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title: The co-occurrence of overweight/obesity and anaemia among adult women, adolescent
girls and children living in fifty-two low- and middle-income countries
authors:
- Ana Irache
- Paramjit Gill
- Rishi Caleyachetty
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991775
doi: 10.1017/S1368980021002512
license: CC BY 4.0
---
# The co-occurrence of overweight/obesity and anaemia among adult women, adolescent girls and children living in fifty-two low- and middle-income countries
## Body
For women, adolescent girls and children living in low- and middle-income countries (LMIC), micronutrient deficiencies and overweight (including obesity) are common nutrition-related disorders. Globally, the prevalence of anaemia, a proxy for micronutrient deficiencies in the absence of micronutrient data[1], is 32·8 % among women of reproductive age (15–49 years old) and 43 % among children under-five[2,3], whereas 39·2 % of women (≥ 18 years), 17·5 % of children and adolescents girls (5–19 years old) and 5·6 % of children under-five are currently living with overweight or obesity[2]. These estimates indicate modest reductions in anaemia alongside rapid increases in overweight/obesity(2–4), resulting in a double burden of malnutrition (DBM) with multiple health and economic consequences(5–7).
Anaemia can cause physical and cognitive impairments, fatigue and low productivity(8–10), while overweight/obesity has been associated with a higher risk of developing non-communicable diseases (e.g. diabetes and CVD)(11–13). Maternal anaemia contributes to maternal deaths and low birth weight[9,14], and maternal overweight/obesity increases maternal morbidity, preterm birth and infant mortality[14].
With no country on track to meet the 2025 Global Nutrition Targets of anaemia and adult obesity[2], dual burdens of malnutrition present a major opportunity for integrated action to end malnutrition in all its forms by 2030[15]. Quantifying the extent and distribution of the DBM is the first step to achieve this, to guide the development of context-specific programmes and policies that address the full spectrum of malnutrition and that take into account inequalities, leaving no one behind[2,16].
According to a recent review[17], little research has focused on the coexistence of overweight/obesity with anaemia or micronutrient deficiencies at the individual level, when compared with other forms of DBM (e.g. overweight/obesity and stunting). Moreover, anaemia data were also excluded from the most comprehensive analysis on the DBM to date[5], which might have underestimated the overall magnitude of the dual burden in LMIC[18]. Previous epidemiological studies on the magnitude of concurrent overweight/obesity and anaemia have been conducted predominantly in individual countries and have focused on women of reproductive age(19–26) or children[21,24]. Thus, the co-occurrence of overweight/obesity and anaemia among adolescent girls remains poorly understood. Another review including Latin American countries found the proportion of concurrent overweight/obesity and anaemia to range from 3·4 % to 13·6 % among women and from 1·2 % to 1·4 % among children under-five[27]. More recently, the co-occurrence of overweight/obesity and anaemia has been estimated in a larger, yet limited number of countries (LMIC and high-income countries spanning different regions) among women of reproductive age (n 16)[28] and pre-school children (n 21)[29]. The latter DBM study showed a positive association between higher socio-economic status and concurrent overweight/obesity and anaemia among women of reproductive age living in LMIC[28]; however, stratified analyses by socio-demographic characteristics are missing, masking subgroups within countries who might be most vulnerable to be simultaneously affected by both forms of malnutrition. Identifying subgroups at higher risk to develop concurrent overweight/obesity and anaemia is of utmost importance in order to design appropriate interventions that reduce health inequalities[30].
To address the gaps in knowledge, this study examined the magnitude and distribution of concurrent overweight/obesity and anaemia by household wealth, education level, area of residence and sex, among adult women, adolescent girls and children living in LMIC.
## Abstract
### Objective:
To investigate the magnitude and distribution of concurrent overweight/obesity and anaemia among adult women, adolescent girls and children living in low- and middle-income countries (LMIC).
### Design:
We selected the most recent Demographic and Health Surveys with anthropometric and Hb level measures. Prevalence estimates and 95 % CI of concurrent overweight/obesity and anaemia were calculated for every country, overall and stratified by household wealth quintile, education level, area of residence and sex (for children only). Regional and overall pooled prevalences were estimated using a random-effects model. We measured gaps, expressed in percentage points, to display inequalities in the distribution of the double burden of malnutrition (DBM).
### Setting:
Nationally representative surveys from fifty-two LMIC.
### Participants:
Adult women (n 825 769) aged 20–49 years, adolescent girls (n 192 631) aged 15–19 years and children (n 391 963) aged 6–59 months.
### Results:
The pooled prevalence of concurrent overweight/obesity and anaemia was 12·4 % (95 % CI 11·1, 13·7) among adult women, 4·5 % (95 % CI 4·0, 5·0) among adolescent girls and 3·0 % (95 % CI 2·7, 3·3) among children. Overall, the DBM followed an inverse social gradient, with a higher prevalence among the richest quintile, most educated groups and in urban areas; however, important variations exist. The largest inequality gaps were observed among adult women in Yemen by household wealth (24·0 percentage-points) and in Niger by education level (19·6 percentage-points) and area of residence (11·9 percentage-points). Differences were predominantly significant among adult women, but less among girls and children.
### Conclusions:
Context-specific, multifaceted, responses with an equity lens are needed to reduce all forms of malnutrition.
## Data sources and study participants
This study was based on the most recent Demographic and Health Surveys (DHS) from all LMIC (conducted between January 2000 and January 2019) with available anthropometric data (weight and height) and Hb levels for adult women, adolescent girls and children under-five. DHS are internationally comparable, nationally representative household surveys, conducted in LMIC about every 5 years, that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health and nutrition. Complete descriptions of country DHS sampling, validation of questionnaires, data collection and data validation are published elsewhere[31]. DHS follow a multistage stratified random sampling technique. In the first stage, the number of households needed per geographical areas is determined, and clusters (or census enumeration areas) are randomly selected with probability proportional to size. The second stage consists of a random selection of households within the selected clusters. For each sampled household, standard model questionnaires are then employed to collect primary data at the household and individual level. Informed consent to participate in the study is taken from the participant (or from a parent or legal guardian for children and unmarried adolescents), before conducting any questionnaire or biomarker tests. In all households, members eligible for biomarker collection include women aged 15–49 years and children under 59 months[31]. We excluded women and girls who were pregnant or who have given birth in the 2 months preceding data collection, due to weight gain during pregnancy, and following DHS guidelines[31]. Individual participants with missing anthropometric measures and data on anaemia status (missing values or data not recorded) and those with biologically implausible height, weight or Hb values were also excluded from the analytic sample (see online Supplemental Fig. S1).
Ethical approval for conducting the DHS surveys was obtained centrally by the ORC Macro Institutional Review Board and by individual review boards within the individual countries participating in the programme. The DHS data sets, both the individual (IR) and children’s (KR) recodes used for this study, are publicly available and accessible at https://dhsprogram.com.
## Measure of anthropometry and Hb levels
DHS trained personnel weighed and measured children under-five and their mothers (15–49 years old), using a SECA digital scale and Shorr Productions measuring board. For children younger than 24 months old, recumbent length was obtained. BMI was calculated by dividing body weight in kilograms by squared height in squared metres. To define overweight/obesity, we used the Quetelex index for adult women (20–49 years old)[32], and the WHO 2007 growth reference data for adolescent girls (15–19 years old)[33]. Women were categorised as having overweight/obesity if their BMI was ≥ 25·0 kg/m2, whereas among adolescent girls, overweight/obesity was defined as BMI-for-age Z-score >1 SD above from the median of the reference population. Among children under-five, a BMI-for-age Z-score >2 SD was classified as overweight/obesity[34,35].
Diagnosis of anaemia was confirmed by measuring Hb concentration levels in blood through a stick capillary blood sample, using HemoCue® 201+ or the 301+ system, a portable Hb analyser. Anaemia in adult women and adolescent girls aged 15–49 years old was defined as Hb concentration levels <12·0 g/dl, and <11·0 g/dl in children (6–59 months)[36,37]. Hb levels were adjusted for altitude and smoking (when data on smoking status were collected) in women of reproductive age, and for altitude in children, as these are known factors to increase Hb concentrations[31]. Similarly, Hb levels were not measured in infants aged 0–6 months, considered a low-risk group for anaemia, for presenting higher Hb concentration levels in the first 6 months of life[31].
## Defining the double burden of malnutrition at the individual level
The double burden was defined as the simultaneous presence of overweight/obesity and anaemia among: (i) adult women (20–49 years old); (ii) adolescent girls (15–19 years old) and (iii) children (6–59 months).
## Covariates
DHS collect information on a wide range of socio-demographic factors. Household wealth was generated using principal component analysis with data collected at each household including assets (e.g. bicycles, cars or radios) and dwelling characteristics (e.g. flooring material, drinking water source or type of toilet facility)[38] and was further divided into five quintiles (Q1: poorest; Q2: poorer; Q3: middle; Q4: richer; Q5: richest). Education level, or maternal education level for children, was assessed by self-report of the completed educational level (E1: no education; E2: primary education; E3: secondary education; E4: higher education). Among adolescent girls, the third and fourth education levels were combined into one category as not all girls might have reached higher education, and thus, avoid low sample sizes. Place of residence was defined according to country-specific definitions and categorised as urban or rural. We also included sex for children, categorised as girls or boys.
## Statistical analysis
Prevalence estimates and 95 % CI of concurrent overweight/obesity and anaemia were first calculated for every country. We also ran separate stratified analyses by wealth quintile, education level, area of residence and sex (for children only). Because some of the combinations in the stratified analysis resulted in small sample sizes, we excluded prevalence estimates for which the sample size was lower than twenty-five observations, in accordance with DHS guidelines[31]. Pooled prevalence estimates were then calculated (command ‘metaprop’) using a random-effects model, overall and by WHO region (i.e. African, Eastern Mediterranean, European, Americas, Southeast Asian and Western Pacific). The Western Pacific region only had one country with available data (Cambodia); hence, the regional pooled prevalence could not be generated.
We measured inequality gaps, defined as the absolute difference in percentage points between the prevalence of DBM among the highest and lowest household wealth quintile (Q5-Q1), the highest and lowest education level (E4/E3-E1), urban v. rural areas and boys v. girls. Positive gaps depict a higher prevalence of concurrent overweight/obesity and anaemia in the richest quintile, most educated group, urban residents and boys than in the poorest quintile, least educated, rural residents and girls, respectively. Negative gaps represent the opposite. P-values <0·05, obtained through χ 2 tests and tests for trend, mean that differences in the prevalence of DBM observed across the different groups were significant and not due to chance.
All analyses were conducted using Stata version V.16.0. ( Statacorp.). We used sampling weights and the Stata’s survey estimation procedures (‘svy’ command) throughout the analyses to account for the clustering and stratification in the sample design of DHS surveys.
## Characteristics of surveys and participants
Overall, fifty-two LMIC had a DHS between 2003 and 2018 with available data on anthropometry and anaemia, comprising a total of 825 769 adult women (20–49 years old), 192 631 adolescent girls (15–19 years old) and 391 963 children (6–59 months). By WHO region, per total number of LMIC with a DHS survey from 2000 onwards (n 69), 31 (86·1 %) of 36 in the African region, 3 (50·0 %) of 6 in the Eastern Mediterranean region, 6 (75·0 %) of 8 in the European region, 6 (66·7 %) of 9 in the Americas region, 5 (71·4 %) of 7 in the Southeast Asian region and 1 (33·3 %) of 3 in the Western Pacific region. We found anthropometric and anaemia data missing in Angola for adult women and adolescent girls. For Madagascar and Jordan, height and weight measurements among children were deemed unreliable in the most recent DHS survey and thus, we used the second most recent survey with available data for this age group. As a result, fifty-one DHS surveys were included in the analysis for adult women and adolescent girls, and fifty-two DHS surveys for children.
Characteristics of participants included in the study are provided in Supplemental Tables 1–3. The median age was between 30 and 43 years old among adult women, 17 and 18 years old among adolescent girls and 28 and 35 months among children. The largest sample size was found in India for the three age groups (n > 100 000), whereas sample sizes were smaller among adolescent girls (n < 1000 in 16 countries) when compared with adult women or children. The overall bivariate prevalence of overweight/obesity and anaemia was 37·5 % and 38·7 % among adult women, 11·3 % and 38·8 % among adolescent girls and 6·3 % and 55·9 % among children, respectively (Fig. 1 and see online Supplemental Table S4).
Fig. 1Bivariate prevalence of overweight/obesity and anaemia in the studied population by WHO regions and overall. Each dot is the pooled prevalence of overweight/obesity or anaemia. AFRO: African region; EMRO: Eastern Mediterranean region; EURO: European region; PAHO: Americas region; SEARO: Southeast Asian region. The Western Pacific region is missing as it only has one country with available data (Cambodia)
## Overweight/obesity and anaemia among adult women
The pooled prevalence of concurrent overweight/obesity and anaemia among adult women was 12·4 % (95 % CI 11·1, 13·7; I 2 99·6 %), ranging from 1·7 % in Ethiopia to 33·6 % in Maldives (Fig. 2 and see online Supplemental Table S4). The regional pooled prevalence ranged from 11·1 % (95 % CI 9·2, 13·0) in the African region to 23·8 % (95 % CI 17·0, 30·7) in the Eastern Mediterranean region.
Fig. 2Prevalence of concurrent overweight/obesity and anaemia among adult women, adolescent girls and children living in LMIC. AFRO: African region; EMRO: Eastern Mediterranean region; EURO: European region; PAHO: Americas region; SEARO: Southeast Asian region; WPRO: Western Pacific region; DRC: Democratic of the Congo; STP: Sao Tome and Principe. Jordan and Madagascar are missing because data were from different DHS surveys. Angola was not included because data were missing for women of reproductive age. The three missing countries were included in the calculation of the regional and overall pooled prevalence The full distribution of the magnitude of the DBM among adult women is presented in Supplemental Tables 5–7. Overall, the highest DBM prevalence was found in the richest quintile (16·5 %), third education level (13·6 %) and urban women (15·3 %). The European region presented a distinct pattern for two of the three socio-economic measures, with the poorer quintile (13·9 %), third education level (16·8 %) and rural women (13·3 %) bearing the largest burden of DBM.
Figure 3 shows the absolute inequality of the prevalence of overweight/obesity and anaemia among adult women by the three socio-economic measures. Countries showed more or less inequalities regardless of the magnitude of DBM (see online Supplemental Tables 4–7). The largest gaps were observed in Yemen, with a 24·0 percentage-point difference ($P \leq 0$·001) in DBM prevalence by household wealth; and in Niger, with a 19·6 percentage-point difference ($P \leq 0$·001) by education level and 11·9 percentage-point difference ($P \leq 0$·001) by area of residence. Gaps were positive in 86·3 % ($\frac{44}{51}$), 76·1 % ($\frac{35}{46}$) and 92·2 % ($\frac{47}{51}$) of countries by household wealth, education level and area of residence, respectively, indicating higher prevalence of concurrent overweight/obesity and anaemia among the richest quintile than the poorest, the most educated than the least educated and urban than rural residents (Fig. 3). The opposite (i.e. higher prevalence among the poorest than the richest, the more educated than the least educated and rural than urban residents), was only observed in 11·8 % ($\frac{6}{51}$) of countries by household wealth, 23·9 % ($\frac{11}{46}$) of countries by education level and 7·8 % ($\frac{4}{51}$) of countries by area of residence. In one country (Albania), the inequality gap in the prevalence of DBM was 0·0 percentage-points among the richest and the poorest group ($$P \leq 0$$·278) (Fig. 3(a)). Differences observed across groups were significant in 80·4 % ($\frac{41}{51}$) of countries by household wealth and 78·4 % ($\frac{40}{51}$) by area of residence (Fig. 3(a) and (c)), and in 60·9 % ($\frac{28}{46}$) of countries by education level (Fig. 3(b)).
Fig. 3Absolute gap difference of concurrent overweight/obesity and anaemia by wealth quintile (a); education level (b); and area of residence (c) among adult women. Positive values mean that concurrent overweight/obesity and anaemia are more prevalent in the richest quintile (Q5), highest education level (E4) and in urban areas when compared to the poorest quintile (Q1), lowest education level (E1) and rural areas. Negative values mean the opposite. (*) P-value <0·05. In Fig. b, Yemen was not included because data on education level was missing. Likewise, countries with a sample size <25 observations for E1 or E4 were excluded. DRC: Democratic of the Congo; STP: Sao Tome and Principe
## Overweight/obesity and anaemia among adolescent girls
The pooled prevalence of concurrent overweight/obesity and anaemia among adolescent girls was 4·5 % (95 % CI 4·0, 5·0; I 2 96·2 %), ranging from 0·5 % in Madagascar and Timor-Leste to 21·5 % in Jordan (Fig. 2 and see online Supplemental Table S4). The pooled regional prevalence ranged from 2·5 % (95 % CI 1·5, 3·4) in the European region to 11·8 % (95 % CI 4·2, 19·3) in the Eastern Mediterranean region.
Patterns in the distribution of concurrent overweight/obesity and anaemia were similar to those of adult women, although with more variation across and within regions. Overall, the highest prevalence was found in the fifth richest quintile (5·7 %), third education level (4·3 %), and urban residents (5·4 %) (see online Supplemental Tables 8–10). A distinct pattern was also observed in the European region, where the prevalence of DBM was higher in rural residents (see online Supplemental Table 10), and in the Americas region, with a higher prevalence of DBM observed among the least educated (see online Supplemental Table 9).
The largest gaps were observed in Togo, with a 15·3 percentage-point difference ($P \leq 0$·001) in DBM prevalence by household wealth; in Uganda, with a 19·5 percentage-point difference ($P \leq 0$·001) by education level; and in Yemen, with a 9·0 percentage-point difference ($P \leq 0$·001) by area of residence (Fig. 4 and see online Supplemental Tables S8–S10). Gaps were positive in 81·6 % ($\frac{40}{49}$), 64·5 % ($\frac{20}{31}$) and 76·0 % ($\frac{38}{50}$) of countries by household wealth, education level and area of residence, respectively, whereas these were negative in 18·4 % ($\frac{9}{49}$) of countries by household wealth, 35·5 % ($\frac{11}{31}$) by education level and 24·0 % ($\frac{12}{50}$) by area of residence (Fig. 4). Differences observed across groups were significant in less than half of the countries for the three socio-economic measures. For example, by education level, differences observed between the least and most educated were only significant in Uganda, Nigeria, Mozambique, Burkina Faso, India and Haiti.
Fig. 4Absolute gap difference of concurrent overweight/obesity and anaemia by wealth quintile (a); education level (b); and area of residence (c) among adolescent girls. Positive values mean that concurrent overweight/obesity and anaemia are more prevalent in the richest quintile (Q5), highest education level (E3) and in urban areas when compared with the poorest quintile (Q1), lowest education level (E1) and rural areas. Negative values mean the opposite. (*) P-value <0·05. Countries with a sample size <25 observations were excluded. In Fig. b, Yemen was not included because data on education level was missing. DRC: Democratic of the Congo; STP: Sao Tome and Principe
## Overweight/obesity and anaemia among children
The pooled prevalence of concurrent overweight/obesity and anaemia among children was 3·0 % (95 % CI 2·7, 3·3; I 2 97·1 %), ranging from 0·4 % in Nepal to 9·1 % in South Africa (Fig. 2 and Supplemental Table S4). The pooled regional prevalence ranged from 1·2 % (95 % CI 0·8, 1·7) in the Southeast Asian region to 3·8 % (95 % CI 2·4, 5·2) in the European region.
Overall, the prevalence of concurrent overweight/obesity and anaemia among children was the lowest across the three age groups studied (see online Supplemental Table 4); however, Fig. 2 shows that the prevalence of DBM among children was higher than that of adolescent girls in fourteen countries and in the European region.
Patterns in the distribution of concurrent overweight/obesity and anaemia differed from those among adult women and adolescent girls (see online Supplemental Tables 11–13). Overall, the highest prevalence was in the second and fifth wealth quintile (2·8 %), second maternal education level (2·7 %) and in rural areas (3·0 %). Nevertheless, in the African region the distribution of the DBM by the three socio-economic measures among children emulated that of adult women and adolescent girls, with the highest prevalence in the fifth quintile, fourth education level and urban residents. For all WHO regions, the prevalence of overweight/obesity and anaemia was slightly higher among boys than girls (3·2 % v. 2·5 %) (see online Supplemental Table 14).
The widths of inequality gaps were smaller than those of adult women and adolescent girls overall, with only fifteen instances where gaps were greater than 3·0 percentage-points (Fig. 5(a), (b), (c) and (d)). The largest gaps were observed in Uganda, with a 9·5 ($$P \leq 0$$·891) and a 4·5 percentage-point difference ($$P \leq 0$$·857) in DBM prevalence by household wealth and area of residence, respectively; and in Sierra Leone, with a 15·9 percentage-point difference ($$P \leq 0$$·028) by maternal education level (Fig. 5). The prevalence of DBM was the same, with a gap of 0·0 percentage-points, among the richest and poorest groups in Mozambique and Guyana; urban and rural residents in Maldives and Burundi and boys and girls in India, Peru, Cote d’Ivoire and Benin. Differences observed among groups were significant in 11·5 % ($\frac{6}{52}$), 11·1 % ($\frac{5}{45}$), 11·5 % ($\frac{6}{52}$) and 21·2 % ($\frac{11}{52}$) of countries by household wealth, maternal education level, area of residence and sex, respectively.
Fig. 5Absolute gap difference of concurrent overweight/obesity and anaemia by wealth quintile (a); maternal education level (b); area of residence (c); and sex (d) among children. Positive values mean that concurrent overweight/obesity and anaemia are more prevalent in the richest quintile (Q5), highest education level (E4), urban areas and among boys, when compared with the poorest quintile (Q1), lowest education level (E1), rural areas and girls. Negative values mean the opposite. (*) P-value <0·05. In b, Yemen was not included because data on education level were missing. Likewise, countries with a sample size <25 observations for E1 or E4 were excluded. DRC: Democratic of the Congo; STP: Sao Tome and Principe
## Discussion
The present study provides evidence of the magnitude and distribution of overweight/obesity and anaemia at the individual level among adult women, adolescent girls and children, using nationally representative DHS samples from fifty-two LMIC. We show that concurrent overweight/obesity and anaemia were common among adult women, with more than 1 in 10 simultaneously affected by the two forms of malnutrition; however, it was low among adolescent girls and children. The overall pooled prevalence estimate for adult women (12·4 %) was almost three times that of the prevalence estimate for adolescent girls (4·5 %) and was four times higher than that of children (3·0 %). Important variations exist in the prevalence of concurrent overweight/obesity and anaemia across LMIC and WHO regions.
Williams and colleagues[28] recently reported that the prevalence of overweight/obesity and anaemia at the individual level among women of reproductive age (15–49 years old) ranged between 1·0 % and 18·6 % (median = 8·6 %); however, these data were estimated in sixteen countries, including LMIC and high-income countries, using surveys from the BRINDA project (https://brinda-nutrition.org/). The prevalence estimates that we present for adult women and adolescent girls are not directly comparable with those from the above-mentioned study[28], as we calculated separate estimates for adult women (20–49 years old) and adolescent girls (15–49 years old), and only included LMIC (n 51). By providing separate estimates, we show that the prevalence of concurrent overweight/obesity and anaemia among adolescent girls was similar to that of younger children (6–59 months), and that the prevalence of DBM among adult women was as high as 30·0 % in countries such as Maldives, Jordan or Gabon. Another study analysing anthropometric data to quantify the magnitude of DBM at the individual level (i.e. concurrent overweight/obesity and stunting) also found a low burden among adolescent girls (12–15 years old) living in LMIC, with prevalence estimates ranging from 0·0 % to 7·7 %[39]. We additionally observed that the difference in estimates among women and girls (7·9 percentage-points) was primarily driven by a higher prevalence of overweight/obesity among the adult population (37·5 % v. 11·3 %), with both groups bearing a similar burden of anaemia (38·7 % v. 38·8 %).
Our estimates among children under-five are slightly higher than those previously reported[29]. The earlier study calculated the prevalence of concurrent overweight/obesity and anaemia, ranging from 0·0 % to 5·0 % (median= 1·4 %)[29]. We found, however, the highest prevalences of DBM among children ranging from 6·0 % to 9·1 % in Gabon, Mozambique, Bolivia, Eswatini, Sierra Leone, Sao Tome and Principe and South Africa; none of which were included in Engle-Stone and colleagues’ study[29]. We present overlapping estimates for five countries, with a difference between 2·8 percentage-points in Cameroon (4·5 % v. 1·7 %) and 0·7 percentage-points in Malawi (2·6 % v. 3·3 %). Only in Cote d’Ivoire, our estimate is lower (2·9 %) than the previously reported value (3·7 %)[29], although data analysed was collected 3 years apart, and thus, changes in the burden of overweight/obesity and anaemia would be expected. Nevertheless, we use data from the same year for Cambodia [2014] and Malawi (2015–2016) and obtained a higher prevalence of concurrent overweight/obesity and anaemia for both countries. Although the difference is low (<1·5 percentage-points), presenting different estimates can cause confusion for governments, or policy and programme planners, and hinder appropriate monitoring of the DBM. Significant differences in Hb distributions across different surveys have been discussed previously and attributed to factors such as humidity, the HemoCue® model used for data collection, or the use of different survey sampling procedures[40]. This may have had an influence in the prevalence of DBM; however, it is worth noting, that for Cambodia, our sample size is significantly higher (3799 v. 406), which may explain the difference in estimates from a 0·0 %[29] to a 1·4 % in our study.
Overall, the co-occurrence of overweight/obesity and anaemia followed an inverse social gradient, emulating the distribution of overweight/obesity in LMIC(2,41–45). The prevalence of DBM was higher among the richest quintile and most educated groups, and urban residents had consistently a larger burden than their rural counterparts in most countries, especially among adult women and adolescent girls. This is in alignment with previous studies reporting a higher prevalence of overweight/obesity and anaemia at the individual level among the most socio-economically advantaged and in urban areas[28,46]. Among children, we observed little inequalities by household wealth, maternal education level, area of residence and sex; however, the prevalence of DBM was slightly higher in rural areas, with the exception of the African region, and in boys.
Larger inequality gaps were observed among adult women and adolescent girls, when compared to children, for which gaps were overall small (< 3·0 percentage-points). Across the three age groups, the largest gaps by the three socio-economic measures were found in African countries (e.g. Niger, Uganda, Togo and Sierra Leone) and in Yemen. There were variations in the level of inequality across countries with a similar burden of DBM. For example, among adult women, the difference between the richest and the poorest group was 7·1 percentage-points in Maldives, where the prevalence of concurrent overweight/obesity was 33·6 %, whereas in Gabon, with a 30·1 % prevalence, the inequality gap was 17·7 percentage-points. This points to the need for context-specific solutions that appropriately address the nutritional needs of a country’s population. In countries with a high prevalence of concurrent overweight/obesity and anaemia across all socio-economic groups, population-based interventions that target both forms of malnutrition might be more suitable than targeting only the richest quintiles.
Differences observed by household wealth, education level and area of residence were significant for most countries among adult women; however, these were significant in <50 % of countries among adolescent girls, and only a handful of countries among children. Other studies had previously identified no significant associations between socio-demographic measures (i.e. sex, area of residence, household wealth and education) and concurrent overweight/obesity and anaemia among children under-five[29] and mixed results among women of reproductive age[28].
Our study has several limitations that need to be considered. First, we used anaemia as a proxy for micronutrient deficiencies in the absence of individual micronutrient data (e.g. Fe, vitamin A, etc.) in DHS surveys. The complex aetiology of anaemia in LMIC has been described in detail somewhere else[47]. In countries with a low infection burden, the proportion of anaemic women of reproductive age and children under-five with Fe deficiency is believed to be 71·0 % and 50·0 %, respectively, whereas in countries with a high infection burden, the proportion was estimated to be 58·0 % among children and as low as 35·1 % among women[48,49]. Second, we did not measure the independence of overweight/obesity and anaemia. Excess weight has been previously linked with Fe deficiency(50–52), but this association would not necessarily be true for anaemia[53]. Recent evidence found that overweight/obesity and anaemia were either two independent conditions in LMIC or were negatively associated (i.e. odds of anaemia were higher among normal weight than overweight/obese women)[28,29]. Third, some categories created for the stratified analysis resulted in small sample sizes, and therefore, we could not include certain countries into the regional prevalence, and in some cases, the regional estimate could not be calculated. Fourth, for some WHO regions, the number of countries with data on both, anthropometric measures and anaemia was low, and thus, they might be underrepresented. For example, the Western Pacific region, only had one country with available data (i.e. Cambodia), and the Eastern and Mediterranean region had three countries (i.e. Egypt, Jordan and Yemen). Although most DHS surveys include weight and height measurements, not all collect data on Hb levels, and thus, a number of potential countries could not be included in our study. Fifth, it is important to note that we did not use complex measures of inequalities (i.e. slope index of inequality) which would have allowed to take into account differences across all socio-economic groups, including the intermediate groups; however, in this study, we were only interested in measuring differences between the least and most disadvantageous groups (e.g. Q5 v. Q1 and E4 v. E1). Sixth, although we sought to include the most recent DHS surveys available for each country, these were spread out over several years (from 2003 in Madagascar to 2018 in Nigeria). This might have influenced the prevalence estimates obtained and might not reflect the current magnitude of concurrent overweight/obesity and anaemia, particularly for those countries with data from older surveys.
Despite these limitations, our study included a large number of LMIC (n 52), and we were able to analyse overall large sample sizes from nationally representative surveys. Additionally, we were able to present the magnitude of concurrent overweight/obesity and anaemia for adult women and adolescent girls separately and stratified by different socio-economic measures, which could aid in informing more precise policy responses within individual countries.
The mechanisms underpinning the simultaneous presence of overweight/obesity and anaemia across the three age groups in LMIC are likely to be multifactorial due to the varied and complex aetiology of anaemia(47–49). Nevertheless, the fact that we found the highest prevalence of concurrent overweight/obesity and anaemia among adult women living in urban areas and those from the richest quintiles (where the risk of infectious diseases is likely to be lower) could point to unhealthy dietary practices and/or overweight/obesity as plausible factors contributing to the DBM for this age group. Nevertheless, food insecurity could also lead to a higher consumption of energy-dense nutrient-poor foods and explain why some LMIC had a high prevalence of DBM across all wealth quintiles[54]. First, rapid changes in the food systems of LMIC are resulting in an increased availability of ultra-processed foods, which are easily accessible and affordable[2,5,55,56]. A high consumption of these foods rich in fats and poor in vitamins and minerals could make women more prone to develop both weight gain and anaemia as a result of Fe deficiency (or other micronutrient deficiencies) from the diet. Second, overweight/obesity can also lead to Fe deficiency due to increases in hepcidin, which reduces the absorption of Fe from the diet[52]. Data on dietary practices, as well as on the causes of anaemia, are needed in nationally representative surveys (e.g. DHS), in order to better elucidate the causes of concurrent overweight/obesity and anaemia.
Double-duty actions, proposed to address forms of undernutrition and overweight/obesity[15], might have a positive effect in preventing and reducing the dual burden of overweight/obesity and anaemia (e.g. changes in the food environment conducive to supporting healthy diets, scale up of programmes that protect breast-feeding and offer a better guidance for complementary feeding practices, counselling about healthy eating during antenatal care, etc). Yet, the effectiveness of double-duty actions still needs to be tested in this context. For women who are already living with overweight/obesity, weight reduction could translate into better absorption of micronutrients (e.g. Fe) from the diet and hence increased Hb levels.
Furthermore, further research is needed to elucidate the full implications of concurrent overweight/obesity and anaemia, particularly among women of reproductive age, for whom the burden was highest. Women who enter pregnancy with concurrent overweight/obesity and anaemia might be confronted with adverse maternal, obstetric and birth outcomes related to both, anaemia and adiposity. Management of both of these conditions would be particularly challenging for many health systems in LMIC which are underdeveloped[57] and might need to be redesigned to contend with women presenting with the dual burden of overweight/obesity and anaemia. Likewise, adolescent girls, who showed an overall low prevalence of DBM, could be a crucial second window of opportunity to act early on and prevent the detrimental intergenerational consequences of malnutrition, as well as to improve pregnancy outcomes[6].
In conclusion, our study demonstrated a high prevalence of concurrent overweight/obesity and anaemia among adult women and a much lower prevalence among adolescent girls and children under-five. Concurrent overweight/obesity and anaemia were unequally distributed across wealth quintiles, education levels and area of residence. As the prevalence of overweight/obesity continues to increase rapidly across LMIC in response to the nutrition transition[2,5], this may translate in increases in concurrent overweight/obesity and anaemia. Similarly, changes in the distribution of this form of DBM might also occur, in view of the obesity shift towards rural residents and the poor(42,57–59). Given the variation in the magnitude and distribution observed, context-specific, multifaceted and equity-focused programmatic and policy responses that address all forms of malnutrition are needed.
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|
---
title: 'Evaluating an integrated nutrition and mathematics curriculum: primary school
teachers’ and students’ experiences'
authors:
- Berit M Follong
- Elena Prieto-Rodriguez
- Andrew Miller
- Clare E Collins
- Tamara Bucher
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991777
doi: 10.1017/S1368980022000386
license: CC BY 4.0
---
# Evaluating an integrated nutrition and mathematics curriculum: primary school teachers’ and students’ experiences
## Body
Childhood overweight and obesity presents an ongoing public health concern[1] with an extensive number of prevention interventions, including school-based nutrition interventions, proposed to improve children’s dietary behaviours and prevent childhood obesity[2,3]. Previous reviews have demonstrated school-based nutrition education programmes to be moderately effective in reducing the prevalence of overweight and obesity[4], lowering energy intake[5], increasing fruit and vegetable consumption[4,5] and improving nutrition knowledge[5]. Despite evidence for a small but positive impact of nutrition education on children, researchers suggest that effectiveness could be enhanced by addressing issues related to programme implementation(5–7).
Implementation of school-based nutrition interventions largely depends on barriers and facilitators experienced by teachers[8,9]. Teacher characteristics, such as previous experience/knowledge, motivation and interest, as well as comprehensive educational material, teaching training and support from programme staff contribute to enhanced implementation of nutrition curricula(9–11). Furthermore, many studies have investigated barriers and consistently found that particularly limited time and competing demands hinder teaching of nutrition(8,12–20). Therefore, the use of cross-curricular or integrative teaching strategies has been proposed as a solution to combat these barriers[14,16,18,19,21]. Embedding nutrition within core subjects could support the uptake of nutrition education without reducing the time spent on mandatory subjects[14,22]. Additionally, nutrition concepts could provide a real-life context that enhances the learning of other core subjects[9,23,24]. Although this approach has gained some interest, it remains unknown whether it addresses teachers’ time constraints and how teachers and students perceive this teaching strategy[25].
A recent scoping review concluded that more research is needed to explore the effectiveness of cross-curricular approaches[25]. These future trials require comprehensive process evaluations to understand why some interventions succeed while others fail[26]. Many studies use quantitative data to examine intervention fidelity and acceptability, while only few provide insights from qualitative data[26]. Not surprisingly, previous research warrants the need for rigorous process evaluations including both quantitative and qualitative outcomes(6,25–27).
The Cross-curricular Unit on Portion Size (CUPS) program was evaluated using a pilot cluster randomised controlled trial (RCT) in Australian primary schools. CUPS was designed to improve children’s portion size estimation skills and nutrition knowledge while using an integrative teaching approach[28]. The CUPS intervention comprised six lessons on nutrition and mathematics concepts for Stage 2 students (aged 8–10 years). Teachers received training and resources to implement the programme into their classrooms across 1–4 weeks. The programme proved effective towards students’ nutrition knowledge scores ($P \leq 0$·01)[29], and whilst non-significant due to the sample size not being large enough to provide sufficient statistical power, a slight improvement in portion size estimation skills was observed among children receiving the programme in comparison to a control condition (Follong et al., unpublished results).
The current paper describes the findings from the process evaluation, embedded within a cluster RCT examining the programme effectiveness, to identify teachers’ and students’ experiences with the programme and the use of an integrative teaching strategy. This process evaluation goes beyond examining programme acceptability and explores barriers and facilitators that affected its implementation and effectiveness. We sought to distinguish which aspects of the intervention were successful and which elements would need amendments to guide future studies using a similar teaching strategy.
## Abstract
### Objective:
To present the process evaluation of a curricular Cross-curricular Unit on Portion Size (CUPS) program that integrates nutrition and mathematics, describing teacher and student perspectives on the intervention.
### Design:
Semi-structured interviews and focus groups were conducted following the implementation of the CUPS program during a pilot randomised controlled trial designed to evaluate efficacy for improved portion size estimation. Lessons involved experiential learning using food models and mathematics cubes and focussed on portion size, food groups, volume and capacity. Data were collected immediately post-intervention and analysed using an inductive thematic approach.
### Setting:
Primary schools in Newcastle, Australia.
### Participants:
Year 3 and/or 4 teachers (n 3) and their students (n 15).
### Results:
Teachers believed the programme supported the learning of nutrition concepts, with the majority of students enjoying the lessons, cubes and food models. Teachers indicated most students were engaged and became more aware of healthy eating and serve size recommendation. Although teachers enjoyed and valued the lessons, they suggested that the integration of volume and capacity should be further improved in order to address the time barrier for teaching nutrition.
### Conclusion:
The process evaluation reports on challenges and successes of implementing an integrative nutrition programme. This teaching approach could be useful and successful when aligned with teacher’ and student’ needs. Based on participant feedback, lessons could be refined to enhance integration of mathematics content and to support student learning.
## Methods
The CUPS intervention followed a mixed method design. It consisted of a cluster RCT designed to investigate the effectiveness of integrative nutrition education for primary school children and qualitative data collection embedded within the trial to explore teachers’ and students’ perceptions of the educational programme and teaching approach. The trial was designed according to the Consolidated Standards of Reporting Trials and registered with the Australian and New Zealand Clinical Trial Registry (ACTRN12619001071112). A recent publication describes the intervention and methods used to conduct the research in detail[28]. Student nutrition knowledge (survey) and portion size estimation skills (estimation task) were assessed at baseline, immediately post-intervention and 4 weeks later as part of the RCT component. To complement the quantitative student data obtained during the RCT, interviews and focus groups were held with participating teachers and students, respectively. Only qualitative data are reported in this paper.
## Participants
Four primary schools (5 Year 3 and/or 4 teachers) from the Newcastle and Hunter region (New South Wales (NSW), Australia) participated in the RCT. Teachers (n 3) randomised to the intervention condition and a selection of their students were involved in the current evaluation study. Voluntary consent was sought from all participants. On average, teachers had 9 years of experience in teaching a variety of primary school levels. None of the teachers had prior experience in teaching nutrition, nor were they trained in any specialist areas. A mix (e.g. gender, age and achievement level) of five students were selected by their teachers to participate in the evaluation. This ensured the selected students represented the wider student population involved in the CUPS program.
## Intervention
The CUPS intervention involved the implementation of an integrated teaching unit that targeted student learning on both nutrition and mathematics concepts such as appropriate portion sizes per food group and volume and capacity. Experienced primary school teachers and experts in the field of Nutrition research collaborated to develop the final six lesson plans. The NSW K-10 syllabus for Mathematics and Personal Development, Health and Physical Education, and materials on the Australian Guide to Healthy Eating recommendations were used to create the content. A recent protocol paper outlines the CUPS lesson content, sequence and learning outcomes[28]. In contrast to what was stated in the protocol, content was spread over six instead of the five lessons. Lesson two was split into two identical lessons except for the example foods used to measure portion size (i.e. example foods from the five food groups were divided over the two lessons instead of all examples being discussed during one lesson). This ensured students had adequate exposure to the content across two lessons. The researchers believed students would need more time and practice to get a grasp of portion size estimations given that a previous study with children concluded that they may need multiple lessons[30].
Before programme implementation occurred, teachers attended a 3-h professional development workshop delivered by the research team. The aim and content of this workshop are described elsewhere[28]. Briefly, teachers were taught how to prepare, plan and implement the CUPS lessons. The relevance and rationale for the integrative and experiential approach was discussed. Moreover, teachers were familiarised with content on healthy eating guidelines and portion size estimation, with the majority of the time focussing on how to integrate this content into the Stage 2 Mathematics curriculum. Following completion of the workshop, teachers were given a CUPS school pack containing all resources (i.e. mathematics cubes, food models, measuring cups, plastic containers, Australian Guide to Healthy Eating brochures and posters, lesson plans, worksheets and presentation slides). Teachers completed six pre-planned lessons of each approximately 40 min in duration, which were taught across 3 to 4 weeks (approximately two lessons/week).
## Evaluation outcomes
The acceptability, experiences and potential of the CUPS program were examined in the process evaluation using two qualitative research methods. Semi-structured interviews and focus groups were conducted to explore both teachers’ and students’ perspectives on the intervention. The full research method protocol including all interview and focus group questions has been recently published[28]. Questions were adapted from a previous physical activity programme in schools using a similar methodology[31].
Interviews focusing on perceptions, facilitators and barriers of the programme implementation and delivery, and content and resources were conducted by phone shortly after finishing the last lesson. Teachers were asked about their experiences with the teaching unit in comparison with regular volume and capacity lessons. Additionally, 20-min focus groups were held with the selected students within 1 week of completing the intervention at their schools. Questions were designed to explore students’ thoughts on the CUPS lessons in comparison with their usual mathematics classes, as well as their opinion on enjoyment regarding both classroom activities and materials, learning outcomes and further improvements of the programme. Furthermore, teachers and students were asked to rate their agreement with several statements using a five-point Likert-type scale (high scores representing high agreement). The interviews and focus groups were audio recorded and transcribed verbatim by a secure transcription service.
## Data analysis
Data were analysed in line with Creswell’s [2014] six steps for qualitative analysis by the lead author[32]. Audio recordings were transcribed, and digital transcripts were subsequently managed using NVivo software (Version 12). First, all transcripts were repeatedly reviewed to gain a general sense of the data. Themes were inductively identified and defined by a process of sequential revision, refinement and coding from the raw interview and focus group data[33]. The lead author generated a list of themes, followed by the identification of subthemes. The next step involved interpreting the significance of these themes. Lastly, quotes that corresponded with the (sub-)themes were extracted from the transcripts. Average scores for statement agreement were calculated.
## Teacher interviews
Five themes were identified that highlight the teachers’ experiences and perceptions on the CUPS program. The themes are described with example quotes below and in Table 1.
Table 1Themes and subthemes including illustrative quotes that emerged from teacher interviews on the CUPS program (n 3)Themes and subtheme descriptionQuotes CUPS lesson content The overall lesson content, sequence and integrative concept was well received‘The lessons themselves were really good. […] It was really informing and I think the kids enjoyed it as well. Yeah, it was a good concept. It just needed a little bit of tweaking with those lessons, I think. That’s all.’ Teacher 2‘I think it’s great to bring in the maths, because if we were just teaching nutrition, we’d just be doing a lot of talking and a lot of looking at pictures. […] Yeah, it made it very practical, so I think it sunk in more.’ Teacher 3Nutrition content was perceived as really good‘It was definitely more nutrition based […] So yeah, it had good knowledge about the nutrition side of things.’ Teacher 2Lacking content and depth on volume and capacity concept‘However, with the volume and capacity, I just don’t know how much it did link to volume. It was definitely a nutrition lesson but […] I don’t know how closely connected it was to volume and what the kids need to know about volume.’ Teacher 2Does not fully replace usual lessons on volume and capacity‘I don’t know if it could replace it completely, but I think if a few things are maybe just changed a little bit, yes, I think so. […] and then if we just get a bit more in-depth about the cups, millilitres and grams of the different foods, then I think it could be.’ Teacher 1Includes a variety of mathematics concepts‘Yes, they definitely built their maths skills too. It was just they were building a variety of maths skills. […] They were doing fractions, they were doing multiplying, they were doing estimating, and they were doing a little bit of volume. They’re all good things. […] Yeah, it was very cross-curricular in terms of tapping into other aspects of maths, rather than just being about volume.’ Teacher 3 CUPS resources and materials The materials such as the lesson plans, worksheets and PowerPoint Presentations were perceived as very detailed and easy-to-use‘I thought the lessons were very detailed and all the worksheets were great, and the PowerPoints were really easy to explain. Yeah, so I thought the rest was quite good.’ Teacher 1The food models, mathematics cubes and measuring cups were great hands-on tools to engage the students and visualise the concepts taught‘Well they were very engaged when the food and the cubes were out, yeah very hands-on which was good because my kids really liked that.’ Teacher 1‘It’s just good to hold it. I think the more physical it can be, where they’re holding it in their hands - I mean, it is a very physical program’ Teacher 3 Student learning and achievement Students had a better understanding of the nutrition concepts, and they were able to put the knowledge and skills gained into practice‘Then even just understanding what might be a healthier option than something else, like when they were doing that the follow-up assessment, I noticed that I think they understood that a bit better, so that benefitted their results.’ Teacher 1 ‘I think - well it will be interesting to see, but I think the kids will retain a lot about nutrition, and estimating, that they’ll carry with them into their real lives.’ Teacher 3Some students might have understood the estimations and mathematical conversions, whereas others did not as a few concepts might have been confusing‘I think they understood, once we went through it, the cubes, and how it matched up to the serve sizes and they definitely got better as the lessons went on. I think they were a little bit confused by that at the start, but they got better at that, I thought.’ Teacher 1The majority of the students enjoyed the lessons‘Yes, I think most of them did enjoy most of the lessons. I think at times there’s always students that get a bit distracted or aren’t paying attention the whole time. But I think they were mostly engaged and I think having the fake foods there, and the cubes, they liked playing with the cubes. So I think they were mostly engaged and enjoying it.’ Teacher 1‘I think some of them found it a little bit repetitive but I’d say the majority of them did enjoy it.’ Teacher 2Students were engaged most to the time, and they might have had a greater engagement as a result of the nutrition integration compared with their usual maths lessons“For the most part, the kids totally engaged and learnt from each other, as well as from me.” Teacher 3‘Oh, yeah [I think the nutrition integration contributed to a greater engagement compared to our usual maths lessons]. I would say anything integrated because it’s something a little bit different than the norm is exciting and different. I did think that that they did benefit in it having that integration into it.’ Teacher 2 Teaching experience The professional development was informative and very valuable‘Yes, I did enjoy that. It really opened up my eyes to nutrition in general. […] I found it really valuable. To be able to see a lesson being taught as well was really good for me to be able to go in and then teach my own class. Yeah, I thought it was quite valuable and good information.’ Teacher 2Teachers enjoyed teaching the CUPS lessons‘I enjoyed teaching the lessons because it was something different.’ Teacher 2“*It is* always enjoyable to do those subjects that are hands-on.’ Teacher 3Teachers felt moderately confident about teaching the CUPS lessons mostly because of having limited knowledge about nutrition‘Yeah, I think I was [confident]. Once I’d done a little bit of research myself […] like as you do with all lessons I always like to go and have a look at the lesson and see what the content is and all that kind of stuff before I teach it. Once I had actually, I guess you could say, do a mini research myself before I taught the lesson, I was confident…’ Teacher 2Teachers expressed that they would like to use more integrative strategies in their future teaching, and that it is important to incorporate nutrition into the curriculum‘I believe in cross-curricular integration. At this day in age you need to do cross-curricular as you don’t fit a lot of the stuff in. […] In terms of time constraints it needs to be integrated. It’s unfortunate that it can’t be its own program. […] I would [like to use cross-curricular teaching], you just need to plan for it.’ Teacher 2 Programme benefits and challenges Teachers indicated that one of the major benefits of the programme was that they were able to teach their students about nutrition‘Well as I said we don’t get to teach nutrition that much, so to go over that was really good. I think the kids need to be aware of it, of what they should be eating and how much they should be eating, because some of them just have no idea that there’s even guidelines for that. I think that was good.’ Teacher 1‘I think just the fact of being able to teach nutrition to the students because I haven’t. […] I think that was probably the benefits that I had out of it…’ Teacher 2Teachers observed that the students benefitted from by programme as their nutrition knowledge improved‘I found that a lot of students actually didn’t know [about the food groups or that you weren’t supposed to just have a balanced meal] so it was good to see the benefits of the students actually understanding.’ Teacher 2Not enough resources were available for all children to participate in the activities at the same time‘Yeah, I think that was probably just my main challenge was just that, was just like the resources in terms of trying to get the kids to, all of them, to participate and not just my children who were super involved in it. Not just getting them to take over.’ Teacher 2‘I think that we probably need a few more resources.’ Teacher 3Not being able to differentiate the lessons for students at varying levels‘There was only that one lesson that I stressed out a little bit about which had integrated a lot of the maths concepts. The reason I did stress out a bit at that was because the kids in my class were quite differentiated, if you know what I mean. I had children who were working really low who would not understand that concept and then I had children who were working quite high that would understand that concept.’ Teacher 2‘Varying student knowledge, I guess, is sometimes an issue.’ Teacher 3
## Cross-curricular Unit on Portion Size lesson content
*In* general, the lesson content was perceived as really good by all the teachers. Each teacher highlighted aspects that they thought were good and aspects that could use improvements. According to all teachers, the lesson content sufficiently supported the learning of nutrition concepts but missed depth on volume and capacity. As a result, two teachers commented that they believed the CUPS lessons could not fully replace their usual mathematics lessons on these concepts. The following quotes illustrate these three findings:‘I think 100 % [supports the learning of] nutrition [concepts]. If you wanted to tackle nutrition, that’s the way to do it.’ Teacher 3 ‘It definitely complements the mathematics of volume and capacity, but it does lend itself more so, I’d say, to a health lesson.’ Teacher 3 ‘I guess it’s so different to anything else in the maths syllabus, because you’ve combined the two [subjects]. It steers quite far away from your traditional way to teach volume and capacity. So, you couldn’t really say it would be a replacement to volume and capacity as a maths lesson.’ Teacher 2 Although the lessons might have not contained in-depth volume and capacity concepts, the lessons ‘tapped into other aspects of maths, rather than just being about volume’ (Teacher 3). Teachers reported students learning about estimating, problem solving, reading tables, fractions, multiplying and adding multiples.
## Cross-curricular Unit on Portion Size resources and materials
The CUPS resources and materials were of great value to the teachers. The ‘ready-to-go’ materials were ‘really appealing’ (Teacher 3), easy to use and very detailed. The lesson plans were good to refer to during the lessons and therefore supportive of teaching. ‘The lessons were really thorough. You couldn’t say you didn’t have enough information. The slides were really clear, but not over the top. […] I can pay for a subscription to a resource, and not get the level of detail.’ Teacher 3 In terms of the food models and mathematics cubes, two teachers commented that these resources were great hands-on tools that were engaging for the students and helped visualising the food volumes:‘Yeah it was good to have it, to actually have all those fake foods for the kids to see, because I think a lot of kids need that visualising of the actual food as well; instead of just telling them what a serve size is, when they see it they’ll be able to better understand it and remember it. So yes, I thought that was good.’ Teacher 1 Other positive aspects of the programme were the resources and materials. Except for two students, all children stated that they enjoyed using the food models in the classroom. The food models were perceived as fun and looking like real foods. The reason for not enjoying the food models was the fact that they were not edible. In addition, almost all students liked using the cubes in the lessons as they were ‘cool’ (Student, class 1), ‘fun’ (Students, class 1) and ‘colourful’ (Student, class 3). About one-third of the children commented that the cubes kept falling apart, which was perceived as annoying by some of these children. ‘Yes [I liked the cubes], it can help combine health and maths together.’ Student, class 1 The use of food models and cubes resulted in the mathematics activities being reported as more interesting for the students (score 4·$\frac{1}{5}$·0). Children from one class in particular said that the food models could be used as visual examples.
Although many students said nothing needed to change in the programme, two students suggested the instructions and group work could be improved by simplifying the instructions and decreasing the number of students to collaborate with.
## Student learning and achievement
Students clearly showed improvements in their nutrition knowledge according to all teachers. Teachers stated that the students have a better understanding of the food groups, serve sizes and the number of serves they are allowed to have, healthy and unhealthy foods and balanced meals. This sparked students’ interest to apply their knowledge outside of the classroom:‘…I feel like they were really starting to get it and they were linking it to real life. In the playground they were looking at the nutrition patterns on the back of their foods and stuff like that.’ Teacher 2 A major part of the mathematical content of the CUPS lessons involved estimating volumes and the conversion of cubes to cups and vice versa. All teachers commented on their students’ ability or understanding of these mathematical concepts which showed that this varied widely. Some children struggled with visualisations of squared cubes fitting into round cups, whereas others found it hard to conceptualise estimating volumes of a group of food shapes. Others might have memorised the conversions rather than understanding the mechanism behind it. The quotes below demonstrate the large differences in student understanding:‘The estimating skills hugely improved by using the CUPS cubes.’ Teacher 3 ‘I think mathematically that’s a hard concept to look at it in terms for the Year 4 students and Year 3 students. […] Once they worked out [a pattern] then they were okay with it, but I think they would not have probably understood that mathematical conversion of how to work it out unless they just memorised it.’ Teacher 2 Overall, many students enjoyed the CUPS lessons and the majority were engaged (score 3·$\frac{3}{5}$·0). Two out of three teachers also mentioned their students showed greater engagement compared with their usual mathematics lessons as a result of the nutrition integration being ‘different than the norm’ (Teacher 2) and ‘tangible’ (Teacher 3). However, these two teachers also briefly stated that the lessons may have been somewhat repetitive for a few of their students. ‘Yes, definitely [nutrition integration contributed to a greater engagement compared to our usual maths lessons]. Because it was tangible, and it was real.’ Teacher 3 The last theme relates to student understanding, learning and achievements. All students were able to tell that the CUPS program was about healthy eating, food groups and serve sizes. However, only some may have understood that this content was linked to mathematics.
The majority of the students thought that the real-life nutrition topics helped them learn. In two focus groups (classes 1 and 3), all students reported that the nutrition topics supported their mathematics learning in regard to ‘volume and capacity’, ‘measuring with cubes’ and ‘estimating’. ‘Yes, estimation you mainly use maths.’ Student, class 1 In one focus group (class 2), the students felt that the real-life context did ‘not really’ help them learn. According to the students in this group, the lessons supported the learning of some mathematical concepts including counting and multiplications and that the lessons ‘enforced more nutrition than maths’.
After receiving the CUPS lessons, all students believed that they had improved their knowledge on nutrition and healthy eating. Examples given by the children on topics that they know more about compared with before the CUPS program included the food groups, guidelines on serve sizes, number of recommended serves and differences between gender and age:‘I didn’t know this before that girls actually have to eat more dairy and boys actually have to eat more grains.’ Student, class 3
## Teaching experience
The professional development workshop was informative, valuable and ‘a great starting point’ (Teacher 3).‘Yes, I thought it was very informative and it helped me to understand what I was going to be teaching the kids.’ Teacher 1 All teachers enjoyed teaching the lessons (score 4·$\frac{0}{5}$·0), and they felt moderately confident while doing this (score 3·$\frac{7}{5}$·0). They were less confident about their nutrition background knowledge:‘My confidence grew obviously, and as I got more and more into it, as did the kids. Yes, I felt pretty confident. I was caught out a couple of times, but it was mainly about nutrition questions I couldn’t answer.’ Teacher 3 After teaching the CUPS lessons, each of the teachers expressed they would like to continue using an integrative approach to teach nutrition. ‘I would definitely like to [continue using an integrated approach to teach nutrition]. Sometimes it can be tricky, but if I was given the opportunity to, I would. It makes it a bit easier and probably more interesting for the kids as well.’ Teacher 1
## Programme benefits and challenges
All teachers agreed that being able to teach nutrition to their students was a major benefit of the programme. They expressed that they usually do not get to teach nutrition often. Another benefit was that the teachers noticed significant improvements in students’ nutrition knowledge. ‘I think, yeah probably the best thing was that I could link nutrition into the classroom because I really haven’t done that at all this year with this class. So actually teaching them about something that I am, I guess, quite passionate about as well was good.’ Teacher 1 ‘Definitely an understanding of what is in each food group, and the number of serves that they can have of each food group in a day, I think that point got across really well. I think that they have a better understanding of just everyday healthy food, v. discretionary, sometimes food.’ Teacher 3 In contrast, the teachers experienced several challenges to teaching the lessons such as limited resources and not being able to differentiate the lessons. Students might have not been able to participate due to groups being too large and a few teachers felt they were not always able to accommodate varying student knowledge as some lessons did not allow for differentiation. The following quotes substantiate these challenges, respectively:‘I think maybe just when we had to measure, the kids had to be in groups and they were estimating […]. I kind of found there probably wasn’t enough of the fake foods for the groups were so big, so I’d found some kids just kind of sitting there, not doing anything, and that’s when I think they got a bit distracted.’ Teacher 1 ‘I think it was more like I was trying to work out a way that I could differentiate it so I could make it easier for most of my class to be able to answer that […]. I found that was a little bit tricky because I could tell for some of my kids it was just going over their heads. Because that lesson was only one lesson, usually if it was something like that, a concept that I knew the kids needed to know but I couldn’t really differentiate it, then I would probably teach it over a couple of lessons. But other than that, I think the kids really got it in the end.’ Teacher 2
## Student focus groups
Focus groups were held with five students from each class including both boys and girls (n 15). Student answers were classified into three themes that are summarised in Table 2.
Table 2Themes and subthemes including illustrative quotes that emerged from student focus groups on the CUPS program (n 15)Theme and subtheme descriptionQuote Programme enjoyment All students enjoyed the CUPS lessons‘It was fun. It was nice learning something new.’ Student, class 3Student opinions varied on the activities that they enjoyed the most and the least‘I liked the sugar one where we had to figure out which had more sugar.’ Student, class 1‘All of [the activities]. Especially the lunchbox one.’ Student, class 3‘[Lesson five] was confusing.’ Student, class 2The repetitiveness of the lessons and group work were perceived as less enjoyable by a few students‘My least favourite was the one where you had to estimate the cubes because once we did it a few times it kind of got a little boring.’ Student, class 1The best things about being involved were the fun lessons and learning about nutrition‘You get to do fun lessons.’ Student, class 1‘That I learned about healthy eating.’ Student, class 2 CUPS resources and materials Almost all students enjoyed using the food models and mathematics cubes‘I liked the cubes, but they kept falling apart.’ Student, class 2‘[The food models] were so realistic.’ Student, class 3Using food models and mathematics cubes made the activities more interesting‘It makes it less confusing. […] because you don’t have to guess it.’ Student, class 2Lessons do not need many improvements except for the instructions and group work‘Just maybe more instructions.’ Student, class 2‘In smaller groups.’ Student, class 3 Student learning and achievement The real-life nutrition context helped some students learn about mathematics‘Yes, estimation you mainly use maths.’ Student, class 1Students gained knowledge on healthy eating and the guidelines‘[I now know] how much of serves we’re supposed to have a day and that.’ Student, class 1
## Programme enjoyment
All students reported that they enjoyed the CUPS lessons with a variety of examples or reasons given (score 4·$\frac{4}{5}$·0). Students stated they enjoyed the programme because it could ‘help with your health’, ‘it was challenging’ and ‘it was fun and nice learning something new’ (Students, class 3).‘I enjoyed them because they were fun.’ Student, class 1 Answers to the question ‘What kind of activities did you enjoy doing in the CUPS program?’ varied greatly. Students referred to liking specific lessons such as the lessons on food groups and serve sizes (n 1), serve size estimations (n 2), sugar volume (n 3), creating your own lunchbox (n 6) and all lessons (n 2). Furthermore, some students indicated that they enjoyed completing the assessments as part of the effectiveness trial at the start and end of the programme (n 4).
Whereas two students enjoyed the serve size estimation activities the most, others expressed they did not enjoy these lessons (n 8). Three students highlighted that these lessons were repetitive and therefore less enjoyable. Other than being repetitive, these three lessons also involved a lot of group work. A few students found it difficult or frustrating to work in such large groups sharing only limited food models:‘Well, maybe not so much the ones where we had to go around in the groups. Maybe it was just some of the people in my group weren’t cooperating well. […] Because some of the people on my group weren’t letting any other people have a turn of like giving them what their opinion of it and what they think of it, so it’s a bit frustrating.’ Student, class 3 Furthermore, four children stated that they ‘liked them all’ (Students, class 1 and class 2) or that there was ‘nothing’ (Student, class 3) they did not enjoy. Similar to the teachers, students thought the best thing about being involved in the programme was learning about healthy eating in a fun way.
## Discussion
The current study evaluated experiences and acceptability of the CUPS program that involved teaching about portion size and mathematics in primary schools. The intervention used an integrative approach and experiential learning to teach students about healthy eating, serve sizes and portion size estimations while embedding educational standards from the NSW *Mathematics syllabus* on unit of measurement. The programme was generally well received by both teachers and students, with programme-specific challenges suggesting future improvements.
Results from the interviews and focus groups revealed that the programme acceptance was relatively high. Teachers and students reported enjoying teaching and receiving the lessons, respectively. In particular, the nutrition content on healthy eating, food groups and serve size recommendations was liked and the ability to learn about these topics was valued the most. Teachers and students stated that they noticed considerable improvements in nutrition knowledge and students used the gained knowledge outside of the classroom. These positive attitudes and observations are in line with our findings of the programme’s impact on student nutrition knowledge scores which significantly increased for students receiving the lessons compared with students who did not (Follong et al., unpublished results). This clearly shows that the CUPS program could contribute to enjoyable and effective nutrition education within the primary school setting.
Student enjoyment and engagement could be further enhanced by small amendments to the teaching unit and intervention design that would result in less repetitiveness and improved group work. Lesson content was purposefully designed to be somewhat repetitive and to some extent overlap. Reasoning for this was that previous research suggested that to improve estimation accuracy, children needed more than one training session[30]. Strategies to refine the programme could be to reduce the number of lessons that involve portion size estimations by consolidating lessons. Two lessons contained the same activities but only differed in the food models used. This would also decrease the number of students per food item and thereby improve the group work that was reported as troublesome. In addition, spreading the lessons across a longer intervention period might diminish the perception of repetition. It is therefore recommended to take into account the intervention design in terms of the intervention length and spread of the lessons when developing new educational programmes to ensure effective programme implementation and optimal student engagement.
Connecting real-life nutrition topics to abstract mathematics concepts has been suggested to enhance student engagement and academic achievement(23,34–36). In the present study, teachers expressed that students were more engaged with the lessons due to its integrative nature, and some students thought that the real-life nutrition context helped them learn about mathematics. Furthermore, the use of both food models and mathematics cubes made the lessons more interesting for the students. However, teachers noted that the lessons steered quite far away from their typical teaching, and as highlighted in both interview and focus group data, most students did not identify the mathematics content in the lessons. While the connection between nutrition and mathematics was explicit within the lesson plans and activities, integration might needs to be made more explicit to the students in order for them to recognise the relevance of mathematics in real-life settings and to produce meaningful connections[37]. Therefore, it is essential that educational programmes using a similar approach incorporate teacher instructions and classroom activities that focus on the rationale an importance of the curricular integration. Future trials should assess student mathematics achievement to examine the impact of the programme in comparison with traditional mathematics lessons.
The integrative lessons were designed to incorporate learning outcomes from the NSW *Mathematics syllabus* that target volume and capacity. Despite the lessons involving learning of various mathematical concepts, teachers would prefer more in-depth content on volume and capacity. The learning of volume and capacity was perceived as very different to their usual teaching and therefore did not fully match the syllabus. Consequently, teachers indicated the CUPS lessons could not replace their usual teaching unit on this mathematical topic and might therefore add additional time to their teaching schedule[10]. It can thus not be assumed that any form of integration with core subjects would be beneficial for teachers’ classroom time. These findings highlight the complexity of integrating two or more subjects and the need for a seamless fit with the teachers’ standard teaching practices. Although previous research[18,19] and the current study show that teachers believe curricular integration may be an effective approach to enhance the implementation of nutrition education in schools, it remains unclear what teachers expectations, preferences and needs are towards using this teaching strategy. While the current study involved nutrition education that integrates mathematics concepts, teachers might prefer mathematics lessons that incorporate nutrition examples. Future research should therefore investigate teachers’ preferences and needs and explore the most suitable and effective way to use an integrative teaching approach.
Another concern that arises as a result of the limited mathematics content is the programme’s impact on student learning and understanding of portion size estimations. To accurately estimate food portions, knowledge on mathematical concepts such as volume and capacity is essential[38]. Students’ ability to understand the concepts behind portion size estimation might have been hindered by the insufficient link with volume and capacity. This was further supported by teacher’ comments on confusing content. Some students might have understood the concepts taught, whereas others found it difficult to comprehend. A reason for this might be that not all teachers were able to differentiate some lessons and therefore could not adjust for varying student abilities. There was no guidance provided in the professional development workshop and written materials for teachers to adapt or differentiate the lessons. A process evaluation of the Active for Life Year 5 program on diet and physical activity reported similar feedback from teachers, where some teachers expressed that the lessons did not always fit their students learning abilities[27]. As with this previous programme, amendments should be made to the workshop and CUPS lessons to account for different levels of ability, particularly on the mathematics content[27]. Future trials might need to consider developing educational resources together with participating teachers as this potentially increases feasibility, uptake and alignment with traditional teaching content[18,27], especially when using an integrative approach[25]. This way, teachers would also be able to ensure that the programme accommodates their students’ abilities.
Additionally, the limited number of resources was suggested to influence student learning. Adequate provision of resources is paramount for successful implementation of experiential nutrition programmes[21]. Some students might have simply not been able to participate in the activities as groups were too large to all work with the food models and cubes at the same time. As stated earlier, supplying a sufficient amount of resources would also contribute to higher student engagement and enjoyment by allowing the teachers to divide the classroom into smaller groups. This addresses the students’ comments on programme improvements and should be taken into account in future trials to enhance programme implementation and effectiveness.
Students enjoyed using the hands-on tools (e.g. food models and mathematics cubes) to estimate portion size. According to both teachers and students, these physical resources were particularly useful as visual reference when learning about appropriate serve sizes. Food replicas are frequently used in nutrition and portion size research[39,40]. Food models have several benefits over real food that make them practical for use in the educational setting. Food models do not require preparation and food waste is avoided by repeatedly using the same models[40]. Except for some of the cubes breaking apart while handling them, they were perceived as a fun tool to combine nutrition and mathematics. Mathematics cubes are commonly used in the classroom to teach children about volume and capacity[38]. These cubes have also been tested as a new portion size estimation aid in the adult population, with results showing improved accuracy compared with other aids[41]. Although the cubes were fun and familiar to the students, the intervention was not able to replicate these previous observations. In comparison with students not receiving the CUPS intervention, students did not significantly improve estimation accuracy of food portions (Follong et al., unpublished results). Age-appropriateness of the cubes as a portion size estimation aid for primary school children should be further explored.
After completing the professional development workshop, teachers felt relatively confident about teaching the unit. Their level of confidence was primarily affected by their restricted nutrition knowledge, which required some teachers to further research the content in preparation of the lessons. Similar findings were observed for a school-based diet and physical activity intervention in the United Kingdom in which teachers indicated that for almost half of the lessons more preparation time than usual was required[27]. The extra time and effort associated with lessons involving new concepts and strategies might diminish over time when teachers become more familiar with it[31]. However, the intervention length and number of lessons might have not provided the teachers with enough time and practice to do so and they may have benefitted from regular contact with the researchers throughout the intervention period. Furthermore, adequate teacher training is an important factor to successfully implement nutrition education[42]. Training has proven to improve teachers’ self-efficacy and may thus lead to more effective nutrition education[43] and better programme implementation[44]. The FoodMASTER program used a similar approach to integrate nutrition, mathematics and science in American primary schools. Findings showed that teachers involved in a 1-d training significantly increased their self-efficacy towards teaching nutrition, with largest improvements observed for teachers’ understanding of nutrition concepts[43]. A recent systematic review concluded that it is critical for teacher training to focus on nutrition content and behaviour change techniques, with some research suggesting a minimum of 20 h being ideal[45]. Therefore, expanding the duration and nutrition content of the CUPS professional development workshop might be necessary to adequately support teachers implementing the programme and ultimately facilitating student learning.
## Strength and limitations
Limitations should be considered when interpreting the data. With three teachers implementing the intervention, the sample size of the current study was small. The number of interviews and focus group conducted was therefore low and saturation might have not been obtained. Another limitation might be that both teachers and students may have given socially desirable answers. Furthermore, all teachers had an interest in nutrition and thought nutrition education was important to support their students’ health. This might have influenced the teachers’ motivation to provide the lessons and may have impacted their interview answers. Verification of findings by a larger group of participants that better represents the wider teacher and student population is essential. Although all lessons were delivered to the students by trained teachers, intervention fidelity was not examined. Potential differences in the implementation of the programme between classes might have influenced the teacher’ and student’ experiences and therefore could have impacted the findings of this evaluation study. Lastly, data analysis was performed by a single researcher, and codes were not checked by the other authors.
The current study describes qualitative data exploring both teachers’ and students’ experiences with the programme. An in-depth evaluation of the programme was performed that highlights the areas that need improvement not only for the CUPS intervention but also for nutrition education programmes in general. Taking into account the perspectives of not only the teachers but also the students ensured that programme enhancement benefits both parties. Since the need for qualitative research on integrative nutrition education is warranted[25], our observations provide novel insights that inform future research about integrating nutrition on what works well and what aspects require further investigation.
## Conclusions
Our findings provide insight into major challenges and successes of implementing an integrative nutrition programme, its effect on student learning and enjoyment and suggestions for improvements. The current study is one of only a few reporting on the impact of integrative teaching on teachers’ time barriers to implement nutrition education. Due to insufficient level of depth on specific mathematical concepts and teachers’ lack of nutrition knowledge, this programme might have not reduced teachers’ time constraints. This, together with difficulties differentiating for student’ ability and limited resources, may have impacted on the effectiveness of the intervention to demonstrate improved estimation accuracy. Nevertheless, teachers and students experienced the programme as enjoyable and valuable specifically in regard to nutrition. To build a more complete picture of cross-curricular teaching, a better understanding of teachers’ preferences and needs towards nutrition integration into the primary school curriculum is fundamental. It is recommended that researchers work closely together with participating teachers to develop effective nutrition lessons that are embedded into other school subjects.
## Financial support:
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
## Conflict of interest:
There are no conflicts of interest.
## Authorship:
B.M.F., EP, A.M., C.E.C. and T.B. conceived of the study design. B.M.F. and A.M. collaborated with primary school teachers to develop the teaching unit. B.M.F. recruited participants, trained participating teachers, performed the interviews and focus groups and analysed the transcripts. B.M.F. led manuscript preparation with support from E.P., A.M., C.E.C. and T.B. All authors reviewed, edited and approved the final manuscript.
## Ethics of human subject participation:
The current study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by The University of Newcastle Human Research Ethics Committee (H-2018-0492) and the Catholic Diocese of Newcastle-Maitland in New South Wales, Australia. Written informed consent was obtained from all subjects.
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|
---
title: Implementation of food education in school environments improves pupils’ eating
patterns and social participation in school dining
authors:
- Aija L Laitinen
- Amma Antikainen
- Santtu Mikkonen
- Kaisa Kähkönen
- Sanna Talvia
- Silja Varjonen
- Saila Paavola
- Leila Karhunen
- Tanja Tilles-Tirkkonen
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991779
doi: 10.1017/S1368980022002154
license: CC BY 4.0
---
# Implementation of food education in school environments improves pupils’ eating patterns and social participation in school dining
## Body
Eating patterns are established during childhood(1–4) and influence well-being into adulthood(5–8). Despite the acknowledged importance of healthy eating patterns on well-being, eating patterns among Finnish school-aged children seldom fulfill the dietary recommendations(9–12). Consumption of vegetables, whole grain products and products high in unsaturated fat is lower than recommended, whereas consumption of products high in sugar, salt and saturated fat is above recommended levels. In addition, children’s meal patterns are often irregular, and snacking is common[13].
School is an excellent arena for promoting healthy eating patterns, as it reaches practically all children in Finland[14,15]. The Finnish national curriculum[16] guides primary schools to set objectives and implement food education but does not offer any practical tools or model to support implementation. A previous study showed that the ‘Tools for Feeling Good’ Finnish food education model was effective in promoting regular meals, vegetable consumption and eating varied school lunches among fifth graders[17].
A multitude of individual factors (e.g. age, genetics, body image), social and physical environments (e.g. parental feeding practices, peers, school) influence the eating patterns of school-aged children[18]. Children’s eating patterns and the prevalence of body dissatisfaction have been raised as an acute public health concern, and thus multicomponent health-promotion strategies are needed at various levels[19]. One promising approach is the Satter Eating Competence Model (ecSatter), which emphasises positive and flexible attitudes towards food and eating[20]. Eating competence has been found to be related to healthier eating patterns among children and adolescents[21].
In Finland, a free hot meal is offered at school daily to all pupils from pre-school to upper secondary level. According to the recommendations of the Finnish National Nutrition Council[22], a school meal should include a warm main dish, salad, fibre-rich bread with margarine and milk, buttermilk or a plant-based drink. Each pupil consumes approximately 2500 hot school lunches over their school years. A previous study demonstrated that eating a balanced school meal was associated with more regular meal patterns, the availability of healthier foods at home and an overall healthier diet[13]. However, only a small proportion (9 %) of pupils eat the recommended balanced school meal, although it is available for everyone[23].
The ‘Tasty School’ model[24] provides tools for implementing food education in primary schools and thus helps schools to meet the requirements of the School Meal Recommendations[22] and Finnish National Curriculum[16]. The Tasty School model was developed in cooperation with primary schools and nutrition, food education and basic education experts by utilising a previous nutrition curriculum, Tools for Feeling Good, as a starting point[17]. The food education model extended the earlier curriculum to cover the entire primary school system (grades 1–6, age 7–12 years) and was diversified through a website that included an idea bank with learning materials, a self-assessment questionnaire and online training for teachers. The model was based on several theoretical approaches: Self-Determination Theory[25], the Health at Every Size approach[26], Eating Competence[20], Sensory-Based Learning[27] (i.e. Sapere), Mindful Eating[28] and Intuitive Eating[29]. The Tasty School has a holistic approach as it integrates food education pedagogy in school subjects, school dining and the school environment. The model offers a wide set of tools for evaluating, planning and implementing food education in primary schools. At the pupils’ level, the aims of the Tasty School were promoting healthy eating patterns especially during school days, increasing positive attitudes towards school dining, and increasing social participation in food education in schools, as well as supporting positive body image and eating competence.
The aim of the current study was to investigate the effects of the Tasty School food education model on pupils’ eating patterns and experiences of school dining, eating competence and perception of body image. We also aimed to assess the implementation strength of the Tasty School.
## Abstract
### Objective:
Schools can be an effective arena for food education. The Tasty *School is* a tailored teacher-driven food education model that provides tools for implementing food education in primary schools. This study aimed to investigate the effects of the Tasty School model on pupils’ eating patterns and experiences. We also aimed to assess the implementation strength of the Tasty School.
### Design:
A quasi-experimental study was conducted during one school year 2019–2020 in fifteen intervention and ten control schools. The intervention schools implemented the Tasty School food education model. The pupils completed web-based baseline and follow-up questionnaires in class during a school day. The principals were interviewed after the intervention. The data were analysed using a mixed-effects model for repeated measures, accounting for the implementation strength and selected standardisation effects.
### Setting:
A total of twenty-five general Finnish primary schools.
### Participants:
1480 pupils from grades 3−6 (age 8–12 years) from five municipalities in Finland.
### Results:
Percentages of pupils eating a balanced school meal increased in schools where food education was actively implemented ($$P \leq 0$$·027). In addition, pupils’ experience of social participation in school dining strengthened in schools where the Tasty School model was implemented (5-point scale mean from 2·41 to 2·61; $$P \leq 0$$·017).
### Conclusions:
Healthy eating patterns can be promoted by the active implementation of food education in primary schools. The Tasty School model offers a promising tool for developing healthy eating patterns and increasing social participation among pupils not only in Finland, but also potentially in other countries as well.
## Setting and participants
The present study had a quasi-experimental design[30] with fifteen intervention schools, which were supported to implement food education based on a tailored, teacher-delivered Tasty School model and ten control schools that did not receive food education support[24]. The study was conducted during the school year 2019–2020 and included baseline and follow-up questionnaires addressed to all grades 3–6 pupils in participating schools. Pupils’ parents were informed about the study via the schools’ web interface tool. The participation of the pupils was voluntary, but none of the parents declined to approve their child‘s participation in the study. Additionally, all principals of the intervention and control schools were individually interviewed by telephone after the intervention in June or October 2020.
The recruitment process commenced in January 2019 by contacting municipalities’ directors of education and inviting them to participate in the study. Based on their previous cooperation, six municipalities were contacted, and five municipalities located in southern and eastern Finland participated in the study. The population of these municipalities ranged from 10 000 to 120 000 and eight-three primary schools were located across the five municipalities.
Recruitment of schools from participating municipalities was carried out in spring 2019 by the directors of education. The aim was to recruit fifteen intervention and fifteen control schools in keeping with the project’s resources. Due to the varied resources of schools, the participating schools were able to choose their status in the study, either as an intervention school or a control school. The directors of education were not willing to order schools to participate and thus, all forms of participation were known in advance. At the end of the recruiting process when the maximum number of intervention schools had been recruited, the rest of the interested schools were invited to participate as control schools. At least one control school was recruited from each participating municipality.
Altogether fifteen intervention and thirteen control schools participated in the study (Fig. 1, three control schools withdrew). Each municipality had at least four participating schools, and all primary schools participated in one municipality. The number of pupils ranged from 51 to 424 (mean 192) in intervention schools and from 50 to 400 (mean 205) in control schools. In Finland, in 2019, the average number of pupils in a primary school was 169[31].
Fig. 1Study design, study population and measurements
## Measures
Pupils from grades 3–6 (aged 9–12 years) in the intervention and control schools completed a web-based study questionnaire in their classrooms with the support of a teacher two times, at baseline (August or September 2019, n 2814) and at the end of the school year (April 2020, n 1781). Pupils answered web-based questionnaires on the Formjack platform (version 3.1, Eduix Ltd, Finland 2008) during the school day with tablets or computers using personal response codes. The questionnaire contained items concerning eating patterns during school days, experiences of school dining, eating competence and body image.
Frequencies of eating school lunch were evaluated using a four-point scale: never, 1–2 times a week, 3–4 times a week, every school day, scored 1, 2, 3 and 4. Frequencies of eating different components of school meals according to the school meal recommendations[22] warm main dish, salad or vegetable, salad dressing or oil, bread, margarine and drink (milk, buttermilk or a plant-based drink) were evaluated with the same scale as frequency of school lunch described above. In the current study, the balanced school meal was defined to contain a main dish, salad, bread, margarine and drink (milk, buttermilk, or a plant-based drink).
Experiences related to school dining were assessed using 16 statements (Table 1). These statements were evaluated using a five-point Likert scale: totally disagree, somewhat disagree, neither agree nor disagree, somewhat agree, totally agree, scored 1, 2, 3, 4 and 5. The statements were based on a previously published questionnaire[32] that was developed further for the purposes of the current study by the study group. The questionnaire did not include a point-scale but instead contained individual statements. Nine statements were partly modified from the previously used questionnaire, and the other seven statements were developed by the research team.
Table 1Pupils’ experiences related to school diningStatement* (scores: min 1, max 5)Committed intervention group n 536Uncommitted intervention group n 367Active control group n 243Inactive control group n 334 P value† The personnel are friendly Mean at baseline (sd)4·360·874·100·934·430·764·370·90 Mean at follow-up (sd)4·350·824·081·054·350·864·300·860·665It is peaceful in the dining hall Mean at baseline (sd)2·911·162·431·222·921·112·801·11 Mean at follow-up (sd)2·991·102·631·082·941·052·991·130·183There is no too much noise in the dining hall Mean at baseline (sd)2·791·262·431·342·851·252·671·25 Mean at follow-up (sd)3·011·222·651·203·031·182·881·230·987It is cozy in the dining hall Mean at baseline (sd)3·821·163·331·243·901·083·671·20 Mean at follow-up (sd)3·891·023·361·193·801·123·671·130·441The food queue runs smoothly Mean at baseline (sd)3·661·133·541·223·631·193·841·13 Mean at follow-up (sd)3·651·093·571·073·711·023·881·080·841School lunch is a nice moment in a day Mean at baseline (sd)4·111·013·711·183·911·113·881·19 Mean at follow-up (sd)4·210·973·701·174·160·943·961·080·062Teachers guide dining appropriately Mean at baseline (sd)4·061·063·741·124·101·094·021·13 Mean at follow-up (sd)4·070·993·951·09I 4·031·15I 4·151·04U,A 0·013I get to eat the amount I want at the school meal Mean at baseline (sd)4·111·144·081·204·081·234·221·16 Mean at follow-up (sd)4·201·14I 4·111·134·141·224·021·25C 0·012I have enough time to eat Mean at baseline (sd)4·191·073·921·264·311·034·321·07 Mean at follow-up (sd)4·201·074·001·214·311·034·271·050·503My table mates are very well behaved Mean at baseline (sd)3·951·024·120·924·190·874·110·96 Mean at follow-up (sd)4·000·934·110·954·130·914·130·860·692School meals are healthy Mean at baseline (sd) 4·320·933·991·024·190·914·160·98 Mean at follow-up (sd)4·350·874·071·004·340·864·191·020·421School meals are tasty Mean at baseline (sd) 3·781·093·161·243·481·133·421·22 Mean at follow-up (sd)3·821·043·071·283·601·013·341·150·073School meals look good Mean at baseline (sd)3·691·113·111·263·531·093·271·23 Mean at follow-up (sd)3·641·062·991·243·571·043·281·130·291School meals help me to stay healthy Mean at baseline (sd)3·971·053·631·173·821·023·821·12 Mean at follow-up (sd)4·060·973·691·134·041·043·831·140·153School meals help me to manage Mean at baseline (sd)4·161·063·821·214·061·044·081·16 Mean at follow-up (sd)4·270·95U,I 3·921·15C,A 4·230·96U 4·011·14C 0·024I get to participate in planning school dining Mean at baseline (sd) 2·411·302·411·352·291·272·531·39 Mean at follow-up (sd)2·671·28A,I 2·551·282·441·36C 2·461·31C 0·017C = committed intervention group, U = uncommitted intervention group, A = active control group, I = inactive control group.*Perspectives and experiences of school dining were evaluated using a 16-item query with a five-point Likert-scale: totally disagree, somewhat disagree, neither agree nor disagree, somewhat agree, totally agree. Questionnaire was edited from the study of Nutrition and Wellbeing of Secondary School Pupils[28].† P value of the interaction. The data were analysed with a mixed-effects model for repeated measures accounting for the intervention effect and selected standardising effects. The top index indicates which group the significance is given with. Gender and class level were standardised in the analysis.
Eating competence was measured using a Finnish translation[21] of the Satter Eating Competence Inventory (ecSI 2·0TM)[33,34]. The Satter Eating Competence *Inventory is* composed of four subscales (eating attitudes, contextual skills, food acceptance and internal regulation) comprising sixteen statements with a five-point Likert-scale: never, rarely, sometimes, often, always and scored 0, 0, 1, 2 and 3. A total score of at least thirty-two points indicates eating competence.
Body image was evaluated using three statements (I feel good about my body, I am attentive to my body’s needs and I am comfortable in my body) from a Finnish translation of the Body Appreciation Scale-2[35,36]. As the Body Appreciation Scale-2 for children[37] was not available in Finnish. Thus, Body Appreciation Scale-2 was piloted with a small group of children and, based on the pilot, three questions were selected which best answered our research question. Body image was evaluated with a five-point Likert-scale: never, rarely, occasionally, often, always, scored 1, 2, 3, 4 and 5. Subjective experience of one’s own health status was evaluated by one question from the School Health Promotion Study[38] with a modified five-point Likert scale: very poor, quite poor, moderate, quite good, very good, scored 1, 2, 3, 4 and 5.
## Evaluation of the implementation strength
All principals of the intervention (n 15) and control schools (n 10) were individually interviewed to investigate the implementation strength of food education during the past school year. The interviews were conducted by telephone after the intervention in June or October 2020. Principals in the intervention schools were asked: [1] whether the Tasty School was included into the school year plan; [2] whether food education projects in which the entire school participated were implemented during the previous school year; [3] whether any positive changes were noticed at school (subjective evaluation) and [4] whether the school intended to take advantage of the Tasty School next school year. Principals in the control schools were asked: [1] whether food education projects in which the entire school participated were implemented during the previous school year and [2] whether the school took advantage of the Tasty School materials (freely available on the internet). In addition, principals were invited to give open feedback about the programme during the interview.
## Procedures
All intervention schools were supported to implement the Tasty School model during 1 school year from September 2019 to March 2020. Each intervention school selected 1–2 coordinating teachers who encouraged school personnel to implement food education in the school. Before the intervention, a researcher introduced the model to coordinating teachers. These coordinating teachers received a 1-d live training about the Tasty School at the beginning of the school year in September 2019. Researchers kept in contact with the coordinating teachers by email or telephone once a month throughout the school year.
For each intervention school, the starting point of the implementation was to complete a self-assessment questionnaire at the beginning of the school year concerning the state of their school’s food education and school lunch arrangements. The survey covered five themes: management and engagement, integration of food education, implementation of school meals, collaboration and support. The questionnaire was completed by a multi-professional group. Schools were instructed to invite at least a principal, a teacher and a food service employee into the multi-professional group, but others were also welcomed to participate. Based on the self-assessment questionnaire, each intervention school was advised to choose development targets to guide the implementation of the Tasty School at school level. The self-assessment questionnaire was used only for assessing the current state of food education and setting targets for schools. The information gathered from it was not used as research data.
Teachers in the intervention schools were instructed to utilise the Tasty *School idea* bank, which contained over 100 development or action ideas for food education in school. Each classroom teacher was instructed to put into practice at least one food education idea monthly (duration at least 30 min). This would mean at least seven ideas per school year (from September to March). Teachers were instructed to integrate these ideas into the school’s daily routines during the school year. The idea bank was freely available on the website (www.maistuvakoulu.fi, only in Finnish) and had three main sections: ideas for lessons, school dining and collaboration in food education. The ideas were grouped by topic for example, food culture, food systems, sensory-based learning (Sapere), media literacy, sustainable diets, body image and nutrition and health. Classroom teachers were also encouraged to complete during the autumn semester a 3-hour independent online training about food education that was freely available on the Tasty School website.
## Supplemental intervention support during the school year
A researcher visited each intervention school two to three times during the intervention year. A monthly newsletter including practical ideas for food education was emailed to all the teachers in the intervention schools. Weekly food education tips were given through the Tasty School Facebook and Instagram pages. A monthly food education blog entry including topical issues and tips was published on the Tasty School website. Additionally, during the intervention year, one optional webinar was available for teachers and one for principals. The intervention schools received a printed copy of the Finnish Recommendations for School Meals and a toolkit for sensory-based food education activities. No financial support was given to any school.
## Data analysis
The principals’ interviews were analysed using qualitative content analysis. Data were first coded and then codes were organised into thematic statements. An intervention school was classified as a committed school if at least three statements were completed concerning implementation of the Tasty School food education model (see Table 2). A control school was classified as an active school if at least three statements in the principals’ interview were completed (see Table 3). To investigate the effects of the implementation strength of food education, schools were divided in the data analysis into four groups (committed intervention, uncommitted intervention, active control and inactive control) according to the strength of implementation reported in principals’ interviews.
Table 2Description of planning and implementation of the tasty school at school level and whole school commitment to the intervention according to principal interviewsStatementIntervention schoolI1I2I3I4I5I6I7I8I9I10I11I12I13I14I15Planning The self-assessment questionnaire was completed at baseline. XXXXXXXXXXXXXX The self-assessment questionnaire was completed at follow-up. XXXXXXX Coordinating teacher(s) participated the 1-d live training. XXXXXXXXXXXXXXX The Tasty School was written into the school year plan. XXXXXXXXXXXXXXX The school also planned to use the Tasty School during the next school year. XXXXXXXXXXXXImplementation The school staff co-operated with the food service staff. XXXXXXXX The principal was active in promoting food education on the whole school level. XXXXXXXX The school actively improved school dining. XXXXXXXXX The school organised a food educational project or workshop that involved all pupils. XXXXXXXSummary* The school was committed to the intervention. XXXXXXX*The school was classified as a committed school if at least three statements were completed concerning implementation of the model.
Table 3Description of control schools’ food education activity according to principal interviewsStatementControl schoolC1C2C3C4C5C6C7C8C9C10The school staff co-operated with the food service staff. XXXXXXThe principal was active in promoting food education on the whole school level. XXXXThe school actively improved school dining. XXXXXThe school organised a food educational project or workshop that involved all pupils. XXXXXSummary* The school was classified as active in food education. XXXX*The school was classified as an active school if at least three statements were completed. Three control schools did not participate in follow-up measurements and thus were not included.
Statistical analysis was performed using SPSS (IBM SPSS, version 27.0, 2020). The criterion for significance was set to be $P \leq 0$·05. Descriptive statistics (means, standard deviations and frequencies) were calculated separately for baseline and follow-up. Associations with frequencies of eating school lunch, perspective variables and explanatory variables were analysed using linear mixed-effects models for repeated measures in order to account for the multi-level data structure by clustering the repeated outcome measures at baseline and follow-up within pupils, who in turn, were clustered within schools. However, the clustering effect of school was found negligible when the activity of schools was considered. Thus, to avoid unnecessary complexity in the analysis, clustering effect of school was not included in the final model. The model was adjusted for gender and class level (grades 3–6), and included main effects for time and four-level intervention groups with intervention group × time interaction. Cross-tabulated frequencies between eating competence and the variable describing eating a balanced school meal were analysed with the χ 2-test.
## Dropout analysis
Dropout analysis was conducted with the Mann–Whitney U test to examine possible selection bias between pupils (n 1329) who withdrew after the baseline and pupils (n 1480) who remained in the study. Few differences in perceived health status and food choices at school lunch were found. The mixed-model analysis was able to account for these differences.
A comparison of variable means showed that pupils who did not respond to the follow-up questionnaire experienced their health status as being significantly worse (means 1·7 and 4·30); in addition, they ate a main dish and salad at school meals less often than those pupils who answered both baseline and follow-up questionnaires. Nevertheless, there were no differences between the two groups in eating a balanced school meal every day, perspectives on school dining, eating competence total scores and perception of body image.
## Final study population and classification of participating schools
The final study sample consisted of 1480 pupils from grades 3 to 6 who had answered questionnaires both at baseline and at follow-up. Supplemental Table 1 displays descriptive information about participating pupils. In total, seven schools were classified as committed intervention, six schools as uncommitted intervention, four schools as active control and four schools as inactive control group (Tables 2 and 3). Descriptive information about study participants reveals that pupils in uncommitted intervention schools experienced their health status as being worse than in other schools reported (see online Supplemental Table 1).
## Eating patterns at school meal
At baseline, 88 % of pupils ate school lunch every school day, but only 10 % of pupils ate a balanced school meal every school day, including main dish, salad, bread with margarine and drink (milk, buttermilk or a plant-based drink). Consumption of a balanced school meal increased in the committed (from 10·7 % to 16·4 %) and uncommitted (from 8·1 % to 9·0 %) intervention groups as well as in the active control group (from 4·7 % to 11·2 %) during the school year ($$P \leq 0$$·027, Fig. 2). In the inactive control group, the consumption of a balanced school meal decreased. The change was greater among pupils in the committed intervention and active control groups than in the uncommitted intervention group.
Fig. 2Pupils eating a balanced school meal daily at baseline and follow-up situations. The data were analysed with a mixed-effects model for repeated measures accounting for the intervention effect and selected standardising effects. P value of the interaction is 0·027. The significant differences in pairwise comparisons made with least-squares difference method are given between ○ Committed intervention group and Uncommitted intervention group, as well as ● Committed intervention group and Active control group. The balanced school meal contains main dish, salad, bread, margarine and drink (milk, buttermilk or a plant-based drink)
## Experiences related to school dining
Pupils’ experiences of school dining at baseline and follow-up are presented in Table 1. At baseline, 45 % of pupils expressed that there is not too much noise in the dining hall, 62 % that the dining hall is cozy, 70 % that the school meal is a nice moment in the day, 78 % that they have enough time to eat and 56 % that school meals are tasty. In the follow-up pupils, especially in the committed intervention group, experienced that their opportunities to participate in school dining planning increased, and pupils in the inactive control group felt their opportunities decreased ($$P \leq 0$$·017). Pupils’ opportunities to eat the amount of food they desired at the school meal increased in both committed and uncommitted intervention groups ($$P \leq 0$$·012). At follow-up situation, the pupils in committed intervention, uncommitted intervention and active control groups were more likely to report that the school meal helps them to manage, whereas in the inactive control group this experience decreased ($$P \leq 0$$·024). Teachers’ appropriate guidance at the school meal increased in the inactive control group ($$P \leq 0$$·013).
## Eating competence
46 % of all pupils were classified as competent eaters (girls 47 %, boys 44 %), and the eating competence mean total score was 30·0 at baseline. Eating competence was associated with eating more commonly a balanced school meal (compare 5·5 % to 15·0 %, $$P \leq 0$$·001). Eating competence total score increased ($$P \leq 0$$·025, see online Supplemental Fig. 1) in the committed intervention group (baseline 29·4 and at follow-up 31·5). The increase was found specifically in food acceptance subscore ($$P \leq 0$$·001), which increased significantly in all the groups, except in the uncommitted intervention group ($$P \leq 0$$·001, see online Supplemental Table 2).
## Perception of body image
A total of 41 % of pupils (girls 35 %, boys 47 %) always felt good and 35 % often felt good (girls 35 %, boys 35 %) about their bodies. A total of 9 % of pupils (girls 12 %, boys 6 %) never or rarely felt good about their bodies. No significant changes were found in perception of body image during intervention or between study groups (see online Supplemental Table 3).
## Discussion
This study found that only a minority of pupils (10 %) consumed all components of school meals as recommended by the Finnish National Nutrition Council[22]. Thus, the current study strengthened earlier study findings that balanced school meals are consumed rarely[13], although they are offered to all pupils free of charge. The current study also found that active implementation of food education in the school environment can improve pupils’ eating patterns—eating a balanced school meal, even without making any actual changes to the composition of the school meal, as previously reported[17]. In practice, school personnel selected actions they felt were needed and many schools changed the way the salad was offered to be more attractive, encouraged tasting and enhanced the atmosphere of the dining hall by lowering noise with weekly quiet dining.
The main aim of the Tasty School model was to offer primary schools an inclusive model for food education, which has shown to be an effective strategy[39]. According to the results, the model achieved this goal. The previous study showed that according to teachers’ and principals’ experience, the Tasty School model is feasible for food education in primary schools[24]. The current study adds to our understanding of the effects of Tasty School on pupils, i.e. whether the model achieves its intended outcomes. The pupils in the committed intervention school experienced higher social participation in planning school dining than pupils in control school. In practice, teachers in the intervention schools created common rules for school dining together with pupils and organised workshops where pupils carried out SWOT (strengths, weaknesses, opportunities, threats) analysis for school dining.
In this study, 46 % of pupils were classified as eating competent. The amount is slightly lower than in an earlier study in which 58 % of pupils were classified as eating competent[21]. However, it should be noted that in this study, subjects were younger than in the previous study. More information is needed on the development of eating competence with age. In an earlier study involving adults, eating competence was more common among older subjects[40]. Interestingly, eating competence was statistically associated with eating a balanced school meal. The finding reinforces the earlier findings[21] that the thematic areas of the ecSatter model could be important targets for health promotion. Eating competence did not increase after the intervention except in food acceptance, one of the eating competence subscores. In the present study, the use of Sapere[27] sensory-based education activities may be one reason for increased food acceptance among the pupils in the committed intervention school. Sapere is an essential part of the Tasty School model. In practice, by using Sapere activities, pupils were offered an opportunity to explore vegetables and other foods by smelling, tasting, touching, seeing and hearing. Earlier interventions using the Sapere method in nutrition education have also been successful in reducing food neophobia, encouraging school-aged children to try new, unfamiliar foods and expand their food repertoire[27,41,42].
Traditionally food and nutrition education at school has not focused on pupils’ body image[43]. Themes related to body image were included in the Tasty School model, as body image dissatisfaction affects eating patterns negatively[44]. At baseline, fewer than half (41 %) of pupils always felt good about their bodies, and 9 % of pupils never or rarely felt good about their bodies. Similar results were found in Spain, where 43 % of pupils of the same age (9–12 years old) were satisfied with their bodies[45]. In future research, an adapted version of the questionnaire Body Appreciation Scale-2 for children (BAS-2C)[37] could be a useful measure to get more specific information on children’s body image.
No changes in perception of body image were observed during the intervention in the current study. The result is similar to the findings of our previous study among fifth grade pupils[17]. In line with the principles of the Health at Every Size approach[26], this can also be regarded as a positive result, indicating that implementing the Tasty School model did not cause harm to pupils’ body image. The results of the current study suggest that there might be gender differences in body image of children. Moreover, body image is constructed of multi-component factors and strongly affected by, for example, media[46] and social media[47]. It could thus be assumed that changes in perception of body image require a more intensive or longer period of intervention. The development of a balanced body and food relationship for children and adolescents needs support from adults, and it is therefore important to consider the manner in which food education is delivered.
The implementation strength of food education influenced the observed changes in pupils’ eating patterns and experiences related to school dining. Our findings suggest that commitment at the whole school level is important, and school administrators could increase the effectiveness of the actions implemented by teachers to promote food education. The crucial role of school administrators or principals has also been demonstrated in previous studies from South Korea and China, which concluded that a school administrator’s lack of priority for nutrition education or an administrator not requiring teachers to teach nutrition is a barrier to implementation[48,49].
The current study demonstrated that in addition to evaluating results of individual food education actions, it is also important to evaluate implementation. Approximately half of the intervention schools were classified as uncommitted intervention schools. These schools were not able to implement Tasty School as expected. The results of the current study do not reveal reasons for unsuccessful implementation. However, the self-assessed health status of pupils was worse in uncommitted intervention schools. This might have affected the whole school culture and routines, challenging teachers’ work and limiting their resources for further developmental work. Finally, almost half of the control schools were classified as active control schools based on the post-intervention interviews of school principals. In Finland, food education is included in the Finnish Basic Education Curriculum[16], and thus we could not prevent control schools from applying active food education as part of their school culture. It was thus encouraging to observe that any activities related to food education were accompanied with favourable outcomes.
## Strengths and limitations
A strength of the current study was the large number of study subjects[50]. The research data were also collected extensively across Finland; thus, the results are not limited to a particular area. Furthermore, the schools varied in size and operated in several municipalities, so the results are not the result of an individual municipality’s resources or curriculum. Another strength was that the intervention was carried out as part of normal schoolwork (e.g. conducted by teachers, not researchers), improving the applicability of the results to practice.
A further strength of this study was that the interpretation of the results considered the intensity of the implementation of food education and thus was able to compare schools with respect to implemented food education. The data were collected using internet-based questionnaires that the pupils completed on schooldays. In this way, the study did not select participants and the data can be seen as descriptive. However, data were collected using self-reported questionnaires instead of objective measures to assess eating patterns.
The study also has several limitations. As a real-life quasi-experimental study, this intervention may have been subject to selection bias. Participating schools self-selected to participate either as an intervention or control school and thus schools could not be randomised into research groups. However, the schools were divided into four groups according to strength of food education implementation, which decreased the possibility of bias in the results. To confirm our findings, future studies should use randomised study designs and intention to treat analyses.
The intervention study was conducted in the 2019–2020 school year, during which the COVID-19 pandemic emerged and forced schools to shift into remote teaching 1 month before the follow-up measurements. Although pupils were instructed to think of an ordinary school day at school when answering the follow-up questionnaire, we cannot rule out the potential effects of this unusual situation. However, the situation was the same in all participating schools, and thus the intervention–control study design likely alleviated these possible effects. Instead, the COVID-19 pandemic might not have greatly impacted teachers’ resources and thus the implementation strength of food education because the pandemic was present only during the last month of the intervention.
On the other hand, the COVID-19 pandemic might have affected the dropout rates. Lower dropout rates in intervention groups indicated their higher commitment to the study. However, the baseline dropout analysis found no significant differences between the pupils who withdrew compared to those who completed the study. Finally, as a quasi-experimental study, the study frame might be exposed to selection bias.
The observed changes in eating patterns were, though encouraging, rather small. The duration of the current study was one school year, and it could be expected that with a longer duration of food education, stronger effects could be seen. Additionally, the holistic approach to food education should be more acknowledged and promoted[43] and should also be considered in the education of future teachers.
## Conclusions
Commitment at the school level and active implementation of food education had beneficial effects on pupils’ eating patterns and experiences of school dining. The Tasty School food education model offers an effective tool to primary schools for promoting healthy eating patterns and social participation in school dining among pupils not only in Finland, but also potentially in other countries as well.
The current study indicates that whole school commitment and the activity of food education are crucial for beneficial outcomes on pupils’ eating patterns. School administrators and curricula must encourage planning and implementing food education activities and practices through the whole school year to ensure the effective strength of the implementation of food education.
## Conflicts of interest:
All authors of this article declare they have no conflicts of interest.
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|
---
title: Neighbourhood food typologies, fast food outlet visitation and snack food purchasing
among adolescents in Melbourne, Australia
authors:
- Venurs HY Loh
- Maartje Poelman
- Jenny Veitch
- Sarah A McNaughton
- Rebecca Leech
- Anna Timperio
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991780
doi: 10.1017/S1368980021004298
license: CC BY 4.0
---
# Neighbourhood food typologies, fast food outlet visitation and snack food purchasing among adolescents in Melbourne, Australia
## Body
Overweight and obesity during childhood and adolescence is a global public health concern[1]. It has been established that individuals who are overweight in the early years are linked to obesity, chronic diseases and premature death in later years[2]. Although obesity and chronic diseases are largely preventable by healthy lifestyles, including a healthy diet, young people worldwide are increasingly consuming fewer healthy foods (e.g. fruits and vegetables, wholegrains and dairy) and more unhealthy foods (e.g. ultra-processed and energy-dense food)(3–6). Fast food and pre-packaged snacks are major sources of energy-dense food intake, and consumption of these foods has increased substantially among adolescents in high-[4,7] and low-to-middle-income countries[8] in the past decades.
There are multiple socio-ecological factors that shape dietary behaviour among young people, including the local food environment[9]. Local food outlets facilitate the opportunity for residents to visit, purchase and consume both healthy and unhealthy food items[9]. Over the past decade, the food environment in Australia has changed remarkably, and there is an overabundance of unhealthy food options compared with healthy food options available(10–12). For example, a recent study in Metropolitan Melbourne observed a shift towards a greater dominance of unhealthy food outlets relative to healthy food outlets across Melbourne from 2008 to 2016[13]. An abundance of energy-dense and unhealthy food has been shown to influence an individual’s propensity to choose healthy food options[14].
The neighbourhood food environment may have a particularly strong influence on adolescents’ dietary behaviours as they may be more restricted than adults in terms of their ability to travel independently beyond their neighbourhood[15]. Adolescence is also a developmental period characterised by asserting their independence in relation to food choices and the need for social affirmation from peers[16]. However, studies that have explored neighbourhood food outlet exposure and dietary behaviours among adolescents are few and the findings are inconsistent. Studies among adolescents from the USA[17,18] and Canada[15,19] have found positive associations between the proximity, availability or density of unhealthy food outlets near home (fast food, convenience stores and corner stores) and the purchase of snack food and fast food, while other studies from the USA[20] and Denmark[21] found no associations between the proximity and availability of stores in neighbourhood food environment and the purchase of fast food and junk food.
A possible explanation for the mixed findings may be due to the heterogeneity of food environment exposure measures used across studies[22]. Geographic Information Systems are one of the most commonly used methods to assess food environments objectively. Geographic Information System-based measures have been used to assess availability of food outlets using binary (i.e. presence/absence) or non-binary (i.e. density, count) data as proxies for exposure. However, these measures may show different associations with dietary behaviours[23]. A common limitation of the literature is the assessment of only one or a limited selection of food outlets, usually fast food outlets or supermarkets, to assess the relationship between neighbourhood food outlet exposure and dietary intake or behaviours. However, other types of food outlets (e.g. cafes and bakeries) and the mix of food outlets available may influence food choice and shape dietary behaviour. Studies that have incorporated various food outlets using measures of relative availability (e.g. ratio or proportion of supermarkets to fast food outlets) have observed positive and more robust associations with dietary or weight-related outcomes than absolute availability of food outlets[10,24,25]. While this suggests that relative measures may be better predictors for dietary behaviour as they reflect the balance of the food environment, the use of relative measures has limitations[26,27]. For example, three convenience stores and a café would be treated the same as six convenience stores and two cafés; however, these food environments could have different associations with dietary behaviours.
Previous studies have found evidence to suggest that characteristics of the food environment tend to aggregate to form typologies[27,28]. Exploring clustering of different types of food outlets to develop typologies of food environments may offer a data-driven way to incorporate data on a range of outlets to determine influences on dietary behaviour. However, few studies have used data-driven approaches to characterise typologies within food environments[29] and the relationship between these typologies and food choice and purchasing. One study conducted among children in Melbourne found that those exposed to environments close to home characterised as having a variety of food outlet types had a healthier dietary pattern during adolescence than those residing in a neighbourhood characterised as having few types of outlets (mainly convenience stores and cafes/restaurants)[30]. While this suggests that exposure to a range of food outlets may alter the impact of convenience stores and fast food outlets by providing more options, that study was based on presence or absence of food outlet types and did not consider the number of outlets.
To better reflect how food outlets cluster to form typologies, it is important to incorporate both the variety and quantity of food outlets near home. This study aimed to examine associations between typologies of neighbourhood food environments and fast food outlet visitation and snack food purchasing behaviour among adolescents living in metropolitan Melbourne, Australia.
## Abstract
### Objective:
Despite the increased attention on neighbourhood food environments and dietary behaviours, studies focusing on adolescents are limited. This study aims to characterise typologies of food environments surrounding adolescents and their associations with fast food outlet visitation and snack food purchasing to/from school.
### Design:
The number of food outlets (supermarket; green grocers; butcher/seafood/deli; bakeries; convenience stores; fast food/takeaways; café and restaurants) within a 1 km buffer from home was determined using a Geographic Information System. Adolescents’ self-reported frequency of fast food outlet visitation and snack food purchasing to/from school. Latent Profile Analysis was conducted to identify typologies of the food environment. Cross-sectional multilevel logistic regression analyses were conducted to examine the relationships between food typologies, fast food outlet visitations and snack food purchasing to/from school.
### Setting:
Melbourne, Australia.
### Participants:
Totally, 410 adolescents (mean age= 15·5 (sd = 1·5) years).
### Results:
Four distinct typologies of food outlets were identified: [1] limited variety/low number; [2] some variety/low number; [3] high variety/medium number and [4] high variety/high number. Adolescents living in Typologies 1 and 2 had three times higher odds of visiting fast food outlets ≥1 per week (Typology 1: OR = 3·71, 95 % CI 1·23, 11·19; Typology 2: OR = 3·65, 95 % CI 1·21, 10·99) than those living in Typology 4. No evidence of association was found between typologies of the food environments and snack food purchasing behaviour to/from school among adolescents.
### Conclusion:
Local government could emphasise an overall balance of food outlets when designing neighbourhoods to reduce propensity for fast food outlet visitation among adolescents.
## Methods
This study uses data from the Neighborhood Activity in Youth (NEArbY) study conducted between August 2014 and December 2015 among adolescents residing in Melbourne, Australia. The NEArbY study is part of the multicounty International Physical Activity and the Environment Network Adolescent project[31]. This study adhered to the STROBE-nut reporting guidelines (Appendix 1).
## School recruitment
School and participant recruitment have been detailed elsewhere[32]. Briefly, the selection of schools was based on statistical area level 1 (SA1) walkability and income quadrants in order to maximise heterogeneity in built environment and socio-economic position. Eighteen of 137 invited secondary schools consented to participate (18 % response rate). Participating schools selected year levels between years 7 and 12 to take part, and students were given a recruitment package, which consists of the study information, a parent survey and a consent form. A total of 528 students provided parental consent and student assent. Of these, 468 students completed an online survey at school and 473 had their residential addresses geocoded at the SA1 level. In total, 465 students had survey and residential address data. Parents also completed a survey, but it was only used here to supplement missing data for age and sex.
## Fast food outlet visitation
Students reported how often they visit fast food outlets in a usual month using items adapted from Thornton et al. [ 33]. The fast food outlets were McDonalds, KFC, Subway, Hungry Jacks, Red Rooster, Nando’s and ‘Others’. The response categories for visiting each fast food outlet, with scoring in parentheses, were ‘never/rarely’ [0], ‘once/fortnight’ (0·5), ‘1–2 times/week’ (1·5), ‘3–4 times/week’ (3·5), ‘5–6 times per week’ (5·5) and ‘at least once a day’ [7]. Summary scores were computed by adding scores for each type of outlet. Due to the zero-inflated and left-skewed distribution, fast food visitation was then categorised into (i) once a week or more and (ii) less than once a week.
## Snack foods purchasing behaviours to and from school
Two items about purchasing ‘snack foods’ (defined as food eaten between meals, such as muesli bars, chocolates, pastries and lollies) were included. Participants were asked to report how often they bought snack foods to eat: [1] on the way to school and [2] on the way home from school. The response categories were ‘not in the last month’, ‘1–2 times/month’, ‘1–2 times/week’, ‘most days’ and ‘every day’. Due to the zero-inflated and left-skewed distribution, responses were dichotomised into (i) once a week or more and (ii) less than once a week for snack purchasing to school and from school separately.
## Neighbourhood food environment
Using ESRI ArcGIS 10·3 (Redlands, CA, US), the number of food retailers within a 1 km street network buffer around participants’ residential addresses, a distance deemed to be walkable according to adolescents[34], was determined. Seven types of food retailers were examined: (i) supermarkets (supermarkets and ethnic grocers); (ii) green grocers; (iii) butchers, poultry and seafood; (iv) bakeries; (v) convenience stores; (vi) fast food outlets and major takeaways and (vii) café and restaurants. The locations of major supermarket chains and fast food outlets were obtained from company websites, butchers, poultry and seafood from PrimeSafe, and fast food outlets from company websites. All other categories of food retailers were sourced from Local Government food registries or phone directories (Yellow Pages, White Pages), where Local Government records were unable to be obtained.
## Socio-demographic variables
Adolescents self-reported their age and sex. The parent survey supplemented missing information on age (n 7). Neighbourhood disadvantage was determined at the SA1 level based on residential address using the Index of Relative Socioeconomic Disadvantage[35]. The IRSD score reflects each SA1’s overall level of disadvantage based on seventeen aspects that capture a range of socio-economic factors, including occupation, education, income, unemployment rate and household structure (among others). A higher IRSD score reflects a relatively advantaged area than an area with a lower score. The IRSD score in this sample ranged from 380 to 1137, with a mean of 995 (sd = 101·4).
## Statistical analyses
Of the 465 participants, those with missing data for age (n 9) and food purchasing behaviours to and from school (n 45), and whose 1 km network buffer was not covered by the food outlet mapping described earlier (n 1), were excluded. This reduced the analytic sample to 410 participants.
To identify neighbourhood typologies based on the seven types of food outlets within 1 km from home, latent profile analysis (LPA) with a Poisson link function was conducted using maximum likelihood estimation. LPA is a data-driven method of identifying groups of individuals (sub-populations) based on similarities in patterns within a set of variables[36,37] and is capable of handling count data[36]. The method assigns individuals into a user-specified number of groups (latent classes) based on probability of group membership. The LPA input variables were the frequency counts of the seven food outlets within the 1 km buffer from participants’ home addresses. Then, the LPA models assigned participants to groups based on the number of food outlets. While each food outlet added to the LPA was a count, the typology derived is distinguishable by variety (the mix of different types of food outlets) and number of food outlets (count of food outlets). To determine the appropriate number of latent classes, a sequence of models with increasing numbers of classes (2–6 classes) were tested. These models were compared, and a combination of criteria was considered to select the most appropriate number of classes to represent typologies for this sample. These included model fit indicators generated by the LPA (i.e. the Akaike Information Criteria, Bayesian Information Criterion) and likelihood ratio statistical test methods, where lower values indicate better model fit, as well as practical criteria regarding the size of each class and the interpretability of the classes[36] The ANOVA was conducted to assess whether neighbourhood disadvantage scores differ by neighbourhood food typologies. Separate multilevel logistic regression models were conducted to assess associations between neighbourhood food typologies, fast food visitations and snack purchasing behaviours. School (the unit of recruitment) was entered as a random effect variable to account for clustering. All models were adjusted for age, sex and neighbourhood disadvantage. The precise threshold to indicate statistical significance was not used in this study[38,39]. As such, 95 % CI and exact P-values are presented to indicate the level of evidence they provide: $P \leq 0$·005 providing strong evidence, $P \leq 0$·05 providing some evidence,.05 < $P \leq 0$·1 providing weak evidence and P ≥ 0·1 providing no evidence[40]. All analyses were undertaken using STATA/SE 15·0.
## Participant characteristics
Mean age was 15·5 (sd = 1·5) years and 244 (59 %) participants were girls. Overall, 47 % of adolescents visited fast food outlets at least once a week or more, 11 % bought snack foods on the way to school at least once a week or more and 21 % bought snack foods to eat on the way from school at least once a week or more.
## Neighbourhood food typologies
The six and five class solutions had the best fit based on the Akaike Information Criteria, Bayesian Information Criterion and the log likelihood values (Table 1). However, the cell sizes for some of the subgroups in these solutions were small (e.g. < 2 % of sample) and had similar characteristics between subgroups, making it difficult to interpret meaningful differences between them. Based on a combination of model fit and interpretability of the solution, the four-class solution was chosen as it had meaningful distinction between subgroups with reasonable cell sizes[36].
Table 1Comparisons of latent profile solutions of 2–6 according to the model fit indicatorsNumber of classes23456Log likelihood−5327·8−4429·2−4151·3−3928·7−3870·8Degree of freedom1523313947AIC10 685·78904·58364·67935·57835·6BIC10 745·98996·88489·18092·18024·4Cell sizes per subgroup$\frac{350}{60246}$/$\frac{126}{38213}$/$\frac{133}{39}$/$\frac{25202}{126}$/$\frac{42}{27}$/$\frac{7117}{151}$/$\frac{82}{31}$/$\frac{22}{7}$AIC, Akaike Information Criteria; BIC, Bayesian Information Criterion.
Figure 1 presents the median distribution and interquartile range for each food outlet within the four typologies in the sample, and Table 2 shows the number of participants in each typology and the percentage of participants within each typology that have availability (≥ 1) of each type of food outlet. The variety and number of food outlets increases from Typology 1 to Typology 4 (Fig. 1). In particular, the largest difference was observed for café/restaurants and fast food/other major takeaways from Typology 1 to Typology 4. Typology 1, the most prevalent (52 %), is characterised by having the least number and limited variety of food outlets, but with relatively higher availability of convenience stores, café/restaurants and fast food outlets/major takeaways compared with supermarkets, bakeries and green grocers (Table 2). Typology 2 (10 %) is characterised by having some variety, with relative higher availability of café/restaurants, convenience stores and fast food outlets/major takeaways, but low median counts of food outlets; Typology 3 (32 %) is characterised by having all types of outlets present, particularly fast food/major takeaways and cafés/restaurants; and Typology 4, the least prevalent (6 %), is characterised by having a variety and abundance of all food outlets compared with the other typologies. Although Typologies 1 and 2 had low variety, the availability of convenience stores, fast food/major takeaways and café/restaurants were more common than availability of other food outlets (Table 2). Conversely, each type of food outlet was available to the majority of participants in Typologies 3 and 4. No differences were found between the four typologies and neighbourhood disadvantage ($F = 0$·96, $$P \leq 0$$·41).
Fig. 1Median and interquartile range of food outlet counts within 1 km street network buffer by neighbourhood typologies (4-class solution) Table 2Percentage of participants (n 410) in each typology with availability (at least one) of each food outlet within 1 km buffer n 410Typology 1: Limited variety/low number of food outlets n 213 (52 %)Typology 2: Some variety/low number of food outlets n 133 (32 %)Typology 3: High variety/medium number of outlets n 39 (10 %)Typology 4: High variety/high number of food outlets n 25 (6 %)Supermarket14·769·287·2100·0Green grocer1·836·987·296·0Butcher/poultry/seafood6·151·277·084·0Convenience stores36·779·787·296·0Bakery8·072·289·896·0Fast food/major takeaway16·583·594·9100·0Café/restaurant21·294·7100·0100·0 Associations between each of the four neighbourhood food environment typologies and fast food outlet visitation and snack food purchasing behaviour are shown in Table 3. Compared with those living in neighbourhoods with a variety and abundance of food outlets (Typology 4), there was some evidence that those living in a neighbourhood characterised as having the lowest number and variety of food outlets (Typologies 1 and 2) had three times higher odds of visiting fast food outlets once or more a week. No evidence of associations was found between neighbourhood food typologies and snack foods purchasing on the way to and from school.
Table 3Odds ratios (95 % CI) of the associations between neighbourhood food typologies, fast food visitations and purchasing behaviours among adolescents (n 410)Snack food purchasingFast food visitation ≥ once/weekBought something on the way to school ≥ once/weekBought something on the way from school ≥ once/weekTypologiesOR95 % CI P-valueOR95 % CI P-valueOR95 % CI P-valueTypology 4: High variety/high number of food outlets (Reference category)1·01·01·0Typology 3: High variety/medium number of food outlets1·780·51, 6·230·361·090·24, 4·760·901·210·40, 3·660·52Typology 2: Some variety/low number of food outlets3·651·21, 10·990·020·860·19, 4·000·851·380·44, 4·290·57Typology 1: Limited variety/low number of food outlets3·711·23, 11·190·021·070·18, 6·250·931·520·41, 5·570·52All models adjusted for age, sex, neighbourhood disadvantage and clustering within school.
## Discussion
This study used a data-driven approach to characterise neighbourhood food environments and examine associations with fast food outlet visitation and snack food purchasing behaviour among adolescents in Melbourne, Australia. Four typologies of neighbourhood food environments were identified: the least number and limited variety of food outlets (Typology 1); some variety but low numbers of food outlets (Typology 2); variety of all food outlets present, with relatively more fast food/major takeaways and cafes/restaurants (Typology 3) and a variety and abundance of all food outlets (Typology 4). We found that those living in neighbourhoods with less variety and fewer food outlets were more likely to visit fast food outlets once or more a week compared with those living in neighbourhoods with an abundance of food outlets of all types. However, no evidence of associations was found between neighbourhood food environment typologies and snack food purchasing behaviour on the way to and from school.
In this study, more than half of the sample lived in neighbourhoods characterised as having the least number and limited variety of food outlets (Typology 1). This is similar to another study conducted with children in the same city, which also found that most children in the sample lived in neighbourhoods with little variety of food outlets[30]. The low number and diversity of food outlets within 1 km of participant homes observed in this study may be a reflection of the relatively small 1 km buffer. It is also possible that many of these 1 km buffers represented predominantly residential land uses. Larger buffer sizes (e.g. 2 km and 3 km) and other types of geographical buffers (e.g. sausage buffer, Euclidian buffer) may have impacted the number of food outlets captured for each latent profile in the study[41]. For example, a recent study compared the use of Euclidian buffer (circular buffer created by drawing a line out from a given distance from home address to form a circle) v. sausage network buffer (line-based buffers along all street networks at a given distance from home) and found that the sausage buffer showed a more robust positive association between the count/density of businesses and minutes of walking per week[42]. There are policy guidelines recommending that for growth areas in the state of Victoria, where *Melbourne is* situated, at least 80 % of residents should have access to a supermarket within 1 km[43]; however, similar policy guidelines for supermarkets and other types of food outlets for other local government areas in Victoria are yet to be implemented[13]. In this study, conducted across Melbourne, only a small number of adolescents were living in neighbourhoods characterised as Typology 4, where all had a supermarket within 1 km, whereas only 15 % adolescents in Typology 1 (the most prevalent typology) had a supermarket within 1 km. Several jurisdictions around the world have introduced planning policies to limit certain types of food outlets [44,45]. For example, some municipalities across the USA have legislated zoning bans on fast food outlets and drive-through services [44,45]. Similarly in Ireland, ‘No Fry Zones’ within 400 m around schools have been implemented [46]. However, policies that consider a range of food outlets appear rare.
In our study, we found that adolescents living in neighbourhoods characterised as Typologies 1 and 2 (little variety but relatively greater availability of convenience stores and fast food outlets compared with other outlets) were much more likely to visit fast food outlets once a week or more compared with those living in Typology 4, which had the widest variety and the greatest abundance of each type of outlet. Prior research indicated that food purchasing decisions are not made merely based on awareness of one type of food outlet available, but are made in consideration of other potential alternatives within the neighbourhood food environment[14]. It is possible that adolescents visited fast food outlets more frequently in neighbourhoods characterised as Typologies 1 and 2 because there were a lack of other options available. For example in Typology 1, fewer than 10 % had access to green grocer/butcher, bakery. This finding is similar to a study that examined the association between relative density of fast food outlets within 10-min walk of residential areas and body weight status: they found that adults living in a neighbourhood with a high proportion of fast food outlets (five outlets and above) relative to other food outlets were 2·5 times more likely to be obese[25]. Other possible mechanisms for this association could be due to social normalisation of fast food visitation after school with peers[47,48], higher demand for fast food due to preference[49], affordability of fast food or lower price due to higher competition between fast food outlets[50].
Although one in five adolescents reported to have bought snack food on the way home from school once a week or more, there was no evidence of associations between neighbourhood food typologies and snack food purchasing behaviours on the way to or from school. The lack of association may be due to the small geographical scale (1 km buffer) applied to characterise the neighbourhood food environment, as the exposure measure did not account for what adolescents actually experience en-route to and from school. It is also possible that most purchasing occurs with peers within close proximity to school with may have been beyond the 1 km buffer. Previous studies that used Global Positioning Systems to track activity spaces have confirmed the importance of environmental exposure on dietary behaviours [15,51,52]. Sadler et al. [ 15], for example, examined exposure to ‘junk food’ outlets during adolescents’ trips to and from school using combined data from the Global Positioning Systems and Geographic Information Systems and found that the number of minutes adolescents was exposed to junk food outlets was positively associated with junk food consumption/purchasing behaviour en-route to and from school [15].
## Study implications
Findings from this study highlight that a variety and abundance of all types of food outlets may potentially reduce the propensity to visit fast food outlets among adolescents. The local exposure to the concomitant presence of a high number of potentially ‘healthy’ and ‘unhealthy’ food outlets may affect adolescents’ food purchasing decisions, with healthy food options potentially competing with the unhealthy food options. Conversely, local exposure to environments with less variety and predominantly fast food outlets may increase adolescents’ propensity to visit fast food outlets through cumulative exposure, which may ultimately contribute to normalisation of fast food consumption. Therefore, a concurrent consideration of the optimal mix of retail food outlets in the neighbourhood environment may be more effective in promoting healthy food choices than solely targeting a single type of food outlet (e.g. fast food outlet).
## Strengths and limitations
Strengths of this study include the assessment of a wide variety of food outlets using objective data, not just a specific type of food outlet in isolation. The use of LPA may offer a more comprehensive representation of the food environment than other approaches such as relative measures (ratio or proportion) of the food environment. However, given that the LPA approach is data driven, there may be a lack of generalisability to other jurisdictions as other data will likely result in different typologies. Thus, our findings on neighbourhood food typologies may be specific to Melbourne only, and the generalisability of our study findings will likely depend on a city’s similarities to Melbourne. Also, the study was not based on an a priori protocol or registered prospectively and uses data collected in 2014 and 2015. Information on exact response rates for individual students are unavailable for the study, and this may have implications on study generalisability. The cross-sectional design of our study means that claims about causality cannot be implied. A longitudinal design or natural experiment (e.g. opening or closing of certain food outlets) would have strengthened the study findings. The reliance on self-reported fast food visitation and snack food purchasing behaviours may be subject to recall bias. Of note, these behaviours may have occurred outside the 1 km buffer from home, particularly given that, on average, journeys to secondary school in Melbourne are greater than 7 km[53]. The dichotomisation of the fast food visitation and snack food purchasing variables in the analyses may have over- or underestimated the extent of variation in outcome between groups [54]. Further, our study only examined snack food purchasing to and from school, and purchasing of snack foods outside of school-related travel (e.g. weekends, at night or during school holidays) was not examined. We also examined fast food visitation, rather than purchasing. It is possible that some adolescents visit fast food outlets for social reasons, without making purchases. In addition, we have focused only on typologies of food environments near home. Food outlets in other environments where adolescents spend time, such as in the area around school, may also be important. For example, evidence from activity space studies suggested that food environments other than the home environment, such as school or places where adolescents socialise or are active, are important settings for adolescents’ dietary behaviours [15]. Future research should consider the use of ecological momentary assessment (i.e. real-time surveys to assess participants’ ongoing experiences and interactions) [55] with geographic momentary assessment (i.e. GPS tracking, wearable camera) [56] to further unpack the complexity of food environments and behaviour interactions among adolescents over time. It is also important to acknowledge that food outlets, including supermarkets, stock a range of foods and options, some of which could be considered healthy and others unhealthy. Thus, we did not designate outlets as ‘healthy’ or ‘unhealthy’. Point of choice marketing, product placement techniques and price and promotions can impact the sales of discretionary food in supermarkets [57,58], especially when the purchasing decisions are unplanned [59]. In addition, it is important for future research to explore whether food environment typologies differ by neighbourhood socio-economic disadvantage in other cities.
## Conclusion
Using a data-driven approach, our study found four distinct neighbourhood food typologies surrounding adolescents living in Melbourne. Adolescents living in neighbourhood typologies characterised by having limited variety and a low number of food outlets but relatively greater number of fast food outlets and convenience stores were more likely to visit fast food outlets than those living in neighbourhoods characterised by having both variety and an abundance of food outlets. The findings highlight important implications for local government, planners and other stakeholders involved in the regulation, modification and management of adolescents’ food environments. In particular, a collective consideration of the overall mix of retail food outlets to reduce the propensity for fast food visitation among adolescents may be important.
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|
---
title: 'Beyond food swamps and food deserts: exploring urban Australian food retail
environment typologies'
authors:
- Cindy Needham
- Claudia Strugnell
- Steven Allender
- Liliana Orellana
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991784
doi: 10.1017/S136898002200009X
license: CC BY 4.0
---
# Beyond food swamps and food deserts: exploring urban Australian food retail environment typologies
## Body
The prevalence of obesity continues to increase worldwide[1,2]; and despite recommendations to address major drivers of the obesity epidemic such as the food system, interventions remain largely focused on individual lifestyle changes[3,4]. The effectiveness of interventions at the individual level is limited by the obesogenic nature of the food system which does not support communities to make healthy choices, e.g. individuals cannot overcome/overpower the entrenched environmental drivers[3]. Analysis of data from member countries of the Organisation for Economic Co-operation and Development (OECD) indicates that it is increased supply and consequent consumption of calories that have contributed directly to the increased global prevalence of obesity[5,6]. Per capita caloric supply is estimated by collecting data on food supply, calculating the quantity of foodstuffs produced and imported by a country and distinguishing between foods available for human consumption at the retail level and that for other uses (e.g. stock feed)[7].
The observed increase in caloric supply is at least in part driven by the changing distribution and accessibility of food resources (i.e. number, type and location of food outlets) in the ‘food retail environment’[3]. Food retail environments provide physical access to the food available to buy and play a key role in influencing food purchasing and subsequent dietary behaviours and prevalence of people with obesity[4]. Little is known about how the food retail environment is changing (i.e. quantity and healthiness of outlets) over time, the exception being a handful of studies set in the United Kingdom, North America and Australia which reported increasing numbers of food retail outlets[8], which varied by type and density across geographic areas[9] and by measures of socio-economic position(10–13). For example, in the United Kingdom, one study identified an 80 % growth in food outlets between 1980 and 2000, with the most dramatic growth observed for takeaways and restaurants[10]. A second study reported that the density per 10 000 population (using data from the 2001 United Kingdom Census) of takeaway food outlets in Norfolk (United Kingdom) almost doubled between 1990 and 2008, supermarket density also increasing albeit by a smaller margin (29 %)[13]. In the current study, takeaway food outlet density increased at a more rapid rate in deprived areas, indicative of the non-uniform way in which food is retailed across and within communities[13]. Similarly, in a sample of neighbourhoods in the Bronx (New York) between 2008 and 2017, the growth in food retail establishments was twice that of the population growth (5·7 %) over the same period, with a significantly larger number of less healthy outlets opening in lower-income areas compared with high-income areas[12]. Over a shorter period of 10 months (2016–2017), using a sample of urban streets in the Bronx, modest growth in food retail outlets was observed, with a trend of increasing availability of less healthy compared with healthy food options from within food outlets[8].
While there is growing evidence of the relationship between the food retail environment, dietary behaviours and obesity, strong evidence on the relationship is lacking and limits the development and implementation of healthy food retail environment policies[14]. Mixed results across studies are likely a consequence of heterogeneity in methods and measures, as ongoing debate exists as to what aspects of food retail environments are most influential on health(15–17). A large portion of the literature seeks to examine disparities in food access and availability, to understand how this might be related to the disproportionate geographical distribution of people with obesity(15–17). To do this, a common approach in the food retail environment literature is to examine absolute measures of access and availability for a single type of food outlet (i.e. density of supermarkets or fast-food outlets only) as a representation of the food retail environment. For example, the well-known term ‘Food Desert’ first used in the 1990s[18] generally refers to areas with limited access to food retailers (supermarkets or grocery stores in most instances), where residents are restricted by physical and in some cases economic barriers to accessing healthy foods[19]. The term has successfully been used by the USA Federal Government to implement the ‘Healthy Food Financing initiative’ (HFFI). The HFFI provides funding to support the establishment of new supermarkets and grocery stores in areas identified as Food Deserts; in this instance defined as a low-income census tract within an urban area where at least 33 % of the population cannot access a supermarket or large grocery store within one mile from home[20].
The use of absolute measures involving only one food outlet type has, however, been critiqued due to its simplistic nature[15,21]. Results from earlier research suggest that studies encapsulating a broader range of food outlets are more likely to report associations in the expected direction (e.g. greater availability of healthy outlets associated to lower prevalence of people with overweight/obesity)[15]. Recent research suggests using relative measures of the food retail environment (i.e. the relative availability of healthy outlets from the sum of healthy and unhealthy food outlets), return more consistent findings in association with food purchasing and consumption behaviour (i.e. greater proportion of healthier outlets associated with healthier purchasing and lower prevalence of people with obesity) than those using only absolute measures[22,23]. Using relative measures, the term ‘Food Swamp’ has emerged to describe unhealthy food retail environments where the density of unhealthy food outlets (i.e. independent takeaways and global fast food chains) is much higher relative to healthy food outlets(24–26). While encapsulating more food outlet types, relative measures of the food retail environment have also been critiqued due to inclusion of only outlets classified healthy or unhealthy, this simplistic classification leading to the exclusion of food outlets that are not clearly healthy or unhealthy; and as a result produce a simplified description of the complex and multidimensional food retail environment[23,27]. Excluding a large proportion of food outlets because they are difficult to categorise limits the ability to examine health effects, as it seems logical that less healthy and/or independent specialty stores may play an important role in health outcomes, an aspect missing in earlier studies[28,29].
In an attempt to incorporate all food retail outlets, a recently developed tool by Moayyed et al.[30] classifies all food retail outlets into twenty-four food outlet types and uses a Food Environment Score (FES) to categorise their healthiness on a scale from –10 to +10, with zero included as a possible value in the scale. Using this tool, the healthiness of the food retail environment for a given area (i.e. suburb) is calculated by the sum of all food outlets FES, divided by the total number of outlets[30]. While inclusive, the FES provides only a measures of healthiness and does not provide a measure of accessibility (i.e. density of food outlets per population or area) which is an important aspect of consideration. Meyer et al.[23] used Latent Class Analysis to longitudinally examine the neighbourhood food and physical activity environment using measures of accessibility (using 3 km buffers) around participant homes and their association with weight-related outcomes (diet quality, fast-food consumption, BMI and physical activity). This holistic approach identified six neighbourhood classes associated to some obesity-related outcomes. The classes were not restricted by pre-existing classifications.
In the current study, we propose to use a census of the food outlets available in Greater Melbourne conducted at four different time points across eight years to identify trends in the food retail environment in an area experiencing rapid population and urban growth. The current study aimed to:Identify the most prominent food retail environment typologies in small geographical areas in Greater Melbourne, Victoria, based on a diverse set of measures of accessibility to all types of food outlets and relative availability of healthy food outlets. Describe how the prevalence of food retail environment typologies: (a) changed across the study period (2008–2016) and (b) varied according to distance from the Central Business District (CBD) or area-level socio-economic position (SEP).
## Abstract
### Objective:
‘Food deserts’ and ‘food swamps’ are food retail environment typologies associated with unhealthy diet and obesity. The current study aimed to identify more complex food retail environment typologies and examine temporal trends.
### Design:
Measures of food retail environment accessibility and relative healthy food availability were defined for small areas (SA2s) of Melbourne, Australia, from a census of food outlets operating in 2008, 2012, 2014 and 2016. SA2s were classified into typologies using a two-stage approach: [1] SA2s were sorted into twenty clusters according to accessibility and availability and [2] clusters were grouped using evidence-based thresholds.
### Setting:
The current study was set in Melbourne, the capital city of the state of Victoria, Australia.
### Subjects:
Food retail environments in 301 small areas (Statistical Area 2) located in Melbourne in 2008, 2012, 2014 and 2016.
### Results:
Six typologies were identified based on access (low, moderate and high) and healthy food availability including one where zero food outlets were present. Over the study period, SA2s experienced an overall increase in accessibility and healthiness. Distribution of typologies varied by geographic location and area-level socio-economic position.
### Conclusion:
Multiple typologies with contrasting access and healthiness measures exist within Melbourne and these continue to change over time, and the majority of SA2s were dominated by the presence of unhealthy relative to healthy outlets, with SA2s experiencing growth and disadvantage having the lowest access and to a greater proportion of unhealthy outlets.
## Study region
The current study is set in Greater Melbourne (hereinafter referred as to ‘Melbourne’) the capital city of the state of Victoria, Australia. Victoria is experiencing the fastest population growth in Australia[31].
## Food retail environment data source and classification
A retrospective census of food outlets was undertaken for 2008, 2012, 2014 and 2016; stores were classified by ‘types’ and ‘healthiness’[11]. Retrospective food outlet data (name, type, address) for all food outlets located in Melbourne were extracted from hard copy business directories called the Yellow and White Pages, which publish government and commercial lists of businesses from information provided by telecommunications services[32]. Limited virtual ground truthing was performed in 2019 using Google and Google Street View to confirm current operation status and premise type[11]. Food outlets were classified into 17 types using an Australian food outlet classification tool (adapted to include an additional food outlet type ‘salad bar/sushi bar’) and allocated a FES representing outlet ‘healthiness’ using a 21-point scoring system ranging between –10 (least healthy) and +10 (most healthy) (see online supplementary material, Supplemental Table 1)[11,30]. Store types were collapsed into three groups according to their FES. Then, by type into seven groups of which we included only supermarkets in the current study, given they operate at a larger scale than most other retailers and serve a greater proportion of the Australian population (68 % of food purchases were from supermarkets in 2019)[33], warranting consideration independently as well as a contributor to healthier food retail availability (Table 1; see online supplementary material, Supplemental Table 1).
Table 1Food retail environment: food outlet types and classificationsFood outlet types included within each accessibility measure* 1. Supermarkets: Minor & Major supermarkets.2. Healthy (Healthiness score range: +5 to +10): Supermarkets, Fruit and greengrocer, Butcher, Fish, Poultry shop, Salad/Sandwich/Sushi bar.3. Less Healthy (Healthiness score range: –4 to +4): Cafes and Restaurants (Independent and Franchise), Bakers, Delicatessen.4. Unhealthy (Healthiness score range: –10 to –5): Fast-food, Takeaway independent, Pubs, General stores and Specialty extra.5. Relative Healthy Food Availability: Healthy food outlets (Supermarkets and Greengrocers) Unhealthy food outlets (Fast-food and Takeaway independent).Adapted from Needham et al.[11] *Descirptions of each food outlt type are listed in Supplemental Table 1.
## Food retail environment: geographic scale
Food outlet data were summarised at the Statistical Area 2 (SA2) level, which are medium-sized general purpose geographical zones (i.e. suburbs, residential districts) where communities interact together socially and economically[34,35]. With an average population of 10 000 people, SA2s are the smallest area for which population *Census data* are released[34]. In 2016, there were 302 SA2s located entirely within the borders of Melbourne. The SA2 ‘Melbourne’ (i.e. the CBD) was excluded due to the fact that food outlets in this area mainly service visitors[36]; therefore, 301 SA2’s were included in the analysis. Food outlets were geocoded and then spatially joined to the 2016 SA2 boundaries Shapefile[34] creating a data set that indicated, for the purpose of analysis, which SA2 each food outlet was located in.
## Food retail environment measures
Food outlet data for each SA2 were used to create two dimensions of the food retail environment, 1) healthy food availability using the measure of Relative Healthy Food Availability (RHFA) and 2) accessibility using four measures referred to conjointly as the Food Retail Accessibility Measures (FRAMs) in the current study. Together, these measures indicate how much healthy food is available in a neighbourhood and how far on average people need to travel to access a range of different food outlet types within their neighbourhood.
## Relative Healthy Food Availability
Relative healthy food availability is increasingly being used as a measure of food retail environment ‘healthiness’[15]. In the current study, the RHFA represents the percentage of healthy food outlets available relative to the total number of food outlets (healthy plus unhealthy) within each SA2 boundary. To be consistent with previous literature and allow for comparability with former studies using more limited food outlet data, the RHFA only included supermarkets and greengrocers as healthy food outlets and only fast-food and independent takeaway for unhealthy food outlets[37].
## Food retail accessibility measures
Currently, there is no gold standard for measuring access to various types of food outlets. Building on previous work,[23,26] we considered four measures of accessibility: density of ‘supermarkets’, ‘healthy’, ‘less healthy’ (i.e. neither clearly healthy nor unhealthy) and ‘unhealthy’ food outlets. Density of ‘supermarkets’ was included because the largest proportion of food is purchased at these retailers. Accessibility (density) within an SA2 was calculated as the number of outlets in each classification per km2. This measure indicates the average distance a person needs to travel within the SA2 to access one of these outlets, under the assumption that population and outlets are uniformly distributed across the SA2[21,38].
## Identification of Food Retail Environment Typologies
A two-stage approach was followed to identify typologies: [1] SA2s were grouped into clusters using a K-means algorithm and [2] clusters were collapsed into typologies guided by the existing research evidence. Over the study period, some of the SA2s had no food outlets identified for some of the study years (n 29); therefore, 1175 ‘observations’ (i.e. all SA2s with food outlets over the study period) were included in the cluster analysis described in Stage 1.
## Stage 1. Unsupervised clustering
The K-means algorithm with Euclidean (L2) distance was used to sort, based on measures of availability and accessibility, 1175 observations into $K = 20$ mutually exclusive groups[39]. $K = 20$ was chosen to avoid collapsing large clusters, while retaining atypical clusters with few observations. Five measures of the food retail environment (RHFA and the four FRAMs; density of supermarkets, healthy, less healthy and unhealthy outlets per km2) were used as input variables. Variables were standardised using robust measures of location (median) and scale (median absolute deviation, MAD). Input variables were summarised by cluster. The cluster analysis was generated using SAS software version 9.4.
## Stage 2. Evidence-based grouping of clusters in typologies
With the aim of further collapsing similar clusters into a smaller number of typologies, meaningful thresholds for each of the five measures were derived from earlier studies that examined the effect of RHFA and accessibility measures on behaviours (i.e. food purchasing behaviour and diet) and health outcomes (i.e. obesity prevalence). Supplementary file 2 provides supporting information for thresholds.
First, the cluster RHFA mean was classified into three levels (i.e. ≤ 25 %, > 25 to < 50 %, ≥ 50 %). Then, each of the four FRAM means in a cluster was categorised in levels. For healthy, less healthy and unhealthy density we defined: ‘Low’ (< 1 per km2), ‘Moderate’ (≥ 1 to < 2 per km2) or ‘High’ (≥ 2 per km2) as described in Table 2. These FRAM categories reflect ‘access’ as a measure of distance only and do not reflect what would be considered ‘good’ access required for health. For supermarkets, given their larger size and scale of operation[33], we defined access as ‘Low’ (< 0·625 per km2), ‘Moderate’ (0·625 to < 1·25 per km2) and ‘High’ (≥ 1·25 supermarkets per km2). The four dimensions of access were highly correlated; i.e. where the four access measures were low they were all very low (see online supplementary material, Supplemental Table 3, clusters 13 and 8); when one measures was moderate the other measures whilst low were higher in comparison (see online supplementary material, Supplemental Table 3, cluster 15); when one measure was high all others tended to be high or moderate. Therefore, the four access dimensions were summarised in one of three ‘access’ categories (low, moderate and high) based on the categorisation of the FRAMs: ‘Low access’ if all FRAMs were ‘low’; ‘Moderate access’ where at least one (regardless of type) FRAM was ‘Moderate’ and ‘High access’ where at least one (regardless of type) FRAM was ‘High’.
Table 2Thresholds used for the classification of each food retail environment measureMeasuresCategoriesClassificationRelative Healthy Food Availability (RHFA)Percentage of healthy food resourcesRHFA≤ 25 %Low> 25 to < 50 %Moderate≥ 50HighFood Retail Accessibility Measures (FRAM)Density per km2AccessHealthy, Less Healthy, Unhealthy< 1Low≥ 1 to < 2Moderate≥ 2HighSupermarkets< 0·625Low0·625 to < 1·25Moderate≥ 1·25High
## Exploratory analysis of typologies by geographical location and socio-economic position
Each SA2 was classified (based on the local government area (LGA) in which they were located) relative to distance from Melbourne’s CBD as: ‘Inner Ring’ (< 15 km), ‘Middle Ring’ (15–25 km) and ‘Outer Ring’ (25–55 km)[11]. A fourth group included SA2s located in LGAs identified as Growth Areas (30–70 km from CBD; areas housing a large proportion of urban growth located on the urban fringe)[40].
The Australian Bureau of Statistics Socio-Economic Index for Areas, Index of Relative Socio-Economic Advantage and Disadvantage (SEIFA-IRSAD) at the SA2 level was used to define SEP quartiles, Q1 (lowest SEP) to Q4 (highest SEP). SEIFA-IRSAD incorporates 25 collected measures of SEP (i.e. income, occupation, education, internet connection) which are used to summarise the relative disadvantage of the population within an area(41–43). Food retail environment data for years 2008 and 2012 were matched to the SEIFA-IRSAD quartiles from the 2011 census[41] and 2014 and 2016 food retail environment data to the 2016 census[42]. Four SA2s had missing SEIFA-IRSAD due to the low population or low response rate for that census year[43].
We report prevalence of typologies for each time point to explore trends over the study period by geographic location or area-level SEP.
## Results
Six food retail environment typologies were identified for Melbourne SA2s, five using the two-stage procedure and a last typology corresponding to zero food outlets (Table 3).
Table 3Summary of food retail environment measures for each food retail environment typology by year2008201220142016YearMean sd Mean sd Mean sd Mean sd Typology: Low access – High % healthyNumber of SA2s (%)237·6227·3196·3175·7RHFA %64·317·565·017·368·819·866·319·0Density Healthy per km2 0·20·50·30·60·40·80·30·8Density Less Healthy per km2 0·30·70·40·90·40·90·30·4Density Unhealthy per km2 0·20·30·10·20·10·20·10·2Density Supermarkets per km2 0·10·20·10·20·10·20·10·9Typology: Low access – Low % healthyNumber of SA2s (%)8126·95919·64615·34214RHFA %3·14·93·05·24·25·63·55·1Density Healthy per km2 0·10·20·10·20·20·20·20·2Density Less Healthy per km2 0·60·60·50·60·70·70·70·7Density Unhealthy per km2 1·00·90·80·80·90·80·90·9Density Supermarkets per km2 0·00·10·00·10·00·10·00·1Typology: Moderate access – Low % healthyNumber of SA2s (%)8528·29932·910133·69932·9RHFA %22·06·924·36·824·97·324·07·6Density Healthy per km2 0·50·30·50·30·50·30·40·3Density Less Healthy per km2 0·90·70·90·60·80·60·90·7Density Unhealthy per km2 1·20·71·30·81·20·91·20·8Density Supermarkets per km2 0·20·10·20·10·20·10·20·1Typology: High Access – Low % healthyNumber of SA2s3913·04515·04314·34916·3RHFA %18·19·919·37·819·08·118·98·1Density Healthy per km2 1·91·32·11·82·41·92·52·3Density Less Healthy per km2 10·512·111·614·811·213·611·914·8Density Unhealthy per km2 5·74·16·15·56·55·76·75·9Density Supermarkets per km2 0·50·50·70·70·70·50·70·6Typology: High access – Moderate % healthyNumber of SA2s5919·66922·98829·29029·9RHFA %32·010·632·58·130·98·131·59·6Density Healthy per km2 2·31·62·41·82·61·92·41·5Density Less Healthy per km2 7·410·75·55·46·29·05·98·2Density Unhealthy per km2 3·83·53·92·64·13·14·13·1Density Supermarkets per km2 0·80·41·00·51·00·51·00·6Typology: Zero Food Retail* Number of SA2s224·7152·3121·3121·3*Represents Statistical Area 2’s with zero food retail outlets. RHFA %: percentage of healthy food outlets relative to healthy and unhealthy food retail outlets within each SA2.SA2: Statistical Area 2: medium-sized general purpose areas representing geographical areas where community interact together socially and economically[34].
Typology 1. Low access – High % healthy: comprised a single cluster of 81 ‘observations’ (SA2s across years) with low food retail accessibility measures and the highest percentage of healthy food outlets. Typology 2. Low access – Low % healthy: comprised a single cluster of 228 observations (SA2s across years) with low food retail accessibility measures and the lowest percentage of healthy food outlets. Typology 3. Moderate access – Low % healthy: comprised one single cluster comprising 384 observations with moderate accessibility to unhealthy outlets and low accessibility to supermarkets, healthy and less healthy outlets; and a low percentage of healthy outlets. Typology 4. High Access – Low % healthy: comprised 10 clusters with a total of 176 observations (SA2s across years) with high access to all outlet types except supermarkets which was moderate in some of the clusters and a low percentage of healthy food outlets. Typology 5. High Access – Moderate % healthy: comprised seven clusters which together contained 306 observations (SA2s across years) with high access to all outlets excluding supermarkets for which there was moderate access on average across the clusters and a moderate percentage of healthy outlets. Typology 6. Zero Food Retail: included 29 observations (SA2s across years) that had zero food retail outlets.
Over the study period (2008–2016), the food retail environment experienced an increase in RHFA and accessibility to food retail (Table 3). In 2008, the two most dominant typologies were Low access – Low % healthy and Moderate access – Low % healthy, together accounting for 55·1 % of SA2s. In 2016, Moderate access – Low % healthy and High Access – Moderate % healthy typologies accounted for 62·8 % of all SA2s. Over time, the proportion of Zero Food Retail, Low access – Low % healthy and Low access – High % healthy SA2s slightly decreased by 3·4 %, 12·9 % and 1·9 %, respectively. In contrast, an increase of 4·7 % was observed for Moderate access – Low % healthy and 3·3 % for High access – Low % healthy, with the largest increase (10·3 %) observed in High Access – Moderate % healthy.
## Distribution of food retail environment typologies across geographical location and time
Table 4 presents the distribution of food retail environment typologies within years (rows) and within LGA-Ring (columns). The prevalence of typologies representing High access decreased when moving away from the CBD (Table 4, rows). This pattern was seen in all four years, although there was a small increase in the proportion of Moderate access – Low % healthy typologies in the Growth Area LGA-Ring over the study period (18·8 % to 33·3 %). The Inner and Middle Ring had the highest proportion of SA2s classified as High Access – Moderate % healthy (27·1 % and 57·6 %) in 2008, slightly decreasing over time (Table 4, columns). Of all typologies, the proportion of High Access – Low % healthy was highest in the Inner Ring in 2008 (51·3 %), decreasing over time with the Middle Ring having the highest proportion (44·9 %) in 2016. In 2008, close to half (48·1 %) of the SA2s in the Growth Ring were classified as Low access – Low % healthy and two-thirds (64·3 %) were classified as Zero food retail. Over time the proportion of Moderate access – Low % healthy SA2s in the Growth Area Ring increased (18·8 % to 33·3 %). Over the study period, the prevalence of SA2s classified as Zero food retail decreased across all LGA-Rings except for the Outer Ring where it remained relatively stable. The Middle Ring experienced an increase in the proportion of High access – Low% Healthy (35·9 % to 44·9 %) and a decrease in Moderate access – Low % healthy (44·7 % to 33·3 %) and High Access - Moderate % Healthy (57·6 % to 47·8 %) typologies. Supplemental File 4 presents maps of ‘typologies’ across Melbourne over time by LGA Ring.
Table 4Food retail environment typology prevalence across years and geographic distance from CBD, in Greater MelbourneLGA-RingINNERMIDDLEOUTERGROWTHFood Retail Environment TypologyNo. SA2s% within Inner% within yearNo. SA2s% of within Middle% within yearNo. SA2s% within Outer% within yearNo. SA2s% within Growth% within yearTotal SA2s% of totalYear: 2008Zero food retail12·27·110·97·134·321·4911·864·3144·6Low access – High % healthy24·48·732·813·01014·143·5810·534·8237·6Low access – Low % healthy36·73·71917·423·52028·224·73951·348·18126·9Moderate access – Low % healthy36·73·53834·944·72839·432·91621·118·88528·2High access – Low % healthy2044·451·31412·835·934·27·722·65·13913·0High access – Moderate % healthy1635·627·13431·257·679·911·922·63·45919·645100·014·9109100·036·271100·023·676100·025·2301100·0Year: 2012Zero food retail00·00·0000·00·034·242·945·357·172·3Low access – High % healthy24·49·110·94·51014·145·4911·840·9227·3Low access – Low % healthy48·96·81412·823·71521·125·42634·244·15919·6Moderate access – Low % healthy24·42·03733·937·43042·330·33039·530·39932·9High access – Low % healthy2248·948·91715·637·845·68·922·64·44514·9High access – Moderate % healthy1533·321·74036·758·0912·713·056·67·26922·945100·0014·9109100·036·271100·023·676100·025·2301100·0Year: 2014Zero food retail00·00·0000·00·0022·850·022·650·041·3Low access – High % healthy24·410·510·95·31115·557·956·626·3196·3Low access – Low % healthy24·44·31311·928·31216·926·11925·041·34615·3Moderate access – Low % healthy48·94·03027·529·72940·928·73850·037·610133·5High access – Low % healthy1737·839·51816·541·957·011·634·07·04314·3High access – Moderate % healthy2044·422·74743·153·41216·913·6911·810·28829·245100·014·9109100·036·271100·023·676100·025·2301100·0Year: 2016Zero food retail00·00·0000·00·034·275·011·325·0041·3Low access – High % healthy12·25·910·95·9912·752·967·935·3175·6Low access – Low % healthy24·44·8109·223·81115·526·21925·045·24213·9Moderate access – Low % healthy36·73·03330·333·33042·330·33343·433·39932·9High access – Low % healthy2146·736·74339·544·91318·310·21317·18·29029·9High access – Moderate % healthy1840·023·32220·247·857·014·445·314·44916·345100·014·9109100·036·271100·023·676100·025·2301100·0CBD: Central business district; LGA: local government area; SA2: Statistical Area 2; Inner = local government areas surrounding the CBD; Middle = local government areas surrounding the Inner ring; Outer = local government areas surround the Middle ring; Growth = designated areas to house population growth located on the urban fringe.
## Food retail environment typology over time by area-level socio-economic position: Descriptive analysis
Figure 1 and Supplemental File 4 present the distribution of ‘typologies’ within SEP (SEIFA-IRSAD) quartiles across Melbourne over time. It should be noted that Melbourne had an over-representation of the second highest (Q3) and highest (Q4) SEP quartiles. SA2 typologies representing RHFA and accessibility were not evenly distributed across SEIFA-IRSAD quartiles. There was a greater prevalence of High access typologies in areas of high SEP (Q4) compared to low SEP (Q1). Over time there was an increase in overall accessibility and RHFA across all SEP quartiles (Fig. 1). Supplementary Table 5 presents the distribution of typologies across SEP quartiles within each year (across rows). High SEP SA2s (Q4) housed over half (56·4 %, n 22) of all High access – Low % healthy SA2s, this amount slightly increased over time. The highest SEP (Q4) SA2s maintained the largest portion of High access – Moderate % healthy (45·8 %, n 27 in 2008; 42·7 %, n 38 in 2016) across the study period. The proportion of Low access – High % healthy was highest in high SEP SA2s (Q4), remaining constant over time (range 40·9 %, n 9 in 2008; 50 %, n 8 in 2016). In 2008 Low access – Low % healthy typologies was highest in the second highest SEP SA2s (Q3: 34·6 %, n 27) this trend remaining over time. The second lowest SEP SA2s remained relatively stable in the mix of typologies over time, made up by predominantly Low and Moderate access typologies.
Fig. 1Food retail environment typology distribution by area-level socioeconomic position quartiles within years. SEIFA-IRSAD: Socio-Economic Index for Areas, Index of Relative Socio-Economic Advantage and Disadvantage (Q1 = low socioeconomic position, Q4 = high socioeconomic position)., High access – moderate % healthy;, High access – low % healthy, Moderate access – low % healthy, Low access – low % healthy, Low access – high % healthy, Zero food retail
## Discussion
We identified six distinct food retail environment typologies across the 301 SA2s in Melbourne between 2008 and 2016. All but one had low RHFA (i.e. low availability of healthy food stores relative to the sum of healthy and unhealthy outlets), and all had greater accessibility to unhealthy and less healthy food outlets, when compared with healthy food outlets and supermarkets. Three of the possible combinations of accessibility and availability were not identified in Melbourne: Moderate access – Moderate % healthy, Moderate access – High % healthy and High access – High % healthy.
The majority of typologies were considered Low % healthy, with the average proportion of healthy food outlets available ranging from as low as 3·1 % in SA2’s classified as Low access – Low % healthy to 24·9 % in Moderate access – Low % healthy SA2s. Considered alongside the estimated population density of each SA2, this reflects approximately two-thirds of Melbourne residents (70 % in 2008; 62 % in 2016) living in SA2s where the food retail environment includes a large majority of unhealthy food outlets. An increase in access to food outlets was observed across Melbourne over time, with prevalence of Moderate and High access typologies increasing and Low access and Zero decreasing.
If increasing availability (RHFA) of healthy food and better accessibility were considered an indicator of typology healthiness, the High Access – Moderate % healthy typology would be the healthiest, albeit access to unhealthy and less healthy outlets in this typology far exceeded that of access to supermarkets and healthy outlets. Only one typology was identified as High % healthy (i.e. greater than 50 % healthy outlets); however, this was associated with a limited overall availability of outlets (e.g. ≤ 0·4 healthy, less healthy, unhealthy outlets; and 0·1 supermarkets per km2). The SA2s closest to the CBD (Inner) and in the highest SEP quartile (Q4) were classified predominantly as High Access (i.e. not Food Deserts). The lowest SEP quartile (Q1) showed a small increase in High Access – Moderate % healthy over the study period.
Outer and Growth Area Rings housed the largest proportion of Zero, Low and Moderate Access typologies. Moderate access typologies became more prevalent over time in both Outer and Growth Area LGAs reflective of an increase in access to unhealthy food outlets to approximately 1 per km2, while access to all other food outlet types remained ‘Low’.
The current study indicates that the characteristics of the food retail environment are likely heightening the risk of unhealthy dietary behaviours and increasing prevalence of people with obesity in Melbourne[37]. The retail mix reflects similar characteristics in the food retail environment to that reported in New Zealand, Canada and parts of the USA, where ‘Food Swamps’ (areas with greater access to unhealthy outlets relative to healthy outlets) dominate the food retail environment[25,26,44]. If we were to have used the term ‘Food Swamp’ to define the food retail environments in the current study, all SA2s excluding the Low Access – High % healthy SA2s would be considered ‘Food Swamps’, despite their vastly different characteristics. Parts of Melbourne would also be considered ‘Food Deserts’ when applying the United States Department of Agriculture definition, which considers ‘Food Deserts’ areas where at least 33 % of the population (particularly in low-income areas) cannot access a supermarket or large grocery store within one mile (1·6 km) from home[20]. This definition includes SA2s with Low and Moderate Access, as they have limited supermarket access (from 0·1 to 0·2 supermarkets per km2), only High Access areas would be considered as having sufficient access. Thus, the approach used in the current study to classify the food retail environment highlights the simplistic nature of these two terms (Food Deserts and Swamps), emphasising the need for a more integral analysis of food retail environment measures, to reflect the complexity and multidimensional aspects of food retail environments across areas.
## Health implications – food retail environment research
Evidence suggests that both having poor access to healthy food outlets and high access to unhealthy outlets relative to healthy outlets are associated with unhealthy weight[17,37,45,46]. For example, an Australian study set in Adelaide found that one sd increase in the ratio of unhealthy to healthy food outlets within 1 km of participant’s home address was associated with an 11 % higher risk of participants having abdominal obesity[47]. Another study among adults (≥ 45 years) in Sydney (Australia) also found significantly higher BMIs where unhealthy outlets accounted for ≥ 25 % of all food outlets within a 1·6 and 3·2 km buffer from home[37]. Another study in Perth (Australia) found that with each additional healthy food outlet within 800 m of home, there was a 20 % decrease in the risk of a child being overweight or obese after controlling for SEP, physical activity, sedentary behaviour, takeaway consumption, age and the presence of unhealthy outlets[46]. Given the evidence presented in the current study, findings suggest the characteristics of food retail environment in Melbourne are likely increasing the risk of unhealthy diet and weight[37].
## Food retail environment disparities
These findings highlight the inequities that exist within the food retail environment, with communities living in areas of lower SEP and further from metropolitan centres exposed to unhealthier food retail environments[11,15,19]. Several studies have found food retail environment disparities and negative health outcomes among lower SEP populations[48,49]. Evidence suggesting healthier food retail environments (i.e. areas with greater access to healthy food outlets) within 800 m and 1 km of home are supportive of a healthy BMI in areas of high disadvantage, but not so for those in areas of less disadvantage[48]. Similarly, in Melbourne, women without high school degrees or above living in low SEP areas had a higher BMI, partially explained by lower access to supermarkets, the coastline and sports facilities, when compared with women with the same education level in high SEP areas[50].
Food retail environment disparities have also been reported across Melbourne with people residing in Established Areas (urban areas not experiencing significant development and population growth) having significantly lower BMI and greater proximity and access (density) to supermarkets (within 800 m, 1·6 km and 2 km and 3 km) and fast-food (within 800 m, 1 km, 1·6 km, 2 km and 3 km) when compared with people in Growth Areas (i.e. new housing development areas)[48,51]. Unexpectedly, further analysis of the data indicated fast-food density was positively associated with BMI in more established areas of Melbourne (within 800 m and 1000 m buffers), but negatively associated in Growth Areas (within 800 m and 1600 m buffers); after adjustment for a number of factors, including supermarket access, age, gender, measures of SEP and food and beverage consumption[51].
It has been postulated in earlier research that the relationship between the food retail environment and obesity or dietary behaviours across areas, based on location and SEP, is driven by having greater access to healthier food outlets (e.g. supermarkets), which may play a protective role for BMI[15]. This is exemplified in the USA, where individuals with limited access to public transport and who did not own a vehicle appeared more vulnerable to the negative health impacts of living in areas where access to unhealthy food was disproportionately higher compared with healthy food, even after controlling for measures of SEP, recreation/fitness facilities and food deserts (absence or presence of supermarkets)[25]. In these instances, the physical environment in lower density areas (e.g. Growth Areas) where heavy car dependency, poor public transport, lower housing density and higher relative unhealthy food outlet accessibility is evident, we can expect an increased risk of unhealthy weight independent of SEP[25,51].
## Strengths
Our approach to examining the food retail environment extends previous methods to identify new typologies, and our census of food outlets in 301 geographic areas gives a more detailed perspective of the food retail environment over time than has previously been reported. Results highlight the limitations of considering the food retail environment using only absolute measures of a single food outlet type, or use of terms such as Food Swamp and Food Desert, which by definition are simplistic. For example, despite extensive differences across measures of accessibility when considering only RHFA almost the entirety of Melbourne would be classified as a Food Swamp. However, when considering accessibility to healthy food outlets and supermarkets, a large proportion (all bar typologies that identify as High Access SA2s) would also be identified as a food desert. Using a combination of a data-driven and evidence-based approaches, we highlight the complexity of the food retail environment that exists across areas. The inclusion of supermarkets, which account for the bulk of food purchases in Australia (68 % of purchases in Australia in 2019)[33], and healthy and unhealthy food retailers, which are the most influential food outlet types on purchasing and dietary behaviours, are strengths of the study[52]. The current study also included the often overlooked food outlets that cannot be clearly categorised as healthy or unhealthy, termed ‘less healthy’ food outlets, further strengthening results[11]. By measuring accessibility with density of food outlets within a predefined area unit (e.g. postcode, suburb), we proposed a method that can be applied at scale in public health settings[15,17].
## Weaknesses
The food retail environment data set was extracted from hard copy business listings in the Yellow and White Pages, ground truthing was performed in 2019 on a sample of outlets and was limited by its retrospective nature[11]. Therefore, caution should be taken when assuming all food outlets are represented, as some outlets (e.g. those without a fixed-line telephone service or business listing) may have been overlooked. Supermarkets are often considered a the major source of fruit and vegetables within the food retail environment[22] and are commonly used as a proxy for healthy food retail outlet availability[15]. However, they are also a large retailer of unhealthy food products[53] and have a tendency to promote unhealthy food products heavily instore[54,55]. Following the Australian food outlets classification tool (range –10 to +10), supermarkets (as well as salad, sushi and sandwich outlets) received a rating of +5, to reflect their contribution in retailing unhealthy as well as healthy food. Accessibility measures represent the average travel distance within each SA2 to access a food outlet, under the strong assumption that food outlets are spread evenly across the entire SA2 as is the population, which may not be a valid assumption. Additionally, evidence-based thresholds used to collapse clusters into typologies were drawn from earlier studies set in different countries and contexts and may be different in relation to food offered, behaviours and health outcomes in metropolitan Melbourne, Australia. It is acknowledged that over the study period some change to SA2 boundaries may have occurred. However, between 2011 and 2016, approximately 95 % of SA2 boundaries remain effectively unchanged in Australia[34]. Nevertheless, for comparability, we used the year 2016 SA2 boundaries throughout. Finally, consistent with previous food retail environment studies, the SA2 ‘Melbourne’ (i.e. the CBD) was excluded due to the fact that food outlets in this area mainly service visitors[36].
## Recommendations/implications for practice
The evidence to date suggests manipulating the food retail environment to support healthy food choices presents a potentially powerful opportunity to reduce the prevalence of obesity at the population level[3,25]. We present a key element for a comprehensive surveillance system which could provide evidence for planners, policy makers and interventionists, seeking to improve health through changing food retail environments. Implemented as part of a routine monitoring system, it would provide insight into drivers, trends and disparities in access to food resources, with particular emphasis on areas of low SEP and areas experiencing rapid population growth and expansion[11,25,56]. It is proposed that census measures of the entire food retail environment become the gold standard for future research, alongside measures guided by data-driven techniques that allow for identification of a broad range of food retail environment typologies. Additionally, it would be ideal for data to be provided by the relevant authority that regulates food retail (i.e. local governments) as these data are likely to have a higher level of accuracy[57]. These techniques are of international relevance for countries seeking to monitor, examine and identify emerging food retail environment trends and disparities alongside relationships with public health outcomes in greater detail. The evidence produced provides a source of data that could be linked to population health statistics to understand the relationships and trends over time, building the evidence base to support decision makers in favour of population health when challenged by the commercial interests of ‘big food’[1]. Further research using typologies and both child and adult BMI and dietary behaviours alongside other factors that may also influence access to food retail (e.g. income, employment and car ownership)[58,59] are required to examine the impact of different food retail environments on populations according to socio-economic and geographic strata.
## Conclusion
We identified six food retail environment typologies representing relative healthy food availability and accessibility to the full spectrum of food outlets in Melbourne. All typologies were inherently unhealthy and pose potential increased risks to public health. Disparities across food retail environments were evident across areas of differing SEP and geographic locations and evolved over the eight-year study period. Communities living in low SEP areas and further from the CBD had largely low access to food outlets, and only a small proportion of these outlets were healthy. Whilst those living in areas of higher SEP and/or closer to the CBD were more likely to have high access to food outlets in general, and a marginally higher percentage of healthy outlets. This research provides new methods to understand the food retail environment and supports the need for food retail environment monitoring for the purposes of future research, strategic planning and enforcement of regulatory approaches to improve public health.
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|
---
title: Nutrient profiles of commercially produced complementary foods available in
Cambodia, Indonesia and the Philippines
authors:
- Eleonora Bassetti
- Elizabeth Zehner
- Susannah H Mayhew
- Nadine Nasser
- Anzélle Mulder
- Jane Badham
- Lara Sweet
- Rachel Crossley
- Alissa M Pries
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991791
doi: 10.1017/S1368980022001483
license: CC BY 4.0
---
# Nutrient profiles of commercially produced complementary foods available in Cambodia, Indonesia and the Philippines
## Body
Early life is a critical window of opportunity to prevent all forms of malnutrition, including stunting, wasting, overweight/obesity and diet-related non-communicable diseases[1,2]. While breastmilk is sufficient for optimal infant growth until the age of 6 months, after this time infants’ nutritional needs increase beyond what exclusive breastfeeding can provide[3]. This period is an important time to focus on preventing growth faltering and future physical and neurocognitive limitations[4]. The introduction of safe, adequate and nutrient-dense complementary foods is necessary at this point, while breastfeeding continues to 2 years and beyond[3]. Older infants’ and young children’s (IYC) nutrient demands are very high and cannot be met if intake of nutrient-rich foods is low[5].
Over the last decade, there has been substantial growth in the processed foods market in South-East Asia, including commercially produced complementary foods (CPCF), which have gained popularity due to their convenience and aggressive promotion(6–8). Total sales of CPCF in the Philippines in 2020 were over US$ 30 million, having grown 10 % between 2015 and 2020[9]. Prior research also indicates that consumption of CPCF is increasingly prevalent among older IYC across the South-East Asia region, particularly in urban areas[10,11]. While nutrient-rich, home-prepared and locally available foods are preferable, commercial fortified food products added to diets of older IYC can improve micronutrient intake[2]. However, CPCF are heterogeneous in their nutritional quality. Some products may be of optimal nutrient composition, providing micronutrients that are typically missing in the diets of older IYC[5,6,12], but others may be of concern due to high levels of added salt, sugar and other potentially harmful additives[6]. Unhealthy CPCF can also conceivably displace consumption of other nutritious foods and breastmilk, undermining optimal breastfeeding and complementary feeding practices[7,13,14]. Additionally, food preferences, tastes and habits established in early years of life can persist into later childhood and beyond[2,15].
In 2016, the World Health Assembly (WHA) welcomed the WHO Guidance on Ending the Inappropriate Promotion of Foods for Infants and Young Children (WHA resolution 69.9)[16]. The WHO guidance called for restrictions on promotion of CPCF so that they do not interfere with breastfeeding, contribute to overweight and non-communicable diseases and create a dependency on commercial products or mislead caregivers (e.g. via inappropriate health and nutrition claims), whilst ensuring that products have high nutritional quality[16]. The WHO guidance suggested the development of nutrient profile models (NPM) to accompany decisions about which CPCF meet composition standards and ensure that only those that meet composition and labelling standards may be appropriately promoted[16]. Subsequently, in 2019, the WHO Regional Office for Europe created a NPM (hereafter referred to as WHO Europe NPM) to guide decisions on which products for children aged 6–36 months (older IYC) in the European region are suitable for promotion; in the WHO Europe NPM, a product must comply with both nutrient composition thresholds and labelling requirements to be suitable for promotion for older IYC[17]. The nutrient composition thresholds in the WHO Europe NPM are based on compositional requirements set out in regional and global guidance documents, including European Commission directives and Codex Alimentarius composition standards for CPCF[17]. In addition to classification of CPCF as suitable/unsuitable, the WHO Europe NPM also details a front-of-pack warning for products determined as having high sugar content.
As the double burden of malnutrition is a growing issue for IYC across the South-East Asia region, focus should be placed on the food environment, including the appropriateness of commercial complementary foods available on the market, to ensure optimal complementary feeding practices. Given their increasing demand in the South-East Asia region, it is essential that CPCF are of optimal nutritional quality[17]. The WHO Europe NPM has been validated for products in European markets; however, its applicability to other regions has not yet been evaluated. The application of such a NPM to products in other regions is a first step in enabling the use of nutrient profiling for CPCF in areas where consumption of these products is rising, such as the South-East Asia region, and where minimum standards for CPCF have yet to be established. This paper aims to assess the nutritional quality of CPCF available on the market in three South-East Asian contexts (Cambodia, Indonesia and the Philippines) to determine their nutritional suitability for older IYC. The objectives are [1] to determine the nutrient content of CPCF by capturing nutritional information declared on labels, including total sugar, added sugar, sodium and total fat, [2] to assess the nutritionally suitability of CPCF to be marketed to older IYC by applying the WHO Europe NPM, and [3] to determine the proportion of CPCF that would require a ‘high sugar’ warning based on the WHO Europe NPM.
## Abstract
### Objective:
To assess the nutritional suitability of commercially produced complementary foods (CPCF) marketed in three South-East Asian contexts.
### Design:
Based on label information declared on the products, nutrient composition and content of CPCF were assessed against the WHO Europe nutrient profile model (NPM). The proportion of CPCF that would require a ‘high sugar’ warning was also determined.
### Setting:
Khsach Kandal district, Cambodia; Bandung City, Indonesia; and National Capital Region, Philippines.
### Participants:
CPCF products purchased in Cambodia (n 68) and Philippines (n 211) in 2020, and Indonesia (n 211) in 2017.
### Results:
Only 4·4 % of products in Cambodia, 10·0 % of products in Indonesia and 37·0 % of products in the Philippines fully complied with relevant WHO Europe NPM nutrient composition requirements. Sixteen per cent of CPCF in Cambodia, 27·0 % in Indonesia and 58·8 % in the Philippines contained total sugar content levels that would require a ‘high sugar’ warning.
### Conclusions:
Most of the analysed CPCF were not nutritionally suitable to be promoted for older infants and young children based on their nutrient profiles, with many containing high levels of sugar and sodium. Therefore, it is crucial to introduce new policies, regulations and standards to limit the promotion of inappropriate CPCF in the South-East Asia region.
## Study design
This study involved a cross-sectional assessment of information declared on the labels of CPCF products available on the market in: Khsach Kandal district, Cambodia; Bandung City, Indonesia; and National Capital Region (NCR), Philippines. For this study, CPCF were defined as commercially produced foods specifically marketed as suitable for feeding children below 36 months of age, not including infant formula or other breastmilk substitutes. CPCF identified in the three locations had to meet at least one of the following criteria[17]: [1] recommended for introduction at an age of less than 3 years; [2] labelled with the words ‘baby’, ‘infant’, ‘toddler’, ‘young child’ or synonym; [3] labelled with an image of a child who appears to be younger than 3 years of age; or [4] in any other way presented as being suitable for children under the age of 3 years. Only food products, not beverage products, were included in this study.
## Store sampling and CPCF product identification
Store sampling for CPCF product purchasing applied the following procedures. In both Bandung City, Indonesia, and NCR, Philippines, store sampling followed WHO international procedures for the assessment of CPCF availability and promotion[18]. In Bandung City and NCR, forty-three stores were sampled in each location. Researchers purposively selected one small store per thirty-three randomly sampled public health facilities offering child health services in each city, resulting in thirty-three small stores visited in each location. Specifically, four small stores in closest proximity to each of the thirty-three health facilities were listed based on identification using Google Maps and Street View. Small stores included corner stores, neighbourhood cooperative grocery stores, minimarts and pharmacies. During data collection, these stores were visited in order of proximity and the first found to sell CPCF product was sampled for that facility. If a store not identified through Google Maps and Street View was found in closer proximity to the health facility and met study criteria, it was used instead. In addition, ten large retail outlets each in Bandung City and NCR were purposively sampled for their large variety of products that were assumed to be a representative sample of products available across the city. In each location, these ten large retail outlets were identified through store scoping and in consultation with local research staff familiar with the cities. Large retail stores included grocery stores/supermarkets, hypermarkets and baby stores. In NCR, five of the largest online retailer websites were also reviewed for CPCF products; however, no additional products were identified online that were not found in stores.
In Cambodia, CPCF products were identified and purchased in a peri-urban district, Khsach Kandal, outside Phnom Penh, using exhaustive sampling of stores within a subset of communes. Nine of eighteen communes in the district were selected for store sampling; these included the three most urban communes, the three more rural communes with the greatest population of children under 1 year of age and an additional three communes chosen by random selection. For each of the communes selected, researchers visited the village considered to be the most commercially developed and the village chief assisted researchers in identifying the main commercial road(s) in the village. The researchers went to the identified roads and located all stores selling baby goods (e.g. baby stores), medicines (e.g. pharmacies) or commercially produced foods (e.g. grocers, corner/convenience stores and kiosks) along the road(s) within the village boundaries. A total of 188 stores were visited, of which 13 % (n 25) sold CPCF.
Across all three locations, identification of CPCF within stores utilised the following procedures. All areas inside each store were surveyed (e.g. baby food section, milk section, baby supplies section and discount section) to identify all unique CPCF products for sale. The following criteria were used to determine whether CPCF products were unique or the same products, based on the assumption that these unique products may differ in nutrient content: (a) single serving and multi-serving packages of the same product were considered to be the same product; (b) different sizes of multi-serving packages were considered to be the same product; (c) bundles of single-serving sachets/packages were considered to be the same product; (d) products with the same name but different types of packaging (e.g. aluminium tin v. cardboard box) were considered to be the same product; (e) products with the same product name but different manufacturers (e.g. a local manufacturer v. an imported product with an international manufacturer) were considered to be unique products; (f) products that varied by brand/sub-brand were considered to be unique products; and (g) different flavours of the same product were considered to be unique products. CPCF products were purchased in 2017 in Bandung City, Indonesia, and in 2020 in Khsach Kandal, Cambodia and NCR, Philippines.
## Data management and analysis
After product purchase, the labels of the products were photographed/scanned. Only labels with information in English or the national language of the country were analysed. Label information in the national languages for Cambodia and Indonesia (Khmer and Bahasa Indonesia, respectively) were translated to English, while in the Philippines the label information was in both Filipino and English (as required by local regulations) and did not require translation. Data extraction was performed by entering all relevant information from the product labels into Microsoft Excel datasheets, including: ingredient list; declaration of nutrition information per serving and per 100 g (per ready-to-eat/powdered product); serving size; and nutrient content claims. Extracted data underwent a detailed error check in each location.
The labels of CPCF were assessed against the WHO Europe NPM to determine adherence to nutrient composition thresholds. The WHO Europe NPM contains two components to assess if a CPCF product is suitable for promotion – assessment of nutrient composition and assessment of labelling practices. As the aim of this study was to assess nutritional suitability of CPCF, only the nutrient composition component was used. First, product names and ingredients were reviewed, and CPCF products were placed in one of the sixteen categories proposed in WHO Europe CPCF NPM. After product categorisation, the ingredient list and nutritional content of products were cross-checked against category-specific nutrient/ingredients thresholds. These thresholds are typically applied to nutrient content per 100 kcal or per 100 g, but several thresholds are based on the presence of a nutrient/ingredient or another weight specification (e.g. per serving). Since the categories proposed in the WHO Europe NPM were created considering the products on the European market, they did not comprehensively reflect the types of CPCF on the market in the three South-East Asian contexts. Therefore, it was necessary to operate some adaptations. Specifically, thirteen products were dry powder products that were not applicable to category ‘1·1 Dry or instant cereal/starch’ because their main ingredient was dehydrated dairy/fruit. Though assessment of powdered products is intended to be based on non-reconstituted nutrient values, as the WHO Europe NPM is intended to cover foods ‘as sold’ rather than ‘as eaten’, these thirteen products were categorised under applicable puree categories and nutrient values assessed were based on reconstituted values as per manufacturers’ instructions. A considerable number of analysed products presented incomplete nutrient content information on the label. When product labels were missing nutrient content information for nutrient composition thresholds, these products were excluded from that specific nutrient assessment. A product was categorised as nutritionally suitable for older IYC if it achieved all category-specific nutrient thresholds.
The following listed ingredients were classed as added sugars/sweeteners: sugar or sucrose, dextrose, fructose, glucose, maltose, galactose, trehalose, (any) syrup, honey, malt extract, malted barley, molasses and juice/concentrate (other than lemon or lime juice, as they are not sweet tasting). The proportion of energy from sugar, fat and protein was determined using the Atwater factor system by multiplying the number of grams/100 g product of sugar, fat or protein by four, nine and four, respectively, and then dividing by the total energy content of the product (expressed in kcal/100 g). Where salt content, instead of sodium, was provided on the package, sodium content was estimated (total sodium = salt/2·5). Categorisation of products as fortified was based on review of ingredient lists for the addition of minerals/vitamins. The presence of nutrient content claims on products labels was determined by reviewing label information for statements/images that described the level of nutrient content[19].
Analysis of the CPCF products’ performance in the WHO Europe NPM was conducted using a pre-designed Microsoft Excel spreadsheet developed by a team of researchers at Leeds University’s Nutritional Epidemiology Group, which serves as a WHO Collaborating Group. A user enters products’ ingredients and nutrient content based on the label declarations, and the spreadsheet contains automated calculations that evaluate each product against the WHO Europe NPM nutrient composition thresholds. Statistical analysis was conducted in Stata 14. Descriptive statistics were calculated and summarised using proportion and medians with minimum–maximum range for non-normally distributed data. Differences in proportions of products were tested using the Pearson chi-square test, with significance defined as $P \leq 0$·05.
## Results
A total of sixty-nine unique CPCF were purchased in Khsach Kandal district, Cambodia. One product did not provide any label information in either English or Khmer and was excluded, resulting in a final sample of sixty-eight CPCF products. In Bandung City, Indonesia, a total of 217 unique CPCF were purchased. Six of these did not meet the definition of CPCF used for this study and were excluded, resulting in a final sample of 211 products. In NCR, Philippines, a total of 211 unique CPCF were purchased. Characteristics of the CPCF identified in the three locations, including companies and brands, are provided in Supplemental Table 1. The majority of CPCF in Cambodia and Philippines were made by internationally headquartered companies, while almost half (44·5 %, n 94) of products purchased in Indonesia were made by national companies.
Instant cereals and ready-to-eat finger foods/snacks were the predominant categories of CPCF identified in both Khsach Kandal district, Cambodia (35·3 % and 54·4 %), and Bandung City, Indonesia (43·6 % and 33·6 %), while pureed foods/meals accounted for 57·3 % (n 121) of products identified in NCR, Philippines (Table 1). Only 4·4 % (n 3) products in Cambodia, 10·0 % (n 21) of products in Indonesia and 37·0 % (n 78) of products in the Philippines were found to be nutritionally suitable to be promoted for older IYC based on relevant WHO Europe NPM nutrient composition thresholds.
Table 1WHO Europe NPM nutrient composition assessment of commercially produced complementary food products* Met all relevant nutrient thresholdsNo added sugar/sweetener† Low/no added fruit‡ Less than 15 % energy from sugar§ *Met sodium* threshold||Met energy density threshold¶Met protein threshold** Met total fat threshold†† Food category n % n % n % n % n % n % n % n % n Products identified in Khsach Kandal district, Cambodia (n 68)Instant cereals1·1 Dry or instant cereals/starch2412·5320·8591·722NA58·314NA87·52187·521Pureed foods/meals2·1 Dairy-based desserts and cereal products70·000·0042·93NA0·00100·07100·07100·07Ready-to-eat finger foods/snacks4·3 Snacks and finger foods370·0010·84NA48·71821·68NANA75·728Products identified in Bandung City, Indonesia (n 211)Instant cereals1·1 Dry or instant cereals/starch927·6731·52994·687NA34·832NA92·48594·687Pureed foods/meals2·1 Dairy-based desserts and cereal products140·007·1185·712NA100·01478·61121·4378·6112·2 Fruit puree2045·0970·014NANA85·01765·013NA100·022·3 Vegetable-only puree250·01100·02100·02NA100·02NANA100·022·5 Pureed meal with cheese250·01100·02100·02NA100·0250·01100·02100·022·6 Pureed meal with meat/fish mentioned in product name633·3283·35100·06NA50·0366·7483·35100·062·7 Pureed meal with meat/fish not mentioned in product name425·01100·04100·04NA75·0325·01100·0475·03Ready-to-eat finger foods/snacks4·3 Snacks and finger foods710·001·41NA9·9742·330NANA62·044Products identified in National Capital Region, Philippines (n 211)Instant cereals1·1 Dry or instant cereals/starch3554·31971·42582·929NA88·631NA100·035100·035Pureed foods/meals2·1 Dairy-based desserts and cereal products80·0050·0450·04NA100·08100·0887·57100·082·2 Fruit puree7153·53885·961NANA94·46764·846NA100·0712·3 Vegetable-only puree875·06100·08100·08NA87·57NANA100·082·4 Vegetable puree with cereals540·02100·05100·05NA100·0540·02NA100·052·5 Pureed meal with cheese580·04100·05100·05NA100·0580·04100·05100·052·6 Pureed meal with meat/fish mentioned in product name922·22100·09100·09NA77·8766·7622·22100·092·7 Pureed meal with meat/fish not mentioned in product name150·00100·01593·314NA66·71033·3526·74100·0153·1 Chunky meal with meat/fish/cheese10·00100·01100·01NA100·01NA0·00100·013·2 Chunky meal with vegetables1100·01100·01100·01NA100·01NA100·01100·01Ready-to-eat finger foods/snacks4·1 Confectionary, sweet spreads, and chews/melts40·0025·01NA0·0025·01NANA100·044·3 Snacks and finger foods4912·2622·511NA53·12642·921NANA91·845*Values are presented as % (n); NA, not applicable based on category.†The following were considered added sugar/sweetener: sugar, juice (except lemon/lime), sucrose, dextrose, fructose, glucose, maltose, galactose, trehalose, syrup, nectar, honey, malted barley, malt extract and molasses.‡Requirement definition per applicable category – 1·1: < 10 % by weight dried/powdered fruit; 2·$\frac{1}{2}$·$\frac{5}{2}$·$\frac{6}{2}$·$\frac{7}{2}$·$\frac{8}{3}$·$\frac{1}{3}$·2: ≤ 5 % by weight fruit puree; 2·$\frac{3}{2}$·4: no added fruit/fruit purée.§Applicable to category 4·3 only.||Threshold per applicable category – 1·1: sodium < 50 mg/100 kcal; 2·$\frac{1}{2}$·$\frac{2}{2}$·$\frac{3}{2}$·$\frac{4}{4}$·$\frac{1}{4}$·$\frac{2}{4}$·3: sodium < 50 mg/100 kcal and < 50 mg/100 g; 2·5: sodium < 100 mg/100 kcal and 100 mg/100 g; 2·$\frac{6}{2}$·$\frac{7}{2}$·$\frac{8}{3}$·$\frac{1}{3}$·2: sodium < 50 mg/100 kcal and < 50 mg/100 g (or < 100 mg/100 kcal and < 100 mg/100 g if cheese is listed in front-of-pack name).¶Threshold per applicable category – 2·$\frac{1}{2}$·$\frac{2}{2}$·$\frac{4}{2}$·$\frac{5}{2}$·$\frac{6}{2}$·7: energy density ≥ 60 kcal/100 g.**Threshold per applicable category – 1·1: < 5·5 total protein g/100 kcal; 2·1: ≥ 2·2 total protein g/100 kcal; 2·5: ≥ 3 total protein g/100 kcal; 2·6: ≥ 4 total protein g/100 kcal from the named source and protein named as the first food(s) in the product name must be ≥ 10 % by weight of the total product; 2·7: ≥ 3 total protein g/100 kcal and protein source mentioned in the product name must be ≥ 8 % by weight of the total product; 2·8: ≥ 7 total protein g/100 kcal; 3·1: ≥ 4 total protein g/100 kcal and protein source mentioned in the product name must be ≥ 10 % by weight of the total product; 3·2: ≥ 3 total protein g/100 kcal.††Threshold per applicable category – 1·$\frac{1}{2}$·$\frac{1}{2}$·$\frac{2}{2}$·$\frac{3}{2}$·$\frac{4}{2}$·$\frac{7}{3}$·$\frac{2}{4}$·$\frac{1}{4}$·$\frac{2}{4}$·3: ≤ 4·5 g/100 kcal total fat; 2·$\frac{5}{2}$·$\frac{6}{2}$·$\frac{8}{3}$·1: ≤ 6 g/100 kcal total fat.
Of instant cereals, 12·5 % (n 3), 7·6 % (n 7) and 54·3 % (n 19) in Cambodia, Indonesia and the Philippines, respectively, were profiled as nutritionally suitable for promotion. The variance in performance of instant cereals was driven by variation in added/total sugar and sodium content in products; the presence of added sugar/sweeteners and failure to meet the sodium standard was more prevalent among instant cereals in Cambodia and Indonesia, as compared to the Philippines. Of pureed foods/meals, 0 %, 29·2 % (n 14) and 43·0 % (n 52) in Cambodia, Indonesia and the Philippines, respectively, were profiled as nutritionally suitable for promotion. The variance in performance of pureed foods/meals across the three locations was driven by the proportion of fruit-only and vegetable-only purees, which made up 65·3 % (n 79) and 45·8 % (n 22) of pureed foods/meals in the Philippines and Indonesia, as compared to 0 % in Cambodia. A greater proportion of fruit-only and vegetable-only purees (45–75 %) were profiled as nutritionally suitable, as compared to other puree categories. No dairy-based puree products in any of the three locations met their relevant WHO Europe NPM nutrient composition thresholds, primarily due to the presence of added sugar/sweeteners. The proportion of pureed foods/meals with meat/fish that were profiled as nutritionally suitable ranged from 0 to 33 % across the three locations, with failure to meet energy density and protein thresholds being the most common reasons for unsuitability. Ready-to-eat finger foods/snacks were consistently profiled as not nutritionally suitable for the promotion for older IYC across all three locations with 0 % in both Cambodia and Indonesia and only 11·3 % (n 6) in the Philippines meeting relevant WHO Europe NPM nutrient composition thresholds. This was primarily driven by the presence of added sugars/sweeteners and total sugar content in finger foods/snacks, but 78·4 %, 57·7 % and 57·1 % of finger foods/snacks in Cambodia, Indonesia and the Philippines, respectively, also failed to meet the sodium threshold. In Cambodia and Indonesia, a greater proportion of finger foods/snacks also failed to meet the total fat threshold, as compared to instant cereals or pureed foods/meals. Eighty or more per cent of all products in each of the three locations were able to achieve the nutrient composition threshold for total fat content.
Sixteen per cent (n 11) of CPCF products in Cambodia, 27·0 % (n 57) in Indonesia and 58·8 % (n 124) in the Philippines contained total sugar content levels that would require a ‘high sugar’ front-of-pack label warning based on WHO Europe NPM guidance (Table 2). This trend was driven primarily by ready-to-eat finger foods/snacks, such as rusks, biscuits and yogurt melts, where median total sugar content ranged from 12·8 to 55·8 g per 100 g of product across the three locations.
Table 2Sugar warning and nutrient content of commercially produced complementary food products with relevant nutrient declarations* Requires ‘high sugar’ warning‡ Total sugar per 100 g (g)Sodium per 100 g (mg)Protein per 100 g (g)Total fat per 100 g (g)Food category n † % n n † MedianMinimum –Maximum n † MedianMinimum –Maximum n † MedianMinimum –Maximum n † MedianMinimum –MaximumProducts identified in Khsach Kandal district, CambodiaInstant cereals1·1 Dry or instant cereals/starch90·0099·20–15·0211615–3252112·55·2–16·0217·50·8–10·0Pureed foods/meals2·1 Dairy-based desserts and cereal products0–0–0–73·33·0–3·572·82·5–3·0Ready-to-eat finger foods/snacks4·3 Snacks and finger foods2937·9112912·80–66·7302790–500335·10–13·3330·80–28·6Products identified in Bandung City, IndonesiaInstant cereals1·1 Dry or instant cereals/starch7811·597815·11·7–40·0792600–12568712·04·2–22·9877·00–14·6Pureed foods/meals2·1 Dairy-based desserts and cereal products1145·55115·71·3–11·014132–29141·00–4·5142·71·2–2·92·2 Fruit puree850·0486·40·1–10·81900–452000–2·92000–1·22·3 Vegetable-only puree0–0–200–021·90·9–2·920·70–1·42·5 Pureed meal with cheese20·0020·70·5–0·825242–6322·72·5–2·822·11·2–2·92·6 Pureed meal with meat/fish mentioned in product name20·0021·20·8–1·563018–6462·92·5–4·861·60–2·92·7 Pureed meal with meat/fish not mentioned in product name20·0021·01·0–1·04240–5742·92·9–3·542·10–2·9Ready-to-eat finger foods/snacks4·3 Snacks and finger foods4684·8394620·02·5–32·147230–1595475·30–10·0477·50–32·5Products identified in National Capital Region, PhilippinesInstant cereals1·1 Dry or instant cereals/starch355·72336·80–40·035200–360359·00·7–16·0352·30–11·0Pureed foods/meals2·1 Dairy-based desserts and cereal products862·5589·75·3–14·283615–4082·21·2–3·183·21·0–4·32·2 Fruit puree71100·0717110·44·2–16·47140–80710·80–1·7710·20–2·22·3 Vegetable-only puree850·0483·01·8–8·0880–3181·30·6–2·4800–1·52·4 Vegetable puree with cereals560·0352·81·7–7·85107–2751·91·5–2·750·40–1·02·5 Pureed meal with cheese520·0151·81·4–2·85323–5952·82·1–3·252·41·5–2·92·6 Pureed meal with meat/fish mentioned in product name933·3391·50·5–3·89177–8293·20·6–3·791·50·4–2·52·7 Pureed meal with meat/fish not mentioned in product name1546·77152·40·9–38·5152711–45152·91·8–3·5151·20·7–2·23·1 Chunky meal with meat/fish/cheese10·0012·4--118--13·0--10·9--3·2 Chunky meal with vegetables1100·0112·8--115--12·0--10·3--Ready-to-eat finger foods/snacks4·1 Confectionary, sweet spreads and chews/melts4100·04455·84·0–57·1421114–286416·61·0–28·6400–11·84·3 Snacks and finger foods4946·9234712·90–57·149710–929494·60–40·0496·00–28·6*Values are presented as % (n) and median (minimum–maximum).†Products without relevant nutrient content declarations on label are excluded.‡Front-of-pack ‘high sugar’ warning required if the percentage energy from total sugar content is greater than or equal to the standard for that product category – 1·1: 40 %; 2·$\frac{1}{2}$·$\frac{2}{2}$·3: 30 %; 2·4: 20 %; 2·$\frac{5}{2}$·$\frac{6}{2}$·$\frac{7}{3}$·$\frac{1}{3}$·$\frac{2}{4}$·$\frac{1}{4}$·3: 15 %.
Over two-thirds of CPCF products in Cambodia and Indonesia were fortified (72·1 % (n 49) and 65·9 % (n 139), respectively), while 28·4 % (n 60) of products in the Philippines were fortified. Nutrient content claims were present on 66·2 % (n 45) of CPCF purchased in Cambodia, 66·8 % (n 141) in Indonesia and 83·9 % (n 177) in the Philippines. The WHO Europe NPM performance of fortified v. non-fortified products and products with nutrient content claims v. no claims varied across the three locations (Fig. 1). In Cambodia, there was no statistically significant difference in products’ ability to meet nutrient composition thresholds of the WHO Europe NPM nor in products that would require a ‘high sugar’ front-of-pack warning. A similar trend was found in the Philippines, with the exception that a lower proportion of fortified products were required to carry a ‘high sugar’ front-of-pack warning as compared to non-fortified products ($P \leq 0$·001). While in Indonesia, a greater proportion of non-fortified products and products without nutrient content claims met all nutrient composition thresholds as compared to fortified products ($P \leq 0$·001) and products with claims ($P \leq 0$·001). In addition, a greater proportion of fortified products and products with nutrient content claims required ‘high sugar’ front-of-pack warnings as compared to non-fortified or products without claims ($P \leq 0$·001 and $$P \leq 0$$·052, respectively).
Fig. 1Nutrient profiling performance by fortification and nutrient content claim status. CPCF, commercially produced complementary foods.
## Discussion
This study assessed the nutritional suitability of CPCF marketed in: Khsach Kandal district, Cambodia Bandung City, Indonesia; and NCR, Philippines. Nutrient profiles were evaluated against the nutrient content component of the WHO Europe NPM for CPCF. To our knowledge, this is the first study to conduct nutrient profiling of CPCF across the South-East Asia region and the first study to apply the WHO Europe NPM in another region. Ninety-six per cent of products in Cambodia, 90·0 % in Indonesia and 63·0 % in the Philippines were classified by the WHO Europe NPM as not suitable to be promoted to older IYC based on their nutrient content. Median sugar content was greatest among snack/finger food products, at 12·8 g and 20·0 g per 100 g product in Cambodia and Indonesia, respectively, and among confectionary items in the Philippines, at 55·8 g per 100 g product.
Most of the CPCF products were not compliant with WHO Europe NPM recommendations due to the presence of added sugars and/or excessive sodium content. These findings are consistent with recent studies conducted in Europe that also used the WHO Europe NPM[20,21] and found prevalent use of added sugars in CPCF. High sugar content in CPCF has also been found among products in other countries using different evaluation approaches. A study that evaluated the impact of Chilean front-of-package warning label found that 40 % of formulas and foods for older IYC available in Chile had high sugar content that would warrant a front-of-package warning[22]. Likewise, 45 % of CPCF available in the USA were classified as having ‘high’ sugar content when evaluated in light of American Heart Association recommendations, with over 20 % of their energy content derived from sugar[23]. This goes against recommendations of several international health agencies and associations to limit free sugars and avoid added sugars in foods for older IYC. The WHO recommends limiting free sugar intake to below 10 % of total energy intake for adults and suggests a further reduction to below 5 %[24]. However, for children below 2 years of age, the European Society for Paediatric Gastroenterology Hepatology and Nutrition (ESPGHAN)[25], the American Heart Association recommends to avoid added sugars[26] and the WHO Europe NPM recommends avoiding any added sugars or sweetening agents for children below '36 months of age. In addition, excessive sodium content was particularly problematic in Cambodia and Indonesia, where only one-third and one-half of products, respectively, fell below the WHO Europe NPM sodium standard. While sodium content standards for CPCF do not exist in Cambodia, in Indonesia, the sodium standard for CPCF is below 100 mg/100 kcal[27], twice as high as the WHO Europe NPM standard (50 mg/100 kcal). The high sugar and salt content of CPCF analysed in this study is extremely concerning. As the consumption of CPCF in South-East *Asia is* rising, older IYC might be increasingly exposed to unhealthy products that contribute to their daily sugar and sodium intakes and may displace the consumption of more nutritious whole foods. High sugar intake is linked with the development of dental caries[28], weight gain and increased risk of non-communicable diseases[29], and high salt intake in early life is correlated with high blood pressure in childhood[30]. Moreover, infants are born with a preference for sweet and salty tastes[15], and exposure to these tastes early in life can establish long-lasting taste preferences and unhealthy dietary patterns[15,31]. Therefore, it is crucial to minimise added sugar and salt content in the diet of older IYC and to diversify exposure to different flavours to ensure the acceptability of a wider range of nutritious foods later in life[32]. A growing amount of data suggests that promotion of unhealthy CPCF may jeopardise appropriate nutrition during the complementary feeding period[2] and later in life[33]. Consequently, prohibiting added sugars and limiting salt and total sugars in CPCF should be a policy priority for national governments, as well as among manufacturers, to ensure that older IYC are not exposed to products that are unnecessarily sweet or salty on a regular basis.
Despite widespread recognition of the need to reduce sugar intake in children[2,24], progress in lowering the sugar content of products marketed to IYC has been insufficient. It is critical to advocate a new approach to sugar reduction that focuses on avoiding added sugars and sweetness intensity[34]. While the recommendation to prohibit added sugars in CPCF may be considered challenging to implement, it could be an effective strategy[35,36] to reduce sugar intake of older IYC at the population level in South-East Asia, given their increasing consumption of CPCF. The food industry may be concerned that consumers will find reformulated low-sugar products less acceptable; however, a recent study showed that a low amount of sugar in CPCF instant cereals did not compromise their acceptability[37]. National CPCF nutrient composition standards are typically guided by Codex Alimentarius (Codex) standards in the South-East Asia region. However, Codex does not have a compositional requirement for total sugar or added/free sugar. This gap in Codex guidance may therefore be exploited by the food industry in the production and marketing of CPCF and must be addressed with stricter standards. The WHO Guidance on Ending the Inappropriate Promotion of Foods for Infants and Young Children in recommendation 3 specifies that ‘Codex standards and guidelines should be updated, and additional guidelines developed in line with WHO’s guidance to ensure that products are appropriate for IYC, with a particular focus on avoiding the addition of free sugars and salt’[16]. As the amount of added sugar is not routinely indicated on CPCF packages and thus difficult to monitor in contexts within South-east Asia, it would be more practical to prohibit added sugars rather than setting a limit on the authorised amount of added sugar.
Over half of the CPCF identified in Cambodia, one-third in Indonesia and one-quarter in Philippines were snack/finger foods and confectionary items. The CPCF snack/finger food products identified in this study included sweet/savoury puffs, biscuits/cookies, yogurt candies and instant noodles, which are common types of snack food products for the general population. There is growing evidence that older IYC consume commercial snack products during the critical complementary feeding period[7,38]. A survey conducted in Phnom Penh in 2014 found that 55 % of children 6–23 months of age had consumed commercially produced snack foods in the previous 24 h[39], and a 2018 survey found such consumption among 82 % of children 6–35 months of age in Bandung City, Indonesia[10]. The ubiquitous presence of snack food products on the market may normalise older IYC snacking on commercial foods and encourage undesirable habits throughout childhood[17]. Furthermore, the CPCF snack/finger foods identified in this study had particularly poor nutrient profiles, with none in Cambodia and Indonesia and only 11 % in Philippines found to be nutritionally suitable to be promoted for older IYC. These products contained excessive sugar, with 38–85 % requiring a ‘high sugar’ warning across the three locations. Median total sugar content for these products was 13–56 g per 100 g across the three locations; in comparison, total sugar content per 100 g of commercial biscuits in the United Kingdom ranges from 17 to 45 g[40]. It is of great concern that a substantial portion of the CPCF product landscape in these South-East Asian countries is commercial snack food products high in sugar/sodium and that they are promoted as products suitable for older IYC when their nutrient profiles indicate they are not appropriate for this age group. Overconsumption of energy-dense, nutrient-poor snack foods products can contribute to undernutrition and micronutrient deficiencies by displacing consumption of whole foods rich in essential nutrients[13], as well as overweight and obesity[41], exacerbating the double burden of malnutrition already prevalent in the region.
The use of nutrient content claims on CPCF products in Cambodia, Indonesia and the Philippines was widespread. Nutrient content claims were present on 66 %, 67 % and 84 % of labels, respectively. Nutrient content claims are likely to attract caregivers, creating a healthy ‘halo effect’, establishing brand -loyalty and idealising the product[42], which can put home-made foods at a disadvantage[17]. Such claims can be misleading, especially when placed on products with high levels of nutrients of public health concern[42]. In this study, although many CPCF claimed to offer nutrient content benefits, particularly the presence of vitamins and minerals, most of them contained concerning levels of sugar and sodium. In Indonesia and the Philippines, fewer products with nutrient content claims met all WHO Europe NPM nutrient composition thresholds than those without nutrient content claims, and in Cambodia and *Indonesia a* greater proportion of products with such claims were required to carry a ‘high sugar warning’ as compared to products with no nutrient content claim. While the Philippines prohibits the presence of nutrient content claims on CPCF products[43] and Indonesia prohibits such claims on products promoted for infants below 12 months[44], findings from this study indicate that stronger enforcement of national regulations and company compliance are urgently needed to protect older IYC diets from misleading and confusing marketing and promotional tactics.
Introducing new national policies, regulations and standards to address unhealthy food environments and to limit the promotion of inappropriate CPCF will simultaneously tackle malnutrition in all its forms. The balance between the need for additional micronutrients in the diet of older IYC and the potential harm from consuming CPCF that additionally contain high sugar and sodium content poses many challenges. Implementing an adapted version of the WHO Europe NPM in South-East Asia, which aligns with national dietary guidelines, could be a valuable way to assess CPCF sold in the region. An adapted NPM could include the evaluation of relevant micronutrient content, such as iron, zinc and calcium, to ensure that CPCF provide adequate amounts of micronutrients of public health concern. Moreover, enforcing a standardised front-of-package labelling system could be a cost-effective intervention, since it would encourage healthier food choices, help caregivers in making informed purchasing decisions[17,45,46] and urge manufacturers to reformulate unhealthy CPCF[47,48]. Further research is needed to define what standardised front-of-package labelling system would be most effective and acceptable in assisting caregivers in South-East Asia contexts to make informed CPCF purchases.
This study has several limitations. Firstly, the study was restricted to the information provided on product labels. Not all nutrients appeared on all labels, making it challenging to determine whether the product met all relevant nutrient standards. When a product was missing data relative to a nutrient, it was excluded from assessment for that specific nutrient. This could have led to an underestimation of the products meeting WHO Europe NPM criteria. Secondly, the analysis relied on manufacturers’ reported content, rather than independent laboratory analyses. Thus, actual nutrient content may be higher or lower than that declared on the label. Third, the study focused on the nutritional quality of CPCF and did not assess the overall quality of the diet of older IYC. Nonetheless, the analysis highlights the concerning amount of total sugar and sodium contained in CPCF, which could potentially contribute to sugar and sodium intake in the diet of older IYC consuming these products. Further research is needed to determine CPCF’s contributions of these problematic nutrients to diets of older IYC in South-East Asia. Finally, the WHO Europe NPM was developed for the European region. It was not designed specifically for the diet/nutritional status of South-East Asian older IYC and does not consider the importance of micronutrient content in CPCF for this population.
## Conclusion
This study contributes evidence to a currently limited field of research investigating the nutrient profiles and nutrient content of CPCF. The findings reveal that many products provide excess nutrients of public health concern, such as sugar and sodium, and do not comply with WHO recommendations for nutrient composition for diets of older IYC. As food companies increase their market penetration in low- and middle-income countries, CPCF will become more abundant and even more pervasively promoted. To reduce the availability of CPCF high in sugar and salt, further restrictions on food promotion are therefore necessary in the South-East region to improve children’s diets, foster healthy food environments and prevent malnutrition in all its forms. Stricter and more comprehensive standards related to labelling practices should be enacted and enforced to provide clearer and more comprehensive label information and to prohibit deceptive nutrient content claims on products that are not nutritionally suitable for this vulnerable age group. To address the increasing prevalence of the double burden of malnutrition in South-East Asia, regulations and enforcement to hold manufacturers accountable are urgently needed.
## Conflict of interest:
All authors have no conflicts of interest to declare.
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|
---
title: The correlation between food insecurity and infant mortality in North Carolina
authors:
- Lisa Cassidy-Vu
- Victoria Way
- John Spangler
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991794
doi: 10.1017/S136898002200026X
license: CC BY 4.0
---
# The correlation between food insecurity and infant mortality in North Carolina
## Body
The US Department of Agriculture defines food insecurity (FI) as a condition of reduced quality and variety of food in the diet causing disrupted eating patterns and reduction in food intake[1]. Despite a gradual improvement in the percentage of Americans affected by FI over the last decade, in 2018, there was an estimated 11·1 % of American households reporting FI, which amounts to 14·3 million Americans lacking access to food[2]. The Covid-19 pandemic has highlighted the need to address FI. The crisis has exaggerated resource disparities and increased rates of FI, particularly in vulnerable populations already at risk for FI(3–5).
Certain subgroups of Americans are more likely to be food insecure. FI is more prevalent in households below the 185 % federal poverty level (31·6 %); those headed by a single mother (31·6 %) or single father (21·7 %) v. those without children (8 %); and in black (22·5 %) and Hispanic households (18·5 %) v. white households (9·3 %)[6]. Rates of FI are also higher in families with adult smokers, those with disabled children or grandchildren, those with American Indians and those at risk of homelessness(7–13). Residential segregation compounds these factors. One study found that there are 30 % fewer supermarkets in lowest income neighbourhoods[14]. In smaller stores within low-income neighbourhoods, food prices are higher with less selection and poorer quality of healthy foods[14]. A low-income, single mother may not have access to transportation to shop at a supermarket, or she may be limited by the time it takes to do so while working and caring for her children[15].
FI has significant consequences for pregnant women and their unborn children. While definitions vary in the literature, FI is associated with greater weight gain and other prenatal complications, such as gestational diabetes mellitus[16], and with poor intake of micro- and macronutrients[17]. During pregnancy, adequate intake of Fe and the fatty acid DHA improves fetal brain growth and neurodevelopmental outcomes[18]. Low folic acid stores are associated with neural tube defects, preterm delivery, intrauterine growth restriction and low birth weight (LBW)[19,20]. In a recent study of ninety-five lower income countries, decreased access to food was related to an increase in undernourishment and overall increase in infant mortality (IM)[21]. In rural Gambia, increasing the intake of protein, Ca and Fe led to an increase in birth weight and decreased perinatal mortality[22]. Apart from LBW and IM, birth defects from FI include tetralogy of Fallot, transposition of the great arteries, cleft palate and even anencephaly[23].
Outside of the perinatal period, FI affects matriarchal heads of households. The mother may self-reduce her food intake to prevent hunger in children residing in the home[24]. FI negatively influences initiation and duration of breastfeeding[25]. FI is associated with decreased well-being of entire households; including higher rates of maternal depression and higher rates of family conflict[26]. Maternal depression from FI can lead to neglect of her children by reducing her ability to shop for food and care for herself and her children[27]. This can exacerbate the lack of food available to her children.
Although it is well known that FI in lower income countries is a significant factor in IM(28–32), there are less data in the USA since the development of the Women, Children and Infant nutritional program. When instituted in the 1970’s, Women, Children and Infant showed a decrease in IM[33]. Research shows that the current leading causes of IM in the USA are due to congenital malformations, preterm delivery and LBW[34]. Since FI is linked to PTD and LBW in lower income countries(35–37), it might be argued that FI has the same influence in the USA. However, since the initiation of Women, Children and Infant, FI in the USA and the impact on IM has not been evaluated in a meaningful way in a state such as North Carolina (NC) that did not expand Medicaid under the Affordable Care Act. This study will aim to examine the relationship between FI and IM in NC specifically.
## Abstract
### Objective:
Food insecurity (FI) affects approximately 11·1 % of US households and is related to worsened infant outcomes. Evidence in lower income countries links FI and infant mortality rates (IMR), but there are limited data in the USA. This study examines the relationship between FI and IMR in North Carolina (NC).
### Design:
NC county-level health data were used from the 2019 Robert Woods Johnson Foundation County Health Rankings. The dependent variable was county-level IMR. Eighteen county-level independent variables were selected and a multivariable linear regression was performed. The independent variable, FI, was based on the United States Department of Agriculture’s Food Security Supplement to the Current Population Survey.
### Setting:
NC counties.
### Participants:
Residents of NC, county-level data.
### Results:
The mean NC county-level IMR was 7·9 per 1000 live births compared with 5·8 nationally. The average percentage of county population reporting FI was 15·4 % in the state v. 11·8 % nationally. Three variables statistically significantly predicted county IMR: percent of county population reporting FI; county population and percent population with diabetes (P values, respectively, < 0·04; < 0·05; < 0·03). These variables explained 42·4 % of the variance of county-level IMR. With the largest standardised coefficient (0·247), FI was the strongest predictor of IMR.
### Conclusions:
FI, low birth weight and diabetes are positively correlated with infant mortality. While correlation is not causation, addressing FI as part of multifaceted social determinants of health might improve county-level IMR in NC.
## Methods
We used NC county-level health data from the 2019 County Health Rankings[38], a database of the nation’s 3000 plus counties assembled and annually updated by the University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation. The County Health Rankings include both health outcomes such as morbidity and mortality rates; and health factors such as health behaviours, clinical care and socio-economic factors. Data are derived from publically available datasets including the National Center for Health Statistics Mortality and Natality files, the Behavioral Risk Factor Surveillance System, Map the Meal Gap from Feeding America, the American Community Survey, the Current Population Survey, the Census Bureau and others. A full list of 2019 County Health Ranking measures and sources is available at: https://www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/2019-measures [39]. All data exists in the public domain and therefore the study is exempt from institutional review board review.
We were particularly interested in the relationship between infant mortality rates (IMR) and FI. We used IMR from the North Carolina State Center for Health Statistics, defined as the number of deaths of infants under 12 months of age per 1000 live births for the combined years 2011–2017, a 6-year range because infant death is a rare event for many counties[40]. FI was defined as the percentage of the population who lack adequate access to food. The Economic Research Service of the United States Department of Agriculture derives this composite measure. It is based on answering affirmatively to three out of eighteen questions on Food Security Supplement to the Current Population Survey in 2018[1]. This survey includes such household questions as ‘We worried whether our food would run out before we got money to buy more’; ‘In the last 12 months, were you ever hungry, but didn’t eat, because there wasn’t enough money for food?’; and ‘. In the last 12 months, were the children ever hungry but you just couldn’t afford more food?’ ( see online supplementary material, Supplemental material 1).
Multiple regression analysis was employed to predict 2011–2017 county-level IMR (the dependent variable) using eighteen county characteristics as independent variables from the 100 NC counties (Table 1). These variables were selected because of their importance to IMR as mentioned in the literature. The county-level independent variables comprised socio-demographics (population size, race/ethnicity, uninsured, college education, household income), health status (diabetes and obesity prevalence), health behaviours (smoking, excessive drinking, physical inactivity) and health outcomes (teenage pregnancy rates, LBW). We selected dates of independent variables to be as close to 2017 as possible so that any policy implications of the analysis would be more current. To normalise distributions, percent population by race/ethnicity and population per square mile were transformed to the natural log for analysis. Four counties (Davie, Greene, Hertford and Swain) were excluded from the data because their residuals were ≥ 3 standard deviations from the mean which made the distribution of the residuals non-normal. Running the analysis with and without these counties did not materially change the regression coefficients. In multiple regression, we first carried out bivariate analysis for all independent variables and IMR. Then variables were excluded from or included into the model stepwise based on the probability of F > 0·20 and < 0·10. Visual inspection showed homoscedasticity based on the dependent variable’s P–P plot of expected-to-observed cumulative probabilities of IMR; and the scatter plot of regression standardised value of IMR to regression standardised residual value. The data were not multicollinear (tolerance > 0·1 and Variable Inflation Factor < 10). Statistical significance was set at $P \leq 0$·05. All analyses were carried out with IBM SPSS®, version 26.
Table 1Averaged characteristics of NC’s 100 counties (se) along with corresponding US national ratesCounty characteristicAveraged rates of NC counties (n 100) se US ratesIMR, 2011–2017* 7·90·215·8Population of county 2017105 99617 083NAPercent population African American 2017** 20·21·612·3Percent population American Indian 2017** 1·70·40·7Percent population Hispanic 2017** 7·40·418·1Percent population white non-Hispanic 2017** 68·01·860·6Population per square mile, 2016** 19527·375·2Median household income in dollars, 201747 33391361 372Percent uninsured adults, 201716·70·37·9Primary care physicians per 100 000 population, 201753·43·2Percent adult population 25 years or older with some college, 2013–201758·30·960·8Percent adult population reporting FI, 2017*** 15·40·411·8Percent population with diabetes, 201613·20·48·5Number of births per 1000 female population ages 15–19, 2011–201731·61·018·8Percent births < 2500 g, 2011–20179·30·28·3Percent adult population with obesity, 201633·70·631·6Percent adult population who smoke, 201717·40·217·4Percent adult population with physical inactivity, 201627·70·523·1Percent adult population who drink excessively, 201715·80·218·0NC, North Carolina; IMR, infant mortality rate; FI, food insecurity.*Per 1000 live births.**To normalise distributions, the natural log of these variables were used in the regression equation.***FI defined as household-level economic and social conditions of limited or uncertain access to adequate food.
## Results
Averaged characteristics of NC’s 100 counties are listed in Table 1 along with corresponding US national rates. Compared with the national 2017 demographic composition, the state’s counties have a higher percent population of African American (20·2 % v. 12·3 %), American Indian (1·7 % v. 0·7 %) and non-Hispanic white (68 % v. 60·6 %). Conversely, there is a lower average percent of Hispanics in NC counties (7·4 % v. 18·1 %). The county-level median household income ($47 333) is lower than American households ($61 372) as is the percentage of adults 25 years or older who reported some college (2013–2017 rates: 53·4 % in NC v. 60·8 % in the USA). Mean county population per square mile is 195, but ranged greatly from 4 (Hyde County) to 1932 (Mecklenburg County). Granville County holds the state’s median population residents per square mile [109].
All NC county health-related variables in Table 1 except excessive drinking are less favourable (‘less healthy’) compared with the US population (NB: these are for illustrative purposes only since cannot derive inferential statistics without full US datasets). Contrasting with national averages, the average percent of county population reporting FI in 2017 is 15·4 % in the state v. 11·8 % in the American population. In 2017, NC counties have a higher percent of uninsured adult residents (16·7 % in NC v. 7·9 % in the USA) and diabetes (13·2 % in NC v. 8·5 % in the USA). The 2011–2017 mean county-level IMR is 7·9 deaths per 1000 live births in NC compared with 5·8 nationally. Two adverse gestational characteristics—teenage pregnancy rate and LBW—are higher in the state’s counties: Average county and national teenage birth rate 31·6 v. 18·8 births per 1000 teenage women, respectively; and average percent of LBW (< 2500 g) 9·3 % v. 8·3 %, respectively. Compared with other Americans, more NC adults smoke (17·4 % v. 13·7 % in 2017); are obese (33·7 % v. 31·6 %, 2016) and are physically inactive (27·7 % v. 23·1 %, 2016). Fewer NC adults by county drink excessively in 2017 compared with US rates (15·8 % v. 18·0 %).
Table 2 shows the multivariable regression analysis. Four variables remain in the equation, three of which are statistically significant: average percent of county population reporting FI in 2017; average county population in 2017 transformed to the natural log and percent population with diabetes in 2016 (p values, respectively, < 0·04; < 0·05; < 0·03). Percent LBW (< 2500 g) between 2011 and 2017 approaches statistical significance ($$P \leq 0$$·086). County level FI, diabetes and LBW are positively associated with IM. Each regression coefficient indicates how much the predicted value of the dependent variable (IMR) changes (increases or decreases) each time the dependent variable (FI, diabetes or LBW) changes (increases or decreases) by one unit[41]. Theoretically, for each 1 % increase (or decrease) in county-level FI, there will be an increase (or decrease) of 0·12 infant deaths per 1000 live births between 2011 and 2017; 10 % increase (or decrease) in county FI will add (or subtract) 1·2 additional infant deaths per 1000 live births for the time period. Population, log transformed, is negatively associated with IM. A 1 % increase in population yields approximately a 0·003 decrease in IM; and a 10 % increase in population yields 0·03 decrease in IM. This four variable model explains 42·4 % of the variance in IMR from 2011 to 2017 (adjusted R2 = 0·424). FI is a robust predictor, remaining in models even when limiting data from County Health Ranking years to 2013–2017. The standardised coefficient of FI (Beta = 0·247) has the largest absolute value indicating its importance compared to the three other variables. This value implies that for each one sd increase (or decrease) in FI by county, there will be a corresponding increase (or decrease) in county-level IM of 0·247 sd of deaths per 1000 live births.
Table 2Regression analysis of county-level predictors of county-level IMR in NC counties (n 100)ModelB se Adjusted Beta P valuePercent population reporting FI, 2017* 0·1180·0560·247< 0·04Ln (population 2017)** −0·2900·144−0·179< 0·05Percent population with diabetes, 20160·2090·0920·241< 0·03Percent births < 2500 g, 2011–20170·2450·1410·2080·086IMR, infant mortality rate; NC, North Carolina; FI, food insecurity.*FI defined as household-level economic and social conditions of limited or uncertain access to adequate food.**To normalise distribution, the natural log of this variable was used in the regression equation.
As mentioned, multivariate analysis confirmed that FI (Table 2) was the strongest correlate of IMR. The initial and final models displaying the unstandardised coefficients and their se are shown in Supplemental Table 2. The absolute value of FI unstandardised coefficient decreased when the final model controlled for all variables (0·265 ± 0·169 to 0·118 ± 0·056), as did the absolute values of the unstandardised coefficients for percent population with diabetes and percent LBW births (0·327 ± 0·183 to 0·209 ± 0·092; and 0·325 ± 0·221 to 0·245 ± 0·141, respectively). By contrast, the absolute value of the unstandardised coefficient for county population increased (–0·040 ± 0·53 to –0·290 ± 0·144).
## Discussion
Four county-level variables in NC explain a substantial amount of the variance (42·4 %) in the 2011–2017 IMR—percent of population experiencing FI, LBW, rate of diabetes, and low total population of each county. Apart from population, which has a negative relationship with IM, the other variables positively correlate with IM such that increases or decreases in these variables also imply increases or decreases in IMR.
Moreover, of the four variables remaining in the model in our study, FI has the strongest standardised coefficient (Beta = 0·247). Even though county-level data cannot be extrapolated down to specific infants or pregnant women, the data can be applied to county policy for each of the counties. Indeed of the four variables, FI is theoretically the most modifiable. Food assistance can be given directly to populations suffering from FI. It is known that FI is inversely related to food quality and food assistance programs such as community-based meal delivery services, SNAP and Women, Children and Infant decrease FI(42–46). Could ensuring adequate food supply in NC’s counties lower county-level IMR?
These results are largely consistent with the current literature on IMR. While our study demonstrates that counties with a higher rate of diabetes in the population in general also have a higher rate of IM, the current data on this subject support the link specifically between maternal diabetes and IMR. For instance, a study done in Indonesia showed that diabetes during pregnancy may lead to an increased risk of IM[47], though it is worth noting that with adequate control and prenatal care, the outcomes for premature/LBW infants born to mothers with diabetes are not significantly different than those with non-diabetic mothers[48,49].
The relationship between IM and LBW is well studied, and our results are overall consistent. When evaluating IMR in Delaware (a state historically with a higher IMR than the national average), IM is linked to very LBW[50]. In the USA as a whole from 1983 to 2005, there is a very strong correlation between very LBW and IMR[51]. While the rates of IM overall decreased during that period, it is speculated that the number of LBW infants born prevents the rate of decline from being more drastic[52]. Although our data do not show a significant difference between races, there are data to suggest that the African American community is more likely to experience LBW and thus higher IM compared with the white community[52].
As in our study, IMR are much higher in areas considered non-metropolitan compared with metropolitan areas[52]. Risk factors (maternal obesity, maternal smoking) for higher IMR appear to be more commonly found in non-metropolitan, or rural, areas. Rates are also higher in areas with decreased access to healthcare resources[53]. These factors may be confounding.
Our finding that FI positively correlates with increased IM in NC supports the prior findings of multiple studies largely performed in lower income countries. In nations ranging from Niger to Nepal[28,54,55], FI is related to both IMR and mortality for children under 5 years[30,31,56]. Moreover, supplemental feeding and micronutrient provisions decrease IMR[54]. For example, in Nepal in 2003, it was found that Fe and folic acid deficiencies are thought to play a significant role in IM[54], as supplementing mothers with these vitamins decreases IMR when compared with the control group of vitamin A supplementation alone[56]. Among pregnant women in poor communities in Bangladesh, treatment with multiple micronutrients and early food supplementation results in decreased childhood mortality[21].
While not widely studied in the USA, our data reflect global trends. More than one-third of child deaths are due to maternal and/or child undernutrition[57]. It is estimated that effects of FI such as stunting, severe wasting and intrauterine growth restriction are jointly the cause of 2·2 million deaths and 21 % of disability-adjusted years of life for children younger than 5 years globally in the year 2005 alone. Vitamin A and Zn deficiencies are estimated to be responsible for 0·6 million and 0·4 million deaths, respectively. While Fe deficiencies in infants do not cause many deaths, lack of sufficient maternal Fe added 115 000 deaths in a single year[57]. It is not an unreasonable deduction to draw a link between the vitamin deficiencies listed above and food insecure status.
There is a scarcity of studies in the USA on FI and its effects on IM. As mentioned, there are several studies focussed on lower income countries, and our findings are congruent with these. We think it important to initiate a body of literature for FI in the USA and its relationship to IM, which is the impetus for this study.
As health professionals, we have to be more cognizant of the health determinants that affect our communities. On a community level, there are many possible solutions to the FI crisis. Residential segregation and zoning should be examined. Supermarkets with better accessibility to families can reduce LBW[58]. Supporting community gardens and local farmers markets can help with accessibility and affordability of fresh fruits and vegetables. Encouraging policies that mandate higher minimum wages can help impoverished families with transportation and location. Nationwide, we need a system in place that prompts us to ask our patients and clients about FI with readily available resources and solutions.
Our study is not without limitations. First, this information in this study is unique to NC and may not be generalisable outside the state. NC does not represent the entire country. We only assessed the relationship of IM to eighteen variables that are hypothesised to have clinical significance, though there are many other variables that are not addressed that may have shown a significant difference. These include but are not limited to illicit drug use, maternal comorbid medical conditions apart from diabetes and infant medical conditions/congenital malformations. There are several variables assessed that surprisingly do not show a clinically significant relationship with IMR. Specifically, smoking and race, which is somewhat conflicting with the current literature. Because of the type of data used (population level), there is potential for overshadowing and for confounding factors to limit the strength of the results.
## Conclusion
This study shows that FI is a potential predictor of IM. FI is also a serious public health issue and needs to be addressed as such. Our paper provides justification to study FI at the national level not only to see if our NC findings are validated, but also to determine if such findings can be translated by societal interventions that actually reduce IMR. Therefore, more research still needs to be done to look not only at FI and at its relationship to IMR, but also its relationship to other social determinants of health.
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|
---
title: 'Individual and contextual factors associated with under- and over-nutrition
among school-aged children and adolescents in two Nigerian states: a multi-level
analysis'
authors:
- Adeleye Abiodun Adeomi
- Adesegun Fatusi
- Kerstin Klipstein-Grobusch
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991795
doi: 10.1017/S1368980022000258
license: CC BY 4.0
---
# Individual and contextual factors associated with under- and over-nutrition among school-aged children and adolescents in two Nigerian states: a multi-level analysis
## Body
Obesity is a global challenge, and the prevalence of overweight and obesity is increasing at a faster rate in low- and middle-income countries as compared with high-income countries[1]. At the same time, the challenge of under-nutrition persists still in many low- and middle-income countries[2]. Thus, many low and middle-income countries are confronted with a double burden of malnutrition[3], with co-existing high level of under-nutrition and an increasing level of over-nutrition[4]. Several factors are associated with malnutrition, and the ecological systems model has been proposed to understand child nutrition processes[5]. Ecological systems are the contextual factors within which individuals are nested[6,7].
While the ecological systems theory recognises the impact of the child’s individual factors in affecting development, it conceptualises that these factors are just one group out of many others[6,8]. In the original work on the ecological systems theory, four different inter-related environments are identified, with the microsystem as the most proximal level, followed by the mesosystem, the exosystem and the macrosystem level. While the most proximal level reflects individual and intrafamilial processes, the outermost system reflects the cultural, religious and socio-economic organisation of the community[6,8]. The chronosystem is the fifth and final level of Bronfenbrenner’s ecological systems theory; it encompasses the concept of time and consists of the environmental changes and transitions that occur over the lifetime and influence the child’s development. The theory was later updated to emphasise the important role the individual plays in the development process[7]. It was hypothesised that human development was the result of the interplay among four processes: person, context, process and time, with ‘person’ factors to mean individual characteristics, the ‘context’ to refer to the external factors described in the original work on the ecological systems theory, the ‘process’ to refer to the interaction between the person and the context and this development to be understood with reference to ‘time’[7,8].
Most of the research efforts targeted at identifying the determinants of the nutritional status of school-aged children and adolescents have focussed mainly at the individual factors alone. A number of studies have reported a significant relationship between nutritional status and such factors as age and gender of the child[9,10], residence[11,12], physical activity[13,14] and feeding patterns[11,15] of children. However, there is little evidence on the relationship between the nutritional status of school-aged children and adolescents and the communities within which the children live. Little or no evidence exists in Nigeria about the relationship between the nutritional status of this group of children and community-level factors, and very few studies have explored ecological factors as determinants of child nutrition beyond the individual factors.
There is a need to focus on the determinants of the nutritional status of school-aged children and adolescents in Nigeria, because identifying the determinants has not been the focus of most research efforts on the subject in Nigeria(16–18). The interest of majority of the researchers in this field in Nigeria has been the assessment and description of the nutritional status. Identifying the determinants is important, not only in improving the understanding about the subject, but especially in planning appropriate nutritional interventions for the children.
Identifying the individual and contextual determinants, using multi-level modelling however, has added advantages. Firstly, conventional regression models assume the units of analysis are not dependent, and this is not usually true. This error may then lead to over-estimation of statistical significance[19]. Designing interventions based on erroneous evidence may lead to ineffectiveness of such interventions. Additionally, identifying the contextual determinants will help to understand the dynamics of the influence of the contextual units[19], and especially the potentials they hold for reducing the burden of a complex health challenge such as the double burden of under- and over-nutrition.
While multi-level modelling has been used to explain the determinants of the nutritional status of children under the age of 5 years in Nigeria[20,21], to date, data are lacking for school-aged children and adolescents in Nigeria. This study, therefore, aimed to identify individual and contextual factors that are associated with either under- or over-nutrition among both school-aged children and adolescents in two Nigerian states.
## Abstract
### Objective:
This study aimed to identify individual and contextual factors that are associated with under- and over-nutrition among school-aged children and adolescents in two Nigerian states.
### Design:
Community-based cross-sectional study.
### Setting:
The study was carried out in rural and urban communities of Osun and Gombe States in Nigeria.
### Participants:
A total of 1200 school-aged children and adolescents.
### Results:
Multi-level analysis showed that the full models accounted for about 82 % and 39 % of the odds of thinness or overweight/obese across the communities, respectively. Household size (adjusted OR (aOR) 1·10; $$P \leq 0$$·001; 95 % CI (1·04, 1·16)) increased the odds, while the upper wealth index (aOR 0·43; $$P \leq 0$$·016; 95 % CI (0·22, 0·86)) decreased the odds of thinness. Age (aOR 0·86; $P \leq 0$·001; 95 % CI (1·26, 8·70)), exclusive breastfeeding (aOR 0·46; $$P \leq 0$$·010; 95 % CI (0·25, 0·83)), physical activity (aOR 0·55; $$P \leq 0$$·001; 95 % CI (0·39, 0·78)) and the upper wealth index (aOR 0·47; $$P \leq 0$$·018; 95 % CI (0·25, 0·88)) were inversely related with overweight/obesity, while residing in Osun State (aOR 3·32; $$P \leq 0$$·015; 95 % CI (1·26, 1·70)), female gender (aOR 1·73; $$P \leq 0$$·015; 95 % CI (1·11, 2·69)) and screen time > 2 h/d (aOR 2·33; $$P \leq 0$$·005; 95 % CI (1·29, 4·19)) were positively associated with overweight/obesity.
### Conclusions:
The study shows that selected community and individual-level factors are strongly associated with thinness and overweight/obesity among school-aged children and adolescents.
## Study design and setting
This community-based cross-sectional study was carried out in two Nigerian states. School-aged children and adolescents aged between 6 and 19 years and their mothers formed the study population. The sample size was calculated to get an absolute precision of ± 5 % for prevalence estimates using STATCALC on the Epi-Info software[22], and a design effect of 1·5, which is an adjustment for the sampling technique (multi-stage) used. After correcting for an anticipated non-response rate of 10 %, the sample size came to 561 and was rounded off to 600 for each state, making a total of 1200.
The classification of Nigeria’s six geo-political zones in Nigeria according to their wealth index by the Nigeria demographic and health survey[23] was used in selecting the states for this study. One state each from the zones with the lowest (North-east zone) and the highest wealth index (South-west zone) were selected using simple random sampling technique (balloting method). Selection of states was done according to wealth index and geo-political zones because these were the two most consistent factors that were significantly associated with malnutrition from previous similar studies in Nigeria[20,21,24]. Gombe and Osun States were selected from the North-east and the South-west zones, respectively. The 1200 children 6–19 years old and their mothers who constituted the study population were then selected using the multi-stage sampling technique.
## Data collection
Data collection was carried out with interviewer-administered pre-tested structured questionnaires using REDCap[25], a data collection software installed on electronic tablets. The mothers were the respondents for the sections on general characteristics of the child, household/family characteristics and community factors. The school-aged children or adolescents responded to the sections on the dietary diversity, physical activity patterns and pubertal staging. Anthropometric measurements of the children and mothers were taken according to standard protocols recommended by the International Society for the Advancement of Kinanthropometry[26]. Weight was measured in 0·1 kg by use of Omron® electronic bathroom weighing scale. Height was measured to the nearest 0·1 metre using a stadiometer. Weighing scales were routinely standardised by the use of known weights.
Screen time was the time in hours that the child spent with television, computer, video games or phones/d. The physical activity was assessed using the physical activity questionnaire for older children and adolescents by Kowalski et al.[27] from which a composite score of 1 to 5 was derived (higher scores represent higher physical activity levels).
## Measures
The primary outcome/dependent variable was the nutritional status, which was assessed using the BMI-for-age according to WHO reference values[28]. It was categorised into: [1] thinness; [2] normal and [3] overweight/obese. The independent variables used were those that had been previously reported from similar studies[20,21] and are presented in Table 1. The independent variables consist of three groups of variables: individual, household and community-level factors, but the individual and household-level factors were collapsed into the individual-level factors for the multi-level analysis. Communities were taken as those who shared a common enumeration area, which served as the primary sampling unit. Household wealth index was calculated using ownership of some household possessions, as it was used by the Nigeria demographic and health survey[23]. Principal component analysis was then used to produce a common factor score (household wealth index score) for each household. These scores were used to categorise wealth index into three: [1] poor; [2] middle and [3] rich. The community wealth index was calculated by finding the median wealth index score for each community, and these were then categorised into: [1] low and [2] high using the median value. Pubertal staging was assessed using the Tanner pubertal self-rating scale[29].
Table 1Definitions for independent variablesIndependent variablesDescriptionIndividual-level factors Age of child (years)Expressed as a continuous variable Sex of childCategorised into [1] male [2] female Birth orderCategorised into [1] 1 [2] 2–4 [3] > 4 EducationCategorised into [1] currently in school [2] previously in school [3] never in school Birth weightCategorised into [1] low < 2·5 kg [2] normal 2·5–4 kg [3] > 4 kg Breastfeeding durationExpressed as a continuous variable (months) Immunisation statusCategorised into [1] complete [2] incomplete Birth placeCategorised into [1] others [2] hospital Exclusively breastfed for 6 monthsCategorised into [1] yes [2] no Child’s healthThis is the mother’s perception of the child’s health and it was categorised into [1] very good, [2] good and [3] not too good Puberty stageCategorised into stages 1–5 (Tanner staging) Screen time (in hours)Categorised as [1] < 2 h [2] ≥ 2 h Physical activityExpressed as continuous variable (scores) Household/family factors Household sizeExpressed as continuous variable Number of childrenExpressed as continuous variable Wealth indexCategorised into [1] Poor, [2] Middle and [3] Rich Marital statusCategorised into [1] single, [2] married and [3] previously married Family typeCategorised into [1] monogamous and [2] polygamous Maternal educationCategorised into [1] less than secondary and [2] secondary or more Father’s educationCategorised into [1] less than secondary and [2] secondary or more Mother’s unemploymentCategorised into [1] employed and [2] unemployedCommunity-level factors StateCategorised into [1] Gombe and [2] Osun ResidenceCategorised into [1] rural and [2] urban Community wealth indexCategorised into [1] low [2] high, using the median of the community wealth index scores as reference Maternal educationCategorised into [1] low [2] high, using the median as reference Safe waterCategorised into [1] low [2] high, using the median as reference
## Data analysis
The data were analysed by use of STATA version 15.1[30]. At the bivariate level, cross-tabulations were done using Pearson’s chi-squared test for the categorical variables. The Mann–Whitney U and Kruskal–Wallis tests (non-parametric) were used to test for association among the continuous variables with two and more than two independent variables, respectively. These tests were used because the continuous variables were not normally distributed.
Two-level multi-level binary logistic regression analysis was done to investigate the extent to which the individual and community-level factors explained the variation in under- and over-nutrition in the two Nigerian states. This involved 1200 school-aged children and adolescents (level 1) nested within forty communities (level 2). Enumeration areas, as demarcated by the National Population Commission for 2006 population census in Nigeria[31], were used as communities in this study, and a total of forty enumeration areas were selected (twenty in each of the two states). Two separate multi-level analyses identified the individual and contextual factors associated with either under- or over-nutrition. Hence, the nutritional status of the school-aged children and adolescents, which was initially categorised into: [1] thinness; [2] normal and [3] overweight/obese, was recoded to form two different dependent variables which were thinness (categorised as [1] thinness [0] otherwise) and overweight/obesity (categorised as [1] overweight/obese [0] otherwise). Firstly, thinness was retained as ‘thinness’, while normal and overweight/obesity were merged as ‘otherwise’ (1 – Thinness, 0 – otherwise). For the second dependent variable, overweight/obesity were retained as such, while normal and thinness were merged as ‘otherwise’ (1 – overweight/obesity, 0 – otherwise). Six models each (total of twelve models) were fitted in all. The first model (Model 0) was the empty model, and the second model (Model 1) considered only the states (Osun and Gombe States), while the third model (Model 2) incorporated the child characteristics to Model 1. The fourth model (Model 3) incorporated the household/family characteristics into the first model. The community-level factors alone were considered in the fifth model (Model 4), while the sixth model (Model 5) is the full model that incorporated all factors into the multi-level analysis. Ethnicity was not included because of a high variance inflation factor when multi-collinearity diagnostics were done.
The fixed effects were used as the measures of association and expressed as adjusted OR (aOR) with the 95 % CI and the P values. The random effects, which measured variations, were intra-class correlation or the variance partition coefficient and the proportional change in variance. Akaike information criteria were used to determine the goodness-of-fit of the models, where a lower value indicated a better fit[32]. The independent structure, which is the default for the STATA software, was used in the present study.
## Results
The prevalence rates of thinness and overweight/obesity were 10·3 % and 11·4 %, respectively, while 21·7 % had one form of malnutrition or the other (Fig. 1). Gombe State has a higher prevalence of thinness (13·8 % v. 6·7 %) and lower prevalence of overweight/obesity (6·8 % v. 16·0 %). The prevalence rate of malnutrition (under- and over-nutrition) was 22·7 % for Osun State and 20·6 % for Gombe State.
Fig. 1Distribution of the nutritional status among school-aged children and adolescents by state The description of all individual- and community-level factors according to the states is shown in Supplemental Table 1. Table 2 shows that all the child characteristics had a statistically significant association with the nutritional status of the respondents ($P \leq 0$·05) at the bivariate analysis level, except education ($$P \leq 0$$·86), birth order ($$P \leq 0$$·06) and pubertal staging ($$P \leq 0$$·06). All the household/family factors had a statistically significant association with the nutritional status of the older children ($P \leq 0$·05), except marital status ($$P \leq 0$$·61) and family type ($$P \leq 0$$·67). All the community-level factors had a statistically significant association with the nutritional status of the children ($P \leq 0$·001).
Table 2Association between the individual- and community-level factors and nutritional status at bivariate analysis levelVariablesNutritional status (%)StatisticsThinnessNormalOverweight/obesityIndividual-level factors – child characteristics Age of the child (IR)12·07·012·06·010·04·0 † $P \leq 0$·001* Breastfeeding duration (months) (IR)18·08·018·09·018·08·0 † $$P \leq 0$$·046*Sex Male6711·148180·0538·8 $$P \leq 0$$·014* Female569·345976·68414·0Child education Currently in-school11610·586678·312411·2 $$P \leq 0$$·855 Previously in-school47·04578·9814·0 Never attended school38·12978·4513·5Birth weight of the child Small (< 2·5 kg)1710·213580·8159·0 $$P \leq 0$$·003* Normal (2·5–4 kg)9110·962975·611213·5 Big (> 4 kg)157·517687·6105·0Birth place of the child Others3214·616776·3209·1 $$P \leq 0$$·042* Hospital919·377378·811711·9Exclusively breastfed No6513·138577·6469·3 $$P \leq 0$$·006* Yes588·255578·89112·9Immunisation status of child Not immunised628·61361·929·5 $$P \leq 0$$·012* Partially immunised2616·511673·41610·1 Fully immunised868·877379·511311·6Not sure510·23877·6612·2Birth order of the child 1589·746077·27813·1 $$P \leq 0$$·063 2–45311·236677·55311·2 > 4129·111486·464·5 Screen time (hours) (IR)2·03·02·83·23·03·0 † $P \leq 0$·001* Physical activity scores (IR)2·01·12·31·02·10·8 † $P \leq 0$·001*Child’s health Very good397·839178·46913·8 $P \leq 0$·001* Good579·747380·6579·7 Not too good2723·77666·7119·6Pubertal staging Stage 1329·726881·2309·1 $$P \leq 0$$·061 Stage 2379·232881·2399·7 Stage 33510·723872·85416·5 Stage 41613·88976·7119·5 Stage 5313·01773·9313·0Individual-level factors –household/family characteristics Household size (IR)7·05·06·03·05·02·0 † $P \leq 0$·001* Children in the household (IR)5·03·04·02·03·01·0 † $P \leq 0$·001*Household wealth index Poor6115·329473·54511·3 $P \leq 0$·001* Middle4411·031278·04411·0 Rich184·533483·54812·0 *Marital status* Single112·5562·5225·0 $$P \leq 0$$·606 Married11710·487978·112911·5 Previously married57·55683·669·0Family type Monogamous10710·183378·312411·7 $$P \leq 0$$·667 Polygamous1611·810778·7139·6Mother’s education Less than secondary6116·328375·7308·0 $P \leq 0$·001* Secondary or more627·565779·510713·0Father’s education Less than Secondary3515·217073·62611·3 $$P \leq 0$$·023* Secondary or more889·177079·511111·5Mother’s employment Employed778·175979·511912·5 $P \leq 0$·001* Unemployed4618·818173·9187·3Community-level factors State Gombe8313·847679·3416·8 $P \leq 0$·001* Osun406·746477·39616·0 Residence Rural7712·848180·2427·0 $P \leq 0$·001* Urban467·745976·59515·8Community wealth index Low7913·247779·5447·3 $P \leq 0$·001* High447·346377·29315·5Mother’s education Low8514·247278·7437·2 $P \leq 0$·001* High386·346878·09415·7Safe water Low6914·437077·137077·1 $P \leq 0$·001* High547·557079·29613·3IR, interquartile range; Screen time, time spent watching television, with phone, computer or computer.*Statistically significant.†Kruskal–Wallis test (non-parametric) was used because the variables were not normally distributed.
Table 3 shows the results of the multi-level analyses for thinness (under-nutrition), highlighting the fixed and random effects. The full model shows that household size (aOR 1·10; $$P \leq 0$$·001; 95 % CI (1·04, 1·16)) and the uppermost wealth index (rich) (aOR 0·43; $$P \leq 0$$·016; 95 % CI (0·22, 0·86)) had a significant positive and inverse associations with thinness, respectively. Concerning the measures of variations for thinness, as shown with the random effects on Table 3, the intra-class correlation for the intercept-only model (i.e. no explanatory variable) was 21·6 %. This implies that 21·6 % of the variation in the odds of thinness was attributable to community-level variables, and this was statistically significant ($P \leq 0$·001). The Models 1, 2, 3, 4 and 5 account for about 22·1 %, 51·8 %, 41·2 %, 56·7 % and 80·2 % in the odds of under-nutrition across the communities, as explained by the proportional change in variance. The model with the best fit is Model 3, which controlled for State and the Household/family characteristics, with Akaike information criteria of 740·41 compared with 753·3 for the empty model (Model 0).
Table 3Individual and contextual factors associated with thinness among school-aged children and adolescents in two Nigerian states using a two-level multi-level analysisVariablesModel 0Model 1Model 2Model 3Model 4Model 5OR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CIStates Gombe ® Osun*0·4990·25, 0·990·5210·25, 1·090·5760·29, 1·140·6200·33, 1·160·6960·33, 1·48Individual-level factors – child characteristics Age0·9770·92, 1·040·9760·91, 1·04 Breastfeeding (months)1·0310·99, 1·081·0290·98, 1·08Gender Male ® Female0·9330·61, 1·430·9140·59, 1·42Birth weight Small (< 2·5 kg) ® Normal (2·5–4 kg)1·2480·67, 2·311·0810·58, 2·02 Big (> 4 kg)0·8360·37, 1·910·6030·26, 1·42Hospital birth No ® Yes0·9500·55, 1·651·2840·71, 2·31Child education Currently in-school ® Previously in school0·6490·21, 1·970·6130·19, 1·97 Never in school0·3410·09, 1·290·3790·10, 1·43Immunisation Not immunised ® Partially immunised0·7860·23, 2·711·0630·26, 4·28 Fully immunised0·5660·16, 1·970·9580·23, 3·99 Not sure0·5590·12, 2·580·5080·09, 2·78Birth order 1 ® 2–41·0170·65, 1·591·0220·64, 1·63 > 40·7370·36, 1·530·5370·22, 1·32Exclusive breastfeeding No ® Yes0·8280·49, 1·390·8950·52, 1·54Screen time < 2 h ® ≥ 2 h0·6770·43, 1·080·8050·49, 1·32 Physical activity scores1·0720·80, 1·450·9630·71, 1·30Child’s health Very good ® Good1·0790·64, 1·830·9510·55, 1·65 Not too good1·4580·63, 3·381·1270·46, 2·76Puberty stage 1 ® 21·1930·67, 2·111·2820·71, 2·30 31·1790·63, 2·201·4630·77, 2·78 41·9370·87, 4·321·5060·65, 3·48 52·6020·70, 9·682·3500·60, 9·19Individual-level factors – household/family characteristics Household size**1·0761·02, 1·13***1·0971·04, 1·16 Children in the household0·9360·85, 1·030·9720·86, 1·10Wealth index Poor ® Middle0·8270·51, 1·330·9210·55, 1·53 Rich***0·3500·19, 0·66*0·4320·22, 1·86Marital status Single ® Married0·5960·06, 5·560·4490·05, 4·32 Previously married0·4080·04, 4·580·3510·03, 4·25Family type Monogamous ® Polygamous0·5190·25, 1·070·4520·20, 1·02Maternal education < secondary ® Secondary or more0·7750·45, 1·320·7960·44, 1·44Father’s education < Secondary ® Secondary or more1·0420·60, 1·801·2040·67, 2·17Mother’s employment Employed ® Unemployed1·1110·66, 1·871·3970·81, 2·41Community-level factors Residence Rural ® Urban1·8730·63, 5·551·6770·62, 4·53Community wealth index Low ® High0·5530·28, 1·070·5920·32 1·10Maternal education Low High ®*0·3320·12, 1·920·4590·18, 1·19Safe waterLow ®High0·6370·33, 1·210·7330·41, 1·32Random effects Community levelVariance (se)***0·9050·34***0·7050·29***0·4360·23***0·5320·25***0·3920·210·1790·19 VPC = ICC (%)21·57 %17·65 %11·69 %13·91 %10·64 %5·16 % Explained variation (ie PCV in %)Reference22·10 %51·82 %41·22 %56·69 %80·22 % Log likelihood−374·65−372·85−356·26−357·20−367·15−337·18 Model fit statistics (AIC)753·30751·70762·51740·41748·31752·35R, reference value; Screen time, time spent watching television, with phone, computer or computer games; VPC, variance partition coefficient; ICC, intra-class correlation; PCV, proportional change in variance; AIC, Akaike information criteria. Statistically significant: *$P \leq 0$·005; **$P \leq 0$·010; ***$P \leq 0$·001.
Table 4 shows the results of the multi-level analysis for overweight/obesity (over-nutrition). Age (aOR 0·86; $P \leq 0$·001; 95 % CI (1·26, 8·70)), exclusive breastfeeding (aOR 0·46; $$P \leq 0$$·010; 95 % CI (0·25, 0·83)), physical activity (aOR 0·55; $$P \leq 0$$·001; 95 % CI (0·39, 0·78)) and the rich wealth index (aOR 0·47; $$P \leq 0$$·018; 95 % CI (0·25, 0·88)) had an inverse relationship with overweight/obesity, while residing in Osun State (aOR 3·32; $$P \leq 0$$·015; 95 % CI (1·26, 1·70)), female gender (aOR 1·73; $$P \leq 0$$·015; 95 % CI (1·11, 2·69)) and screening time > 2 h/d (aOR 2·33; $$P \leq 0$$·005; 95 % CI (1·29, 4·19)) were positively associated with overweight/obesity. The empty model (Model 0) shows a statistically significant variation in the odds of childhood overweight/obesity across the communities ($P \leq 0$·001). As indicated by the intra-class correlation, 28·5 % of the variance in the odds of overweight/obesity among the children could be ascribed to community-level factors. The Models 1, 2, 3, 4 and 5 account for about 23·4 %, 35·3 %, 18·2 %, 48·1 % and 39·2 % in the odds of over-nutrition across the communities, as explained by the proportional change in variance. The statistically significant variation across communities persisted even after controlling for all the variables ($P \leq 0$·001). Model 2, which controlled for the state and child characteristics, had the best fit with Akaike information criteria of 752·76. ( Table 4).
Table 4Individual and contextual factors associated with over-nutrition among school-aged children and adolescents in two Nigerian states using a two-level multi-level analysisVariablesModel 0Model 1Model 2Model 3Model 4Model 5OR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CIStates Gombe ® Osun*2·4991·16, 5·38**3·9301·61, 9·572·1380·93, 4·91**2·5431·23, 5·28*3·3171·26, 8·70Individual-level factors – child characteristics Age***0·8640·81, 0·93***0·8610·80, 0·92 Breastfeeding (months)0·9860·95, 1·030·9900·95, 1·03Gender Male ® Female*1·7481·13, 2·70*1·7311·11, 2·69Birth weight Small (< 2·5 kg) ® Normal (2·5–4 kg)1·1430·57, 2·291·1800·57, 2·45 Big (> 4 kg)0·4910·19, 1·290·5520·21, 1·48Hospital birth No ® Yes1·1380·61, 2·140·9930·51, 1·92Child education Currently in-school ® Previously in school2·0570·82, 5·162·0230·79, 5·16 Never in school1·3090·35, 4·871·1770·30, 4·57Immunisation Not immunised ® Partially immunised1·0350·17, 6·201·1430·19, 6·75 Fully immunised0·6820·12, 3·880·7520·13, 4·23 Not sure1·1710·16, 8·471·5530·22, 11·18Birth order 1 ® 2–41·0680·69, 1·641·1290·72, 1·78 > 40·5620·21, 1·480·6150·20, 1·91Exclusive BF No ® Yes*0·5140·29, 0·91**0·4550·25, 0·83Screen time < 2 h ® ≥ 2 h**2·1591·23, 3·78**2·3291·29, 4·19 Physical activity scores***0·5510·39, 0·77***0·5500·39, 0·78Child’s health Very good ® Good0·7250·42, 1·250·7120·41, 1·24 Not too good1·8220·65, 5·081·9240·65, 5·66Puberty stage 1 ® 21·0730·61, 1·891·0900·61, 1·93 31·2720·67, 2·421·2830·67, 2·46 41·0280·43, 2·461·0820·44, 2·65 50·5600·06, 4·960·6010·07, 5·41Individual-level factors – household/family characteristics Household size1·0030·93, 1·080·9970·91, 1·09 Children in the household0·9370·82, 1·070·9910·85, 1·16Wealth index Poor ® Middle0·7600·46, 6·150·6860·39, 1·20 Rich0·5890·34, 5·05*0·4720·25, 0·88Marital status Single (R) Married0·9330·14, 6·151·1080·13, 9·63 Previously married0·6440·08, 5·050·6770·07, 6·98Family type Monogamous (R) Polygamous1·4400·67, 3·121·6460·69, 3·90Maternal education Less than secondary (R) Secondary or more1·7300·93, 3·232·0520·98, 4·32Father’s education Less than Secondary (R) Secondary or more0·6170·33, 1·150·6220·32, 1·21Mother’s employment Employed (R) Unemployed0·9230·49, 1·750·7310·36, 1·47Community-level factorsResidence Rural (R) Urban2·2750·55, 9·481·1950·24, 5·89Community wealth index Low (R) High1·6960·78, 3·711·5650·65, 3·76Maternal education Low High (R)0·9360·26, 3·401·6120·37, 7·05Safe water Low (R) High0·6980·31, 1·580·7310·30, 1·80Random effects Community-level variance (se)***1·3110·48***1·0050·39***0·8480·37***1·0730·43***0·6810·30***0·7960·36 VPC = ICC (%)28·50 %23·40 %20·49 %24·60 %17·14 %19·48 % Explained variation (i.e. PCV in %)Reference23·34 %35·32 %18·15 %48·05 %39·21 % Log likelihood−391·56−389·07−351·01−384·50−385·41−343·87 Model fit statistics (AIC)787·11784·15752·03795·00784·82765·73R, reference value; Screen time, time spent watching television, with phone, computer or computer games; VPC, variance partition coefficient; ICC, intra-class correlation; PCV, proportional change in variance; AIC, Akaike information criteria. Statistically significant: *$P \leq 0$·005; **$P \leq 0$·010; ***$P \leq 0$·001.
## Discussion
This study, to the best our knowledge, is the first attempt to simultaneously consider the influence of individual- and community-level factors as predictors of under- and over-nutrition among school-aged and adolescents in Nigeria. Most previous studies have focussed on various indicators of under-nutrition among categories of under-five children. It is also the first study that used multi-level analysis to identify the predictors of overweight/obesity among any group of children or adolescents in Nigeria. Furthermore, the present study controlled for a wide range of independent variables, making it one of the most comprehensive studies on the determinants of nutritional status of any category of children and/or adolescents in Nigeria.
The ecological systems theory has underscored the importance of not only the compositional factors (i.e. the individual-level factors), but also the contextual factors (i.e. community-level factors) in trying to understand a complex and highly important process like child nutrition[6,7]. The multi-level analysis that was done in this study therefore helped to account for the variations in under- and over-nutrition of older children across the different contextual units, as well as to identify the compositional and contextual factors that were associated with under- and over-nutrition among the older children in the two Nigerian States.
A major finding of this study is the importance of community variation and community-level factors in the prevalence of thinness and overweight/obesity among children 6–19 years old in the two Nigerian states. As indicated by the intra-class correlation of the intercept-only model (i.e. no explanatory variable incorporated), about 22 % and 29 % of the variance in the odds for thinness and overweight/obesity among the children could be ascribed to community-level factors, respectively. Apart from the full model that included all explanatory variables, the model which consisted of community-level factors only (these factors include the state, residence, wealth index, maternal education and safe water), accounted for the highest variation observed for children that were thin (57 %) and overweight/obese (48 %). This suggests that school-aged children and adolescents from the same communities are influenced by common factors, and hence the potential for community-level interventions.
The present study found the odds for thinness increased by 10 % for every unit increase in household size. Not many studies among children have explored the relationship between under-nutrition and household size, but some studies have however looked at other variables that could serve as proxy for the household size. For example, Nnebue et al.[33] found a statistically significant relationship between under-nutrition and the number of siblings a child has. This finding further underscores the need for increased efforts in promoting family planning in Nigeria, because a larger number of children may put a household at higher risk of poverty and hence of under-nutrition. The household wealth index was also significantly associated with thinness, such that those in the ‘rich’ category had 57 % lesser odds of being thinner than those in the ‘poor’ category. Furthermore, wealth index had a statistically significant association with household size, such that those from richer households significantly had a lower household size. Hence, large family size, which increased the likelihood of thinness, may be a proxy for poverty. The relationship between poverty and under-nutrition, especially in low and middle-income countries has been well established[34]. Therefore, effective interventions for thinness (under-nutrition) in these two states and Nigeria as a whole may be interventions against poverty.
The present study found that for every unit increase in age, the odds of being overweight/obese reduced by 14 %. The association between age and overweight/obesity may not be unconnected with the pubertal staging of the respondents. Although the pubertal stage was not significantly associated with overweight/obesity, it had a strong statistically significant association with age ($P \leq 0$·001). Females were also found to have two times higher odds of being overweight/obese than males. The relationship between overweight/obesity, age and sex of school-aged children and adolescents has been similarly reported by other studies within and outside Nigeria(35–37). Screen time (i.e. time spent with television, computers, video games and phones) of 2 h or more daily had two times higher odds of being overweight/obese, while the odds of being overweight/obese reduced by 45 % with a unit increase in physical activity. This finding is in line with previous studies linking overweight/obesity to reducing physical activity and increasing sedentary lifestyle, of which screen time plays a major role[14,15,38,39].
Increasing screen time has also been associated with higher consumption of snacks, which also has been reported to significantly increase the likelihood of overweight/obesity in children[40]. Exclusive breastfeeding was found to significantly reduce the odds of being overweight/obesity by as much as 54 % in line with the results of previous studies(41–43). This finding is important as it underscores the importance of exclusive breastfeeding in reducing, not only childhood under-nutrition and mortality[21,44], but also overweight/obesity. An interesting finding was that being part of a rich household reduced the odds of overweight/obesity by 53 %. This is different from what previous researchers in Nigeria have reported[37,39], but similar to the finding in the high-income countries[45]. This may be due to community-level factors which were probably not assessed in this study, since 30 % of the variance in the odds for overweight/obesity in this study is attributable to community-level factors. Community-level factors such as availability, accessibility and proximity to fast-food shops and recreational facilities or programmes in communities have been shown to influence the nutritional status of children and adolescents(46–48). Another plausible reason for this is that, by reason of exposure, the richest households in Nigeria are already adopting the lifestyle and values of the rich in developed countries where emphasis is placed on healthy food, exercises and a slim figure.
The school-aged children and adolescents living in Osun State had three times higher odds of being overweight/obese than those from Gombe State, and this may be a reflection of the higher socio-economic status and urbanisation of Osun State and the southwestern part of Nigeria compared with the northeastern part of the country where Gombe *State is* located[49]. In the present study, although the wealth index was not significantly different between the two states, Osun state did significantly better for almost all other indices of better socio-economic status than Gombe state, including household size, number of children in the family, family type, mother’s education, father’s education and mother’s employment.
Comparing the findings of the present study with those from previous multi-level analyses in *Nigeria is* challenging. Firstly, the previous studies were undertaken for under-five children, and they also focussed on under-nutrition alone. Furthermore, no previous research effort has used multi-level modelling to understand the determinants of overweight/obesity among any group of children/adolescents in Nigeria[20,21,24]. Additionally, the reference values used in the present study are different from those used by other previous studies. Previous studies used height-for-age (for stunting)[20,21], weight-for-age (for underweight)[24] and weight-for-height (for wasting)[24] reference values, which are all indicators of under-nutrition. The present study used the BMI-for-age reference values, which has the advantage of measuring both under-nutrition (measured as thinness) and over-nutrition (measured as overweight and obesity)[28].
A limitation of this study is that the findings of this study may not be generalisable to all of Nigeria, because only two out of thirty-six states were involved in the study. Another limitation is that, as to date, there was no data about the contextual determinants of overweight/obesity among older children in Nigeria using multi-level analysis, hence making the comparison of the findings of the present study and others challenging. The cross-sectional nature of the study also makes it impossible to establish causality.
## Policy implications
The findings of this study have some important policy implications. The present study observed that the community-level factors contributed significantly to the odds of under- and over-nutrition, indicating the need to explore community-based nutritional interventions for school-aged children and adolescents. There is also a need to review current interventions to assess whether/how they could be scaled-up and targeted at reducing socio-economic in-equalities, and especially poverty among households in the study location. The food systems approach of the Food and Agriculture Organization to create an enabling environment for improved nutrition[50] can guide governance for improved nutrition, evidence-based policies and programmes and financial investment to facilitate changes in food systems. The findings of the present study underscore the importance of reduced physical activity and prolonged screen time in increasing the odds for overweight/obesity among the school-aged children and adolescents. There is a need, therefore, for the development of recreational, sports or games centres and programmes for children and adolescents in different communities that will increase engagement in physical activity and reduce screen time among them.
## Conclusion
This study showed that Nigeria faces the challenge of a double burden of malnutrition among its school-aged and adolescents (6–19 years), with over a fifth experiencing either under-nutrition or over-nutrition. The study showed thinness and overweight/obesity among school-aged children and adolescents were strongly influenced by their communities, individual-level factors and their residence. Predictors of thinness in this study were household size and household wealth index. Overweight/obesity was significantly associated with the age, sex, exclusive breastfeeding, physical activity and household wealth index. Policymakers and stakeholders should therefore plan community-based educational programs to address, especially, socio-economic status, physical activity patterns among the children and the control of family/household size in the two Nigerian states.
## Financial support:
The research work was supported by the Consortium for Advanced Research Training in Africa (CARTA). CARTA is jointly led by the African Population and Health Research Center and the University of the Witwatersrand and funded by the Carnegie Corporation of New York (Grant No G-19-57145), Sida (Grant No: 54100113), Uppsala Monitoring Centre and the DELTAS Africa Initiative (Grant No: 107768/Z/15/Z). The DELTAS Africa *Initiative is* an independent funding scheme of the African Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (UK) and the UK government. The statements made and views expressed are solely the responsibility of the Fellows.
## Conflict of interest:
The authors declare that there is no conflict of interest.
## Authorship:
All the authors were involved in the conceptualisation of the research idea and topic, the design of the methodology and the proposal. A.A. carried out the study as part of his PhD work, while A.F. and K.K. supervised, provided useful suggestions and the mentorship that helped to shape the study into the present form. All the authors read and approved the final version of the manuscript.
## Ethics of human subject participation:
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Human Research Ethics Committee of University of the Witwatersrand (certificate No: M190514) and the ministry of health in Osun (certificate No: OSHREC/PRS/569T/155) and Gombe (certificate No: MOH/ADM/$\frac{621}{1}$/142) States. Written informed consent was obtained from all subjects/patients.
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|
---
title: Development of a protein energy malnutrition screening tool for older Thais
in public residential homes
authors:
- Thitima Phodhichai
- Warapone Satheannoppakao
- Mathuros Tipayamongkholgul
- Carol Hutchinson
- Siriphan Sasat
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991796
doi: 10.1017/S1368980021004250
license: CC BY 4.0
---
# Development of a protein energy malnutrition screening tool for older Thais in public residential homes
## Body
The ageing population is increasing worldwide as a result of declining fertility and improved longevity. Data from the United Nations predict that the proportion of people aged 60 years and older will increase from 12·5 % in 2017 to 20 % in 2050, globally[1]. Similarly, data from the Foundation of Thai Gerontology Research and Development Institute, Thailand, reported that the ageing population (aged 60 years and older) increased from 8·4 % of the population in 2010 to 17·1 % in 2017[2]. Furthermore, the proportion is projected to increase to 19·9 % in 2037. Therefore, there are many challenges for health care professionals who have to address their needs.
Older adults have special dietary requirements; however, not all of them can achieve an optimal intake. Consequently, this age group is at high risk of protein energy malnutrition (PEM). PEM is defined as a wasting condition in which the body has inadequate protein, energy and/or other nutrients as a consequence of insufficient food and nutrient intake over time(3–5). It is caused by many factors and reflects deteriorating physical and mental health including poor sensory function[6], poor appetite[5,6], poor cognitive function, difficulty chewing or swallowing, restricted mobility, chronic illnesses, poverty, social isolation and other factors[6]. PEM can be linked to many serious health outcomes including increased risk of falls(7–9), reduced functional capacity[7,9], increased risk of complications[10], poorer cognitive function[11], poorer quality of life[12], delayed discharge and increased risk of mortality[9]. In addition, it imposes an increased financial burden on older adults, caregivers and communities.
Early detection using screening tools is useful to identify older adults who are at risk of malnutrition. Then a proper nutrition intervention can be provided early. Even though many tools have been developed, there is no gold standard method for the early detection of malnutrition in older adults. Moreover, differences in anthropometry, nutritional characteristics and factors contributing to nutritional status are present between older people in different countries[13]. These factors limit the adoption of validated screening tools[14]. Therefore, many studies about screening tool development and validation have been conducted.
The most widely used nutrition screening tool in *Thailand is* the Mini Nutritional Assessment (MNA). Some studies have examined the reliability and validity of this existing nutrition screening tool among older Thai people; however, the effectiveness of the MNA has not been confirmed[15,16]. Most importantly, although the MNA was translated into Thai (Thai MNA), questions were developed based on the characteristics of older French citizens, which do not fit the Thai context in terms of dietary habits, BMI and other anthropometrical measurements. Indeed, Chumlea stated that the translation of MNA might not be applicable to non-Western countries due to differences in culture, dietary habits or health care system[17]. This is one major justification for developing a new screening tool specifically to detect malnutrition among older Thai people. A second justification point for a new nutrition screening tool is that previous Thai-developed nutrition screening tools focussed on patients in hospital settings and were not tailored towards screening older people(18–20). These tools are the Vajira Nutritional Screening Tool, Bhumibol Nutrition Triage and Nutrition Alert Form. Besides, two of them require the results of biochemical tests. Prior to the completion of this study, there was no published nutrition screening tool tailored towards older Thai residents in long-term care facilities, which have limited specialist human resources.
Institutionalised older adults are mostly dependent, disabled, highly afflicted with functional impairments[21,22] and have chronic illnesses[23] that may compromise energy and nutrient adequacy. Even though nutritious meals are served to them, older adults may dislike foods that are provided as they tend to be unpalatable due to limited salt or sugar content; this may be related to unintentional weight loss[23]. Additionally, meals may be difficult for older residents to chew and swallow, which possibly causes older people to eat less[24]. Moreover, isolation from their families and living in a new environment may lead to psychological stress[25,26], which in turn puts them at a higher risk of PEM[22].
This current study focussed on older Thai people living in public residential homes that were operated by government or provincial administrative organisations. Even though older adults are physically independent upon admission to public residential homes, they may become dependent later in their life. In addition, this older group is poor, lonely or cannot stay with their family. These scenarios might put them at risk of PEM. Also, some research evidence suggested that these residents were mostly dependent (60·3 %) and had health problems (86·8 %) such as hypertension, cognitive impairment, renal disease, depression and other risk factors for PEM[27]. Therefore, a nutrition screening tool for early detection of PEM risk among older Thais in residential homes must be developed and validated, and tested for practicality. The objective of this study was to develop and validate such a tool, and to test its practicality.
## Abstract
### Objective:
This study aimed to develop and validate protein energy malnutrition (PEM) screening tool for older adults in public residential homes, and to test its practicality.
### Design:
This cross-sectional study consisted of two phases: tool development/validation and tool practicality evaluation. In Phase 1, the questionnaire was developed based on literature review and tested for content validity. Older residents were interviewed using this questionnaire to identify potential PEM risk factors. A 24-h recall was used to collect dietary data, and body composition and serum albumin were measured. In Phase 2, practicality of new PEM screening tool was evaluated by intended users. Data were analysed by χ 2 test, Fisher’s exact test, t-test, Mann–Whitney U test and multiple logistic regression. Akaike Information Criterion (AIC) was used to estimate the best fit model.
### Setting:
Four public residential homes in central region, Thailand.
### Participants:
249 older residents residing in public residential homes and eight intended users.
### Results:
26·9 % had PEM (serum albumin <3·5 g/dl). According to multiple logistic regression and AIC values, PEM predictors were having pressure ulcer, experiencing significant weight loss and taking ≥ 9 types of medicine daily. These predictors were included in PEM screening tool. Regarding the tool performance test, area under the ROC curve was 0·8 ($P \leq 0$·001) with sensitivity and specificity of 83·9 and 45·5 %, respectively. For its practicality, eight intended users reported that it was useful and easy to use.
### Conclusions:
New screening tool may be capable of identifying PEM in older residents, and further testing is required before being recommended for use.
## Study design and participants
This cross-sectional study was carried out in public residential homes from 2016 to 2017. Across Thailand, there were twenty-five public residential homes at the time that this study was conducted. Ten public residential homes were located in the central region followed by six homes in the northeastern, five in the southern and four in the northern regions[27]. A simple random sampling technique was used to select 50 % of a total of ten public residential homes in the central region of Thailand. Then five public residential homes were enrolled as study settings. Participants from the five public residential homes were sampled by using the probability proportion to size technique. This multicentre study was divided into two phases: Phase 1 tool development/validation and Phase 2 tool practicality testing.
Participants in Phase 1 were aged 60 years and older and residing in public residential homes of provincial administrative organisations and the Ministry of Social Development and Human Security, Thailand. Older residents who were unconscious, receiving enteral or parenteral nutrition and suffering from critical illness were excluded. Sample size (n 469) was determined by using a sample size calculation for a single proportion[28,29]. We determined the standard, which was estimated under the normal curve at Type I error = 0·05 with a PEM prevalence of $17\%$[27] and a margin of error equal to 3·4 % at a CI of 95 %. Owing to the small population, finite population correction for proportions was used to adjust the sample size[29], then 333 older residents were recruited.
Participants in Phase 2 were composed of nurses and care assistants (defined as intended users) in the selected public residential homes. Two intended users per public residential home were recruited for the tool practicality testing. The researcher excluded nurses and/or care assistants who were not on duty during data collection.
## Research assistants
Before starting data collection, research assistants with, or studying for, a university degree in nutrition were recruited via an announcement placed on notice boards in the Faculty of Public Health, Mahidol University. Research assistants were given an overview of the research project and their roles. They subsequently received operation manuals and training about anthropometric measurement, the 24-h recall method and interview technique. The first author monitored and supervised the whole process of data collection.
## Phase 1: tool development/validation
The process of development/validation of the PEM screening tool is described in Fig. 1. Briefly, it was composed of: [1] A literature review of the possible risk factors associated with PEM in older adults based on previous studies both in Thailand and other countries; [2] Development of the questionnaire by importing the potential risk factors into the questionnaire; [3] Content validity of the questionnaire, which was examined by an expert panel in nutrition and gerontology. After that, the questionnaire was revised based on the experts’ suggestions; [4] data collection was performed; and [5] statistical analysis was conducted for selecting the best model and scoring system. The first author and trained research assistants interviewed participants using a questionnaire that covered potential factors linked to PEM. They were general characteristics (e.g. sex, age, education, income, source of income, current smoking and drinking habit), activities in daily living and health status (e.g. medication, presence of pressure ulcer, oral health, depression, etc.).
Fig. 1The process of developing and validating the PEM screening tool. Note: PEM, protein energy malnutrition Additionally, body composition was determined by using standardised methods and tools by trained research assistants. Measurements included weight, height, calf circumference, mid-upper arm circumference and triceps skin fold thickness. Participants were weighed whilst they were wearing light clothing. Measurements were made to the nearest 0·1 kg by using portable standardised electronic scales (Tanita BC-587). Height was measured to the nearest 0·1 cm by using a stadiometer. Participants’ heels, buttocks, shoulders and head touched the stadiometer, and they looked straight ahead. BMI was then calculated from weight (kg) divided by height in metre squared. Calf circumference was measured to the nearest 0·1 cm using a non-stretchable measuring tape at the widest circumference of the right calf in a sitting position. For mid-upper arm circumference, arm circumference midway between the acromion and olecranon-on the left arm was marked and measured using a non-stretchable measuring tape. Triceps skinfold thickness was measured at the upper arm mid-point mark on the posterior surface of the right arm by Harpenden calliper. Moreover, information regarding weight in kg during the past 6 months (weight recorded in the last 1–6 months) was obtained from each participant’s health record. Then significant weight loss over time (i.e. 5 %, 7·5 % and 10 % weight loss in the previous 1, 3 and 6 months, respectively)[30] was calculated.
Furthermore, dietary intake data were collected using a single 24-h recall by the first author and research assistants with a university degree in nutrition. Information on the type, brand names and amount of food consumed was collected. To increase the accuracy of portion size estimation, household measures and visual aids were used. Due to concerns about participants’ memory (ability to recall), the food weighing method was utilised to validate the outcomes from the 24-h recall method. In this study, a subsample of 20 % of individual lunches was randomly selected for the validation. To validate the 24-h recall by using the food weighing method, we spent 2 d at each residential home. Day 1 was set for weighing lunchtime meals among 20 % of participants. All meal components (served and leftover) were weighed by using a digital kitchen scale (Tanita KD-321) and recorded. On day 2, these participants were interviewed about the food and drink they consumed yesterday using the 24-h recall method. Then intake amounts (for lunchtime meals) from both methods were compared.
To test the new screening tool, a reference standard that is used to diagnose PEM had to be utilised. However, while many criteria are employed to define PEM, there is no universally agreed on reference standard for screening and diagnosing older people with PEM[31]. Serum albumin concentration is commonly used for PEM screening, and has some advantages including being easy to measure, relatively cheap and reproducible[32]. As a result, serum albumin concentration was used to identify nutritional status; serum albumin < 3·5 g/dl was indicative of a malnourished state[31]. For determination of serum albumin concentration, blood was taken by a registered nurse and transferred to serum tubes without anticoagulant. Within 8 h, blood samples were transported at room temperature to a laboratory at the Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University. Serum was separated by centrifugation and stored at −70°C on the same day as sample collection. Then, blood samples were analysed for serum albumin concentration using the dye-binding bromcresol purple technique[33] by laboratory staff.
These factors (i.e. general characteristics, activities in daily living, health status, body composition and energy and macronutrient intake) were analysed as independent variables predicting PEM risk. Statistical analysis was performed for selecting the best model and scoring system to develop a new PEM screening tool.
## Phase 2: tool practicality testing
The PEM screening tool was used by intended users including residential home nurses and care assistants who regularly provided care to the older people. Practicality was assessed to reflect the feasibility of administration and interpretation of this tool. Data were collected by using a self-administered questionnaire consisting of questions about: (i) time taken for each participant to complete the PEM screening tool; (ii) the completeness of items on this tool; (iii) ease of use and (iv) user preferences.
## Nutrient analysis
Nutrient intakes were analysed by using INMUCAL-Nutrients Software Version 3.0[34]. Then intakes of energy and macronutrients (carbohydrate, protein and fat) were reported.
## Statistical analysis
After cleaning and coding, the data were analysed by using SPSS version 18.0 (SPSS Inc. Released 2009. PASW Statistics for Windows, Version 18.0 SPSS Inc.). Descriptive statistics were used for explaining participants’ characteristics and general information. The normality of continuous data was examined by the Kolmogorov–Smirnov test. In order to identify potential predictors, the independent variables predicting PEM risk were derived from two main parts because of the large number of independent variables possibly related to PEM. Part 1 was the comparison of differences in characteristics between participants with and without PEM by using the χ 2 test, Fisher’s exact test, t-test or Mann–Whitney U test. As for Part 2, factors predicting PEM risk were determined by using simple binary logistic regression. Independent variables with $P \leq 0$·20[35] and OR ≥ 1·5 were considered to be important factors for PEM. The independent variables which met the aforementioned criteria were entered into multiple logistic regression to identify models for predicting PEM risk. In this step, the Akaike information criterion (AIC) was used for estimating the likelihood of each model to predict PEM. The model that provided the minimum AIC was selected. The performance of the PEM screening tool was explained by sensitivity, specificity, AUC and receiver operating characteristic curve. Sensitivity and specificity were calculated to test the quality of the tool. A receiver operating characteristic curve was determined to discriminate between the residents who were and were not at risk of PEM. For the scoring system, the score of each factor derived from the coefficient of each variable was divided by the lowest β value, multiplied by a constant and rounded to the nearest integer to identify participants at risk of PEM. $P \leq 0$·05 was considered to be statistically significant.
## Characteristics of participants
Initially, 306 older Thai residents from five settings agreed to participate in this multicentre study. However, among the five study settings, fifty-seven participants of one setting declined to have their blood drawn. For that reason, there were serum albumin data (PEM indicator) for only 249 participants. Thus, the response rate was equal to 74·8 % (249 of 306 older residents) as illustrated in Fig. 2, and the data of these 249 participants from four study settings were used. Almost two-thirds of them were female (66·7 %) and had their own income (68·3 %) mainly from individual donors. More than two-fifths were aged 70–79 years (45·4 %). Half of them had completed primary school (55·5 %). Over 80 % were not current smokers, and over 98 % were not current drinkers. Almost 50 % perceived their health status as fair, and 79·5 % did not have depression. The prevalence of PEM (serum albumin < 3·5 g/dl) was 26·9 %.
Fig. 2Flow of recruiting participants for tool development/validation phase. Note: PEM, protein energy malnutrition
## Factors associated with PEM risk
For the univariate analysis (n 249), the dependent variable was PEM risk classified by serum albumin level. Independent variables which were possibly associated with PEM ($P \leq 0$·20) were age, educational level, personal income, perceived health status, received therapeutic diet, difficulty swallowing, activities in daily living score, taking ≥ 9 types of medicine daily, triceps skinfold thickness and experiencing significant weight loss (Table 1). Independent variables which considerably predicted PEM (OR ≥ 1·50) included older age group, educational level, difficulty swallowing, drinking alcohol, having pressure ulcers, taking ≥ 9 types of medicine daily and experiencing significant weight loss (Table 2).
Table 1Factors possibly associated with PEM risk determined by serum albuminVariablesTotalNo PEMPEM P n % n % n %Sex0·320* Male8333·35731·52638·2 Female16666·712468·54261·8Age (years)0·140† Mean76·9976·5478·12 sd 7·567·427·89Educational level0·140* No formal education4317·43016·81319·1 Primary school13755·59854·73957·4 Secondary school4016·22715·11319·1 Diploma/college52·031·722·9 Bachelor and higher228·92111·711·5Personal income0·050* No7931·75128·22841·2 Yes17068·313071·84058·8Smoking0·820* Yes3514·12614·4913·2 No21485·915585·65986·8Drinking0·300‡ Yes41·621·122·9 No24598·417998·96697·1Perceived health status0·130* Poor3915·72815·51116·2 Fair12249·09451·92841·2 Good7028·14424·32638·2 Excellent187·2158·334·4Presence of pressure ulcer0·410‡ Yes176·8116·168·8 No23293·217093·96291·2Receive therapeutic diet0·090‡ No23795·217596·76291·2 Yes124·863·368·8Number of meals0·840‡ 110·410·600·0 2187·3147·845·.9 322992·316591·76494·1Chewing problem0·630* No problem12349·68848·93551·5 Had minor problem10843·68145·02739·7 Had major problem176·9116·168·8Swallowing difficulty0·020* No22289·915687·26697·1 Yes2510·12312·822·9Sense of smell0·740* No difference20582·314781·25885·3 Poorer2510·01910·568·8 Better197·6158·345·9Sense of taste0·430* No difference20080·314479·65682·4 Poorer249·62011·045·9 Better2510·0179·4811·8Appetite0·290* No difference15763·111463·04363·2 Better124·8116·111·5 Poorer8032·15630·92435·3ADL score0·010§ Median20·0020·0020·00 25th, 75th percentiles19·00, 20·0019·00, 20·0019·00, 20·00Depression0·710* No19879·514580·15377·9 Yes5120·53619·91522·1Taking ≥ 9 types of medicine daily0·010* No20684·115787·74974·2 Yes3915·92212·31725·8BMI (kg/m2)0·470§ Median23·2123·5322·16 25th, 75th percentiles20·47, 26·1820·52, 26·2020·23, 26·13MUAC (cm)0·670§ Median28·3028·3028·30 25th, 75th percentiles25·50, 30·5025·60, 30·3825·00, 31·50Triceps skinfold thickness (mm)0·080§ Median15·4516·0014·00 25th, 75th percentiles11·20, 20·0011·40, 20·9010·40, 18·20Calf circumference (cm)0·470† Median33·0033·4032·75 25th, 75th percentiles30·80, 35·5030·75, 35·6030·88, 35·53Experienced significant weight loss (n 142)0·048* No7351·46156·01236·4 Yes6948·64844·02163·6Energy intake (kcal)0·700§ Median920·40926·36916·70 25th, 75th percentiles627·47, 1210·14614·36, 1210·24628·64, 1210·90Carbohydrate intake (g)0·750§ Median141·93140·34144·14 25th, 75th percentiles102·57, 193·58100·30, 195·06110·52, 187·29Protein intake (g)0·590§ Median29·9430·2729·44 25th, 75th percentiles21·13, 43·0521·43, 43·8420·43, 40·68Fat intake (g)0·920§ Median22·7322·4823·13 25th, 75th percentiles13·84, 32·5614·29, 32·2213·21, 34·38Note: ADL, activities in daily living; MUAC, mid-upper arm circumference; PEM, protein energy malnutrition.* χ 2 test.† t-test.‡Fisher’s exact test.§Mann Whitney U test.
Table 2Univariate analysis of factors associated with PEM classified by serum albumin using simple binary logistic regressionVariablesCoefficient P OR95 %CISex Male0·3300·2671·3910·777, 2·491 Female1·000Ref. Age group (years) 60–691·000Ref. 70–790·2630·5271·3010·576, 2·941 ≥ 800·4340·3081·5430·671, 4·742Educational level No formal education2·0950·0528·1290·982, 67·310 Primary school2·1230·0418·3571·087, 64·279 Secondary school2·3140·03210·1111·223, 83·598 College2·6390·05414·0000·952, 205·841 Bachelor and higher1·000Ref. Personal income No1·000Ref. Yes−0·5230·0790·5930·330, 1·063Smoking Yes−0·0710·8640·9310·412, 2·105 No1·000Ref. Alcohol drinking Yes1·0190·3132·7690·382, 20·064 No1·000Ref. Perceived health status Poor0·6750·3521·9640·474, 8·145 Fair0·3980·5511·4890·402, 5·517 Good1·0220·1332·7780·733, 10·529 Excellent1·000Ref. Presence of pressure ulcer Yes0·4250·4221·5290·542, 4·312 No1·000Ref. Receive therapeutic diet Yes−0·7010·2460·4960·152, 1·620 No1·000Ref. Number of meals 1−20·2341·0000·0000·000 2−0·2840·6280·7530·239–2·374 ≥ 31·000Ref. Chewing problem No problem1·000Ref. Had minor problem−0·2270·4510·7970·442, 1·438 Had major problem0·3160·5631·3710·471, 3·994Swallowing difficulty Yes1·5600·0384·7611·091, 20·781 No1·000Ref. Sense of smell No difference1·000Ref. Poorer−0·1990·6880·8200·312, 2·157 Better−0·3680·5290·6920·220, 2·175Sense of taste No difference1·000Ref. Better−0·6400·2620·5270·172, 1·612 Poorer0·2160·6371·2410·507, 3·039Appetite No difference1·000Ref. Better−1·4230·1790·2410·030, 1·923 Poorer0·0670·8251·0700·588, 1·945Total ADL score−0·0100·9220·9900·810, 1·210Depression Yes0·1570·6511·1700·592, 2·310 No1·000Ref. Taking ≥ 9 types of medicine daily Yes0·8020·0282·2291·091, 4·552 No1·000Ref. BMI Normal1·000Ref. Underweight0·1890·6761·2090·497, 2·941 Overweight−0·3630·3970·6960·301, 1·611 Obesity−0·3320·3360·7180·365, 1·410MUAC−0·0010·9870·9990·933, 1·071Triceps skinfold thickness−0·0400·0960·9610·917, 1·007Experienced significant weight loss (n 184) Yes0·7300·0772·0750·924, 4·657 No1·000Ref. Energy intake0·0000·5411·0000·999, 1·000Fat intake0·0010·8971·0010·982, 1·021Carbohydrate intake−0·0010·4760·9990·994, 1·003Protein intake−0·0070·3860·9930·977, 1·009Note: Ref., reference category; ADL, activities in daily living; MUAC, mid-upper arm circumference; PEM, protein energy malnutrition. Experienced significant weight loss (i.e. 5 %, 7·5 % and 10 % weight loss in the previous 1, 3 and 6 months, respectively, was calculated[30].
These variables were then incorporated in to multiple logistic regression, in order to select the best model to predict PEM risk. However, data from 110 participants were excluded due to missing weight loss data. Consequently, data from 139 participants were utilised to develop the PEM screening tool. In this step, the AIC was used for estimating the likelihood of a model to predict PEM. The model providing minimum AIC, which contained significant factors from the literature review, was selected. The model that included taking ≥ 9 types of medicine daily, having pressure ulcers and experiencing significant weight loss was used for developing the PEM screening tool (Table 3).
Table 3Models predicting the occurrence of PEM classified by serum albuminVariablesModel 1Model 2Model 3Model 4Model 5Educational level✓Swallowing difficulty✓Taking ≥ 9 types of medicine daily✓✓✓✓✓Limited in ADL✓Presence of pressure ulcer✓Experienced significant weight loss✓✓Low triceps skinfold thickness✓Loss of appetite✓ n 241141139139139AIC267·2122·1120·1118·1120·8Note: ADL, activities in daily living; PEM, protein energy malnutrition.
## Scoring system
The possibility of scoring ranged from 0 to 7 points. The score of each factor derived from the coefficient of each variable was divided by 1·11 (lowest β value), multiplied by the constant 2 and rounded to the nearest integer. The scoring system of the PEM screening tool is shown in Table 4.
Table 4Scoring system of the PEM screening toolRisk factorCoefficientOR95 % CI P PointPresence of pressure ulcer1·725·61·2, 26·70·033Significant weight loss1·113·01·1, 8·30·032Taking ≥ 9 types of medicine daily1·323·81·2, 12·20·032Note:PEM, protein energy malnutrition.
The PEM screening tool consisted of three questions including whether or not a participant had a pressure ulcer, experienced significant weight loss or took ≥ 9 types of medicine. The risk level was defined as at risk of PEM and not at risk of PEM. The sensitivity and specificity of the predicted model are shown in Table 5. It indicated that a participant was at risk of PEM if they answered ‘Yes’ to only 1 out of 3 questions, which equated to a score of 2.
Table 5Score, sensitivity and specificity of predicted modelScoreSensitivity95 % CISpecificity95 % CI−1·01·0000·0002·00·8390·751, 0·9270·4550·383, 0·5275·00·2900·238, 0·3420·8730·838, 0·9087·00·2580·209, 0·3070·9090·879, 0·9399·00·0970·067, 0·1270·9820·969, 0·99512·00·0000·000, 0·0000·9910·982, 1·000 The AUC, sensitivity and specificity of the PEM screening tool are described in the receiver operating characteristic curve (Fig. 3). The receiver operating characteristic curve was used to determine the cut-off point of the screening tool and determine the scoring system. The AUC was 0·795 ($P \leq 0$·001), meaning there is 79·5 % chance that this model is able to distinguish between PEM and no PEM groups. The best cut-off point was 1·0. It provided the best sensitivity and specificity (83·9 (95 % CI 75·1, 92·7) and 45·5 (95 % CI 38·3, 52·7), respectively).
Fig. 3Receiver operating characteristic (ROC) curve of PEM screening tool. Note: PEM, protein energy malnutrition.
## Phase 2: tool practicality test
Participants of this phase were eight intended users from four public residential homes, namely four nurses and four care assistants who had more than 1-year work experience. These intended users were asked to interview thirty-nine residents (eight males and thirty-one females) of their residential homes by using the PEM screening tool. Collection of data using this PEM screening tool could be completed within 5 min (data not shown). Furthermore, the eight participants from the four different settings agreed that the screening tool was useful (100·0 %), easy to use (87·5 %) and easy to interpret (100·0 %). They also reported that the questions were easy for older residents to understand. Yet, the clarity of one question (regarding significant weight loss) needed to be improved.
## Discussion
Institutionalised older adults are at risk of PEM. Early screening for PEM risk factors is important. This study aimed to develop a PEM screening tool for older adults in public residential homes and to validate and test the practicality of this tool. Three hundred and six older residents were recruited. Fifty-seven from one setting were excluded from data analysis due to missing serum albumin data, leaving 249 older participants from only four settings. The study response rate was 74·8 %. As reviewed, a response rate of approximately 60 % is considered to be acceptable[36]. Thus, the response rate in this study was more than satisfactory. Furthermore, there were no statistically significant differences between the participant group (n 249) and the excluded group (n 57) in terms of seven out of eight of their general characteristics, namely sex ($$P \leq 0$$·080), age ($$P \leq 0$$·290), educational level ($$P \leq 0$$·220), income ($$P \leq 0$$·060), smoking behaviour ($$P \leq 0$$·150), drinking habit ($$P \leq 1$$·000) and perceived health status ($$P \leq 0$$·110).
Regarding a biomarker that makes use of the definition of PEM in this study, serum albumin concentration was selected to test against the new screening tool because serum albumin concentration is easy to measure, relatively cheap, reproducible and commonly used for PEM screening[32]. Even though it has a long half-life and its level may be affected by infection, burns, fluid overload, hepatic failure and nephrotic syndrome[37], serum albumin is a mainstay in the screening and monitoring of malnutrition[38]. Furthermore, to reduce these confounders, older residents who had all of the aforementioned health problems, except infection, were excluded from this study; thereby increasing the likelihood that serum albumin concentration provided a truer reflection of nutritional status.
Using a serum albumin cut-off of < 3·5 g/dl[31], around one quarter (26·9 %) of participants had PEM. Due to a lack of research using serum albumin to assess nutritional status in older Thais living in public residential homes, it is difficult to draw comparisons. An extensive review of the literature revealed only one study that measured serum albumin concentration. The study was conducted in 1997 by Charoonruk and it examined the nutritional status of 139 older Thai residents in one public residential home[39]. The prevalence of PEM in Charoonruk’s study 23 years ago was 14 %. Nevertheless, when the same setting as that used by Charoonruk was included in this study, the PEM prevalence was slightly lower than that reported by Charoonruk (12·7 v. 14·0 %, respectively). Thus, PEM prevalence in Thailand may not have markedly increased over two decades. We recruited participants from four residential homes, whereas Charoonruk collected data from only one setting. The current study uncovered wide variation in PEM prevalence (12·7 % to 37·2 %) across the four settings (data not shown). However, it is recognised that PEM has been a common problem among older Thai residents in public residential homes for some time, and prevention and treatment of PEM continue to challenge health professionals.
In the development/validation phase, factors associated with PEM risk were firstly investigated by univariate analysis. All independent variables that were probably linked with PEM risk ($P \leq 0$·20 or OR ≥ 1·50) were selected as candidate PEM predictors for multiple logistic regression. As only 139 out of 249 older residents had complete data, it should be noted that the reduced number of participants might have affected the predictors of PEM risk.
The AIC, which was applied in this study, is generally considered to be the first model selection criterion to use in practice[40]. Even though the model with the lowest AIC is considered to be the best model (contained taking ≥ 9 types of medicine daily, low triceps circumference and loss of appetite), we selected the second lowest AIC model as the significant predictors for this PEM screening tool. The reason being that the main goal was to produce a screening tool which is easy to use, concise, economical and usable by people without nutrition expertise[41]. Measurement of triceps skinfold circumference (in the model with the lowest AIC) requires a specific and high-cost instrument, which may be difficult for residential home staff to access. Well-trained and experienced staff are also required to take accurate skinfold measurements. Furthermore, a screening tool with multiple items must meet standards of reliability[42].
The AUC was 0·795, which provided the best sensitivity (83·9 %) and specificity (45·5 %). The sensitivity and specificity of a screening test are characteristics of the test’s performance at a given cut-off point (criterion of positivity)[43]. Ideally, a test should provide high sensitivity and specificity[43]. PEM is a health problem which can be prevented, so we focussed on sensitivity because this test is more likely to correctly identify older adults who are at risk, confirm risk and then provide a nutrition intervention. Furthermore, high sensitivity is important where an undetected condition has serious consequences but is treatable(43–45). The AUC is a measure of the cut-off accuracy of a test and the figure obtained in this study (0·795) indicated that the tool performed well in distinguishing older adults with PEM and without PEM[46]. PEM screening tools can be easy to administer, but accuracy remains essential.
Nutrition screening tools contain a variety of risk factors, in terms of type and number[47]. Most screening tools are based on basic questions covering weight loss, current BMI, dietary intake, disease severity or some other measurement[44,48,49]. Some nutrition screening tools include physical examination, such as Subjective Global Assessment Test, which is dependent on the availability of a health professional who is a skilled and experienced observer[47]. In reality, many nutrition screening tools often require experienced clinicians and dietitians or longer periods of time[32] to collect data and/or interpret outcomes. As is the case in low- and middle-income countries in general, residential homes in Thailand do not employ full-time nutritionists or dietitians to provide food services and nutrition care. Hence, early detection of PEM is rare.
In this study, having pressure ulcers (OR 5·6 (95 % CI 1·2, 26·7)), experiencing significant weight loss (OR 3·0 (95 % CI 1·1, 8·3)) and taking ≥ 9 types of medicine daily (OR 3·8 (95 % CI 1·2, 12·2)) were associated with PEM occurrence, and these factors were included in the PEM screening tool. This outcome is in line with other studies conducted in clinical, community or long-term care settings in other countries, which also demonstrated that older adults with pressure ulcers[50], polypharmacy[51,52] and weight loss[53] had a higher risk of PEM and/or malnutrition. A cross-sectional multicentre study by Bonetti and colleagues examined factors related to malnutrition among patients admitted to twelve hospitals in northern Italy. Presence of pressure ulcer was significantly associated with malnutrition (OR 4·95 (95 % CI 2·63, 9·31), according to multivariate logistic regression)[50]. Additionally, greater use of medicine can lead to malnutrition. Nevertheless, there is no consensus on the definition of greater use of medicine or polypharmacy. According to previous publications, the number of medicines required to be considered polypharmacy varies from more than 4 to 10[13,54,55]. Therefore, the number of medicines that we used as the cut-off point varied from 4 to 10. Based on the outcomes of binary logistic regression and multiple logistic regression, taking more than or equal to nine types of medicine was a predictor of PEM risk in this study. Medeiros et al. performed a cross-sectional study among older adults living in seventeen nursing homes in Brazil to examine factors linked to frailty and malnutrition. They found that older adults taking more medicines had a higher chance of frailty and malnutrition (adjusted PR 1·016 (95 % CI 1·006, 1·027)). Medeiros et al. proposed that the relationship between greater medicine use and malnutrition may be due to medication side effects, including appetite and sensory alterations[52]. As for weight loss, it seems to be allied with malnutrition in several age groups. de Aquino and Philippi investigated malnutrition risk factors among Brazilian hospital patients who were aged 18–64 years old. The strongest predictor was weight loss (OR 58·03 (95 % CI 18·46, 182·41))[53].
However, compared to other nutrition screening tools(18–20) developed in the Thai context, the factors that predicted PEM in this screening tool were different. The Vajira Nutritional Screening Tool, which was designed to assess the nutritional status of hospital patients, is composed of four significant factors including BMI < 18·5 kg/m2, weight loss within 3 months, decreased food intake within a week and chronic illnesses or surgery[18]. Regarding the Nutrition Alert Form developed by Komindr et al. [ 19], PEM predictors are arm span, BMI, albumin or total lymphocyte count, weight change within 4 weeks, body shape, gastrointestinal problems, food accessibility and morbidities. Differences in the characteristics of participants in each setting (hospital-based or residential home-based) might explain the variation in PEM risk factors.
Questions in this PEM screening tool also differ from the Mini Nutritional Assessment Short-Form (MNA-SF) which includes questions about appetite loss, weight loss over 3 months, mobility, acute disease, BMI and neuropsychological problems[56]. MNA-SF has some advantages for use in long-term care facilities, for example, it does not require a laboratory test. However, to our knowledge, some factors in the MNA-SF might be of limited use for screening residents in a long-term care facility. For example, its questions about neuropsychological problems or psychological stress should not rely on only residents’ self-evaluation of themselves, but also require judgment from specialists in this field. Apart from the differences between this new screening tool and others mentioned above, as noted, one factor predicting PEM risk that has been commonly found in all screening tools is a loss or change in weight in these vulnerable groups. Consequently, weight loss or weight change should be a concern.
Residential homes provide services at all levels of care because the majority of residents have chronic health problems and need moderate to high levels of care. Thus, reliable and valid assessment of instruments and adequate health care services are required in order to appropriately assess and address these needs[57]. Nutrition screening or assessment in secondary and tertiary care is widely considered to be a useful tool to identify older people who are at risk of malnourishment[47]. Some evidence suggests that screening prior to admission to care homes may also be beneficial[48,58]. Green and Watson hinted that earlier identification might help to reduce the malnutrition trajectory and the negative outcomes associated with poor nutritional status[47]. The screening process should be simple, acceptable to intended users and older participants, and should not require any nutrition expertise.
The main strength of this study was its inclusion of several residential homes (four out of ten) located in the central region of Thailand. This helped to expand the number of participants that met the eligibility criteria. The participants were also representative of the older adults living in public residential homes in this region. Furthermore, this screening tool had high sensitivity and AUC. Consequently, it was able to screen older residents in public residential homes who were at risk of PEM. Additionally, this screening tool was accepted by intended users, namely nurses and care assistants, as it is easy to use and interpret.
Some limitations are presented. The first is related to incomplete secondary data used for predicting the potential PEM risk factors. For example, weight loss data were not recorded regularly in all study settings. Therefore, the analysis of PEM predictors by multiple logistic regression included fewer than expected participants with complete data. Hence, due to incomplete data, further testing of this proposed PEM risk screening tool is required. Secondly, in this multicentre study, albumin was used as a biomarker of PEM. It is generally acknowledged that albumin is not the most sensitive biomarker of malnutrition due to a long half-life and potential interference from several factors[59]. However, older residents who had potentially confounding health problems, with the exception of infection, were excluded from participating. Furthermore, research has demonstrated that serum albumin remains a suitable indicator for screening and monitoring malnutrition[38]. Nevertheless, using an imperfect reference standard may affect estimates of diagnostic accuracy. In light of this, the ranges of bounded values for estimated sensitivity and specificity are presented to explain their uncertainty[60]. Another limitation concerns the collection of dietary intake data covering only 1 d. A single day dietary account might not be representative of an individual’s habitual dietary consumption. Nonetheless, it is suitable for estimating the average intakes of a group or population[61]. Using the 24-h recall method to collect information on dietary intake from older adults can be questionable, due to respondent memory lapses. In this study, this method was used because it presents a low burden to participants and does not affect their dietary habits. Household measures and visual aids were used to help assist participants to more accurately recall portion sizes. Moreover, we weighed 20 % of participants’ meals at lunchtimes. It was found that average intake amounts ascertained by weighing and dietary recall were comparable ($r = 0$·61, $P \leq 0$·001), thereby indicating that the 24-h recall data were acceptable in terms of its accuracy in estimating the amounts consumed. As noted, in this study, the median energy intake was lower than the 2003 and 2020 Thai recommendations for these age groups[62,63]. It was also somewhat lower than the energy intakes reported in the Thai National Health Examination Survey IV[64]. The relatively low energy intakes in this study may reflect the study population group, which only included older adults residing in residential care homes, who tend to have health conditions which can adversely affect their dietary intakes(21–23).
In conclusion, intended users reported that the PEM screening tool developed in this study was useful and easy to use and interpret. Besides, the questions in this screening tool were easy for older residents to understand. This screening tool could be useful for detecting PEM among older adults who live in public residential homes; however, further testing of the tool is required before it can be recommended for use.
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|
---
title: 'Influence of parental physical activity on offspring’s nutritional status:
an intergenerational study in the 1993 Pelotas birth cohort'
authors:
- Cauane Blumenberg
- Rafaela Costa Martins
- Shana Ginar da Silva
- Bruna Gonçalves Cordeiro da Silva
- Fernando C Wehrmeister
- Helen Gonçalves
- Pedro C Hallal
- Inácio Crochemore-Silva
- Ana MB Menezes
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991797
doi: 10.1017/S1368980021004079
license: CC BY 4.0
---
# Influence of parental physical activity on offspring’s nutritional status: an intergenerational study in the 1993 Pelotas birth cohort
## Body
In 1953, the study by Morris and colleagues revealed that physical activity could prevent coronary heart diseases[1]. As the time passed by, the benefits of physical activity were found to be even more important, promoting well-being and preventing not only coronary heart diseases, but also other non-communicable diseases. Current knowledge links physical activity to other health benefits, such as preventing all-cause mortality, several types of cancer, type 2 diabetes, mental illnesses, hypertension and chronic obstructive pulmonary disease(2–4). Besides, physically active individuals have a higher life expectancy compared to inactive individuals[5].
In the 1990s, David Barker proposed the foetal origins hypothesis, which suggests that mother’s behaviours during pregnancy can impact future outcomes on their children[6]. This hypothesis brought attention to the role of intergenerational effects, also investigating the role of the father and other periods of parental life course, including those previous to the pregnancy(7–10). Some studies showed that children born to obese or hyperglycaemic mothers are more likely to develop diabetes or obesity later in life[7,11,12], that parents’ alcohol consumption influences adolescent’s drinking behaviour[8], and that parental drug use predicts adolescent’s drug use[9]. However, few studies investigated the intergenerational effects of parental physical activity on offspring’s health-related indicators[13], especially regarding their nutritional status.
The limited evidence about the intergenerational effects of physical activity on nutritional status is mixed. A study showed that reduced maternal physical activity over time was associated with higher offspring’s BMI[10], while another showed no influence[14]. In order to further explore this subject, the members of the 1993 Pelotas birth cohort and their children were assessed to investigate the influence of parental physical activity measured in three moments previous to the birth of their child on the offspring’s nutritional status.
## Abstract
### Objective:
To investigate the influence of parental physical activity on offspring’s nutritional status in the 1993 Pelotas (Brazil) birth cohort.
### Design:
Birth cohort study.
### Setting:
The main outcomes were overweight and obesity status of children. The main exposure was parental physical activity over time, measured during the 11, 15 and 18 years of age follow-ups. The exposure was operationalised as cumulative, and the most recent measure before the birth of child. We adjusted Poisson regression models with robust variance to evaluate crude and adjusted associations between parental physical activity and offspring’s nutritional status. All analyses were stratified according to the sex of the parent.
### Participants:
A total of 874 members from the 1993 Pelotas (Brazil) birth cohort followed-up at 22 years of age with their first-born child were analysed.
### Results:
Children were, on average, 3·1 years old. Crude analyses showed that the mother’s cumulative physical activity measure had an indirect association with the prevalence of children’s obesity. The most recent maternal physical activity measure before the birth of the child was associated with 41 % lower prevalence of obesity in children, even after adjustment for confounders.
### Conclusions:
The most recent maternal physical activity measure was indirectly associated with the prevalence of obesity in children. No associations were found for fathers, reinforcing the hypothesis of a biological effect of maternal physical activity on offspring’s nutritional status.
## Sample
This study is inserted in the context of the 1993 Pelotas birth cohort. Between January and December 1993, all mothers living in the urban areas of Pelotas (Southern Brazil) and who gave birth to one or more liveborn children in maternity hospitals of the city were invited to enrol their children into the 1993 Pelotas birth cohort. A total of 5249 children born in 1993 (99·7 % of all births occurring in the city) agreed to participate in the longitudinal study[15]. Apart from the perinatal study, that occurred in 1993, the members of the birth cohort are routinely accompanied, and 13 follow-ups have been carried out so far. In 2015, as part of the 22 years of age follow-up of the 1993 birth cohort, the research team tried to locate through phone calls, social networks and official registries all its 5249 members, of which 3810 were interviewed and examined.
When invited to participate, the members of the birth cohort were also asked if they had any children. In case of an affirmative answer, they were asked to bring their children along to participate. A total of 948 members of the birth cohort (24·9 % of those followed-up at 22 years of age) attended the research clinic with their children, totalising 1213 children assessed (mean of 1·3 children per member of the birth cohort) – however, in this study only the first-born child is considered to simplify analyses and interpretation (n 948). This was the first follow-up of the ‘second generation’ of the 1993 Pelotas birth cohort. The second generation was assessed from January to December 2016, being composed of children born from mothers or fathers that are original members of the 1993 Pelotas birth cohort. Further details about the 22 years of age follow-up and the second generation sample can be found elsewhere[16].
## Outcomes
BMI-for-age of the second generation was the main outcome analysed. Using children’s sex, age, height and weight, the Anthro Plus Stata macro was used to estimate the anthropometric index based on the 2007 WHO reference population[17]. Children’s age was calculated in months by subtracting the date that the children attended the research clinic from their date of birth. The height for children younger than 3 years was measured in centimetres with 0·1 cm precision with children lying down on an infant anthropometer. Children older than 3 years were measured standing on a stadiometer with 0·1 cm precision. The weight was assessed in kilograms using a regular scale with 0·1 kg precision. Children younger than 3 years were weighted with their mothers or fathers, followed by a weighting of the mother or father alone. A simple subtraction between both weights was performed to achieve children’s weight. In turn, children older than 3 years were weighted directly on the scale. All parents and children were weighted in very light clothing.
Outcomes were analysed as binary variables (overweight and obesity). Overweight was defined as children with Z-scores of BMI-for-age higher than +1 SD, and obesity was defined as children with Z-score of BMI-for-age higher than +2 SDs[17,18].
## Exposures
Exposures analysed were based on parental physical activity. Total time on physical activity was investigated when the parents (original members of the 1993 Pelotas birth cohort) were 11, 15 and 18 years of age. In order to investigate how many minutes individuals spent on physical activity during a regular week, a list of physical activities practiced was applied in the 11 and 15 years follow-ups. For the 18 years follow-up, the transport and leisure time section of the International Physical Activity Questionnaire (IPAQ) was applied[15,16,19]. Both questionnaires investigate the number of days and the length of practice (in minutes) of each activity in the week before the interview. The number of minutes was multiplied by the number of days that each activity was practiced in the previous week to generate the minutes of physical activity in a regular week. Since different physical activity intensities were combined into a single measure, minutes on vigorous physical activities were weighted and further multiplied by two, following recommendations[20,21]. The intensity of each activity from the list used in the 11- and 15-year follow-ups was determined based on metabolic equivalents. Activities with metabolic equivalents higher than six were considered vigorous[22]. In contrast, the IPAQ already has established categories of physical activities according to different intensities[19].
Two operationalisations of the exposure were analysed: a cumulative measure and the most recent physical activity measure before the birth of the child. The cumulative measure is the sum of follow-ups (0, 1, 2 or 3) previous to the birth of the second generation in which the parents were considered physically active. For instance, if the second generation was born when the parent was 16 years of age, only the 11- and 15-year follow-ups were considered in the sum.
For the most recent physical activity measure before the birth of the child, only the status (active or inactive) of the parent in the follow-up immediately before the birth of the second generation was considered. A total of 159 parents did not attend the follow-up immediately before the birth and were not considered in the analyses based on the most recent physical activity measure before the birth of the second generation. For all exposures, the parent had to spend at least 300 min/week in physical activity in the 11- and 15-year follow-ups, and at least 150 min/week in the 18-year follow-up to be considered active, according to WHO’s recommendations[23].
## Statistical analyses
Crude and adjusted analyses were performed to assess the influence of exposures (father’s and mother’s physical activity) on the outcomes (offspring’s nutritional status). BMI-for-age when parents were 11 years of age, and the age of the child from the second generation were considered as confounders (all inserted in adjusted models as continuous variables). The parents’ BMI-for-age at 11 years of age was estimated using the same methodology applied for the second generation. We did not adjust for parental BMI at age 15 or 18 as these measures would lie within the pathway between exposures and outcomes, being mediators and not confounders of the associations. All analyses were stratified according to the sex of the parent.
Poisson regression models with robust variance were fitted to assess the influence of each parental physical activity exposure on second generation’s overweight and obesity status (binary outcomes). For binary outcomes, it is possible to interpret incidence rate ratios produced by Poisson regressions as prevalence ratios – as described by Barros and Hirakata[24].
Our units of analysis were the second generation children, and when the parent had more than one child, we considered only the first born for each parent in order to simplify analyses and interpretation. All fathers and mothers in the sample had the same age (all born in 1993), however the number of follow-ups considered in the operationalisation of the exposures could vary. This could happen since only the physical activity measures previous to the birth of the second generation were considered in the operationalisation of exposures. Hence, the number of follow-ups considered for each parent depended on the age that the parent was when the first child was born. To correct for the different lengths of exposure (represented here by the number of follow-ups), the natural logarithm of the parental age at the moment the first child was born (age from conception) was added as an offset to Poisson regression models.
All analyses were performed using Stata software version 16.1 (StataCorp., LLC).
## Results
Out of the 3810 members of the 1993 Pelotas birth cohort interviewed during the 22 years of age follow-up, 948 attended the research clinic with their children. However, we analysed 874 parent-child pairs – since only those with complete information on the outcomes were considered (n 74 excluded due to missing information). A flowchart presenting the full sample and subsamples analysed is presented in Supplemental Fig. 1. Compared to the sample interviewed when they were 22 years old (n 3810), parents of the second generation (n 874) were poorer and mainly represented by females (73·5 % in analysed sample and 53·2 % in interviewed sample). Median minutes in physical activity were similar for the 11- and 15-year measures, but almost 50 % higher at 18 years of age for the full sample compared to the analysed. Mean BMI values were similar between the compared samples (see online Supplemental Table 1). The characteristics of the second generation samples analysed (n 874) and not included in analyses due to missing information on the outcome (n 74) are compared in Supplemental Table 2. *Second* generation’s children not included in the analyses were older and more frequently of male sex compared to the analysed sample. The median parental physical activity levels were similar.
The second generation was, on average, 3·1 years old (sd = 2·1) and mainly female (Table 1). Slightly more than one-third of the second generation presented overweight and 12·4 % was obese. Most of the 1993 Pelotas birth cohort members analysed were female (73·5 %). The median minutes of physical activity practice were similar in the 11- and 18-year follow-ups (approximately 290 min), and around 30 min lower in the 15-year follow-up. Almost 35 % of the parents were active only in one follow-up, and 70 % were considered active in the follow-up immediately before the birth of the second generation.
Table 1Second generation’s and parent’s characteristics of the analysed sampleSecond generation’s characteristics (n 874)Mean95 % CIAge (years)3·12·96, 3·24BMI-for-age (Z-score)0·750·66, 0·84Sex n %95 % CI**Female47354·150·8, 57·4**Male40145·942·6, 49·2Overweight31736·333·2, 39·6Obesity10812·410·3, 14·7Parent’s characteristics (n 874)Physical activity (minutes)MedianIQR**11 years follow-up290140, 555**15 years follow-up265125, 540**18 years follow-up292·5120, 725Sex n %95 % CI**Female64273·570·4, 76·3**Male23226·523·7, 29·6Physically active follow-ups**017219·717·1, 22·5**130534·931·8, 38·1**227331·228·2, 34·4**312414·212·0, 16·7Most recent physical activity status before the birth of the child**Inactive21229·626·4, 33·1**Active50370·466·9, 73·6IQR, interquartile range.
The cumulative physical activity measure, analysed as the number of physically active follow-ups, had no influence on the second generation’s prevalence of overweight (Table 2). In turn, crude analyses showed that the higher the number of active follow-ups in which mothers were active, the lower was the prevalence of obesity for the second generation. However, the association did not persist after adjustment for parental BMI-for-age at 11 years and child’s age. No association between cumulative physical activity and obesity was found for fathers, either in crude or adjusted analyses.
Table 2Crude and adjusted prevalence ratio for overweight and obesity according to the number of physically active follow-ups by the parents (cumulative physical activity measure)OverweightObesityCrude PR95 % CIAdjusted PR95 % CI* Crude PR95 % CIAdjusted PR95 % CI* Father’s active follow-ups $$P \leq 0$$·322 $$P \leq 0$$·117 $$P \leq 0$$·323 $$P \leq 0$$·512**01·001·001·001·00**11·700·60, 4·792·670·85, 8·342·180·30, 15·753·090·49, 19·45**21·300·46, 3·642·020·66, 6·211·610·23, 11·422·480·42, 14·43**31·790·65, 4·962·910·94, 9·000·840·11, 6·631·650·24, 11·21Mother’s active follow-ups $$P \leq 0$$·177 $$P \leq 0$$·161 $$P \leq 0$$·014 $$P \leq 0$$·069**01·001·001·001·00**10·810·62, 1·040·830·64, 1·060·550·34, 0·890·610·38, 0·97**20·740·56, 0·980·750·57, 1·000·490·28, 0·840·540·32, 0·92**30·900·62, 1·311·000·68, 1·470·370·14, 0·990·500·19, 1·34PR, prevalence ratio.*Adjusted by father’s and mother’s BMI-for-age at 11 years of age and age of the child.
The effect of the most recent physical activity measure before the birth of the child, which only considered the follow-up immediately before the birth of the second generation, was also analysed (Table 3). No associations between paternal nor maternal physical activity and second generation overweight were found. In crude analyses, the prevalence of obesity in children born to active mothers was 41 % lower (prevalence ratio = 0·59; (95 % CI 0·39, 0·89)) compared to those born to inactive mothers. The association persisted even after adjustment for confounders, being the prevalence of obesity in children born to active mothers also 41 % lower (prevalence ratio = 0·59; (95 % CI 0·39, 0·87)) compared to their peers. In turn, there was no association between the most recent physical activity measure before the birth of the child and obesity of the second generation when fathers were analysed.
Table 3Crude and adjusted prevalence ratio for overweight and obesity according to parental physical activity status in the most recent measure before the birth of the second generationOverweightObesityCrude PR95 % CIAdjusted PR95 % CI* Crude PR95 % CIAdjusted PR95 % CI* Father’s physical activity status $$P \leq 0$$·569 $$P \leq 0$$·372 $$P \leq 0$$·871 $$P \leq 0$$·878**Inactive1·001·001·001·00**Active1·150·71, 1·871·240·78, 1·970·930·37, 2·311·070·45, 2·56Mother’s physical activity status $$P \leq 0$$·246 $$P \leq 0$$·404 $$P \leq 0$$·012 $$P \leq 0$$·008**Inactive1·001·001·001·00**Active0·920·74, 1·150·910·74, 1·130·590·39, 0·890·590·39, 0·87Analyses for this exposure consider only parents that attended to the most recent follow-up before the birth or the second generation (n 715).PR, prevalence ratio.*Adjusted by father’s and mother’s BMI-for-age at 11 years of age and age of the child.
## Discussion
This study examined the influence of parental physical activity on offspring’s nutritional status. Our results showed that the effect of the most recent maternal physical activity measure before the birth of the child was relevant to reduce the prevalence of obesity among children born to active mothers. In contrast, no associations between cumulative, or the most recent physical activity measure before the birth of the second generation and offspring’s nutritional status were found when fathers were analysed.
It is well-documented that genetic factors influence the offspring’s nutritional status(25–27). However, shared environmental factors also play a substantial role regarding offspring’s overweight/obesity[10,25,26,28,29]. Nonetheless, few studies have focused on the influence of parental physical activity over time on offspring’s nutritional status, especially in population-based samples. The evidence available does not show consistent results. In a previous study, conducted in Norway with over 4000 parent-offspring dyads, a decrease in maternal physical activity levels was associated with a 0·1 increase on BMI Z-scores in adolescents. Father’s lifestyle changes, however, did not significantly affect adolescent’s BMI[10]. In the HUNT study, which included 3681 adolescents (15·9 years of age), Fasting and co-authors [2011] did not find any direct association between low levels of parental physical activity and offspring’s overweight[30]. In a population-based birth cohort from the Netherlands with 1554 children, Sijtsma et al. [ 2015] revealed no correlation between total, light, moderate or vigorous physical activity of the mother or father, assessed in one time point, with the children’s BMI or waist circumference Z-scores (3·9 years of age). However, the same study demonstrated that more active commuting by the mother was related to lower offspring’s BMI[14]. This last finding is in line with our results on the association between active mothers in the most recent measure before the birth of the child and reduced offspring’s obesity prevalence.
Intergenerational transmission, which is the transfer of individual traits, abilities and behaviours from parents to their children, is described to be one of the factors that can impact children’s outcomes and health behaviour[31]. Previous studies found strong correlations between a large range of parents’ and offspring’s outcomes, such as self-assessed health[32], obesity and anthropometric measures[33,34], mental health[35], chronic health conditions[36] and health-related behaviours(8,37–40). For example, parental smoking experience is significantly associated with their offspring’s initiation and lifetime smoking[38,39]. Also, it is well established that BMI and obesity are strongly correlated among biological parent–child dyads[34]. However, in terms of the influence of parents’ physical activity over time on offspring’s nutritional status, the intergenerational transmission is not completely understood and lacks supportive evidence. Our findings showed an indirect association between the most recent maternal physical activity measure and children’s obesity prevalence. However, we found no association between paternal physical activity and offspring’s nutritional status. Thus, we hypothesise that biological factors might play a stronger role compared to environmental factors in the relationship between parental physical activity and offspring’s nutritional status.
To the best of our knowledge, there are no reports addressing the biological mechanisms involved in the potential intergenerational effects of the parents’ physical activity on their children’s growth. One hypothesis that might explain this influence is the theory of foetal programming, which indicates that the environment surrounding the foetus during its development, including maternal behaviours during pregnancy, plays a seminal role in determining its disease risk and health behaviours during the life[6]. An experimental examination demonstrated that a higher level of physical activity during pregnancy was related to a lower percentage of body fat at age five[41]. Also, the literature has shown that mothers who perform physical activity before and during pregnancy are less likely to have obese children in childhood and adulthood[42,43]. We found that the prevalence of obesity was lower in children born to mothers who were active in the follow-up immediately before the child’s birth. Our hypothesis for this finding is that mothers who were active in the follow-up prior to the child’s birth would be more likely to be active also during pregnancy. In our study, we did not have information regarding the measures of physical activity during pregnancy. However, the literature has shown that one of the strongest determinants of physical activity during pregnancy is the pre-pregnancy physical activity status[44].
Parental lifestyle has a strong effect on children’s health, playing an important role in their habits, including healthy diet and physical activity[45,46]. This was also why we expected to find an association between parental physical activity and children’s nutritional status. However, it is known that child nutritional status is multifactorial and complex, driven by an interaction between genetic, biological and environmental factors(47–49). Perinatal factors, birth size, catch-up growth, breastfeeding status, eating behaviours and physical activity are some of the factors affecting child nutritional status[47]. Parental BMI and height are also associated with offspring’s BMI and height[48,50,51]. Although it is important to understand the complete mechanism between parental physical activity and offspring’s nutritional status, our study is limited to understand the total effect of this association. Our findings are an important starting point to guide future studies specifically focused on understanding the direct, indirect and total effects of this association.
Our study has important strengths. It was carried out in a population-based birth cohort with high rates of follow-up, minimizing the likelihood of selection bias. The weight and height were assessed by trained technicians, providing high-quality measures. Parental physical activity was explored from different perspectives, which allowed us to evaluate the cumulative, and the effects of the most recent father’s and mother’s physical activity measure before the birth of the child on the offspring’s nutritional status.
Some limitations also have to be mentioned. Self-reported data and the different questionnaires used to assess parental physical activity might have overestimated the amount of physical activity. A second limitation is that we were not able to estimate the direct or indirect effects of parental physical activity on offspring’s nutritional status, but only the total effect. This happens because some perinatal mediators, such as offspring’s diet, physical activity and genetic factors were not available in our study. Also, we did not have information on maternal physical activity during pregnancy – which may have a specific effect on offspring’s body composition. Our analyses were adjusted by the parental BMI-for-age when they were 11 years old, at the same moment the first physical activity assessment was performed. Thus, we are not able to rule out the possibility that the parental physical activity influences the BMI-for-age at 11 years of age. The second generation includes participants that were evaluated at different ages. We performed the analyses with nutritional status variables standardised for age in order to minimise this difference. However, we can speculate that the effect of parental physical activity on offspring’s nutritional status may be different in early and late childhood – but we could not assess this due to the limited sample size. Additionally, there is some evidence that adolescent pregnancy may affect foetal growth[52,53]. However, in our study, only one-third of female participants were younger than 18 years old when became pregnant. Still, our results should be interpreted in light of the characteristics of the sample included in this study – young adults of up to 22 years of age. Finally, performing complete case analyses reflected on excluding around 8 % (n 74) of the children due to missing information. This could have affected the statistical power of our study by reducing the sample size and influenced the representativeness of the sample included in our analyses – since the frequency of male children and their mean age were higher in non-analysed children compared to our sample.
## Conclusion
Our results showed that the most recent physical activity measure before the birth of the child was associated with lower prevalence of obesity among children born to active mothers. Although without statistical significance, the findings showed a consistent direction of the effects for cumulative physical activity measures – where children born to active mothers had, in general, lower prevalence of overweight and obesity. In contrast, fathers’ physical activity did not influence offspring’s nutritional status. The fact we found association only for mothers, reinforce the hypothesis of a possible biological effect of maternal physical activity on the offspring’s nutritional status. Further studies, comprising increased sample sizes and exploring the mediators involved in the intergenerational effect of parent’s physical activity on their children’s growth are encouraged to provide a better understanding of the mechanisms of this association.
## Financial support:
This article is based on data from the study “Pelotas Birth Cohort, 1993" conducted by Postgraduate Program in Epidemiology at Universidade Federal de Pelotas with the collaboration of the Brazilian Public Health Association (ABRASCO). From 2004 to 2013, the Wellcome Trust supported the 1993 birth cohort study funding its face-to-face follow-ups. The European Union, National Support Program for Centers of Excellence (PRONEX), the Brazilian National Research Council (CNPq), and the Brazilian Ministry of Health supported previous face-to-face phases of the study. The 22-year face-to-face follow-up was supported by the Science and Technology Department/Brazilian Ministry of Health, with resources transferred through the Brazilian National Council for Scientific and Technological Development (CNPq), grant $\frac{400943}{2013}$-1.
## Conflict of interest:
There are no conflicts of interest.
## Authorship:
The corresponding author attests that all listed collaborators meet authorship criteria. C.B., R.C.M., S.G.S., B.G.C.S. and I.C.S. proposed the design of the study and drafted substantial parts of the manuscript. C.B. also performed the statistical analyses. F.C.W., H.G., P.C.H. and A.M.B.M. coordinated the data collection process of the 1993 Pelotas birth cohort. All authors critically reviewed and approved the final version of this manuscript.
## Ethics of human subject participation:
The Ethics Committee of the Faculty of Social Medicine of the Federal University of Pelotas approved all 1993 Pelotas birth cohort follow-ups (registry number 1.250.366). Individuals were informed about the objectives of the study and were asked to sign a consent form to be eligible to participate. A guardian had to sign the consent form if participants were younger than 18 years of age. Syntax files used in the analyses could be shared upon reasonable request.
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|
---
title: '‘You know what, I’m in the trend as well’: understanding the interplay between
digital and real-life social influences on the food and activity choices of young
adults'
authors:
- Jodie Leu
- Zoey Tay
- Rob M van Dam
- Falk Müller-Riemenschneider
- Michael EJ Lean
- Charoula Konstantia Nikolaou
- Salome A Rebello
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991798
doi: 10.1017/S1368980022000398
license: CC BY 4.0
---
# ‘You know what, I’m in the trend as well’: understanding the interplay between digital and real-life social influences on the food and activity choices of young adults
## Body
The importance of healthful eating and physical activity for preventing obesity and decreasing disease risk is well established[1]. However, young people in Singapore, as elsewhere, tend to adopt dietary behaviours, such as not meeting recommended daily servings of fruit and vegetables[2] and frequently eating at ‘Western’ fast-food venues[3], associated with excessive weight gain[4,5]. While 13·1 % of Singaporean adults in their 20s (18–29 years) have high-risk BMI of ≥27·5, according to BMI cut-offs for Asian populations[6], this proportion almost doubles to 22·4 % for adults who are in their 30s (30–39 years)[7]. Similar patterns of weight gain have been observed internationally such as in Europe, Australia and the USA[8]. As losing weight and maintaining weight loss is challenging, preventing excessive weight gain is important for having a healthy body weight. Emerging adulthood, a developmental period typically defined as an age between 18 and 25 years, is also a time of increasing autonomy, when social identities are explored, and habits which are likely to be long standing in nature develop[4,5]. High value is also placed on fitting in and finding friends as young people transition from secondary to tertiary education[9,10]. This period, therefore, presents an important opportunity for health-directed dietary and physical activity interventions.
The role of environmental factors, including the affordability, availability, accessibility and marketing of food, that can influence dietary behaviours are increasingly recognised[11,12]. Growing evidence also suggests that social influences may have a strong impact on young adults. Social networks can influence food and activity choices via interacting pathways of peer influence(13–15), social support[13,14,16] and social norms[17]. Peers can positively affect dietary practices and physical activity by supporting health-promoting practices to achieve weight loss goals[15], and by extending invitations to participate in sports or cooking meals together[13,14]. Conversely, peers may discourage health-promoting practices by engaging in sedentary social activities or dissuading physically active social pursuits[13,14]. Social support, including the types of information and resources shared within social networks, can also influence the dietary and activity behaviours of network members[13,14,18]. Further, social norms influence multiple aspects of consumption such as whether it is socially acceptable to dine alone and to use mobile devices during mealtimes[17]. Social norms also influence dietary choices as people tend to model their food choice and consumption based on their companions, such as mirroring others’ portion sizes and food choices and women eating less in the presence of men[19].
These interacting pathways of peer influence, social support and social norms which play out in real life may be amplified in digital spaces as young adults are concerned with how they are perceived on social media and are receptive to social influences. As social media engagement becomes more pervasive, social networks have expanded and can have transformative effects. Globally, social media use is highly prevalent, with those aged 18–34 making up the majority of social media users across various social media platforms[20]. Singapore is a multi-ethnic, high-income, urban nation located in Southeast Asia with a representation of 3 major Asian ethnic groups, Chinese, Indians and Malays. Eating out is a pervasive norm with 77·3 % of Singaporeans usually having either breakfast, lunch or dinner outside and is supported by a highly accessible food environment catering to a diverse range of palates and budgets[3]. In Singapore, 84·4 % of the population were active social media users in 2021[20]. These aspects suggest that social networks, both real and virtual, can have a strong influence on the food and activity choices of the Singaporean population. Most studies that explore this phenomenon are based on Western populations[17,19,21] with some in university settings[13,14] and few in Asian settings.
This study is part of a larger qualitative cross-cultural study on social, ethical and moral perspectives of young adults around the consumption of food and physical activity in Europe and Singapore. We have investigated young adults’ perceptions of the social environment on their food and activity choices through a qualitative approach, in an English-speaking Asian context. The findings may help inform current and future health interventions directed towards young adults.
## Abstract
### Objective:
To understand young adults’ perceptions of online and real-life social influences on their food and activity choices.
### Design:
A qualitative study involving 7 focus groups. Thematic analysis using both deductive and inductive techniques were performed.
### Setting:
A polytechnic and a university in Singapore.
### Participants:
A total of 46 full-time students, 19–24 years of age.
### Results:
Participants revealed that social media meets multiple needs, contributing to its ubiquitous use and facilitating content spread between social networks. Food-related content shared on social media were mostly commercial posts, marketing foods and eateries showcasing price promotions, emphasising sensory properties of foods or creating narratives that activated trends. Subsequently, real-life social activities frequently revolve around marketed foods that were not necessarily healthy. In contrast, physical activity posts were rarely being followed up in real life. Portrayals describing a toxic gym culture could contribute to negative perceptions of peers’ physical activity posts and a disinclination towards sharing such posts. Participants expressed that close, supportive social networks in real life strongly influenced initiating and maintaining healthy lifestyles. However, in a society that highly values academic achievements, participants prioritised studying and socialising over healthy eating and physical activity.
### Conclusions:
Overall, our findings reveal that virtual and real-life social influences have complex interactions affecting Asian young adults’ behavioural choices and should be considered when designing interventions for this group. Regulations related to the digital marketing of unhealthy food, and improving the availability, accessibility and affordability of healthier food options, particularly in the foodservice sector, would be of value to consider.
## Study design
A qualitative study design was utilised to explore and gain in-depth insights of factors that influence young adults’ dietary and physical activity practices[22]. Purposive sampling was used to facilitate the recruitment of participants similar in age and life experiences who were knowledgeable and experienced with the phenomena being studied[22]. As the study is exploratory in nature, the use of focus group discussions (FGD) allowed for the collection of a wide range of viewpoints and subsequent exploration of expressed viewpoints through synergistic interactions between group participants[22]. The 32-item consolidated criteria for reporting qualitative studies (COREQ) checklist was followed for the preparation of this paper[23].
## Study setting
The study was conducted in English amongst students from the National University of Singapore (NUS) and Republic Polytechnic (RP). Differing from polytechnics in the USA and the UK, most Singaporean polytechnics admit students who have completed lower secondary schooling at around 16 years of age. RP is a diploma-granting higher education institute with an enrolment of 13 566 students[24], most of whom live off-campus, typically at parental homes. NUS is a degree-granting institute with approximately 40 000 students, a quarter of whom are postgraduates[25]. Approximately 10 000 students live on campus, usually during their first and second years of undergraduate study.
## Participant recruitment
To be included, participants had to be full-time students at either RP or NUS, at least 18 years of age, own and use a smartphone and be Singaporean citizens or permanent residents. We excluded participants who were pregnant or breastfeeding.
We purposively recruited students at NUS using email blasts and at RP using onsite recruitment and friend referrals. A total of 163 students (62 men and 101 women) expressed their interest via email or text messages and 112 students (47 men and 65 women) were screened while 51 students (15 men and 36 women) were not screened due to the following reasons: decided not to participate following their expression of interest, unable to be reached following their expression of interest or FGD numbers had been reached. Of the screened participants, 103 students (44 men and 59 women) were eligible, and 9 students (3 men and 6 women) were not eligible. We were unable to schedule discussions for 57 students (25 men and 32 women) who were eligible. This was due to: them not wanting to join the study following their expression of interest, scheduling conflicts or a decision we made that the study had reached thematic saturation. A total of 46 students (19 men and 27 women) of mixed academic disciplines participated in the study. Recruitment ended when discussions between JL, ZT and SAR deemed data saturation had been reached.
## Study procedure
Prior to the start of FGD, participants were asked to fill out a short demographic survey to collect information on their age, height, weight, gender, ethnicity, marital status, institution of study, faculty, course of study, year of study and their use of social media platforms (Facebook, Instagram, Twitter and Weblogs).
FGD were conducted between March and May 2017 at designated conference rooms at the institutions. Separate discussions were conducted with men and women as some topics such as body image were considered sensitive. Among men (n 19), 2 FGD with 8 and 7 participants took place at NUS and 1 FGD with 4 participants took place at RP. Among women (n 27), 2 FGD took place at NUS with 7 and 8 participants and 2 FGD took place at RP with 6 participants each. Where possible, friends were not scheduled in the same focus group. The discussions were facilitated by JL and ZT, who were female postgraduate students trained in qualitative methodologies and had no prior relationships with the participants. All participants provided verbal informed consent prior to participation.
A discussion guide was developed and piloted by the study team, and included topics on social media use, factors influencing food and physical activity behaviours and mobile application preferences. For the purposes of this paper, social media is defined as internet-based platforms where users can create individual profiles, contribute and access searchable digital content (e.g. content intended to inform, entertain or sell products) and form online connections with other social media users[26]. Discussions lasted 90 min on average. Debrief sessions took place after the focus groups between JL, ZT and SAR. Data saturation was discussed between JL, ZT and SAR and seemed to have been reached despite a small number of focus groups. All FGD were audio-recorded, transcribed verbatim by a contracted transcription company and verified by focus group facilitators. Member checking was not conducted to limit the burden on participants[27].
## Thematic analysis
Each transcript was reviewed and coded using thematic analysis which allows for using both inductive and deductive approaches[28]. With the deductive approach, transcripts were coded for themes that were identified from the literature[13,14,19,21]. With the inductive approach, additional themes were added if relevant during the coding process allowing for the inclusion of unanticipated themes. Each transcript was coded by JL and ZT independently. Upon the completion of coding, JL and ZT compared, reviewed and finalised themes. Further discussions of the themes and the relationships between them took place between JL, ZT and SAR to develop a mind-map that illustrated the relationships between themes (Fig. 1)[28]. Nvivo software (version 11, QSR International, Australia) was used to organise and analyse the transcripts. To maintain confidentially, all participants have been assigned codes.
Fig. 1Thematic relationship to illustrate the role of real and virtual social influences on food and activity behaviours Fig. 2Frequency of social media use of young men (n 19) and women (n 27)
## Participant characteristics
A large proportion of participants were ethnic Chinese (80·4 %, n 37) which is broadly reflective of the national profile (Chinese: 74·3 %, Malays: 13·5 %, Indians: 9·0 %, Others: 3·2 %)[29]. All participants were studying full time, of ages ranging 19–24 years old, and all were unmarried (Table 1). Participants represented a wide spread of study majors and years of tertiary education. Close to 40 % (n 18) of participants were overweight according to *Asian criteria* (BMI ≥ 23 kg/m2) (Table 1). All participants engaged with at least one social media platform with Facebook being the most popular (n 43, 93·5 %) followed by Instagram (n 40, 87·0 %), Twitter (n 20, 43·5 %) and Weblogs (n 14, 30·4 %). Facebook and Instagram had high usage (Fig. 2). Participants were all able to speak and express themselves freely in English with the occasional use of colloquial Singaporean English (Singlish).
Table 1Participant characteristics across 7 focus groups by genderTotal (n 46)Male (n 19)Female (n 27)Number of focus groups734Mean sd Mean sd Mean sd Age (years)20·91·821·61·920·41·6BMI (kg/m2)22·13·023·23·021·32·8 n Percentage n Percentage n Percentage < 18·512·200·013·7 18·5–22·92758·7842·11970·4 23–27·51532·6947·4622·2 ≥ 27·536·5210·513·7Ethnicity n Percentage n Percentage n Percentage Chinese3780·41473·72385·2 Malay24·315·313·7 Indian48·7315·813·7 Other36·515·327·4Marital status n Percentage n Percentage n Percentage Single46100·019100·027100·0Year of study* n Percentage n Percentage n Percentage 1 (RP and NUS)1123·9526·3622·2 2 (RP and NUS)1532·6631·6933·3 3 (RP and NUS)1123·9421·1725·9 4 (NUS only)817·4421·1414·8Course of study n Percentage n Percentage n Percentage Business† 48·7315·813·7 Design and environment12·200·013·7 Law24·315·313·7 Arts and social science‡ 1226·1526·3725·9 Applied science§ 2043·5842·11244·4 Life science510·9210·5311·1 Physical science‖ 24·300·027·4*Numbers do not add to 100 % due to missing data. Polytechnics usually offered 3-year diplomas while NUS usually offered 4-year degrees.†Includes: Accountancy, Business Administration.‡Includes: Communications and New Media, Economics, Geography, Political Science, Psychology, Sociology, Undeclared Arts and Social Science student.§Includes: Medicine, Pharmacy, Health Management and Promotion, Outdoor and Adventure Learning, Biomedical Science, Computer Science (includes Information Technology related courses), Engineering.‖Includes: Environmental Science. Percentages may not add up to 100 % due to rounding.
Our findings are presented under 4 main themes as shown in Fig. 1: needs met by social media engagement, sources of influence on social media, real-world family and peer influences on health-promoting habits and the role of norms. The relationships between each of the themes and how they ultimately influence young adults’ food and activity behaviours are described under each of the themes.
## Theme 1. Needs met by social media engagement
Most participants had social media accounts that they used daily, with Facebook (daily use: men: 78·9 %; women: 59·2 %) and Instagram (daily use: men: 78·9 %; women: 66·7 %) being the most popular (Fig. 2). This high use was motivated by a need to connect and be informed of events and interests in the lives of family and friends and of news in the world. Through social media, participants interacted with people within and outside their close social networks to share content and promotional deals, and to pick up new skills. Illustrative quotes for this theme are presented in Table 2.
Table 2Quotes for theme 1: needs met by social media engagementSub-ThemeIllustrative quotesT1·1 Platform for maintaining and forming connectionsSo, the way I use social media is not indeed to actually like put myself out there or like um, update people about myself. But more of like viewing what other people has been doing. Yeah, and more of like taking in information.– F04, female, 19, Infocomm Security ManagementUm, okay, for me I usually update Instagram when there are, like, occasions, or, like, events that happen in my life, and I just want to share it, because I feel like, um, you think, like, social media helps you, it’s not just like for other people to update you but also, like, you can easily update, like, a lot of people around you without, like, personally messaging every single one of them, or, like, hey, this thing happened in my life, you know, it’s like – when you just post it, like, it’s easier to facilitate this kind of information. So, for me that’s why I use Instagram for and, sometimes also just ’cause I find some photos nice, then I just want to post it, just to share it with my friends. And then *Facebook is* usually – I just – I don’t really post anything, but I just, like, share, what I find, like, interesting or funny that, like, other friends have shared before. Yeah– C04, female, 19, PsychologyI follow the Instagram accounts of my, of the Olympic medalists for my sport. So, just to check them out. So, uh, to see them skills-wise and try to imitate them.– B08, male, 21, Life ScienceT1·2 Source of information and recommendationsI actually like bookmarked a lot of them. [ other participants laughed] Like “get toned in like 30 days” that kind. I have so many of them on my Facebook like bookmark list, but I never go back to them. [ background laughter and other participants vocalizing agreement]– A05, female, 21, Life ScienceUm, for me, I rarely notice um apps related to fitness, but maybe food, but usually I don’t really respond to these apps unless, um, there’s promotion, or I’m interested in it, or my friends have talked about it, yeah.– C07, female, 23, EconomicsI’m not sure if y’all know [popular local lifestyle blogger]. Like when the Chizza [*Chizza is* a portmanteau of ‘chicken’ and ‘pizza’. This product was sold at Kentucky Fried Chicken in Singapore] just came out and she was eating it, I wanted to try it. Yeah. So that was one of the factors of advertisements that was quite good I think… Lucky I didn’t [try] ’cause my friend told me it’s not nice.– A02, female, 23, Project and Facilities ManagementT1·3 Platform for expression and validationI think, sometimes when I see those café stuff, right, I feel like people don’t always go there just to try them, but out of social pressure. It’s like everybody’s going there, so it’s like I have to go there and try also, otherwise I would be left out. Then people will be talking about it like Art Box [A huge flea market with more than 300 fashion and food stalls that happened from April 14th – 23rd, 2017]. Like, initially I don’t think a lot of people was interested in it. Most people go out of social pressure because all their friends are posting on Instagram, like “I am going to Art Box, trying new foods”, so like, okay, you know what, I should go and try also. Even though people say it sucks, but everybody is going, so I just go… Yeah, it’s like to show people that “you know what, I’m in the trend as well”.– E05, female, 19, Environmental ScienceI have a friend, who’s a bodybuilder, and he always posts like his own selfies on Facebook and he puts some stupid caption that I don’t even know what, how does that relate to the photo?– D07, male, 24, Political ScienceYeah, and they also have, like, uh trainer, they will like promote themselves on Instagram, like they will suddenly pop up at the explorer thingy. Yeah, so like, I follow them ah, like… It motivates me to like, exercise and eat healthy. And like, when you see your friends also, like they, their body, or like nice right? Yeah, then like, make me feel down also lah, sometimes. But it also boost you up ah actually. Why you laugh? [ participants laughed] Yeah, it actually motivate you ah, to like, have that body you know. Like yeah.– F04, female, 19, Infocomm Security ManagementIt gets a bit annoying. I don’t know, I see Facebook and oh your friend just completed a 6km run and I’m just like…hmmm…[Participants laughed in the background hence the complete sentence cannot be heard clearly] then like.– B01, male, 23, Life ScienceI don’t see the need to publicize my, whatever you have done ah.– B02, male, 22, EconomicsSame. Like it gets annoying when you see people posting. Like, okay, so what? What are you trying to prove? But yeah, I think it depends on the individual lah. Like are you doing it for yourself or are you doing to show people that you are doing it?– B08, male, 21, Life ScienceI…I think that… Actually, let’s take Nike running or any of those other running apps for examples, you can add friends within the app. So, if there are people who are actually interested in that information, they would probably add you as a friend. You guys can talk about it and add through the app, as opposed to on Facebook. Because the people on Facebook don’t care if I ran 9 km last night. [ some laughs from other participants]– B04, male, 21, Business AdministrationUm, I think, for myself, like I see a lot of the, like those home boxing clubs, or like gym kind of, free trial classes that they always offer at the beginning, and things like that, and then, uh, for me um, I generally don’t respond unless, like, I really have an inclination towards it lah, like, [unclear 00:14:39], yeah, or unless it’s something really novel and, like, hey like I could try this out, then I would like just respond… Uh, there’s this thing, there’s this um, new workout place called B-Bounce [Singapore’s first dedicated rebounding fitness studio], so basically what they did, what they do is, like, you work out on the trampoline, and then it’s something, like, really interesting, I mean, it’s not like trampoline park where you just bounce around, but, like, you follow, like, what the instructor does, and it actually like is really quite effective, I feel. [ laughs] Yeah…. Yeah, I tried it. Like, I tried with my friend, so basically this friend, I will always like to share these kinds of things between ourselves.– C03, female, 21, Business Administration
## Subtheme T1·1 Platform for maintaining and forming connections
Keeping in touch with friends and family on social media by being up to date with events and interests was one of the main reasons why the participants had social media accounts even if they were not visibly active on their own accounts. Through their digital persona, participants sustained relationships and created social capital through interactions with their social networks. Participants also used social media to connect with people and entities outside their social networks especially when seeking new information thus effectively expanding their social networks. For instance, participants recalled following a personal trainer on social media to view free exercise programmes that they may also share with their social networks.
## Subtheme T1·2 Source of information and recommendations
Participant responses suggest that the content they are exposed to often serve to pique their interest in certain ideas based on which they may conduct their own research. While participants recognised the limitations of digital sources of information, they also described ways of managing this, as D01 (male, 21, chemical engineering) describes, ‘Usually I’ll source a few different websites, ’cause a lot of uh, sensationalist news now.’ Although participants were often suspicious of the veracity of the content on social media, they admitted that there were also times when they shared information based solely on headlines. Nonetheless, several participants reported using social media sites as inspiration for eating healthfully, cooking or being active, even though they may not always follow this through with long-term behaviour change.
Social media served as a platform where participants actively searched and shared recommendations on activities and price promotions for future social occasions. Some examples voiced by participants included ‘buy one get one free’ deals from a coffee shop chain, gym promotions and discounted buffets. The types of information shared also generally reflected the interests of their friends in a social group. Young adults in Singapore also turn to social media accounts of influential and prolific bloggers for information on food and activities. While recognising that some of these may be industry-sponsored posts, they were nevertheless regarded as being somewhat persuasive. Opinions of friends, however, were more trusted when these differed from influencers. Consequently, content shared with friends had the effect of being a personal recommendation, which may have a stronger influence than marketing advertisements as participants tend to trust posts from their social network.
## Subtheme T1·3 Platform for expression and validation
Social media serves as a platform for young adults to express themselves and showcase their experiences. Some participants talked about how sharing information on social media was the primary motivator for going to food places or events, many of which were planned as part of social gatherings. A degree of ‘fear of missing out’ also provided motivation for participants to eat specific foods and participate in activities that were trending with their friends. By sharing their own experiences of trendy activities on social media, participants sought to gain validation from their peers. *In* general, there seems to be a need to stay informed, and a sense of unease related to missing out on news, events in friends’ lives, trends, promotions and recommendations. However, friends’ posts were not always positively regarded, and achievements such as gym workouts or completing a run were viewed by some participants as being pretentious or ‘annoying’. Seeking validation from others or facilitating accountability were perceived as motivations for why peers share physical activity achievements. Participants also seemed disinclined to post their own physical activity achievements widely, describing this information as something they might not want anybody to know, unless it was with a specific group, such as a training group that shared the same interest. Sometimes, posts by peers or fitness instructors may motivate participants to exercise, though at times, some participants described feeling negatively due to comparing themselves with the person in the post.
## Theme 2 – Sources of influence on social media
Whilst on social media platforms, participants described encountering various sources of influences including from institutional entities such as food companies, gyms and government organisations and influences from peers. Illustrative quotes for this theme are presented in Table 3.
Table 3Quote for theme 2: sources of influence on social mediaSub-ThemeIllustrative quotesT2·1 Exposure to companies on social mediaEspecially McDonald’s, right, even if you don’t have it for a very long time. And then it release a new burger and your friend was like, “have you tried their new burger?” [ participants laughed]. Well looks like I’m going there. Or they will jio [Singaporean slang for “invite”] you go. And, and it works, you know. It works so well. Uh, every time they do this promotional campaign, a bunch of people go just for the hype. [ A few participants making “mm” sounds in agreement to what was said] … They [the marketing companies], they know it. They’re cashing in on it. There weren’t so many specialty burgers in the past. Now there’s like a few every year.– D01, male, 21, Chemical EngineeringSo, because the companies, since they are big companies, and all, they will actually pay people to try their stuff or their… ‘Cause, um, I do have some friends who are like influencers, but, although not mainly, not mainly food. They are like, they are people who actually take very beautiful photos and things like that. The companies would actually contact them, like, “hey, we have a party for you at this time, this place. So, uh, come and then, you know, we can reveal about us”, things like that. Then, you know, most often than not, they’ll write very pleasing reviews and things like that.– G04, male, 18, Information TechnologyYeah, food bloggers, and they will influence how we eat overall, yeah. Because most people nowadays, we consume a lot of social media, so if person A says, so if person A says, like, say the main person who blogs right, say, hey this was very good, we should all try it. Then the next person who hops on to, like, the blog site, then they would go try it, and then this chain just continues on to more and more people.– G02, male, 19, Information TechnologyNormally how they shape the marketing campaign is based on the public sentiments. So, if you believe that their marketing strategies are not very ethical right, it’s because there are vulnerabilities in how the society thinks and they’re just tapping on it. So, if, if you don’t, we want to weed out the bad or unethical marketing strategies, right, the people have to change their mind-set first.– D04, male, 24, Mechanical EngineeringMm. So what’s your personal take towards this? Like, how do you respond, then, to such marketing strategies?– Focus Group FacilitatorI mean, it’s, it’s… If you don’t want them to do this, you have to… become more witty and to know what’s right and what’s wrong lah. Because if the marketing campaign doesn’t stick, right, it will show in the sales, in all the figures that they have lah. Usually, they will have a… after they have an advertising campaign there will always be numbers, then they will do surveys on how the reception is like. So, it’s actually the people lah, if you ask me. Like, to put another perspective lah, it’s not necessarily their fault. The people are the ones who have the wrong, uh, perception of certain things. And they’re just making use of that weakness ah, that people have, yeah.– D04, male, 24, Mechanical EngineeringSo how do you personally feel towards that?– Focus Group FacilitatorThink uh, the smart people will know lah. That’s what I feel lah. It’s only the like more gullible ones who will fall for these kind of things.– D04, male, 24, Mechanical EngineeringI don’t really look at these kinds of advertisements unless, unless it’s for promotions. Like, food promotions. Like, there was a period of time when there was, like, a lot, like, Uber Eats [an online meal ordering and delivery platform] discount codes, then they [the company] posted, then everyone just went, like you know, take advantage of the situation.– C04, female, 19, PsychologyBut then, um, I guess sometimes when you like scroll through Insta [Instagram] or what, then they have those adverts, you know those mini-adverts that they started doing. Then sometimes got food or like certain commercials on going out all that kind, yeah then I will, I would click and see ah. Yeah… sometimes…Like there was the… The Manhattan Fish Market was showing, and KFC also had the, you know those online coupon thingies…Yeah yeah, so they had that on Insta, so I was like, okay, I’ll get mine now.– D05, male, 21, EconomicsUh my gym membership from Anytime Fitness…Yeah, so I saw it online and I found it quite interesting to have a 24-hour gym, because most of the time I spend it in school, so during midnight I will be going to gym…So I actually went to sign up. It’s quite costly actually, but if I were to look for a new gym, usually the flat rate is 88 per month…So I saw this discount where they have a new gym opening. So, I just went to sign up. It’s 68 per month, yeah, quite cheap.– G02, male, 19, Information TechnologyT2·2 Exposure to government health campaigns on social mediaI used the HPB [Health Promotion Board] step tracker thing but only because it gives you NTUC vouchers [participants laughing in the background] and then I would just fake the steps by shaking it.– B03, male, 22, LawYes. It’s mainly for the rewards, like [another participant] said as well. So, because I was thinking like, definitely, you’re bound to walk every day, no matter how little you walk. So eventually you do… Can clock some steps. So, like you mentioned, there’s a total of seven tiers as well. I think I only managed to get about four or five because you need to consistently clock on average 10 000 per day. But, yes, since I’ve grown accustomed to it, I just keep on wearing it lah. Yeah. But the numbers of the steps themselves don’t really matter to me.– B05, male, 21, AccountancyThe healthy SG, yeah. Healthy365. That’s a lousy name for an app. [ participants laughed] But I think it works because of the monetary incentive. I found myself jogging more because I wanted to hit the target… I saw my parents using, like walking more because of that as well. They are very nua [Singaporean slang for “lazing around”] people.– D04, male, 24, Mechanical EngineeringFor me it’s like uh, okay, you know those like sponsored ads on Instagram, or something. So, like sometimes ActiveSG [a Singaporean Sport portal with sports news, events calendar as well as facilities and coaches directory for everyone to watch and play sports; available as a mobile application] they will come out, something like uhm, spend your credits today or something. So, for me it’s like a reminder lah, like, I need to spend my $100 free credit ah… I think they do like sponsor ads, yeah. ’Cause I’m very sure I didn’t follow them [on social media]– D02, male, 22, Mechanical EngineeringI do use [ActiveSG] to book badminton courts, because I’ve been doing badminton since young. So actually that’s, that’s what I find it really useful, and it’s very cheap to grab one court maybe like $5 for one hour, so they give you the $100 free credit so you can book like quite a number of courts. Yeah, and then, um, tsk, uh I think, uh I think having like monetary incentive really does, uh, make people want to work out. But after that, for it to be sustainable right, people have to realize that uh, tsk, exercising must become a part of your lifestyle. Like you do it, not because uh, you want to earn money but because you know that it is healthy for you. Yeah, so hopefully by having all these monetary incentives you can actually motivate people to think in this manner. So I think that’s how it will eventually work.– D03, male, 24, Pharmacy[When] I’m looking for healthier choices. I actually use this quite a lot, the Healthier Choice sign. I think the government did a good job in implementing that. I actually look for that if I’m shopping for groceries, quite often, yeah.– D01, male, 21, Chemical EngineeringYeah. But, but then again, it’s like, it’s kind of scary too. I, I remember one that says that if you eat a slice of roti prata [South-Indian flat bread served with curry], you have to run, like, I don’t know.– D07, male, 24, Political ScienceThose are, those aren’t really helpful at all. [ participants laughed] I mean, like, this pineapple tart, you can run for three days, it won’t go away. [ participants laughed] And the thing is, they, they completely ignore, like, the calories you lose while you’re sleeping, that kind of thing… And they like to….– D01, male, 21, Chemical EngineeringExaggerate ah.– D04, male, 24, Mechanical EngineeringFear monger.– D07, male, 24, Political ScienceI think for my family, like slowly we are becoming more and more, in a sense, health conscious. I don’t know, like, every time we go grocery shopping then we will always check the labels and things like that. I mean, it feel a bit like, over – it feels a bit too over-exaggerated sometimes, but I feel like it’s quite necessary, because even things like, yeah, granola bars and cereal, like, as long as there’s, um, like high fructose corn syrup, then that in itself is really not good, even though they like to package it is as, oh, something healthy, even if it’s like oat-based. My family also starts to, like, shift to, like, more healthier food choices, so for example like – ’cause like, my sister and I, we don’t really care, what my mum buys at the supermarket. We just eat. So, like, she’ll she change from, like, white rice to brown rice, but like we don’t – we don’t really mind. And also ’cause brown rice, it doesn’t taste as bad as it used to last time. My mum always says that she always – like, “why do you hate brown rice? It tastes exactly like white rice”, like, like she always – she always, like, tells my sister like that. And like bread in our house is no longer white bread. But, like, like, whole meal bread or, yeah, we try and, we try and… like, so, like, we just changed, like, what we already eat to, like, slightly healthier options, but we won’t, like, completely remove, like we won’t remove bread cause oh bread is like carbs or whatever. But we will still eat bread, but we change to, like, whole meal bread, yeah.– C03, female, 21, Business AdministrationT2·3 *Social media* and peer influenceI don’t follow any food related accounts on Facebook or Instagram, but I see them on my feed, because people share them. And for Instagram I see it as well, because it appears on my explore page.– C05, female, 20, Communications and New MediaLike the… I think like for social media they actually uh, they actually show you things that you search for? So, they’re kind of like tracking your preferences, so the more you search for food the more food they’ll show you. So it doesn’t really help if you’re, like, you’re not exercising and stuff, and they just keep showing you food and things like that. And like the workouts on Facebook, like I mean those that I’ve seen, are very simplified? They make it seem like you can work out anywhere, anywhere, those, those kind of things. But I, I tried. But it’s, it’s damn lame, like, it doesn’t even work… I mean, it’s just like push-up challenge thing. I think it’s a 30-day thing, and like every day you do one [more than the day before]. It doesn’t train me. It doesn’t do anything to me.– D07, male, 24, Political Science
## Subtheme T2·1 Exposure to companies on social media
Participants were aware of how food companies used advertisements to influence their food and activity choices. They discussed social media influencers posting sponsored content, with some recalling experiences of friends who write sponsored posts, resulting in inadvertent exposure to food advertising due to their social networks. Perceptions towards sponsored posts tend to be negative as participants believe that the people who write sponsored posts are ‘doing it for the money’ (A04, female, 20, life science) or exposure. Moreover, most of the participants agree with the sentiment expressed by C08 (female, 23, Faculty of Arts and Social Sciences; undecided academic discipline) on commercial advertising campaigns, ‘I think in general, like, commercials, all that, like, I think they’re just trying to lure customers. Yeah, it’s just a money-making business lah.’. ( Note: ‘lah’ is a commonly used particle, typically used at the end of sentences or words as part of colloquial Singaporean English (Singlish) whose meaning is different (can place emphasis, affirm, and dismiss what is said) depending on the tone, syntax, and context.) Thus, many participants actively browse past advertisements on social media unless they spot promotional deals such as discount codes and coupons.
Meanwhile, some participants install Adblockers on browsers to avoid seeing any advertisements. However, this does not mean that participants are entirely unexposed to commercial advertisements, as they will still be exposed to their friends’ posts and social media activity, which may also initiate a discussion or activity related to the advertised products and services. Nonetheless, social media continued to be a resource for finding promotional deals for products and experiences for many participants. However, personal interest, friends’ influence and promotional deals had more influence on whether they chose to engage with marketing campaigns.
## Subtheme T2·2 Exposure to government health campaigns on social media
Government health campaigns on social media were met with mixed reception. One of the most discussed campaigns was the annual nationwide National Steps Challenge™ organised by the Singapore Health Promotion Board. Participants received free pedometers to track step counts and used a mobile application called ‘Healthy 365’ to record their step counts in exchange for incentives. Participants recalled that the campaign was heavily advertised on social media and in real life. Much of the discussion centred around barriers to participation, including technical issues with pedometers and the mobile application, and ways to inflate the step count to get the incentives. Whilst many participants stopped using the pedometer and mobile application after the incentives were discontinued, some participants continued their use as they got used to being more active. Overall, at least during the challenge, many participants and their friends and family took part in more activity.
Another government health campaign that was frequently discussed was the ActiveSG campaign. With ActiveSG, patrons can visit affiliated gyms at SGD2·50 (USD 1·88) per session and book recreational facilities at very low prices. Additionally, an annual monetary credit of SGD100 was provided by the government to use at these facilities[30]. Many participants recalled being first exposed to the campaign on social media through sponsored posts, which also served as reminders to use their credit. While some participants made use of the credit and facilities, others did not due to lack of time, poor accessibility, the gyms being overcrowded or having old equipment and the perception that social gatherings generally do not involve physical activity. Similar to the National Steps Challenge™, long-term maintenance of physical activity habits once gifted credit has been used was questioned.
Healthy eating social media campaigns hosted by the government also elicited critical responses. A campaign which required students to add a hashtag on social media in exchange for a SGD2 discount on a ‘healthier’ choice lunch item was faulted for not incentivising a wider range of more healthful meals or for discount redemption being troublesome. Participants who engaged with this campaign primarily did so for the discount, while others did not want to be affiliated with a health campaign on social media. Another heavily promoted health campaign on social media and at food outlets was ‘Healthy Plate’ where more healthful food portions were promoted. ‘ Healthy Plate’ was also perceived as having limited success by participants as they tend to eat whatever portions they wanted and felt that at least on campus, they did not have many healthful options. General perceptions towards health campaigns amongst most participants concur with sentiments expressed by D06 (male, 24, sociology), ‘I think sometimes like all these health tips, right, it’s a bit too, uh, like it assumes like you have a choice lah. But sometimes we are really just limited by our circumstances’.
Participants largely appeared to trust information from government entities, such as the Singapore Health Promotion Board and Singapore Ministry of Health, referring to contents of health campaigns, or product endorsements as a benchmark for what they considered healthful in their discussions. However, at times, due to the way some health messages were framed and interpreted by the participants, some participants expressed apprehension about its veracity. For instance, participants voiced that caloric content of food items were inflated in a government-endorsed food-tracking mobile application to scare them into being more health conscious and were generally not helpful to them. Conversely, some participants talked about improvements in family members’ food literacy and preferences due to health promotion efforts. For instance, after one participant’s mother learned about healthful alternatives through government campaigns, the family’s diet incorporated these switches for more healthful meals at home, in addition to reading nutritional content labels. Consequently, that participant continues to follow these beneficial changes outside of the home as well.
## Subtheme T2·3 Social media and peer influence
The influence of peers was evident by the narratives participants recounted on the types of content they were exposed to, engaged with and shared amongst their networks. While in some instances, posts by friends initiated or reinforced health-promoting behaviours, a more commonly shared experience was being tempted by shared posts with new and promotional food deals as experienced by F06 (female, 18, aviation management), ‘Like yeah, you get tempted by your friends like Instagram post like, they’ll post like, uh, “try this place”, “it’s a new place”, then they post the food, then like, you [are] also like tempted to go and buy’. Social media also provides an avenue to explore other content of interest as algorithms suggest content to users based on personal interests and their friends’ use. However, some participants shared that this may work against their goals of making healthier lifestyle choices. Nonetheless, responses of the participants suggest that they purposefully use social media to seek information on trends, experiences, promotional deals and recommendations within and beyond their immediate social network. Generally, shared promotional deals and recommendations often result in related social activities in real life, such as a group outing to visit a newly opened café offering promotions, demonstrating how shared information on social media influences food and activity choices in reality.
## Theme 3: Real-world family and peer influences on health-promoting habits
Apart from interactions on social media, participants discussed how real-life interactions with their social networks influence their perceptions and practices around food and physical activity. Friends and family can influence behaviour in several ways such as by providing information, serving as role models, shaping perceptions about body image and acting as arbiters of accountability. Illustrative quotes for this theme are presented in Table 4.
Table 4Quotes for theme 3: real-world family and peer influences on health-promoting habitsSub-ThemeIllustrative quotesT3·1 Role modellingAt certain times, like, all your friends are like eating healthy, then, like, you are subconsciously, like, oh, okay, maybe I should, like, hop on the bandwagon and try it too, that kind of thing. [ participant smiled] or like um, yeah, I don’t know lah. It’s just phases lor, yeah.– C05, female, 20, Communications and New MediaThen for me with regards to exercise, occasionally I have those friends that – they will post their, like, workout videos, and like, oh cool, I wish I could be like you, but too bad I don’t have the self-control, time, or energy to do that. Yeah, although, like, uh I do kind of – like, as in I try to exercise, but I just – just fail lah.– C06, female, 20, PsychologyT3·2 Health literacy and practicesBecause my friend, like, she is quite, like, a health conscious person. Then, like, the time, like, I had stayed over with her, then she show me her six packs [referring to her friend’s abs] …Then she say, like she – she achieved that ’cause she didn’t drink any sugary drinks lah. So I’m, like, very motivated, I’m like, okay. [ participants laughed]– C08, female, 23, Faculty of Arts and Social Sciences;Undecided academic disciplineT3·3 Recognition, accountability, and body imageI play soccer sometimes also. Then uh, like for example, if my teammates notice that my stamina improved. Then they tell me like, “eh your stamina become better sia”, and say like, “good job” or something, then I’ll feel better lah. Then I guess that’s one of the benefits, rec - recognize your effort. Then like what he [another participant] said just now, sometimes my girlfriend will say, like, “wow, so fit”. Then whoa shiok [Singaporean expression for expressing admiration or approval] [other participants laughed].– D06, male, 24, SociologyLike you see all these like clothes [on social media] and then you’re like, oh my gosh, if I could like be like that woman wearing that clothes. You see like these models and things like that. And then it’s like, um… Or I used to like want to like really like wear bikinis and everything, and stuff… But then like, um, sometimes I’m just like, yeah, I just feel fat so then I was like I will just exercise. But, um, this is very weird, but like honestly I think that like, um, my… like look-wise I’ve been less and less motivated to like exercise for looks because, um, increasingly I’ve been surrounded by people, like close… people who are close to me who accept me for who I am. [ participants laughed] So like they are very like accepting and like they’re very loving, and like they don’t make me feel inadequate. So, I don’t feel inadequate, so I don’t work, so I don’t like engage in physical exercise to improve my looks ’cause I don’t think that my looks are that bad.– A08, female, 20, Political ScienceI think there was – there was this period of time like in school when, like, I think it was very stressful and stuff, so my friends and I, like, we went to the gym every day, and then, like, we make sure, like, we kept each other accountable about what we ate… Like, let’s say in the canteen, like, if we are, I don’t know, if we are – let’s say we go to the [Mixed Vegetable Rice] stall then, like, we see what [is available], and we’ll [be] like, “yeah, no fried food okay?”, that kind of things… And then, like, uh yeah, like, you ask for less rice, or, like, brown rice, that kind of options, yeah. So, we were just – like, I don’t know, just keep in check on one another.– C03, female, 21, Business AdministrationWhen I go to my grandma’s house, she never fails to tell me that I get fatter. Like, every two weeks, like every two weeks when I visit her, and then like, my aunts or my grandma or my mum, like, they will just like comment. ’Cause like, now I don’t – like now that I don’t live at home, like, I think it’s more obvious, like, to my mum also… She will notice that there are, like, stark differences… I’m usually in hall every day. So, people in hall wouldn’t be able to tell me, like, anything, because they see me every day, but like, when I go back home, everyone’s like, “Hey”, I’m not sure why or how come [I’ve gained weight], so I think that’s like a very big problem for me, yeah– C04, female, 19, PsychologyI’m a guy… My, my parents always tell me, like, “eh, you’re a guy, you must eat heartily, must eat a lot”. You know, eat a lot of rice, then you will grow taller, you will grow bigger. Yeah, but then I know for a girl it would be different lah, like my sister, she, it’s different for her. Like they tell her, “Hey, why do you eat so much? Uh, why you eat so much white rice?”– D06, male, 24, SociologyI think it’s more the people. Um, because, like, for instance, okay, like myself, if I go for a run, right, I find it very difficult to run longer distances by myself, compared to when I’m running with a friend. And, like, the rate of my – the speed at which I run is, like, faster when I run with a friend. I don’t know why. It’s, like, I just feel less tired. And like, we’re not even talking, but it’s, like, I just feel, like, I cover more distance in a shorter amount of time. But if you do it yourself, then it’s, like, it just feels like it takes forever, and, I don’t know, maybe it’s because there’s that sense of like, competition, so you’re, like, oh, I – like, oh don’t want to lose face, like oh, I don’t want to like yeah, trail behind and stuff lah.– C05, female, 20, Communications and New MediaI hate going to the gym. I don’t like it. I don’t know. I just feel like all the gym rats are there. [ other participants laughed] It’s very intimidating [another participant says ‘yes’ in the background, agreeing with A08’s statement] and like I… Hmm. Some of the machines are so weird [other participants laughed] and I’m not sure what to do with them. I’m not sure if I’m doing it right. Then I’m like… I’m, I’m not sure if like my… what’s like… I don’t know. What do they call it? Like whether their posture is correct. Oh, whether their form is on point or not [another participant says ‘oh yeah’ in the background, agreeing with A08’s statement], right. Then like there, there will be people who are more experienced who are judging me and my bad form. And like, I don’t know. There’s too much like social pressure and like it navigates the whole situation. Like it feels too social for me. Like I prefer exercising alone personally. So, like, um, sometimes like asking a friend is like the person will probably have to be a very good friend to watch me die. Because [other participants laughed] I will just die. Like, yeah. And then like I prefer being alone and then like, like that moment when like your… the exercise takes over and then like you can’t think of anything. You can only focus on breathing because you’re dying. [ other participants laughed] Like I think that’s quite nice. [ other participants laughed] Yeah, like I think is… Yeah, I don’t know.– A08, female, 20, Political Science
## Subtheme T3·1 Role modelling
Peers who incorporated healthy meals and exercise into their daily routine were often represented positively by participants as being a ‘role model’ or ‘disciplined’. When friendship groups are interested in health-promoting practices, participants may also find themselves eating healthier. Likewise, when friends are also interested in exercise, they can influence participants as D05 (male, 21, economics) comments, ‘I think friends are important uh. Like if your friends exercise then you nothing to do, “Okay lah, I’ll follow you ah”.’ *While this* was true for several participants, there are also those who remain unmotivated due to personal barriers.
Peers were variably described as providing motivation to engage in healthy behaviours or serving as sources of temptation. While peer behaviours were sometimes described as being aspirational, incongruity between personal aspirations, perspectives and practices was sometimes observed. For instance, while D07 (male, 24, political science) thinks that having free time means that you can have a healthier lifestyle and that it is a sign of discipline to maintain these practices he also thinks that ‘eating healthy is a bit, is a bit sad? Like everyday life is so stressful and you’re eating healthy, it’s kind of not helping?’.
In other situations, peers may entice individuals to make unhealthy food choices by saying words to the effect of ‘It’s just once in a while’. Some participants reported strategies to maintain healthier food practices even when friends had different food preferences such as eating before the gathering or choosing a healthier alternative when dining with friends at fast-food restaurants. However, most of the time, participants found it hard to resist the temptations of enticing food and their friends’ invitation.
## Subtheme T3·2 Health literacy and practices
Participants described how family members’ health literacy and practices can influence their behaviour. For example, some participants explained that they have no choice but to eat late at the behest of their family members, a practice which they feel adversely affects health. Similarly, peers may also be a source of information on health-promoting practices when they are interested in health-related information. For instance, a participant became motivated to curb her sugary drink intake when she found out that her physically fit friend did not drink sugary beverages to maintain her figure. Though at times, some participants may oppose their peers’ ideas on health, such as with G01 (male, 19, information technology) who is interested in bodybuilding, ‘I have talked to, like, a few of my female friends who are, like, into dieting and stuff also. So, like, um, what usually their mind-set was that lesser calories equals to a healthier diet so that they don’t gain weight and stuff, yeah. So, like, this doesn’t really make any sense lah because… they just have like this very broad idea that calories is bad, so… [trails off]’
## Subtheme T3·3 Recognition, accountability and body image
Positive self-image and personal development were partly attributed to having a supportive group of friends who motivated, validated and recognised young adults’ health-promoting efforts. *In* general, having supportive friendships improved participants’ body image. Participants who lost weight or kept up healthy practices attributed it to a supportive group of friends that is respectful of participants’ individual preferences. Participants also felt positively about these friendships especially if they have had past struggles with exercise and weight gain. *In* general, joining sports clubs, interest groups, having a goal or joining competitions also encouraged more exercise. Male participants reported joining friends to train for annual fitness tests post-conscription. This demonstrates how friendship groups can have considerable influence, holding members of the group accountable for their health behaviours.
Outside of friendship groups, some participants described their family and relatives as ‘mirrors’ that compel them to reflect on their appearance. Such comments may have an impact on how participants perceived themselves, especially if the comments are negative. There was a gendered aspect on food practices in the family, in particular with regards to portion sizes, with young men being encouraged to eat more and young women cautioned against eating large servings. Gendered perceptions were also visible with regards to exercise as women remarked that they preferred to exercise with friends with whom they felt comfortable revealing their untidy selves, especially if they struggled with exercise. Most participants, both men and women expressed that they would be more likely to exercise, and to do so more vigorously, when accompanied by a friend.
## Theme 4: Role of norms
The activities that participants took part in and accounts of their peers’ behaviours revealed social norms that influence daily living and health-promoting practices. Illustrative quotes for this theme are presented in Table 5.
Table 5Quotes for theme 4: role of normsSub-ThemeIllustrative quotesT4·1 Student priorities – studying and socialisingAnd what makes it [physical activity] difficult, there are so many things that make it difficult. Like what [another participant] said lah, like she’d rather watch a drama. Like, why go to the gym and, like, tire yourself out more when you can, like, relax and watch a drama and stuff. Yeah.– C05, female, 20, Communications and New MediaI’m not getting enough sleep, I guess, so to fit in my gym into my schedule, usually if I were to go home around 10 plus? Yeah, I will be gyming around 11 onwards, so usually I would get around four hours of sleep to five hours of sleep.– G02, male, 19, Information TechnologyI mean, you only get three to four years in Uni, why not just play and enjoy it first before you actually go out and start working then you won’t have these kind of supper nights anymore… while you are still in Uni, yeah, just go along with it lah.– B02, male, 22, EconomicsT4·2 Gym cultureLike those uh, like, I know the few guys on Instagram, so they’re just all the time like admiring. Like, like they started using words they’ve never used before, and it feels very… weird.– D01, male, 21, Chemical EngineeringYeah. I just can’t stand guys who do that.– D07, male, 24, Political ScienceGoes off into a world of their own. [ Participant laughed] … Then it sort of estranges [everyone] away.– D01, male, 21, Chemical EngineeringThere is one more reason why I don’t go uh gym because that time after a long time never go to gym I went with my uh a bit oversized friend, ’cause she was determined to lose weight. Then we went to the gym right, there was like these guys who were like laughing at her [other participants saying “so bad” together] and we felt so offended for her. So apparently after that like she didn’t want to go to gym anymore. Then also, we also like feel bad already and we don’t go gym already.– E02, female, 19, IT Service ManagementI have friends who like don’t like to gym, because they feel very conscious of the people around them. And then for me that was how, like, I felt when I first started going to the gym, and like – there’s a – like, last time it used to be, oh, only, like, guys go to the gym, like, girls don’t really do it that kind of feeling. Girls just, I don’t know, jog on the treadmill, but you don’t touch the weights, yeah. But then, um, subsequently I realised, actually that’s not really the case ah. Like in fact, like guys will be more conscious of girls being in the gym than – because they want to look good. But also, at the same time, like, people actually don’t really care what you’re doing, about what you do ah, people aren’t looking at, like, you, they are looking at, like, they prefer to look good themselves. [ fellow participants laughed] Yeah they want to, yeah they rather make sure that they are, they are the ones looking good, as opposed to, oh, how you are doing. Yeah, that kind of feeling At least that’s what – like, that’s what I personally feel uh, yeah.– C03, female, 21, Business Administration
## Subtheme T4·1 Student priorities – studying and socialising
Valuing academic achievements as a priority appeared to be a common mindset in most men and women, with a male participant, who served in the nationwide compulsory conscription, quoting ‘Now I am a full-time student, not a soldier’ to demonstrate his priority as a tertiary student taking precedence over other activities. Along with studies, other priorities may include part-time jobs that the students take on to support themselves. Several participants described prioritising time to rest or study over taking part in exercise. Some participants who chose to take part in physical activity noted that it cuts into their time for sleep.
Socialising was also perceived as a priority and participants reported spending time to form and maintain connections and friendships during this phase of life. Thus, peer influences on dietary behaviours may be particularly pronounced as commensality or shared eating facilitates occasions for young adults to forge new and strengthen existing friendships. For instance, NUS participants who lived on the university campus expressed that it is a norm to have late-night suppers with other students, especially as first year students. These suppers provided a way to socialise and form friendship groups despite the unhealthy practice of eating calorie-dense foods, which were perceived as being the only options available at late hours. Upon forming friendship groups, participants reported feeling less pressure to join suppers, which were positively viewed by some participants as saving time and money.
Thus, these priorities influence the amount of time participants have to take part in health-promoting activities, especially when they often prioritise studying over exercise and eating healthfully. This can result in phases of inactivity and unhealthy eating during exam periods and more activity and healthier eating during non-exam periods.
## Subtheme T4·2 Gym culture
Some virtual norms and behaviours for exercise are also enacted in real life. Many participants described online and real-life gym culture as being showy, obnoxious and alienating to those who do not take part. These negative social experiences reinforce barriers to physical activity. Many women described gyms as male-dominated spaces, resulting in a perceived hostile environment for women, especially if they were to exercise alone. Participants also shared negative experiences such as when their friend was fat-shamed by other patrons, which resulted in participants never going back to the gym. However, some women shared experiences where they felt better able to exercise in the gym if they went with a friend or once they noticed that fellow gym-goers were more focused on their personal performances.
## Discussion
This study explored young adults’ perspectives of social environments, both real and virtual in relation to their food and physical activity choices. Our results suggest that social media influences dietary and activity choices through pathways of meeting physiological and social needs, creating or reinforcing norms and serving as sources of influences from peers, family and commercial and governmental entities. Real-world social connections similarly served as sources of information, role modelling and determinants of body image. Participants seemed to navigate these 2 worlds in a complementary manner and when presented with conflicting views such as whether a popular café has tasty food, the opinions from real-world sources, i.e. close social networks, generally took precedence.
Comparable to other studies[10,31], the high prevalence of our participants’ social media use was driven by the need to connect with others, express themselves and seek validation. Social media complements traditional ways of bonding with friends and families and facilitates new connections with others on a virtual platform that fuels interactions with online content. Social media is also an efficient platform to share and promote information, create social capital and set the scene for social norms that persist in real life, such as gym culture. Simultaneously, social media was viewed as a platform that informs users of current trends, a way of seeding trends and creating normative behaviours. *In* general, the types of information viewed and shared reflect the interest of individuals and their social networks, especially as social networks tend to be homophilic since similarity breeds connection[32]. However, the high frequency of use also suggests that there is an underlying ‘fear of missing out’ on trends and events[33].
Consistent with results from a study in European adults, our participants also reported using sources apart from social media as alternate channels of information[34]. While our participants valued content from reliable sources, they also admitted to instances where they shared content based on the headline without perusing the content. Experimental evidence suggests that apart from the perceived credibility of the news publisher, social network tie-strengths can also influence the credibility of the news shared, with closer ties being more influential[35]. In line with these findings, we observed that our participants were more likely to trust shared food and product recommendations from their close social networks at face value rather than those from social media influencers. Nevertheless, the source of information was still important with governmental sources being regarded as more legitimate and less likely to be fact-checked by participants as compared with unreferenced articles of interest.
Commercial marketing campaigns often target young adults with aims to promote sales, recognition and brand loyalty – often successfully[36]. Our results suggest that marketing campaigns that use price promotions, leverage on the latest trends and present trusted sources for recommendations were more successful amongst young adults. These campaigns seemed to initiate or influence more food-based rather than activity-based social occasions. Virtual recommendations of eateries or foods triggered activities in real life and were followed up by sharing these activities on virtual spaces, thus fuelling trends. Several contextual factors may act together to facilitate this in Singapore, as elsewhere. The wide range of eating outlets with a competitive retail food industry[37] means that various foods are available to suit a range of budgets and there are usually new places to discover. With over 75 % of adults eating out daily, eating out is socially acceptable and can be viewed as normative behaviour in Singapore[3].
Clever marketing by food companies by use of social influencers and native advertising also makes it harder to distinguish paid advertisements from genuine recommendations[38]. Our participants were aware of the persuasive elements of social media algorithms and marketing content. However, social media engagement seems to sustain trends that are stimulated in part by the ‘fear of missing out’. Simultaneously it provides content to share with social networks and opportunities to participate in new experiences with their friends in real life, thus facilitating the spread of advertised content. Successful social marketing of modern manufactured foods and meals is closely related, historically at least, to the epidemic of obesity. Consequently, this has included a muddying of the water for consumers by appropriation of the language used for health promotion as the term ‘healthy’ has no real meaning when used in food marketing and where pursuit of health through healthful eating is often confounded with, or limited, to slimming[39,40].
Unlike industry marketing campaigns, which were generally viewed with a level of mistrust, participants largely trusted government-endorsed media, a finding that is consistent with survey data[41] and bodes well for receptivity to health promotion efforts. Although there were no clear answers on how to improve young adults’ engagement with government health campaigns on social media, participants were receptive to government health campaigns in terms of viewing promoted content before deciding on whether to take part in promoted activities. While concerns about programme sustainability were raised, physical activity-related health campaigns seemed to have successfully used incentives to facilitate engagement to some extent. Our results diverges from a study which found that Scottish young adults viewed health campaigns as unengaging due to dissemination methods and messages that neither help overcome perceived barriers to health promotion nor were tailored to young adults[42]. Though, as suggested from our findings, improving some aspects such as the naming and usability of mobile applications, ease of access and engagement to promoted activities and the framing of health messages may help improve engagement for government health campaigns directed towards young adults.
Similar to young adults in the USA[43], our participants were also exposed to a wide range of diet and exercise-related content on social media which rarely resulted in them taking part in these activities in real life. Posts related to food were commonly shared, suggesting that young adults are highly interested in food-related topics. However, shared posts were mainly related to eatery or food recommendations which tended to promote high-calorie foods, presenting contradictory messaging to healthy eating campaigns. These observations are consistent with those of Holmberg and coworkers who found that Scandinavian teens typically shared calorie-dense, nutrient-poor foods on social media[44]. In contrast to commercial food posts, and resulting real-life activities, our participants expressed reluctance to share physical activity posts of their own achievements. The reasons for this hesitancy were similar to those observed amongst young adults in the USA who did not wish to post physical activity-related information due to a perceived lack of interest from their peers, not wanting to appear annoying and perceiving physical activity as a personal activity[45]. Likewise, although posts related to physical activity promotion received more likes than posts on health education amongst Norwegian social media users, they were less likely to receive comments and shares[46]. Similar to a study from the USA, we found that while peers’ physical activity posts can sometimes be motivational, comparing their appearance or activity level with those of the post can lead to some participants feeling negatively about themselves[47]. Thus, due to their interest and use of social media, shared posts of young adults seem to compete with the dissemination and actualisation of health-promoting information and activities.
As young adults spend a lot of time with their social group, group preferences also have a marked influence on whether social media content affects lifestyle behaviours. This prioritisation of socialisation could be due to transitions in young adulthood where students shift from secondary schooling to an institute of higher education where in addition to students acclimatising to their new circumstances and studies, they also experience a need to form and maintain social bonds whilst they navigate this stage of life[4,9,10]. Similar to young adults in Belgium[13,14], our participants prioritised studying and social occasions over other activities, including physical activity and healthy eating. The role of commensality as a mechanism for facilitating the start and maintenance of new and existing social bonds is well recognised[19,48]. Indeed, in our study, several discussions of social activities centred around eateries, many of which were promoted on social media platforms. Further, social eating is related to enjoying foods that are perceived as being tasty, which are usually foods that are low in nutritional content[49]. Taste and cost are important drivers of food choice in budget-conscious young adults[14], and may have contributed to sharing of food posts that are related to these aspects.
The types of food-related posts may also be indicative of the eating behaviours of young adults[50], especially as they can model their eating behaviours on those around them[14,19]. Consequently, young adults were more likely to eat calorie-dense foods if their friends did[19,51] and these types of foods are also more likely to be promoted on social media[44]. Hence, the prioritisation of socialising can work against healthy eating. Although commercial advertising was viewed with cynicism, they were nevertheless persuasive and participants actively accessed social media for food-related promotional deals. Similarly, studies in China and Indonesia showed that the abundance of food-related posts on social media, such as price promotions, snacks and brand name food products contributed to increased intake of unhealthy foods amongst their youth[52,53]. Our findings emphasise the need for regulating the marketing of unhealthy foods on digital platforms. As young adults tend to view their close social networks as particularly trustworthy sources of information, measures to regulate the re-posting of unhealthy commercial food advertising to limit inadvertent exposure to unhealthy food marketing are important. In Singapore, whilst guidelines to limit unhealthy food advertising to children of 12 years or younger exist, these are limited in scope[54]. Regulatory measures related to mandatory front-of-pack labelling and advertising prohibition for unhealthy beverages are being implemented and extending similar measures to food would be of value to consider[12,55]. Additionally, supporting the use of healthier ingredients by the foods service sector[12,55] can be further strengthened.
Similar to young women in the UK[16], our participants often felt judged for their appearance and ability whilst participating in physical activity. Further, our participants were subjected to gender norms regarding dietary intake which can influence their self-image. Studies have found body image, food preferences and practices to be highly influenced by family[56,57]. The combination of these gendered perceptions presents limitations on the circumstances under which women feel comfortable taking part in physical activity. Further, like their counterparts in the USA[43], our participants perceived gym culture presented on social media and in real life as obnoxious which deterred some women from going to the gym. Negative perceptions of gym culture may have also contributed to participants’ reluctance towards widely posting their own physical activity achievements as people who did so were perceived as being annoying or showy. Although changing the narrative around physical activity in the social media sphere may promote more physical activity as social media users who posted physical activity related posts were more committed to doing physical activity and had higher levels of physical activity[45,58].
Overall, maintaining health-promoting activities was commonly perceived as challenging, unless the young adult has incorporated these practices into their daily life. Taking part in these activities may also form a part of a participant’s identity as a health-conscious individual. However, most of the participants who tried to incorporate exercise into their routines were likely to decrease time spent on studies, or more commonly, lose sleep. Nonetheless, our participants commonly perceived peers who manage to maintain health-promoting practices as role models which can help encourage the maintenance of these habits. These views were also expressed by middle-aged Canadian men, suggesting that these perceptions could be common amongst a broad range of age groups[59].
Our study also demonstrated that virtual and real-life social environments can motivate individuals and their social networks to take part in health-promoting practices, although strong support is needed from their real-life social networks for these to be maintained. This is consistent with studies showing how supportive family and friends have a major influence on increasing physical activity and healthy eating[13,51]. However, some participants who have supportive and accepting friend groups may not feel the motivation to take part in health-promoting activities because their currently comfortable social state supports continuing their less healthful behaviours. Ultimately, the interactions that take place between virtual and real-life social environments can influence lifestyle habits that take place in real life and affect health-promoting practices. The social media environment has massive potential to generate global changes in attitudes, among and driven by young people. Social movement for climate change, or against US gun laws, are recent examples, and the opportunity clearly exists for young people to harness social media to combat obesity and diet-related chronic diseases[8].
## Strengths and limitations
This study is one of the few studies on understanding digital media influences on Asian young adults and is the first to investigate the influence of the social environment on young adults’ food and activity choices through a qualitative approach in an Asian context.
The use of qualitative methods allows in-depth insights into the research topic and the exploration of the factors that influence young adults’ dietary and physical activity practices. The inclusion of young adults, both men and women, from various academic backgrounds provided diverse perspectives to be explored. Further, the use of focus groups allows for interactive discussions which more closely simulates real-life conversations as compared with one-on-one interviews, and can present a broad range of viewpoints[22]. Conversely, participants with contrary views may not be able to express their real thoughts in group settings. However, efforts were made by the facilitators to create inclusive environments. Study rigor was also improved by pilot testing the interview guide, coding by 2 authors (JL and ZT) and discussions about FGD content between JL, ZT, and SAR to maximise reflexivity. Due to the nature of qualitative methods, sample sizes were smaller than quantitative studies, however, as additional topics were not raised in the last few discussions, we deemed thematic saturation to have been achieved. With limited literature concerning the social influences on young adults’ food and activity choices in Asian countries, our focus was on exploring the range of opinions, ideas and themes which help elucidate and conceptualise factors of social influences[60]. Discussions about FGD content between JL, ZT and SAR also helped mitigate issues related to thematic saturation, such as reflexive discussions on collated data and derived themes. Our study does not consider the perspectives of young adults who are not studying at tertiary institutes. However, a majority of young adults in Singapore do attend a tertiary institution, with 80·4 % of 25–29-year-old Singapore residents attaining a university degree or a diploma and professional qualification in 2020[29]. Also, as most participants were Singaporean Chinese, reflecting the ethnic mix in Singapore, the findings may not all be generalisable to other populations.
The personal interests and backgrounds of the participants may have affected the content of the focus group. For example, participants from health-related disciplines could have been influenced by course content, resulting in leading discussions or overcontribution. This issue was mitigated by having focus groups with students from mixed disciplines and facilitators ensuring that each participant had time to express their views.
Self-reported information such as height, weight and food choices were not verified by objective measures. Our study sample had a lower proportion of participants with high-risk BMI of ≥ 27·5 (6·5 %) as compared with national statistics of 13·1 % in adults aged 18–29 years[7]. Although students were asked about their dietary choices, without tracking what participants actually ate, their nutritional intake was not measured. A previous study found that Singaporean university students were not eating healthfully[2] and this was also implied by the types of social media influences and resultant food choices that the participants discussed in our study.
## Conclusion
Overall, our results highlight the growing influence that social media and digital social interactions have on young adults’ dietary and physical activity practices. The frequency of social media use is unlikely to decrease as it is an easily accessible medium and meets the multiple social, informational and utilitarian needs of young adults. The type of content accessed on social media also reflects the interests of individuals and their friendship groups, which influences their choice of real-life social activities and practices, creating and reinforcing trends in virtual spaces and real life. Tapping into the needs of young adults on social media, commercial food marketing campaigns utilising limited time offers, novel flavours or price promotions are often used to generate interest in trendy and affordable foods or eateries. This translates into food-based social activities in real life and competes with government health promotion messaging which is viewed as trustworthy but seems to be less persuasive. Prioritisation of studying and socialisation, and unhelpful cultural norms including those of gym culture in real life, also present important challenges to healthful behaviours. The presence of close and supportive social networks can also help overcome perceived barriers.
Taken together, our findings support the growing call for regulating digital marketing of unhealthy foods[12,61] and emphasises the importance of the food service retail sector’s contribution to the dietary behaviours of young adults. Regulatory approaches, such as those addressing barriers towards access and affordability of healthy food items and limiting the availability and accessibility of unhealthy food, are also important to improve dietary behaviours in young adults[12,51]. Our findings also suggest that marketing strategies that meet young adults need for finding affordable food options, fitting-in and providing opportunities for socialising are particularly effective, and this can be informative for the design of government health promotion campaigns. Mitigating the negative perceptions of gym culture, addressing gendered norms around physical activity, and promoting the incorporation of physical activity in social gatherings, as done with food may be ways to encourage physical activity in young adults. Given the limited success in existing population-level obesity interventions, co-designing health campaigns with young adults for improved engagement and efforts to understand and improve digital platforms to support healthful choices[8] presents a valuable area of future research.
## Financial support:
This paper presented collaborative research amongst the authors and was funded by the NUS Physical Activity and Nutrition Determinants in Asia (PANDA) Research Programme.
## Conflict of interest:
There are no conflicts of interest.
## Authorship:
J.L.: Conceptualisation, methodology, formal analysis, investigation, writing – original draft, visualisation, project administration, data curation, writing – review and editing. Z.T.: Conceptualisation, methodology, formal analysis, investigation, visualisation, project administration, data curation, writing – review and editing. R.M.v. D.: Conceptualisation, methodology, resources, writing – review and editing, funding acquisition. F.M-R.: conceptualisation, writing – review and editing, funding acquisition. M.E.J.L.: conceptualisation, writing – review and editing. C.K.N.: conceptualisation, writing – review and editing. S.A.R.: conceptualisation, methodology, resources, supervision, project administration, writing – review and editing.
## Ethics of human subject participation:
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the NUS Institutional Review Board (NUS-IRB Reference Code S-17-007). Verbal informed consent was obtained from all subjects/patients. Verbal consent was witnessed and formally recorded.
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---
title: 'Eating patterns in a nationwide sample of Japanese aged 1–79 years from MINNADE
study: eating frequency, clock time for eating, time spent on eating and variability
of eating patterns'
authors:
- Kentaro Murakami
- M Barbara E Livingstone
- Shizuko Masayasu
- Satoshi Sasaki
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991801
doi: 10.1017/S1368980021000975
license: CC BY 4.0
---
# Eating patterns in a nationwide sample of Japanese aged 1–79 years from MINNADE study: eating frequency, clock time for eating, time spent on eating and variability of eating patterns
## Body
Efforts to overcome the limitations of evaluating the health impact of consuming single nutrients and foods in isolation have led to a gradual shift in nutrition research to dietary patterns[1,2]. While dietary patterns are generally examined using the daily intake of individual foods(3–6), an increasing number of studies now focus on dietary intake on an eating occasion basis (i.e. breakfast, lunch, dinner and snacks) or eating patterns(7–9). Evaluation of eating patterns instead of overall dietary intakes or patterns might increase relevance by accounting for physiological synergies and interactions occurring during digestion and metabolism[10]. Moreover, eating pattern-based dietary advice would resonate better with consumers if it reflects actual eating behaviours[9].
Because of the complex nature of eating patterns, existing research has used a variety of variables in terms of eating patterns, including eating frequency(11–17), timing of eating(13,18–21) and variability of eating patterns(22–27), with equivocal outcomes. This may be mainly due to a lack of clear definitions of these variables. To develop more consistent and clearer definitions of eating pattern variables, comprehensive reports on eating pattern variables based on actual eating behaviour assessment (such as dietary record) are imperative. While there are several papers in this regard based on large-scale observational studies in free-living situations(11–14,18), these are limited with regard to the number of dietary assessment days (usually only 1 or 2 d)(11–13,18) or assessed only a limited aspect of eating patterns[11,14,18]. In particular, we are unaware of descriptive information on time spent on eating and variability of eating patterns in general populations despite the potential health effects of eating rate[28,29] and regularity of eating(22–26). Moreover, most of previous studies have been conducted in adult populations, while information in children is sparse. Investigation of this issue in children is merited from a prevention perspective.
Therefore, the aim of the present study was to describe eating patterns, namely eating frequency, clock time for the start of eating, time spent on eating and variability of eating patterns, in a nationwide sample of Japanese aged 1–79 years, based on information on actual eating behaviors collected using 2-d dietary record in each season over a year (total 8 d).
## Abstract
### Objective:
Although there is growing evidence suggesting that eating patterns are important determinants of health status, comprehensive information on patterning of eating behaviours is almost lacking. The aim of this cross-sectional study was to describe eating patterns in Japan.
### Design:
Information on actual eating behaviours was collected using 2-d dietary record in each season over a year (total 8 d). Eating occasions were defined as any discrete intake occasion (with a discrete start clock time and name) except for eating occasions consisting of water only, which were excluded.
### Setting:
Japan.
### Participants:
A nationwide sample of 4032 Japanese aged 1–79 years.
### Results:
The mean value of eating frequency of meals (i.e. breakfast, lunch and dinner), snacks and total eating occasions was 2·94, 1·74 and 4·68 times/d, respectively. The mean clock time for the start of breakfast, lunch and dinner was 07.24, 12.29 and 19.15 h, respectively. The mean time spent consuming breakfast, lunch, dinner and snacks was 19, 25, 34 and 27 min/d, respectively. On average, variability (i.e. average of absolute difference from mean) of meal frequency was small compared with that of snack frequency and total eating frequency. Both mean variability of clock time for the start of eating (<1 h) and mean variability of time spent on meals (<10 min/d) were also small. Conversely, mean variability of time spent on snacks was large (>18 min/d).
### Conclusion:
The present findings serve as both a reference and an indication for future research on patterning of eating behaviours.
## Study procedure and participants
This analysis was based on data from MINNADE (MINistry of health, labour and welfare-sponsored NAtionwide study on Dietary intake Evaluation) study. The ultimate purpose of MINNADE study was to describe nationwide data on dietary characteristics and eating behaviours in Japan. The study consisted of two rounds of 1-year data collection (first round: November 2016 to September 2017; second round: October 2017 to September 2018). The target population comprised apparently healthy Japanese aged 1–79 years living in private households in Japan. Initially, thirty-two (of forty-seven) prefectures, which cover >85 % of total population in Japan, were selected on the basis of geographical diversity and feasibility of the survey, particularly the recruitment of collaborators (research dietitians). During sampling procedure, the proportion of population number in each region in Japan was reflected (i.e. Hokkaido 4 %, Tohoku 7 %, Kanto I 28 %, Kanto II 8 %, Hokuriku 4 %, Tokai 12 %, Kinki I 13 %, Kinki II 3 %, Chugoku 6 %, Shikoku 3 %, Kita-kyushu 7 % and Minami-kyushu 5 %[30]).
A total of 441 research dietitians agreed to support the study and were responsible for recruitment of participants from communities as well as data collection. Based on feasibility and human and financial resources (assuming 5–6 persons per research dietitian), we decided to include 256 individuals (128 for each sex) for each of nine age groups (i.e. 1–6, 7–13, 14–19, 20–29, 30–39, 40–49, 50–59, 60–69 and 70–79 years) during the first round of data collection (n 2304 in total). Considering the difference in dropout rate between age-sex groups observed during the first round, the number of recruited participants in the second round varied from 110 to 119 for each sex-age group, with a total of 2051 individuals (4–5 persons per research dietitian).
The key inclusion criterion for this study of community-dwelling (free-living) individuals was their willingness to complete a dietary record. Excluded from the study were dietitians, individuals living with a dietitian, those working together with a research dietitian, those who had experienced dietary counselling from a doctor or dietitian, those taking insulin treatment for diabetes, those taking dialysis treatment, pregnant or lactating women (at the start of study) and infants habitually drinking human milk. We did not exclude overnight workers from this study but asked not to conduct dietary record on overnight working days as well as days before and after these days. Participation of only 1 person per household was permitted. Participants in this study were not randomly selected. Consequently, a total of 4268 individuals aged 1–79 years participated in this study.
## Dietary assessment
Dietary data were collected using 4 × 2-d (total 8 d) weighed dietary records. After receiving written and verbal instructions by a research dietitian, as well as an example of a completed diary sheet, each participant was requested to maintain a record of all items eaten or drunk, both in and out of the home. This was done over 2 nonconsecutive days once per season at an interval of around 3 months, namely November for fall, February for winter, May for spring and August for summer. The sets of two recording days comprised 2 weekdays (Monday to Friday) for half of participants and 1 weekday and 1 weekend day (Saturday or Sunday as well as national holidays) for the remaining participants. However, not all days of the week were evenly represented. This allocation was maintained throughout the study; thus, it was expected that half of participants provide 8-weekday dietary data while the remaining participants provide 4-weekday data and 4-weekend day data. This strategy was adopted to obtain dietary data with an approximate proportion of weekdays and weekend days (3:1 compared with the actual ratio of 5:2) as a whole, while not compromising feasibility and simplicity for the conduct of the survey. The recording schedule for each participant was arranged by the assigned research dietitian.
Children aged ≥13 years (as well as adult participants) were expected to be able to complete the record themselves, whereas for children aged <13 years, the parent/guardian was asked to complete the record with input from the child as appropriate. Irrespective of age, however, we encouraged participants to get support from the main cook (e.g. mother and wife) when necessary. Within a few days after each recording day (usually the next day), the research dietitian collected the recording diary, checked the completeness of recording and recorded additional information if necessary. All the collected diaries were checked by trained dietitians at the central office in terms of coding, recorded weights and descriptions of items consumed. Data on dietary intake were not available in this study because the use of these data is not currently permitted by the Ministry of Health, Labour and Welfare, Japan.
## Definition and creation of eating pattern variables
The food diary sheet used was based on a typical Japanese eating pattern, which comprises breakfast, lunch, dinner and snacks, and these eating occasions were prescribed in the diary. Thus, the eating event (eating occasion) name used in the present analysis was based on this classification. Multiple entries of eating events into a section of breakfast, lunch or dinner were extremely rare in this study (only six cases); in these cases, the first eating event was considered the corresponding eating event (i.e. breakfast, lunch, or dinner), and the following eating events were considered snacks.
During the diet recording, participants were asked to report the clock time when a food or beverage was consumed (both start and finish times). Consequently, all items reported in an eating event were given the same clock time and event name in the food diary. Based on these data, several eating pattern variables were created mainly based on previous studies(11–13,26) as described below. Unless otherwise indicated, the mean value over 8 d was used for each participant.
## Eating frequency
In this study, eating occasions were defined as any separate intake occasion (with a discrete start clock time and name) except for eating occasions consisting of water only (tap and mineral water), which were excluded[11]. Thus, eating frequency was defined as the total number of eating occasions per day, which consist of foods only, drinks only or foods and drinks combined. Based on participant-identified name of eating occasions, eating frequency variables for meals (breakfast, lunch and dinner combined), snacks and all eating occasions were calculated for each participant.
## Clock time for the start of eating
Based on information on start clock time of eating occasions, clock time for the start of eating breakfast, lunch, dinner and the first and the last eating occasions were created. For each individual, we calculated mean values over data-available days for clock time for the start of first and last eating occasions. We also calculated mean values on consumption days with data needed for clock time for the start of breakfast, lunch and dinner; participants who reported no consumption of these meals on any of 8 recording days had missing values on these variables.
## Time spent on eating
Time spent on eating (breakfast, lunch, dinner and all snacks combined) was defined as time difference between the finish and start time of eating occasions. For these variables, we calculated mean values on consumption days with data needed, and participants who reported no consumption on any of 8 recording days had missing values on these variables.
## Time between eating occasions
Time between eating occasions was defined as time between end of meal and start of next meal. For each day, the mean value of time between eating occasions was calculated, and then mean value over data-available days was calculated for each participant.
## Length of ingestion period
Length of ingestion period was defined as time between the start clock time of the first eating occasion and the finish clock time of the last eating occasion. For each participant, we calculated mean value based on data-available days.
## Variability of eating patterns
Variability in each eating pattern variable was calculated by adding the absolute difference between the mean value and that in each day divided by the number of days, with a higher value indicating a larger variability in eating patterns[26]. For each individual, mean daily values over 8 dietary recording days were used for eating frequency variables; mean values over data-available days for clock time for the start of first and last eating occasions, time between eating occasions, and length of ingestion period; mean values on consumption days with data needed for clock time for the start of breakfast, lunch and dinner and time spent on eating breakfast, lunch, dinner, and snacks. For variables based on breakfast, lunch, dinner and snacks, only participants who reported consumption of the corresponding eating episode on ≥ 4 d (with complete information) were included.
## Assessment of basic characteristics
Age at the time of start of the study was calculated based on birth date. Anthropometric measurements were performed by either family members or research dietitians using standard procedures. Body height (nearest 0·1 cm) and weight (nearest 0·1 kg) were measured, while the participants were barefoot and wearing light clothes only. When measurement was not available (n 23), self-reported (or parent-reported) height and weight were used. BMI (kg/m2) was calculated by the commonly used formula, namely weight (kg) divided by height squared (m2). Information on annual household income was collected using a question with possible sixteen categories, which were subsequently aggregated into three categories (< 4 million, ≥ 4 to < 7 million and ≥ 7 million Japanese yen). Information on education level and employment status was also collected for adult participants (aged 20–79 years), who were grouped into three categories for the former (junior high school or high school, college or vocational school and university or higher) and into four categories for the latter (full-time job, part-time job, student and unemployed).
## Analytic sample
For analysis, we excluded from the initial sample of 4268 participants 111 participants with < 8-d dietary record data, 102 participants who had 8-d dietary data but whose dietary assessment was conducted on 2 consecutive days at least in 1 season and seven participants who had nonconsecutive 8-d dietary data but whose dietary assessment was not conducted in appropriate months (i.e. October, November and December for fall, January, February and March for winter, April, May and June for spring and July, August and September for summer). After further excluding twelve participants who became pregnant during data collection and four participants who lived in a different region (which we recognised after the start of data collection), the final analysis sample comprised 4032 participants (see online supplementary material, Supplemental Figure S1). Further exclusion of participants whose dietary assessment was not conducted in accordance with the schedule assigned (n 324) did not alter the findings of the study (data not shown); therefore, these participants were retained in the analysis.
## Statistical analysis
All statistical analyses were performed for adults (aged 20–79 years) and children (aged 1–19 years) separately, using SAS statistical software (version 9.4, SAS Institute Inc.). Data are presented as means and sd for continuous variables and as the numbers and percentages of participants for categorical variables. First, the number and proportion of participants by number of reporting days of consumption of breakfast, lunch, dinner and snacks were calculated. Then, distribution of clock time for the start of eating breakfast, lunch, dinner and snacks was described. Finally, descriptive statistics on eating pattern variables was provided for the whole sample as well as by sex and age group (20–39, 40–59 and 60–79 years for adults and 1–6, 7–13 and 14–19 years for children). Differences in eating pattern variables between sex and across age categories were examined on the basis of independent t test and ANOVA, respectively. All reported P values are two-tailed, and P values <0·05 were considered statistically significant. When the overall P value from ANOVA was <0·05, a Bonferroni’s post hoc test was performed.
## Results
This analysis included 2681 adults aged 20–79 years (1325 men and 1356 women) and 1351 children aged 1–19 years (680 boys and 671 girls) who completed nonconsecutive 8-d dietary record over a year (Table 1). A total of 16·3 % of dietary record came from Monday, 15·6 % from Tuesday, 16·0 % from Wednesday, 15·0 % from Thursday, 13·9 % from Friday, 8·3 % from Saturday and 14·9 % from Sunday. The percentage of participants (adults and children combined) who reported consumption of breakfast, lunch and dinner on all the 8 dietary recording days was 88·1 %, 92·6 % and 95·9 %, respectively (see online supplementary material, Supplemental Table S1). In total, 81·4 % of participants reported consumption of all three main meals on all 8 d, with additional 12·6 % of participants reporting consumption of two of three main meals on all 8 d as well as that of the remaining meal on at least 4 d. In contrast, the prevalence of no consumption of each of these meals on all 8 d was very low (0·7 % for breakfast, 0·1 % for lunch and 0·05 % for dinner). For snacks, 54·9 % of participants reported consumption on all 8 d, with 4·1 % of them reporting no consumption on any of 8 d.
Table 1Basic characteristics of study populationAdults (aged 20–79 years)Children (aged 1–19 years)All (n 2681)Male (n 1325)Female (n 1356)All (n 1351)Male (n 680)Female (n 671)Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Age (years)49·716·949·617·149·716·89·95·49·85·49·95·5Body height (cm)162·79·0169·36·3156·26·0133·628·7135·730·7131·426·4Body weight (kg)61·212·268·011·254·69·234·618·236·120·033·116·0BMI (kg/m2)23·03·623·73·422·43·617·73·417·73·617·73·3Annual household income (%)* <4 million Japanese yen36·033·638·515·315·015·5 ≥4 to < 7 million Japanese yen34·035·832·239·741·537·8 ≥7 million Japanese yen30·030·629·345·143·546·7Education level (%)† Junior high school or high school38·037·138·8––– College or technical school30·720·041·2––– University or higher31·342·820·0–––*Employment status* (%)‡ Full-time job66·672·860·6––– Part-time job12·68·516·7––– Student1·31·41·3––– Unemployed19·417·221·5–––Diet recording group (%) 8 weekdays45·445·045·847·546·948·0 4 weekdays and 4 weekend days45·045·444·747·648·147·1 Other combinations9·69·79·55·05·04·9* n 2656 for adults and 1331 for children (because of missing information).†Available for adults only (n 2664 because of missing information).‡Available for adults only.
Figure 1 shows distribution of clock time for the start of breakfast, lunch, dinner and snacks reported by adults (a) and children (b) in 8-d dietary record. All three meals had a clear peak in timing (07.00–07.59 for breakfast, 12.00–12.59 for lunch and 19.00–19.59 for dinner). Within 3-h time slots with the central slot corresponding with the peak (i.e. 06.00–08.59 for breakfast, 11.00–13.59 for lunch and 18.00–20.59 for dinner) was reported a very large proportion of breakfast (85·1 % for adults and 90·1 % for children), lunch (93·8 % for adults and 96·8 % for children) and dinner (85·0 % for adults and 90·4 % for children). For the timing of snacks, there were three peaks in both adults and children (10.00–10.59, 15.00–15.59 and 20.00–20.59). Snacking after dinner was reported in 33 % of all dietary recording days.
Fig. 1Distribution of clock time for the start of eating breakfast, lunch, dinner and snacks reported by 2681 Japanese adults aged 20–79 years (a) and by 1351 Japanese children aged 1–19 years (b). The total number of breakfast, lunch, dinner and snacks reported by adults in 8-d dietary record (with information on clock time) is 20 369, 20 982, 21 269 and 37 227, respectively. The corresponding number in children is 10 576, 10 732, 10 753 and 18 960, respectively. Breakfast; lunch; dinner; snacks For the population overall (n 4032), the mean value (sd) of eating frequency of meal, snacks and total eating occasions was 2·94 (0·19), 1·74 (1·18) and 4·68 (1·20) times/d, respectively. The mean (sd) clock time for the start of breakfast, lunch and dinner was 07.24 (00.48), 12.29 (00.31) and 19.15 (00.51) h, respectively. The mean (sd) time spent on breakfast, lunch, dinner and snacks was 19 [8], 25 [8], 34 [15] and 27 [34] min/d, respectively.
Tables 2 and 3 present descriptive data on eating pattern variables for adults and children, respectively, by sex and age group. Eating frequencies of meals, snacks and all eating occasions were higher in female adults (2·94, 1·85 and 4·79 times/d, respectively) than male adults (2·90, 1·62, and 4·52 times/d, respectively; all $P \leq 0$·0001), while eating frequencies of snacks and all eating occasions were higher in male children (1·83 and 4·80 times/d, respectively) than female children (1·68 and 4·65 times/d, respectively; both P ≤ 0·01). There was no sex difference in clock time for the start of eating, except for earlier start time for lunch in men (12.30 v. 12.34 h), earlier start time for dinner in women (19.16 v. 19.22 h) and earlier start time for the last eating occasion in girls (19.35 v. 19.46 h; all P ≤ 0·005). Longer time was spent on breakfast and lunch in both female adults (20 v. 18 min/d and 25 v. 23 min/d, respectively; both $P \leq 0$·0001) and female children (21 v. 19 min/d and 27 v. 25 min/d, respectively; both P ≤ 0·0006). Female adults also spent shorter time for dinner and snacks and had shorter time between eating occasions (34 v. 36 min/d, 25 v. 32 min/d and 3·2 v. 3·6 h, respectively; all P ≤ 0·0008). Female children also spent longer time for dinner and had shorter length of ingestion period (33 v. 31 min/d and 12·6 v. 12·8 h, respectively; both P ≤ 0·008). In terms of age, compared with adults aged 20–39 years (and to a lesser extent with adults aged 40–59 years), adults aged 60–79 years had higher eating frequencies of meals and snacks or all eating occasions, earlier clock time for the start of all meals as well as the first and last eating occasions, longer time spent on meals but shorter time on snacks, shorter time between eating occasions and shorter length of ingestion period. Similar characteristics were shared by children aged 1–6 years (compared with those aged 7–13 years and 14–19 years).
Table 2Eating patterns of Japanese adults aged 20–79 years as assessed by eating frequency, clock time for the start of eating, time spent on eating, time between eating occasions and length of ingestion period, by sex and age group* AllMaleFemaleAge 20–39 yearsAge 40–59 yearsAge 60–79 years n Mean sd n Mean sd n Mean sd P † n Mean sd n Mean sd n Mean sd P ‡ Eating frequency (times/d) Meals§ 26812·920·2113252·900·2513562·940·16< 0·00018782·85a 0·288982·93b 0·219052·98c 0·09< 0·0001 Snacks26811·741·2413251·621·2413561·851·23< 0·00018781·61a 1·318981·81b 1·259051·80b 1·150·0006 All eating occasions26814·661·2713254·521·2813564·791·25< 0·00018784·45a 1·378984·73b 1·279054·78b 1·15< 0·0001Clock time for the start of eating (hh:mm) Breakfast26557:240:5313047:230:5513517:260:500·228647:40a 0:578867:16b 0:519057:18b 0:46< 0·0001 Lunch267612:320:32132012:300:33135612:340:310·00387812:37a 0:3789412:34a 0:3090412:24b 0:28< 0·0001 Dinner267919:190:55132319:220:59135619:160:490·00187619:38a 0:5489819:31b 0:5190518:48c 0:45< 0·0001 First eating occasion26817:311:0813257:331:1413567:291:030·168788:01a 1:218987:19b 1:039057:13b 0:50< 0·0001 Last eating occasion268119:571:16132519:581:18135619:561:130·5287820:17a 1:1489820:09a 1:1390519:25b 1:10< 0·0001Time spent on eating (min/d) Breakfast265519913041891351208< 0·000186416a 788618b 790523c 9< 0·0001 Lunch267624813202381356258< 0·000187823a 789423a 890426b 8< 0·0001 Dinner2679351713233619135634150·000887633a 1589836b 1890537b 17< 0·0001 Snacks (total)|| 254129381230324313112532< 0·000182736a 4585529b 4285921c 22< 0·0001Time between eating occasions (h)26813·41·213253·61·313563·21·0< 0·00018783·6a 1·28983·4b 1·29053·1c 1·0< 0·0001Length of ingestion period (h)268113·01·5132513·01·5135613·01·40·5887812·8a 1·689813·4b 1·490512·8a 1·2< 0·0001*For each individual, mean daily values over 8 dietary recording days were used for eating frequency variables; mean values over data-available days for clock time for the start of first and last eating occasions, time between eating occasions and length of ingestion period; mean values on consumption days with data needed for clock time for the start of breakfast, lunch and dinner and time spent on eating breakfast, lunch, dinner and snacks. For variables based on breakfast, lunch, dinner and snacks, participants who reported no consumption on any of 8 recording days were excluded (n 26, 5, 2 and 139, respectively).†Sex difference examined based on independent t test.‡Age group difference examined based on ANOVA. When the overall P from ANOVA was < 0·05, a Bonferroni’s post hoc test was performed; values within each variable with unlike superscript letters are significantly different ($P \leq 0$·05).§Including breakfast, lunch and dinner.||One participant who reported consumption of snacks but provided no information on clock time was excluded.
Table 3Eating patterns of Japanese children aged 1–19 years as assessed by eating frequency, clock time for the start of eating, time spent on eating, time between eating occasions and length of ingestion period, by sex and age group* AllMaleFemaleAge 1–6 yearsAge 7–13 yearsAge 14–19 years n Mean sd n Mean sd n Mean sd P † n Mean sd n Mean sd n Mean sd P ‡ Eating frequency (times/d) Meals§ 13512·970·116802·970·116712·970·110·714482·99a 0·054612·99a 0·054422·92b 0·17< 0·0001 Snacks13511·761·036801·831·046711·681·020·0084482·19a 1·004611·62b 0·934421·47b 1·03< 0·0001 All eating occasions13514·731·056804·801·066714·651·040·014485·18a 1·004614·61b 0·94424·39c 1·05< 0·0001Clock time for the start of eating (hh:mm) Breakfast13497:240:396797:240:396707:240:390·974487:30a 0:334617:13b 0:294407:31a 0:50< 0·0001 Lunch135112:240:2768012:240:2767112:250:260·4544812:04a 0:2246112:29b 0:1544212:40c 0:27< 0·0001 Dinner135119:070:4368019:080:4467119:060:420·3044818:47a 0:3246119:10b 0:3644219:33c 0:47< 0·0001 First eating occasion13517:290:486807:290:486717:290:480·904487:29a 0:344617:14b 0:324427:44c 1:07< 0·0001 Last eating occasion135119:411:1068019:461:1467119:351:060·00544819:18a 1:0346119:32b 0:5844220:13c 1:18< 0·0001Time spent on eating (min/d) Breakfast13492076791976702180·000644825a 746119b 644015c 5< 0·0001 Lunch13512676802576712770·000444831a 746125b 644222c 6< 0·0001 Dinner13513296803196713390·000344835a 846131b 944229c 9< 0·0001 Snacks (total)13262324669242465722240·1544718a 1545327b 2742624b 28< 0·0001Time between eating occasions (h)13513·20·96803·10·96713·20·90·134482·6a 0·64613·3b 0·94423·7c 1·0< 0·0001Length of ingestion period (h)135112·71·168012·81·167112·61·10·00844812·3a 0·846112·8b 0·844213·0c 1·4< 0·0001*For each individual, mean daily values over 8 dietary recording days were used for eating frequency variables; mean values over data-available days for clock time for the start of first and last eating occasions, time between eating occasions and length of ingestion period; mean values on consumption days with data needed for clock time for the start of breakfast, lunch and dinner and time spent on eating breakfast, lunch, dinner and snacks. For variables based on breakfast and snacks, participants who reported no consumption on any of 8 recording days were excluded (n 2 and 25, respectively).†Sex difference examined based on independent t test.‡Age group difference examined based on ANOVA. When the overall P from ANOVA was < 0·05, a Bonferroni’s post hoc test was performed; values within each variable with unlike superscript letters are significantly different ($P \leq 0$·05).§Including breakfast, lunch and dinner.
Variability of eating patterns (calculated by adding the absolute difference between the mean value and that in each day divided by the number of days) is shown in Supplemental Table 2 (for adults) and Supplemental Table 3 (for children). On average, variability of meal frequency was small compared with that of snack frequency and total eating frequency. Both mean variability of clock time for the start of eating (< 1 h) and mean variability of time spent on meals (< 10 min/d) were also small. Conversely, mean variability of time spent on snacks was somewhat large (21·2 min/d for adults and 17·8 min/d for children).
Compared with male adults, female adults showed smaller variation in all eating pattern variables, except for no difference in snack frequency, total eating frequency, clock time for lunch and the last eating occasion and time spent on breakfast, as well as larger variability of time spent on lunch. For children, there was no sex difference, except for smaller variability of snack frequency and total eating frequency and larger variability of clock time for dinner and time spent on breakfast and dinner in females. In terms of age, adults aged 60–79 years had smaller variability of all eating pattern variables than younger adult groups, except for no difference in snack frequency. Smaller variability of eating patterns was also observed in children aged 1–6 years (compared with older child groups), except for no difference in variability of snack and total eating frequency and larger variability of time spent for breakfast and lunch.
## Discussion
To our knowledge, this is the first comprehensive report on patterning of eating behaviours under free-living conditions, particularly time spent on eating and variability of eating patterns. Eating frequency has been the most widely investigated variable of eating behaviours in adults. In the European Prospective Investigation into Cancer and Nutrition (EPIC) study consisting of ten European countries, mean total eating frequency (using the definition of eating occasion identical to that used in this study) varied across countries (4·9 to 7·0 times/d), with a trend for lower eating frequency in Mediterranean countries (Greece, Spain, Italy and France) compared with central European (Germany, the Netherlands and UK) and Nordic (Denmark, Sweden and Norway) countries[11]. While the definition of eating occasions is inconsistent across studies, mean total eating frequency in a national representative adult population has also been reported from the USA (5·0 times/d)[13], Australia (ranging from 4·9 to 5·9 times/d, depending on sex and age group)[12] and the UK (7·8 times/d for men and 7·6 times/d for women)[14]. In contrast, mean total eating frequency in the present Japanese adults (4·7 times/d), which was identical to that in a small previous Japanese study[31], was consistently lower than that observed in Western countries. Given that daily meal frequency is generally close to three times in many studies(11–13), this is clearly due to difference in snack frequency, with a lower mean in Japanese population (1·7 and 1·8 times/d in the present and previous[31] studies, respectively) compared with Western populations (ranging from 2·1 to 4·2 times/d, depending on studies)(11–13).
In contrast, fewer studies have attempted to characterise other potentially important features of eating patterns. It appears that meal skipping is rare in Japan (86 %, 91 % and 96 % of adults reporting consumption of breakfast, lunch and dinner, respectively, over all 8 d of dietary recording) compared with Western populations. In EPIC study, the proportion of consumers on a diet recall day ranged from 86·0 % to 99·8 % for breakfast, from 72·4 % to 100 % for lunch and from 89·2 % to 100 % for dinner[11]. The corresponding value in US adults from NHANES 2009–2014 was 85 %, 79 % and 93 %, respectively[13]. With regard to timing of eating, the mean clock time for the start of eating in US adults was 08.08 h for the first eating occasion, 08.11 h for breakfast, 12.43 h for lunch 18.24 h for dinner and 20.18 for the last eating occasion[13]. Compared with US population, our Japanese participants tended to consume earlier meals at earlier time (mean: 07.31 h for the first eating occasion, 07.25 h for breakfast and 12.32 h for lunch) but later meals at later time (19.19 h for dinner and 19.57 h for the last eating occasion). Consequently, the length of eating period was longer in the present Japanese (mean: 13.00 h) than the US adults (mean: 12.20 h)[13]. Nevertheless, time between eating occasions was longer in the present Japanese (mean: 3.40 h) than the US adults (mean: 2.50 h)[13], mainly because of lower eating (snack) frequency in the former.
In this study, snacking after dinner was observed in only 33 % of total dietary recording days, which is a little more frequent compared with Mediterranean countries (21 % to 33 %) but much less frequent than other Western countries (49 % to 87 %)[11,13]. In a small study based on 16-d dietary record, the Japanese eating pattern was, on average, characterised by small snacks (11 % of total energy intake), as well as relatively large three main meals (percentage of total energy intake: 21 % for breakfast, 32 % for lunch and 40 % for dinner)[32]. A similar eating pattern (three main meals, with infrequent snacks particularly at night) was also observed in a representative sample of Taiwanese adults[33]. It would be of interest to clarify if this eating pattern is prevalent in East Asian countries with similar social and cultural backgrounds.
For time spent on eating, we are unaware of any previous studies which can be compared with the present findings. In the present population, a longer time was spent consuming later meals (mean: 19 min for breakfast, 24 min for lunch and 35 min for dinner), which is consistent with a previous observation in Japanese that a larger amount of energy was consumed in later meals (mean: 23 %, 30 % and 40 %, respectively)[34]. We are also unaware of previous studies investigating variability of eating patterns. In this population, it seemed that variability of eating patterns related to meals was relatively small compared with those for snacks.
Taken together, eating patterns of Japanese adults may be characterised by a relatively stable nature of frequency, timing and time spent with regard to meals (breakfast, lunch and dinner) as well as snacks which are less frequently consumed but exhibit greater variability in both timing and time spent. It seems that these are somewhat in common in eating patterns in Mediterranean regions characterised by less eating frequency[12] and later consumption of meals and snacks[18]. As these characteristics may be favourably associated with health outcomes beyond food selection and nutrient composition[9], further studies on the association of eating patterns with food and nutrient intake, diet quality and health outcomes would merit more exploration.
Eating patterns in children as well as association between eating patterns and basic characteristics have been poorly investigated. In this study, eating patterns in children were on average comparable with those in adults, which may be reasonable given that adults are generally responsible for children’s diet. For the associations of eating patterns with sex and age, the most consistent finding was that older adults and younger children have more stable and regular eating patterns with regard to frequency, timing and time spent on eating compared with younger adults and older children, respectively. This may reflect busy lifestyle in younger adults and older children. In any case, further studies on these topics are warranted.
The advantages of the present study include the use of actual eating behaviour data based on food diary with a large number of recording days (8 d) collected throughout a year, use of a wide range of eating pattern variables with clear definitions and a nationwide large sample. However, there are also several limitations. First, although the sampling was conducted so that regional difference in population proportion is reflected, the present population is not a nationally representative sample of general Japanese but rather volunteers. Given the burden required for dietary recording, it is conceivable that the participants were more representative of the health conscious nature. Nevertheless, distribution of annual household income in the present population was similar to that in a national representative sample (45·0 %, 26·9 % and 28·0 % for <4, ≥4 to <7 and ≥7 million Japanese yen, respectively, in all households; 19·3 %, 36·2 % and 44·5 %, respectively, in households with a child/children)[35], although education level in the present adult population was somewhat high compared with a national representative sample (54·6 % for junior high school or high school, 20·8 % for college or vocational school and 24·6 % for university or higher)[36]. Further, mean (sd) values of body height, weight and BMI in our adult participants were also similar to those in a national representative sample aged ≥ 20 years (men: 167·6 (7·0) cm, 67·0 (11·5) kg and 23·8 (3·4) kg/m2, respectively; women: 154·1 (6·9) cm, 53·6 (9·4) kg and 22·6 (3·7) kg/m2, respectively)[37]. Thus, there may be no strong reason for considering that the present participants largely differ from general Japanese population.
Second, while all the dietary data were derived from dietary record, nature and extent of measurement error of self-reported (parent-reported) information on eating patterns are largely unknown. However, given that there are no objective markers of eating patterns examined here, we had no choice but relying on self-report on eating patterns. In this regard, we believe that dietary record is an optimal solution because we are able to collect a wide range of information (e.g. time) based on actual eating behaviours without relying on memory. Third, there is currently no consensus about what constitutes an eating occasion, a meal, a breakfast, a lunch, a dinner and a snack[12,16,17]. Although we used a widely used definition of eating occasions[11] as well as meals and snacks(11–13) to maximise comparability between studies, the present findings should be interpreted with these caveats in mind, and different results may be obtained if different definitions were applied. Finally, by design, dietary recording was conducted avoiding overnight working days (as well as days before and after these days). For overnight workers, dietary data might not fully represent their usual eating patterns because of a lack of data on overnight working days. Thus, further studies are needed to investigate eating patterns in overnight workers.
In conclusion, we provided comprehensive pictures of a range of eating pattern variables in a nationwide sample of Japanese. The major findings are as follows: meal skipping is quite rare in Japan; compared with Western populations, Japanese have on average a lower eating frequency (because of lower snack frequency); clock time for the start of breakfast and lunch is relatively early while that for lunch is relatively late; time spent on eating meals gradually increases during the day; variability of eating patterns is relatively small except for snacking and is smaller in older adults and younger children compared with younger adults and older children, respectively. The present findings serve as both a reference and an indication for future research on patterning of eating behaviours. The next step is to investigate associations of a variety of eating pattern variables with food and nutrient intake, diet quality and health outcomes.
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|
---
title: Are dietary patterns in early childhood associated with alcohol consumption
at the age of 17 years? Analysis of data from the Avon Longitudinal Study of Parents
and Children (ALSPAC) prospective cohort study
authors:
- Katherine Yorke
- Kate Northstone
- Louise R Jones
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991802
doi: 10.1017/S1368980021004183
license: CC BY 4.0
---
# Are dietary patterns in early childhood associated with alcohol consumption at the age of 17 years? Analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort study
## Body
Alcohol use is a leading cause of mortality and morbidity worldwide(1–3). The 2016 Global Burden of Disease identified alcohol as the leading risk factor in male and female deaths aged 15–49 years in 2016, and the seventh leading risk factor for all ages for deaths and disability-adjusted life years[1]. Alcohol use also places a high burden on healthcare systems, costing the National Health Service (NHS) in the UK an estimated £3·5 billion per year[4,5]. Alcohol consumption most commonly begins in adolescence in countries with a high prevalence of alcohol use[6,7]. A recent study in the UK identified that 90 % of young people within their study population had their first taste of alcohol by the age of 17 years[6]. In addition, it has been shown that young people who binge drink in adolescence are more likely to report binge drinking as young adults[8] and to be exposed to greater alcohol harm as they grow older[7,9]. Understanding the factors that contribute to higher alcohol consumption in the adolescent population is a key part of longer-term strategies for reducing population level alcohol harm in adults[8]. Identifying sensitive periods where early intervention may have an impact is important for the development of appropriate interventions both in childhood and in adolescence[8,10]. There is no single contributory factor to a young person’s propensity to over-consume alcohol[7]. However, each of the factors that do contribute merit attention as they could prove to be influential in identifying points for intervention.
There is evidence to suggest that sugar and alcohol addiction may be related[11]. Studies in animals have identified that higher consumption of sugar, and possibly fat, may mimic some properties of addictive substances[12,13]. In particular, binging of sugar and fat becomes more pronounced in rodents the longer that they are exposed to increased levels of sugar and fat (i.e. levels in excess of what would normally be fed in a laboratory environment) on a regular basis[13]. Animal studies have also shown that exposing rats to cycles of increased sugar in the diet followed by complete withdrawal of sugar causes a similar neurochemical response to opiate withdrawal[12,14], suggesting that sugar may be addictive. Understanding how this research may apply to humans is complex. Firstly, if certain foodstuffs were found to be addictive, whether a human becomes addicted to them is likely to be multifactorial[10,15]. In addition, linking this to other addictive substances or behaviours, such as patterns of alcohol use or addiction, adds further complexity and identifying causality is very challenging. Some addictive behaviours have been studied alongside diet and, for example, it has been identified that people with high scores on the Yale–Brown Obsessive Compulsive Scale – modified for Pathological Gambling (YPG-YBOCS) – also had higher total fat intake than others included in the study[16]. Biological children of parents with alcohol dependence are more likely to over-consume sugar, which indicates an association in the opposite direction[12].
Recent research has identified that dietary intake in childhood may be a contributing factor to alcohol use in adolescence. A 2018 study by Mehlig et al. [ 10] using data, the Identification and prevention of Dietary-and lifestyle-induced health Effects in Children and infantS (IDEFICS)[10], showed that children with a high propensity to consume sugar and fat were at 2·46 times at greater risk (RR: 2·46; (95 % CI 1·47, 4·12)) of alcohol consumption in adolescence compared to those with low propensity, after adjustment for confounders[10].
The literature on children’s dietary intake and later alcohol consumption is limited. To contribute to research in this area, we used data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a prospective UK cohort study. Given the fact that nutrients are not consumed in isolation[17], we wanted to examine the association between dietary patterns, looking at the diet as a whole in early childhood with later alcohol consumption. The aim of the study was therefore to examine the associations between dietary patterns in early childhood and alcohol intake and harmful behaviour at the age of 17 years. In addition to patterns, we specifically identified sugar as a factor of interest[10]. We hypothesised that children following a dietary pattern high in ‘processed’ food or sugar would be more likely to develop harmful alcohol behaviours in adolescence and more likely to consume alcohol at a higher frequency than those children adhering to a dietary pattern deemed ‘healthy’ or ‘traditional’ or having low sugar consumption.
## Abstract
### Objective:
To examine the relationship between a posteriori dietary patterns in early childhood and alcohol consumption in adolescence.
### Design:
Data were obtained from the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort study. Dietary information was obtained using FFQ at the age of 3 and 7 years. The association between dietary patterns, derived using principal components analysis and the Alcohol Use Disorders Identification Test (AUDIT) scores (to assess harmful intake) and frequency of alcohol consumption at the age of 17 years were examined. Secondary analysis considered sugar intake as a percentage of total energy intake.
### Setting:
Women who gave birth between 1 April 1991 and 31 December 1992 in the *Avon area* in southwest England were eligible for the ALSPAC cohort study.
### Participants:
Totally, 14 541 pregnancies were enrolled in ALSPAC during its initial recruitment phase. For this analysis, complete data were available for between 3148 and 3520 participants.
### Results:
Adherence to the ‘healthy’ dietary pattern at both 3 and 7 years of age was positively associated with consuming more than one alcoholic drink per week at 17 years of age, whilst adherence to the ‘traditional’ dietary pattern at both ages was protective of harmful alcohol intake at 17 years of age. Sugar intake was not associated with either alcohol outcome after adjustment for ethnicity, maternal level of education, parental social class and maternal AUDIT score.
### Conclusions:
For the population studied, changes to diet in early childhood are unlikely to have an impact on harmful alcohol use in adolescence given the lack of consistency across the results.
## Participants
Pregnant women resident in Avon, UK, with expected dates of delivery 1 April 1991 to 31 December 1992 were invited to take part in ALSPAC. The initial number of pregnancies enrolled was 14 541. Of these, there were 14 062 live births and 13 988 children alive at 1 year of age[18,19]. Data were primarily collected using self-completed questionnaires which were administered during pregnancy and at set ages once the child was born[20,21]. The ALSPAC participants included in this study were those who had dietary intake data available at both 38 months and 81 months, as well as alcohol use data at the age of 17 years. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Please note that the ALSPAC study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool: http://www.bristol.ac.uk/alspac/researchers/our-data/.
## Measurement of exposures
Dietary intake was measured using a FFQ when the children were aged 38 months and 81 months. The main carer was asked to provide information on how regularly their child consumed a wide range of items, including everyday basic foods as well as snacks and drinks (thirty-four items at 38 months and four-one items at 81 months). The FFQ gave the following options for response: (i) never or rarely; (ii) once in 2 weeks; (iii) 1–3 times a week; (iv) 4–7 times a week or (v) more than once a day[20]. Portion sizes were not asked about. For everyday items, such as milk and bread, more detailed questions were asked, for example, the number of slices of bread per d and the type of bread[20,22]. Dietary patterns were obtained using principal component analysis (PCA) at each time point(20–22). In brief, frequency of consumption options were converted to times per week as follows: (i) 0; (ii) 0·5; (iii) 2; (iv) 5·5 and (v) 10 times per week. All items were standardised by subtracting the mean and dividing by the standard deviation for each variable. The data were then entered into a PCA with a Varimax rotation[23,24], and the number of components that best represented the data were chosen using a scree plot. A score for each child was calculated for each component identified at each time point, each score has a mean of 0 and a standard deviation of 1 in the population in which it was derived and a higher score indicates a higher adherence to that dietary pattern.
Non-milk extrinsic sugar (NMES) (free sugars in the diet not including milk) intake was estimated from the FFQ as grams per week based on the fifth edition of McCance & Widdowson’s The Composition of Foods[25]. The estimated intake was adjusted to account for overall energy intake for each participant and is presented as the percentage of overall energy intake. We have previously shown that estimated sugar intake was strongly correlated with the ‘processed’ pattern at 3 (previously labelled ‘junk’) and 7 years of age[22] ($r = 0$·475 and 0·637, respectively).
## Measurement of outcomes
There were two outcome measures used in the study. Alcohol consumption was measured at the age of 17 years using a computerised questionnaire, which was completed by the young person in a clinic setting. The questionnaire included the Alcohol Use Disorders Identification Test (AUDIT)[26] and AUDIT score was used as the first outcome measure. Clinically, a score of 8 or more in the AUDIT test is considered to indicate an increased risk of harm from alcohol[26,27], and we therefore chose to use this dichotomy.
The second outcome was a measure of the frequency of alcohol consumption and was based on responses to a question that asked how often the young person had a drink containing alcohol. The options were never, monthly or less, 2 or 4 times a month, and 2 or 3 times a week or 4 or more times a week. We collapsed the five categories into ‘at least weekly’ (‘2 or 3 times a week’ and ‘4 or more times a week)’ compared with less than weekly: (‘never’, ‘monthly or less’ and ‘2 or 4 times a month’) in order to facilitate comparison with the IDEFICS study[10].
## Statistical methods
STATA v. 15.1 was used to perform logistic regression to assess the relationship with each of the exposure variables and both outcomes. OR and 95 % CI are presented. The exposure variables were categorised into quintiles due to our interest in the extremes of each dietary pattern. This also enhanced interpretation of any associations (i.e. making comparisons to the lowest quintile rather than a unit-less continuous variable).
A Direct Acyclic Graph (DAG) diagram was used to identify potential confounders from the available data. Relevant literature was consulted while developing the DAG to inform the decision on which variables could be potential confounders and should be included. For example, alcohol harm[28] and poor nutrition[29] are more prevalent in deprived communities and therefore social class was included as a potential confounding variable. Based on this approach, four confounding factors were included in the adjusted analysis which were the child’s ethnicity, mother’s highest educational qualification, household social class and the mother’s AUDIT score. There is evidence that there is a difference in the reporting of levels of alcohol consumption between girls and boys, and therefore an interaction test for gender was completed[10,30].
Multiple imputation by chained equation was used to impute missing data with the use of the ice command in STATA[31]. Twenty-five datasets were generated and ten switching procedures were undertaken. The results can be seen in Supplemental Tables A–D. The following variables were used to impute all outcomes, main predictors, and other variables included in the adjusted analyses plus the Family Adversity Index score, which is comprised of early parenthood, housing (adequacy, basic living facilities (e.g. hot water) and defects), financial difficulties, partner (present/absent), relationship with partner (affection/cruelty/support), family size, major family problems (child in care, not with natural mother or on at risk register), maternal depression/anxiety, substance abuse and crime (trouble with police or convictions). The analyses of associations between dietary patterns and sugar intake with AUDIT and number of drinks consumed were then repeated with the imputed data.
## Results
PCA was completed on all participants with dietary data at each time point (n 10139[20] and n 8286[21] at 38 and 81 months, respectively). AUDIT score data were available for 4148 at the age of 17 years. Scores ranged from 0 to 40 with a median of 6 and a mean of 7 (sd 4·8). Alcohol consumption data were available for 3969 young people at the age of 17 years. Data on both dietary intake and alcohol consumption were available for between 3148 and 3520 of the original cohort in the unadjusted analysis.
PCA identified four major dietary components from the FFQ at 38 months, which accounted for 23·5 % of the variation within the sample[20]. Each child was given a score for the four components to indicate their predominant dietary pattern or patterns[20]. The four components identified were ‘processed’ (note that in previous papers this was referred to as ‘junk’, high positive loadings for snack foods and foods high in fat such as crisps, sweets, biscuits, chocolate sausages, burgers, chips and takeaway foods), ‘healthy’ (high positive loadings for vegetables, fruit, rice, pasta, pulses and meat substitutes), ‘traditional’ (high positive loadings for meats, poultry, potatoes and vegetables) and ‘snack’ (high positive loadings for finger foods such as bread, fruit, biscuits, crisps and cheese). Analysis at 81 months identified three dietary patterns, ‘processed’, ‘healthy’ and ‘traditional’, which had very similar loadings for foods as the 3-year patterns and explained 18·2 % of variation within the sample[21]. The data available on NMES intake was adjusted to account for overall energy intake for each participant and is presented as a percentage of total energy intake. The percentages for NMES ranged from 0·6 % to 39·8 % at 38 months, and 1·1 % to 40·3 % at 81 months.
Associations were evident between each of the confounders considered and at least one of the outcomes (see Table 1). An AUDIT score ≥ 8 was more likely in children of White ethnicity and those whose mothers also reported an AUDIT score ≥ 8. Adolescents who reported consuming more than one drink per week were more likely to have lower educated mothers, be from White backgrounds and have mothers with a high AUDIT score.
Table 1Association between dietary pattern scores and confounders with outcome measures; n (%) for categorial variables, mean (sd) for continuous variablesAUDIT scoreFrequency of consumption< 860·1 %≥ 839·9 % P value* ≤1 drink/week>1 drink/week P value* n % n % n % n %Maternal education level High† (52·2 %)119860·7 %77639·3 %0·535146077·1 %43322·9 %< 0·001 Low‡ (47·8 %)108059·7 %72940·3 %123671·5 %49228·5 %Household socio-economic status High§ (79·4 %)174960·6 %113939·4 %0·810203573·7 %72526·3 %0·010 Low‖ (20·6 %)45060·1 %29939·9 %56678·4 %15621·6 %Ethnicity White (97·8 %)220459·8 %148340·2 %0·0004262974·3 %90925·7 %0·013 Non-White (2·1 %)6475·3 %2124·7 %6082·2 %1317·8 %Maternal AUDIT score < 8 (53·0 %)73466·1 %37333·9 %< 0·00183078·9 %22221·1 %< 0·001 ≥ 8 (47·0 %)52252·946447·1 %65067·4 %31532·6 %Gender Male (43·9 %)106258·3 %76041·7 %0·036121269·5 %53330·5 %< 0·001 Female (56·1 %)143061·5 %89538·5 %174578·5 %47821·5 %Dietary pattern scores at 3 years of age Processed Mean−0·24−0·160·023−0·18−0·270·009 sd 0·900·920·920·89 Healthy Mean0·050·090·2530·030·17< 0·001 sd 0·981·010·971·03 Traditional Mean0·03−0·020·1610·010·010·946 sd 1·001·021·001·01 Snacks Mean0·010·100·9860·090·120·451 sd 0·940·980·950·97Dietary pattern scores at 7 years of age Processed Mean−0·16−0·120·336−0·14−0·160·494 sd 0·950·90-0·170·92 Healthy Mean0·040·080·2910·010·19<0·001 sd 0·991·010·971·07 Traditional Mean0·09−0·030·0750·17−0·010·484 sd 0·960·950·960·95 Sugar % overall energy intake at 3 years of age Mean14·1614·430·05314·3114·170·344 sd 3·953·973·973·73 Sugar % overall energy intake at 7 years of age Mean14·0214·250·07014·1414·170·793 sd 3·543·403·543·33AUDIT, Alcohol Use Disorders Identification Test.* P-values from χ 2 tests for categorical variables and t-tests for continuous variables.†Degree or A levels ((optional) exams taken at the age of 18 years).‡GCSE/O levels (compulsory exams taken at the age of 16 years) or vocational qualifications.§Classes I, II, III (non-manual): professional, managerial/technical or skilled non-manual occupations.‖Classes III (manual), IV, V: skilled manual, partly skilled or unskilled occupations.
There were no differences in mean dietary pattern scores at 3 or 7 years of age with AUDIT score. However, children consuming more than one alcoholic drink per week had lower mean scores for the ‘processed’ dietary pattern at 3 and higher mean scores for the ‘healthy’ dietary pattern at both 3 and 7. Sugar intake was slightly higher in those children with an AUDIT score ≥ 8 (although this was < 0·5 %), but there was no difference in mean sugar intake according to frequency of alcohol consumption.
Supplemental Table A outlines the baseline differences in participants who did and did not provide alcohol consumption data at the age of 17 years. Mean maternal age at delivery was 1·3 years higher on average in mothers of participants who provided alcohol data. Participants who provided alcohol data were also more likely to have mothers with a higher level of education and to be from a higher social class, as well as less likely to have mothers with an AUDIT score of ≥ 8. Following multiple imputation, the descriptive characteristics of the participants who did and did not have alcohol data were similar to the non-imputed data (see online Supplemental Table A).
## Dietary patterns and harmful alcohol consumption
Table 2 presents the associations between dietary pattern scores at 3 and 7 years of age and AUDIT scores ≥ 8 (i.e. harmful alcohol consumption). Children in the highest quintile for the 3-year ‘processed’ pattern were more likely to have harmful alcohol drinking compared to those in the lowest quintile (unadjusted OR: 1·41 (95 % CI 1·13, 1·77)). This association was attenuated after adjustment (OR: 1·35 (95 % CI 0·96, 1·90)). A linear association was also evident with an unadjusted OR of 1·09 (95 % CI 1·00, 1·17), but again this was attenuated after adjustment (OR: 1·06 (95 % CI 0·94, 1·19)). There were no associations evident between the ‘processed’ pattern at 7 years and harmful alcohol drinking. For the ‘healthy’ dietary pattern, there was a linear adjusted effect at the age of 7 years (OR: 1·10 (95 % CI 1·00, 1·21)) but not at 3 years of age. The ‘traditional’ pattern showed a protective linear adjusted effect at the age of 3 years (OR; 0·90 (95 % CI 0·82, 1·00)) and this was stronger at the age of 7 years (OR: 0·87 (95 % CI 0·70, 0·96)). In addition, being in the highest quintile of for the ‘traditional’ pattern was associated with a reduced risk of harmful alcohol consumption (OR of 0·71 (95 % CI 0·53, 0·96) and 0·64 (95 % CI 0·47, 0·87) at the age of 3 and 7 years, respectively). The 3-year ‘snack’ pattern was not associated with harmful alcohol consumption. In the imputed analyses presented in Supplemental Table B, similar patterns of association were seen across all dietary patterns, although associations tended to be weaker. This was particularly the case for the ‘traditional’ pattern at the age of 7 years (adjusted OR: 0·94 (95 % CI 0·88, 1·00).
Table 2Association between dietary patterns at the age of 3 and 7 years, and AUDIT score of 8 or greater at the age of 17 years; associations with quintiles of dietary pattern score and continuous pattern scoreExposureUnadjusted OR95 % CI P valueAdjusted OR95 % CI* P value‘Processed’ diet pattern – 3 years of age Number of cases/total$\frac{1393}{3520764}$/1935 Quintile 1 (baseline – lowest ‘processed’ pattern)11 Quintile 21·321·08, 1·600·0061·260·98, 1·620·077 Quintile 31·210·98, 1·480·0701·120·85, 1·460·431 Quintile 41·100·89, 1·360·3990·960·71, 1·300·776 Quintile 5 (highest ‘processed’ pattern)1·411·13, 1·770·0031·350·96, 1·900·083 Linear effect1·091·00, 1·170·0231·060·94, 1·190·353‘Processed’ diet pattern – 7 years of age Number of cases/total$\frac{1323}{3311759}$/1905 Quintile 1 (baseline – lowest ‘processed’ pattern)11 Quintile 21·150·93, 1·410·1931·170·90, 1·530·237 Quintile 31·050·85, 1·300·6670·920·69, 1·220·541 Quintile 41·311·06, 1·620·0131·250·94, 1·670·131 Quintile 5 (highest ‘processed’ pattern)1·070·85, 1·350·5641·090·79, 1·500·618 Linear effect1·040·96, 1·120·3361·030·92, 1·150·599‘Healthy’ diet pattern – 3 years of ageNumber of cases/total$\frac{1393}{3520764}$/1935 Quintile 1 (baseline – lowest ‘healthy’ pattern)11 Quintile 21·130·90, 1·410·2961·260·92, 1·730·151 Quintile 31·070·86, 1·340·5261·270·94, 1·730·122 Quintile 41·080·87, 1·350·4751·130·83, 1·550·435 Quintile 5 (highest ‘healthy’ pattern)1·180·95, 1·470·1291·320·97, 1·800·079 Linear effect1·040·97, 1·110·2531·080·98, 1·190·143‘Healthy’ diet pattern – 7 years of age Number of cases/total$\frac{1323}{3311759}$/1905Quintile 1 (baseline – lowest ‘healthy’ pattern)11 Quintile 20·980·78, 1·230·8700·990·72, 1·350·930 Quintile 30·740·59, 0·930·0100·790·58, 1·080·142 Quintile 40·850·68, 1·070·1650·790·58, 1·080·144 Quintile 5 (highest ‘healthy’ pattern)1·090·87, 1·360·4521·140·83, 1·550·420 Linear effect1·040·97, 1·110·2911·101·00, 1·210·063‘Traditional’ diet pattern – 3 years of age Number of cases/total$\frac{1393}{3520764}$/1935 Quintile 1 (baseline – lowest ‘traditional’ pattern)11 Quintile 20·990·80, 1·220·8920·780·59, 1·050·100 Quintile 30·950·77, 1·180·6420·800·59, 1·070·137 Quintile 40·870·70, 1·080·2050·730·54, 0·970·031 Quintile 5 (highest ‘traditional’ pattern)0·890·72, 1·100·2900·710·53, 0·960·024 Linear effect0·950·89, 1·010·1610·900·82, 1·000·034‘Traditional’ diet pattern – 7 years of ageNumber of cases/total$\frac{1323}{3311759}$/1905 Quintile 1 (baseline – lowest ‘traditional’ pattern)11 Quintile 20·870·70, 1·090·2230·780·58, 1·050·105 Quintile 30·960·77, 1·200·7170·830·62, 1·120·220 Quintile 40·920·74, 1·150·4480·840·63, 1·130·254 Quintile 5 (highest ‘traditional’ pattern)0·750·60, 0·940·0110·640·47, 0·870·005 Linear effect0·940·87, 1·010·0750·870·70, 0·960·007‘Snack’ diet pattern – 3 years of age Number of cases/total$\frac{1393}{3520764}$/1935 Quintile 1 (baseline – lowest ‘traditional’ pattern)11 Quintile 20·780·62, 0·990·0370·740·53, 1·030·073 Quintile 30·810·64, 1·010·0650·790·58, 1·090·154 Quintile 40·840·67, 1·050·1230·810·59, 1·110·190 Quintile 5 (highest ‘traditional’ pattern)0·910·73, 1·140·4070·870·64, 1·190·390 Linear effect0·990·93, 1·070·9860·970·88, 1·070·572AUDIT, Alcohol Use Disorders Identification Test.*Data adjusted for ethnicity, maternal level of education, parental social class and maternal AUDIT score.
## Dietary patterns and frequent alcohol consumption
Associations between dietary pattern scores and whether participants consumed more than one alcoholic drink per week at the age of 17 years are presented in Table 3. Being in the highest quintile of the ‘processed’ pattern score at the age of 7 years was protective (OR: 0·70 (95 % CI 0·54, 0·91)) compared to being in the lowest quintile; however, this association was lost after adjustment for confounders. Similarly, the unadjusted linear association with the ‘processed’ pattern scores pre-adjustment was no longer evident after adjustment. The 7-year ‘processed’ pattern was not associated with consuming more than one alcoholic drink per week at the age of 17 years. The ‘healthy’ pattern at both 3 and 7 years of age was linearly associated with this outcome after adjustment, although the effects were stronger at 7 years of age compared to 3 years of age (OR: 1·21 and 1·11, respectively). There were no associations between the ‘traditional’ dietary pattern at either age or the ‘snacks’ pattern at the age of 3 years and later alcohol consumption at the age of 17 years. All these patterns of association were replicated in the imputed analyses (see online Supplemental Table C). There was evidence of an interaction between gender and the unadjusted 3-year ‘processed’ pattern and the adjusted 7-year ‘traditional’ pattern. Additional stratified analyses were therefore carried out (see online Supplemental Table E). This indicated that boys with the highest ‘processed’ dietary pattern quintile were 0·55 (95 % CI 0·37, 0·80) times less likely to consume alcohol, whilst girls in the same quintile had no association with an OR of 0·92 (95 % CI 0·64, 1·33). No association was shown for girls or boys separately for the ‘traditional’ pattern.
Table 3Association between dietary patterns at ages 3 and 7 years, and consumption of more than one drink per week at the age of 17 years; associations with quintiles of dietary pattern score and continuous pattern scoreExposureUnadjusted OR95 % CI P valueAdjusted OR95 % CI P value‘Processed’ diet pattern – 3 years of age Number of cases/total$\frac{860}{3371492}$/1856 Quintile 1 (baseline – lowest ‘processed’ pattern)11 Quintile 20·900·72, 1·120·3400·830·62, 1·100·199 Quintile 30·970·78. 1·220·8051·000·75, 1·350·976 Quintile 40·740·58, 0·950·0180·720·50, 1·020·061 Quintile 5 (highest ‘processed’ pattern)0·700·54, 0·910·0090·900·61, 1·320·597 Linear effect0·890·82, 0·970·0090·940·82, 1·080·376‘Processed’ diet pattern – 7 years of age Number of cases/total$\frac{809}{3175485}$/1832 Quintile 1 (baseline – lowest ‘processed’ pattern)11 Quintile 20·940·75, 1·190·6210·830·61, 1·120·221 Quintile 30·920·72, 1·170·5080·860·62, 1·170·332 Quintile 40·990·78, 1·270·9651·080·79, 1·480·638 Quintile 5 (highest ‘processed’ pattern)0·800·61, 1·050·1020·760·52, 1·100·147 Linear effect0·970·89, 1·060·4941·000·89, 1·140·960‘Healthy’ diet pattern – 3 years of age Number of cases/total$\frac{860}{3371492}$/1856 Quintile 1 (baseline – lowest ‘healthy’ pattern)11 Quintile 20·870·67, 1·140·3080·800·55, 1·150·221 Quintile 31·090·84, 1·410·5200·830·59, 1·180·311 Quintile 41·210·94, 1·560·1371·050·75, 1·490·768 Quintile 5 (highest ‘healthy’ pattern)1·371·07, 1·750·0131·130·80, 1·580·494 Linear effect1·151·06, 1·23<0·0011·111·00, 1·240·056‘Healthy’ diet pattern – 7 years of age Number of cases/total$\frac{809}{3175485}$/1832 Quintile 1 (baseline – lowest ‘healthy’ pattern)11 Quintile 21·030·78, 1·340·8540·960·67, 1·370·813 Quintile 30·820·62, 1·080·1560·660·45, 0·960·028 Quintile 41·130·87, 1·470·3520·950·66, 1·350·763 Quintile 5 (highest ‘healthy’ pattern)1·521·18, 1·960·0011·350·95, 1·910·093 Linear effect1·191·10, 1·29<0·0011·211·09, 1·350·001‘Traditional’ diet pattern – 3 years of age Number of cases/total$\frac{860}{3371492}$/1856 Quintile 1 (baseline – lowest ‘traditional’ pattern)11 Quintile 21·210·95, 1·570·1201·100·79, 1·520·576 Quintile 30·970·75, 1·250·8110·950·68, 1·330·776 Quintile 41·050·82, 1·340·7180·970·70, 1·350·874 Quintile 5 (highest ‘traditional’ pattern)1·020·80, 1·300·8840·880·63, 1·240·469 Linear effect1·000·92, 1·080·9460·950·85, 1·050·312‘Traditional’ diet pattern – 7 years of age Number of cases/total$\frac{809}{3175485}$/1832 Quintile 1 (baseline – lowest ‘traditional’ pattern)11 Quintile 20·890·69, 1·150·3840·940·67, 1·320·736 Quintile 31·000·78, 1·290·9791·020·73, 1·430·899 Quintile 40·930·72, 1·200·5980·970·70, 1·360·880 Quintile 5 (highest ‘traditional’ pattern)0·890·69, 1·150·3750·890·63, 1·260·506 Linear effect0·970·89, 1·060·4840·980·87, 1·090·663‘Snack’ diet pattern – 3 years of age Number of cases/total$\frac{1393}{3520764}$/1935 Quintile 1 (baseline – lowest ‘traditional’ pattern)11 Quintile 20·850·65, 1·100·2050·770·53, 1·120·169 Quintile 30·830·64, 1·080·1600·880·62, 1·260·481 Quintile 40·900·70, 1·160·4240·820·58, 1·170·275 Quintile 5 (highest ‘traditional’ pattern)1·040·81, 1·340·7651·020·71, 1·450·928 Linear effect1·030·95, 1·110·4501·010·91, 1·140·804
## Sugar consumption and alcohol consumption
There was an increased risk of harmful alcohol consumption for those children in the highest quintile of sugar intake at the age of 3 years, but the OR was attenuated after adjustment (Table 4), and there were no associations with sugar intake at the age of 7 years. There were no associations between sugar consumption at either age and having more than one alcoholic drink per week at 17 years of age. In the imputed analysis (see online Supplemental Table E), a similar pattern was seen.
Table 4Association between percentage of overall energy intake as NMES at the age of 3 and 7 years, and alcohol consumption at the age of 17 years; associations with quintiles of NMES and continuous NMES intakeOutcome 1 – AUDIT score of 8 or greater at the age of 17 yearsExposureUnadjusted OR95 % CI P valueAdjusted OR95 % CI P valueSugar % overall energy intake – 3 years of age Number of cases/total$\frac{1381}{3472784}$/1917 Quintile 1 (baseline – lowest sugar)11 Quintile 20·940·77, 1·160·5881·090·82, 1·440·566 Quintile 31·070·87, 1330·5021·080·81, 1·450·580 Quintile 41·010·82, 1·250·9141·050·78, 1·420·726 Quintile 5 (highest sugar)1·240·99, 1·540·0571·300·95, 1·780·105 Linear effect1·021·00, 1·030·0531·010·98, 1·040·309Sugar % overall energy intake – 7 years of age Number of cases/total$\frac{1975}{3288757}$/1898 Quintile 1 (baseline – lowest sugar)11 Quintile 21·210·97, 1·500·0841·090·83, 1·440·526 Quintile 31·331·07, 1·650·0091·250·94, 1·660·123 Quintile 41·240·99, 1·540·0560·990·74, 1·320·926 Quintile 5 (highest sugar)1·190·95, 1·500·1391·040·76, 1·420·792 Linear effect1·020·99, 1·040·0701·000·97, 1·030·927Outcome 2 – more than one drink per week; associations with quintiles of NMES and continuous NMES intakeSugar % overall energy intake – 3 years of age Number of cases/total$\frac{853}{3332491}$/1841 Quintile 1 (baseline – lowest sugar)11 Quintile 20·890·70, 1·130·3510·880·64, 1·200·417 Quintile 31·050·83, 1·330·6841·060·77, 1·460·730 Quintile 41·030·81, 1·310·8361·060·76, 1·470·720 Quintile 5 (highest sugar)0·830·64, 1·080·1701·020·71, 1·460·915 Linear effect0·990·97, 1·010·3441·010·98, 1·040·608Sugar % overall energy intake – 7 years of age Number of cases/total$\frac{804}{3148484}$/1824 Quintile 1 (baseline – lowest sugar)11 Quintile 21·060·83, 1·360·6180·970·71, 1·320·837 Quintile 31·060·83, 1·360·6560·910·66, 1·260·580 Quintile 41·090·85, 1·400·5030·970·70, 1·350·876 Quintile 5 (highest sugar)1·060·82, 1·380·6451·030·73, 1·460·853 Linear effect1·000·98, 1·030·7931·000·77, 1·040·831NMES, non-milk extrinsic sugar; AUDIT, Alcohol Use Disorders Identification Test.
## Discussion
In this study, we report an association between a ‘healthy’ dietary pattern score at both 3 and 7 years of age and consuming more than one alcohol drink per week at the age of 17 years. In addition, the ‘traditional’ dietary pattern at both ages was protective of harmful alcohol intake at the age of 17 years. However, we found no association between sugar intake and either measure of alcohol consumption after adjustment for ethnicity, maternal level of education, parental social class and maternal AUDIT score.
This study adds to a limited and new area of literature. Nutrition in childhood has been shown to impact on a wide range of later health and behavioural outcomes[32,33]. Given the negative impact that alcohol can have on health, exploration of factors that contribute to increased usage is important. It is reassuring that in this population, a poor diet in childhood (represented by adherence to the ‘processed’ pattern) is not associated with an increased risk of harmful drinking in early adulthood. The fact that adhering to a ‘healthy’ pattern in early childhood is associated with an increased likelihood of consuming more than one alcoholic drink a week may warrant further investigation. However, harmful levels of intake were not associated with this dietary pattern.
We are not aware of any other studies examining the association between dietary patterns in childhood and alcohol consumption in adolescence. However, other studies have identified associations between dietary patterns and alcohol consumption in adulthood which helped to inform our hypothesis. For example, a positive association was identified between heavier alcohol consumption during pregnancy and scoring highly on the ‘processed’ dietary pattern among mothers in the ALSPAC cohort[34]. The FinDrink project in Finland identified mixed results from a study of the relationship between alcohol consumption and dietary patterns[35], and they identified that moderate alcohol consumption (compared to non-drinkers) was identified with higher fish intake but also higher energy intake from total fats and monosaturated fats. A key conclusion from the FinDrink study was that further study into the relationship between alcohol consumption and dietary habits is needed.
The study by Mehlig et al. also informed our hypothesis, and specifically our interest in examining the relationship between NMES percentage intake and alcohol consumption. The results from the ALSPAC study are notably different to the IDEFICS study and could be due to multiple factors. Firstly, there are likely to be differences between the demographics, eating habits and alcohol use of the populations from different countries(12–14). Secondly, the ALSPAC data offered the opportunity to study a larger population than the IDEFICS study. Approximately 25·5 % of the adolescents were consuming at least one drink per week and 39·9 % had an AUDIT => 8, which provided a rich data source for identifying if any associations exist. This contrasts with the study undertaken by Mehlig et al. [ 10] which had 107 participants regularly consuming alcohol. This is a small sample size when considering that the results are spread across eight countries. A further strength of the ALSPAC dataset is that the study participants were 17 years of age. In the UK, the legal age for alcohol consumption is 18 years; however, by the age of 17 years, most adolescents have tried alcohol[6] and could have developed patterns of drinking which can be assessed using the AUDIT scoring system[36]. In IDEFICS, the adolescents were aged 11–16 years[10], so it is more unlikely that this cohort would be regularly consuming alcohol[6]. Reporting of alcohol intake may have been affected as the participants were underage. The use of AUDIT scores also strengthens our study. AUDIT is a widely used, validated tool[25], and the overall score offers a broad understanding of a person’s risk of alcohol harm. To add sensitivity to the study, the measure of alcohol frequency was included as an additional outcome variable. This additional measure also had more similarity to the outcome measure in the IDEFICS study, providing an opportunity to better consider our paper in the context of previous findings. Finally, it should be highlighted that the IDEFICS study concluded that both sugar and fat intake were associated with later alcohol consumption. High fat intake was shown to be independently associated with alcohol consumption, but high sugar intake was not[10]. Our findings support this lack of independent association with sugar; however, we did not find any evidence to suggest that increased ‘fat’ intake, using our ‘processed’ pattern as a proxy was associated with alcohol consumption.
As well as the strengths outlined above, this paper has a number of limitations. Some children were lost to follow-up, reducing the amount of data available at the later time points. However, the results of the imputation indicated that this did not bias our findings. On the whole, effect sizes in the imputed analyses were smaller than those in the complete case analysis. The loss of participants to follow-up reduces the external validity of the study, because the adolescents included were more likely to be from advantaged backgrounds, more likely to be from a White British background, more likely to have highly educated mothers and less likely to have mothers with an AUDIT => 8.
A further potential limitation is the reliability of self-reported alcohol data amongst adolescents[37,38]. Williams et al. [ 37] have shown that self-report of substance abuse by adolescents only had fair validity and recommended biochemical corroboration be routinely used for this age group, although Winters et al. [ 38] suggest that adolescents reliably self-report alcohol intake. The data collected on alcohol were part of a much wider clinic visit with many other measures being obtained. Whilst there is still a risk that participants may have under-reported their alcohol consumption, it is less likely in such a setting where multiple facets of an individual are being examined.
The external validity of the study may also be affected by the changes in children’s diets over the last 30 years. The dietary intake data used in the study were collected in the 1990s, and between data collection and the present day, childhood obesity has increased and some elements of childhood diet have changed in association with this[39]. It is likely that the broad patterns found in the PCA would remain, but some loadings and scores that contributed to the patterns may have strengthened, for example, to accommodate an increase in consumption of sugary drinks[40].
## Conclusion
In this large prospective cohort study, no association was found between sugar intake in childhood and alcohol consumption in later adolescence, which is in contradiction to previous research on this topic. However, we do report an association between adherence to a ‘healthy’ dietary pattern at both 3 and 7 years of age and consuming more than one drink per week and adherence to a ‘traditional’ dietary pattern intake at the age of 7 years being protective of harmful alcohol intake. We do not know enough from this study alone to know if adherence to a specific dietary pattern in early childhood would affect harm from alcohol. However, the study adds to a field of relatively limited literature and further research is required to elucidate the associations under study. For the population studied, it suggests that changes to diet in early childhood are unlikely to have an impact on harmful alcohol use in adolescence given the lack of consistency across the results.
## Acknowledgements:
The authors are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.
## Financial support:
The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and Kate Northstone will serve as guarantor for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf); This research was specifically funded by the Medical Research Council (Grant number: G$\frac{0800612}{86812}$).
## Conflict of interest:
There are no conflicts of interest.
## Authorship:
K.Y. (lead author; data analysis). K.N. (co-author; imputed data analysis). L.R.J. (co-author).
## Ethics of human subject participation:
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the ALSPAC Ethics and Law Committee. Written informed consent was obtained from all subjects. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Please note that the ALSPAC study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool: http://www.bristol.ac.uk/alspac/researchers/our-data/.
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|
---
title: 'Multilevel exploration of individual- and community-level factors contributing
to overweight and obesity among reproductive-aged women: a pooled analysis of Bangladesh
Demographic and Health Survey, 2004–2018'
authors:
- Benojir Ahammed
- Md. Alamgir Sarder
- Subarna Kundu
- Syed Afroz Keramat
- Khorshed Alam
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991804
doi: 10.1017/S1368980022001124
license: CC BY 4.0
---
# Multilevel exploration of individual- and community-level factors contributing to overweight and obesity among reproductive-aged women: a pooled analysis of Bangladesh Demographic and Health Survey, 2004–2018
## Body
Globally, the rising prevalence of overweight and obesity is a severe public health concern. Overweight and obesity are the result of imbalance between energy consumption and energy expansion. Excessive body weight is a risk factor for chronic diseases, such as hypertension, diabetics, heart diseases and cancer[1]. The number of obese women has increased from 69 million in 1975 to 390 million in 2016[2]. This problem severely affects reproductive-aged women because it harms their health and affects the health status of their offspring[3]. Women with overweight and obesity have a high risk of infertility and different pregnancy complications, including gestational diabetics, hypertension, haemorrhage and eclampsia[3,4]. Understanding the pattern of overweight and obesity and their determinants, especially among reproductive-aged women, is vital to secure the health of current and future generations. Consequently, the prevalence and determinants of overweight and obesity among reproductive-aged women are of particular interest to researchers and policy-makers. The rate of overweight and obesity is rapidly increasing in Bangladesh as a result of rapid urbanisation, economic growth, changes in dietary habits and lifestyle and modernisation[5]. The recent Bangladesh Demographic and Health Survey (BDHS) report showed that overweight and obesity among reproductive-aged women have increased from 12 % to 32 % between 2007 and 2017[6]. Unlike undernutrition, overweight or obesity is a kind of malnutrition that receive little attention in Bangladesh[7]. Because of the rising prevalence of overweight and obesity in Bangladesh, it is necessary to take this issue into consideration while developing health and nutritional policy. Several studies have attempted to discover the associated factors of overweight and obesity in different settings using a single survey, including Bangladesh(8–10). Individual factors associated with overweight and obesity have been discovered previously. However, the risk factors for overweight and obesity at the community level have received scant attention. A recent study looked at the prevalence and risk factors of overweight and obesity among Bangladeshi reproductive-aged women[11]. They determined the risk factors for underweight, overweight and obesity using pooled data from 1999 to 2014 and a multinomial logistic regression model. However, community-level factors were not considered in this study. Individual-level factors are deep setters in the community-level factors. Thus, disregarding community-level factors is a flaw of the previous research.
There is strong evidence that community factors, such as wealth status and literacy, have played a role in people being overweight and obese[7,12]. As a result, making inferences without considering individual- or community-level factors may result in erroneous information. This study aimed to investigate the individual- and community-level factors responsible for overweight and obesity among reproductive-aged women by conducting a multilevel analysis that considers the clustering and hierarchical structure of different survey years. Multilevel analysis has been utilised in a few research, although the results are outdated. The reason for this is that numerous changes in other socio-economic elements have happened over time, including media campaigns using radio, television and educational status. Bangladesh has raised literacy rate (58·6 % to 72·3 %) and per capita income in recent years[13]. Women who live in highly educated societies and have access to the media are more aware of their own health and well-being[7]. The reason for this is that the public has access to nutritional information through the media[14]. As a result, the literacy rate of women in the community, wealth position, media access and employment may all have an impact on nutritional status and individual-level variables. Using the most recent nationally representative data from Bangladesh, this study examined the patterns and prevalence of overweight and obesity among reproductive-aged women and evaluated its association with individual- and community-level factors.
## Abstract
### Objectives:
Overweight and obesity have been related to a variety of adverse health outcomes. Understanding the overweight and obesity epidemic in Bangladesh, particularly among reproductive-aged women, is critical for monitoring and designing effective control measures. The purpose of this study was to determine the prevalence of overweight and obesity in reproductive-aged women and to identify the risk factors of overweight and obesity.
### Design:
A total of 70 651 women were obtained from the five most recent and successive Bangladesh Demographic and Health Surveys (BDHS). The multilevel logistic regression model was used to explore the individual- and community-level factors of overweight and obesity.
### Setting:
Five most recent nationally representative household surveys across all regions.
### Participants:
Reproductive-aged (15–49 years) non-pregnant women.
### Results:
Approximately 35·2 % (95 % CI: 34·9–35·6 %) of women were either overweight or obese in Bangladesh. At the individual- and community-level, higher age (adjusted odds ratio (aOR) = 5·79, 95 % CI: 5·28–6·34), secondary or higher education (aOR = 1·69 [1·60–1·78]), relatively wealthiest households (aOR = 4·41 [4·10–4·74]), electronic media access (aOR = 1·32 [1·26–1·37]) and community high literacy (aOR = 1·10 [1·04–1·15]) of women were significantly positively associated with being overweight or obese. Whereas, rural residents (aOR = 0·79 [0·76–0·82]) from larger-sized households (aOR = 0·80 [0·73–0·87]) and have high community employment (aOR = 0·92 [0·88–0·97]) were negatively associated with the probability of being overweight or obese.
### Conclusion:
Individual- and community-level factors influenced the overweight and obesity of Bangladeshi reproductive-aged women. Interventions and a comprehensive public health plan aimed at identifying and addressing the growing burden of overweight and obesity should be a top focus.
## Data and sampling design
This study analysed data from five consecutive surveys undertaken by the BDHS in 2004, 2007, 2011, 2014 and 2017–2018. The Bangladesh Ministry of Health and Family Welfare and the National Institute of Population Research and Training conducted the survey. All BDHS employed a two-stage stratified cluster sampling procedure for selecting the sample. In the first stage, enumeration areas were randomly selected according to the probability proportional to the size of each enumeration area. In the second stage, the average number of households per enumeration area was chosen for each division to provide reliable sample estimates for the whole country. The surveys included 11 440, 10 996, 17 842, 17 863 and 20 250 married reproductive-aged women from five consecutive cross-sectional household surveys. Missing values on the outcome and explanatory variables and women who were pregnant or had given birth within the previous 2 months of survey visits were excluded. A total of 70 651 women were evaluated for the subsample analyses after the exclusion criteria were met. The detailed information on the sampling techniques, survey design, survey instruments, measuring system, data collections and quality control has been previously described(6,15–18).
## Outcome variables
Overweight and obesity status was considered as the outcome variable and measured by the BMI of reproductive-aged non-pregnant women. The BMI cut-off points recommended by the WHO have been used to measure Bangladeshi women’s overweight and obesity status[5,10,11]. According to the WHO, women are considered overweight when their BMI is 25·0 to 29·99 kg/m2, obese when their BMI is ≥ 30 kg/m2 and overweight/obese when their BMI is ≥ 25·0 kg/m2[19]. However, Bangladesh being an Asian country, this study used BMI cut-off point for Asians which is lower than the WHO criteria and categorised weight status into four groups as follows: underweight (< 18·5 kg/m2), healthy weight (18·5–22·9 kg/m2), overweight (23–27·5 kg/m2) and obese (>27·5 kg/m2)[20]. Finally, the primary outcome variable was formed by combining overweight and obesity for a woman with a BMI of ≥23 kg/m2.
## Independent variables
The following individual-level variables were finally chosen following previous studies[10,19,21]: women’s age (15–19, 20–29, 30–39 and 40–49 years), women’s education (no formal education, primary and secondary or higher), working status (not working and working), wealth index (poorest, poorer, middle, wealthier and wealthiest), religion (Muslim and non-Muslim), household size (1–2, 3–4 and 5+), number of children (0, 1–2 and 3+), electronic media access (no and yes) and marital status (married and widowed/divorced/separated).
Demographic and health survey data do not directly provide community-level characteristics except the place of residence (urban and rural)[19,21]. Therefore, other community-level factors were created by aggregating the individual-level factors inside their clusters. The distribution in each community is considered to develop community-level characteristics. This study used the mean or median as a cut-off point to create community-level factors. Current analyses also included four derived community-level variables, namely community poverty (low and high)[21], community women literacy (low and high), community electronic media access (low and high)[21] and community women employment (low and high). Community-level factors such as community poverty, community women literacy, community electronic media access and community women employment were derived from the individual-level factors, wealth index, women’s education, electronic media access and women’s working status, respectively. The detailed descriptions of the individual- and community-level factors are described in supplementary Table 1.
Table 1Individual- and community-level factors associated with overweight/obesity in reproductive-aged (15–49 years) women in Bangladesh from univariate analysisVariableTotalPrevalence P-value n %Overweight, %Obesity, %Overweight/obesity, %95 % CIOverall70 65110024·310·935·234·9, 35·6Individual-level factorsSurvey year< 0·001 200410 15614·413·84·718·517·7, 19·2 2007989814·016·76·423·122·3, 23·9 201116 05122·721·69·631·230·5, 32·0 201416 28423·028·211·739·939·1, 40·6 2017–201818 26425·933·217·250·449·7, 51·2Women’s age (in years)< 0·001 15–1967859·611·63·314·814·0, 15·7 20–2924 76935·122·98·631·530·9, 32·1 30–3922 14131·328·114·042·041·4, 42·7 40–4916 95624·026·913·640·539·8, 41·3Women’s education< 0·001 No education18 80126·617·96·624·523·9, 25·1 Primary21 25730·123·39·432·732·1, 33·3 Secondary and higher30 59343·329·414·944·343·7, 44·8Working status0·596 Not working48 65368·923·911·435·334·9, 35·7 Working21 99831·125·39·935·134·5, 35·8Wealth index< 0·001 Poorest12 50617·713·74·017·717·0, 18·4 Poorer13 03718·518·15·623·723·0, 24·4 Middle13 60519·323·67·931·430·7, 32·2 Wealthier14 63120·729·211·440·739·9, 41·5 Wealthiest16 87223·935·224·259·558·7, 60·2Religion< 0·001 Islam63 29189·624·410·935·334·9, 35·6 Others736010·424·010·934·933·9, 36·0Household size< 0·001 1–234014·827·311·438·637·0, 40·3 3–424 91935·325·611·937·536·9, 38·1 ≥ 542 33159·923·310·333·633·1, 34·0Number of living children< 0·001 ≤ 263449·019·58·628·026·9, 29·1 3–435 43950·225·611·537·136·6, 37·6 ≥ 528 86840·923·910·734·634·0, 35·1Electronic media access< 0·001 No31 55044·718·86·425·224·7, 25·7 Yes39 10155·329·014·743·743·2, 44·2Marital status< 0·001 Married65 61292·924·711·035·735·3, 36·0 Widowed/divorced/separated50397·119·49·929·328·0, 30·6Community-level factorsPlace of residence< 0·001 Urban25 37535·930·919·150·049·4, 50·6 Rural45 27664·122·07·929·929·5, 30·3Community poverty< 0·001 Low36 66751·926·513·039·539·0, 40·0 High33 98448·122·08·630·730·2, 31·1Community women literacy< 0·001 Low33 98148·122·29·331·631·1, 32·0 High36 67051·926·412·538·838·3, 39·3Community electronic media access< 0·001 Low33 24547·122·18·430·429·9, 30·9 High37 40652·926·313·139·438·9, 39·9Community women employment< 0·001 Low35 99751·024·211·335·535·0, 36·0 High34 65449·024·410·635·034·5, 35·5
## Statistical analysis
Descriptive analyses were employed to calculate the frequencies and percentages for each of the studied variables, followed by the estimation of the prevalence of overweight and obesity by the individual- and community-level factors. The Chi-square test was used to examine the differences between the group of overweight and obesity, and the significant ($P \leq 0$·05) variables for the Chi-square test were considered for multilevel analysis. Multilevel logistic regression modelling was applied to explore the relationships between individual- and community-level factors with the risk of being overweight or obese. Given that the individual-level factors were nested inside the community-level factors, a two-level multilevel modelling technique was used. Associations between individual-level predictors and overweight or obesity were measured by level 1 modelling, and the community-level determinants of overweight/obesity were assessed by level 2 modelling. Four separate regression models were run in this study. Without consideration for any individual- or community-level predictor variables, Model 1 is an empty model developed to estimate the random cluster effect and measures variation among the communities (primary sampling units). Model 2 includes all selected individual-level factors in the bivariate analysis. Model 3 includes only community-level factors. The final model (Model 4) consists of individual- and community-level factors. To evaluate the best-fitted model, this study considered log-likelihood, Akaike’s information criterion and Bayesian information criterion to explain the variation of these models. A 5 % significance level and a 95 % CI were used to measure the association between selected individual- and community-level factors and overweight/obesity. Multicollinearity was checked before establishing the final models. There is no multicollinearity identified because variance influential factor is less than the threshold value, 10. Therefore, multicollinearity diagnostic results were not reported in the paper. STATA 13.0 (StataCorp, USA) was used for all statistical analyses.
## Results
Table 1 presents the distribution of overweight and obesity by individual- and community-level factors in Bangladesh. A total of 70 651 non-pregnant women were included. Among them, the highest percentage (35·1 %) was from the 20–29 years age group, 43·3 % of the women had secondary and a high level of education, and more than two-thirds of the women were not working (68·9 %). The majority (89·6 %) of the women’s religious faith were Islam. More than half of the participants had five and above household members (59·9 %) and 3–4 children (50·2 %), and 55·3 % of their families had electronic media access. At community-level factors, approximately two-thirds (64·1 %) of women were from rural areas and 52·9 % of women had high community-level electronic media access.
Figure 1 presents the prevalence of overweight/obesity according to the WHO recommended BMI cut-off point and BMI cut-off point for the Asian population. The prevalence of overweight/obesity according to the WHO suggested BMI cut-off points was lower since it followed higher cut-off points than the BMI cut-off point for the Asian population. Fig. 2 also provides the prevalence of overweight and obesity according to the BMI cut-off point for the Asian population. The prevalence of overweight and obesity has increased in recent years.
Fig. 1Prevalence of overweight/obesity according to WHO recommended BMI cut-off point and BMI cut-off point for the Asian population Fig. 2Prevalence of overweight and obesity according to BMI cut-off point for the Asian population Table 1 provides the prevalence of overweight, obesity and overweight/obesity by individual- and community-level factors in Bangladesh. The overall prevalence of overweight and obesity were 24·3 % and 10·9 %, respectively. At the individual level, the prevalence of overweight/obesity has increased in recent years. The prevalence of overweight/obesity was the highest among the age group of 30–39 years (42·0 %) and lowest among the age group of 15–19 years (14·8 %). This study showed the highest rate of overweight/obesity among secondary and higher educated (44·3 %) women in Bangladesh. The prevalence of overweight/obesity increased with the wealth status of women. A small family (1–2) had a high prevalence of overweight/obesity (38·6 %). The prevalence of overweight/obesity was high among families with access to electronic media (43·7 %). Half (50·0 %) of women in the urban areas were overweight or obese at the community level. The prevalence of overweight/obesity was high among highly educated women in the community (38·8 %) and those with increased community electronic media exposure (39·4 %). All considered individual- and community-level factors except women’s working status were significantly ($P \leq 0$·05) associated with overweight/obesity among women.
## Multilevel analysis
Table 2 describes the results of the multivariate multilevel regression analysis for empty (Model 1), individual (Model 2), community (Model 3) and individual and community (Model 4)-level factors to measure the random effect of community and fixed effect of factors associated with overweight/obesity in non-pregnant women aged 15–49 years in Bangladesh.
Table 2Individual- and community-level factors associated with overweight/obesity in reproductive-aged (15–49 years) women in Bangladesh from multivariable logistic regression analysisVariable (Category)Model-2Model-3Model-4aOR95 % CI P-valueaOR95 % CI P-valueaOR95 % CI P-valueIndividual-level factors Survey year 20041·001·00 20071·301·21, 1·40< 0·0011·281·19, 1·37< 0·001 20111·981·85, 2·11< 0·0011·971·84, 2·10< 0·001 20142·952·77, 3·15< 0·0012·932·75, 3·13< 0·001 2017–20184·464·18, 4·76< 0·0014·424·15, 4·72< 0·001 Women’s age (in years) 15–191·001·00 20–292·802·58, 3·03< 0·0012·932·57, 3·02< 0·001 30–395·475·02, 5·96< 0·0015·455·01, 5·94< 0·001 40–495·795·28, 6·34< 0·0015·795·28, 6·34< 0·001 Women’s education No formal education1·001·00 Primary1·301·24, 1·37< 0·0011·311·25, 1·38< 0·001 Secondary or higher1·671·59, 1·76< 0·0011·691·60, 1·78< 0·001 Wealth index Poorest1·001·00 Poorer1·251·17, 1·33< 0·0011·251·17, 1·33< 0·001 Middle1·701·60, 1·82< 0·0011·661·56, 1·77< 0·001 Wealthier2·382·23, 2·54< 0·0012·242·09, 2·39< 0·001 Wealthiest5·034·69, 5·39< 0·0014·414·10, 4·74< 0·001 Household size 1–21·001·00 3–40·890·81, 0·970·0340·890·82, 0·970·012 ≥ 50·790·72, 0·86< 0·0010·800·73, 0·87< 0·001 Religion Muslim1·001·00 Non-Muslim0·840·79, 0·89< 0·0010·850·80, 0·90< 0·001 Number of living children ≤ 21·001·00 3–41·040·97, 1·120·2331·040·97, 1·120·252 ≥ 50·940·86, 1·020·1460·940·86, 1·020·150 *Electronic media* access No1·001·00 Yes1·351·29, 1·41< 0·0011·321·26, 1·37< 0·001 *Marital status* Married1·001·00 Widowed/divorced/separated0·740·69, 0·80< 0·0010·740·69, 0·79< 0·001Community-level factors Residence Urban1·001·00 Rural0·450·43, 0·46< 0·0010·790·76, 0·82< 0·001 Community poverty Low1·001·00 High0·880·82, 0·94< 0·0010·990·93, 1·050·803 Community women literacy Low1·001·00 High1·251·18, 1·32< 0·0011·101·04, 1·150·001 Community electronic media access Low1·001·00 High1·050·99, 1·120·0891·020·96, 1·080·436 Community women employment Low1·001·00 High1·010·95, 1·050·9180·920·88, 0·970·002Model 1 is an empty model.
In the final full model (Model 4), individual-level factors (such as women’s age, education, wealth index, household size, religion, electronic media access and marital status) and community-level factors (such as place of residence, community women literacy and community women employment) were significantly associated with overweight/obesity among non-pregnant women.
At the individual level, the odds of being overweight/obese were 2·93 times (adjusted odds ratio (aOR) = 2·93, 95 % CI: 2·57, 3·02) higher among those aged 20–29 years, 5·45 times (aOR = 5·45, 95 % CI: 5·01, 5·94) higher among those aged 30–39 years and 5·79 times (aOR = 5·79, 95 % CI: 5·28, 6·34) higher among those aged 40–49 years compared with women aged 15–19 years. Primary educated women (aOR = 1·31, 95 % CI: 1·25, 1·38) and secondary and higher educated women (aOR = 1·69, 95 % CI: 1·60, 1·78) were more likely to be overweight/obese compared with women who have no formal education. The odds of being overweight/obese were also significantly higher in women from the poorer (aOR = 1·25, 95 % CI: 1·17, 1·33), middle-class (aOR = 1·66, 95 % CI: 1·56, 1·77), wealthier (aOR = 2·24, 95 % CI: 2·09, 2·39) and wealthiest (aOR = 4·41, 95 % CI: 4·10, 4·74) households compared with those in women from the poorest households. Women with access to electronic media were 1·32 times (aOR = 1·32, 95 % CI: 1·26, 1·37) more likely to be overweight/obese than women with no access to electronic media. Widowed/divorced/separated women were less likely to become overweight/obese compared with married women (aOR = 0·74, 95 % CI: 0·69, 0·79). Furthermore, the odds of being overweight/obese were lower in non-Muslim (aOR = 0·85, 95 % CI: 0·80, 0·90) compared with Muslims women. However, the chance of being overweight/obese was significantly lower among women with 3–4 (aOR = 0·89, 95 % CI: 0·82, 0·97) and ≥5 (aOR = 0·80, 95 % CI: 0·73, 0·87) family members compared with their counterparts.
At the community level, the odds of being overweight/obese were lower in women from rural areas (aOR = 0·79, 95 % CI: 0·76, 0·82) and had high employment (aOR = 0·92, 95 % CI: 0·88, 0·97) than in women from an urban area and low community employment of women, respectively. The probability of being overweight and obese was increased in women from communities with high literacy (aOR = 1·10, 95 % CI: 1·04, 1·15).
Table 3 shows the results of model variation for overweight and obesity at the cluster level by multilevel logistic regression analysis. The empty model (Model 1) illustrated significant variability in the odds of overweight and obesity across communities (τ = 0·132; 95 % CI: 0·113, 0·154; $P \leq 0$·001). Similarly, significant variations in overweight/obesity of women existed in Model 2 (τ = 0·041; 95 % CI: 0·031, 0·052; $P \leq 0$·001) and Model 3 (τ = 0·062; 95 % CI: 0·050, 0·074; $P \leq 0$·001). After controlling the effect of individual- and community-level factors, the variance at the community level had a significant impact (τ = 0·036; 95 % CI: 0·027, 0·047; $P \leq 0$·001) in Model 4. The empty model (Model 1) reported an overall 3·88 % variation in the odds of overweight/obesity among women by involving the cluster difference of the characteristics (intra-cluster correlation = 3·88 %). The variability between clusters declined for the consecutive models from 3·88 % in the empty model (Model 1) to 1·23 % in the individual-level model only (Model 2), 1·84 % in the community-level model only (Model 3) and finally 1·09 % in the final model (Model 4). Proportional change in variance specified that the accumulation of forecasters to the empty model clarified an increased proportion of variation in overweight/obesity. Parallel to the intra-cluster correlation values, the proportional change in variance showed high values in the combined model (Model 4), that is, 1·09 % of the variation in overweight/obesity could be explained by the combined individual- and community-level factors. On the basis of the log-likelihood, Akaike’s information criterion and Bayesian information criterion values, Model 4 is the best-fitted model.
Table 3Results from the random intercept model (a measure of variation) for overweight and obesity at cluster level by multilevel logistic regression analysisModel summary (random effect)Model 1* Model 2† Model 3‡ Model 4§ Communities Variance0·1320·0410·0620·036 se 0·0140·0130·0120·01395 % CI0·113, 0·1540·031, 0·0520·050, 0·0740·027, 0·047ICC (%)3·88 %1·23 %1·84 %1·09 %PCV (%)Ref68·34 %52·65 %71·78 %Model fit statisticsLog likelihood−45821·433−39089·874−44733·955−39012·142AIC91646·8778223·789481·978078·28BIC91665·278425·3989546·0778325·75ICC, intra-cluster correlation; PCV, proportional change in variance; AIC, Akaike’s information criterion; BIC, Bayesian information criterion.*Model 1 is the empty model, a baseline mode l without any determinant variable.†Model 2 is adjusted for individual-level factors.‡Model 3 is adjusted for community-level factors.§Model 4 is final model adjusted for both individual- and community-level factors.
## Discussions
This study draws required information from the five most recent BDHS data to assess individual- and community-level factors associated with overweight and obesity among non-pregnant reproductive-aged women in Bangladesh. Multivariate regression techniques were used to quantify the contribution of the individual- and community-level factors to overweight or obesity. Several studies used socio-economic and demographic characteristics, and most of the reports applied a single model such as binary logistic regression. However, the investigation of community- and individual-level factors from the Bangladesh perspective is minimal. The findings of this study revealed that approximately one-third (35·2 %) of the non-pregnant women in Bangladesh were either overweight or obese. At the individual level, the prevalence of overweight/obesity was high among women of the 30–39 years age group, wealthiest, married non-pregnant, with secondary and higher education, have 1–2 household members and with electronic media access. At the community level, the prevalence of overweight/obesity was high among non-pregnant women living in the urban area, from low community poverty, high community literacy and increased community electronic media access.
In this study, the overall pooled prevalence of overweight/obesity in the nationally representative Bangladeshi non-pregnant women was 35·2 %, and the prevalence rates of overweight and obesity were 24·3 % and 10·9 %, respectively. These values are higher than those in Nepal[9] and Tanzania[22]. Therefore, overweight/obesity among women remains a serious concern in Bangladesh.
Several individual- and community-level factors affect the overweight/obesity of non-pregnant women. Individual-level factors (such as women’s age, education, wealth index, religion, household size, religion, number of children, electronic media access and marital status) and community-level factors (such as place of residence, community women literacy and community women employment) had a significant effect on overweight/obesity among non-pregnant women in Bangladesh. The risk of overweight/obesity increases with the women’s age. Furthermore, the high age of non-pregnant women was significantly associated with higher odds of being overweight/obese compared with those in women of the low age group (15–19 years). An earlier study conducted in Bangladesh found that older women were at a high risk of being overweight/obese[23]. Another study conducted in India found that overweight and obesity increased significantly with age[24]. The fat mass may be the main reason for being overweight/obese. The results of another study suggested that older age is associated with considerable changes in body composition because of the gradual decrease in fat-free mass and increase in fat mass after 30 years of age[25]. Women’s education has a positive relationship with the risk of being overweight/obese. Similar results have been reported in Bangladesh[23,26] and other countries such as Tanzania[22], Nepal[9], Malawi[19] and Ghana[27]. A possible reason could be that highly educated people lead a sedentary lifestyle[26]. Previous studies in low- and middle-income countries also revealed a significant positive relationship between education and overweight/obesity[28]. Even research conducted in developed countries found a negative relationship between education and excess body fat[29].
Non-pregnant women from better-off households had higher odds of being overweight and obese than women from poorer households. This finding is consistent with a study conducted in low- to middle-income countries that found a significant positive association between wealth index and overweight/obesity[30]. A similar finding was also found in India[24]. The positive association between increased wealth and being overweight could be attributed to the dietary behaviour changes with high income. The intake of high energy density foods and consumption of animal and processed foods were high among the high-income households and significantly associated with overweight and obesity[31]. Upliftment in the wealth index has given great access to food and discharge from physical work to people in lower-income countries, leading to a high risk of being overweight and obese[11]. Additionally, the economically solvent and educated family often passes their time outside of the home due to job responsibilities and habituates to eating junk or fast foods outside. These activities may lead to an unhealthy life and increase the risk of overweight/obesity[32]. Furthermore, non-pregnant women whose families had access to electronic media were more likely to be overweight/obese than those whose families had no access to media. Television and radio are essential media sources, and women who watch television have a high risk of being overweight and obese[27,33,34]. Mainly television watching has been used as a proxy for sitting time. Studies that followed participants over long periods have consistently found that people who spend more time watching television are likely to be overweight and obese[35].
In the context of residence, women who lived in a rural area were less likely to be overweight/obese than those who lived in an urban area. The association between place of residence and overweight and obesity is evident in past studies conducted in India[36], Senegal[37] and low-and middle-income countries[22,38]. A possible reason could be that rural women are highly engaged in physical activities such as agricultural activities, homework and other manual work as their occupation. These physical activities helped them reduce weight and hinder excessive weight gain[39]. Another possible reason could be that the availability of fast-food consumption, processed, packed and refrigerated foods is lower among rural women than among urban women[22]. Women from communities with high literacy were likely to be overweight/obese. However, high community-level literacy increases the risk of overweight and obesity[40]. Even health literacy is essential to understand consumer needs related to overweight and obesity[41]. An imminent investigation should discover the independent effects of community-level literacy on overweight and obesity among non-pregnant women in Bangladesh.
This study identified the individual- and community-level determinants of overweight and obesity among non-pregnant women aged 15–49 years in Bangladesh. The findings have important policy implications. The use of multilevel analysis helped solve practical and methodological constraints. Owing to the use of the nationally representative dataset, the findings can also be generalised among non-pregnant women.
The key strength of this study is its extensive pooled data from five successive BDHS rather than focusing on a specific survey year. Thus, the study is more diverse, and the results are more cohesive than any cross-sectional studies. This study identified important individual- and community-level factors predicting overweight and obesity among reproductive-aged women in Bangladesh. It has described community-level variables within a multilevel model framework. Therefore, unlike studies with only individual factors, it has minimised omitted variable biases. Furthermore, the results are based on five successive nationally representative surveys with large sample sizes, making the results generalisable to other similar socio-economic settings.
This study has also some limitations. The analysis was based on the secondary data, and some important variables, such as food habits, smoking, smoking habits, some non-communicable diseases risk and physical activity of women, were not included due to unavailability. The inclusion of these variables in the models might be helpful to fully understand the relationship of the selected independent variables with the overweight and obesity status of women. This study followed a cross-sectional design that cannot provide a causal relationship between explanatory variables and overweight and obesity. Further, the analysis was limited to only non-pregnant women aged 15–49 years; thus, the generalisability of the findings to all women may be limited. Despite these limitations, the results significantly contribute to the existing literature on the association of individual- and community-level factors with women’s overweight and obesity status.
## Conclusions
Individual- and community-level factors of overweight and obesity among reproductive-aged women in Bangladesh were investigated in this study. The study found that the prevalence and likelihood of being overweight/obese sharply increased over the survey period. At the individual level, women’s age, women’s education, wealth index, religion, household size, numbers of children and electronic media exposure have a significant relationship with overweight/obesity. At the community level, place of residence and community women literacy are significantly associated with a higher likelihood of being overweight/obese. Older, educated and women from the wealthiest households should be conscious about overweight/obesity and its harmful effects on health and quality of life. Media exposure should play an essential role in providing health awareness messages across all communities. Finally, executing protective interventions by government and non-government organisations may reduce the growing burden of overweight/obesity, particularly among the reproductive-aged women in Bangladesh.
## Conflict of interest:
There are no conflicts of interest.
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---
title: Associations of percentage energy intake from total, animal and plant protein
with overweight/obesity and underweight among adults in Addis Ababa, Ethiopia
authors:
- Elena C Hemler
- Sabri Bromage
- Amare Worku Tadesse
- Rachel Zack
- Yemane Berhane
- Chelsey R Canavan
- Wafaie W Fawzi
- Walter C Willett
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991810
doi: 10.1017/S1368980022001100
license: CC BY 4.0
---
# Associations of percentage energy intake from total, animal and plant protein with overweight/obesity and underweight among adults in Addis Ababa, Ethiopia
## Body
Malnutrition is the top risk factor for death and disability in Ethiopia, responsible for 24 % of all deaths[1]. The country faces a high double burden of malnutrition, with 22 % of women and 33 % of men underweight, and 8 % of women and 3 % of men overweight or obese. In Ethiopia’s capital city, Addis Ababa, the prevalence of overweight and obesity (29 % of women and 20 % of men) has overtaken that of underweight (13 % of women and 18 % of men)[2]. Overweight and obesity, along with unhealthy lifestyles, are important risk factors for non-communicable diseases such as CVD, type 2 diabetes and some cancers[3]. Obesity is particularly harmful in low-income settings with high rates of childhood malnutrition, as exposure to undernutrition in early life exacerbates the relationship between obesity and risk of non-communicable diseases[4]. As in other urban areas in sub-Saharan Africa (SSA), non-communicable diseases such as diabetes and CVD have become major causes of morbidity and mortality in Addis Ababa, responsible for 31 % of deaths reported by hospitals[5].
Innovative strategies are needed to address the double burden of malnutrition, especially in low-income, urban settings. Increasing the proportion of energy intake from protein could be a potential intervention to simultaneously prevent or treat underweight and overweight. Protein deficiency, contributing to protein-energy malnutrition, is a major cause of disability in Ethiopia[6]. In 2013, an estimated 49 % of women and 69 % of men in Ethiopia were below the estimated average requirement for protein intake[7]. Protein intake in low-income countries such as Ethiopia tends to be limited, as the majority of energy intake comes from cereal-based staple foods, with on average only 3 % of energy from meat, 11 % from roots and tubers and 6 % from pulses, nuts and oilseeds[8]. Low protein intake can lead to stunting and wasting, especially in childhood[8]. An analysis of 180 countries found that higher national-level estimates of total protein intake were associated with lower prevalence of child stunting[9]. Higher utilisable protein estimates (which take into account the digestibility of the protein sources and the essential amino acid composition) were associated with lower prevalence of child stunting, independent of energy intake[9]. One study estimated a potential 1·19-year increase in life expectancy at birth if the adult population of Addis Ababa ate enough protein to meet the required daily amounts and a 0·42–2·0 percentage point decrease in child stunting, with animal-source foods more efficacious than plant-source foods in reducing stunting[10].
While evidence from low-income settings is limited, studies in Western populations suggest that increased protein intake may have favourable effects on body weight and cardiometabolic health. In the USA and Canada, protein intake above the RDA has been associated with lower body weight, more favourable body composition and lower waist circumference[11,12]. A meta-analysis of short-term randomised trials suggests that high protein diets (20–35 % of energy from protein) may lower cardiometabolic risk through changes in body composition and/or weight[13]. Higher-protein diets often have a relatively low energy density, which can aid appetite suppression, reduce overall food intake, preserve lean body mass and promote weight management[14].
While evidence from short-term studies indicates that overall protein intake may be beneficial for improving cardiometabolic health, it is important to consider potential divergent health effects of distinct protein sources. Traditionally, animal-source protein has been thought to be beneficial for the reduction of undernutrition[15,16], but the relationship between animal protein, plant protein and cardiometabolic health is unclear. In the USA, diets higher in both plant and animal protein, independent of other dietary factors, have been associated with cardiometabolic benefits including decreased BMI and waist circumference[17]. In Korea, increased amounts of both plant and animal protein have been found to decrease BMI and waist circumference[18]. However, in Belgium, plant protein intake was associated with lower BMI and waist circumference in males and females, while animal protein was associated with higher BMI and higher waist circumference in males[19]. Plant protein intake has also been associated with favourable changes in waist circumference in the USA[20] and lower risk of metabolic syndrome in Australia[21]. In two large US cohorts, total protein intake was not associated with total mortality, but animal protein was associated with higher mortality and plant protein was associated with lower mortality. However, these findings may reflect in part the types of fat in animal and plant foods and other unhealthy components of animal-source foods commonly consumed in Western settings (such as sodium, nitrates and nitrites found in processed red meat) rather than just the type of protein[22].
Nearly all evidence on protein intake and cardiometabolic health is from high-income countries, which may not be generalisable to low-income settings where animal protein consumption is much lower and food sources of protein differ. While shifts from animal to plant sources of protein are encouraged in Western populations for health and environmental benefits, promotion of animal-source foods in place of carbohydrate in low-resource settings in SSA, where the majority of energy is from starchy carbohydrates, may improve dietary quality, micronutrient intake and have metabolic benefits through reducing glycaemic load[15,23]. Ethiopia is currently creating dietary guidelines to address the double burden of malnutrition[24]; however, there is a lack of robust dietary data on protein consumption in Ethiopia and a need to clarify how distinct protein sources are related to underweight and overweight in order to form evidence-based guidelines. Therefore, we aimed to examine the associations between proportion of energy from total, animal and plant protein, as well as food sources of protein, with underweight and overweight/obesity in Ethiopian adults.
## Abstract
### Objective:
This study investigated associations between types and food sources of protein with overweight/obesity and underweight in Ethiopia.
### Design:
We conducted a cross-sectional dietary survey using a non-quantitative FFQ. Linear regression models were used to assess associations between percentage energy intake from total, animal and plant protein and BMI. Logistic regression models were used to examine the associations of percentage energy intake from total, animal and plant protein and specific protein food sources with underweight and overweight/obesity.
### Setting:
Addis Ababa, Ethiopia.
### Participants:
1624 Ethiopian adults (992 women and 632 men) aged 18–49 years in selected households sampled using multi-stage random sampling from five sub-cities of Addis Ababa.
### Results:
Of the surveyed adults, 31 % were overweight or obese. The majority of energy intake was from carbohydrate with only 3 % from animal protein. In multivariable-adjusted linear models, BMI was not associated with percentage energy from total, plant or animal protein. Total and animal protein intake were both associated with lower odds of overweight/obesity (OR per 1 % energy increment of total protein 0·92; 95 % CI: 0·86, 0·99; $$P \leq 0$$·02; OR per 1 % energy increment of animal protein 0·89; 95 % CI: 0·82, 0·96; $$P \leq 0$$·004) when substituted for carbohydrate and adjusted for socio-demographic covariates.
### Conclusion:
Increasing proportion of energy intake from total protein or animal protein in place of carbohydrate could be a strategy to address overweight and obesity in Addis Ababa; longitudinal studies are needed to further examine this potential association.
## Household survey
Between January and March 2018, we conducted a cross-sectional study among 1050 urban households in Addis Ababa including 1050 adult women and 635 adult men 18–49 years of age to characterise patterns in protein-source food production, access and consumption. We used multi-stage random sampling to select households from five sub-cities of Addis Ababa, Ethiopia. Eligible households included a woman of reproductive age (18–49) and at least one child between 6 and 59 months of age. If there was an adult male age 18–49 years available at the household, they were also included in the sample. If more than one adult male or adult female was living in the household and present at the time of the interview, one was randomly selected. If the woman of reproductive age was pregnant, she was included in the survey; however, pregnant women were excluded from this analysis (n 56). We invited 1083 eligible households to participate and 1050 provided informed consent (response rate of 97 %). The woman of reproductive age completed a household survey including questions on socio-economic status and demographic characteristics. Both the adult man and woman completed a seventy-three-item non-quantitative FFQ assessing the number of days each food was consumed out of the past 7 d, which was administered at the participant’s household by a trained interviewer. Portion sizes and frequencies of intake were not collected. This FFQ was adapted from a semi-quantitative FFQ previously validated for use against two 24 h diet recalls among urban adults in Dar es Salaam, Tanzania[25] and was modified to reflect foods commonly consumed in Ethiopia. Height was measured for all men and women to the nearest 0·1 cm with the subject barefoot using a stadiometer. Weight was measured to the nearest 0·1 kg with the subject barefoot and in light clothes using a SECA digital scale. BMI was calculated as weight in kilograms divided by height in metres squared. Based on BMI, individuals were classified using standard cut-offs as underweight (< 18·5 kg/m2), healthy weight (18·5–24·9 kg/m2), overweight (25–29·9 kg/m2) or obese (≥ 30 kg/m2). All participants had complete height, weight and dietary data.
## Assessment of dietary intake
We calculated energy, protein, carbohydrate and fat intakes using [1] FFQ dietary intake data, [2] nutrient contents from the Ethiopian Food Composition Table[26] and [3] portion sizes for men and women obtained from previous nutrition surveys in Addis Ababa[27]. We used the Tanzanian Food Composition Table[28] where Ethiopian estimates were missing. No participants had implausibly high energy intakes (greater than 4500 kcal/18 828 kJ per day), but five participants with energy intakes less than 500 kcal/2092 kJ per day were excluded from the analysis. We estimated the prevalence of inadequate protein intake by applying the estimated average requirements for protein intake for each individual (0·66 g/kg/d for adult men and non-lactating women and 1·05 g/kg/d for lactating women)[29].
## Covariate Measurements
Covariates were collected in the socio-demographic household survey administered to women and included age, highest level of school completed, religion, marital status, employment, household size, indicators of household living standards and household food insecurity. The women’s covariates were used for the men living in their households. All households had complete covariate data. To measure household wealth, participants were categorised into quartiles based on a wealth index that was constructed using principal components analysis and a set of twelve indicators describing household living standards and possessions owned[30]. Household food insecurity was assessed using the Household Food Insecurity Access Scale, which is a standardised scale ranging from 0 to 27 based on a nine-item questionnaire including the following food security domains: uncertainty about the household food supply, insufficient quality of food and insufficient food intake. This scale has been used across cultural contexts and categorises households and populations as food secure, mildly food insecure, moderately food insecure or severely food insecure[31].
## Statistical analysis
Descriptive statistics were presented as means and standard deviations; medians and 25th/75th percentiles or proportions. Relationships between socio-demographic characteristics and protein intake were assessed by testing for linear trends in socio-demographic characteristics across quartiles of percentage energy from total protein intake using linear regression (for continuous variables), logistic regression (for binary variables) and ordered multinomial logistic regression (for categorical variables with more than two levels). Linear models adjusting for socio-demographic variables were used to examine associations between percentage energy from total protein, plant protein and animal protein and continuous BMI. Percentage energy from total protein, plant protein and animal protein were examined in linear models as continuous exposures and also in quartiles, with the first quartile representing the lowest percentage energy intake from total, plant or animal protein. We assessed potential nonlinearity in the associations between BMI and percentage energy from total, plant and animal protein by modelling each exposure in multivariate-adjusted models as cubic splines with 2 df.
Logistic regression models were used to examine the associations of percentage energy from total, plant and animal protein (both in quartiles and as continuous exposures) with a binary variable for overweight/obese (defined as BMI ≥ 25 kg/m2) and a binary variable for underweight (BMI < 18·5 kg/m2) to examine effects of protein sources on underweight and overweight/obesity independently. Overweight/obese and underweight models were run separately excluding underweight participants from models with an outcome of overweight/obesity and vice versa to maintain a comparison group of those with a healthy weight. Analyses were also performed separately by sex because women and men may have different risks of underweight and overweight[32,33]. In addition, the top ten food sources of protein intake in the sample were identified and logistic regression models were used to investigate the associations between percentage energy from each protein food with overweight and underweight.
All dietary factors were expressed as nutrient densities (percentage of total energy intake) to examine diet composition without constraining total energy intake, as this may be the primary mediator of how dietary exposures affect outcomes of body weight[34,35]. We used these nutrient density models to estimate the effect of substituting percentage energy from total, animal and plant protein for an equal percentage energy from carbohydrate as well as substituting animal for plant protein. In all analyses, multivariable-adjusted models controlled for female’s age, sex, female’s educational attainment (never attended school, completed primary or less, completed higher than primary), if female is married or living with partner (yes, no), wealth index (poorest, poor, wealthy and wealthiest), female’s religion (orthodox Christian or not orthodox Christian), household size (continuous) and female employed (yes, no). Carbohydrate substitution models controlled for the same covariates as the multivariable-adjusted models plus percentage of calories from total fat (continuous). Carbohydrate substitution models with plant protein as the exposure additionally adjusted for percentage energy from animal protein and models with animal protein as the exposure additionally adjusted for percentage energy from plant protein. Animal and plant protein substitution models were also included to estimate the effect of substituting animal for plant protein (or vice versa) by adjusting for all socio-demographic covariates in the multivariable-adjusted models, plus percentage of calories from carbohydrate (continuous) and percentage of calories from fat (continuous).
Secondary analyses examined the associations between percentage energy from total protein, plant protein and animal protein and continuous BMI separately excluding underweight participants and excluding overweight participants as the relationship between protein intake and BMI may differ in the context of overfeeding and underfeeding[36]. Secondary analyses were also performed adjusting for total energy intake as a continuous variable in the linear models examining percentage energy from total protein, plant protein and animal protein with continuous BMI; as well as the logistic models with binary outcomes of overweight and underweight. In addition, because 29 % of participants reported currently lactating, secondary analyses were conducted adjusting for lactation status to rule out potential confounding due to postpartum weight retention. To confirm our estimation of substitution effects using the nutrient density models, we also estimated substitutions using the energy partition model[34] by including percentage of energy from fat, carbohydrate and protein as continuous variables in the same multivariable model. For this secondary analysis, the difference in the coefficients for each macronutrient plus their covariance was used to estimate substitution effects[34].
A two-sided probability value < 0·05 was considered to indicate a statistically significant difference. Statistical analyses were conducted using Stata 16·1 (StataCorp LP).
## Results
Characteristics of the survey population are presented in Table 1. After excluding participants with implausible energy intakes (n 5) and pregnant women (n 56), there were 632 men and 992 women included in the final analysis. Most participants were Orthodox Christian, had completed primary school or higher and were married. According to the Household Food Insecurity Access Scale, 38 % of households were food insecure[31]. Female’s age, male sex, Orthodox Christian (v. not), married (v. not), household size, household wealth quartile, household food security and BMI were all positively associated with percentage energy intake from total protein in bivariate analyses ($P \leq 0$·05; Table 1). Median energy intake (P25, P75) was 1695 [1353, 1986] kcal/d for men and 1556 [1223, 1890] kcal/d for women. Mean protein intake was 14 % of total energy for men and 14 % of total energy for women. Mean percentage energy from carbohydrates and fat was 69 % and 17 %, respectively. The proportion of participants who did not meet the estimated average requirement for protein intake was 21 % of men, 23 % of non-lactating women and 62 % of lactating women. Plant protein made up 83 % of total protein intake in men and 84 % in women. Major sources of dietary protein for both men and women included teff (36 % of protein), beef meat (12 %), peas (10 %), lentils (9 %) and wheat (5 %). In this sample, 10 % of women and 8 % of men were underweight, 24 % of women and 23 % of men were overweight and 10 % of women and 3 % of men were obese.
Table 1Demographic characteristics of 1624 men and women aged 18–49 in Addis Ababa, Ethiopia by quartiles of percentage energy intake from total proteinPercentage energy from total proteinQ1† (n 406)Q2 (n 406)Q3 (n 406)Q4 (n 406)Overall (n 1624) % n % n % n % n % n Socio-demographic‡ Female’s age years** Mean29·129·729·930·629·8sd 5·65·65·95·75·7Female** 64·326166·527063·125650·520561·1992Female is Orthodox Christian* 2·729573·229777·831678·131775·41225EducationNever attended school10·8449·1379·13711·34610·1164Completed less than primary44·818240·416437·415230·512438·3622Completed primary or higher44·318050·520553·421758·123651·6838Marital status** Married or lives with partner84·534390·936990·436793·337989·81458Female employed36·214735·714536·014630·512434·6562Household size* Mean4·34·54·54·74·5sd 1·51·61·61·81·6Wealth index** Poorest30·012227·611221·78818·27424·4396Poor27·811326·810925·110223·69625·9420Wealthy23·29420·98527·811325·910524·4397Wealthiest19·07724·610025·410332·313125·3411Household is food insecure** 40·616544·318037·715327·611237·6610 Anthropometric and nutritional BMI* Mean23·123·623·323·923·5sd 4·04·03·94·34·1Underweight (BMI < 18·5)11·6478·4347·1299·4389·1148Overweight (25 ≤ BMI < 30)22·49126·310721·98924·19823·7385Obese (BMI ≥ 30)5·7237·9325·4229·6397·1116Total energy intake (kcal/d)Median12871621170617821613P25, P751027, 16161328, 19111397, 20681438, 21351279, $1939\%$ Energy from plant proteinMean11·111·812·111·911·7sd 1·11·61·92·31·$8\%$ energy from animal proteinMean0·81·92·64·62·5sd 1·11·61·92·72·4Protein intake (g/d)Median38·354·962·973·457·0 P25, P7529·7, 49·544·4, 65·652·0, 76·360·5, 87·043·2, 72·6 Estimated average requirements by population group % of men below the estimated average requirement (EAR) for protein intake§ (n 632 men)60·08716·2227·3115·01020·$6130\%$ of non-lactating women below the EAR for protein intake§ (n 529 non-lactating women)55·57620·12810·5144·2523·$3123\%$ of lactating women below the EAR for protein intake|| (n 463 lactating women)88·711066·48743·95441·23561·8286* $P \leq 0$·05.** $P \leq 0$·001.†Demographic characteristics are presented by quartiles of percent energy from protein intake, with the first quartile representing the lowest percent energy intake from protein.‡Data are presented as mean ± sd or median (P25, P75) for continuous measures and % (n) for categorical measures.§EAR for protein intake are 0·66 g/kg/d for adult men and women aged 19–70 years.||EAR for protein intake are 1.05 g/kg/d for lactating women.
In linear models adjusted for socio-demographic covariates, percentage energy from total protein was not associated with continuous BMI (Table 2). A greater percentage energy from plant protein was associated with lower BMI in linear models adjusted for age and sex, but this was NS after controlling for covariates. In age- and sex-adjusted linear models, a greater percentage energy from animal protein was positively and significantly associated with increased BMI (β for 1 % energy increment 0·15; 95 % CI: 0·07, 0·23; $P \leq 0$·001). However, this was NS after adjusting for socio-demographic characteristics. Of all covariates, wealth and education were strong confounders of this relationship and inclusion of either in the model nullified the positive association between animal protein and BMI. Additionally adjusting for percentage energy from fat (which changes the model into a substitution of animal protein for carbohydrate) flipped the association to inverse, although this was NS (β for 1 % energy increment –0·09; 95 % CI: –0·22, 0·05; $$P \leq 0$$·20) (Table 2). There was no evidence of nonlinearity for the associations between BMI and percentage energy from total protein, plant protein and animal protein in multivariate-adjusted models (see online supplementary material, Supplemental Fig. 1) or multivariate-adjusted carbohydrate substitution models (see online supplementary material, Supplemental Fig. 2).
Table 2Beta coefficients (95 % CI) for percentage energy intake from total protein, plant and animal protein in relation to BMI among 1624 adults in Addis Ababa, EthiopiaQ2Q3Q4Per 1 % difference‡ Q1† Beta coefficients95 % CIBeta coefficients95 % CIBeta coefficients95 % CIBeta coefficients95 % CI P trend Percentage energy from total proteinMean % energy11·813·614·816·514·2Age- and sex-adjusted§ 0·0 (Ref)0·38–0·16, 0·920·05–0·49, 0·590·61* 0·06, 1·150·09–0·02, 0·190·07Multivariable-adjusted|| 0·0 (Ref)0·22–0·31, 0·75−0·18–0·71, 0·350·24–0·30, 0·780·01–0·10, 0·110·65Multivariable-adjusted carbohydrate substitution¶ 0·0 (Ref)0·11–0·43, 0·65−0·33–0·88, 0·22−0·03–0·62, 0·56−0·06–0·18, 0·060·60Percentage energy from plant proteinMean % energy9·411·112·314·111·7Age- and sex–adjusted0·0 (Ref)−0·47–1·01, 0·07−0·42–0·96, 0·12−0·71* –1·25, –0·17−0·17* –0·27, –0·070·01Multivariable-adjusted0·0 (Ref)−0·27–0·80, 0·26−0·04–0·57, 0·50−0·04–0·60, 0·51−0·02–0·13, 0·080·94Multivariable-adjusted carbohydrate substitution0·0 (Ref)−0·21–0·78, 0·36−0·05–0·65, 0·55−0·04–0·70, 0·62−0·03–0·17, 0·100·93Multivariable-adjusted animal protein substitution†† 0·0 (Ref)−0·12–0·67, 0·430·09–0·46, 0·640·13–0·47, 0·730·01–0·11, 0·130·54Percentage energy from animal proteinMean % energy0·01·22·95·72·5Age- and sex-adjusted0·0 (Ref)0·82* 0·28, 1·360·66* 0·13, 1·201·00** 0·46, 1·540·15** 0·07, 0·23< 0·01Multivariable-adjusted0·0 (Ref)0·47–0·07, 1·010·09–0·46, 0·650·12–0·45, 0·700·02–0·07, 0·110·89Multivariable-adjusted carbohydrate substitution0·0 (Ref)0·27–0·28, 0·83−0·30–0·93, 0·32−0·56–1·33, 0·21−0·09–0·22, 0·050·07Multivariable-adjusted plant protein substitution‡‡ 0·0 (Ref)0·27–0·29, 0·83−0·28–0·89, 0·33−0·51–1·23, 0·21−0·08–0·20, 0·030·06* $P \leq 0$·05.** $P \leq 0$·001.†Percentage energy from total protein, plant protein and animal protein were examined in linear models as continuous exposures and also in quartiles; Q1 represents the lowest percent energy intake from total, plant or animal protein.‡Beta coefficients are in kg/m2 per 1 % difference in percent energy§The age- and sex-adjusted model is adjusted for female’s age (years, continuous) and sex (female, male)||The multivariable-adjusted model is adjusted for female’s age, sex, female’s educational attainment (never attended school, completed primary or less, completed higher than primary), if female is married or living with partner (yes, no), wealth index (poorest, poor, wealthy, wealthiest), female’s religion (orthodox Christian or not orthodox Christian), household size (continuous) and female employed (yes, no).¶The multivariable-adjusted carbohydrate substitution model is the multivariable-adjusted model additionally adjusted for percentage of calories from total fat (continuous). When plant protein is the exposure, the model is additionally adjusted for percentage of calories from animal protein. When animal protein is the exposure, the model is additionally adjusted for percentage of calories from plant protein.††The multivariable-adjusted animal protein substitution model is adjusted for female’s age, sex, percentage of calories from fat, percentage of calories from carbohydrate (continuous), female’s educational attainment, if female is married or living with partner, wealth index, female’s religion, household size and female employed.‡‡The multivariable-adjusted plant protein substitution model is adjusted for the same covariates as the multivariable-adjusted animal protein substitution model.
Total protein intake was associated with lower odds of overweight/obesity (OR per 1 % energy increment 0·92; 95 % CI: 0·86, 0·99; $$P \leq 0$$·02) compared with healthy weight, after adjusting for age, sex, percentage energy from total fat, educational attainment, marital status, wealth index, religion, household size and employment status (Table 3). Participants in the third quartile of percentage energy from total protein had a lower odds of overweight/obesity compared with those in the lowest quartile (OR 0·69; 95 % CI: 0·49, 0·97; $$P \leq 0$$·04) when substituting protein for carbohydrate, but no significant associations were observed when comparing the highest and lowest quartiles (OR 0·89; 95 % CI: 0·62, 1·28; $$P \leq 0$$·54). These results were consistent after adjustment for lactation status. In fully adjusted models, total protein was associated with a lower odds of overweight/obesity when substituting for carbohydrate (OR per 1 % energy increment 0·88; 95 % CI: 0·81, 0·97; $$P \leq 0$$·009) in women, but no significant associations were observed in men (OR per 1 % energy increment 0·99; 95 % CI: 0·88, 1·12; $$P \leq 0$$·91) (see online supplementary material, Supplemental Table 1). In multivariable-adjusted carbohydrate substitution models, total protein intake was not significantly associated with odds of underweight (compared with healthy weight) in the overall sample (OR per 1 % energy increment 0·95; 95 % CI: 0·85, 1·07; $$P \leq 0$$·40), in women (OR per 1 % energy increment 0·92; 95 % CI: 0·80, 1·06; $$P \leq 0$$·24) or in men (OR per 1 % energy increment 1·09; 95 % CI: 0·89, 1·32; $$P \leq 0$$·39) (Table 3; see online supplementary material, Supplemental Table 1).
Table 3OR (95 % CI) for percentage energy intake from total protein, plant protein and animal protein in relation to prevalence of underweight or overweight/obesity among 1624 adults in Addis Ababa, EthiopiaQ2Q3Q4Per 1 % differenceQ1† OR95 % CIOR95 % CIOR95 % CIOR95 % CI P trend Percentage energy from total proteinOverweight/Obese‡, n 1476, 501 overweight or obeseAge- and sex-adjusted§ 1·00 (Ref)1·240·91, 1·700·860·63, 1·191·250·92, 1·741·000·94, 1·070·46Multivariable-adjusted|| 1·00 (Ref)1·130·82, 1·560·750·54, 1·041·020·73, 1·410·960·90, 1·020·58Multivariable-adjusted Carbohydrate substitution¶ 1·00 (Ref)1·070·77, 1·480·69* 0·49, 0·970·890·62, 1·280·92* 0·86, 0·990·21Underweight‡, n 1123, 148 underweightAge- and sex-adjusted1·00 (Ref)0·790·49, 1·290·59* 0·36, 0·980·980·61, 1·570·960·87, 1·060·56Multivariable-adjusted1·00 (Ref)0·850·52, 1·380·630·38, 1·051·030·63, 1·680·970·88, 1·070·71Multivariable-adjusted Carbohydrate substitution1·00 (Ref)0·830·51, 1·360·620·37, 1·040·980·57, 1·670·950·85, 1·070·56Percentage energy from plant proteinOverweight/obese, n 1476, 501 overweight or obeseAge- and sex-adjusted1·00 (Ref)0·760·56, 1·040·880·64, 1·190·780·57, 1·070·940·89, 1·000·20Multivariable-adjusted1·00 (Ref)0·820·60, 1·131·050·76, 1·451·100·78, 1·531·010·95, 1·080·40Multivariable-adjusted Carbohydrate substitution1·00 (Ref)0·760·54, 1·070·900·63, 1·290·900·60, 1·340·960·88, 1·050·85Multivariable-adjusted Animal protein substitution** 1·00 (Ref)0·890·64, 1·231·130·81, 1·571·270·88, 1·821·050·97, 1·130·11Underweight n 1123, 148 underweightAge- and sex-adjusted1·00 (Ref)1·000·59, 1·701·500·91, 2·461·290·77, 2·141·080·98, 1·190·19Multivariable-adjusted1·00 (Ref)0·970·56, 1·661·370·83, 2·281·080·64, 1·841·040·94, 1·150·58Multivariable-adjusted Carbohydrate substitution1·00 (Ref)0·890·50, 1·591·180·67, 2·090·890·48, 1·681·000·88, 1·130·87Multivariable-adjusted Animal protein substitution1·00 (Ref)1·030·59, 1·811·450·86, 2·451·240·69, 2·211·080·96, 1·210·31Percentage energy from animal proteinOverweight/obese n 1476, 501 overweight or obeseAge- and sex-adjusted1·00 (Ref)1·40* 1·02, 1·921·310·95, 1·811·290·93, 1·771·040·99, 1·080·26Multivariable-adjusted1·00 (Ref)1·130·81, 1·580·920·66, 1·300·790·55, 1·120·960·92, 1·020·07Multivariable-adjusted Carbohydrate substitution1·00 (Ref)0·990·70, 1·400·710·49, 1·050·50* 0·31, 0·810·89* 0·82, 0·96< 0·01Multivariable-adjusted Plant protein substitution†† 1·00 (Ref)1·010·71, 1·420·740·51, 1·090·55* 0·35, 0·860·91* 0·85, 0·98< 0·01Underweight n 1123, 148 underweightAge- and sex-adjusted1·00 (Ref)0·910·56, 1·450·850·53, 1·380·700·42, 1·150·920·85, 1·000·16Multivariable-adjusted1·00 (Ref)0·960·59, 1·560·960·58, 1·590·850·49, 1·470·950·87, 1·040·59Multivariable-adjusted Carbohydrate substitution1·00 (Ref)0·910·55, 1·510·900·50, 1·590·780·37, 1·650·900·79, 1·030·54Multivariable-adjusted Plant protein substitution1·00 (Ref)0·920·56, 1·520·890·51, 1·560·770·39, 1·530·910·81, 1·030·47* $P \leq 0$·05†Percentage energy intake from total protein, plant protein and animal protein were examined in logistic regression models as continuous exposures and also in quartiles; Q1 represents the lowest percentage energy intake from total, plant or animal protein.‡Overweight/obese is defined as BMI ≥ 25 and is compared with a reference category of non-overweight/obese (BMI < 25). All underweight participants (n 148) were excluded from the overweight analysis. Underweight is defined as BMI < 18·5 and is compared with a reference category of non-underweight (BMI ≥ 18·5). All overweight participants (n 501) were excluded from the underweight analysis.§The age- and sex-adjusted model is adjusted for female’s age (years, continuous) and sex (female, male)||The multivariable-adjusted model is adjusted for female’s age, sex, female’s educational attainment (never attended school, completed primary or less, completed higher than primary), if female is married or living with partner (yes, no), wealth index (poorest, poor, wealthy, wealthiest), female’s religion (orthodox Christian or not orthodox Christian), household size (continuous) and female employed (yes, no).¶The multivariable-adjusted carbohydrate substitution model is the multivariable-adjusted model additionally adjusted for percentage of calories from total fat (continuous). When plant protein is the exposure, the model is additionally adjusted for percentage of calories from animal protein. When animal protein is the exposure, the model is additionally adjusted for percentage of calories from plant protein.**The multivariable-adjusted animal protein substitution model is adjusted for female’s age, sex, percentage of calories from fat, percentage of calories from carbohydrate (continuous), female’s educational attainment, if female is married or living with partner, wealth index, female’s religion, household size and female employed.††The multivariable-adjusted plant protein substitution model is adjusted for the same covariates as the multivariable-adjusted animal protein substitution model.
Among men and women combined, plant protein intake was not significantly associated with odds of underweight or overweight/obesity compared with healthy weight (Table 3). However, plant protein intake was associated with increased odds of overweight/obesity in women when substituted for animal protein (OR per 1 % energy increment 1·11; 95 % CI: 1·01, 1·22; $$P \leq 0$$·03), but not when substituted for carbohydrate (OR per 1 % energy increment 0·96; 95 % CI: 0·86, 1·07; $$P \leq 0$$·50) (see online supplementary material, Supplemental Table 1). Greater animal protein intake was significantly associated with lower odds of overweight in fully adjusted models when substituted for total carbohydrate (OR per 1 % energy increment 0·89; 95 % CI: 0·82, 0·96; $$P \leq 0$$·004) and for plant protein (OR per 1 % energy increment 0·91; 95 % CI: 0·85, 0·98; $$P \leq 0$$·008) (Table 3). Participants in the highest quartile of animal protein intake had a 50 % (95 % CI: 19, 69; $$P \leq 0$$·005) lower odds of overweight/obesity compared with those in the lowest quartile, substituting animal protein for carbohydrate. These results were consistent after adjustment for lactation status. Women had 68 % (95 % CI: 41, 83; $P \leq 0$·001) lower odds of overweight/obesity comparing the highest to lowest quartile of animal protein intake, substituted for carbohydrate, but this association was not seen in men. No associations between animal protein intake and underweight were observed in the fully adjusted models overall, in women, or in men (Table 3; see online supplementary material, Supplemental Table 1).
In food-based analyses, percentage energy from most commonly consumed protein sources was not related to underweight or overweight/obesity (Table 4). In fully adjusted models, a greater percentage energy from milk was associated with lower odds of underweight but the CI was wide (OR per 5 % energy increment 0·54; 95 % CI: 0·30, 1·00; $$P \leq 0$$·05) and this finding was NS after adjusting for lactation status (OR per 5 % energy increment 0·55; 95 % CI: 0·30, 1·01; $$P \leq 0$$·06). Although not statistically significant, a greater percentage energy intake from barley was related to lower odds of overweight/obesity (OR per 5 % energy increment 0·85; 95 % CI: 0·72, 1·01; $$P \leq 0$$·06).
Table 4OR (95 % CI) for percentage of total energy intake from top ten consumed protein foods in relation to prevalence of underweight and overweight/obesity among 1624 adults in Addis Ababa, EthiopiaProtein source† Mean % energy intakeOverweight/obese (n 1476)‡ Underweight (n 1123) § Teff42·71Age- and sex-adjusted|| 0·980·94, 1·021·020·96, 1·08Multivariable-adjusted¶ 1·010·97, 1·061·000·94, 1·06Beef6·11Age- and sex-adjusted1·070·98, 1·160·940·82, 1·07Multivariable-adjusted0·970·89, 1·060·990·86, 1·14Peas5·88Age- and sex-adjusted0·980·89, 1·071·060·92, 1·21Multivariable-adjusted1·050·95, 1·161·020·89, 1·17Lentils6·22Age- and sex-adjusted1·000·92, 1·101·000·87, 1·15Multivariable-adjusted0·980·90, 1·080·980·85, 1·13Wheat5·46Age- and sex-adjusted1·050·97, 1·150·900·78, 1·04Multivariable-adjusted1·050·97, 1·150·890·77, 1·02Beans2·83Age- and sex-adjusted0·950·86, 1·051·070·92, 1·24Multivariable-adjusted0·970·88, 1·081·060·91, 1·24Milk1·11Age- and sex-adjusted1·070·80, 1·430·50* 0·28, 0·91Multivariable-adjusted0·820·60, 1·120·54* 0·30, 1·00Rice2·95Age- and sex-adjusted1·000·88, 1·131·060·88, 1·28Multivariable-adjusted0·960·84, 1·091·100·91, 1·33Barley1·14Age- and sex-adjusted0·860·73, 1·010·720·50, 1·03Multivariable-adjusted0·850·72, 1·010·730·50, 1·05Chickpeas0·62Age- and sex-adjusted0·860·61, 1·231·350·84, 2·18Multivariable-adjusted0·890·62, 1·291·440·89, 2·34* $P \leq 0$·05.†Protein source foods were included in models as percentage of total energy intake from each food. Models are examining a 5 % increase in percentage of total energy intake of each food.‡Overweight/obese is defined as BMI ≥ 25. The overweight/obese analysis was conducted among 1476 healthy weight, overweight and obese participants. 148 underweight participants were excluded from this analysis.§*Underweight is* defined as BMI < 18·5. The underweight analysis was conducted among 1123 healthy weight and underweight participants. 501 overweight and obese participants were excluded from this analysis.||Age- and sex-adjusted model is adjusted for female’s age (years, continuous) and sex (female, male)¶Multivariable-adjusted model is adjusted for female’s age, sex, female’s educational attainment (never attended school, completed primary or less, completed higher than primary), if female is married or living with partner (yes, no), wealth index (poorest, poor, wealthy, wealthiest), female’s religion (orthodox Christian or not orthodox Christian), household size (continuous) and female employed (yes, no).
For linear models with BMI as an outcome, results from secondary analyses adjusting for total energy intake (see online supplementary material, Supplemental Table 2) and excluding overweight participants (n 501) (see online supplementary material, Supplemental Table 3) were largely consistent with the primary analyses. When excluding all underweight participants (n 148), total protein was weakly associated with lower BMI when substituted for carbohydrate comparing the third and lowest quartiles of total protein intake (β –0·55; 95 % CI: –1·08, –0·01; $$P \leq 0$$·05), but not when comparing the highest and lowest quartiles (β –0·07; 95 % CI: –0·19, 0·04; $$P \leq 0$$·22) (see online supplementary material, Supplemental Table 4). Animal protein intake was related to lower BMI when substituted for plant protein in analyses excluding underweight participants, but this association was marginally significant (β per 1 % energy increment –0·11; 95 % CI: –0·23, 0·00; $$P \leq 0$$·05; P trend across Q1–Q4 = 0·04).
In secondary analyses adjusting for total energy intake with overweight/obesity as an outcome, the association between total protein and overweight/obesity was NS in the fully adjusted carbohydrate substitution model (OR per 1 % energy increment: 0·93; 95 % CI: 0·87, 1·00; $$P \leq 0$$·06) (see online supplementary material, Supplemental Table 5). However, when adjusting for total energy intake, greater percentage energy from animal protein was still associated with lower odds of overweight/obesity in fully adjusted models substituting animal protein for carbohydrate (OR per 1 % energy increment: 0·90; 95 % CI: 0·83, 0·98; $$P \leq 0$$·01) and for plant protein (OR per 1 % energy increment: 0·91; 95 % CI: 0·85, 0·98; $$P \leq 0$$·01).
Results from secondary analyses using multivariable-adjusted energy partition models to examine substitution effects, which did not control for total energy intake, were consistent with the primary analyses. Among all participants, substituting fat for carbohydrate was associated with higher BMI, but substituting protein for carbohydrate was not. Among overweight/obese and healthy weight participants, substituting percentage energy from fat for carbohydrate was associated with higher odds of overweight/obesity, while substitution of protein for carbohydrate and protein for fat was associated with lower odds of obesity (see online supplementary material, Supplemental Table 6).
## Discussion
In this cross-sectional analysis among 1624 adults in Addis Ababa, Ethiopia, we found that percentage energy intake from total protein and animal protein were both significantly associated with lower odds of overweight/obesity when substituted for carbohydrate, especially among women. This study population had a very high intake of carbohydrates and very low intake of animal protein, which is consistent with other studies in low-income countries[8]. In addition, one-third of the study population was overweight or obese (34 % of women and 25 % of men), which is similar to previous estimates in Addis Ababa[2,7].
To our knowledge, this is the first study in SSA to examine the associations between total, animal and plant protein intake in relation to underweight and overweight/obesity. Our findings are consistent with some previous studies in Western and Asian populations that have found that increased total protein as well as animal protein were associated with improved waist circumference[18] and body composition[17]. Some randomised trials comparing higher protein diets to lower protein diets have reported decreases in body weight(11,37–40) and greater fat loss with higher protein diets[11,38,41]. However, other trials have found that higher protein diets did not result in increased weight loss compared with other diets, especially over the long term(41–43).A meta-analysis of thirty-two trials with greater than 12 months of follow up found that although higher protein diets showed benefits for weight loss in the short term, these benefits persisted to a smaller degree in the long term. However, greater benefits were observed long term in those with better compliance to higher-protein diets[44].
Although additional evidence is needed from long-term trials, existing evidence from shorter-term trials suggests that higher-protein diets may have beneficial metabolic effects when protein is used to partially replace carbohydrates (especially carbohydrates from refined sources)[41]. Replacement of carbohydrate with protein may promote diet-induced thermogenesis, increase satiety which can lead to reduced subsequent energy intake and lower glycaemic load[41,45]. Protein intake may also protect against loss of lean body mass[14]. In the Ethiopian context, increasing protein intake in place of carbohydrate may be beneficial because carbohydrate intake is very high and food sources of carbohydrates are often refined[7]. In our sample, 69 % of calories were derived from carbohydrates and the major sources were teff (a cereal common in Ethiopia), refined wheat, maize and pasta.
Interestingly, we also found percentage energy from animal protein was associated with lower odds of overweight/obesity when substituted for plant protein, which conflicts with some previous studies in Western populations that have found beneficial effects of plant protein and harmful effects of animal protein on waist circumference, BMI and metabolic syndrome(19–21). Food sources of animal protein tend to be higher in cholesterol, energy and saturated fats while food sources of plant protein could help to control body weight and improve body composition because of their associations with lower intakes of energy, total fat, cholesterol and saturated fat[19]. However, these findings and mechanisms in Western populations are likely not generalisable to low-income settings, especially to Ethiopia, where animal-source food consumption is much lower due to economic, cultural and religious factors and carbohydrate intake is very high[46]. The study populations in these Western studies were consuming 9–11 % of total energy from animal protein[19,21], compared with 3 % of energy from animal protein in our Ethiopian study population. In addition, much of the protein intake in our study was from cereals (such as teff, wheat and rice) which may have less favourable effects on body weight than non-cereal plant proteins (such as pulses and nuts). A randomised controlled trial of protein intake and weight loss maintenance found that while substituting overall plant for animal protein was not associated with effects on body weight, a higher intake of cereal plant protein at the cost of non-cereal plant protein was associated with a larger increase in body weight[47]. Non-cereal plant protein sources such as pulses tend to have a lower glycaemic load and may increase satiety, compared with cereal protein sources (some of which may be refined carbohydrates)[47]. It is possible that in low-income settings with high intakes of carbohydrate and low consumption of animal-source foods, protein from animal sources may be beneficial in preventing overweight or obesity, given that much of the protein and energy in the diet comes from cereals.
A strength of this study is that we were able to approximate usual dietary intake through a non-quantitative FFQ adapted for use in Ethiopia. Most previous dietary surveys in Ethiopia have relied on a single 24-hour recall, which are not considered representative of usual dietary patterns. We also conducted a detailed assessment of confounders related to protein intake, overweight/obesity and underweight. Consistent with previous studies, we found a number of socio-demographic factors that were strongly related to protein intake including age, sex, religion, marital status, wealth and household food insecurity[48]. It is well-documented that these confounders are also strongly associated with obesity risk in sub-Saharan Africa[5,48].
FFQs are designed primarily to generate estimates of relative intake, which gives us the ability to distinguish between high and low consumers of a given nutrient, but they can only approximate absolute intake and are subject to additional limitations including recall and self-report biases. We were able to use a non-quantitative FFQ in this study because our analyses did not rely on absolute intakes. While it would have been ideal to use an FFQ validated in an Ethiopian population, no such instrument exists to our knowledge; therefore, we adapted this FFQ from one validated in Tanzania. Additionally, we were unable to capture seasonal variations in food intake because this FFQ was administered at a single time point. During the time period of the survey (Jan–Feb), urban energy intake tends to be higher than during the lean season in June and July[49]. Despite this, we found that a large proportion of our sample were below the estimated average requirement for protein intake (21 % of men, 23 % of non-lactating women and 62 % of lactating women). However, these findings should be interpreted with caution because the FFQ was not validated in this population and the low levels of protein intake we found are in part due to the low total energy intake in this sample, which may have been under-reported. That said, levels of intake reported by our study participants were consistent with previous estimates from Ethiopia[7].
An additional limitation of this study is possible residual confounding from measured and unmeasured factors, such as physical activity. We also lack some key covariates for the men in the sample (including age), but were able to use the female’s covariates as the male and female participants were living in the same household and in most cases were married. However, it is possible that age, education and employment of the female participant could be different than that of the male participant, which could lead to bias in the results of the adjusted models for the male participants. There is also potential for selection bias, since the study only included households who consented to participate in the survey and contained young children, which may not be representative of the overall population of Addis Ababa. However, this survey had a very high response rate (97 %) and the characteristics of the study population are similar to those of the greater urban Ethiopian population in terms of age, educational attainment and household size[2]. In addition, Ethiopia has a high fertility rate and a long median duration of breastfeeding (2 years) which may also partially explain the high proportion of lactating women in this sample[2]. To further address these limitations, we adjusted for socio-demographic and household characteristics in all analyses and adjusted for lactation status in secondary analyses. Lastly, this study focused on Addis Ababa, a major city and results may not be generalisable to rural areas of Ethiopia or other SSA contexts, where dietary patterns and prevalence of overweight differ. In Addis Ababa, consumption of animal source foods is twice as high as in rural areas of Ethiopia[50] and overweight/obesity prevalence is six times higher among women and thirteen times higher among men in urban compared with rural areas of Ethiopia[2].
The cross-sectional design of this study is an additional limitation as it restricts our ability to make causal inferences and poses unique challenges when studying bodyweight as an outcome because participants may change their diets due to awareness of their weight. Repeated assessments of changes over time in dietary factors and body weight are needed to understand long-term effects of protein intake on overweight/obesity and underweight[35]. The cross-sectional design could explain why we did not observe any associations between underweight and animal or plant protein intake.
Animal protein has traditionally been thought of as beneficial in the context of undernutrition, as it provides higher-quality protein (defined by the amino acid content) than most plant sources, along with other essential nutrients[8,15]. Although animal-source foods may be valuable in preventing and treating undernutrition, promotion of animal-source foods, especially meat, is controversial as they have greater environmental impact than plant protein sources[51]. One study in Addis Ababa found that meeting adult and child recommended daily protein intakes with plant-based foods would have a lower environmental impact than meeting this gap with animal-based foods, but would still result in an estimated 65 % increase in land and water use and a 2 % increase in greenhouse gas emissions. Meeting this protein gap with a milk and red meat strategy would result in an estimated 190 % increase in land and water use and 257 % increase in greenhouse gas emissions[10].
Despite their environmental impacts, animal-source foods are a valuable source of protein and micronutrients and providing some animal-source foods alongside plant-based foods may offer additional health benefits in the Ethiopian context[10]. In Africa, animal-source food production can be sustainable through using grassland which cannot be used for crop production and by converting inedible crop by-products into edible food. It can also aid in reducing fertiliser use, cycling nutrients within the ecosystem and is an important mechanism to diversify farmers’ income in the case crop production is reduced(52–54). The Ethiopian Government has demonstrated their commitment to transitioning traditional livestock practices to climate-smart practices through policy interventions as outlined in their Livestock Master Plan and Climate Resilience and Green Economy Strategy[54].
For optimal human and planetary health, the EAT-Lancet Commission has recommended adopting a global reference diet, consisting of mainly plant-source foods (vegetables, fruits, whole grains, and legumes and nuts), and limited or modest amounts of animal-source foods (such as red or processed meat)[15]. However, in our sample, animal-source protein intake was only 11 g per day, which is less than half of the world average of animal-source protein availability in 2011 and much less than the amounts in the EAT-Lancet healthy reference diet[15]. In Ethiopia, animal protein consumption tends to be limited due to religious and social norms and the availability, accessibility and affordability of animal-source foods[55]. It is estimated that to achieve global dietary recommendations in SSA, increases in food consumption-related greenhouse gas emissions would be necessary, but that these small increases would be far outweighed by higher-income countries adopting diets lower in meat consumption[51].
## Conclusion
Innovative strategies are needed to combat the complex, coexisting problems of over and undernutrition in SSA. Our results suggest that in the context of diets that are very high in carbohydrate such as those in Ethiopia, a greater proportion of energy intake from protein may be associated with lower prevalence of overweight and obesity. However, due to the cross sectional design of this study and limited generalisability to other contexts, our findings should be interpreted with caution. Community-based longitudinal data is scarce in Ethiopia, but such studies are needed to further clarify associations between dietary intake with underweight and overweight/obesity, track changes over time and account for seasonality and urban/rural differences in consumption. The majority of the literature on dietary protein and bodyweight is from Western settings and is likely not generalisable to SSA, where dietary patterns are very different. Longitudinal dietary surveys in Ethiopia are needed to inform local guidelines, policies and programmes to combat the double burden of malnutrition. Given the constraints limiting animal protein consumption in Ethiopia, future research should also assess the feasibility of incorporating modest amounts of animal products into Ethiopian diets.
## Conflict of interest:
The authors declare no conflicts of interest.
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|
---
title: Racial/ethnic differences in maternal feeding practices and beliefs at 6 months
postpartum
authors:
- Tayla von Ash
- Anna Alikhani
- Cynthia Lebron
- Patricia Markham Risica
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991812
doi: 10.1017/S1368980021005073
license: CC BY 4.0
---
# Racial/ethnic differences in maternal feeding practices and beliefs at 6 months postpartum
## Body
By the time children reach preschool age, racial/ethnic disparities in obesity are present. Specifically, the prevalence of obesity is 16·5 % among Hispanic/Latinx, 11·6 % among Black, 7·0 % among Asian and 9·9 % among White American children aged 2–5 years[1]. Higher prevalence among racial/ethnic minorities suggests a need to examine risk and protective factors for obesity early in life. While numerous studies have identified early-life biological and behavioural factors that influence obesity risk throughout the lifespan, the literature on racial/ethnic differences in maternal feeding factors during infancy has largely focused on breast-feeding status and age of introduction to solids[2,3]. Compounded by the fact that the majority of early childhood obesity prevention studies in general have been conducted with higher socio-economic, non-ethnically diverse samples, there remains a knowledge gap in the field when it comes to understanding the emergence of racial/ethnic disparities in childhood obesity rates[2,4].
Parental feeding behaviours, beyond those directly related to infant dietary patterns (e.g. mode of infant feeding and early introduction of solids), influence obesity risk during early life[5,6]. For example, when faced with concerns about child underweight or overweight, caregivers often assume more control over child nutrition[7]. However, the decisions parents make about feeding their children have immediate and long-lasting implications for child growth and development[8]. For example, maternal control of feeding can affect weight gain acceleration or deceleration in children as early as infancy[9]. Infants naturally regulate their energy intakes, but parents’ behaviours can override hunger and satiety cues[10]. Pressured feeding (i.e. encouraging infants to finish their bottle), ‘bottle propping’ (i.e. giving infants a bottle by leaning it on a pillow, blanket or other support) and giving infants a bottle to go to bed with have been independently found to be associated with increased obesity risk[11,12]. However, responsive feeding, generally defined as developmentally appropriate responses to the infant’s hunger and satiety cues, can have a positive effect on infant weight gain. Although feeding in the first year of life necessitates a great deal of parental control, infants do best when they are allowed to self-regulate their food consumption[9].
Independent of potential confounders, Black and Hispanic/Latinx children have demonstrated increased odds of rapid infant weight gain, greater maternal control of infant feeding, bottle propping and pressured feeding compared with White children[13,14]. Differences in risk and protective factors have been attributed to socio-economic status, culture, acculturation and a history of disadvantage and discrimination[3,15]. Studies have also demonstrated how feeding styles in Black and Hispanic/Latinx parents influence infant energy intake and infant size, and how maternal characteristics can be associated with infant feeding styles[16,17]. Racial/ethnic differences in early-life risk factors for obesity require further understanding and evaluation as they may contribute to the high prevalence of obesity among minority preschool-age children and may thus signify important areas for intervention.
The Infant Feeding Questionnaire (IFQ), developed by Baughcum et al., assesses maternal feeding practices that may promote self-regulation and other healthy eating habits in children to prevent obesity[18]. Specifically, the IFQ was designed to assess nurture-based feeding practices adopted by mothers when feeding their infants during the first year of life as well as the beliefs that guide such practices.
Breast-feeding is less common among low-resourced women[19], who are also more likely to smoke or be smoke exposed[20]. The Baby’s Breath study created and tested an intervention for decreasing smoke exposure of low-income pregnant women and their newborns. The purpose of this study was to use the IFQ to examine racial/ethnic differences in maternal infant feeding practices and beliefs at 6 months postpartum in a sample at high risk for early obesity.
## Abstract
### Objective:
To examine racial/ethnic differences in maternal feeding practices and beliefs in a sample of low-income smoke-exposed women.
### Design:
Cross-sectional analysis using data collected during a randomised control trial. Maternal feeding practices and beliefs were assessed using the Infant Feeding Questionnaire (IFQ), which was administered at 6 months postpartum. ANOVA was used to examine differences in IFQ items by race/ethnicity, while multivariable linear regression models were used to examine differences in IFQ factor scores by race/ethnicity adjusting for potential confounders.
### Setting:
Participants were recruited from prenatal clinics.
### Participants:
343 women (39 % non-Hispanic White, 28 % Hispanic/Latina, 13 % Black, and 20 % other).
### Results:
Racial/ethnic minority mothers were more likely than non-Hispanic White mothers to put cereal in their infant’s bottle so that the infant would stay full longer ($$P \leq 0$$·032), state their infant wanted more than just formula or breast milk prior to 4 months ($$P \leq 0$$·019), allow their infant to eat whenever he/she wanted ($$P \leq 0$$·023) and only allow their infant to eat at set times ($P \leq 0$·001). Adjusting for potential confounders, racial/ethnic minority mothers had higher scores for factors 1 (concern about infant undereating or becoming underweight), 2 (concern about infant’s hunger), 4 (concern about infant overeating or becoming overweight) and 5 (feeding infant on a schedule), and lower scores for factor 7 (social interaction with the infant during feeding) than White mothers. Racial/ethnic differences were not found for the other two factors.
### Conclusions:
Differences in maternal feeding practices and beliefs across race/ethnicity are present at 6 months postpartum.
## Study design
This secondary data analysis cross-sectionally examines data collected for Baby’s Breath, a randomised control trial aimed at limiting environmental tobacco smoke exposure among pregnant women in the state of Rhode Island[21,22]. Participants were recruited from prenatal clinics that largely serve low-income women in the Providence area. Women were eligible if they spoke English, were at least 18 years of age, had access to a working telephone and VCR/DVD player and were either a current smoker, recently quit smoker or a non-smoker with current exposure to second-hand smoke from someone in their household. Women who were pregnant with multiples or further than 16 weeks’ gestation at the time of recruitment were not eligible to participate. Additional recruitment details along with intervention details have been published elsewhere[21]. Briefly, participants were randomised to receive newsletters and videos during pregnancy and the first 6 months of the postpartum period aimed at either smoking cessation and avoidance or other healthy pregnancy topics. Any information regarding infant feeding was given to both experimental groups. Informed written consent was obtained from all participants, and they were compensated for their time. The analytic sample for this analysis includes participants for which maternal feeding practices and beliefs were assessed at 6-month postpartum (n 343).
## Main exposure
The primary exposure in this analysis was mother’s racial/ethnic background. At baseline (16 weeks’ gestation), participants reported whether or not they identified as Hispanic or Latina. They were then asked if they identified as American Indian or Alaskan Native, Asian, Black or African American, Caucasian or White, Native Hawaiian or Pacific Islander, multiracial and/or other. The Department of Finance method of racial/ethnic classification, in which Hispanic/*Latinx is* deemed a mutually exclusive racial category, was used, such that Hispanic/Latina mothers were classified as Hispanic/Latina regardless of their racial identity[23]. Given the sample distribution, we examined race/ethnicity as four-level categorical variable in our analyses, with categories non-Hispanic White, Hispanic/Latina, non-Hispanic Black and other (which included multiracial individuals).
## Outcomes
The outcomes of interest were maternal infant feeding practices and beliefs self-reported by mothers at 6 months postpartum using the IFQ[18] (see Table 1). The IFQ consists of twenty items, for which factor analysis resulted in the following seven factors: [1] concern about the child undereating or becoming underweight, [2] concern about infant’s hunger, [3] awareness of infant’s hunger and satiety cues, [4] concern about the infant becoming overweight or overeating, [5] feeding the infant on a schedule, [6] using food to calm the infant’s fussiness and [7] social interaction during feeding. Items are assessed using a 5-point Likert scale. The response options for the 12 items assessing maternal feeding practices or child eating behaviours include 0 = never, 1 = rarely, 2 = sometimes, 3 = often and 4 = almost always. The response options for the remaining eight items, which assess maternal beliefs, are 0 = disagree a lot, 1 = disagree a little, 2 = no strong feelings either way, 3 = agree a little and 4 = agree a lot. Higher scores thus indicate a higher frequency of the specified maternal feeding practice or a higher agreement with the maternal belief statement. In addition to the continuous scales, we also dichotomised the items to report the proportion of mothers who commonly (i.e. sometimes, often or almost always) engaged in each practice and agreed (either a little or a lot) with each belief statement.
Table 1Items in the Infant Feeding QuestionnaireMaternal feeding practices/child eating behaviours* Factor† 1. Since your baby was born, how often have you let your baby eat whenever he or she wanted to?‡ 52. How often have you worried that your baby was eating enough?13. How often have you only allowed your baby to eat at set times?54. When your baby became fussy, how often was feeding him/her the first thing you have done?65. How often have you worried that he/she was eating too much?46. How often has it been a struggle to get your baby to eat?17. How often have you become upset if your baby was eating too much?48. How often have you talked or sang to your baby while you fed him/her?79. How often have you put infant cereal in your baby’s bottle so he/she would sleep at night?210. How often have you held your baby when giving him/her a bottle?711. When your baby was less than 4 months old, how often did he/she want more than just formula and/or breast milk?212. How often have you put cereal in your baby’s bottle so he/she would stay full longer?2Maternal beliefs§ 13. How often have you felt that if you didn’t encourage your baby to eat, then he/she wouldn’t eat enough?114. Feeding your baby is the best way to stop his/her fussiness615. You know when your baby is hungry316. You are worried that he/she will become underweight117. You know when he/she is full318. He/she knows when he/she is hungry319. You worry that he/she will become overweight420. He/she knows when he/she is full3*Response options were 0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = almost always.†Factors are: [1] concern about the child undereating or becoming underweight, [2] concern about infant’s hunger, [3] awareness of infant’s hunger and satiety cues, [4] concern about the infant becoming overweight or overeating, [5] feeding the infant on a schedule, [6] using food to calm the infant’s fussiness and [7] social interaction during feeding.‡Item reverse scored when calculating factor score.§Response options were 0 = disagree a lot, 1 = disagree a little, 2 = no strong feelings either way, 3 = agree a little, 4 = agree a lot.
Consistent with Baughcum et al., factor scores were created by calculating the mean score of the items included in each factor[18]. If any item was missing for factors consisting of just two or three items (i.e. factors 2, 4, 5, 6 and 7), the factor score was considered missing. Factors with four items (i.e. factors 1 and 3) were considered missing if more than one item was missing; if only one item was missing, the missing item score was replaced with the mean score of the other items in that factor before calculating the factor score. Also consistent with Baughcum et al., the first item ‘Did you let him/her eat whenever he/she wanted to?’ was reverse scored because of negative loading on factor 5 (feeding infant on a schedule)[18]. Cronbach’s α scores for these factors in the Baby’s Breath sample were similar to those reported by Baughcum et al. [ 2001][18].
## Other measures
Socio-demographic and other sample characteristics were collected at baseline via survey, with the exception of parity, which was abstracted from hospital records. Participant age was reported as a continuous variable while all other variables were categorical. Marital status was dichotomised as either married/cohabitating or not married/cohabitating. Parity was dichotomised as either multiparous (previous live births) or not. For employment status, participants indicated whether they were employed full time, employed part time, not employed, a student or other. Participants reported their highest level of education which was categorised as less than high school diploma, high school diploma or general education degree, or at least some college or technical school. As this was a low-income sample, annual household income was categorised as less than $10 000 per year, between $10 000 and $30 000 per year or more than $30 000 per year. Finally, we assessed breast-feeding status in the postpartum period via survey by asking participants if they had initiated breast-feeding after birth, as well as whether or not they were exclusively breast-feeding, exclusively formula feeding or mixed feeding (i.e. partially breast-feeding) at both 12 weeks and 6 months postpartum.
## Statistical analyses
Bivariate associations between race/ethnicity and socio-demographic characteristics and breast-feeding status were analysed using χ 2 tests (with Fisher’s exact tests used when there were cell values less than five), while associations with continuous scores from the IFQ were analysed using ANOVA. We then constructed multivariable regression models to assess associations between racial/ethnic background with IFQ factor scores adjusting for potential confounders. Potential confounders, informed by the literature, included age, marital status, parity, education, household income and breast-feeding status. Multiple imputation was used for missing covariate data which was assumed to be missing at random. The statistical significance level was set at 0 05 for all procedures, with analyses performed using Stata 15.0 (Stata: Software for Statistics and Data Science, 2020).
## Results
The racial/ethnic composition of the sample was 39 % non-Hispanic White (n 134), 28 % Hispanic/Latina (n 95), 13 % Black (n 46) and 20 % other (n 68). More than half (56 %) of the mothers who were classified as other identified as multiracial; American Indians/Alaskan Natives (22 %) and Asians (16 %) were also represented in this group (See Table 2). The average age of participants at baseline was 24·0 (sd 5·0) years, just over half (53 %) of the participants were married or cohabitating and half (51 %) had no prior children. Employment status and highest level of education varied across the sample, with 47 % of participants not employed and 37 % not having received a high school diploma. Three-quarters of the sample (74 %) had an annual household income of $30 000 or less, with 42 % of the overall sample having an annual household income less than $10 000. This distribution slightly over-represents non-White populations in comparison to the overall population in 2015 as 57 % non-Hispanic White, 7 % non-Hispanic Black, 25 % Hispanic, 12 % other or mixed race exclusive of these groups. ( PRAMS) and 13 % with less than 12 years of education, though the proportion married was similar[24].
Table 2Sample demographics, overall and by race/ethnicityOverallNon-Hispanic WhiteHispanic/LatinaBlackOther* P-value n % n % n % n % n %Sample size34310013439952846136820Age0·278 <2110430443235379201624 21–25128374936323417373044 26–35101293627252620342029 >3610354330023Marital status0·038 Married/cohabitating180537960464817373857 Not married/cohabitating160475340495229632943Multiparous (previous live births)0·017 No163517962374420442744 Yes155494838475625563556Employment status0·207 Full time75222318222315331522 Part time77233023212214301218 Not employed161476650495214303247 Student12465221234 Other17586112469Highest level of education0'.437 < High school diploma127374534323417373349 High school diploma/GED120355038373916351725 Some college/tech. school93273728262713281725Annual household income0.200 <$10 000145425239474919412739 $10 000–30 000109323828303215332638 >$30 0004212221655613913 Do not know/refused47142216121461369Breast-feeding status Initiated breast-feeding209617153757930653349<0.001Breast-feeding at 12 weeks0.066 Exclusive1961310221234 Partial561616122223920913Breast-feeding at 6 months0.018 Exclusive10397001200 Partial2376410114934Ns may not add to the total sample size due to missing values; GED, general education degree.*Includes fifteen individuals identifying as American Indian/Alaskan Native, eleven Asian, one Native Hawaiian or Other Pacific Islander, three other and thirty-eight multiracial; P-value is for χ 2 test, Fisher’s exact used when there were cell values <5.
Demographic differences were observed across race/ethnicity with Hispanic/Latina and Black participants less likely to be married or cohabitating and more likely to be multiparous compared with non-Hispanic White children. Differences across race/ethnicity were also observed for breast-feeding status. While more than half of the sample (61 %) initiated breast-feeding after birth, initiation was highest among Hispanics/Latinas (79 %, $P \leq 0$·001) compared with the other groups. However, by 12 weeks only 22 % of the sample was still breast-feeding, with only 6 % of the sample exclusively breast-feeding and no differences by race/ethnicity. By 6 months, only 10 % of the sample was still breast-feeding (3 % exclusively). Of the mothers exclusively breast-feeding at 6 months, all but one was non-Hispanic White.
Table 3 displays the sample averages for maternal feeding practices and behaviours at 6 months as well as differences in means across racial/ethnic categories. While nearly half of the mothers in this sample (48 %) reported (either sometimes, often or almost always) worrying about whether or not their infant was eating enough, only 11 % of mothers worried that their infant would become underweight. Mothers commonly endorsed at least sometimes, often or almost always putting cereal in their infant’s bottle so that they would stay full (46 %) or sleep longer (43 %), and half (48 %) of the sample reported at least sometimes, often or almost always feeling that their infant wanted more than just formula and/or breast milk prior to being 4 months old. The large majority of mothers (92–99 %) felt agreed that both they and their infant knew when he or she was hungry and full. However, 37 % at least sometimes, often or almost always worried that their infant was eating too much and 22 % agreed with the statement that they worried that their infant would become overweight. Most mothers at least sometimes, often or almost always allowed their infant to eat whenever he or she wanted (84 %) and reported that at least sometimes, often or almost always feeding was the first thing they did when their infant was fussy (76 %). Most mothers also reported that they at least sometimes, often or almost always held their infant when giving a bottle (96 %) and at least sometimes, often or almost always talked to him or her while feeding (93 %).
Table 3Maternal feeding practices and beliefs at 6 months, overall and by race/ethnicityOverallNon-Hispanic WhiteHispanic/LatinaBlackOther* P-value n %† Mean sd Mean sd Mean sd Mean sd Mean sd Factor 1: concern about infant undereating or becoming underweight Worried infant was not eating enough164481·771·691·491·471·791·601·761·541·771·690·439 Struggled to get infant to eat38110·400·860·380·830·380·910·400·690·460·940·938 Worried infant would become underweight‡ 3190·530·990·380·760·571·040·741·100·611·220·130 Infant needed encouragement to eat enough‡ 2060·471·080·280·810·591·130·631·370·561·200·072Factor 2: concern about infant’s hunger Cereal in bottle so infant would stay fuller longer157461·311·471·031·381·471·551·641·401·421·500·032 Cereal in bottle so infant would sleep longer at night147431·591·461·381·371·731·532·001·481·541·500·064 Before age 4 months, infant wanted more than just formula and/or breast milk164481·421·471·161·411·571·491·891·431·401·490·019Factor 3: awareness of infant’s hunger and satiety cues Infant knew when hungry‡ 332973·930·323·930·343·970·183·930·333·880·410·399 Infant knew when full‡ 314923·780·663·790·583·760·743·650·903·880·500·326 I knew when infant was full‡ 323943·880·473·900·373·860·593·840·523·880·410·916 I knew when infant was hungry‡ 338993·650·873·640·903·740·733·531·043·620·900·589Factor 4: concern about infant overeating or becoming overweight Worried infant was eating too much126371·161·311·011·241·251·381·371·451·191·260·347 Worried infant would become overweight‡ 74220·300·720·220·620·300·730·330·730·460·870·170 Upset if infant ate too much2880·951·360·811·231·071·501·151·490·911·310·361Factor 5: feeding infant on a schedule Allowed infant to eat whenever wanted§ 284842·931·312·201·222·711·362·721·292·851·370·023 Only allowed infant to eat at set times183541·831·581·261·451·761·542·091·702·201·49<0·001Factor 6: using food to calm infant’s fussiness Feeding infant was the best way to stop fussiness‡ 84252·191·202·191·142·261·292·241·272·091·180·847 When fussy, feeding infant was first thing you would do259761·601·341·571·221·721·441·571·281·531·450·804Factor 7: social interaction with the infant during feeding Talked or sang to infant while feeding320933·251·023·370·893·161·093·260·883·161·230·393 Held infant while giving a bottle324963·530·913·640·793·431·043·530·873·430·940·267*Includes those identifying as multiracial.† n (%) represents those agreeing either a little or a lot to belief statements and those who either sometimes, often or almost always practised behaviours in practice statements.‡Responses were recorded using the following scale: 0 = disagree a lot, 1 = disagree a little, 2 = no strong feelings either way, 3 = agree a little, 4 = agree a lot; response options for all other items were: 0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = almost always.§Item was reverse scored; P-value is for ANOVA test.
Racial/ethnic differences in several feeding practices and behaviours were observed. Racial/ethnic minority mothers more frequently put cereal in their infant’s bottle so the infant would stay full longer ($$P \leq 0$$·032) and stated their infant wanted more than just formula or breast milk prior to 4 months ($$P \leq 0$$·019) than non-Hispanic White mothers. Allowing the infant to eat whenever he/she wanted ($$P \leq 0$$·023) was also more frequently reported by racial/ethnic minority mothers than non-Hispanic White mothers, however, so too was only allowing the infant to eat at set times ($P \leq 0$·001).
Table 4 shows the multivariable linear regression models for each IFQ factor. In bivariate analyses, we found racial/ethnic differences for factors 2 (concern about infant’s hunger), 5 (feeding infant on a schedule) and 7 (social interaction with the infant during feeding). These associations persisted after adjustment for maternal age, marital status, parity, education, household income and breast-feeding status at 6 months. After adjustment, Hispanic/Latina and Black mothers were more concerned about their infant’s hunger (β: 0·40 (95 % CI 0·09, 0·71) and β: 0·61 (95 % CI 0·22, 0·99), respectively) than non-Hispanic White mothers. They were also more likely to feed on a schedule (β: 0·70 (95 % CI 0·37, 1·02) and β: 0·60 (95 % CI 0·20, 1·00), respectively), as were mothers classified as other compared with non-Hispanic White mothers (β: 0·60 (95 % CI 0·25, 0·95)). Compared with non-Hispanic White mothers, Hispanic/Latina mothers and mothers classified as other reported less social interaction with their infant during feeding (β: –0·23 (95 % CI –0·43, –0·04) and β: –0·26 (95 % CI –0·47, –0·04), respectively).
Table 4Multivariable adjusted associations of race/ethnicity and Infant Feeding Questionnaire factorsUnadjusted β 95 % CIAdjusted β 95 % CI1. Concern about infant undereating or becoming underweight Race/ethnicity Hispanic/Latina0·200·00, 0·400·250·04, 0·46* Black0·24–0·02, 0·500·22–0·04, 0·49 Other0·22–0·01, 0·440·240·01, 0·46* Age0·030·01, 0·05* Married/cohabitating−0·01–0·17, 0·16Multiparous−0·13–0·32, 0·06Education level some college0·01–0·18, 0·21Annual household income0·00–0·12, 0·13 Breast-feeding at 6 months Exclusive0·22–0·27, 0·72 Partial0·09–0·24, 0·412. Concern about infant’s hunger Race/ethnicity Hispanic/Latina0·410·11, 070* 0·400·09, 0·71* Black0·630·25, 1·01* 0·610·22, 0·99* Other0·26–0·07, 0·590·24–0·10, 0·58Age0·02–0·01, 0·05Married/cohabitating−0·03–0·28, 0·21Multiparous−0·34–0·63, –0·05* Education level some college0·09–0·19, 0·38Annual household income−0·07–0·26, 0·12 Breast-feeding at 6 months Exclusive−1·01–1·74, –0·28* Partial−0·21–0·69, 0·273. Awareness of infant’s hunger and satiety cues Race/ethnicity Hispanic/Latina0·02–0·09, 0·130·02–0·09, 0·13 Black−0·07–0·21, 0·06–0·08–0·22, 0·06 Other0·00–0·12, 0·120·00–0·12, 0·12Age0·00–0·01, 0·01Married/cohabitating−0·06–0·15, 0·03Multiparous−0·03–0·13, 0·08Education level some college0·01–0·09, 0·11Annual household income0·02–0·04, 0·09 Breast-feeding at 6 months Exclusive−0·06–0·32, 0·21 Partial−0·05–0·23, 0·134. Concern about infant overeating or becoming overweight Race/ethnicity Hispanic/Latina0·20–0·03, 0·420·250·01, 0·49* Black0·27–0·02, 0·550·310·01, 0·61* Other0·17–0·08, 0·420·20–0·06, 0·46Age0·01–0·02, 0·03Married/cohabitating0·07–0·12, 0·26Multiparous−0·15–0·37, 0·07Education level some college0·08–0·14, 0·30Annual household income0·01–0·14, 0·17 Breast-feeding at 6 months Exclusive0·09–0·47, 0·65 Partial−0·17–0·54, 0·205. Feeding infant on a schedule Race/ethnicity Hispanic/Latina0·750·44, 1·05* 0·700·37, 1·02* Black0·650·26, 1·04* 0·600·20, 1·00* Other0·640·30, 0·98* 0·600·25, 0·95* Age0·00–0·02, 0·03Married/cohabitating−0·16–0·42, 0·10Multiparous−0·06–0·36, 0·23Education level some college0·14–0·15, 0·44Annual household income−0·07–0·26, 0·13 Breast-feeding at 6 months Exclusive−0·65–1·41, 0·10 Partial−0·30–0·80, 0·206. Using food to calm infant’s fussiness Race/ethnicity Hispanic/Latina0·10–0·18, 0·380·21–0·08, 0·51 Black0·02–0·34, 0·370·04–0·32, 0·41 Other−0·07–0·38, 0·24−0·03–0·34, 0·29Age0·030·00, 0·06* Married/cohabitating0·08–0·15, 0·32Multiparous−0·14–0·41, 0·13Education level some college−0·09–0·36, 0·17Annual household income0·10–0·09, 0·28 Breast-feeding at 6 months Exclusive0·45–0·24, 1·14 Partial−0·14–0·60, 0·317. Social interaction with the infant during feeding Race/ethnicity Hispanic/Latina−0·21–0·40, –0·01* −0·23–0·43, –0·04* Black−0·10–0·34, 0·14−0·16–0·41, 0·08 Other−0·21–0·42, 0·01−0·26–0·47, –0·04* Age0·01–0·01, 0·02Married/cohabitating−0·10–0·26, 0·05Multiparous−0·05–0·23, 0·13Education level some college0·05–0·13, 0·22Annual household income0·04–0·08, 0·15 Breast-feeding at 6 months Exclusive−1·15–1·73, –0·56* Partial−0·35–0·65, –0·04* * $P \leq 0$ 05.Referent group for race/ethnicity is non-Hispanic White, for marital status is not married/cohabitating, for multiparous is no, for education is some college and for breast-feeding at 6 months is formula only; age and income are continuous.
In the adjusted models, we additionally observed racial/ethnic differences for factors 1 (concern about infant undereating or becoming underweight) and 4 (concern about infant overeating or becoming overweight). Mothers classified as Hispanic/Latina or other reported greater concern about their infant undereating or becoming underweight than non-Hispanic White mothers (β: 0·25 (95 % CI 0·04, 0·46) and β: 0·24 (95 % CI 0·01, 0·46), respectively), while Hispanic/Latina and Black mothers reported greater concern about their infant overeating or becoming overweight than non-Hispanic White mothers (β: 0·25 (95 % CI 0·01, 0·49) and β: 0·31 (95 % CI 0·01, 0·61), respectively).
## Discussion
In this secondary data analysis with a high-risk sample, racial/ethnic minority mothers reported feeding practices and beliefs that demonstrated greater concern about their infant’s hunger, as well as the amount of food their infant consumed (i.e. concern about under- and overeating) and his or her weight status (i.e. concern about the infant becoming underweight or overweight) compared with non-Hispanic White mothers at 6 months postpartum. Racial/ethnic minority mothers were also more likely to feed their infant on a schedule and less likely to socially interact with their infant while feeding, than White mothers. Observed differences were independent of maternal age, marital status, parity, education, household income and breast-feeding status.
The findings from this study are consistent with the previous literature on racial/ethnic differences in maternal feeding practices in early childhood[13,14]. Perrin et al. found that racial/ethnic minority mothers, especially Hispanics/Latinas, were more likely to encourage their infant to finish feedings[13]. Similarly, in this study, racial/ethnic minority mothers expressed greater concern regarding underweight or undereating. However, unlike Perrin et al., feeding as a first response to a crying or fussy baby did not differ by race/ethnicity in this sample. Our finding that racial/ethnic minority mothers were more likely to feed their infant on a schedule is consistent with findings by Taveras et al., in which Hispanic/Latina and Black mothers had nearly two times the odds of engaging in restrictive feeding practices compared with White mothers[14]. This study advances the literature by examining and observing differences in additional maternal feedings practices and beliefs (e.g. concern about infant overeating or becoming overweight and social interaction during feeding), across race/ethnicity. Our findings suggest that mothers, especially racial/ethnic minorities, may simultaneously be concerned about their infant under- and overeating or becoming under- or overweight, and that social interaction during feeding, which may foster responsive feeding, varies across groups.
Differences with regard to a mother’s concern about her infant being hungry or becoming underweight may explain why racial/ethnic minority infants are more likely to be introduced to solids early[13,14]. Further underscoring this point, we found that racial/ethnic minority mothers were more likely to mix cereal in their infant’s bottle so he or she would stay full longer and felt as though their infant wanted more than just formula and/or breast milk prior to 4 months of age. This is an important topic for future intervention efforts to consider as early introduction to solids (i.e. prior to 4 months of age) and may increase obesity risk[25,26]. The presence of significant differences across race/ethnicity in regard to feeding on a schedule may be another important intervention target as responsive feeding has long been recommended to foster the development of infant self-regulation, though more research explicitly measuring dimensions of appetite regulation (e.g. hunger, satiety and satiation) is needed[10,27]. Our findings also suggest that racial/ethnic minority mothers are relatively concerned about their infant’s weight status and/or weight gain trajectory. Thus, interventions targeting maternal feeding should address concerns about both infant’s undereating or becoming underweight and overeating or becoming overweight.
In targeting maternal infant feeding practices and beliefs, interventions must consider the contextual factors that shape those practices and beliefs. In addition to being influenced by factors related to socio-economic status and family structure, maternal infant feeding practices are also shaped by cultural influences[28]. For example, some racial/ethnic minorities prefer chubby infants and toddlers and frequently do not identify their overweight children as being overweight[29]. Cultural preferences in which heavier infants are perceived as healthier infants may shape the way mothers feed their children. Cultural preferences like these can also be exacerbated by life experiences like food insecurity; for example, one study described how Latina immigrant mothers not only needed to resist indulgent feeding practices but they also need to resist the pressure to give in that arises from the scarcity they faced during their own childhood[30]. Cultural preference and experiences like these likely contribute to the higher rates of racial/ethnic minority mothers engaging in feeding practices that increase obesity risk such as encouraging their infant to finish their bottle, supplemental feeding, bottle propping, immediately feeding their infant when crying and putting their infant to bed with a bottle[12,13]. Without consideration for cultural preferences and beliefs, intervention efforts aimed at promoting certain maternal feeding behaviours (e.g. nutrition counselling) are unlikely to be successful.
The findings from this study also highlight additional practices and beliefs interventions, particularly interventions targeted at high-risk low-income samples, should consider. Mothers in this sample frequently reported worrying about whether the amount of food their infant was eating was sufficient or healthy. Prior research has found that low-income mothers are more concerned about their infant’s hunger than high-income mothers and that they may find it difficult to withhold food from their children even when they have just eaten[18,31]. The majority of mothers in this sample also reported that feeding their infant was the first thing they would do when he or she was fussy. Low-income mothers may thus require additional support around picking up on infant hunger and satiety cues and calming without feeding. Low-income mothers are less likely to believe that infants know their own hunger and satiety than high-income mothers[32]; thus, interventions require teaching or other encouragement of mothers about the ability of infants to self-regulate.
Taken together, the findings from this study may be used to inform maternal infant feeding intervention efforts aimed at high-risk populations. Additional efforts in this area are paramount as maternal feeding practices during infancy have been associated with later feeding practices, eating behaviours and obesity risk(33–35). For example, infants who are encouraged to finish their bottles have been shown to be about twice as likely to eat all of the food on their plate at 6 years old than those who were rarely encouraged to finish their bottle during early infancy[34]. Further, young children from both racial/ethnic minority backgrounds and low socio-economic status households have a disproportionate risk for obesity and are more likely to be exposed to feeding practices associated with weight gain[36].
While the present study expands the literature on racial/ethnic differences in the ways in which mothers feed their infants, it is not without limitations. First, as a cross-sectional secondary data analysis, we were limited in the analyses we could conduct and variables we could adjust for. Also, the fact that mothers self-reported their feeding practices means our results may be biased by mothers’ perception. Additional research using more robust study designs and measures would further expand the literature on this topic and allow investigations of changes over time. Second, we classified race/ethnicity using the Department of Finance method[23]. Racial/ethnic classification is a complex task due to the heterogeneity and social and cultural complexity of groups, and method of classification can have important implications for the results. While prior studies used the rarest group method to classify multiracial individuals[23], the majority of multiracial participants in this sample did not further specify their racial/ethnic background; thus, we included individuals identifying as multiracial in the ‘other’ category. Results from this group must be interpreted with caution due to extreme within-group heterogeneity. Finally, the study sample was comprised of low-income and smoke-exposed women living in the Providence area, RI; thus, our results may not be generalisable to broader populations. While the implications of smoke exposure on the relationship between race/ethnicity and maternal infant feeding are unknown, racial/ethnic differences in maternal infant feeding practices are likely more pronounced in samples that represent different levels of socio-economic status given associations between race/ethnicity, socio-economic status and infant feeding behaviours.
## Conclusions
This study revealed differences in maternal infant feeding practices and beliefs across race/ethnicity among low-income mothers at 6 months postpartum. These differences were not explained by mothers’ age, marital status, parity or educational level, nor by household income or breast-feeding status. Given the evidence linking maternal feeding practices during infancy to childhood obesity risk, interventions are needed to further promote responsive feeding among mothers identifying as racial/ethnic minorities and address their concerns regarding their infant’s hunger, amount of food being eaten and weight status.
## Conflict of interest:
There are no conflicts of interest.
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---
title: A qualitative inquiry of food insecurity in Belize
authors:
- Laurel D Stevenson
- Melissa M Reznar
- Elizabeth Onye
- Lynna Bendali Amor
- Andre J Lopez
- Rita DeFour
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991813
doi: 10.1017/S1368980021002615
license: CC BY 4.0
---
# A qualitative inquiry of food insecurity in Belize
## Body
Food insecurity is uncertain availability or access to high quality, nutritious food, as conceptualised by the FAO’s Food Insecurity Experience Scale[1,2]. Globally, one hallmark indicator of food insecurity is undernourishment[3]. Undernourishment, as one form of malnutrition in children, can lead to underweight, wasting and growth stunting[3]. Longitudinally, these can lead to increased risk of metabolic and CVD in adulthood, decreases in cognitive ability and reproductive performance, and intergenerational health and economic consequences[4,5]. Common mental disorders, such as anxiety and depression, have also been linked to food insecurity[6,7]. Due to the devastating nature of these consequences combined, the FAO and affiliated organisations have established ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture by 2030 as one of the Sustainable Development Goals, Goal #2 End Hunger[8]. One target of this goal is to ‘ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round’[8].
Food insecurity can result in both undernourishment due to lack of access to food and overnourishment because of reliance on cheap, calorically dense foods. In Belize, the prevalence of child undernourishment remains higher than other Central American countries[3], and the prevalence of child stunting has held steady at 15·0 % between 2015 and 2018[3,9]. Stunting has decreased but still remains above other Central American (12·9 %) and Caribbean nations (8·3 %)[3]. At the same time, the prevalence of overweight in Belizean children (7·3 %) is higher than other Central American (6·9 %) and Caribbean nations (7·0 %)[3,10]. In the adult population, diabetes (17 %)[11] and obesity (22·4 %) are prevalent[3]. As such, Belize experiences a double burden of malnutrition[10,12], but may also experience the so-called ‘Obesity Paradox’, which suggests that food insecurity can increase risk of obesity due to reliance on low-quality, highly processed foods that are often cheaper than healthier options[13,14].
Less developed countries tend to have higher levels of food insecurity[3]. Though Belize classifies as a middle-income county, over 40 % of the population lives in poverty, and 15 % lives in extreme poverty[15,16]. Belize experienced two major economic slowdowns between 2006 and 2011, coinciding with a rise in the prevalence of undernourishment in the population[3]. A 2016 FAO report estimated Belize’s ‘moderate or severe food insecurity’ prevalence (eating less than should due to lack of money or other resources) at 28 %, and approximately 9 % of this group experiences ‘severe food insecurity’ (going an entire day without eating due to lack of money or other resources)[17,18]. One recent study indicates that food insecurity in *Belize is* significantly related to education level[19]. This is consistent with documentation that suggests that education is a major factor worldwide[20]. However, research on food insecurity in *Belize is* very limited, and there have been no studies to our knowledge that have involved a thorough exploration of factors related to food insecurity in Belize. For this present study, we conducted a qualitative inquiry in the Cayo District, Belize, to provide an in-depth, contextually rich description of issues leading to and arising from food insecurity. These findings will inform a discussion at the broader environmental and policy levels in Belize and to aid in development of tailored intervention strategies that will ease subsequent health issues.
## Abstract
### Objective:
To explore and provide contextual meaning around issues surrounding food insecurity, namely factors influencing food access, as one domain of food security.
### Design:
A community-based, qualitative inquiry using semi-structured face-to-face interviews was conducted as part of a larger sequential mixed-methods study.
### Setting:
Cayo District, Belize, May 2019–August 2019.
### Participants:
Thirty English-speaking individuals (eight males, twenty-two females) between the ages of 18–70, with varying family composition residing within the Cayo District.
### Results:
Participants describe a complex interconnectedness between family- and individual-level barriers to food access. Specifically, family composition, income, education and employment influence individuals’ ability to afford and access food for themselves or their families. Participants also cite challenges with transportation and distance to food sources and educational opportunities as barriers to accessing food.
### Conclusion:
These findings provide insight around food security and food access barriers in a middle-income country and provide avenues for further study and potential interventions. Increased and sustained investment in primary and secondary education, including programmes to support enrollment, should be a priority to decreasing food insecurity. Attention to building public infrastructure may also ease burdens around accessing foods.
## Design
This qualitative inquiry, conducted in 2019, is the second phase of a larger community-driven sequential mixed-methods study and builds upon the findings from a cross-sectional community-based survey distributed in the Cayo District, Belize in 2017[19]. As a follow-up, this present study sought to provide contextual meaning around food security issues in Belize. Thirty in-depth interviews were conducted with Belizean residents of the Cayo District.
## Setting
The Cornerstone Foundation, a Belizean non-governmental, community-based organisation, initiated this study in 2017 to understand more comprehensively needs around food among the community and individuals they serve. The Cornerstone operates as a humanitarian and social service organisation, primarily in the twin towns of San Ignacio and Santa Elena, though its service area extends to the entirety of the Cayo District. With its mandate to protect and care for vulnerable children, one of the Cornerstone’s primary services involves providing hot lunches for local school children; however, this organisation also operates a food pantry from which it feeds hungry families, seniors and individuals facing homelessness or substance abuse issues. With the bulk of its programming focused on supplementing food to those facing food insecurity in its community, Cornerstone staff partnered with an Oakland University faculty member, who has an ongoing working relationship with the organisation, and Oakland University Master of Public Health students to investigate food security issues in its service area.
## Interview protocol and instrument
Participants were recruited using word-of-mouth snowball sampling with the use of a financial incentive equivalent to $20 USD. Word-of-mouth recruitment started with Cornerstone staff as the key informants and dispersed into their social networks and into the community of people they serve. All interviews were conducted in English, lasted approximately 15–30 min, and were recorded by a student who was working with Cornerstone during a practicum placement. Potential participants were provided the contact information of this student at Cornerstone, and willing participants, aged 18 and older, who were food gatekeepers (responsible for food purchasing and food preparation) made interview appointments at the Cornerstone office or other public locations, such as businesses or the local market. Data collection took place until participants repeated similar ideas, and no new themes were emerging from interviews, thus achieving data saturation.
As this research was part of a larger sequential mixed-methods study, formative work was conducted to develop the interview guide and overall protocol. Through cognitive interviewing and in a participatory fashion, Cornerstone staff and several community members reviewed and revised the interview guide to ensure cultural appropriateness, relevancy of topics and high face validity of questions. The interview guide structured three of its domains – availability, access, and utilisation – to mirror three components of food security[1] and was designed using select models of health behaviour theories to elicit responses rooted in these theories[21], namely the Health Belief Model[22], the Social Cognitive Theory[23] and the Theory of Planned Behaviour[24]. In building upon previous work[19], these three theories support a line of questioning around perceived barriers, self-efficacy and perceived behavioural control to provide additional insight into factors affecting food security and food access. The primary prompt for the food availability section read, ‘Tell me about your food’. Each question had optional follow-up questions depending on the loquacity of the participant (e.g. Where do you usually buy your food? Do you grow your own food?). The food access domain contained the questions, ‘What things make it easy or hard for people in your community to get access to healthy food?’ and ‘What sort of foods do you and your family prefer? Are you able to eat those preferred foods? Why or why not?’ This domain contained questions that were geared to reveal any potential perceived barriers and facilitators to food access, reflecting a component of the Health Belief Model and the Theory of Planned Behaviour. The food utilisation section asked participants to describe their typical individual or family meals, foods they choose to purchase and methods of food preparation in addition to inquiring whether participants felt in control of eating healthy. This question of control was intended to reveal participant self-efficacy and perceived behavioural control for healthy eating and is rooted in the Social Cognitive Theory and the Theory of Planned Behaviour.
## Analyses
Interview recordings were transcribed manually using Microsoft Office and Windows Media Player, with field notes added to the transcripts. Analysis broadly followed the steps outlined by Braun and Clark[25]. An interdisciplinary four-member team with backgrounds in public health, health behaviour, nutrition, social work and nursing conducted a thematic analysis through a process of manual coding. Each member read all transcripts and generated initial codes across a series of three identical transcripts, using a blended approach of inductive and theoretical coding. For example, some codes and themes were generated without an existing coding framework, similar to grounded theory where themes are generated from the data themselves[26], while other codes and themes were linked to theoretical frameworks (e.g. distance to food source as a perceived barrier from the Health Belief Model). Codes and potential themes were discussed among members, and two members then (re)coded and collated codes into themes across all interviews. Members continually reviewed and revised codes and themes as a group until agreement was reached. During the overall coding process, Cornerstone staff occasionally provided more context around a code or theme, for example, describing distinct educational costs associated with levels of attendance at different schools in the Cayo District. A conceptual model of themes was generated to describe food access and security in the Cayo District (see Fig. 1). This visual representation helps to provide additional context on how narrative text and themes relate across all interviews.
Fig. 1Conceptual map of interpersonal barriers related to food access and food security
## Results
Interview participants included thirty individuals who ranged in age from 18 to 70. All were residents of the Cayo District – of San Ignacio, Santa Elena, or a nearby village with public transportation or close enough for walking and bicycling to the town centres. In total, eight men and twenty-two women participated. Participants’ ethnicities included Kriol, Mestizo, Chinese, Garifuna and mixes of ethnicities. Participants were an assortment of single individuals and people with families of varying composition.
These in-depth interviews provide insight into family- and individual-level barriers to food access particularly around the interplay and compounding effects between (i) family composition and individual-level barriers, (ii) income, (iii) education, (iv) employment and (v) issues involving transportation and distance to foods (see Fig. 1). Overall, individuals described an interconnectedness between family composition (overall size, number of children/grandchildren and single- or double-parent households), generating income to support the family, education (sending children to school or being able to attend school themselves) and employment opportunities. Participants also broadly described challenges with transportation and distance to food sources and educational opportunities. Representative quotes are shown in Table 1.
Table 1Themes associated with perceived barriers to food access for Belizeans (n 30) living in the Cayo District, Belize, May–August, 2019ThemeIllustrative quotesFamily structure Large family‘And it depends you know like for example, if you’re a big family it could be hard. You have to eat what you get, you know. If it’s a family of six or seven, you can’t be keeping up that diet as how you want all the nutritious.’ [ 28, 2019.06.05, 18:54]‘Not saying it’s all, but the families that are big numbers, it’s harder for them.’ [ P25, female] Single parenting‘I would see some kids that around their parents are single parents, like the mother have two or four kids. It’s hard for them to provide a plate of food for each one of them.’ [ P8, male]‘You know, some of these teenage girls that are mothers, like they don’t have any assistance from the father of the child, you know, because of so many issues going on, like um there’s a lot of young girls that drop out of school.’ [ P14, female]Income, financial status and affordability Poverty/low income‘Sometimes, maybe if I want something else to eat, I don’t…can’t afford to buy that kind of food.’ [ P15, female]‘…Poverty. Maybe some people can’t afford to buy groceries…’ [P12, female]‘*Finances is* the number one thing. The amount of money people make to get to, to actually pay for food and healthy food um it’s sometimes out of reach for a lot of people. That’s one of the major things.’ [ P1, male]‘I think it’s too high, because I remember at one time that we could buy affordable stuff but now it’s like you couldn’t barely buy stuff with the money. I’ve seen it happen around a lot.’ [ P10, female] Taxes‘Well, our government keeps borrowing money and, God knows where the money goes, and they keep taking the taxes up, so prices of the stuff go up, because then the more taxes the stores have to pay, the higher the price of the product have to be. So, I feel like that’s a major factor.’ [ P14, female]‘A lot of things that is going on here, you know. Teachers asking for raise of pay. And people asking for this and that. And um, the government has to raise tax, so prices has to go up. And then, things that comes in imported might be a little bit expensive too…Sometimes, I get frightened when I go to buy. Because, for example, if you buy canned food you see there’s like 15 to 20 dollars in tax that you pay. Like you’re scared, you know. Even if you go to the stores, you see a lot of sales tax they charge you. So, it’s something pricey. You know, I don’t know why we have to pay so much tax.’ [ P28, female] Preference‘Most of the time like when I have like, since I go to school, I get my money, but like I don’t have enough to buy foods that I would want to eat every day, but I get to eat it once or twice a week what I love, what I would desire to eat.’ [ P13, female]‘Well, I think it’s the fact that we don’t have enough for buy more healthy stuff.’ [ P9, male] Meal skipping‘I spend all my week money before the week is over, I probably would go home and eat, or I would just like try to drink water to stay hydrated and I don’t eat during the day.’ [ P13, female]‘Sometimes I eat like two times a day, but I make sure my kids eat three times a day. Yeah, so sometimes I eat three times a day. Sometimes two times a day…Like, especially Sunday. Sunday and Saturday. But during the week, I eat three times a day, because then I try to get for my kids, so we can have food for three times a day’ [P27, female]‘I usually make tortillas, which is basically the cheapest thing. Um, tortillas, bean, cheese, eggs. Um, the most cheapest thing that I can find, because most of the grocery items are much more expensive, especially chicken and canned foods and stuff like that. Yeah. On the weekend, when I’m at home, I would eat two times for the day. It’s only when I’m here at work I, you know, get to eat three times a day. But otherwise, I only eat two times a day, which would be lunch and supper. For lunch, I would cook rice, you know. It’s not all the time I can afford to get coconut milk to put in it, so sometimes it’s just rice and um I would make meatballs from oats.’ [ P14, female]Education Costs of tuition‘Right now I have a lot of expense cuz I have four kids. I have three that are left school, but right now I owe the school for two of them, because one of my daughters she get help from some White people that come from States…And I don’t know, because it hard right now. No money. I can’t work, because if I work, there aren’t nobody to mind them. [ P27, female]‘There’s many factors that causes a child not to get an education. Not saying it’s all, but the families that are big numbers, it’s harder for them. Now as a family of two, it’s easier; the parents could go out and find a job and put both students to school.’ [ P25, female]*Attending a* ‘good’ school‘You know, if I came from [one school] and somebody else came from [a different school] and we went to apply for a job, best believe that other person will get that job, not me.’ [ P14, female]Employment No jobs‘Sometimes people don’t have jobs, so it’s hard for them to make money to go buy their foods.’ [ P10, female]‘They can’t find a job, there’s no jobs, so…I think that’s one of the biggest problems.’ [ P11, female] Jobs do not pay enough‘In Belize it’s kind of rare for everyone to have a good paying job.’ [ P12, female]‘Especially mom, sometimes she don’t have the food to give her kids, and that’s why that’s why she sometimes, she don’t make them eat even her eat no kind of food when they don’t have, because how she done in her fifties, so she cannot get a good job. She get a jobs, but they charge her, when she go to cleans to a place, they charge her just give her $20 or $25 and that is not a good money for they get food for the kid.’ [ P15, female] Cannot get a job because do not have a good enough education‘Even here, so many people come and ask for a job, and they even say they’ll do it for a plate of food or, you know. There’s no job and especially if you don’t have an education, what are you going to do?’ [ P11, female]‘I believe that the resources the government is getting, they’re putting it into building a street, infrastructure of streets and bridges. There is a few people that get contracts to do that. That specific job. Not everybody’s an engineer.’ [ P22, female]Transportation and distance Transportation difficulties‘Well, there are villages here that are a few miles away, so if they don’t have access to come into town, they won’t be getting like the fresh produce….Well, they would need to get a bus or a taxi to like come into town or if they have a private vehicle or if they have a motorcycle, then they would come in with that, get what they need, and go back out. because gas is really expensive, so like to charter a vehicle might cost, you know, a great amount of money that they probably don’t have, so then I guess they would just do without.’ [ P6, male]‘Mainly because it’s too far for them to get out, and some of them lack of transportation. And if they don’t have a transportation, so they don’t come out so much.’ [ P16, female]
## Family composition as a barrier to food access
Though participants were not directly asked about their family composition, the theme of family makeup emerged, especially having a large family with many children, as a factor in accessing food. Some individuals had households with a significant number of children in the family, while others were led by a single parent or had only one working adult in the household. In nearly all instances, family makeup was tied to being able to afford food and provide for everyone in the household. This illustrative quote describes this phenomenon:‘….when there’s too much members, too much siblings in a family, like for example in my family we are 14 kids and a single mother. And, uh, my mom had to, you know, work three jobs and it’s still not enough to feed the kids, so I would say a little bit of poverty in our community and the lack of not having both parents sometimes. It’s a part of the stress that it creates when you can’t bring enough food for the children or the children would not eat enough, because of the shortage of food that they have.’ [ P26, Female] In the case of single parenting or having a single provider in the family, participants described different circumstances that led to difficulty in accessing food. For example, some families experienced the death of a parent which influences the care of children:‘One family, I would say is the family across from my house, my neighbor. They lost their mother last year, last October, and she left behind two small kids and their fathers, both of them drink, yeah. So, mostly we are the ones that get to feed them. It’s really unfortunate because there’s one that don’t have that support in the family.’ [ P25, female] Other families have experienced the incarceration of a parent and provider, influencing not only food purchasing but also food preference when they are left with little choice due to lack of money:‘But right now, like I tell you, my husband go to jail, so I no buy food for them [the children]. But if there’s somebody that come and give them, yes….we eat it. We don’t pick, because we don’t have enough to pick and choose, so what they give us, we eat it….’ [ P27, female] Some households have experienced difficulties due to lack of assistance from one parent, which then compounded difficulties in accessing food:‘You know, some of these teenage girls that are mothers, like they don’t have any assistance from the father of the child, you know, because of so many issues going on’ [P14, female]
## Income and financial status
A consistent theme around income and financial status appeared among interview participants influencing their ability to afford food. Some participants recognised that there are people in their community living in poverty which makes food access difficult in general:‘Some people don’t have the money to buy the food, because, some people are really, really poor, poor. They can’t afford food.’ [ P11, female] Participants expanded on this and recognised that many people work for little wages, thus making it difficult to access foods:‘Here in our country we work for little, a minimum wage, and when we go to buy, it’s too high for us. It’s why we can’t afford like everything.’ [ P3, female] Others cited increasing costs of living in Belize around services and goods as a contributing factor of poverty and food insecurity:‘A lot of people live in the poor class of society in Cayo and in Belize on a whole. So is it more of the income. Housing has become a little more expensive that it was. Utilities have become a lot more expensive. Even gasoline and fuel has become very expensive in Belize. So, it is the income.’ [ P1, male] Many participants overwhelmingly went on to discuss taxes on foods:‘The income of the family is not enough and the prices are high…Everything here has tax. And the tax is too high. And the income is low.’ [ P11, female]
Additionally, there was some differentiation among participants in the ability to afford food that they preferred:‘I don’t eat them [fruits and vegetables] daily, which is kind of sad but I do enjoy fruits a lot, but I cannot afford them, but they’re kind of like, they’re kind of pricey.’ [ P5, female] Some individuals also expressed how the inability to afford foods, or running out of money between paychecks, leads to meal skipping, cutting back on food between paychecks and ultimately food insecurity:‘Okay, I do not provide for her [my daughter] as often as I would like. I would say two Fridays out of the month, we do not eat until nighttime after my husband gets his pay, because it isn’t enough with the rent and the bills and things….So, sometimes I have to cut back from me, so I could have for her to eat….’ [ P25, female]
## Education
Education and access to education for children, in general, were also frequently mentioned themes, tied to future employment opportunities and higher income levels, which influenced ability to access and purchase foods:‘the lack of um education, because if you’re not educated, now you can’t have a job. At first, you could get a job easily just by your experience, but now they call more the education level, so sometimes the kids don’t have education, but they have to work more hours to have more money to buy food for the family…’ [P23, female] Participants noted that some families cannot afford the cost associated with school, including tuition and supply costs, resulting in dropout and limited employment opportunities:‘There have been families in my community that I’ve seen that some of them, some of the kids, stop going to school, because the parents have no money to send them to school, and so they stop going to school, and they start selling goods on the street.’ [ P14, female] Participants also mentioned that some families cannot afford to feed their children either before or at school or pay for bus fare costs associate with travel to and from school:‘Some people don’t have food to feed their kids to send them to school, so they don’t really want to send their kids on empty stomachs to school. And they, I would say, the bus fare to go to school. Sometimes, they don’t have that, too. [ P21, female] Furthermore, participants mentioned that there is a perception around reputations of schools throughout the country and how attendance at different schools can influence hiring opportunities:‘And because like here, if you don’t have, even if you have a high school diploma, if it’s not a well-recognized school or a religious school, they look at it as just a piece of paper, like literally. Because then you have, uh for example [name of local school]. That’s a very good school, a religious school. And then you have um, I’d say [name of school in Belize City], I’m comparing to a school in Belize City. You know, if I came from [school in Belize City] and somebody else came from [local school] and we went to apply for a job, best believe that other person will get that job, not me.’ [ P12, female]
Participants also noted that accessible and affordable education, supported by the government, is important to a society:‘They’re [the government] not injecting capital into human resource, where people can get a better education at a more affordable cost, and they could become an asset to society in the future.’ [ P22, female]
## Employment
Interview participants consistently discussed employment opportunities in Belize, citing various difficulties with gaining employment, and thus being unable to purchase foods. For example, in general terms, participants stated that there were not enough jobs available:‘*There is* enough food, but there isn’t enough people who have the money to buy the food, because we have a lot of people that don’t have access to jobs.’ [ P14, female] Expanding on this, one participant described difficulties getting jobs due to migration into Belize:‘There’s not enough jobs. So, I believe that we Belizeans, we live with what we have. If we have a job, we try to do our best, and we try to keep what we have, because it’s very hard to get a lot of jobs. We have lots of foreigners coming and they will do work for a lower price. And we Belizeans, that’s the reason that the Belizeans are not getting the jobs, because the foreigners come, no? And they get the job, because [they work for less].’ [ P19, female] Additionally, some participants described how jobs that are available do not pay enough to purchase needed food because prices are high:‘Sometime you have to work hard and when you go to the store, the things [are] expensive. Sometimes the money not enough to buy foods. And that’s $12 for a whole chicken and need the rice. So, I see it hard like that.….it’s hard to get a job.’ [ P27, female] Tying multiple challenges together, individuals discussed how they were unable to get jobs because of the difficulty in getting an education, ultimately, resulting in being unable to afford foods:‘….majority of the people in Belize here does not have jobs to support the family, so the foods are expensive…there’s a shortage [of jobs]…. Because we sometimes don’t have the…get the education. Like we don’t have. Belize you have, like most of the jobs you have here is more for people who went through college, and so maybe this is a poor country and majority of the children here don’t get that education and to go to finish college and get a job like that.’ [ P17, female]
## Transportation and distance
Though all participants were residents of the Cayo district, there was a divergence between participants on issues around transportation and distance. For those living in the villages farther from the two major city and town centres (Belmopan and the twin towns of San Ignacio and Santa Elena), there were major issues in accessing foods as the majority of individuals rely on public transportation (buses or taxis) or bicycles:‘lot of people in Cayo—myself included—live very far from the market and there’s only really one major market and that’s here near to us, where we are right now. People have to come all the way from the villages, and travel all the way on buses or even bicycles. Sometimes we walk all the way down here just to get food….I would say the pricing is good, but the consistency isn’t especially on the weekends where there probably are only two buses that run for a day, or three at max…For those persons who can have a ride, a lot of people hitchhike. A lot of people ride their bicycles and that’s a long, long journey…If you have a vehicle it’s easier, but most people don’t.’ [ P1, male] *Though bus* scheduling seems to have improved in recent years, individuals expand on the difficulties in accessing transportation to outlying villages around the major market:Okay, right now they at this point in time, there are more buses than there were like, let me see, four years back maybe. To Santa Familia you get like a bus every hour. There’s a bus every hour. They go as far as Spanish Lookout…But, to the villages like rural villages, for example Cristo Rey and San Antonio, it would be more difficult for people, because there would be maybe one hour they only three schedules for the day. So, if you don’t really catch that bus at one, you have to catch the other one ‘til about three o’clock…’ [P2, female] For the majority that do rely on public transportation, rough travel on unpaved roads is a challenge:‘some of them live very, very far. Way in like bushes…we have to go all in those bushes, sometimes like little, like little roads to reach…Because I think maybe it’s too far or maybe it’s because the roads are bad, and one time even when we went back there, we just got stuck in the bus. The bus got stuck in the big hole with muds right there. We had to stay there ‘bout an hour trying to get it out. Then we were nowhere that had service for phone, nothing. We were like stranded ‘til we got it out.’ [ P11, female] Additionally, the expense of paying for transportation is another burden for individuals:‘some of them don’t have access to transportation to get to these places. For someone that’s working, $2 isn’t much for a cab, $2 isn’t much. But for someone that’s not working, it’s very expensive, because in the city, the shuttle is a dollar, right, but they don’t have any dollar buses here, you know. So, it would be much harder.’ [ P14, female]
Compounding difficulties with transportation in general, families with children experience financial and logistical challenges for their children travelling back and forth for school and lunch recess, as one individual described:‘Yeah, some of them, the kids live very far out and maybe lunch time, they cannot go home for a lunch, because early in the morning, they have to get a bus bring them to school. Then at like 3:30 when they come out, they have to catch that bus to go back home, because they don’t have money to pay a taxi and they live far, like out in the, like some of them live in the bushes.’ [ P3, female] In contrast, for those living closer to the city or town centres where the main markets are located, described the ease of being able to walk to the market:‘we are walking distance from the market, so we would go down to the market and buy whatever we’re going to use for the day, and that’s the way we eat. We eat the freshest of vegetables that we could get.’ [ P18, female]
## Discussion
To our knowledge, this qualitative study is the first to provide contextual meaning around food security in the Cayo District, Belize, building from our previous quantitative findings and underscoring the importance of engaging in mixed-methods research[27]. Individuals highlight a complex and nuanced interplay of individual- and family-level barriers to food access, which ultimately drive and contribute to food insecurity. While these concepts are presented separately in the results, the conceptual map generated through group coding and thematic analysis shows the interplay between these barriers and their contributions to food insecurity.
In this present study, individuals cite difficulties affording food, having to stretch food among family members or missing meals until the next paycheck due to these various individual- and family-level barriers, all of which are hallmarks of food insecurity[2,3]. Participants indicate this is widespread and not localised to a small group of individuals, which is supported by income data in Belize. Poverty and its relationship to food access has been shown to be significant driver of food insecurity[28]. Exacerbating this are unemployment rates within in the Cayo District (13·6 %), which experiences the highest rates of all districts, compared to nationwide rates (9·4 %)[29].
Though not specifically asked, nearly every individual placed family composition within the interaction between employment, income, and education and their broader relationship to food security. These qualitative findings complement previous findings[19] and highlight the complexity of family composition and its contribution to food insecurity and malnourishment in a household(30–33). Additionally, single-parent households, particularly female-led, experience additional burdens and greater vulnerability[32,33]. Family size in *Belize is* typically larger than family size in other Central American and Caribbean countries[34] which may contribute to difficulties in accessing food. Specifically, family sizes in the Cayo District, especially in the rural areas, are larger than most districts, though these numbers are slowly decreasing[35].
Compounding these matters, participants emphasise a troubling issue around access to education which is known to drive employment opportunities, earning power, and ultimately, food security[36]. Belize’s primary educational system (approximate ages 6–13) is compulsory and supported by public funds; however, there are significant fees associated with attendance. Both large and small families often struggle with paying fees and it is common for children to drop out before completion[10,37]. For children who can complete their primary education, families are then faced with significant burden of paying for secondary education, which is not free. Approximately 40 % of children aged 13–16 years in Belize do not attend secondary school, thus reducing employment opportunities and long-term earnings[10]. Specifically, the Cayo District has the highest proportion of children (30 %) not attending school[35]. Often, these children will not have necessary skills or meet employer educational requirements for hiring or advancement in many industries[35].
Though study participants, who were primarily women, emphasised challenges around access to education and its relationship to food access, they did not describe gender differences in access to education. This is supportive of data showing that Belize experiences gender parity in primary schooling and recent favouring towards girls in secondary schooling[35]. Contrary to what so many other countries face in gender disparities around education, this offers some long-term hope in Belize that women will go on to tertiary schooling and have greater standing in higher paying jobs. Women with higher educational levels also tend to have smaller families which allows them to invest more in each child[38,39]. Additionally, women with higher nutritional knowledge have been linked to fostering a healthier home food environment[39,40], since women and mothers are usually responsible for family meal preparation and food purchasing as food gatekeepers(41–43). Certainly though, aligning with the United Nations Sustainable Development Goal 4, equitable and inclusive educational investment for all children and young adults will be beneficial in Belize to create a more qualified workforce and informed population. A focus on developing a more robust scholarship system or pay-gradient scale for tuition, while also investing in a national school-meal programme, would significantly ease burdens on multiple fronts for families.
This study confirms our earlier notion that transportation and distance to food sources and schooling are a significant contributor to food access and overall food insecurity in the Cayo District[19]. For those living in the town centre area, walking is readily accessible. However, for people living farther out, transportation challenges arise. Belize has several bus companies that operate in each district, but, as participants mention, routes are designated to main roads and run on limited schedules. Travel on rough roads makes a trip incredibly time consuming or nearly impossible, particularly during rainy season, when roads are muddy and bridges are washed out. Additionally, climate changes and recent climate events have been shown to have catastrophic impacts, resulting in areas being completely cut off from transportation and food deliveries and people fleeing and losing their homes[44]. The challenges around transportation and distance that our study participants cite are not unlike what other communities in the region face, and until there is considerable investment in public infrastructure, like road and bridge building and flood management, these challenges will persist(45–48).
Data from this study were collected before the COVID-19 pandemic and our intent was to provide contextual meaning around food security. The pandemic has severely decimated the tourism industry in Belize that, for many, provides income and job opportunities. With mandatory lockdowns and travel restrictions, like what most countries have experienced, unemployment rates have soared[49]. School closures have severely impacted learning opportunities and access to school feeding programmes for thousands of children[50]. We believe the COVID-19 pandemic has only exacerbated issues described here and recovery will take years. Continuing this line of inquiry to understand the extent COVID-19 has impacted individuals and the food landscape in Belize will inform future economic and policy decisions.
## Limitations
There are several limitations to this study. First, interviews were conducted in the town’s centre, and therefore, we did not adequately gain perspectives from individuals living out in the rural villages beyond access to public transportation or within a reasonable walking or bicycling distance to the towns’ centre. We suspect these individuals experience even greater challenges around food security. Second, all of our participants were English speakers; therefore, future studies should be carried out with Spanish-speaking populations also, or individuals living in the Cayo District who are immigrants from other Central American countries. These individuals may be more at-risk for food insecurity.
## Conclusion
The findings of this study provide contextually rich descriptions of issues contributing to food insecurity among individuals in the Cayo District, Belize, namely individual- and family-level barriers to food access. Through group coding and thematic analysis, we have generated a simple conceptual map that describes barriers to food access and food security. Immediate attention to developing more robust educational scholarship and financial support systems would improve the situation on multiple fronts for individuals and families. Not only would it encourage attendance for children at school, but it would also impact and reduce other barriers that affect food security. Additionally, investment in public infrastructure would lessen transportation burdens to access food. However, future work is needed to explore Belize’s rich and ever-changing cultural, political and economic situation, both pre- and post-COVID-19 pandemic. Understanding these systematic challenges can help to situate the country in terms of population-level food security and provide avenues for change.
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|
---
title: 'Intake of carbohydrates and SFA and risk of CHD in middle-age adults: the
Hordaland Health Study (HUSK)'
authors:
- Teresa R Haugsgjerd
- Grace M Egeland
- Ottar K Nygård
- Jannicke Igland
- Gerhard Sulo
- Vegard Lysne
- Kathrine J Vinknes
- Kjetil Bjornevik
- Grethe S Tell
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991815
doi: 10.1017/S1368980020003043
license: CC BY 4.0
---
# Intake of carbohydrates and SFA and risk of CHD in middle-age adults: the Hordaland Health Study (HUSK)
## Body
According to Ancel Keys ‘diet-heart’ hypothesis, a habitually high intake of SFA may increase the risk of CHD due to increases in serum total cholesterol (TC)[1,2]. Mensink & Katan[3] published a meta-analysis in 1992, including twenty-seven controlled trials, concluding that the most favourable lipoprotein profile for CHD was achieved if SFA were replaced by unsaturated fatty acids, keeping the intake of total fat unchanged.
The discovery of the additional pathways leading from diet to CHD has made the ‘diet-heart’ hypothesis more complex[4]. Advice to reduce SFA as a means to prevent CHD may have, indirectly, increased the intake of carbohydrates[5,6]. While carbohydrates have been considered a basis of a healthy diet, with grain products at the base of the Food Guide Pyramid[7], a diet rich in added sugars and refined grains promotes visceral adiposity and reduces energy expenditure(8–10), raising concerns of the potential for increased CHD risk. On the other hand, if dietary carbohydrates are replaced by fat, the postprandial rise in blood glucose and insulin decreases while glucagon secretion increases, resulting in lower CHD risk(11–14). However, among adults with obesity, Hall et al.[15] found that restriction of dietary fat was associated with a slightly larger body fat loss than restriction of dietary carbohydrates. Also of twenty-nine diets with different macronutrient compositions tested in mice, only high-fat diets led to overconsumption and weight gain[16]. A review indicated that greater high glycaemic index carbohydrate intake was associated with a higher risk of CVD compared with SFA intake[17]. Further, recent prospective studies and reviews as well as meta-analyses have shown inconclusive associations between self-reported intakes of either SFA or carbohydrates and fatal and non-fatal CHD(18–23).
Given the inconsistencies in the literature, the objective of the current study was to evaluate the associations of carbohydrate and SFA intakes with incident CHD in a sample of middle-age community-dwelling Norwegian adults, where the intake of carbohydrates varied from 21 to 74 energy percentage (E%) with a median intake of 49 E%, and where the intake of SFA varied from 4 to 25 E% with a median intake of 13 E%.
## Abstract
### Objective:
Limiting SFA intake may minimise the risk of CHD. However, such reduction often leads to increased intake of carbohydrates. We aimed to evaluate associations and the interplay of carbohydrate and SFA intake on CHD risk.
### Design:
Prospective cohort study.
### Setting:
We followed participants in the Hordaland Health Study, Norway from 1997–1999 through 2009. Information on carbohydrate and SFA intake was obtained from a FFQ and analysed as continuous and categorical (quartiles) variables. Multivariable Cox regression estimated hazard ratios (HR) and 95 % CI. Theoretical substitution analyses modelled the substitution of carbohydrates with other nutrients. CHD was defined as fatal or non-fatal CHD (ICD9 codes 410–414 and ICD10 codes I20–I25).
### Participants:
2995 men and women, aged 46–49 years.
### Results:
Adjusting for age, sex, energy intake, physical activity and smoking, SFA was associated with lower risk (HRQ4 v. Q1 0·44, 95 % CI 0·26, 0·76, P trend = 0·002). For carbohydrates, the opposite pattern was observed (HRQ4 v. Q1 2·10, 95 % CI 1·22, 3·63, P trend = 0·003). SFA from cheese was associated with lower CHD risk (HRQ4 v. Q1 0·44, 95 % CI 0·24, 0·83, P trend = 0·006), while there were no associations between SFA from other food items and CHD. A 5 E% substitution of carbohydrates with total fat, but not SFA, was associated with lower CHD risk (HR 0·75, 95 % CI 0·62, 0·90).
### Conclusions:
Higher intake of predominantly high glycaemic carbohydrates and lower intake of SFA, specifically lower intake from cheese, were associated with higher CHD risk. Substituting carbohydrates with total fat, but not SFA, was associated with significantly lower risk of CHD.
## Study population
The current study is a prospective cohort study of participants in the community-based Hordaland Health Study (HUSK) (https://husk-en.w.uib.no/). The recruitment was based on a cohort from 1992 to 1993 (The Hordaland Homocysteine Study). In 1997–1999, all living cohort members born 1950–1951 and residing in the city of Bergen or the neighbouring suburban municipalities were invited to participate in HUSK. The baseline examinations for the current study were conducted during 1997–1999 as a collaboration between the National Health Screening Service (now The Norwegian Institute of Public Health), The University of Bergen and local health services. The participation rate was 77 %. Participants underwent a brief health examination and provided a non-fasting blood sample. Information on lifestyle was collected via self-administered questionnaires. A semi-quantitative FFQ was completed by 87 % of the participants. A total of 3107 participants aged 46–49 years who answered the FFQ were eligible to be included in the current study.
We excluded twenty-two men and five women who reported prior CHD, and four men and nineteen women due to missing information. Further, we excluded twenty-seven men and thirty-five women who reported extreme energy intakes (below the 1st percentile: <4707·8 kJ for men and 2951·8 kJ for women; or above the 99th percentile: >18907·9 kJ for men and >14944·0 kJ for women). The final study population thus included 2995 participants.
## Dietary assessment
Information on food intake was obtained at baseline (1997–1999) using a 169-item past-year semi-quantitative FFQ, a slightly modified version of a previously described FFQ[24]. The validity study of the previous version of the FFQ in a younger population found that the Spearman correlation coefficients between intake of SFA and carbohydrates estimated by the FFQ v. weighed food records were 0·44 and 0·57, respectively[24]. The FFQ was handed out on the day of the health examination, filled out at home and returned by mail to the HUSK project centre. It includes frequency alternatives (from once a month to several times/d), the number of units eaten and portion sizes (e.g., slices, glasses and spoons) to capture the habitual diet during the past year. The information is presented as individual food or beverage items, food groups and nutrients. Daily nutrient intakes were computed from a database and software system developed at the Department of Nutrition, University of Oslo (KBS, version 3. 2). The nutrient database is primarily based on the official Norwegian food composition table[25]. During dietary data collection in 1997–1999, margarine was undergoing rapid compositional changes where large amounts of trans-fatty acids, an important contributor to unsaturated fat[5,26], were being reduced due to legislation in Norway[26]. Further, prospectively, there were other changes to unsaturated fat sources[5]; thus, unsaturated fat was not evaluated as a primary dietary exposure in the current study.
Measurements used as independent variables in the current study are the total dietary amount of SFA and carbohydrates, as well as intake of SFA and carbohydrates from different food items. All are expressed as E%.
## Health examination and health habits
Baseline examinations included measurements of height, weight, waist circumference, blood pressure and non-fasting blood samples. After at least 2 min seated rest, systolic blood pressure and diastolic blood pressure were measured three times (Dinamap 845 XT equipment (Criticon)). Serum samples of TC, HDL-cholesterol, TAG and glucose were analysed within 7 d at the Department of Clinical Chemistry, Ullevål University Hospital, Oslo, using enzymatic methods with reagents from Boehringer Mannheim (Roche). The Friedewald equation was used for the calculation of LDL-cholesterol. Information on educational level and medication use was self-reported.
Hypertension was considered present if the mean of at least two consecutive measurements of systolic blood pressure was ≥140 mmHg or diastolic blood pressure ≥ 90 mmHg or if the use of medication for hypertension was self-reported.
Participants taking diabetes medications or reported having been diagnosed with diabetes were defined as having diabetes. Also, participants with a serum glucose level >7 mmol/l who had not eaten a meal during the last 8 h, or with a glucose level >11·1 mmol/l and <8 h since their last meal, were defined as having diabetes. Pre-diabetes was defined as having glucose levels between 5·6 and 7 mmol/l at least 8 h after their last meal or between 7·8 and 11 mmol/l <8 h after their last meal.
Participants answered one question on past-year vigorous physical activity resulting in sweating or shortness of breath (none, <1, 1–2 or ≥3 h/week). This variable was treated as a categorical variable with none as the reference.
Participants were classified as either non-smokers, former smokers or current smokers with non-smokers as the reference.
## Outcome
The study endpoints were incident (first time) hospitalisation with CHD (ICD9 codes 410–414 and ICD10 codes I20–I25) as primary or secondary diagnosis or death with CHD as the underlying cause of death. Participants were followed from baseline through 31 December 2009 for CHD events through the Cardiovascular Disease in Norway project (CVDNOR, http://www.cvdnor.no)[27,28] and The Cause of Death Registry. There were 107 non-fatal and five fatal episodes. Follow-up time was calculated as time from participation until CHD, death from other causes, emigration or 31 December ·2009, whichever occurred first.
## Statistical analyses
Descriptive characteristics include numbers with percentages and medians (25th, 75th percentiles) for categorical and continuous variables, respectively. Spearman’s rank correlation (rho, ρ) was used to evaluate correlations between quartiles of carbohydrate intake and SFA intake with baseline characteristics. In addition, Spearman correlations of intake of carbohydrates with total fat and SFA were evaluated. To evaluate linear trends in baseline characteristics across quartiles of carbohydrate and SFA intakes as percentage of total energy intake, we used ordinal logistic regression for categorical outcome variables, logistic regression for dichotomous outcome variables and linear regression for continuous outcome variables where median intake as E% within each quartile group was used as the independent variable in the analyses. Cox proportional hazard models were used to calculate adjusted hazard ratios (HR) with 95 % CI for CHD associated with continuous and quartile intake of carbohydrates and SFA. The included covariates were potential confounders associated with the intake of carbohydrates and SFA and with CHD, which also modified the association of either SFA or carbohydrate with CHD when included in the multivariable model. Two primary analyses are presented: model 1 adjusted for age (continuous (years)), sex and total energy intake (continuous (kcal/d)); model 2 additionally adjusted for vigorous physical activity (none v. <1 h/week, 1–2 h/week or ≥3 h/week) with none as the reference and smoking habits (non-smokers v. previous smokers and non-smokers v. current smokers). The following additional confounders were also evaluated, but inclusion of the variables did not materially alter the associations of SFA or carbohydrates with CHD: family history of myocardial infarction, educational level and alcohol intake (E%). Further, carbohydrate analyses also evaluated consistency in results after adjustment for energy-adjusted fibre from bread, fruit and vegetables. SFA analyses were further adjusted for energy-adjusted intake of cholesterol, PUFA and protein.
Supplementary analyses evaluated models adjusted for age, sex and energy intake (model 1), with additional adjustments for HDL-cholesterol, LDL-cholesterol, TAG, glucose, systolic blood pressure, diastolic blood pressure and BMI (model 2); with additional adjustments for diabetes/prediabetes, hypertension, family history of myocardial infarction, statins, oral hypoglycaemics (including metformin) and insulin and anti-hypertensive medications (model 3) and with additional adjustments for smoking, physical activity, alcohol consumption in E% and education (model 4) (see online supplementary material, Supplemental Table S1). To test for linear trends across intake quartiles, median intake as E% within each quartile group was used as the independent variable. We also evaluated SFA from cheese and SFA when excluding the contribution from cheese for their associations with incident CHD. In additional supplementary analyses, we stratified intake of SFA on smoking habits (see online supplementary material, Supplemental Table S2) and we also evaluated associations between carbohydrates and SFA from other specific food groups and CHD risk (see online supplementary material, Supplemental Tables S3 and S4). Missing data were handled with listwise deletion.
The proportional hazard assumption was evaluated using Schoenfeld’s test.
To evaluate the continuous association between exposure and outcome, and assess potential non-linear associations, smoothed penalised splines were plotted[29].
We used theoretical substitution analyses to model the substitution of carbohydrates with SFA[30]. Variables for the E% (per 5 E% unit increments) of all macronutrients except carbohydrates (SFA, monounsaturated fat, PUFA, protein and alcohol) were included in a Cox model with adjustment for total energy intake, age, sex, physical activity and smoking habits. The HR for SFA is then interpreted as the change in estimated risk for each 5 E% unit increase in SFA while holding all other variables in the model constant but allowing for concomitant decreases in carbohydrate intake as all sources of macronutrients sum to 100 % of energy intake. The same approach was used to evaluate the theoretical substitution of carbohydrates with other macronutrients: total fat, protein and PUFA intake per 5 E% unit increase in a model with other macronutrients except carbohydrates[30].
Sensitivity analyses were conducted where we excluded the first 2 years of observation following the baseline assessment in all of the above analyses.
Statistical analyses were performed using Stata version 15 (StataCorp LP) and R version 3.4.0 (https://www.r-project.org/), The R Foundation for Statistical Computing. P-values <0·05 were considered statistically significant.
## Characteristics of the study population
At baseline, mean age was 48 (sd 0·7) years, median BMI was 24·9 (25th, 75th percentiles 22·8, 27·4) kg/m2, 33·5 % smoked daily, 45·9 % reported at least 1 h vigorous physical activity per week and 25·3 % had indications of reduced metabolic health defined as having hypertension, pre-diabetes or diabetes. Intake of total fat ranged from 14 to 53 E% with a median intake of 33 E%. Intake of total carbohydrates ranged from 21 to 74 E% with a median intake of 49 E%. Less than 1 and 6 % had an intake of carbohydrates at or below 30 and 40 E%, respectively, while 3 and <1 % had an intake of carbohydrates at or above 60 and 70 E%, respectively. Intake of protein ranged from 6 to 30 E% with a median intake of 16 E%, while intake of SFA ranged from 4 to 25 E% with a median intake of 13 E%. Less than 1 % had an intake of SFA at or below 6 E%, while 14 % had an intake at or above 15 E%.
During a mean 10·8 (sd 1·3) years of follow-up, representing 32 449 person-years among 2995 participants (1282 men and 1713 women), we documented 112 incident CHD events. Due to missing values (2·1 % for smoking habits and 3·8 % for physical activity), multivariable-adjusted analyses included 2820 participants (1224 men and 1596 women) and 105 CHD events. Sixty participants died due to other causes during follow-up. When evaluating Spearman correlations between carbohydrate quartiles and baseline characteristics, all correlations (ρ) were between −0·2 and <0·1. However, evaluation of baseline characteristics by quartiles of carbohydrate intake identified that the proportion of participants performing at least 1 h vigorous physical activity per week was higher in higher quartiles, while the proportions of men, daily smokers and participants with glucose intolerance were lower in higher quartiles (Table 1). Also, waist circumference, serum levels of TC, LDL-cholesterol and HDL-cholesterol were lower in higher carbohydrate quartile groups. Intakes of total fat, protein and alcohol were lower with higher quartiles of carbohydrate intake. Bread was the major contributor to carbohydrates in this population. While intake (g/d per 1000 kcal) of bread, sweetened beverages, juice, and fruit and berries (both fresh and canned/preserved) was higher with higher quartiles of carbohydrate intake, there were less noticeable differences for other carbohydrate sources across quartiles. Vegetable and fibre intakes (g/d per 1000 kcal), for example, were similar in the various carbohydrate intake quartiles (Table 1).
Table 1Baseline characteristics by quartiles of carbohydrate intake (energy percentage (E%)), The Hordaland Health Study*TotalQ1Q2Q3Q4Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/% P trend † Carbohydrates (E%)4946, 534340, 444847, 485150, 525654, 58<0·001Carbohydrates (g/d per 1000 kcal)123114, 132107101, 110119116, 121127125, 129139135, 145<0·001Participants (n)2995749749748749Age (years)4847, 484847, 484847, 484847, 484847, 480·822Men128242·834846·533244·332042·828237·70·001College and/or university education113838·325334·030841·328638·429139·50·059Family history of infarction118640·930142·028339·029741·030541·70·952Smoking habits<0·001 Previous smokers91531·221829·522530·424032·923232·1 Current smokers98233·533745·726836·220327·817424·1Physical activity<0·001 None74525·921830·018826·017424·116523·3 <1 h/week81328·222030·222531·218926·217925·3 1–2 h/week90831·518926·023232·124634·124134·0 ≥3 h/week41514·410113·97710·711315·712417·5Hypertension70823·717122·918424·617823·817523·40·886Glucose intolerance0·050 Pre-diabetes662·2212·8192·6141·9121·6 Diabetes270·991·281·130·470·9Casual glucose (mmol/l)‡ 5·014·64, 5·495·044·69, 5·505·024·65, 5·535·014·64, 5·454·964·60, 5·440·094BMI (kg/m2)24·922·8, 27·425·023·0, 27·524·722·7, 27·224·922·7, 27·424·922·8, 27·40·410Waist circumference (cm)85·077·0, 94·086·077·0, 95·085·076·0, 93·085·077·0, 93·084·076·0, 93·00·003Cholesterol (mmol/l)‡ 5·655·06, 6·305·725·13, 6·455·665·04, 6·285·645·04, 6·275·575·01, 6·220·001LDL-cholesterol (mmol/l)‡ 3·573·01, 4·173·603·03, 4·273·583·01, 4·153·573·02, 4·183·512·96, 4·090·010HDL-cholesterol (mmol/l)‡ 1·281·06, 1·531·291·08, 1·541·291·05, 1·551·281·06, 1·531·261·03, 1·510·017TAG (mmol/l)‡ 1·401·01, 2·041·421·01, 2·11·391·00, 1·941·371·00, 2·011·411·02, 2·100·933Medications for Diabetes140·540·540·520·340·50·842 Hypertension1344·5293·9405·3334·4324·30·879 Hypercholesterolemia501·7192·570·9121·6121·60·262Dietary intake SFA (E%)1311, 141413, 161312, 141211, 13119, 12<0·001 Total fat (E%)3329, 363835, 413432, 363130, 332725, 29<0·001 Protein (E%)1614, 171715, 181615, 171614, 171514, 16<0·001 Alcohol (E%)10, 321, 410, 310, 210, 2<0·001 Cholesterol (mg/d per 1000 kcal)130110, 152152131, 176137119, 156126111, 14210791, 125<0·001 Fibre (g/d per 1000 kcal)1110, 13109, 111110, 121210, 141311, 16<0·001 Energy intake (kcal§/d)20571690, 255021331751, 265421281750, 257820791687, 256119171559, 2399<0·001Intake of food items Cakes g/d per 1000 kcal105, 1783, 14115, 18116, 18105, 160·001 E% carbohydrates1·50·7, 2·61·20·5, 2·11·60·7, 2·61·60·9, 2·81·50·7, 2·6<0·001 Snacks g/d per 1000 kcal21, 531, 621, 521, 410, 4<0·001 E% carbohydrates0·30·1, 0·70·40·1, 0·80·30·1, 0·70·30·1, 0·70·20, 0·6<0·001 Soft drinks with sugar g/d per 1000 kcal250, 65180, 43282, 68272, 63341, 86<0·001 E% carbohydrates1·00, 2·60·70, 1·71·10·1, 2·81·10·1, 2·61·40·0, 3·6<0·001 Fresh fruit and berries g/d per 1000 kcal6235, 984324, 715935, 916642, 1048548, 139<0·001 E% carbohydrates3·31·9, 5·32·31·3, 3·73·21·9, 4·83·62·2, 5·64·52·6, 7·5<0·001 Juice g/d per 1000 kcal202, 49120, 32214, 48266, 58232, 63<0·001 E% carbohydrates0·80·1, 2·00·50, 1·30·80·2, 1·91·00·2, 2·30·90·1, 2·5<0·001 Conserved fruit and berries g/d per 1000 kcal133, 2571, 18134, 24166, 28175, 32<0·001 E% carbohydrates2·30·4, 4·81·00, 3·42·30·5, 4·42·90·9, 5·33·10·7, 6·1<0·001 Bread g/d per 1000 kcal8569, 1057760, 938367, 998872, 1089878, 120<0·001 E% carbohydrates16·413·2, 20·114·711·7, 17·815·813·0, 18·916·813·8, 20·718·714·8, 22·9<0·001 Rice, pasta, flour, cereals g/d per 1000 kcal1912, 281610, 231912, 262013, 292113, 33<0·001 E% carbohydrates3·72·3, 5·93·02·0, 4·63·62·3, 5·54·12·6, 6·44·52·6, 7·3<0·001 Potatoes g/d per 1000 kcal4832, 674730, 634733, 644831, 675233, 71<0·001 E% carbohydrates4·12·7, 5·64·02·6, 5·44·12·9, 5·54·12·7, 5·64·32·8, 5·8<0·001 Vegetables g/d per 1000 kcal8554, 1318253, 1228555, 1298857, 1328752, 1360·136 E% carbohydrates2·21·5, 3·22·21·4, 3·12·21·5, 3·22·31·5, 3·22·31·4, 3·30·038Q, quartile.*Values are presented as n and % and median (25th, 75th percentiles) for categorical and continuous variables, respectively.†Logistic regression for categorical variables with two categories, ordered logistic regression when more than two categories and linear regression for continuous variables where median intake as E% within each quartile group was used as the independent variable in the analyses.‡In serum.§To convert kcal to kJ, multiply by 4·184.
When evaluating Spearman correlations between SFA quartiles and the baseline characteristics, all rhos (ρ) were between –0·1 and <0·1. However, the percentage daily smokers were higher with higher quartiles of SFA intake, while the percentage of participants who were men, performed at least 1 h vigorous physical activity per week or had hypertension was lower with higher quartiles (Table 2). Also, BMI, waist circumference and TAG levels, as well as the percentage taking medications for hypercholesterolaemia, were lower with higher SFA quartiles. While intake of cheese was higher with higher quartiles of SFA intake, there were less noticeable differences for other SFA sources across quartiles. Family history of myocardial infarction did not differ between quartiles of carbohydrate (P trend 0·95) or SFA (P trend 0·23) intake as percentage of total energy.
Table 2Baseline characteristics by quartiles of saturated fat intake (energy percentage (E%)), The Hordaland Health Study*TotalQ1Q2Q3Q4 P trend † Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/%Median/n 25th, 75th percentiles/%SFA (E%)1311, 14109, 111211, 121313, 141515, 16<0·001SFA (g/d per 1000 kcal)1412, 161110, 121313, 141514, 151716, 18<0·001Participants (n)2995749749748749Age (years)4847, 484848, 494847, 484847, 484847, 480·151Men128242·832743·734946·631842·528838·50·016College and/or university education113838·329139·428137·629039·227637·00·121Family history of infarction118640·931643·429540·728439·229140·50·227Smoking habits<0·001 Previous smoker91531·224834·224132·923031·219626·7 Current smoker98233·519727·223331·825935·129339·9Physical activity<0·001 None74525·915021·019627·319927·420027·7 <1 h/week81328·219226·916823·422130·423232·2 1–2 h/week90831·524133·724333·822130·420328·2 ≥3 h/week41514·413218·511215·68511·78611·9Hypertension70823·719626·218324·417924·015020·00·006Glucose intolerance0·296 Pre-diabetes662·2152·0172·3111·5233·1 Diabetes270·960·860·891·260·8Casual glucose (mmol/l)‡ 5·014·64, 5·495·044·66, 5·515·014·66, 5·474·994·65, 5·494·984·60, 5·430·400BMI (kg/m2)24·922·8, 27·425·223·0, 27·625·223·0, 27·524·823·0, 27·424·622·2, 27·10·002Waist circumference (cm)85·077·0, 94·085·077·0, 94·086·078·0, 94·085·077·0, 93·084·075·0, 93·00·021Cholesterol (mmol/l)‡ 5·655·06, 6·305·645·06, 6·355·615·01, 6·265·695·10, 6·285·655·08, 6·350·729LDL-cholesterol (mmol/l)‡ 3·573·01, 4·173·552·96, 4·173·532·99, 4·113·583·07, 4·173·613·02, 4·230·250HDL-cholesterol (mmol/l ‡ 1·281·06, 1·531·291·06, 1·531·251·04, 1·501·291·07, 1·541·321·08, 1·570·064TAG (mmol/l)‡ 1·401·01, 2·041·411·04, 2·151·471·03, 2·081·401·01, 1·991·330·95, 1·930·005Medications for Diabetes140·530·420·360·830·40·704 Hypertension1344·5364·8374·9334·4283·70·270 Hypercholesterolaemia501·7202·7111·5141·950·70·007Dietary intake Carbohydrates (E%)4946, 535451, 585148, 534845, 514542, 48<0·001 Total fat (E%)3329, 362725, 293130, 333432, 363735, 40<0·001 Protein (E%)1614, 171614, 171614, 171615, 171614, 170·876 Alcohol (E%)10, 310, 310, 310, 310, 2<0·001 Cholesterol (mg/d per 1000 kcal)130110, 15211595, 137128110, 149133116, 156143122, 164<0·001 Fibre (g/d per 1000 kcal)1110, 131311, 161210, 131110, 13108, 11<0·001 Energy intake (kcal§/d)20571690, 255019001537, 233520931731, 258421121712, 262321821773, 2584<0·001Intake of food items Butter|| g/d per 1000 kcal0100010103<0·001 E% SFA0100000001<0·001 Cheese g/d per 1000 kcal137, 2194, 14117, 18159, 241911, 32<0·001 E% SFA21, 311, 221, 221, 332, 4<0·001 Margarine g/d per 1000 kcal32, 821, 432, 732, 932, 11<0·001 E% SFA11, 210, 111, 211, 211, 3<0·001 Milk and milk products g/d per 1000 kcal12965, 20513161, 21414071, 21312869, 19611961, 1960·031 E% SFA11, 211, 211, 211, 221, 3<0·001 Meat and meat products g/d per 1000 kcal5541, 704936, 635643, 705743, 745742, 73<0·001 E% SFA22, 322, 322, 322, 332, 3<0·001 Minced meat products g/d per 1000 kcal2415, 342012, 292516, 332617, 362617, 35<0·001 E% SFA11, 211, 111, 211, 211, 2<0·001Q, quartile.*Values are presented as n and % and median and 25th, 75th percentiles for categorical and continuous variables, respectively.†Logistic regression for categorical variables with two categories, ordered logistic regression when more than two categories and linear regression for continuous variables where median intake as E% within each quartile group was used as the independent variable in the analyses.‡In serum.§To convert kcal to kJ, multiply by 4·184.||*Intake is* reported as median and mean because of a large number of zero intake reporting.
## Associations between intake of carbohydrates and SFA and incident CHD
Higher intake of carbohydrates was borderline significantly associated with higher risk of CHD in model 1 (adjusted for age, sex and energy intake) (HRQ(quartile)4 v. Q1 1·63, 95 % CI 0·96, 2·76, P trend = 0·056) (Table 3). This association became stronger and significant after further adjustment for smoking habits and physical activity (model 2) (HRQ4 v. Q1 2·10, 95 % CI 1·22, 3·63, P trend = 0·003). Also, continuous analyses (per 2 E%) showed significantly higher risk of CHD with higher intake of carbohydrates (HR 1·12, 95 % CI 1·05, 1·20), after adjusting for age, sex, energy intake, smoking habits and physical activity. Further adjustments for intermediate factors, relevant medications and potential confounders did not materially influence the association (see online supplementary material, Supplemental Table S1).
Table 3Associations between macronutrients and risk of incident CHD, The Hordaland Health Study. Mean follow-up time 10·8 yearsIntake of macronutrients (range in E%)* n CHDModel 1† Model 2‡ n %HR95 % CIHR95 % CICarbohydrates2995112 Q1 [21, 45]749233·11 (ref)1 (ref) Q2 [45, 49]749253·31·110·63, 1·961·090·60, 1·98 Q3 [49, 53]748293·91·300·75, 2·241·670·96, 2·89 Q4 [53, 74]749354·71·630·96, 2·762·101·22, 3·63 P trend § 0·0560·003Continuous (per 2 E%)29951121·081·01, 1·151·121·05, 1·20Saturated fat2995112 Q1 [4, 11]749445·91 (ref)1 (ref) Q2 [11, 13]749243·20·550·34, 0·910·460·27, 0·78 Q3 [13, 14]748233·10·550·33, 0·920·470·28, 0·79 Q4 [14, 25]749212·80·530·32, 0·900·440·26, 0·76 P trend § 0·0130·002Continuous (per 2 E%)29951120·820·70, 0·970·780·66, 0·92Saturated fat from cheese2995112 Q1 [0, 1]749456·01 (ref)1 (ref) Q2 [1, 2]748283·70·680·43, 1·100·780·49, 1·27 Q3 [2, 3]749253·30·640·39, 1·040·600·35, 1·03 Q4 [3, 18]749141·90·380·21, 0·700·440·24, 0·83 P trend § 0·0020·006Continuous (per 1 E%)29951120·840·74, 0·970·870·75, 0·99Saturated fat after excluding saturated fat from cheese2995112 Q1 [3, 9]748344·61 (ref)1 (ref) Q2 [9, 10]750293·90·850·51, 1·390·710·42, 1·20 Q3 [10, 12]748202·70·560·32, 0·980·460·26, 0·81 Q4 [12, 21]749293·90·810·49, 1·340·580·34, 0·98 P trend § 0·2770·030Continuous (per 2 E%)29951120·940·79, 1·120·850·71, 1·02E%, energy percentage; CHD, incident CHD; n, number of participants; HR, hazard ratio; Q, quartile.*Minimum and maximum intake of the macronutrient.†Cox proportional hazard regression analysis adjusted for age, sex and energy intake.‡Adjusted in addition for physical activity and smoking habits.§ P trend, to test for linear trends across quartiles, we modelled the median intake of each quartile as a continuous variable.
Plotting the data adjusting for model 2 covariates indicated a linear relationship (Fig. 1(a)).
Fig. 1Cox proportional hazards regression with penalized splines, The Hordaland Health Study. Distribution of partial hazard (black line) with $95\%$ CI (shadow) for CHD across the distribution of a) intake of carbohydrates in E%, b) intake of saturated fatty acids (SFA) in E% and c) intake of SFA after excluding contribution from cheese in E%. The model includes age, sex, energy intake, physical activity and smoking habits. Intake above the 5thth percentile and below the 95th percentile is included in the figure When examining the association between carbohydrates from various food items, we found no associations with risk of CHD (see online supplementary material, Supplemental Table S3).
A high intake of SFA was significantly associated with lower risk of CHD in the model adjusted for age, sex and energy intake (model 1) (HRQ4 v. Q1 0·53, 95 % CI 0·32, 0·90, P trend = 0·013) (Table 3). This association became stronger after further adjustment for smoking habits and physical activity (model 2) (HRQ4 v. Q1 0·44, 95 % CI 0·26, 0·76, P trend = 0·002). Also, continuous analyses (per 2 E%) showed significantly lower risk of CHD with higher intake of SFA (HR 0·78, 95 % CI 0·66, 0·92), after adjusting for age, sex, energy intake, smoking habits and physical activity. Further adjustments for intermediate factors, relevant medications and potential confounders did not materially influence the association (see online supplementary material, Supplemental Table S1).
Figure 1(b) illustrates lower risk of CHD with a higher intake of SFA until an intake of about 13 E%, after which the curve levelled off, after adjustment for model 2 variables.
When stratifying on smoking habits, there was a tendency of lower risk of CHD with higher intake of SFA in all groups, but less so among current smokers (see online supplementary material, Supplemental Table S2).
When examining the association between SFA from various food items, we found that only SFA from cheese was significantly associated with a lower risk of CHD (Table 3). The median intake of SFA from cheese ranged from 0·5 E% (Q1) to 4·1 E% (Q4). After adjustments for age, sex, energy intake, physical activity and smoking habits (model 2), SFA from cheese was significantly associated with lower risk of CHD in the quartile analyses (HRQ4 v. Q1 0·44, 95 % CI 0·24, 0·83, P trend = 0·006). Results from the evaluation of SFA from cheese as a continuous variable (per 1 E%) were similar.
We further evaluated the association between SFA and CHD after excluding the SFA contribution from cheese, and in the quartile analyses, we found that intake of SFA after exclusion of cheese was associated with lower risk of CHD (HRQ4 v. Q1 0·58, 95 % CI 0·34, 0·98, P trend = 0·030), after adjustment for age, sex, energy intake, physical activity and smoking habits (model 2, Table 3). Results from the continuous analyses were in the same direction as the quartile analyses. Upon further evaluation (Fig. 1(c)), we observed deviations from linearity in the association between SFA intake and CHD risk after excluding SFA from cheese.
Higher intake of total carbohydrates correlated significantly with lower intake of SFA (ρ = −0·6, $P \leq 0$·001) and lower intake of total fat (ρ = −0·8, $P \leq 0$·001). Results from the theoretical substitution analyses are shown in Fig. 2. Substitution of 5 % of total energy intake from carbohydrates with SFA was associated with a 26 % lower risk of CHD (model 2 HR 0·74, 95 % CI 0·40, 1·36), although not statistically significant. A substitution of carbohydrates with total fat was also associated with lower risk of CHD (model 2 HR 0·75, 95 % CI 0·62, 0·90). To further evaluate whether substitution analyses of carbohydrates with SFA or with total fat could be attributed to an underlying beneficial effect of PUFA, we evaluated results of analyses substituting carbohydrates with PUFA in which we found a non-significant association with incident CHD (model 2 HR 1·42, 95 % CI 0·82, 2·47). Further, the substitution of carbohydrates with protein was not associated with the risk of CHD (model 2 HR 1·09, 95 % CI 0·71, 1·68). When adjusting for age, sex and energy intake only, results of all substitution models were in the same direction as in the fully adjusted model, but were non-significant.
Fig. 2Theoretical substitution analyses, illustrating an isocaloric substitution of 5E% from carbohydrates with total fat, saturated fatty acids (SFA), polyunsaturated fatty acids (PUFA) or protein and its association with CHD. Adjusted for age, sex, energy intake, physical activity and smoking habits. Mean 10·8 years follow-up of the Hordaland Health Study participants Exclusion of events occurring during the first 2 years of follow-up yielded no material differences in results.
## Discussion
In this community-based study population, high intake of carbohydrates and low intake of SFA were associated with higher risk of incident CHD. Intake of SFA from cheese was significantly associated with lower CHD risk. When evaluating SFA intake after excluding the contribution of SFA from cheese, the association became weaker, but remained significant. Substituting 5 % of total energy intake from carbohydrates with SFA and total fat was associated with lower CHD risk (HR of 0·74 and 0·75, respectively), but was statistically significant only for total fat. The lack of a statistically significant finding for SFA may reflect, in part, the narrower range of SFA intake compared with total fat and carbohydrate intake.
Carbohydrates reflect a variety of sources including sucrose, fructose and refined cereals, as well as fibre-rich whole grains, vegetables and legumes. Refined carbohydrates and added sugar accounted for a large part of carbohydrate intake in the Norwegian diet at the time of HUSK baseline in 1997–1999[5]. Even today, few Norwegians comply with the Nordic nutrition recommendations for fibre intake[31,32]. Per capita sales data indicate that intake of sugar-containing foods and beverages peaked at the end of the 1990s[5]. In addition, a nationwide diet survey among men and women 16–79 years of age (1997–1999) found that their diet contained inadequate amounts of food products rich in fibre and that the intake of added sugar was 10 and 9 E% among men and women, respectively[33]. When evaluating baseline characteristics in this cohort, the intake of fruit and berries, sugar-sweetened beverages and juice doubled from the lowest to the highest quartile of carbohydrate intake. In contrast, intake of rice, pasta, flour and cereals was only modestly higher and vegetable intake did not differ across quartiles of total carbohydrate intake. Also, while recommended intake of fibre is ≥25 g/d in women and ≥35 g/d in men[31], the median intake of fibre in the total study population was 24 g/d and the median fibre intake in the highest quartile of carbohydrate intake was 26 g/d. However, FFQ are affected by systematic errors and do not precisely estimate dietary intake; therefore, these data should be interpreted with caution.
Previous cohort studies and meta-analyses have shown diverse results regarding the association between intake of carbohydrates and CHD when evaluating total carbohydrates. A study of men and women 30–59 years of age found that carbohydrate intake was associated with a lower CHD mortality risk (RR 0·96, 95 % CI 0·94, 0·99)[34]. However, a large cohort study of individuals aged 35–70 years found that higher carbohydrate intake was not associated with the risk of CVD (HR 1·01, 95 % CI 0·88, 1·15) or myocardial infarction (HR 0·90, 95 % CI 0·73, 1·10)[23]. Carbohydrate intake was not consistently associated with CHD when different sources of carbohydrates were considered separately. Li et al.[35] found in a cohort study that higher intake of carbohydrates from whole grains was associated with lower risk of incident CHD (HR 0·90, 95 % CI 0·83, 0·98), while carbohydrates from refined starches/added sugars were positively associated with higher risk of CHD (HR 1·10, 95 % CI 1·00, 1·21).
Fung et al.[36] studied the association between consumption of sugar-sweetened beverages and the risk of CHD in the Nurses’ Health Study and found that regular consumption of sugar-sweetened beverages was associated with a higher CHD risk. In addition, a meta-analysis of cohort studies reported that intake of sugar-sweetened beverages was associated with increased risk of myocardial infarction[37]. In randomised controlled trials, dietary sugar intake has been found to increase blood pressure and serum TAG, TC and LDL-cholesterol[38]. While we did not identify any one particular source of carbohydrates to contribute to the overall carbohydrate association with CHD, we did note differences in the quality of carbohydrate sources between low to high carbohydrate intake quartiles where intake of fibre, vegetables and many carbohydrate sources remained essentially stable, while bread, sugar-sweetened beverages, juice, and preserved and fresh fruit and berries increased across the quartiles of carbohydrate intake. Thereby indicating that increased carbohydrate intake in the current study population represented increases in low-fibre and higher sucrose/fructose carbohydrates.
SFA intake in our study population came primarily from dairy products, especially cheese. The intake of cheese more than doubled from the lowest to the highest quartile of SFA intake, and cheese was also the main contributor to SFA intake in the highest quartile. As dairy products are important contributors of SFA, the general recommendation in Norway has been to reduce the intake of high-fat dairy products[39]. However, studies do not consistently support that this recommendation would reduce risk of CHD(40–42). A systematic review and meta-analysis did not report a statistically significant association between total dairy intake and CHD (Summary RR 0·91, 95 % CI 0·80, 1·04)[41]. Further, the only dairy product significantly associated with lower CHD risk was cheese (Summary RR 0·82, 95 % CI 0·72, 0·93)[41]. In addition, Qin et al.[42] reported no association between dairy intake and CHD (RR 0·94, 95 % CI 0·82, 1·07), and CHD risk was lowered by cheese consumption also in this study (RR 0·84, 95 % CI 0·71, 1·00).
We found that intake of SFA from cheese was the only food source associated with a lower risk of CHD. Underlying mechanisms for a potential CHD protective effect of cheese may relate to (i) fermentation which may influence dairy fat’s contribution to LDL-cholesterol[43] and (ii) menaquinones (vitamin K2) which comes primarily from cheese in European diets[44]. Geleijnse et al.[45] found that menaquinone intake was inversely associated with serum TC and aortic calcification and positively associated with serum HDL-cholesterol. Menaquinones transported together with SFA may, therefore, be associated with lower CHD risk. Also, as most cheeses are not homogenised, they still contain milk fat globule membranes. Rosqvist et al.[46] reported that intake of milk fat enclosed by milk fat globule membranes did not impair the lipoprotein profile when compared with butter oil. When evaluating the association between SFA and CHD after excluding the contribution from cheese, intake of SFA was still associated with lower risk of CHD. The penalised spline illustrates almost the same pattern as for total SFA, but with a tendency of higher risk at higher intakes.
A systematic review and meta-analysis found that when comparing the highest v. lowest intake of SFA, there was no association observed between SFA intake and CHD (RR 1·03, 95 % CI 0·98, 1·07)[18]. Another meta-analysis of cohort studies found that the highest v. lowest quintile intake of SFA had a weak association with the risk of CHD (RR 1·06, 95 % CI 0·95, 1·17)[19]. However, the quality of the documentation was regarded as very low, and in an analysis not adjusted for cardiovascular risk factors such as serum cholesterol, there was a significantly higher risk of CHD mortality comparing the highest v. lowest intake of SFA (RR 1·20, 95 % CI 1·02, 1·41)[19].
Our results differ from these meta-analyses and likely reflect that, in the current study population, cheese was the predominant contributor to SFA, there was a narrow range of median SFA intake in the four quartiles, and there was an inverse association between SFA and carbohydrate intake.
## Theoretical substitution analyses
Analysing the effect of one nutrient when considering the nutrients it substitutes provides another means of understanding the observed associations[30]. Another study suggested that reducing the intake of carbohydrates from refined grains and added sugars may produce beneficial metabolic effects that may decrease the risk of CHD[22]. Jakobsen et al.[21] showed that when substituting 5 E% from SFA by carbohydrates, there was no association with fatal CHD (RR 0·96, 95 % CI 0·82, 1·13), but a statistically significant increase in the overall CHD risk (RR 1·07, 95 % CI 1·01, 1·14). When separately evaluating carbohydrates with high and low glycaemic index, only a substitution of SFA with high glycaemic index carbohydrates was associated with a higher risk of myocardial infarction (HR 1·33, 95 % CI 1·08, 1·64)[22]. Chen et al.[47] evaluated the association between dairy fat and CHD in US adults and found no significant benefit of replacing dairy fat with the same energy intake from refined starch and added sugar. However, the substitution of 5 % of energy from dairy fat by carbohydrates from whole grains was associated with a significantly lower risk of CHD (RR 0·66, 95 % CI 0·62, 0·70).
The tendency for a lower risk of CHD when replacing carbohydrates with total fat and SFA may reflect a combination of the beneficial association observed between cheese consumption and CHD as well as the deleterious association between low-fibre carbohydrate intake and CHD. Total fat and SFA intake in the context of high-cheese consumption may not be generalisable to total fat and SFA intake in a low-cheese consumption context.
## Strengths and limitations
Strengths of our study include a community-based sample of men and women with a relatively long follow-up time. Only sixty participants died due to other reasons until 2009; therefore, there was minimal competing risk from other causes of death. Linkage to the CVDNOR project database assured as good as complete follow-up. Also, we had information on health status, medication use, health habits and history of CHD at baseline, enabling us to evaluate incident CHD. Further, the FFQ captured the major sources of carbohydrates and SFA expected in the current study population, and energy adjustment of the statistical models is a well-established approach for reducing the bias related to self-reported dietary data.
Theoretical substitution analyses were performed, modelling the substitution of carbohydrates with PUFA, SFA, protein and total fat. Another strength is the robustness of the results which were similar from model to model after various adjustments.
Limitations include the relatively small number of participants and events limiting stratified analyses and multivariable adjustments.
Another limitation is the lack of information on possible changes over time in diet, medications and other risk factors. Both dietary habits and food products have changed during the study period, due to the recommendations on reducing intake of SFA as the source of fat and increasing intake of whole grains as the source of carbohydrates[31,48]. Intake of cooking oil has tripled from the late 1990s to about 2013, and the consumption of vegetables also increased, while intake of margarine and sugar-containing food decreased according to per capita sales data[5].
Blood samples were non-fasting. Since postprandial TAG remain elevated for several hours, and the Friedewald equation, used for the calculation of LDL-cholesterol, assumes fasting TAG values, LDL-cholesterol may be underestimated[49]. Also, most reference values for serum lipids and glucose are established on fasting blood specimen.
Further, a common problem with FFQ is systematic under- or overreporting of nutrient and energy intake, limiting the estimation of absolute intake. However, the FFQ is well suited to rank participants by dietary intake for evaluation of associations with health endpoints[50]. Given that FFQ are not optimal for determining absolute nutrient intake, caution is required in the interpretation of the theoretical substitution models[51].
There may also be other limitations. A large proportion (75 %) of the participants reported zero intake of butter, likely reflecting underreporting. Also, we did not have extensive information on carbohydrate quality particularly for bread due to lack of historical food recipes, lack of food label details and type of carbohydrate content for the recipes from the dietary database at the end of the 1990s.
Nevertheless, when we adjusted for estimated fibre from bread, vegetables and fruit intake, total carbohydrate intake remained a statistically significant predictor of higher CHD risk. Another limitation of the current study is that the results cannot be generalisable to populations with a greater range in carbohydrate or SFA intake. In the current study, the intake of SFA varied from 4 to 25 E% and the intake of carbohydrates varied from 21 to 74 E% resulting in a narrower range of intake when we evaluated the median intake between the lowest and highest quartiles (i.e., 10–15 E% for SFA and 43–56 E% for carbohydrates), precluding our ability to generalise to lower and higher intake values. The current study can therefore not be compared with previous studies that have shown higher all-cause and cause-specific mortality associated with much lower carbohydrate intakes than our study population[52,53]. Finally, the available data were not appropriate for studying unsaturated fat, given the changing trans-fatty acid composition of unsaturated fat during the study period.
While reverse causation is a general concern in observational studies of dietary habits and disease outcomes, we noted a similar percentage of participants with a family history of CHD and similar baseline BMI values across quartiles of carbohydrate and SFA intake. Further, adjusting for family history of CHD and BMI did not alter our findings. Although we performed multivariable analyses, residual confounding may still be present.
Lastly, it is of note that at the end of follow-up, participants’ age was generally lower than the mean age of acute myocardial infarction in Norway which is 73·8 years for men and 79·1 years for women[54]. Thus, our findings may reflect mechanisms involved in early-onset CHD and may not necessarily be generalisable to older populations.
## Conclusion
The focus of the current study was to evaluate the importance of the interplay between SFA and total carbohydrates, including SFA sources, when evaluating the association between SFA and CHD. A high intake of carbohydrates, reflecting low-fibre and relatively higher sucrose/fructose dietary sources, and a low intake of SFA were associated with higher CHD risk in the current study population. Substituting carbohydrates with total fat was associated with lower risk. Also, SFA from cheese was associated with lower risk of CHD.
Further research evaluating potential benefits of dairy products and their nutritional constituents is warranted. Also, there is a need to clarify the relative health trade-offs between replacing carbohydrate intake with fat intake in study populations with diverse dietary habits and a wider range in carbohydrate and SFA intakes. In addition, results of our study suggest that dietary guidelines development and their communication to the public, especially regarding reductions in certain foods and nutrients, need to consider the potential health impact of alternative sources of energy.
## Disclaimer
The current study used data from the Norwegian Cause of Death Registry. The interpretation and reporting of these data are the sole responsibility of the authors, and no endorsement by the registry is intended nor should be inferred.
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|
---
title: Does the prevalence of promotions on foods and beverages vary by product healthiness?
A population-based study of household food and drink purchases in New Zealand
authors:
- Essa Tawfiq
- Kathryn E Bradbury
- Cliona Ni Mhurchu
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991816
doi: 10.1017/S1368980021004936
license: CC BY 4.0
---
# Does the prevalence of promotions on foods and beverages vary by product healthiness? A population-based study of household food and drink purchases in New Zealand
## Body
It is generally accepted that population rises in non-communicable chronic diseases such as type 2 diabetes, obesity, CVD and cancer have been driven mainly by unhealthy food environments[1,2]. Unhealthy food environments are determined by the ready availability of affordable and heavily promoted foods and drinks high in salt, saturated fat and sugar[1,2], and increases in food energy supply have been associated with increases in the incidence of non-communicable chronic diseases NCD worldwide[3]. Influenced by unhealthy food environments, population overeating of heavily promoted, energy-dense and nutrient-poor food poses a serious global public health concern[4].
Product promotions increase the sales of promoted products[5,6] and lead to stockpiling (buying earlier and/or more than usual) and unplanned purchases of promoted products[5]. In this study, we used the general term ‘promotions’ instead of price promotions because households self-reported promotions and it was not possible to determine if products were price promoted or promoted using some other means (e.g. signage, health claims, gifts in the products, characters in the labels or prominent placement in-store). Product promotions influence consumer purchasing behaviours[7] and can increase brand awareness, maintain or improve brand familiarity, generate perceived value and encourage consumers’ positive self-image as bargain shoppers[7]. There is some evidence that suggests product promotions increase the volumes of foods or drinks purchased during a single shopping visit and do not lead to reduction in the frequency of purchasing at subsequent shopping visits[5]. An industry report in 2018 stated that the proportion of all grocery products sold on promotion was 59 % in New Zealand (NZ), 40 % in Australia, 30 % in the USA, 28 % in Italy, 27 % in the UK, 23 % in Germany, 21 % in Brazil, 20 % in the Netherlands, 20 % in Spain, 18 % in Belgium and 17 % in France[8].
Unhealthy foods and beverages are promoted more often than healthier food and drink options(9–12). A recent study, which used Nielsen New Zealand Homescan® data, October 2016 to October 2017, and examined the prevalence of promotions according to the processing level of food and drink products, found that proportions of unhealthier food options (e.g. ultra-processed and processed foods) promoted were significantly greater than proportions of healthier food options (e.g. unprocessed and ingredient products)[13]. In Australia, a recent study that examined the prevalence of beverage price promotions available online at two major Australian supermarket chains (Coles and Woolworths) over 50 weeks, found that within non-alcoholic beverages, the sugar-sweetened beverages (SSB) subcategory had the greatest proportion of price promotions[14].
To our knowledge, in NZ, household self-reported promotions of foods and beverages according to Health Star Rating (HSR) has not been studied so far. In this study, we aimed to examine the prevalence of household self-reported promotion of food and beverage products according to HSR of the products. We used the HSR system, which is an established Australasian nutrient profiling system, to define product healthiness for packaged and unpackaged products. We hypothesised that the prevalence of products purchased on promotion varies according to product healthiness, measured as HSR ≥ 3·5 (healthy) v. HSR < 3·5 (unhealthy) for foods and beverages, and measured as < 5 g v. ≥ 5 g sugar per 100 ml content of products for beverages.
## Abstract
### Objective:
To assess the prevalence of promotions on foods and non-alcoholic drinks purchased by New Zealand households and to determine if they vary according to healthiness of products.
### Design:
We undertook a cross-sectional analysis of Nielsen New Zealand Homescan® $\frac{2018}{19}$ panel data. We conducted multivariate analyses to examine the variability in quantities of healthy v. unhealthy food and beverage products purchased on promotion. Promotion was self-reported by the panellist. Healthiness of products was measured by the Health Star Rating (HSR) system. We also carried out a subgroup analysis for beverages according to the threshold of < 5 g v. ≥ 5 g sugar per 100 ml content of products.
### Setting:
The Nielsen New Zealand Homescan® data were linked with two New Zealand Food Composition Databases (Nutritrack and the FOODfiles).
### Participants:
Food and beverage purchases data by 1800 panel households were used.
### Results:
Overall, 46 % (1 803 $\frac{601}{3}$ 940 458) of all purchases made were on promotion. Compared with purchases of food and beverage products with HSR < 3·5 (unhealthy), food and beverage products with HSR ≥ 3·5 (healthy) were significantly less likely to be on promotion (OR = 0·78, 95 % CI 0·77, 0·79). The subgroup analysis for beverages shows that products with < 5 g sugar per 100 ml were significantly less likely to be on promotion than those with ≥ 5 g sugar per 100 ml (OR = 0·77, 95 % CI 0·75, 0·79).
### Conclusions:
Policies to improve healthy food retailing should focus on increasing the promotion of healthier food and drink options in stores and supermarkets.
## Study design
This was a cross-sectional analysis of the Nielsen New Zealand Homescan® panel data collected between October 2018 and October 2019. The Nielsen New Zealand Homescan® panel is a sample of 2500 NZ households who are representing NZ households in terms of demographics and geographic locations. We used data from 1800 NZ households as Nielsen New Zealand Homescan® excludes data for households who scan items inconsistently, show sudden changes in scanning behaviour or do not meet the minimum spending criteria. Moreover, data for food purchased at restaurants, takeaways, fast-food outlets and cafés are excluded. The Nielsen Homescan® data is one of the largest commercial food purchasing datasets, and it contains up-to-date data that are used to monitor consumer purchases across twenty-five countries[15]. Since Nielsen New Zealand Homescan® data do not include nutrient information, we linked it with two national food composition datasets (Nutritrack and FOODfiles) to extract data on the nutrient profile (energy, total sugar, Na, saturated fat, dietary fibre, protein, Ca and fruit, vegetable, nut, and legume content) of the products purchased.
Data in Nielsen New Zealand Homescan® panel include 1-year household purchases from different food retail stores across NZ. Nielsen New Zealand Homescan® is an open cohort recruiting households continuously, thus accounting for household attrition and limiting demographic changes over time. At the time of recruitment, information on the demographics and geographic locations of the households is collected. The information collected includes the main household shopper’s age and sex, household composition, household size, postcode, and household income. After a household is recruited, the household receives an electronic scanner with a copy of the User Guide on how to use the scanner and collect data on the household purchases for every shopping trip. The panellist household is asked to record food and beverage purchases made at any stores that are taken back into the home. Household data collection takes place continuously as long as a household remains within the panel. A point-earning system is applied to incentivise panel households. Data included in these analyses were collected over a full 12-month period between October 2018 and October 2019. The point-earning system enables conversion of earned points to monetary rewards. After a product is purchased and brought home, the panel member enters the quantity purchased, price of product, whether it was on promotion (yes/no), the store shopped at, and scans the product barcode. The barcode enables the system to derive information on item description, product category, pack size, unit (e.g. g, kg, l and ml), brand and product department. Product departments include beverages, chilled foods, fresh foods, frozen foods, general grocery, and snack foods and confectionary. For purchases of products that do not have barcodes (e.g. fresh produce), the panellist household chooses a corresponding barcode from a supplied booklet. Promotions are self-reported by panellists, which means that promotions are based on the shoppers’ perception as to whether the product purchased was on promotion or not. The panellist households were not provided with a definition of the term ‘promotion’.
For Nutritrack, trained surveyors collect data from four major supermarket chains in the city of Auckland each year. The four supermarkets contain a large range of packaged food and are owned by the two major supermarket retailer companies (Foodstuffs NZ and Woolworths New Zealand) that together hold 89 % of the national grocery market share[16]. The trained surveyors use a customised smartphone application which scans the barcode of each packaged food and beverage displaying a Nutrition Information Panel (NIP), available at the time of the survey in the store. Photographs of all surfaces of each surveyed product are taken and uploaded into a web-based database. Nuritrack data include product barcode, product name, food group and category, pack size, recommended serving size, HSR displayed on the product, and NIP information. The NIP includes information on the average amount of energy, total sugar, Na, saturated fat, protein, Ca and dietary fibre per 100 g/ml of each packaged product. This study used Nutritrack 2018 and Nutritrack 2019 data. For unpackaged products, we used FOODfiles. The FOODfiles dataset is the main component of the New Zealand Food Composition Database, which is updated and released online every 2 years. The FOODfiles dataset is the most comprehensive collection of generic food composition data for foods commonly consumed in NZ. We used the most recent FOODfiles database released in 2018[17].
## Exclusion criteria
The food and beverage products eligible for inclusion were those purchased in-store from food stores. This means food and drink products purchased online, at pet stores, at liquor stores and at stores where food forms a small proportion of total sales were ineligible for inclusion in this study. Moreover, products which were purchased infrequently were excluded (less than one unit purchased per month on average across the entire dataset of NZ households). We applied the exclusion criteria at two steps. At step 1, the following products were defined as ineligible and excluded: (i) products purchased at pet stores or stores providing only home delivery or online purchases, (ii) products purchased at stores where food constitutes a small part of total sales (e.g. department stores), (iii) products purchased at stores with no recorded name, (iv) Easter and Christmas products, (v) products not required by regulations to display a NIP (e.g. tea, unflavoured coffee, artificial sweeteners, chewing and bubble gums, gelatine, salt, flour, corn flour, self-raising flour, vinegar, herbs and spices, herb tubes and pastes, cream of tartar, mustard, pepper, baking soda, baking powder, tartaric acid, citric acid, cooking ingredients, ice, curry powder, yeast, bicarbonate of soda, and (vi) special products (baby foods, protein bars, protein powders, and fitness or diet products). Alcoholic beverages and food products purchased at liquor stores were also excluded. At step 2, the infrequently purchased products were excluded. Criteria for infrequently purchased products is described below.
## Data linkage
We used barcode details to link products between Nutritrack and Nielsen New Zealand Homescan®. For those products in Nielsen New Zealand Homescan® which could not be linked in this way, we applied the following four-step approach:the products which could not be linked by barcode were listed and ranked based on total units purchased over the 1-year period;infrequently purchased products were defined as those bought fewer than 12 units over the 1-year period and were excluded (less than one unit per month on average);for fresh produce, FOODfiles was used to extract food composition data, as NIP information for fresh produce is not routinely displayed. For every product, its closest match product was identified by a nutritionist (K.E.B.). The second nutritionist (C.N.M.) resolved issues through discussion when uncertainties regarding appropriate matching arose.for the remaining unmatched packaged products, the category-average food composition values were calculated, using the product category nutrient content of Nutritrack products. For example, for a yoghurt product, we assigned the average nutrient composition of all Nutritrack yoghurt products. The nutrient content data used were, energy per 100 g/ml, saturated fat, total sugar, Na, protein, Ca, dietary fibre per 100 g/ml, and the fruit, vegetable, nut, and legume (fvnl) content. Estimated category-level fvnl points data were available in Nutritrack database.
Figure 1 illustrates how from an initial total of 31 470 unique products, 20 419 unique products were included for data analyses, after the two-step exclusion criteria were applied. A unique product was defined as a product with a distinctive barcode among all products in the Nielsen New Zealand Homescan® panel data, October 2018–October 2019. This means for the 20 419 unique products included for data analyses, there were 20 419 barcodes available in the database. As Fig. 1 shows, 23 020 products (23 $\frac{020}{31}$ 470 = 73·1 %) were eligible for inclusion after the first exclusion step was applied. Following the second exclusion step, 20 491 (20 $\frac{491}{23}$ 020 = 89·0 %) of all eligible products were matched to food composition data (Nutritrack and FOODfiles) and included in data analyses. In the second exclusion step, 11 % ($\frac{2525}{23}$,020 = 11 %) were infrequently purchased products, and four unique products were purchased from a retail brand at one location during the 1-year period; therefore, these products were excluded.
Fig. 1Flow diagram showing number of products included in the study. Note: At step 1, the following products were excluded: (i) products purchased online, (ii) products purchased from stores other than food stores, (iii) products purchased from stores where food forms a small part of total sales, (iv) Easter and Christmas products, (v) products not required by regulations to display a NIP, and (vi) special products. At step 2, the infrequently purchased products were excluded. For details, please see Exclusion criteria
## Estimated Health Star Rating
Product healthiness was defined by estimated HSR, which is based on nutrient profiling of products. Nutrient profiling classifies foods according to their food composition[18]. Nutrient profiling plays an important role in labelling food and beverage products in the Organisation for Economic Co-operation and Development (OECD) countries(19–22). HSR is a front-of-pack (FOP) nutrition label, and it provides interpretive front-of-pack nutrition information to assist consumers make healthier choices. The HSR system was introduced as a voluntary front-of-pack labelling policy in NZ and Australia in 2014[23]. Since it is not required that all packaged products display HSR[23], we estimated HSR for all products (packaged and unpackaged) using the Guide for Industry to the Health Star Rating Calculator[24].
We estimated HSR through four steps. First, we categorised all products into one of six categories: (i) Category 1 (beverages other than dairy beverages), (ii) Category 1D (dairy beverages), (iii) Category 2 (all foods other than those included in Category 1, 1D, 2D, 3 or 3D, (iv) Category 2D (dairy foods other than those included in Category 1D or 3D), (v) Category 3 (edible oil, edible oil spreads, margarine and butter) and (vi) Category 3D (cheese and processed cheese with Ca content > 320 mg/100 g). Second, using per 100 g/ml of energy content (kJ), saturated fat, sugar and Na content of each product, we used the published algorithms[24] and calculated baseline points for all products. Third, using per 100 g/ml of protein content and dietary fibre content of each product, we calculated protein points and dietary fibre points where appropriate. The baseline points were modified by subtracting protein points, dietary fibre and fvnl points from the baseline points. Fourth, using the published algorithms[24], the modified points were transformed into HSR, ranging from 0·5 to 5·0 stars in half-star increments.
## Statistical analyses
We applied generalised linear models (GLM) to examine differences in prevalence of foods purchased on promotion according to product healthiness. The unit of analysis was each unique product purchased, and the outcome variable was a binary response variable as to whether the product purchased was ‘on promotion’ or not. We examined overall estimated mean HSR and percentage of purchases on promotion across the ten categories of product healthiness. The analyses from generalised linear model were adjusted for age of main household shopper (< 34, 35–39, 40–49, 50–65 and > 65 years), sex of household main shopper (male and female), number of family members (1–2 and ≥ 3), equivalised household income level (tertiles of low, medium and high), geographic location (Auckland, Bay of Plenty and Waikato, rest of North Island, Wellington, Canterbury, rest of South Island), product healthiness, product price and store type. Store type was defined as (supermarkets, grocery stores, convenience stores, fruit and vegetable stores, meat and fish stores, and bakeries) using criteria we developed for a recent study (see online supplementary material, Supplemental Table 1). Using the OECD equivalence factors, equivalised household income was generated. This approach for equivalised household income was used in a recent study[13]. Household income was categorised based on the midpoint of ten categorical income groups available in the Nielsen New Zealand database. Then the OECD equivalence scales were used to calculate equivalised household income, using the following equivalence factors: 1 for the first adult, 0·5 for each additional adult and 0·3 for each child within the household. The following statistical model was specified for the generalised linear model multivariate analysis: refers to the binary variable for product i (whether promotion equals yes or no), with HSR category of j. HSR refers to the binary variable of product healthiness, that is, HSR ≥ 3·5 v. HSR < 3·5 stars. denotes a vector of confounders, and refers to the number of confounders. β 0 stands for the intercept which is odds of the quantity purchases that were made on promotion for products with an HSR < 3·5 stars (reference category), and β 1 refers to odds ratio (OR) of the quantity purchases made on promotion for products with HSR ≥ 3·5 stars. Food and beverage products with a HSR ≥ 3·5 were considered to be ‘healthy’, and products with HSR < 3·5 stars were considered to be ‘unhealthy’. The cut-off of 3·5 stars was based on a technical report showing that this cut-off aligned with the New South Wales healthy food provision policy[25]. According to that report products classified as Green by the Traffic Light criteria, on average had a HSR of ≥ 3·5 stars, and products classified as amber or red on average received a HSR < 3·5 stars. We applied the model for all products as well as for non-alcoholic beverages. We categorised non-alcoholic beverages based on sugar content of < 5 g/100 ml v. ≥ 5 g/100 ml (reference group). The threshold of 5 g sugar per 100 ml was determined based on the UK Soft Drinks Industry Levy[26]. All analyses were performed using STATA version 13.
## Validity of estimated Health Star Rating
Supplemental Table 2 presents the agreement between the displayed HSR and estimated HSR. It shows that out of 2948 products that displayed HSR in Nutritrack dataset, the agreement was 88·2 % and the kappa statistic was 0·74 ($P \leq 0$·001), showing a substantial level of agreement[27].
## Results
Table 1 shows Nielsen New Zealand Homescan® household demographic and socio-economic characteristics. Out of 1800 households, most of the household main shoppers were in the older age groups of 40–49 years, 50–65 years and > 65 years (86·9 % combined), and most were female (75·8 %). In terms of geographic region, most households were in the North Island (over 75 %) with 29·4 % of these located in the Auckland region. Less than 25 % of households were in South Island with 15·2 % of these in the Canterbury region. The distribution of Nielsen New Zealand Homescan® panel households across the country reflects the population density of the North Island and South Island. Most households consisted of one to two persons (58·2 %), followed by three or more person households (41·8 %). The average monthly household expenditure by store type was highest for supermarkets (median = NZ$446 and mean = NZ$487) and lowest for grocery stores (median = NZ$22 and mean = NZ$32). Product purchases by age group shows that the household main shoppers aged > 65 years had the lowest number of purchases made on promotion (41·9 %), while their purchases were the second highest (1 024 825 units purchased) after those shoppers aged 50–65 years who purchased 1 601 609 units (46·0 % of them on promotion). The age group < 35 years had the second lowest purchases made on promotion (45·1 % of the 220 564 units purchased). The age group 40–49 years had the highest purchases made on promotion (49·0 % of the 794 458 units purchased). Product purchases by income showed that high-income households had the lowest purchases made on promotion (42·5 % of the 1 306 942 units purchased), while low-income households had the highest purchases made on promotion (47·0 % of the 1 299 121 units purchased). The table also shows that 90·0 % of all food and non-alcoholic drink purchases made by the panel were from supermarkets, followed by 7·9 % from fruit and vegetable stores, and the remaining 2·1 % from meat and fish stores, grocery stores, convenience stores, and bakeries combined. In total, there were 3 940 458 product purchases comprising 20 491 unique products. This means for the 20 491 unique products included in this study, on average there were 192 units (3 940 $\frac{458}{20}$ 491) of each unique product purchased by the 1800 households over the 1-year period.
Table 1Demographic and socio-economic status of Nielsen NZ Homescan® panel households, October 2018–October 2019Household characterNumber of householdsSex of household main shopperMale43624·2 %Female136475·8 %Age of household main shopper< 35 years1055·8 %35–39 years1317·3 %40–49 years35719·8 %50–65 years71139·5 %> 65 years49627·6 %Geographic region of householdsAuckland53029·4 %Bay of Plenty and Waikato29216·2 %Wellington24213·5 %Rest of North Island28916·1 %Canterbury27415·2 %Rest of South Island1739·6 %Equivalised household incomeLow-income64335·7 %Middle-income55330·7 %High-income60433·6 %Household size (number of persons)1–2104758·2 %≥ 375341·8 %Monthly household expenditure by store typeMean NZ$Median NZ$Supermarkets487·1445·9Meat and fish stores104·378·4Fruit and vegetable stores96·371·9Convenience stores57·130·0Bakeries34·723·5Grocery stores32·122·0Household purchases made on promotion by age groupUnits purchasedPercentage on promotion< 35 years220 56445·1 %35–39 years299 00247·0 %40–49 years794 45849·0 %50–65 years1 601 60946·0 %> 65 years1 024 82541·9 %Household purchases made on promotion by income groupUnits purchasedPercentage on promotionLow-income1 299 12147·0 %Middle-income1 334 39547·8 %High-income1 306 94242·5 %Quantities of products purchased at food storesUnits purchasedPercentage of purchasesSupermarkets3 545 14190·0 %Fruit and vegetable stores312 3007·9 %Meat and fish stores65 1981·7 %Grocery stores11 4680·3 %Convenience stores36130·1 %Bakeries27380·1 % Figure 2 shows the percentage of products purchased on promotion, by ten categories of HSR. Overall, 46 % of all purchases, with a mean HSR of 3·5, were on promotion. Of all 5·0-star products purchased, 37 % were on promotion compared to 41 % of all 4·5-star and 41 % of all 4·0-star products, 58 % of all 3·5-star, 51 % of all 3·0-star, 50 % of all 2·5-star, 46 % of all 2·0-star, 58 % of all 1·5-star, 52 % of all 1·0-star and 50 % of all 0·5-star products.
Fig. 2Prevalence of promotions on quantity purchase, by product healthiness. HSR, Health Star Rating Table 2 shows the OR of purchasing a product on promotion by healthiness of products. Overall, compared with food and beverage products with an HSR < 3·5, food and beverage products with an HSR ≥ 3·5 were significantly less likely to be on promotion (OR = 0·78, 95 % CI 0·77, 0·79). As for the subgroup analysis, compared with beverages with ≥ 5 g per 100 ml, those with < 5 g per 100 ml were significantly less likely to be on promotion (OR = 0·77, 95 % CI 0·75, 0·79).
Table 2Differences in the prevalence of promotions on quantity purchases, October 2018–October 2019Food and beverage products n 3 940 458OR95 % CI P-valueHSR < 3·5 (ref)1·00HSR ≥ 3·50·780·77, 0·79<0·001Beverage products n 151 872Beverages with ≥ 5 g sugar/100 ml (ref)1·00Beverages with < 5 g sugar/100 ml0·770·75, 0·79<0·001Beverage products refer to non-alcoholic beverages.
## Discussion
In this study of annual household purchases by 1800 NZ households, we found that less healthy food was more likely to be purchased on promotion, compared to more healthy food. Overall, compared with purchases of products with HSR < 3·5, purchases of products with HSR ≥ 3·5 were significantly less likely to be on promotion (OR = 0·78). A similar pattern was seen for promotions of beverages where beverages with a sugar content of less than 5 g/100 ml were significantly less likely to be on promotion than those with a higher sugar content (≥ 5 g/100 ml) (OR = 0·77).
Our study is the first to examine the prevalence of household self-reported promotions on purchases of healthy v. unhealthy foods and drinks, using 1-year of Nielsen New Zealand Homescan® panel data. Measuring the healthiness of foods and beverages, using the HSR system, and using the sugar content cut-off for beverages is another distinction of our study. In a recent study, for which Nielsen New Zealand Homescan®, October 2016–October 2017, data were used, Zorbas et al.[13] defined healthiness of food and beverage products according to the NOVA system[28]. The NOVA system is based on the food processing level (e.g. ultra-processed, processed, unprocessed and ingredients). The authors found that on average 50 % of all annual household grocery items purchased were price promoted. Processed products constituted 59 %, ultra-processed products 55 %, unprocessed products 45 % and ingredient products consisted of 45 % of price promoted purchases. The authors reported that a significantly greater proportion of purchases made by low- and middle-income households were price promoted compared with purchases made by high-income households.
The findings from our study support those from other studies in the Netherlands, UK, Australia and NZ for both food and beverages. In the Netherlands, Ravensbergen et al.[9] studied the prevalence of price promotions for healthy and unhealthy foods, using weekly supermarket flyers over a period of 8 weeks. The authors assessed the product healthiness according to the Dutch ‘Guidelines for Food Choice 2011’ and found greater prevalence of promotions for less healthy products than for healthier products; 70 % of promotions were on unhealthy products. In an Australian study, Riesenberg et al.[29] used online supermarket data on weekly product prices and examined the prevalence of price promotions according to product healthiness, measured by HSR. The authors found that the most price promoted categories during a given week were all discretionary foods (chocolate, 40·3 %; chips, 32·5 %; high-sugar breakfast cereal, 24·0 %; and ice cream, 22·1 %), and the least promoted categories were all core foods (packaged bread, 7·5 %; muesli and oats, 14·7 %; low-sugar breakfast cereals, 15·0 %; and frozen vegetables, 19·2 %). In another Australian study, Zorbas et al.[14] used online supermarket data on weekly product prices from two supermarket chains (Coles and Woolworths) and examined the prevalence of price promotions within different categories of non-alcoholic beverages for 50 weeks. The study found that 26 % and 30 % of all beverages in Coles and Woolworths, respectively, were price promoted in any given week. The authors categorised beverages into four categories: SSB, artificially sweetened beverages (ASB), flavoured milk and 100 % juice, and milk and water. The findings showed that the proportions of price promotions within beverage categories were similar for SSB and ASB (Coles: 30 % of all SSB v. 33 % of all ASB; Woolworths: 37 % of all SSB v. 38 % of all ASB) and lowest for the ‘milk and water’ category with a weekly average of 14 % for Coles and 15 % for Woolworths.
In a study in NZ, Pollock et al.[30] collected discount information for non-alcoholic beverages from four supermarkets in the Wellington region over a 4-week period in 2008. The authors classified the products into green (drink most), amber (drink in moderation) and red (drink less) categories and examined the percentage of discounts for all three categories of beverages. The authors found that a higher percentage of beverage discounts were for amber (40·9 %) or red (44·1 %) beverages rather than green (14·9 %) across all beverage groups except water ($P \leq 0$·001). In a UK study, Nakamura et al.[12] used 1 year of household purchasing data from the Kantar Worldpanel survey. The authors defined healthiness of products according to a nutrient profile model endorsed by the UK Food Standards Agency and examined the prevalence of price promotions for healthier compared with less healthy products. The authors found greater prevalence of promotions for less healthy than for healthier foods, after controlling for the reference price, price discount rate and brand-specific effects. There was an increase in sales from 27·3 % to 35·0 % for less healthy products ($P \leq 0$·01).
The use of nationally representative household food purchasing data collected from a wide range of food stores across NZ over a period of 1 year is a strength of our study. Measuring product healthiness using the HSR system, both for packaged and unpackaged products, including fresh produce, is another strength. This is despite the fact that for some products (e.g. fresh produce), food manufacturers are not required to provide on-pack nutrition information[23], thus making it challenging to use HSR as a measure of product healthiness. In our study, however, using HSR to measure healthiness of packaged and unpackaged products has the advantage that promotion of both healthy and unhealthy products purchased can be examined and compared, and this provides a more complete picture of all food purchased on promotion.
Food policies and interventions can be effective in promoting purchases of healthy foods. Policies and interventions such as increased availability or information as well as monetary incentives for healthy products (e.g. fruits and vegetables) can promote intakes of healthy foods[31]. The recent systematic review assessed the effectiveness of food store interventions in promoting consumption of healthy foods[31] and found that in-store health interventions were effective in promoting purchases of healthy foods. According to the review, most of the interventions aimed to increase sales of healthy foods (e.g. whole grains, fruits and vegetables, lower-fat milk, healthier beverages, lower sugar cereals, low-calorie beverages, fish). Most of the studies reported that in-store interventions were effective in one or more of the targeted outcomes[31]. Some interventions were single component (e.g. increased availability or accessibility or information intervention), while others were multi-component interventions (e.g. combined information and access/availability or combined monetary incentives and information). The review reported that studies that focused on information provision (in the form of shelf labels, product labels, posters, flyers and distribution of brochures) showed mixed results (e.g. some reported higher sales of promoted food items, while some others reported no difference in sales of promoted products). In contrast, the studies with multi-component interventions reported positive effects in one or more of the planned outcomes, especially increased sales of healthier products[31].
Our study has some limitations. Firstly, although the Nielsen New Zealand Homescan® panel is representative of NZ households in terms of certain demographics (household size and household income) and broad geographical region (upper North Island, lower North Island and South Island), the panel is not recruited to be representative of ethnicity and does not include information on ethnicity of households; thus, we could not adjust our results for ethnicity or report results for ethnicity separately. Secondly, promotion was self-reported by the panellist as a binary response (yes/no). Therefore, in this study, it is not possible to distinguish and examine the prevalence of specific types of promotions (e.g. temporary price reduction, multi-buy offers, larger volumes for the same price as standard volumes, prominent placement in-store, end-of-aisle displays, signage or promotional flyers).
Future research should investigate changes over time in the promotion of healthy and less healthy products, and the impact of any new policy or retailer strategy (e.g. restrictions on promotions of less healthy products) on consumer purchasing behaviour and retailer sales. Another important area with potential policy implications to improve food environments is to examine promotions as well as purchases of healthier compared with less healthy products by neighbourhood deprivation (low/high) and region (urban/rural).
## Policy implications
Our study has the potential to influence food policies and actions to promote healthier food environments. The findings from our study may be used by the government and food industry to increase the availability and promotion of healthy food options across all stores and restrict the availability and promotion of less healthy foods in NZ.
## Conclusion
Based on our findings, nearly half of all products purchased were on promotion. Therefore, compared to healthier food options, it was more likely that greater quantities of less healthy foods were purchased on promotion. To contribute to the reduction and prevention of diet-related chronic diseases, food policies and interventions should focus on increasing the availability and promotion of healthier food options.
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|
---
title: Intake of ultra-processed foods is associated with inflammatory markers in
Brazilian adolescents
authors:
- Glauciane Márcia dos Santos Martins
- Ana Karina Teixeira da Cunha França
- Poliana Cristina de Almeida Fonseca Viola
- Carolina Abreu de Carvalho
- Karla Danielle Silva Marques
- Alcione Miranda dos Santos
- Mônica Araújo Batalha
- Janete Daniel de Alencar Alves
- Cecilia Claudia Costa Ribeiro
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991817
doi: 10.1017/S1368980021004523
license: CC BY 4.0
---
# Intake of ultra-processed foods is associated with inflammatory markers in Brazilian adolescents
## Body
The changes in global food consumption are related to diets high in fats, sugars and Na, which are chiefly linked to ultra-processed foods (UPF)[1,2]. In Brazil, the evolution of the household food availability from 2002–2003 to 2017–2018 showed a decrease in the participation of traditional foods such as rice, beans, cassava flour and milk and an increased participation of processed foods and industrialised mixtures in the Brazilian diet[3]. UPF, including breads, sausages, soft drinks, cookies and ready meals, account for 18·4 % of the Brazilian diet[3]. On the other hand, in the year 2000, the household consumption of UPF was recorded as over 50 % in middle- and high-income countries, like Chile, Canada and the United Kingdom(4–6).
Adolescence is the period in life most prone to higher consumption of UPF. This factor leads to great biological and nutritional vulnerability of adolescents[3,7]. Therefore, it is relevant to investigate the effects of the early introduction of an unhealthy dietary pattern on the health of this population.
A higher intake of UPF has been directly associated with energy density, free sugars, saturated and trans fats levels, and inversely associated with the content of fibres, proteins, vitamins and minerals[8,9]. This poor dietary profile can induce postprandial metabolic changes[10] and inflammation processes as it reduces antioxidant defences. It can also trigger oxidative stress and induce the transcription process of inflammatory genes, through the activation of the NF-κB and the innate immune system[11]. Inflammatory responses are characterised by high synthesis and release of pro-inflammatory markers such as C-reactive protein (CRP), TNF-α, IL-6 and IL-8[12], along with a decrease in the circulating levels of anti-inflammatory markers such as adiponectin[13].
Studies have associated higher plasma concentrations of inflammatory markers and dietary patterns based on processed food and UPF or isolated nutrients (simple carbohydrates, saturated fat, trans fats and fibres)[14,15]. Such findings, however, are still limited and present conflicting results, especially in the young population. To date, no studies have investigated the relationship between UPF consumption and inflammatory markers in adolescents.
Because changes in inflammatory markers may be predictors of chronic non-communicable diseases, it is of great importance to study their associated factors such as food consumption. In this context, the objective of this study was to evaluate the association of UPF intake and inflammatory markers in adolescents from public schools in a capital city in the Northeastern region of Brazil.
## Abstract
### Objective:
To evaluate the association of the consumption of foods of the ultra-processed group (UPF) with inflammatory markers in the adolescent population in Northeastern Brazil.
### Design:
A cross-sectional population-based study. Food consumption was evaluated using two 24-h dietary recalls using the NOVA classification for food processing levels. The following inflammatory markers were evaluated: adiponectin, IL-6, IL-8, C-reactive protein (CRP) and TNF-α. Multivariate linear regression was used to investigate the association between the percentage of UPF energy contribution and inflammatory markers.
### Setting:
São Luís, Maranhão, Brazil.
### Participants:
The sample consisted of 391 male and female adolescents, aged from 17 to 18 years.
### Results:
The average daily energy consumption by adolescents was 8032·9 kJ/d, of which 26·1 % originated from UPF. The upper tertile (T3) of UPF consumption presented higher intake of simple carbohydrates, lipids, saturated fat, and Na and lower protein intake. Individuals in T3 presented higher serum leptin and CRP levels ($P \leq 0$·05). Adolescents with UPF energy consumption ≥30·0 % (tertile 3 of UPF) had a 79 % (exp (0·58) = 1·79) increase in IL-8 levels when compared with adolescents in tertile 1 of UPF ($$P \leq 0$$·013).
### Conclusions:
The association between the consumption of UPF, poor quality diet and pro-inflammatory markers have important harmful effects that can be observed as early as in adolescence.
## Study design
The data used in this cross-sectional study originated from a research carried out with adolescents in the city of São Luís, state of Maranhão, between January 2014 and June 2016. The research entitled ‘Are oral disorders in adolescents associated with risk markers for chronic non-communicable diseases?’ investigated the association between nutritional and/or inflammatory markers and the outcomes of tooth decay, tooth loss, tooth infection and periodontal diseases in adolescents.
São Luís is the capital city of Maranhão, a state located in the Northeast of Brazil with a Human Development Index of 0·768[16]. In 2012, the urban area of São Luís had 42 009 high school students enrolled in fifty-two public schools.
## Study sample
The sample consisted of students enrolled in public high schools in São Luís – MA, Brazil. Initially, the high schools in the urban area were identified (n 52) and thirteen schools were randomly selected.
In the second phase, thirty-nine student classes were selected by chance from the previously selected schools (10, 11 and 12th grade). Students aged 17 or 18 years (n 2030) were considered eligible to enroll in the study.
The sample calculation considered a correlation coefficient between the percentage of UPF consumption and inflammatory markers of at least 15 %, power of the study at 80 % and 95 % CI, with a minimum sample size of 347 adolescents. A final sample of 391 adolescents had the UPF consumption assessed.
The Education State Department (SEDUC) provided a list with all public high schools in the urban area of São Luís. Afterwards, a three-stage cluster sampling (school, high school year and class) was carried out using the software Bioestat 5.3.
The sample was composed by students aged 17 or 18 years (n 2030) regularly enrolled in public schools, and the Free and Informed Consent Form was provided by their parents/guardians or on their own. Pregnant and lactating adolescents, individuals with low immunity associated diseases or under medications that could interfere with test results (corticosteroids or cytostatics) and those who did not respond to the dietary survey were not enrolled in this study.
## Data collection
Data were collected in the public schools in the city of São Luís by a multidisciplinary team composed of dentists, nutritionists and nutrition students.
A questionnaire on the socio-demographic and behavioural factors was applied to the adolescent and their guardian/parents. The following variables were analysed: sex (male and female); age (average ± sd); skin colour self-declaration using the classification of the Brazilian Institute of Geography and Statistics (IBGE) (white, black, yellow, brown and indigenous); maternal education (years of school attendance) (≤ 4; 5–8; 9–12 and > 12 years of school attendance); Brazilian Economic Classification in social classes (A/B, C and D/E) according to the Brazilian Association of Research Companies (ABEP)[17]; alcohol and tobacco consumption in the last year (yes and no); physical activity frequency using the Physical Activity Questionnaire for Adolescents (QAFA) (min/d)[18].
## Nutritional assessment
The nutritional assessment was carried out by a group of trained nutritionists using anthropometric measurements and food consumption assessments. The anthropometric measurements (weight, height and waist circumference (WC)) were performed in duplicate considering the arithmetic mean value as final result. Weight was measured in a digital scale (Tanita®, Brazil), with maximum capacity of 150 kg and accuracy of 100 g. Participants were barefoot and wearing light clothing and were guided to stand in the centre of the scale. Height was measured using a portable stadiometer (Alturexata®), with 1·0 cm accuracy. The students stood in an upright position, barefoot, with their arms beside the body and with heels, back and back of the head making contact with the backboard.
WC was measured at the midpoint between the iliac crest and the last rib using a flexible and inelastic measuring tape (Sanny®). Body fat distribution was assessed by cut-off points proposed by Taylor et al.[19]. Abdominal fat was considered high when WC ≥ 80th percentile, adjusted for age and sex.
BMI was used to assess the ratio between weight and height ((kg)/height (m2)) and classified according to Z-score adjusted for sex and age. The recommendations by the WHO and used by the Ministry of Health of Brazil[20] were as follows: underweight (<Z-score −2); eutrophy (≥Z-scores −2 and <Z-scores +1); overweight (≥Z-score- + 1 and <Z-score +2) and obesity (≥Z-score + 2).
Food consumption was assessed using two 24-h diet recall surveys. The first survey was applied on the same day as the anthropometric assessment and the second was applied a week later. The 24-h diet recall surveys were applied on non-consecutive days to minimise variation effects of intrapersonal food intake. Both recalls were considered in the analysis of average food consumption of the participating adolescents.
The 24-h diet recall was used to define and quantify food and beverage intake on the previous day, besides time, place, method of preparation and the brand of the reported items.
A photograph album with household utensils and food serving sizes extracted from the book ‘Photographic Record For Dietary Inquiry’[21] was used to reduce the memory bias and assist in the identification of the reported servings.
Before recording the food consumption data, the collected information underwent quality control with the standardisation of food and beverage quantification, according to the Table for the Assessment of Household Food Consumption Measures[22]. Following, data were entered into the Virtual Nutri Plus® software and the information from the nutritional facts label that were not registered in the programme was included.
Foods were classified according to their level of processing, using the ‘NOVA classification’ with methodology proposed by Monteiro et al.[23] and adapted by the Dietary Guidelines for the Brazilian Population[2]. Foods were classified into four groups: fresh or minimally processed foods; culinary ingredients; processed foods and UPF. In this study, we have classified the foods into three categories, merging the fresh or minimally processed foods group with the culinary ingredients group[2,23].
The energy contribution percentage for each food group was calculated according to the level of processing, and the tertile of UPF contribution to the diet was assessed.
## Biochemical evaluation
Blood samples were used to analyse the following inflammatory markers: adiponectin, leptin, TNF-α, IL-6, IL-8 and high sensitivity CRP. The blood samples were collected by venipuncture in the morning after an overnight fast (12 h) by a nurse in the schools. The samples were identified, stored in a styrofoam box with ice pads, transported to laboratory and processed on the same day. Inflammatory markers were determined using the Magpix-Milliplex technology.
The high sensitivity CRP was classified according to Pearson et al.[24]; values above 3·0 mg/l and below or equal to 10·0 mg/l were considered inflammation; values above 10·0 mg/l were considered acute inflammatory processes. Other inflammatory markers had no reference values.
## Statistical analysis
Categorical variables were presented as frequencies and percentages and all quantitative variables were presented as means and standard deviations (± sd). Variable normality was evaluated by the Kolmogorov–Smirnov test.
Energy and nutrient profiles were analysed according to tertiles of the UPF contribution using ANOVA with Bonferroni correction (Bonferroni post hoc test). The Pearson’s χ 2 test was used to compare socio-demographic, body composition and behaviour with the tertile of the UPF contribution.
A linear regression model was used to assess the association between inflammatory markers adiponectin, leptin, IL-6, IL-8, TNFα and CRP (dependent variable) and tertiles of UPF intake (main independent variable). For each inflammatory marker, a crude and adjusted model was constructed. A univariate linear regression model was initially carried out between inflammatory markers and tertiles of UPF consumption.
The models were adjusted for potential confounding factors (independent variables), such as demographic (sex), socio-economic (skin colour, economic classification and maternal education), anthropometric measures (BMI and WC) and behavioural factors (smoking and alcohol consumption). Variables with non-normal distribution were log transformed. Statistical analyses were performed using the STATA® Program (version 14.0), and the statistical significance level used for all analyses was 5 %.
## Results
The study included 391 adolescents with a prevalence of female students (57·0 %) and age mean of 17·3 (± 0·49) years. The study showed a higher prevalence of participants self-declared as brown (64·9 %), maternal education between 9 and 12 years (40·4 %), belonging to Economic Class C (64·6 %). Most adolescents were insufficiently physically active (51·2 %) and reported not having consumed tobacco (87·7 %) or alcohol (53·2 %) in the last year. WC was within normal range (79·5 %), and BMI showed that 18·7 % were overweight or obese. The comparison between socio-demographic, body composition and behavioural factors and the UPF energy contribution tertiles showed an association between adolescents who consumed tobacco and the lowest tertile (T1) of ultra-processed consumption ($P \leq 0$·05) (Table 1).
Table 1Association of socio-demographic, anthropometric and behavioural characteristics with tertiles of ultra-processed food contribution to the diet of adolescents. São Luís – MA, BrazilUltra-processed food consumptionTotalT1T2T3Variable%n%n%n%nP*Sex Male4316835·15935·15929·8500·446 Female5722332·37231·87135·980Skin colour White15·56033·32040·02426·7160·442 Black18·77226·41936·12637·527 Brown65·825435·49031·58033·184Maternal schooling ≤ 4 years20·97133·82433·82432·4230·398 5–8 years26·08839·83529·62630·727 ≥9 years53·118027·85036·76635·664Brazilian economic classification A/B15·56026·71636·72236·7220·230 C64·625031·67934·48634·085 D/E19·97744·23428·62227·321Physical activity Sufficiently active48·819135·66830·95933·5640·570 Insufficiently active51·220031·56335·57133·066Alcohol consumption in the past year Yes46·317936·96634·16129·1520·234 No53·720830·36333·26936·576Tobacco consumption in the past year Yes11·44450·02229·61320·590·034 No88·634331·210734·111734·7119BMI classification Underweight/eutrophy81·331734·110833·410632·51030·762 Overweight/obesity18·77331·52331·52337·027Waist circumference Normal82·731134·410735·411030·2940·129 High17·36529·21927·71843·128*Pearson’s χ2 test.
The average energy consumption for the adolescent group was 8032·9 kJ/d (1919·0 kcal/d), of this 62·4 % derived from fresh food, minimally processed foods, processed culinary ingredients or culinary preparations; 11·4 % from processed foods and 26·2 % from UPF (Fig. 1). The foods with the highest energy contributions among fresh foods, minimally processed foods and culinary preparations were rice (14·8 %), meat (10·4 %), chicken (7·3 %) and milk (4·9 %). Those with the lowest contributions were roots and tubers (0·6 %) and vegetables (0·3 %). In the UPF group, the items with the highest energy participation were white bread (10·2 %) and canned fish (0·6 %). Among the UPF, cakes, pies and sweet cookies (5·6 %), crackers and chips (4·4 %) and fast food (3·6 %) stood out in the total daily energy consumed by adolescents (Table 2).
Table 2Averages of absolute and relative consumption of fresh foods, minimally processed foods, culinary preparations, culinary ingredients, processed and ultra-processed foods of adolescents, São Luís – MA, BrazilVariablekJ/dkcal/d%total energy intakeFresh, minimally processed, culinary preparations or culinary ingredients5012·71197·562·4 Rice1164·1278·114·8 Meat839·7200·610·4 Chicken572·2136·77·3 Milk407·397·34·9 Other cereals and preparations* 285·568·23·5 Beans232·355·52·9 Fish232·355·53·0 Cassava flour220·252·62·5 Fruits203·448·62·4 Coffee and teas142·734·11·9 Juices and smoothies159·736·0‘1·8 Other food and mixed preparations † 141·133·71·8 Eggs126·430·21·6 Roots and tubers47·311·30·6 Vegetables and legumes20·95·00·3 Sugar217·251·92·6 Oils9·22·20·1 Others ‡ 0·00·00·0Processed foods895·0213·811·4 Bread § 815·9194·910·2 Canned fish and fruit34·38·20·6 Cheese33·58·00·4 Processed meat11·32·70·2Ultra-processed food2122·2507·726·2 Cakes, pies and cookies477·2114·05·6 Salty crackers and snacks360·786·74·4 Fast food meals ‖ 239·157·23·6 Ready and semi-ready meals ¶ 224·853·72·8 Soft drinks and processed juices206·949·52·4 Charcuterie164·739·42·1 Desserts, chocolates, candy142·534·11·8 Dairy beverages129·330·91·6 White bread**, hot dog, hamburger128·130·61·5 Other products †† 23·95·70·2 Margarine and sauces/salsas21·35·10·2 Morning cereals3·30·80·0Total8032·91919·0100·0*Includes maize, oat, wheat and their derived flours and preparations such as pasta and couscous.†Includes nuts, peanuts, culinary preparations such as rice and beans (baião de dois), mashed potatoes, home-made soups and pies.‡Includes vinegar and salt. § Includes ‘French bread’ (bread rolls). ‖ Includes hamburgers, hot dog, fried and cooked pastries. ¶ Includes pizza, pasta, frozen meat dishes, instant pasta and powder soups.**Includes industrialised breads: burger bread, hot dog bread and other similar breads.††Includes food supplementation.
Fig. 1Consumption of fresh foods, minimally processed foods, culinary preparations, culinary ingredients, processed and ultra-processed foods of adolescents, São Luís – MA, Brazil The consumption of UPF ranged from an average of 6·0 % of the total energy in tertile1 (T1) to 48·2 % in tertile 3 (T3). The analysis of the contribution of total energy and nutrients consumed in these tertiles show that there was a higher intake of simple carbohydrates, lipids, saturated fat and Na in T2 and T3 compared with T1 ($P \leq 0$·05). On the other hand, there was a higher protein intake in T1 and T2 compared with T3 ($P \leq 0$·05) (Table 3).
Table 3Tertiles of ultra-processed food contribution to the total energetic and nutritional intake by adolescents. São Luís – MA, BrazilT1T2T3(≤15·9 %)(>15·9 e < 30 %)(≥30·0 %)Energy/nutrientsMeansdMeansdMeansdP*Energy (kJ/d)0·342(kcal/d)1860·5683·11981·6657·81914·2663·80·342Carbohydrate (g)140·624·0146·024·1147·125·60·073Simple carbohydrate (kcal/d)217·8 †, ‡ 157·9301·0166·2342·7183·4< 0·001Protein (g)52·7 †, ‡ 15·146·6 § 10·742·19·6< 0·001Lipids (g)31·2 † 6·332·9 § 6·036·16·7< 0·001Saturated fat (g)9·8 †, ‡ 3·010·83·511·43·5< 0·001Polyunsaturated fat (g)4·52·14·52·05·02·70·087Monounsaturated fat (g)8·13·08·23·28·03·20·863Fibre (g)8·5 ‡ 4·38·34·77·23·40·025Vitamin A (mg)525·81442·9426·7922·1480·21360·70·818Vitamin B6 (mg)0·60·30·60·30·50·30·188Vitamin C (mg)56·4134·959·4101·247·0120·10·684Vitamin D (mcg)1·71·41·50·91·41·00·040Vitamin E (mg)5·93·16·13·46·84·30·111Ca (mg)259·690·0251·8104·7246·8111·10·596Na (mg)1275·4 ‡ 418·41370·8432·91478·6518·80·002Todos os nutrients em g/mg estão ajustados por 1000 kcal (4186 kJ);*ANOVA test, com teste pós hoc de Bonferroni.† P value < 0·05 T1 v. T2.‡ P value < 0·05 T1 v. T3. § P value < 0·05 T2 v. T3.
Mean values of inflammatory markers were: 0·041 mg/l for adiponectin, 0·051 mg/l for lepin, 1·18 mg/l for IL-6, 27·13 mg/l for IL-8, 0·11 mg/l for CRP and 2·11 mg/l for TNF-α.
Results from the adjusted analysis of the linear regression model are shown in Table 4. Adolescents with UPF energy consumption ≥30·0 % (tertile 3 of UPF) had a 79 % (exp (0·58) = 1·79) increase in IL-8 levels when compared with adolescents in tertile 1 of UPF.
Table 4Association between the tertiles of the percentage of energetic contribution of ultra-processed foods (UPF) to the diet of adolescents and inflammatory markers. São Luís – MA, BrazilInflammatory markerNon-adjusted modelAdjusted model* β 95 % CI P β 95 % CI P Model 1 adiponectin Tertile 1 of UPF (≤15·9 %)RefRef Tertile 2 of UPF (>15·9 e < 30 %)−0·02−0·18, 0·140·8000·00−0·18, 0·190·972 Tertile 3 of UPF (≥30·0 %)−0·09−0·26, 0·070·252−0·10−0·28, 0·080·272Model 2 leptin Tertile 1 of UPF (≤15·9 %)RefRef Tertile 2 of UPF (>15·9 e < 30 %)0·06−0·35, 0·470·7720·04−0·24, 0·330·753 Tertile 3 of UPF (≥30·0 %)0·38−0·02, 0·790·0650·38−0·10, 0·450·219Model 3 IL-6 Tertile 1 of UPF (≤15·9 %)RefRef Tertile 2 of UPF (>15·9 e < 30 %)0·07−0·16, 0·300·5640·17−0·08, 0·410·179 Tertile 3 of UPF (≥30·0 %)0·09−0·14, 0·310·4490·16−0·08, 0·400·197Model 4 IL-8 Tertile 1 of UPF (≤15·9 %)RefRef Tertile 2 of UPF (>15·9 e < 30 %)0·24−0·17, 0·650·2470·31−0·15, 0·770·186 Tertile 3 of UPF (≥30·0 %)0·39−0·22, 0·800·0640·580·12, 1·000·013Model 5 TNF-α Tertile 1 of UPF (≤15·9 %)RefRef Tertile 2 of UPF (>15·9 e < 30 %)−0·10−0·29, 0·100·330−0·02−0·23, 0·190·854 Tertile 3 of UPF (≥30·0 %)−0·02−0·22, 0·170·8180·00−0·21, 0·210·999Model 6 C-reactive protein Tertile 1 of UPF (≤15·9 %)RefRef Tertile 2 of UPF (>15·9 e < 30 %)0·01−0·34, 0·360·964−0·05−0·43, 0·330·812 Tertile 3 of UPF (≥30·0 %)0·30−0·05, 0·650·0950·26−0·12, 0·640·182*Model adjusted for sex, skin colour, maternal schooling, economic class, physical activity, alcohol consumption, tobacco consumption, BMI and waist circumference.
## Discussion
In this study carried out in adolescents, over 25 % of energy intake originated from UPF (26·2 %). Subjects of this food group have especially high consumption of simple carbohydrates, lipids, saturated fat and Na, and lower intake of proteins and fibre. The regression model adjusted for the variables (sex, skin colour, maternal education and economic class, physical activity, alcohol and tobacco consumption, BMI and WC) confirmed the association between higher UPF consumption and higher IL-8 levels.
The use of the cross-sectional design in the present study was considered a limitation; thus, the effect of UPF consumption on the occurrence of chronic outcomes may be better determined in a longitudinal study. However, the effect of a diet rich in UPF on metabolic and inflammatory alterations has also been investigated in a short-term randomised clinical trial[25].
In addition, the external validity of the results may have also been limited by the participation of students exclusively from public schools. It is possible that this factor could have underestimated the observed associations because students from private schools, with higher socio-economic status, tend to show a higher consumption of UPF and, hence, the impact on the inflammatory markers may also be greater[3]. Nonetheless, we have found relevant results, which demonstrate that adolescents in the groups with the highest UPF intake may have significant alterations of their inflammatory state.
On the other hand, the strengths of this study include its innovative character, the probability sampling, the assessment of food consumption using the NOVA classification and the use of early inflammatory markers in a young population that may limit reverse causality, which is a common limitation of studying the association between UPF and chronic disease health outcomes cross-sectionally. Another strong point was the investigative method for the study of food consumption, the 24-h diet recall, which despite its limitations, is able to quantify all the food consumed by the interviewees. The questionnaire was applied in two moments, increasing its ability to describe diet habits of the subjects.
The percentage of UPF energy contribution observed in our sample group was lower than that observed in other studies with adolescents in Brazil[26]. A study carried out in 2016 with 2499 adolescents in the same age group and city of the present study found that 35·8 % of the total energetic intake originated from UPF[27].
The lower prevalence of UPF consumption found in the present study can be associated with the difference in the socio-economic levels of the participants. In this work, the sample was composed of adolescents, mostly belonging to Class C from public schools in a city of the Northeast region in Brazil, which is the poorest region of the country. Sousa et al.[27] carried out a study in the same city, but participants belonged mostly to Class B. Other studies conducted in the Southern region of Brazil also included private schools with better socio-economic conditions. It must be noted that in Brazil, lower socio-economic status is correlated with a lower consumption of UPF[3].
This study identified that as the energy participation of UPF increases, there is a decrease in the quality of the diet due to a higher intake of simple carbohydrates, lipids and saturated fat, and lower protein intake. Louzada et al. [ 9] observed higher consumption of sugars, total, saturated and trans fat, and lower consumption of proteins, fibres and K in the highest quintile of this group. Similar results have been reported in other studies carried out in the USA, Colombia, the United Kingdom and Canada(28–31).
In agreement with other studies, for example, those by D’Avila et al. [ 26] and Melo et al.[32], we found no association between the consumption of UPF and overweight/obesity and abdominal fat in adolescents. Contrarily, two other studies that assessed the data from the 2008–2009 POF (Consumer Expenditure Survey) contradicted our results. A study carried out with 30 243 adolescents/adults found that those in the highest quintile of UPF intake had significantly higher BMI, with higher probability of overweight and obesity[33]. Another work assessed 55 970 Brazilian families and found that the domestic availability of UPF was also positively associated with BMI and the prevalence of overweight and obesity[34]. Monteiro et al.[35] identified the same positive association in nineteen European countries.
It is of note that both the above-mentioned studies with data from POF (Consumer Expenditure Survey) had a more robust probability sampling and broader age groups. Thus, although this study with adolescents from public schools showed no association between UPF intake and overweight/obesity and abdominal fat, it is important to keep in mind that these individuals may express harmful effects of this food consumption pattern in future stages of life.
Studies assessing the association of UPF intake with inflammatory markers are still scarce in the literature. So far, the only study on the association between UPF intake and the inflammatory marker CRP was carried out with adults from the ELSA cohort (Brazilian Longitudinal Study of Adult Health)[36]. Their results revealed that, among women, this association was not significant after BMI adjustment, which points to a possible mediation effect by adiposity[36].
This is the first study on the relationship between inflammatory markers (adiponectin, leptin, IL-6, IL-8, CRP and TNF-α) and the consumption of UPF among adolescents using the NOVA classification. Individuals with the highest UPF energy contribution (T3) had increased serum levels of leptin and CRP when compared to the individuals with the lowest contribution (T1), in addition to a higher level of IL-8 even after adjusting for confounding variables.
We suggest that this result can be explained by the ability of certain nutrients to modulate the inflammatory response. The increased intake of simple carbohydrates and saturated fats (widely present in UPF) can lead to the excessive production of reactive oxygen species and promote chronic inflammation with the increase of CRP, leptin, IL-6 and TNF-α, and the reduction of adiponectin(37–39). These markers can control the interactions between immune system cells and regulate local and systemic inflammatory responses[40]. The inadequate control of the inflammatory process may evolve into a low-grade chronic inflammation[41] with harmful effects such as tissue damage due to a prolonged activation of the innate immune system. This could lead to the occurrence and progression of several non-communicable diseases such as CVD, obesity, diabetes mellitus and certain types of cancer[42]. The relationship between inflammatory markers and eating habits in adolescence has been confirmed and assessed by other methods that did not consider the food processing levels[14,43,44].
Thus, the association of UPF consumption with inflammatory markers points to yet another harmful effect resulting from the high consumption of this food group in younger individuals. It indicates that the early exposure to dietary risk factors leads to health consequences and may cause more severe problems in the long term, since the triggering of chronic low-grade inflammation is an important risk factor for the occurrence of non-communicable diseases.
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---
title: Participation in cost-offset community-supported agriculture by low-income
households in the USA is associated with community characteristics and operational
practices
authors:
- Karla L Hanson
- Lynn Xu
- Grace A Marshall
- Marilyn Sitaker
- Stephanie B Jilcott Pitts
- Jane Kolodinsky
- April Bennett
- Salem Carriker
- Diane Smith
- Alice S Ammerman
- Rebecca A Seguin-Fowler
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991818
doi: 10.1017/S1368980022000908
license: CC BY 4.0
---
# Participation in cost-offset community-supported agriculture by low-income households in the USA is associated with community characteristics and operational practices
## Body
Fruit and vegetable (FV) consumption is associated with reduced risk for chronic disease and other positive health outcomes[1,2]. However, most US populations do not consume recommended amounts of FV[3]. One approach to improving FV intake is community-supported agriculture (CSA), which directly connects local farms to consumers by allowing community members to purchase a share of a farm’s anticipated harvest[4]. Because a lump sum payment is typically required before the growing season begins, CSA may be more accessible to households with higher incomes(5–9). However, the economic and health benefits of CSA participation for low-income households have been documented[10,11]. To broaden access to CSA, some programmes offer a subsidy, or cost-offset (CO-CSA), to low-income participants who are at greater risk of food insecurity and poor nutritional intake[12,13]. Understanding the community characteristics and operational practices that support participation in CO-CSA would provide useful implementation information to farms and other CO-CSA programme operators.
To our knowledge, twelve prior studies have examined CO-CSA effectiveness in improving FV access and intake, as well as household food security(14–25) and most, but not all, report beneficial changes in outcomes. For example, a pilot study reported that all participants consumed a greater variety of vegetables, learned new methods for cooking and preparing vegetables and liked new vegetables after participating in the CO-CSA[20]. Descriptive studies suggest that CO-CSA participation is associated with improved FV access[12,20] and intake[18,25], and that participants bought more fresh produce, tried new recipes and cooked with more vegetables after joining a CO-CSA[16]. Longitudinal studies of CO-CSA more often reported increases in FV intake[15,17,19,21,23] than no changes[14,20,22]. Findings from one randomised controlled trial supported the effectiveness of CO-CSA relative to unconditional cash transfer in terms of improved diet quality and reduced food insecurity[17]. Taken together, these studies suggest that CO-CSA participation can have positive effects on FV access, dietary intake and related behaviours.
These studies examined CO-CSA implemented in different community contexts and enrolled participants who had varying characteristics. Seven studies operated in urban areas(14,15,19–22,24), and five in predominately rural and micropolitan communities(16–18,23,25). Almost half enrolled participants from communities that were predominately African American and/or Hispanic[14,15,19,22,24]. Most CO-CSA participants were women (71–92 %) of all ages, but sometimes were limited to caregivers of children[16,18,19,25], Head Start staff[19] or focussed on older adults[21,24].
The CO-CSA programmes examined in prior studies also varied in operational practices. Five studies examined CO-CSA programmes that were free[15,16,21,22,24], but others required either a contribution based on a fixed percentage of share price – 45 %[14], 50 %[18,23,25] or 67 %[19] – or a sliding scale (43–75 %) based on ability to pay[17,20]. CO-CSA programmes varied in the options offered to participants, with multiple farm partners resulting in multiple share sizes[14,16], multiple pick up locations[16,23] and/or delivery[16,21,24], or these options were offered at some farms but not others[18,25]. Four of five CO-CSA programmes that partnered with just one farm offered only one option for share size and pick up location[15,19,20,22]. Three studies assessed CO-CSA programmes that operated with market-style selection of produce items[17,20,24], or in which item selection was available at some farms[18,25]. Of the seven studies of CO-CSA programmes that required payment, five accepted Supplemental Nutrition Assistance Programme (SNAP) benefits as payment[14,17,19,20,23] and two accepted SNAP at some farms[18,25].
Farm Fresh Foods for Healthy Kids (F3HK), the focus of this manuscript, was a summer growing season CO-CSA programme (mean = 21 weeks, interquartile range 19–23)[26], provided at 50 % cost-offset (half-price)[27]. The CO-CSA was supported by nine CSA-tailored, in-person nutrition education lessons that featured seasonal produce items via food tastings, demonstrations, hands-on cooking activities, handouts and recipes, and by providing participants with two to four larger cooking tools (e.g. food processor and crockpot)[27]. The F3HK intervention was implemented in the context of a randomised controlled trial with 1:1 random assignment of child–caregiver dyads to intervention and control groups[27]. F3HK reported overall improvements relative to control in caregiver nutrition attitudes, self-efficacy, FV intake, skin carotenoids, preparation of FV as snacks for children and household food security[28]. However, F3HK was implemented in small and micropolitan communities across four states, each with a unique context for implementation. Although F3HK required farms to receive weekly payments from participants and to accept SNAP as payment[27], all other operational practices were decided by each partner farm.
The focus of this manuscript is to explore the context of F3HK implementation with respect to community and participant characteristics and CO-CSA operational practices and to examine associations with participation levels. First, we describe the population characteristics of communities in which the F3HK intervention was implemented. Second, we explore differences in F3HK participant characteristics across communities and states and contrast participant and population characteristics. Third, we describe CO-CSA operational practices at F3HK partner farms and explore differences across communities and states. Fourth, we examine associations between community characteristics, participant characteristics, CO-CSA operational practices and participation levels (i.e. percent of CO-CSA weeks picked up, percent of CO-CSA nutrition lessons attended). These analyses inform recommendations for the development and implementation of future CO-CSA programmes in varied settings.
## Abstract
### Objective:
Subsidised or cost-offset community-supported agriculture (CO-CSA) connects farms directly to low-income households and can improve fruit and vegetable intake. This analysis identifies factors associated with participation in CO-CSA.
### Design:
Farm Fresh Foods for Healthy Kids (F3HK) provided a half-price, summer CO-CSA plus healthy eating classes to low-income households with children. Community characteristics (population, socio-demographics and health statistics) and CO-CSA operational practices (share sizes, pick up sites, payment options and produce selection) are described and associations with participation levels are examined.
### Setting:
Ten communities in New York (NY), North Carolina (NC), Vermont and Washington states in USA.
### Participants:
Caregiver–child dyads enrolled in spring 2016 or 2017.
### Results:
Residents of micropolitan communities had more education and less poverty than in small towns. The one rural location (NC2) had the fewest college graduates (10 %) and most poverty (23 %) and poor health statistics. Most F3HK participants were white, except in NC where 45·2 % were African American. CO-CSA participation varied significantly across communities from 33 % (NC2) to 89 % (NY1) of weeks picked up. Most CO-CSA farms offered multiple share sizes (69·2 %) and participation was higher than when not offered (76·8 % v. 57·7 % of weeks); whereas 53·8 % offered a community pick up location, and participation in these communities was lower than elsewhere (64·7 % v. 78·2 % of weeks).
### Conclusion:
CO-CSA programmes should consider offering a choice of share sizes and innovate to address potential barriers such as rural location and limited education and income among residents. Future research is needed to better understand barriers to participation, particularly among participants utilising community pick up locations.
## Setting and participants
Target locations for F3HK implementation were small and micropolitan (≤ 50 000 population) communities in New York (NY), North Carolina (NC), Vermont (VT) and Washington (WA) in the USA. Each community also had to be served by a partner farm experienced in CSA and a nutrition educator (through cooperative extension in three of four states). F3HK was implemented in a total of twelve communities. In most communities, one farm provided the CO-CSA. However, in VT1 and WA1 participants chose between two different farms, and in all communities in NC, participants were selected from among the same farms. Throughout this article, ‘community’ refers to the location and ‘farm’ refers to the CO-CSA provider.
In spring 2016 and 2017, F3HK enrolled English-speaking adult caregivers of a child 2–12 years of age who had self-reported household income ≤185 % of the Federal Poverty Level and had not participated in CSA for the past 3 years. Participants agreed to use SNAP benefits or money to pay for the CO-CSA share weekly and to attend nine CSA-tailored nutrition education classes[27]. A total of 685 caregivers were screened for eligibility, 542 (79·1 %) were eligible and 305 of those enrolled (56·3 %), of which 148 were assigned to the intervention group[28]. Two communities (one each in NC and VT) were excluded from this analysis due to low F3HK enrolment (≤7 participants, >1 sd below mean sample size of 12), and the remaining 137 intervention participants in 10 communities are the focus of this analysis. Figure 1 depicts the states, communities and participants included in these analyses.
Fig. 1F3HK implementation states, communities, and participants included in analyses
## Measures
Six community characteristics and seven county health statistics were obtained from publicly available data for the approximate time period of F3HK implementation. Community characteristics included population[29], percent of adult population with at least a bachelor’s degree[30], percent of households in poverty, percent of children in poverty, race and ethnicity[31]. We subsequently categorised towns as small (population ≤15 000) or micropolitan (population >15 000). Rural-urban continuum (RUCA 2010) codes also were recorded[32]. County health statistics included percent of persons food insecure[33], and percent of adult residents with diabetes, high cholesterol, high blood pressure, overweight, obesity[34] and cancer[35].
Seven CO-CSA operational practices were extracted from partnership agreements, farm websites and personal communications and dichotomised for analysis: location (outside or within the participant community), length of summer CSA (≤21 weeks or longer), number of pick up sites offered (one or multiple), number of share sizes offered (one or multiple), payment options (credit or debit cards accepted or not) and whether market-style self-selection of produce items or ‘u-pick’ options were offered or not. U-pick allows consumers to harvest larger quantities of certain produce items themselves. Participant selection of type of pick up site (farm, community location or farmer’s market/farm stand) and type of share size (small (≤8 produce items/week) or large (>8 items)) were recorded as part of a baseline survey.
Participant characteristics were recorded during eligibility screening or online baseline survey. Caregiver characteristics included caregiver sex, general health, marital status, education level, employment status, race and ethnicity. Child’s age, sex and general health also were reported by the caregiver. Household characteristics included number of adults and number of children in household, annual household income and receipt of food assistance benefits through the Special Supplemental Nutrition Program for Women, Infants and Children or SNAP in the past month[27]. Few participant characteristics were missing (maximum 0·7 %), and missing items are noted on tables.
Participation levels were assessed as: [1] percent of CO-CSA weeks picked up as recorded on logs maintained by partner farms and [2] percent of CSA lessons attended as recorded on lesson sign-in sheets[26].
## Analyses
Community characteristics and county health statistics were rounded before reporting to maintain the confidentiality of F3HK communities. Descriptive statistics were used to summarise participant characteristics, CO-CSA options selected by participants and participation levels by community and state. Pearson’s chi-square, Fisher’s exact tests and one-way and Welch’s ANOVA with Bonferroni correction were used to test for differences across communities and states. One-way ANOVA was used to test differences in participation levels across community size, participant characteristics and CO-CSA operational characteristics. Two farms (NY4 and VT2) did not provide reliable CO-CSA pick up data, and the twenty-five participants at these locations were excluded from analyses of weeks picked up. All analyses were performed using SPSS version 26 (IBM Corp.). Results are reported at a 95 % confidence level.
## Community and participants characteristics
Community characteristics varied across the 10 F3HK communities included in this analysis (Table 1). Populations ranged from 7000 to 60 000 (just above the upper bound typically used when classified as micropolitan). The largest communities (60 000, 50 000 and 40 000 population) also had the most education (77 %, 44 % and 53 % held a college degree, respectively). The two communities in NC were distinct from one another: NC1 had the highest percentage of adults with at least a bachelor’s degree (77 %), whereas NC2 had the lowest rate of college graduates (10 %) and the highest rate of poverty (23 % of households). The smallest community (NY4) had the lowest rate of poverty (4 % of households).
Table 1Characteristics of communities where F3HK was implementedCommunity characteristicsNew YorkNorth CarolinaVermontWashingtonNY 1NY 2NY 3NY 4NC 1NC 2VT 1VT 2WA 1WA $2\%$%%%%%%%%%Population* 25 00020 00010 000700060 00010 00040 00015 00050 0009000College educated‡ 21223914771053284419Households in poverty† 201011442312121112Children in poverty† 432022653726262031Race† American Indian/Alaskan Native1000000010 Asian11201326274 Black/African American882110225131 Native Hawaiian/Pacific Islander0000000001 White84869397736185968375 None of the above11102900117 Hispanic† 842074331830County health statistics Food insecurity§ 13121212131112121211 Diabetes‖ 891099127889 High cholesterol‖ 28323232283624292931 High blood pressure‖ 27313131293825302931 At least overweight‖ 61626262626859626365 Obese‖ 27272627293226272930 Cancer¶ per 100 000 pop.533541478541442438459476475469Sources:*US Census Bureau. Total population, 2011–2015 American community survey (ACS) 5-year estimates: US Census Bureau; 2015 (Available from: https://data.census.gov/cedsci/).†US Census Bureau. Population density, household income, families in poverty, people in poverty, race, ethnicity, 2014–2018 American community survey (ACS) 5-year estimates: US Census Bureau; 2018 (Available from: https://data.census.gov/cedsci/).‡US Census Bureau. Educational Attainment, 2015–2019 American Community Survey (ACS) 5-Year Estimates: US Census Bureau; 2019 (Available from: https://data.census.gov/cedsci/).§Feeding America 2017 (Available from: http://map.feedingamerica.org/).‖Centers for disease control and prevention, 2017 BRFSS survey 2017 (Available from: https://www.cdc.gov/brfss/annual_data/annual_2017.html).¶Centers for Disease Control and Prevention and National Cancer Institute. Cancer Incidence, 2013–2017: Centers for Disease Control and Prevention and National Cancer Institute; 2017 (Available from: https://gis.cdc.gov/Cancer/USCS/DataViz.html).
Residents of most communities were predominantly white and non-Hispanic, except in NC2 where 22 % of residents were Black or African American and 43 % were Hispanic and in WA2 where 30 % of residents were Hispanic. NC2 also had the highest rates of diabetes (12 %), high cholesterol (36 %), high blood pressure (38 %), overweight (68 %) and obesity (32 %) but also the lowest rate of cancer (438 per 100 000). All NY locations had high cancer rates (478–541 per 100 000). Across all communities, 11–13 % of households were food insecure.
Some F3HK participant characteristics varied significantly across communities and states (Table 2) and mirrored some of the overall community differences described above. For example, participant race and household income differed significantly by state; NC had the most Black or African American participants (45·2 %). Population data showed that both NC2 and WA2 had high percentages of Hispanic residents (43 % and 30 %, repectively), and both recruited relatively low percentages of Hispanic participants (4 participants or 25 %, and 1 or 9 %, respectively). NC was also the state with the lowest percentage of participants with household income ≥$25 000 (29·0 %). Household income also varied significantly across communities, with NC2 having a notably low percentage of participants with income ≥$25 000 (12·5 %). Three respondents in NC and three in VT (10 % each) had a child in fair or poor health whereas none did in either NY or WA.
Table 2F3HK participant characteristics by community and stateHousehold characteristicsNew YorkNorth CarolinaVermontWashingtonSig. by stateSig. by communityNY1 (n 16)NY2 (n 8)NY3 (n 9)NY4 (n 12)NY TotalNC1 (n 15)NC2 (n 16)NC TotalVT1 (n 16)VT2 (n 13)VT TotalWA1 (n 21)WA2 (n 11)WA Total####%##%##%##%≥2 Adults1457875·612864·511865·515665·6≥3 Children733744·44425·83424·110340·6Income ≥$25 0001257666·77229·010448·38643·8 * * Supplemental Nutrition Assistance Programme in past mo.† 553540·091061·35846·412553·1Caregiver characteristics Race White14591084·45945·2131286·2171084·4 * Black/African American03018·98645·2206·9103·1 Other/Multiple20016·7219·7116·93112·5 Hispanic10002·21416·1000·0219·4 ≥College degree1355562·28438·711658·69646·9 Employed735442·210754·86848·37434·4 Married1235657·84529·011555·28334·4 Fair/poor health023317·85429·04117·23215·6Child characteristics ≤5 years of age923337·85738·712662·111756·3 Fair/poor health00000·0129·72110·3000·0 * Differences across communities and states were tested using Pearson’s chi-square or Fisher’s exact tests all with Bonferroni correction.*Indicates difference at > 95 % confidence.†One observation missing from VT1 for this measure.
## CO-CSA operational practices
Four farms (30·8 %) were located within the participant community, two of which were within VT1 (Table 3). Six farms (46·2 %) operated summer CSA shares lasting 22+ weeks, all of which were in NY and NC. Most farms offered multiple share sizes (9 or 69·2 %) or a community pick up location (7 or 53·8 %), and many offered market-style produce selection (6 or 46·2 %), payment by credit/debit card (6 or 46·2 %) and multiple pick up locations (4 or 30·8 %). When participants were offered a choice of share sizes (n 85), the majority (63·5 %) selected a larger share (>8 produce items/week); and, when offered multiple locations for CO-CSA pick up (n 41), most (61·0 %) chose a community pick up site (data not shown).
Table 3Cost-offset community-supported agriculture (CO-CSA) operational practices by communityNew YorkNorth Carolina* VermontWashingtonTotalNY1NY2NY3NY4NC1 & 2VT1VT2WA1WA2########### of Farms$13111132112\%$ Farm located within community30·8010102000 Longer CSA season (22+ weeks)46·2111030000Farm offered: Multiple share sizes69·2111102111 Multiple pick up sites30·8000020101 A community pick up site53·8000031012 Payment by credit/debit card46·2110130000 Market-style produce selection46·2011012100 U-pick produce items15·4010001000*Partner farms shared enrolment of participants in NC1 and NC2.
Farms in VT and WA offered the most flexibility in share size and pick up options, but pick up locations often shifted over time. For example, in WA1 pick up initially occurred at a public housing complex and shifted to a downtown non-profit serving families with low incomes for the second year. In WA2, one farm initially held pick up at a downtown visitors’ information center and the other at the community programmes office of the local hospital, but both farms shifted for the second year (the first to the local hospital and the second away from the hospital and to their own farm stand). Other states offered fewer options. Almost all NY farms offered a choice in share size, but no NY farm initially offered a community pick up site or a choice of pick up locations. In NY2 however, pick up locations also shifted over time to include a community location at which participants could pick up a missed share and later the farm used that location exclusively when the farmers’ market closed for the season. In NC, on the other hand, no farm offered a choice of share sizes, but two of three farms offered a choice of pick up locations and all three offered a community pick up site.
## Participation level
Participation varied significantly across communities: participants picked up the CO-CSA share 33–89 % of weeks and attended 15–58 % of the education lessons (Table 4). In most communities, participants picked up their CSA share three-quarters of the weeks or more often. CO-CSA participation was lowest in NC2 (33·4 %) and highest in NY1 (89·2 %). Attendance at nutrition education classes also differed significantly across communities, with the lowest attendance in NC2 (15·3 %) and highest attendance in NC1 (57·8 %). CO-CSA pick up and class attendance behaviours were aligned in NC2 (low participation) and in NC1 and VT1 (high participation). In five communities, however, percentages for class attendance were 40–60 percentage points below CO-CSA pick up participation level (NY1–3 and WA1–2). There were no significant differences in participation level across states.
Table 4Mean participation level by community and stateMean participation level n TotalNew YorkNorth CarolinaVermontWashingtonSig. by stateSig. by communityNY1NY2NY3NY4NY TotalNC1NC2NC TotalVT1VT2VT TotalWA1WA2WA Total% CO-CSA weeks picked up11670·989·274·973·744·978·577·933·455·076·681·377·780·661·874·1 * % Education lessons attended13731·131·927·827·231·530·157·815·335·847·930·840·220·119·219·8 * CO-CSA, cost-offset community-supported agriculture.*Indicates difference > 95 % confidence. Differences in means were tested using one-way and Welch’s ANOVA with Bonferroni correction.
Compared to households in small towns, households in micropolitan communities had significantly higher participation in the CO-CSA (80·6 v. 54·4 % of weeks) and education classes (36·7 v. 24·2 % of lessons; Table 5). CO-CSA participation also was higher among households that included at least two adults (78·5 v. 54·5 % of weeks), included a young child (80·3 v. 62·6 %), and those with incomes at least $25 000/year (84·5 v. 57·1 %). Caregivers with a college education (82·6 v. 57·8 %) and those who were married (84·5 v. 60·6 %) also picked up CO-CSA shares a greater percentage of weeks than their counterparts. Married caregivers also attended significantly more education lessons than unmarried caregivers (38·2 v. 25·3 % of lessons), which was the only variation in lesson attendance across any participant characteristic. CO-CSA participation also was notably lower in the six households that included a child in fair or poor health (38·9 v. 72·3 % of weeks).
Table 5Mean participation level by community size, participant characteristics, and cost-offset community-supported agriculture (CO-CSA) operational practices% CO-CSA weeks picked-up (n 116)% Education lessons attended (n 137)NoYesNoYesCommunity size >15 000 population54·580·6 * 24·236·7 * Household characteristics ≥ 2 Adults in household54·578·5 * 25·133·9 ≥ 3 Children in household68·076·133·726·4 Household income ≥$25 00057·184·6 * 27·535·0 Household received Supplemental Nutrition Assistance Programme † 75·865·830·032·3Caregiver characteristics White60·774·931·331·1 Hispanic72·353·931·427·2 Married60·684·5 * 25·338·2 * College educated57·882·6 * 27·734·3 Employed70·271·731·331·0 Fair to poor health73·959·230·533·7Child characteristics ≤ 5 years of age62·680·3 * 32·329·9 Fair to poor health72·338·9 * 31·229·6CO-CSA operational practices Farm located within community70·672·028·738·0 Longer CSA season (22+ weeks)75·466·728·631·4Offered: Multiple share sizes57·776·8 * 34·030·1 Multiple pick up sites74·561·429·634·7 A community pick up site78·264·7 * 34·627·4 Payment by credit/debit card75·965·428·434·2 Market-style produce selection71·469·829·933·1 U-pick options69·775·928·843·9Differences in means were tested using t-tests with Bonferonni correction.*Indicates difference at > 95 % confidence.†One observation missing for this measure in both analyses.
When considering CO-CSA operational characteristics, CO-CSA participation level was higher when farms offered multiple share sizes (76·8 v. 57·7 % of weeks) and lower when farms offered a community pick up site (64·7 v. 78·2 % of weeks; Table 5). Farm location in the community, longer summer CSA length, multiple pick up sites, payment by credit/debit card, market-style selection of produce and u-pick options all were not associated with participation level. Attendance at education lessons did not differ by any CO-CSA operational practices.
## Discussion
This study reported significant differences in CO-CSA and education participation levels across communities but did not vary across the four states in which F3HK was implemented. This provides novel evidence that suggests that the local setting may matter more in supporting participation than does the state context. Further, we identified four inter-related characteristics (micropolitan location, education, income and spouse or other adult in the household) and two distinct operational practices (offering multiple share sizes and community pick up) that were associated with CO-CSA participation level and should be considered when adapting and implementing CO-CSA for rural and micropolitan communities. Five prior studies of CO-CSA took place in predominately rural and micropolitan areas(16–18,23,25), but these studies provided few details about their local contexts. A few prior CO-CSA studies documented widely varying participation levels[14,17,19,22], but only one was in a micropolitan community[17], none operated CO-CSA in multiple states and no study examined how community characteristics, participant characteristics or programme operations were associated with participation levels.
## Characteristics that may support CO-CSA participation
Participants in micropolitan communities had higher participation levels in both the CO-CSA and the CSA-tailored education classes. To our knowledge, no prior research contrasted CO-CSA participation levels in rural and micropolitan communities. Our data provide novel evidence that residents of micropolitan areas may be better able to participate in CO-CSA than their rural counterparts. We noted some population characteristics that differed between micropolitan and rural communities (particularly education) which may contribute to this difference. However, across all communities, more F3HK participants had finished college than was typical in their communities, even in NC2 where a college degree was rare. Three prior CO-CSA studies in rural and micropolitan areas also noted high levels of education among participants[17,18,25]. This suggests that CO-CSA programmes may have more difficulty reaching residents with lesser education either due to recruitment approaches or because programme operations do not meet their needs. Prior research has documented low levels of awareness of CSA among both urban[36] and rural[37] residents with low incomes. Since awareness of and knowledge about CSA is a necessary precursor to participation[38], a lack of familiarity with CSA may inhibit enrolment. Extension educators could conduct community-wide CSA awareness activities to support reach among those with fewer resources. Future research should explore methods for improving reach of CO-CSA to residents with less formal education.
All F3HK participants had incomes at 185 % Federal Poverty Level or below, but participants who had relatively more education or other resources (higher income, and spouse or other adult living in the household) picked-up CO-CSA shares a greater percentage of weeks than their counterparts with fewer resources. Married participants also attended education sessions more often. This suggests that, even if successfully recruited to a CO-CSA, participants from low-income households may need both a cost-offset and relatively more household resources to fully participate. Conversely, CO-CSA programmes may need to provide a cost-offset and other resources or supports to facilitate full participation. F3HK provided nutrition education and cooking tools as additional supports, but the results presented here suggest that more foundational resources like money and time were needed for some participants to fully participate. Future research is needed to disentangle the effects of these inter-related resources on participation and to explore approaches to meeting these needs.
Our data also illustrate that attendance at education sessions was consistently lower than CO-CSA pick up participation, and in five communities was 40–60 percentage points lower. This suggests that local context may matter differently for pick up and attendance, and programme design may require different adaptations for CO-CSA and for education. Future CO-CSA programmes should aim to identify local factors that could potentially hinder participation levels (rural location, low education, lower income, and no other adults in the household), and address these barriers through local programme adaptation. Research is needed to better understand how to encourage increased CO-CSA participation levels among participants with lower socioeconomic status, so as not to exacerbate existing health disparities.
## CO-CSA operational practices that may support participation
This study identified offering multiple share sizes and a community pick up location as operational practices associated with CO-CSA participation levels. Formative research for F3HK had suggested that potential CO-CSA participants desire control over the variety and quality of produce provided through mechanisms such as multiple share sizes and market-style selection[37], as well as convenient pick up location[37,39,40]. Most F3HK partner farms offered flexibility in share size, pick up location or both, which is consistent with the operational characteristics of CO-CSA programmes in prior studies which frequently offered multiple share sizes(14,16–18,25) and multiple pick up locations(16–18,23,25). Farms that offered multiple share sizes generally had participants who both selected the larger share, and had higher levels of CO-CSA pick up, suggesting both positive attitudes toward FV and the logistical capacity for consistent pick up among this subset of participants.
When offered a choice of pick up locations, participants often selected a convenient community site. Prior research also suggests that convenience as perceived by participants also may include a familiar socio-demographic environment in which they feel welcome[40], which many community locations provided (e.g. housing complex, community church). However, participants at community pick up sites also had lower levels of CO-CSA pick up than at other types of locations. For example, in WA1, participants lived in the housing complex that served as the setting for both CO-CSA pick up and classes. Yet despite the convenience of this community location, 9 (43 %) participants attended no classes and 7 (33 %) participants missed pick ups (most of whom eventually dropped out). Together these findings suggest that participants who prefer community pick up locations also may have additional barriers to participation. NC2 operated solely with a community pick up site and the overall poor participation in this location may have influenced these results.
Some prior studies reported on participants’ satisfaction with cost(14,16–18,25), pick up location(14,16–18,25,40) and share volume(14,16–18,25). However, no prior study that reported on participation level[14,17,19,22] also tested associations between programme operations and participation levels. Our study, therefore, provides novel evidence on associations between the choice of share size and a community pick up location with participation levels. In addition, prior studies suggest that CO-CSA farms that offer some flexibility to participants may be more effective than more rigid programmes. Three CO-CSA effectiveness studies offered little flexibility to participants and all reported no change in FV intake[14,20,22]. CO-CSA studies that included flexibility to participants by offering a few options[15,19,21], many options[17] or options that varied because they partnered with multiple farms[16,18,23,25], tended to report positive outcomes.
Assessing and understanding local residents’ needs and desires related to CO-CSA operational practices (especially choice of share size and community pick up location) prior to programme implementation could support the local tailoring of operations to match these needs and, thus, may support participation. Future research should test associations between CO-CSA operational practices and CO-CSA effectiveness using rigorous research design and larger samples.
## Limitations
Several limitations of this study deserve note. First, the sample sizes are small which limits statistical power. Although the F3HK intervention enrolled a total of 148 caregiver–child dyads into the intervention group, the contexts for implementation resulted in small local samples (two of which had fewer than eight participants and were excluded from analyses). In particular, NC2 emerged as unique as having the lowest education and income, and was also the only community classified as entirely rural according to RUCA codes[32]. Second, selection of communities was primarily guided by the availability of both an interested partner farm and an available local extension educator. This was a difficult pairing to identify and was most difficult in NC where one community slightly exceeded population criteria and no appropriate and willing extension educator could be identified and therefore staff provided the CSA-tailored nutrition education lessons. The inclusion of only one entirely rural town and this selection process together may hinder the generalisability of these results to other locations. Third, although the F3HK intervention trial required partner farms to accept both weekly payments and to include SNAP as an accepted form of payment, other operational characteristics were decided upon and implemented by partner farms. Although acceptance of SNAP benefits was required, only 24 % of participants used SNAP benefits all or most weeks[28]. Farms likely select their practices in consideration of the local community context which limits our ability to disentangle associations among contextual characteristics, CO-CSA operational practices and participation levels.
## Conclusion
Small towns and micropolitan communities are highly varied in their population characteristics, and CO-CSA does not appear equally well-suited to all implementation contexts. Understanding community characteristics and familiarity with CSA and adapting models to address potential participation barriers such as limited education and financial resources are important to local CO-CSA programme adaptation. Flexibility in CO-CSA operational practices, and particularly offering multiple share sizes, may support recruitment and participation levels. However, although community pick up locations are desired by participants, these enrollees may face challenges to participation. Future research is needed to better understand barriers to participation in CO-CSA, particularly in rural communities and among participants utilising community pick up locations.
## Conflict of interest:
There are no conflicts of interest.
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|
---
title: 'Household food insecurity during pregnancy as a predictor of anthropometric
indices failures in infants aged less than 6 months: a retrospective longitudinal
study'
authors:
- Karim Karbin
- Fatemeh Khorramrouz
- Ehsan Mosa Farkhani
- Seyyed Reza Sobhani
- Negin Mosalmanzadeh
- Zahra Shahriari
- Golnaz Ranjbar
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991822
doi: 10.1017/S1368980021003591
license: CC BY 4.0
---
# Household food insecurity during pregnancy as a predictor of anthropometric indices failures in infants aged less than 6 months: a retrospective longitudinal study
## Body
Food security is defined as the physical, social and economic accessibility of sufficient, safe and nutritious foods all the times for people to meet their dietary needs and food preferences for active and healthy life[1]. In 2018, the FAO report showed that approximately two billion people across the world are disposed to moderate or severe food insecurity[2]. In Iran, the prevalence rate of food insecurity among households, mothers and children has been reported to be 49, 61 and 67 %, respectively, with an increasing trend during 2004–2015[3].
It is believed that food insecurity mainly affects underprivileged societies, young children and women of the reproductive age[4,5]. Ample evidence suggests that food insecurity is associated with poor health outcomes in women, including higher risk of obesity[6], higher gestational weight gain[6,7], anaemia[8], depression[9], gestational complications (diabetes, hypertension and birth defects)[8,10,11] and the ultimate increase in the burden of healthcare costs on the community[2]. Due to increased nutritional requirements during pregnancy, not reaching the adequate nutrient supply may increase the risk of malnutrition and consequent intrauterine growth restrictions[12,13] which is a significant risk factor for under-five morbidities and mortalities[14].
Extensive research has been focussed on food insecurity, and some of the findings have confirmed the impact of maternal food insecurity on infancy and early childhood growth[15,16]. In this regard, a study conducted in the USA indicated that maternal food insecurity was associated with a threefold increase in the delivery of low-birth weight infants[17]. Also, living in food-insecure households severely affects the health status of children in later life as they are more prone to malnutrition in growth age. Not only may food insecurity lead to increased odds of childhood obesity[18] and poor cognitive performance[19,20], but it also is associated with psychological distress during their transition into adulthood[21].
Food insecurity is assumed to affect the growth of infants by feeding practices such as breastfeeding. As the most cost-effective approach to meeting the nutritional needs of infants and reducing childhood diseases[22], exclusive breastfeeding in the first 6 months of life is adversely affected by food insecurity through changes in the initiation and duration of breastfeeding[23]; therefore, food insecurity seems to adversely impact infant growth variably[9,24]. Nevertheless, only limited studies have been focussed on the effect of household food insecurity (HFI) on infant growth indices, proposing conflicting results[25].
This study aimed to investigate the effects of HFI during the third trimester of pregnancy on the growth indicators of infants aged less than 6 months.
## Abstract
### Objective:
To investigate the impact of household food insecurity during the third trimester of pregnancy on the growth indicators of infants aged less than 6 months.
### Design:
Retrospective longitudinal study.
### Setting:
137 healthcare centres (15 cities) in Khorasan Razavi province, Iran. Data were extracted from the Sina Electronic Health Record System (SinaEHR®).
### Participants:
This study was conducted on 2481 mother and infant dyads during November 2016–March 2019. The Household Food Insecurity Access Scale (nine-item version) was used to measure food insecurity in the third trimester of pregnancy. Women who delivered singleton infants were included in the study, and anthropometric indices of infants were measured throughout the first 6 months of life.
### Results:
Approximately 67 % of the participants were food secure, while 33 % had varying degrees of food insecurity. The children born to the mothers in the food-insecure households were, respectively, 2·01, 3·03, and 3·83 times more likely to be stunted at birth (95 % CI 1·17, 3·46), 4 months (95 % CI 1·21, 7·61) and 6 months of age (95 % CI 1·37, 10·68) compared to their counterparts in the food-secure households. However, there were no significant differences in mean birth weight, birth height and head circumference at birth between the two groups.
### Conclusions:
Household food insecurity during pregnancy is a risk factor for stunting in infants aged less than 6 months. Therefore, national nutrition programs could considerably support women in food-insecure households during and before pregnancy.
## Study design, sampling and inclusion criteria
In this retrospective longitudinal study, data were collected from Sina Electronic Health System (SinaEHR®) during November 2016–March 2019. SinaEHR is an integrated health information system supervised by Mashhad University of Medical Sciences, which contains the health records of more than five million people in Khorasan Razavi province, Iran.
The current research participants were selected from 15 cities (137 healthcare centres) in Khorasan Razavi province among women 18–45 years of age who referred to the healthcare centres for routine check-ups during their third trimester of pregnancy. To maximise the target population (registered in SinaEHR), we pooled data between November 2016 and March 2019, and random sampling was used for selecting study participants. We included only women who delivered singleton infants, but mothers with a history of chronic heart diseases, anaemia, type II diabetes mellitus and gestational diabetes were excluded. In addition, we excluded infants with a history of hospitalisation during the first 6 months of life.
## Sociodemographic and anthropometric measurements
The sociodemographic and anthropometric data, including age, education level, smoking during pregnancy, medical history of the mother and infant, place of residence and maternal pre-pregnancy BMI were collected using a questionnaire which was developed and applied via the SinaEHR. Anthropometric variables of infants (weight, height, and head circumference at birth, second, fourth and sixth months of life) and their mothers (weight and height) were measured by trained personnel using standard protocol.
Infants’ weight was measured with minimal clothing using a Seca scale with an accuracy of 10 g (Seca 725, Hamburg, Germany). Their height (supine position) and head circumference were also measured using a standard measuring tape with 1 mm’s accuracy. Maternal weight and height were measured with light clothing and no shoes using a Seca scale and a stadiometer (Seca 755, Hamburg, Germany) with an accuracy of 1 mm for height and 100 g for weight.
The infants who weighed less than 2500 g were considered low birthweight, and those weighing ≥4000 g were classified as large for gestational age[26]. The WHO Z score system was used to classify the nutritional status, and scores less than −2 of the WHO standards for height-for-age Z scores (HAZ), weight-for-height Z scores and weight-for-age Z scores (WAZ) were considered as stunted, wasted and underweight, respectively. Regarding the weight-for-height Z score standard, infants with the Z scores between −2 and 2 were considered normal, and those with the Z score of above 2 were considered overweight. In addition, infants with the HAZ between the Z-score lines −1 and 2, WAZ between the Z-score lines of −1 and 3 were classified as normal[27].
## Food insecurity measurement
HFI was measured using a standard questionnaire (Household Food Insecurity Access Scale, Household Food Insecurity Access Scale), which has been developed by Food and Nutrition Technical Assistance II project in collaboration with others[28,29]. The validity of the Persian version of this questionnaire has been confirmed by Salarkia et al., who reported the acceptable internal consistency of the scale for Iranian households with the Cronbach’s alpha of 0·95[30].
The Household Food Insecurity Access *Scale is* composed of a set of nine items specific to an experience of food insecurity occurring within the previous 4 weeks. Endorsed a standard scoring procedure was used with 1 point for occurrence and 0 for non-occurrence. The frequency scores are within the range of 0–3, with zero indicating non-occurrence, one showing rare occurrence (once/twice in the past 4 weeks), two indicating sometimes (3–10 times in the past 4 weeks) and three for often (>10 times in the past month)[31,32]. For the purpose of this article, based on the Household Food Insecurity Access Scale total score (nine items based on the frequency score), the study population was classified as food secure (scores 0–1), mildly food insecure (scores 2–7), moderately food insecure (scores 8–14) and severely food insecure (scores 15–27).
## Statistical analysis
Data analysis was performed in SPSS version 22. The continuous variables were expressed as mean and sd, and the categorical variables were expressed as frequency and percentage. Independent samples t-test was used to assess the significant difference in the Z scores between the food-secure and food-insecure subjects, and one-way repeated-measures ANOVA was applied to evaluate the significant difference in the Z scores between various measurement times. The variables were categorised, and the relative risk was calculated at 95 % CI[12]. In addition, multinomial logistic regression was applied considering food insecurity as a factor and the covariates of maternal age, pre-pregnancy BMI, education level and parity. In all the statistical analyses, the P-value of less than 0·05 was considered significant.
## Results
As shown in Fig. 1, more than 175 000 pregnant women were registered in SinaEHR during the study period, and 3474 women were randomly selected for this study. Among these women, 3252 were aged 18–45 years and had live, singleton deliveries. Notably, 202 women were excluded due to medical conditions (e.g. type II diabetes mellitus, gestational diabetes mellitus, anaemia, CHD), 342 women were excluded due to data loss and 227 cases were excluded due to neonatal hospitalisation history.
Fig. 1Flowchart of sampling procedure The sociodemographic characteristics and anthropometric indices of the participants are presented in Table 1. The proportion of the infants by gender was reported at 50 %. In addition, 0·8 % of the women (n 20) had smoking habits within the past 12 months upon enrolment. The majority of study population were urban (95·6 %) and outskirts residents (49·7 %). In terms of education level, 37·2 % of the participants had an academic degree, 35·4 % had a high school diploma and the others had secondary education or were illiterate. Approximately 4 % of the neonates (n 100) were low birth weight, and 4·5 % (n 110) had macrosomia (weight: >4000 g). The infants’ mean birth weight was 3237 g, the mean pre-pregnancy BMI was 26·29 kg/m2 and the mean gravidity was 2·44.
Table 1Sociodemographic characteristics of sample population (number of percentage and mean and sd values)N (%)Mean±sdVariablesN%MeansdGenderMale128051·6Female120148·4Birth weight (g)LBW10043237439Normal227091·5Macrosomia1114·5Birth height (cm)49·932·33 Birth head circumference (cm)34·741·74Maternal age (year)29·525·78Pre-pregnancy BMI26·295·40Mode of deliveryC-section116346·9Natural131853·1Premature birth421·7 Gravidity2·441·39 Exclusive breastfeeding198880·1Smoker mothers200·8Maternal education levelAcademic92337·2Diploma87835·4Below Diploma68027·4Place of residenceRural1104·4Town (<20 K)130·5Town (20–50 K)1646·6Town (50–500 K)823·3Outskirts123349·7City centre87935·4 As presented in Table 2, the highest prevalence of wasting was observed at birth, which gradually decreased with infants’ growth. The comparison of the mean Z scores between the food-secure and food-insecure subjects using independent t-test (Table 3) indicated that birthweight, birth height, head circumference at birth and birth’s Z scores were not significantly different between groups, although the infants in food-secure households had lower WAZ at 6 months of age ($$P \leq 0$$·003) and lower HAZ at the age of 2, 4 and 6 months (P ≤ 0·001).
Table 2Prevalence of classified levels of Z scores (number of percentage (%) values)Birth N (%)Two Months N (%)Four Months N (%)Six Months N (%)CharacteristicsN%N%N%N%WHZWasted2038·21024·1743622·5Normal223890·1229592·4232093·5232093·5Overweight401·6843·4873·5994WAZUnderweight672·7722·9441·8401·6Normal237495·7233594·1235594·9234294·4Other401·674 3 823·3994HAZStunted973·9773·1301·2271·1Normal237795·8238496·1243698·1243298Other70·3200·8150·6220·9Z scores of HAZ, WHZ and WAZ blow −2 considered stunted, wasted and underweight, respectively; WHZ of −2 to 2 considered normal and >2 classified as overweight; HAZ of −1 to 2 and WAZ of −1 to 3 considered normal. HAZ, height-for-age Z scores; WAZ, weight-for-age Z scores; WHZ, weight-for-height Z scores.
Table 3Independent samples t-test for comparison of Z scores in food-secure and food-insecure subjects (mean and sd values)CharacteristicsFood-secureFood-insecuretdfP-valueMean ± sd Mean ± sd Mean sd Mean sd Birth weight (cm)32474313218456−1·524790·054Birth height (cm)49·962·2549·882·49−0·7624530·057Birth head circumference (cm)34·751·6834·721·86−0·5224360·177 Birth WHZ−0·401·17−0·401·200·0418930·971 2-month WHZ−0·051·120·001·141·1423750·254 4-month WHZ−0·051·120·021·051·3223100·186 6-month WHZ0·101·100·091·06−0·3318540·745 Birth WAZ−0·160·87−0·210·94−1·2212340·221 2-month WAZ0·090·980·011·03−1·8323670·067 4-month WAZ0·181·020·101·01−1·7923110·073 6-month WAZ0·271·020·121·01−2·921848 0·003 Birth HAZ−0·081·05−0·161·11−1·5618910·120 2-month HAZ0·241·170·051·25−3·592379 <0·001 4-month HAZ0·401·080·231·12−3·462311 0·001 6-month HAZ0·471·090·231·10−4·431853 <0·001 HAZ, height-for-age Z scores; WAZ, weight-for-age Z scores; WHZ, weight-for-height Z scores. $P \leq 0$·05 considered statistically significant.
Figure 2 shows the prevalence of food insecurity in the sample population. As illustrated herewith, approximately two-thirds of the participants were food secure, while the prevalence of mild, moderate and severe food insecurity was 23 % (n 572), 7 % (n 179) and 3 % (n 70), respectively. Table 4 shows the relative risk of HFI during pregnancy for the anthropometric characteristics of the infants. Accordingly, the children born to the women in food-insecure households were, respectively, 1·96, 2·72, and 3·87 times more likely to be stunted at birth (95 % CI 1·23, 3·12), 4 months (95 % CI 1·28, 5·77) and 6 months of age (95 % CI 1·54, 9·75) compared to the infants of the food-secure women, while they were also 1·85 times more likely to be underweight at the age of 4 months (95 % CI 1·01, 3·41). After adjustment for pre-pregnancy BMI, age, education level, parity, smoking habits and place of living, the risk of stunting remained high at birth (adjusted relative risk (aRR) = 2·01; 95 % CI 1·17, 3·46), 4 months (aRR = 3·03; 95 % CI 1·21, 7·61), and 6 months of age (aRR = 3·83; 95 % CI 1·37, 10·68) in the infants born in food-insecure households compared to those in food-secure households. On the other hand, food insecurity was not significantly associated with weight-for-height Z score and WAZ during the first 6 months of life, and no correlation was observed with birth weight. These results remained persistent after adjustment for potential confounders.
Fig. 2Prevalence of household food insecurity during pregnancy Table 4Unadjusted and adjusted relative risk of household food insecurity during pregnancy in anthropometric characteristics of neonates (number of percentage (%) and RR and 95 % CI values)UnadjustedAdjustedFood security (%)RR (95 % CIRR (95 % CI)CharacteristicsNo (%)Yes (%)RR95 % CIP-valueRR95 % CIP-valueBirth weightLBW4·63·71·260·83, 1·900·2741·100·67, 1·830·706Normal90·592ReferentMacrosomia4·94·31·160·78, 1·720·4701·220·78, 1·900·378Birth WHZWasted8·38·40·960·68, 1·350·1800·940·64, 1·370·746Normal9090·2ReferentOverweight2·41·41·380·67, 2·830·3821·020·47, 2·200·958 2-month WHZWasted44·20·950·61, 1·470·8210·950·59, 1·530·839Normal9292·6ReferentOverweight4·03·21·250·79, 1·970·3371·090·66, 1·790·750 4-month WHZWasted2·13·50·590·33, 1·030·1340·640·34, 1·200·159Normal94·792·9ReferentOverweight3·33·60·900·57, 1·460·6670·820·50, 1·360·439 6-month WHZWasted2·52·50·990·53, 1·840·6671·250·64, 2·430·511Normal94·193·3ReferentOverweight3·54·20·810·48, 1·350·4130·880·50, 1·530·640Birth WAZUnderweight3·42·31·470·84, 2·580·1801·710·84, 3·480·136Normal94·696·3ReferentOther21·41·480·72, 3·070·2911·070·49, 2·350·862 2-month WAZUnderweight3·52·61·340·82, 2·190·2481·340·80, 2·450·244Normal92·994·7ReferentOther3·62·71·350·83, 2·200·2201·250·74, 2·120·412 4-month WAZUnderweight2·61·41·851·01, 3·410·0491·810·91, 3·610·094Normal94·395·1ReferentOther3·13·40·920·56, 1·510·7430·850·51, 1·420·539 6-month WAZUnderweight2·31·61·580·76, 3·270·2711·560·71, 3·440·272Normal94·494·4ReferentOther3·54·30·820·49, 1·370·4430·890·51, 1·550·666Birth HAZStunted5·731·961·23, 3·12 0·005 2·011·17, 3·46 0·012 Normal94·296·7ReferentOther0·20·30·490·06, 4·390·5240·510·05, 4·980·559 2-month HAZStunted3·42·94·460·98, 21·530·0531·140·66, 1·960·632Normal96·396·1ReferentOther0·313·950·91, 17·200·0680·270·06, 1·230·092 4-month HAZStunted2·10·82·721·28, 5·77 0·009 3·031·21, 7·61 0·018 Normal97·498·5ReferentOther0·50·70·740·24, 2·330·6080·560·15, 2·100·386 6-month HAZStunted2·10·63·871·54, 9·75 0·004 3·831·37, 10·68 0·010 Normal97·298·5ReferentOther0·710·690·22, 2·160·5290·850·25, 2·860·795Multinomial logistic regression adjusted for maternal age, pre-pregnancy BMI, education level and parity; $P \leq 0.05$ considered statistically significant. HAZ, height-for-age Z scores; LBW, low birth weight; WAZ, weight-for-age Z scores; WHZ, weight-for-height Z scores.
## Discussion
Despite the fact that HFI has been associated with several adverse health outcomes among children, there is scarce literature regarding the correlation of HFI during pregnancy with the nutritional status of children. This was the first study to determine the effects of HFI during pregnancy on infants’ growth indicators in Iran. Our findings indicated that the mean WAZ and HAZ were significantly lower in infants aged 6 months born to food-insecure mothers. HFI during pregnancy may be a risk factor for stunting in infants aged less than 6 months. However, no significant association was observed between maternal food insecurity and birth weight in this study.
Our results showed that the children of food-insecure households had lower WAZ and HAZ compared to the food-secure ones, which is consistent with the studies conducted in other low- and middle-income countries, such as South Ethiopia[33], Nepal[34], Brazil[16] and Kenya[15]. Another study conducted on children of different age groups in the same region reported that HFI was significantly correlated with the mean height of the infants aged less than 6 years[35]. It seems that food insecurity could affect child development in the early stages of life through at least two mechanisms. First, food-insecure mothers have limited access to nutritionally adequate and safe food, which decreases the resources for the proper growth of their infants during pregnancy and breastfeeding[36,37]. Another mechanism is via the maternal emotional status, as food insecurity could be the driving force behind the disruption of maternal psychological and hormonal balance, which in turn has an adverse impact on the quality and duration of breastfeeding[38,39].
The current research’s initial finding was that HFI was only associated with child stunting, and no correlations were observed with wasting and underweight. Stunting or chronic malnutrition is often an indication of long-term nutritional deprivation. However, this is an issue of greater magnitude than underweight or wasting, more accurately reflecting the nutritional deficiencies and illnesses that occur in the most critical periods of growth and development in the early stages of life[28]. This impaired linear growth, during the first 1000 d of life particularly, tends to remain up to adulthood, which is associated with greater risk of morbidity and mortality[40], poor cognition and educational performance[41] and nutrition-related chronic diseases in adult life[42].
A growing body of evidence shows that HFI is closely linked to greater risk of stunting in early age in developing countries. A study conducted in South Ethiopia reported that children aged 6–59 months living in food-insecure households were 6·7 times more at risk of stunting than their counterparts in food-secure households[33]. Likewise, A cross-sectional study on 2591 children aged 0–60 months confirmed the association of HFI with increased risk of stunting even after adjusting for child, mother and household confounders[43]. In a multi-country study conducted on 800 households in eight countries, Psaki et al. found that food access insecurity was associated with a significant shift in the distribution of children’s HAZ toward lower values; the risk of stunting increased by 12 % among children from food-insecure households.
Since the growth of infants aged less than 6 months largely depends on the nutritional status of mothers through breastfeeding, the adverse conditions in this period may even deteriorate with the initiation of complementary feeding[44]. Hence, our findings highlight this point that there is a clear need to establish early and timely preventive actions to address HFI during pregnancy and the first 6 months after birth.
In contrast to our finding regarding the lack of an association between food insecurity during pregnancy and birth weight, the study by Saeed et al. on 103 pregnant women aged 19–45 years indicated that food-insecure women were at a fivefold increased risk of delivering low birth weight neonates[45]. Moreover, a prospective cohort study of 5044 pregnant women showed that severe HFI is a risk factor for the higher rate of low birth weight[46]. The discrepancy could be because the method used in the mentioned study to assess the HFI status differed from our research. In addition, the rate of food insecurity in our population was low at baseline.
There are some limitations that should be acknowledged. Firstly, there was a lack of access to reliable data on the households’ income status and the inevitable use of the habitation area as a welfare indicator. Secondly, our study was only focussed on the first 6 months of life to clarify the linkage between food insecurity during pregnancy and the growth indicators of infants when they primarily depended on the maternal nutritional status. Therefore, further longitudinal studies are recommended on various age groups in order to confirm the associations. Notably, this was the first multi-centric study regarding the effects of HFI on the growth indicators of infants aged less than 6 months in Iran.
## Conclusion
The results of this longitudinal study indicated that food insecurity during pregnancy might be a risk factor for stunting in infants aged less than 6 months. Our findings could be incorporated into nutritional interventions during and before pregnancy as HFI may have detrimental effects on children’s growth and development in the early stages of life, which could become life-long.
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|
---
title: 'Drivers and distribution of the household-level double burden of malnutrition
in Bangladesh: analysis of mother–child dyads from a national household survey'
authors:
- Abdur Razzaque Sarker
- Zakir Hossain
- Alec Morton
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991823
doi: 10.1017/S1368980022002075
license: CC BY 4.0
---
# Drivers and distribution of the household-level double burden of malnutrition in Bangladesh: analysis of mother–child dyads from a national household survey
## Body
In recent years, the double burden of malnutrition (DBM) has emerged as a global public health issue, particularly for low- and middle-income countries (LMIC)[1,2]. According to the WHO, the DBM means the coexistence of undernutrition along with overweight and obesity, within individuals, households and populations, and across the life course[3]. Due to rapid urbanisation, economic transition and demographic dividend (i.e. accelerated economic growth offered by the changes in the age structure of a population), many developing countries, including Bangladesh, are experiencing a nutritional transition[4]. Children are particularly at risk as poor nutrition in the early years can have lasting consequences throughout life. Yet, various forms of childhood malnutrition, such as stunting, wasting, underweight, overweight and obesity, are very common across various societal groups in many LMIC[5]. According to the latest data, globally, approximately 149·2 million, 45·4 million and 38·9 million children aged under 5 years were stunted, wasted and overweight, respectively, while approximately 20 million new-borns were born with low birth weight[3]. It was estimated that 45 % of all deaths among children aged unde 5 years were directly related to childhood undernutrition, with most of these deaths occurring in Asia and Africa[3]. The number of overweight children (OWC) aged under 5 years increased from 30 million in 2000 to 55·6 million in 2017[6]. Further, a number of mothers in the developing world are also suffering from underweight, overweight and obesity-related malnutrition[7]. Per the latest estimates, approximately 1·9 billion adults are overweight or obese and 462 million people are suffered underweight globally[8].
Although the prevalence of childhood undernutrition has declined substantially, the prevalence of undernourished children and underweight mothers (UWM) is high in rural areas and among the poorest wealth quintile in Bangladesh[1,9]. Further, due to rapid changes in global food systems, increasing urbanisation, decreased physical activity, changes in lifestyle and changes in dietary intake, many developing countries, including Bangladesh, are experiencing overweight-related issues among children and mothers[7,10]. As a consequence, the proportion of people with overweight and obesity has increased substantially, particularly among the wealthiest and most educated individuals and people living in urban areas[1,7].
Like other developing countries, *Bangladesh is* experiencing a coexistence of undernutrition and overweight conditions at the population, individual and household levels, a phenomenon referred to as the DBM, which is an emerging public health problem in Bangladesh[1]. According to WHO guidelines, the DBM is characterised by the coexistence of undernutrition (including wasting, stunting and deficiencies in important micronutrients) with overweight, obesity or diet-related non-communicable diseases[3].
The concept of the DBM has emerged in the past three decades and recently received greater attention due to a recent series of papers in The Lancet, as it appears to be more permanent and widespread than previously perceived[11]. It is well established that both being underweight and overweight have multifaceted consequences for survival, the incidence of chronic diseases, healthy development, and the economic productivity of individuals, societies, and healthcare systems[12]. Both overnutrition and undernutrition are equally harmful. Undernutrition often hinders physical and intellectual development, whereas overnutrition is a significant contributor to various non-communicable diseases, including diabetes and hypertension. Both forms of malnutrition cause huge direct and indirect costs to individuals, families and nations; approximately US $3·5 trillion globally[13].
Bangladesh – a lower-middle-income country – has made remarkable progress in improving its population’s health over the past few decades. This may be due to the pluralistic healthcare system in which public providers, private providers and various non-governmental organisations are engaged in healthcare delivery in Bangladesh. As a consequence, the prevalence of childhood stunting (low height for age) was reduced from 51 % in 2004 to 31 % in 2017–2018, while the prevalence of underweight (low weight for age) was reduced from 43 % in 2004 to 22 % in 2017–2018[14]. While childhood undernutrition constitutes an enormous burden, the prevalence of childhood overweight conditions (2 % in 2018) is an emerging public health problem in Bangladesh[14]. Moreover, in terms of UWM, the prevalence decreased significantly from 30 % in 2007 to 12 % in 2017–2018, while the prevalence of overweight mothers (OWM) has increased alarmingly from 12 % in 2007 to 32 % in 2017–2018[14]. Recently, an increasing trend of overweight or obese has been observed among urban and wealthier individuals in Bangladesh[15]. It has been noted that UWM were found to coexist with OWC, and OWM were found to coexist with stunting, wasting, and underweight conditions in children within the same households in Bangladesh. The WHO policy brief on DBM indicated that most current policies tend to address either undernutrition or overweight and obesity, but not both; therefore, actions that address both conditions should be prioritised globally[3]. Such double-duty actions include interventions, programmes and policies that have the potential to simultaneously lessen the risk or burden of both undernutrition and overnutrition. This is an urgent issue, as the coexistence of various forms of malnutrition among mothers and children has continued to rise not only in Bangladesh but globally. Notably, malnourished women are susceptible to experiencing complications related to pregnancy and childbirth[16].
A number of studies have identified the DBM in various settings globally[1,5]. A recent multi-country study that included Bangladesh estimated the DBM using three separate combinations of overweight or obese mothers with undernourished children (i.e. underweight children (UWC), stunted children (SC) and wasted children (WC))[17]. It was also observed that the prevalence of UWM remained high in rural areas, while the prevalence of OWM increased rapidly in both rural and urban areas, creating a DBM among mothers in Bangladesh[1]. Another study predicted that by the year 2030, the prevalence of UWM would be highest among the poorest segment of society, and the prevalence of overweight and obesity would be highest among the richest segment in Bangladesh[5]. A multi-country study conducted in Bangladesh, Nepal, Pakistan, and Myanmar referred to household-level DBM as the coexistence of OWM and SC in the same household only but did not focus on the other types of DBM[18]. To the best of our knowledge, analysis of the UWM along with the overweight and obesity status of the children and the OWM along with the stunting, wasting and underweight status of the children in the same household to explore the status of DBM using nationally representative data in Bangladesh has not yet been performed. This study aims to provide important information about the prevalence of various forms of DBM at the national level and by urbanity in Bangladesh. The specific objectives of the study are to measure the prevalence, inequality and factors associated with the overall DBM at the household level in Bangladesh.
## Abstract
### Objective:
The double burden of malnutrition (DBM) has become an emerging public health issue in many low- and middle-income countries. This study aims to provide important evidence for the prevalence of different types of DBM at the national and subnational levels in Bangladesh.
### Design:
The study utilised data from the latest Bangladesh Demographic and Health Survey (BDHS) 2017–2018. Multivariable logistic regression was performed to identify the sociodemographic factors associated with DBM.
### Setting:
Nationally representative cross-sectional survey.
### Participants:
8697 mothers aged 15 to 49 years with <5 children.
### Results:
The overall prevalence of the DBM was approximately 21 %, where the prevalence of overweight mother (OWM) & stunted child/wasted child/underweight child (SC/WC/UWC) and underweight mother (UWM) & overweight child (OWC) was 13·35 % and 7·69 %, respectively, with a higher prevalence among urban households (OWM & SC/WC/UWC = 14·22 %; UWM & OWC = 10·58 %) in Bangladesh. High inequality was observed among UWM & OWC dyads, concentration index (CI) = -0·2998, while low level of inequality of DBM were observed for OWM & SC (CI = 0·0153), OWM & WC (CI = 0·1165) and OWM & UWC (CI = 0·0135) dyads. We observed that the age and educational status of the mother, number of children, fathers’ occupation, size and wealth index of the household, and administrative division were significantly associated with all types of DBM.
### Conclusions:
Health policymakers, concerned authorities and various stakeholders should stress the prevalence of DBM issues and take necessary actions aimed at identifying and addressing the DBM in Bangladesh.
## Study population and data source
The study utilised data from the most recent Bangladesh Demographic and Health Survey (BDHS) 2017–2018. The survey was carried out from October 2017 to March 2018 under the authority of the National Institute of Population Research and Training, Medical Education and Family Welfare Division, and Ministry of Health and Family Welfare[14]. The BDHS is a vital source of records of data used in this study, including women’s BMI and records of stunting, wasting, underweight, and overweight conditions of children under 5 years of age. Women aged 15 to 49 years with at least one of their children living in the same household were the population of this study.
## Survey design and sampling procedures
The BDHS 2017–2018 was a cross-sectional survey that used a two-stage stratified random sampling design to cover the entire population by taking a nationally representative sample. This survey used a list of enumeration areas and a standard sampling frame provided by the Bangladesh Bureau of Statistics[14]. A total of 8772 individual mothers aged 15 to 49 years were enrolled in this study. However, seventy-five mothers were excluded, because no children lived in their households. Thus, a total of 8697 samples were analysed, with 2379 and 6319 mothers from urban and rural areas, respectively.
## Outcome variables
The outcome variable was the prevalence of the DBM at the household level, disaggregated by urban and rural households. The DBM was defined as the coexistence of mothers’ underweight condition and children’s overweight condition or the coexistence of mothers’ overweight condition and children’s stunting, wasting, or underweight condition in the same household[3]. Mothers’ nutritional burden was defined as the existence of underweight or overweight conditions in the mother, while children’s nutritional burden was defined as stunting, wasting, underweight or overweight conditions. The 2017–2018 BDHS used the WHO’s guidelines to determine the cut-off values for mothers’ BMI (i.e. underweight and overweight) and the stunting, wasting, underweight and overweight status of the children[14].
## Explanatory variables
A number of explanatory variables were included in this study based on relevance and logical correlation with the DBM among women and children globally[1,2,5]. The series of explanatory variables were as follows: age and educational and working status of the mother, mother’s age at first birth, number of children, father’s education and occupation, sex of children, child’s birth order, household size, toilet facilities, respondent’s exposure to mass media, wealth index, and administrative division of the households. The households of the study participants were categorised based on whether they were residing in urban or rural areas. Respondents’ age was categorised into three groups: 15–19 years, 20–29 years and 30–49 years. Maternal and paternal educational status were reported by the study participants and categorised as ‘no formal education’, ‘primary’, ‘secondary’ and ‘higher’. Mother’s age at first birth was categorised into three groups (less than 18, 18–24 and 25 or above), and working status of the mothers was categorised as ‘yes’ and ‘no’. Likewise, the respondent’s number of children was categorised into three groups (one child, two children, and three or more children). A composite score named the ‘wealth index’ was calculated using principal component analysis based on the household’s ownership of selected assets, availability of electricity supply, television, bicycle, materials used for housing construction, types of water access and sanitation facilities, use of health and other services, and health outcomes. Ultimately, the wealth index was used to categorise households into the ‘poorest’, ‘poorer’, ‘middle’, ‘richer’ and ‘richest’ quintiles[14].
## Measurement of inequality
Measurement of inequality was performed using the concentration curves and concentration indices. The concentration curve provides the distribution of DBM among the socio-economic groups. The cumulative proportion of DBM in the vertical axis is depicted against the cumulative proportion of the samples regarding socio-economic status. If the DBM is more concentrated among poor people, the concentration curve will lie above the equity line and vice versa. If the concentration curve equals the 45-degree straight line exactly, this means that there is perfect equity in DBM with respect to the wealth index. The wealth index was calculated through principal component analysis using BDHS survey data. The concentration index (CI) shows the information contained in each concentration curve and is twice the area between the concentration curves and the equity line[19]. The value of the CI lies between –1 and +1 (i.e. –1 ≤ CI ≤ + 1), where –1 refers to the case where DBM is entirely concentrated among the poorest quintile, and +1 refers to the case where malnutrition is entirely concentrated among the wealthiest quintile. Further, a value of 0 (zero) signifies perfect equality, i.e. there is no socio-economic inequity for the DBM.
## Statistical analysis
Descriptive analysis, such as frequency distribution and cross-tabulation, was applied for measuring the prevalence of DBM according to background variables. The inequality of DBM was measured by generating the Lorenz curve using Microsoft Office Excel version 16.0. Both adjusted and unadjusted logistic regression models were used to examine the significant risk factors. The dependent variable was expressed as binary, and it was represented as ‘1’ for the coexistence of mothers’ underweight condition and children’s overweight condition or the coexistence of mothers’ overweight condition and children’s stunting, wasting or underweight condition in the same household, while ‘0’ was represented for the non-coexistence of mothers’ underweight condition and children’s overweight condition or the coexistence of mothers’ overweight condition and children’s stunting, wasting or underweight condition in the same household. In the multivariable logistic regression models, results were presented as adjusted OR (AOR) with 95 % CIs. Results were considered to be statistically significant at the 5 % α level ($P \leq 0$·05). Since the BDHS survey used a two-stage stratified cluster sampling technique, the recommended sample weights provided by the BDHS were used for the analysis. All statistical analyses were carried out using the statistical package Stata/SE 14 software (Stata Corp.).
## Sociodemographic characteristics of the study participants
The background characteristics of the study participants are described in Table 1. A total of 8697 women with at least one child aged up to 60 months were included in this analysis, with most of the participants living in rural areas (72·65 %). More than half of the respondents were young adults aged 20 to 29 years (62·41 %) and had completed secondary and higher education (63·8 %). Approximately 46 % and 37 % of rural and urban mothers, respectively, had their first pregnancy before 18 years of age, while 65 % of mothers had at least two children. Approximately 59 % of mothers had a BMI within the normal range, while 27 % were overweight or obese and 14 % were underweight. Approximately 52 % of the children were male. Approximately 31 % of the children were stunted, followed by underweight (22 %), wasted (8 %), obese (8 %) and overweight (2 %). Approximately one-third (31 %) of the study households were large in size (six or more family members), while only 13 % of the study households were small in size (<4 family members). Most of the participants (60 %) were using hygienic toilet facilities, and 42 % of the participants had access to mass media. According to the wealth index, 26 % of the rural population enrolled in the study were from the poorest quintile, and 45 % of the urban population enrolled in the study were from the richest quintile. The highest number of participants (26 %) belonged to the Dhaka division (largest administrative unit), followed by the Chittagong division (21 %), while the lowest number of participants (6 %) belonged to the Barisal division.
Table 1Sociodemographic characteristics of the study participant (n 8697)VariablesUrban (n 2379)Rural (n 6319)Overall (n 8697)Frequency%Frequency%Frequency%Double burden (all types) Yes59024·79124019·62182921·03Mothers age 15–19 years27511·5883313·19110912·75 20–29 years146361·51396562·75542862·41 30–49 years64026·92152024·06216124·84Mothers’ educational status No formal education17807·4846607·3764407·40 Primary61025·66189429·98250528·80 Secondary105644·38315649·94421248·42 Higher53522·4880312·70133715·38Mothers age at first birth Less than 18 years87136·61291546·13378543·52 18–24 years130654·91318050·33448651·58 25 years or more20208·4822403·5542604·90Number of children One child92738·96210133·25302834·82 Two children88837·33230036·40318836·66 Three or more child56423·71191730·34248128·53Mothers BMI Underweight25810·8492614·65118313·61 Normal weight118949·96397262·87516159·34 Overweight65627·57110117·43175720·20Obese27711·6331905·0659606·85 Mothers working status Yes77232·44277443·91354640·77Fathers’ education No formal education30612·84115518·28146116·80 Primary69929·40223835·43293833·78 Secondary76332·08203432·18279732·16 Higher61125·6789114·11150217·27Fathers’ occupation Day labour143960·48465073·59608970·01 Business64327·02116618·46180920·80 Service22909·6125404·0148205·55 Unemployed1600·685000·796600·76 Others5202·2019903·1525102·89Sex of children Male120750·74333352·75454052·20 Female117249·26298547·25415747·80Child stunting (n 7818) Yes52425·32187732·66240230·72Child wasting (n 7804) Yes18609·0247308·2465908·44Child underweight (n 8041) Yes40719·10134522·77175321·80Child overweight (n 7781) Yes4502·207201·2511701·50Child obesity Yes25310·6541906·6367207·73Birth order First99641·86234037·04333638·36 Second79933·58198931·48278832·05 Third or more58424·56198931·48257329·59Household size Small (<4)42117·7272211·42114313·14 Medium (4–6)134756·62350855·51485555·82 Large (6 and more)61025·66208933·06270031·04Division Dhaka110946·63112817·85223725·73 Chittagong46619·60134321·25180920·80 Rajshahi18207·6582813·10101011·61 Rangpur13305·6178612·4492010·57 Khulna18407·7461209·6979609·16 Mymensingh12205·1260409·5672608·34 Sylhet10404·3761209·6971608·23 Barisal7803·2840606·4248405·56Toilet facility Hygienic187178·64338953·64526060·48 Unhygienic50821·36292946·36343739·52Mass media exposure Yes150163·08218434·56368442·36Wealth index Poorest20608·64166926·41187421·55 Poorer14906·24161725·59176620·30 Middle27911·72135321·42163218·77 Richer67228·24107316·97174420·05 Richest107445·1660709·60168119·33Total237927·35631972·658697100
## Prevalence of double burden of malnutrition across background characteristics
The prevalence of DBM across background characteristics is described in Table 2. The overall prevalence of DBM characterised by OWM & SC/WC/UWC and UWM & OWC was 13·35 % and 7·69 %, respectively. Although a similar pattern was found for both OWM’s dyad with undernourished child (urban: 14·22 % and rural: 13·02 %) and UWM’s dyad with OWC (urban: 10·58 % and rural: 6·60 %), the differences between urban and rural were much higher among the UWM & OWC dyad. The prevalence of DBM increased gradually as mothers’ age increased, and the highest DBM was found among the mothers aged 30–49 years (OWM & SC/WC/UWC: 15·89 % and UWM & OWC: 9·14 %). The highest prevalence of DBM (OWM & SC/WC/UWC: 15·58 % and UWM & OWC: 11·49 %) was noticed among the mothers who had no formal education, while the urban–rural difference was found higher among OWM & SC/WC/UWC dyads (urban: 19·86 % and rural: 13·94 %) and the lowest prevalence was found for the highest educated mothers. We found that mothers in urban areas who had their first child after age 24 were more prone to both OWM & SC/WC/UWC (15·66 %) and UWM & OWC (13·47 %) dyads. Such pattern was not observed among rural mothers.
Table 2Prevalence of double burden of malnutrition (OWM & SC/WC/UWC, UWM & OWC) by sociodemographic characteristicsVariablesOWM & SC/WC/UWCUWM & OWCUrbanRuralOverallUrbanRuralOverall n % n % n % n % n % n %Mothers age 15–19 years3111·158109·6711110·042208·014805·727006·29 20–29 years19913·6250712·7870613·0115610·6724506·1940107·40 30–49 years10816·9123515·4634315·897311·4712408·1519709·14 P-value0·0250·0010·0000·3940·0230·016Mothers’ educational status No formal education3519·866513·9410015·582212·385211·157411·49 Primary9816·0923912·6133713·466711·0014207·4920908·34 Secondary14613·8442913·5957513·6510910·3017505·5528406·75 Higher5910·949011·2014811·095410·034805·9710207·60 P-value0·0330·2690·0290·4010·0010·001Mothers age at first birth Less than 18 years13115·0536012·3549112·989410·822607·7432008·44 18–24 years17513·4343713·7561313·6613009·9817605·5230606·82 25 years or more3215·662511·245713·342713·471607·104310·12 P-value0·1720·0530·6810·3290·0070·005Number of children One child11011·8824811·7835811·8212213·1316507·8428709·46 Two children13114·7728812·5442013·167408·3112305·3519706·17 Three or more child9717·1728714·9538315·455609·9412906·7418507·47 P-value0·0080·0150·0000·0020·0050·000Mothers working status No22914·2748913·8071813·9414008·7122606·3736607·10 Yes10914·1133312·0244212·4711214·4519106·9030308·54 P-value0·8420·0110·0470·0000·6600·035Fathers’ education No formal education4615·1215013·0019613·443310·6410809·3814109·65 Primary10514·9727412·2637912·907110·1213506·0420607·01 Secondary11515·0128814·1440214·387009·1212005·8918906·77 Higher7311·9011012·3918312·197912·885406·0213208·81 P-value0·0730·6350·3380·0510·0000·000Fathers’ occupation Day labour21014·5663013·5584013·7914710·2129306·2943907·22 Business9114·1613411·4722512·426710·377406·3514107·78 Service3113·462509·755611·512611·442208·574809·93 Others0209·880612·720812·030209·660510·300710·14 Unemployed0509·972813·823313·021019·782411·833413·49 P-value0·7760·3010·6130·0300·0040·000Sex of children Male18315·1542712·8161013·4312610·4822406·7335107·73 Female15513·2539613·2555113·2512510·6719306·4531807·64 P-value0·3130·5610·9020·7000·9790·738Birth order First11811·8128312·1040112·0111811·8115406·5727108·13 Second12115·1925812·9637913·607008·7311605·8118506·65 Third or more9916·9828214·1638114·806411·0014807·4321208·24 P-value0·0050·1340·0040·1170·2230·068Household size Small (<4)6114·4710114·0116214·188520·067510·3915913·95 Medium (4–6)18914·0645512·9764413·2811608·6420805·9332406·68 Large (6 and more)8814·3926612·7535413·125108·3013406·4118506·84 P-value0·8660·8410·8980·0000·0000·000Division Dhaka17916·1013111·6331013·8515614·107706·8123310·42 Chittagong6113·1419414·4625514·124309·219907·3514207·83 Rajshahi2010·9013215·9115215·011407·774605·606005·99 Rangpur2115·568110·2810211·050805·974505·785305·81 Khulna1809·547011·378710·951306·883405·604705·90 Mymensingh1915·906110·168111·120504·453906·514506·17 Sylhet1009·629615·7710614·870807·554607·505407·51 Barisal1113·745714·106814·040405·403007·443407·11 P-value0·0180·0010·1010·0000·5070·000Toilet facility Hygienic toilet facility27414·6346513·7173814·0420911·1720005·9040907·77 Unhygienic toilet facility6512·6935812·2242212·294308·3921707·4126007·55 P-value0·2620·0410·0190·1070·0410·807Mass media exposure Yes21914·5830613·9952414·2315910·6012905·9228807·83 No11913·5951712·5063612·699210·5328806·9638007·59 P-value0·3180·0380·0240·9420·0620·874Wealth index Poorest2612·5820712·3823212·401607·8712607·5314207·57 Poorer2517·1217610·9120211·43905·9611507·1012407·00 Middle3311·6617713·0720912·832007·117205·289105·59 Richer10916·2817216·0628216·147511·106205·7713607·82 Richest14513·509014·9023514·0013212·314307·1017510·43 P-value0·3190·0020·0090·0120·2130·001Overall33814·2282313·02116113·3525210·5841706·6066907·69 P-value0·7300·000OWM, overweight mother; SC, stunted child; WC, wasted child; UWC, underweight child; UWM, underweight mother; OWC, overweight child.
DBM in terms of OWM & SC/WC/UWC dyads (15·45 %) was most prevalent among the mothers who had three or more children at the time of the survey. However, a different scenario was found for the UWM & OWC dyads and mothers with a single child (10 %) were more prone to DBM, while the scenario was more common in urban areas (13·13 %) than rural areas (7·84 %). The results indicated that the DBM was more prevalent among the children whose fathers had no formal education than among those whose fathers had a higher educational level. Male children had a slightly higher prevalence of the DBM than female children, while urban male children had suffered more (OWM & SC/WC/UWC dyads, 15·15 %, and UWM & OWC dyads, 10·48 %) than rural male children. Children whose birth order was third or more were more prone to DBM in both urban and rural areas for all dyads. The prevalence of DBM was found to be highest among the small (<4 family members) households (OWM & SC/WC/UWC: 14·18 % and UWM & OWC: 13·95 %) compared to both the medium (4–6 family members) and large (six or more family members) households. However, the differences between urban and rural were much higher among UWM & OWC dyads, particularly for small households. In terms of division, Dhaka was found to be the most prevalent for UWM & OWC dyads (10·42 %), while the OWM & SC/WC/UWC dyads were more common in Sylhet division (15 %). The prevalence of DBM characteristics by UWM & OWC dyads was highest (10·43 %) among the richest households, while the prevalence of OWM & SC/WC/UWC dyads was high among richer households (16·14 %) followed by the richest households (14 %).
## Prevalence of various forms of double burden of malnutrition among residents of rural and urban areas
The prevalence of various forms of DBM is shown in Fig. 1. The DBM paired households were categorised as: OWM & SC; OWM & UWC; OWM & WC; and UWM & OWC. The overall prevalence of DBM was highest for OWM & SC (4·42 %), followed by OWM & UWC (3·17 %). In urban areas, the prevalence of OWM & SC was higher (4·76 %) than the overall prevalence of this pair and was the most common pair, followed by OWM & UWC (3·53 %). In the rural areas, OWM & SC (4·29 %) and OWM & UWC (3·03 %) were more prevalent DBM pairs.
Fig. 1Prevalence of various forms of DBM among residents of rural and urban areas. DBM, double burden of malnutrition; OWM, overweight mother; SC, stunted child; WC, wasted child; UWC, underweight child; UWM, underweight mother; OWC, overweight child
## Inequality in the prevalence of different types of double burden of malnutrition
Figure 2 shows the inequality of the prevalence of various forms of DBM using concentration curves. High inequality was observed among the UWM & OWC (CI -0·3) dyad, which indicated that poor households were more vulnerable to this type of DBM. A low level of inequality of DBM were observed for OWM & SC (CI 0·015), OWM & WC (CI 0·116) and OWM & UWC (CI 0·013) dyads.
Fig. 2Inequality in the prevalence of different types of double burden of malnutrition. OWM, overweight mother; SC, stunted child; WC, wasted child; UWC, underweight child; UWM, underweight mother; OWC, overweight child
## Factors associated with the double burden of malnutrition
Table 3 shows the various risk factors associated with the DBM (OWM & SC/WC/UWC and UWM & OWC) across background characteristics. We observed that the age and educational status of the mother, number of children, fathers’ occupation, size of the household, administrative division and wealth index of the household were significantly associated with the OWM & SC/WC/UWC dyads, but no significant associations were found for fathers’ education, place of residence and birth order of the child with the same dyads in the adjusted model. We observed a positive relationship between the age of the mother and DBM for such dyads. The risk of DBM was 1·36 (95 % CI (1·08, 1·71); $$P \leq 0$$·01) and 1·73 (95 % CI (1·30, 2·30); $$P \leq 0$$·001) times higher among individual mothers aged 20–29 years and 30–49 years, respectively, than the reference age group (mothers aged 15–19 years). Uneducated mothers (AOR 1·71; 95 % CI (1·21, 2·40); $$P \leq 0$$·001), educated mothers who completed primary education (AOR 1·45; 95 % CI (1·11, 1·90); $$P \leq 0$$·01) and secondary education (AOR 1·39; 95 % CI (1·10, 1·74); $$P \leq 0$$·01) were more likely to exhibit the DBM compared to higher-educated mothers, and this difference was statistically significant for OWM & SC/WC/UWC dyads. However, mothers having two children (AOR 0·76; 95 % CI (0·57, 1·00); $$P \leq 0$$·03) and fathers doing business (AOR 0·83; 95 % CI (0·71, 0·98); $$P \leq 0$$·03) were less likely to DBM for such dyads. A higher risk of DBM was observed among the small households (AOR 1·30; 95 % CI (1·05, 1·62); $$P \leq 0$$·01) compared to the larger households. According to the administrative divisions, the DBM was less prevalent in the Rangpur division (AOR 0·75; 95 % CI (0·57, 0·98); $$P \leq 0$$·03), Khulna division (AOR 0·68; 95 % CI (0·51, 0·91); $$P \leq 0$$·01) and Mymensingh (AOR 0·72; 95 % CI (0·54, 0·96); $$P \leq 0$$·03) than the reference division (Rajshahi) for OWM & SC/WC/UWC dyads. In addition, such DBM was more common among richer (AOR 1·46; 95 % CI (1·18, 1·81); $$P \leq 0$$·001) and richest (AOR 1·38; 95 % CI (1·06, 1·78); $$P \leq 0$$·01) households compared to the poorest households.
Table 3Factors associated with household-level double burden of malnutrition (OWM & SC/WC/UWC, UWM & OWC) among mother–child dyads in BangladeshVariablesOWM & SC/WC/UWC (n 8038)UWM & OWC (n 7537)AOR95 % CIAOR95 % CIMothers age 15–19 years (ref.) 20–29 years1·36** 1·08, 1·711·50** 1·13, 2·01 30–49 years1·73*** 1·30, 2·301·97*** 1·38, 2·83Mothers’ educational status No formal education1·71*** 1·21, 2·402·29*** 1·51, 3·48 Primary1·45** 1·11, 1·901·76*** 1·25, 2·48 Secondary1·39** 1·10, 1·741·35* 1·01, 1·81 Higher (ref.)Number of children One child0·710·49, 1·043·27*** 2·08, 5·14 Two children0·76* 0·57, 1·001·42* 1·00, 2·01 Three or more child (ref.)Fathers’ education No formal education0·950·71, 1·270·910·64, 1·31 Primary0·940·74, 1·210·780·57, 1·07 Secondary1·070·85, 1·350·790·59, 1·06 Higher (ref.)Fathers’ occupation Day labour (ref.) Business0·83* 0·71, 0·981·060·86, 1·31 Service0·870·62, 1·231·220·83, 1·80 Unemployed0·790·37, 1·691·270·55, 2·90 Others1·010·69, 1·501·79** 1·19, 2·70Place of residence Urban1·030·87, 1·211·32** 1·07, 1·62 Rural (ref.)Birth order First (ref.) Second0·970·75, 1·261·55** 1·13, 2·14 Third or more0·770·53, 1·122·36*** 1·51, 3·69Household size Small (<4)1·30** 1·05, 1·621·91*** 1·48, 2·45 Medium (4–6)1·010·88, 1·170·960·79, 1·17 Large (6 and more) (ref.)Division Dhaka0·860·69, 1·071·48** 1·09, 2·02 Chittagong0·900·72, 1·131·39* 1·01, 1·93 Rajshahi (ref.) Rangpur0·75* 0·57, 0·980·940·64, 1·39 Khulna0·68** 0·51, 0·910·950·64, 1·42 Mymensingh0·72* 0·54, 0·960·980·65, 1·47 Sylhet0·950·72, 1·261·210·81, 1·80 Barisal1·000·73, 1·371·250·80, 1·96Wealth index Poorest (ref.) Poorer0·940·76, 1·151·000·77, 1·30 Middle1·070·87, 1·330·750·56, 1·01 Richer1·46*** 1·18, 1·810·990·74, 1·31 Richest1·38** 1·06, 1·781·240·89, 1·72Constant0·12*** 0·07, 0·210·01*** 0·01, 0·03 n 80387537LR χ2[30] 99·48217·97Prob > χ2 0·0000·000Pseudo R2 0·0150·0483Log likelihood−3267·52−2148·57Mean VIF3·103·04OWM, overweight mother; SC, stunted child; WC, wasted child; UWC, underweight child; UWM, underweight mother; OWC, overweight child; AOR, adjusted odds ratio (control factors: mother age at first birth, working status of mothers, sex of children, type of toilet facility and exposure of mass media).* $P \leq 0$·05.** $P \leq 0$·01.*** $P \leq 0$·001.
The age and educational status of the mother, the number of children, the fathers’ occupation, place of residence, birth order of the children, the size of the household and administrative division were significantly associated with the UWM & OWC dyads in the adjusted model. The prevalence of DBM characteristics by UWM & OWC dyads was highest among the mothers aged 30–49 years old (AOR 1·97; 95 % CI (1·38, 2·83); $$P \leq 0$$·001) and the mothers aged 20–29 years old (AOR 1·50; 95 % CI (1·13, 2·01); $$P \leq 0$$·01), respectively. Uneducated mothers (AOR 2·29; 95 % CI (1·51, 3·48); $$P \leq 0$$·001), primarily educated mothers (AOR 1·76; 95 % CI (1·25, 2·48); $$P \leq 0$$·001) and secondary educated mothers (AOR 1·35; 95 % CI (1·01, 1·81); $$P \leq 0$$·03) were significantly more likely to manifest the UWM & OWC dyads compared to higher-educated mothers. Mothers having one child were 3·27 times (95 % CI (2·08, 5·14); $$P \leq 0$$·001) and two children were 1·42 times (95 % CI (1·00, 2·01); $$P \leq 0$$·03) more likely to encounter DBM for such dyads compared to the reference group (three or more child) where the urban households were more prone to DBM characterised by maternal undernutrition and child overnutrition (AOR 1·32; 95 % CI (1·07, 1·62); $$P \leq 0$$·01) than the rural areas for such dyads. According to birth order, second and third or more child were 1·55 and 2·36 times significantly more likely to exhibit DBM, respectively, for UWM & OWC dyads. We noticed a positive relationship between the small households (AOR 1·91; 95 % CI (1·48, 2·45); $$P \leq 0$$·001) and DBM compared to the larger households for similar dyads. Dhaka (AOR 1·48; 95 % CI (1·09, 2·02); $$P \leq 0$$·01) and Chittagong (AOR 1·39; 95 % CI (1·01, 1·93); $$P \leq 0$$·03) divisions were more likely to exhibit DBM characterised by UWM & OWC dyads.
## Discussion
Although Bangladesh has made substantial progress in reducing childhood undernutrition in the past decade, the rapid rise in overweight condition is a major challenge. Further, the prevalence of malnutrition among women of reproductive age is a major concern in Bangladesh[17]. To our knowledge, this is the first study examining the prevalence of DBM that considers all forms of pairwise coexistence of malnutrition among mothers and children at the household level using nationwide representative data. Our findings provide a new perspective to researchers, policymakers and public health agencies, who can take initiatives to reduce this emerging public health burden in Bangladesh.
This study observed that the overall prevalence of the DBM at the household level was approximately 21 %, where the prevalence of OWM & SC/WC/UWC and UWM & OWC was 13·35 % and 7·69 %, respectively, with a significantly higher prevalence among urban households in Bangladesh. Bangladesh is experiencing rapid urban population growth; nonetheless, urban health is often neglected[20]. Further, the large-scale unplanned rural–urban migration resulted in overloaded public services, scarcity of housing, inappropriate diets, unreachable healthcare facilities, and an adverse impact on health and the environment in many urban settings in Bangladesh[21]. In addition, various restaurants, supermarkets and food parks are gaining popularity in urban Bangladesh, serving as places for recreational family activities[22]. This changes the everyday food intake of urban residents, and junk food or ultra-processed food consumption was notably high among these residents due to various enabling factors, such as addictive taste, changing lifestyles, propagandist advertising and instant availability, while it resulted in obesity in children but also undernutrition in mothers because of the lack of essential nutrients for normal growth[23]. According to the latest urban health survey, only a negligible improvement in childhood nutritional status was observed over the last 7 years, with maternal health indicators being particularly unsatisfactory among slum dwellers, who comprise one-third of the urban population in Bangladesh[24]. There are approximately fourteen thousand urban slums in Bangladesh, and these areas exhibit many factors that negatively affect the health and nutrition of both mothers and their children[25]. Other studies found a positive association between urbanisation and BMI in various settings[26,27].
A recent systematic review indicated an increasing trend in overweight and obesity among children, adolescents, and adults over time, with a higher prevalence in urban areas of Bangladesh[15]. A recent study observed that the prevalence of overweight was significantly higher in women (79 % v. 53 %) than in men in urban Bangladesh[28]. In contrast, rural mothers are more prone to underweight than urban mothers in Bangladesh, as has been observed in other resource-poor countries[29,30]. A recent multi-country study reported that women living in rural communities had a greater risk of having UWC than urban mothers[31]. Further, the prevalence of childhood undernutrition is more common in rural than in urban communities in many settings[9,29]. Previous studies observed that the prevalence of childhood malnutrition was higher among Bangladeshi rural children, a phenomenon that has been frequently observed in other developing countries[9,17] This study assessed four different forms of DBM at the household level: UWM & OWC, OWM & SC, OWM & WC, and OWM & UWC. Between 2004 and 2014, there was a 15 % increase in the prevalence of overweight status and a similar decrease in the underweight status of women of reproductive age. The reduction in underweight status was of similar magnitude in both urban and rural areas, whereas there was a greater relative change in overweight status in the rural areas, which is congruent with recent review findings[32]. The underweight prevalence in rural areas remained relatively high, as did the overweight prevalence among urban residents. These findings, indicating a shift of nutritional burdens, are an extension of previous findings, demonstrating consistency with the literature from Bangladesh[1,33]. Similar to what had previously been observed in many LMIC, this study found that the OWM & SC pair was the most prevalent DBM at the household level[17,34]. Compared with the neighbouring countries, the prevalence of OWM & SC we observed is lower than that previously reported in India (8 %) and Pakistan (24 %)[17]. Likewise, a higher prevalence of OWM & SC was also observed in many African and Latin American countries, including Egypt (12·5 %), Ghana (12·5 %), Nicaragua (12·5 %), Bolivia (15 %), Peru (16 %) and Guatemala (23 %)[34]. Although our study did not attempt to identify the underlying reason for this difference, increases in the prevalence of overweight women in South and Southeast Asia in recent decades appear to be an important factor[35]. Regarding the OWM & UWC pair, our results were in accordance with reports from 18 LMIC in South Asia, Africa and Latin America, where the prevalence ranged from 0·3 % to 5·3 %[35]. This study found that the prevalence of OWM & WC at the household level in Bangladesh was lower than that observed in other settings like Nepal (5 %), Myanmar (6 %), India (7 %), Maldives (12 %) and Pakistan (14 %)[34]. This is likely because the prevalence of wasting (8 %) is much lower than that of stunting (31 %) and underweight (22 %) among Bangladeshi children[14].
This study highlights the socio-economic inequality of the DBM, particularly for the UWM and OWC dyads, with poor households at a greater disadvantage than the rich. Of note, the current situation of maternal undernutrition in *Bangladesh is* similar to that observed in other LMIC[36,37]. Various studies found that the wealth index plays a vital role in shaping women’s nutritional status and that mothers from poor households in Bangladesh were more prone to being underweight[33].Social expectations regarding body size, beliefs and cultural practices about food, nutrition, and physical activity may explain the association between overweight status and higher wealth quintiles[2,17]. For instance, a recommendation to follow a reduced-fat diet at the household level can reduce the BMI for those with overweight and obesity, but this intervention could increase the risk for underweight members in the same household. In such a situation, prevention programmes should provide health information that promotes the optimal weight for all individuals in the household. For example, an intervention of reduced energy consumption should be implemented for overweight individuals, particularly for urban residents and those belonging to the wealthiest strata[38]. In such interventions, the target population needs to be motivated to consume food with fewer calories and to increase physical activity such as walking. Awareness programmes about the consequences of being overweight or obese, including prevention activities, should be available in schools, the workplace and the community. In contrast, a poor socio-economic condition is associated with underweight women in Bangladesh, because individuals in such conditions cannot afford expensive items such as milk, meat, poultry, fruits and other nutritious foods. For these individuals, the focus should be on healthy diets (e.g. consumption of fruits and vegetables) that lead to optimal BMI and other health outcomes for vulnerable households.
We observed that the age and educational status of the mother, the size of the household and administrative division were significantly associated with the prevalence of DBM among mother–child pairs at the household level in Bangladesh. We found that older mothers had an increased risk of DBM compared to younger mothers. This result is consistent with several studies that suggested that the prevalence of overweight/obesity was higher in older age groups than in younger age groups[1,5]. Explanation for this includes reduced activity of the mothers as they age, taking in more calories than they require, and slowing of metabolic processes as they age. It was observed that women aged 30 years or older were more likely to be overweight or obese than younger women in Bangladesh[7]. Due to sedentary lifestyles and a reduction in metabolic rates, obesity tends to increase with age among women[7,10]. A previous study observed that the prevalence of underweight and overweight women aged 15–49 years in Bangladesh was 22·4 % and 14·1 %, respectively. These conditions are crucial for determining the overall health condition of a child, as maternal health status plays a significant role in child health[39]. Our study also demonstrated that maternal education was a significant factor for controlling the DBM. Various other studies also observed a negative association between higher education and malnourished children, as improved knowledge of healthy behaviours can help parents nurture their children[40,41]. This is likely because more highly educated mothers tend to have better knowledge of child health and nutrition and can thus choose healthy foods for their household[42]. A study conducted in Bangladesh suggested that secondary or higher education of mothers may have contributed to reducing the risk of DBM in the households studied[43]. Another study indicated that discordant mother–child pairs were significantly less likely to occur in households in which the mother had a secondary or higher education than in those in which the mother had no formal education [34]. Furthermore, knowledge of infant and young child feeding practices was also poor among uneducated mothers in Bangladesh, which emphasises the importance of maternal education for better child health, which could contribute to reducing the household-level DBM in Bangladesh[44]. Various study showed that underweight is more common among less educated mothers, while the overweight is more concentrated among educated woman. This may be because higher-educated individuals often prefer desk jobs where the occupational sitting time is relatively high, which might contribute to overweight status[45,46]. Therefore, target-based educational awareness programmes such as the importance of a balanced diet and sufficient nutrition should be introduced at various levels of society.
This study indicated that the size of the household and the administrative division have important implications for the DBM in Bangladesh. We found that small households were often prone to DBM, probably as due to the lack of extra members in their households, they were often unable to prepare home-cooked meals and tended to use more convenient options (processed foods) that could lead to increased weight[47] Moreover, every member of a small household always tries to feed an excessive amount of food (both home-cooked and processed foods) to the youngest member (children) to show their love and affection in the Bangladeshi context, which puts them at an even higher risk of being malnourished[48]. Although this study did not attempt to explain these findings, increasing maternal and child overweight/obesity may be an important factor. Therefore, further investigation should be conducted. Regarding administrative divisions, we found that households located in the Khulna and Rangpur divisions were less likely to develop the DBM. Khulna and Rangpur are considered high-performing in various health indicators, such as literacy rates, high maternal nutrition, low mortality rate, low fertility rate, low childhood malnutrition and high socio-economic status[40]. In terms of wealth index, we observed that richer and richest households were more likely to generate DBM characterised by OWM & SC/WC/UWC. Our results are similar to many previous studies which have documented a significant positive relationship between the wealth index and household-level DBM[17,18]. It was observed that UWC are more prevalent among poorer households, while being overweight is more common among wealthy mothers in Bangladesh, which is also in line with other settings [45,46]. It may be due to having access to Western or fast food, higher occupational sitting time, and excess energy intake, which often lead to overweight and obesity among mothers [49,50].Various studies also indicated that children from disadvantaged households in Bangladesh are often prone to being stunted, wasted and underweight, while OWC are more concentrated among the wealthiest households[1,9,40].Therefore, policy should focus the mitigation of the unequal wealth distribution for tackling the DBM issues from all strata of society. Our findings and those of other studies suggest that it is high time for policymakers and public health professionals to take the necessary steps to prevent and control the DBM among Bangladeshi women. However, it is quite challenging to implement an intervention in a country in which both undernutrition and overnutrition coexist, as an intervention to address one problem might exacerbate the other. Therefore, target-specific interventions must be formulated and implemented, and health literacy should be encouraged so that people can make the best decisions for themselves given their individual circumstances. The government should sponsor initiatives to educate and encourage affluent women: to embrace a healthy lifestyle and generate awareness of the health impact of being underweight or overweight using mass media; to refashion transport facilities, particularly in urban areas, by making footpaths; and to provide a safe environment for women and adolescent girls to perform physical activities. Physicians and community health workers also can advise their patients, especially pregnant women, so that women receive counselling about weight management before or during early pregnancy. The government should also take the initiative to restrict the production, purchase, and advertisement of junk food, as well as make fruits and vegetables accessible and affordable to people from all socio-economic groups. Furthermore, the development of comprehensive surveillance systems at the household, regional and national levels should be prioritised to tackle the DBM in Bangladesh.
## Strengths and limitations
This study has several limitations. First, the study was based on cross-sectional data, and so we were unable to establish a causal relationship. Second, in the absence of income or expenditure data, we used a household asset-based wealth index as a proxy to assess households’ economic status, and another limitation regarding this was to use the same criteria to assess wealth status in both urban and rural households. Third, due to unavailability of data, various potential confounders (such as physical activity, caregiving practices, cultural influences, postpartum-weight resolution, food taboos, and more detailed components of nutritional status, such as body composition or biochemical or metabolic status) that might affect the DBM cannot be included in the analysis. Therefore, further exploration is warranted to ascertain the contribution of these potential determinants to the development of various forms of DBM in Bangladesh. Despite such limitations, a strength of the present study was that the data were extracted from a nationally representative demographic and health survey with a large randomised sample and low percentages of missing information; thus, our findings can be considered representative of the entire country. The findings of this study will offer strong insights to policymakers and will help them set target-specific, focused public health interventions to tackle the DBM, which is in line with the goal of the latest National Food and Nutrition Security Policy in Bangladesh.
## Conclusion
The current study indicates the overall prevalence of DBM was about 21 %, with a significantly higher prevalence in urban areas of Bangladesh. Higher inequalities in the DBM were observed among the pair of UWM with OWC, which indicated that poor households were more vulnerable to the DBM. In contrast, a low level of inequality of DBM was observed for OWM with SC, WC and UWC. Therefore, health policymakers, concerned authorities and various stakeholders should stress the prevalence of DBM issues and provide the necessary action to tackle this public health problem in Bangladesh.
## Conflict of interest:
There are no conflicts of interest.
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|
---
title: Mealtime media use and cardiometabolic risk in children
authors:
- Joseph Jamnik
- Charles Keown-Stoneman
- Karen M Eny
- Jonathon L Maguire
- Catherine S Birken
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991824
doi: 10.1017/S1368980020003821
license: CC BY 4.0
---
# Mealtime media use and cardiometabolic risk in children
## Body
Excessive exposure to electronic media, including television, computers and handheld devices, during childhood has been associated with delayed language development, aggressive behaviour, smoking and obesity(1–4). Recently, there has been increased interest in the potentially unique effects of media exposure during mealtime and its association with the development of obesity in children. Possible mechanisms to explain the link between mealtime media use and excess body weight include eating despite the lack of hunger, reduced satiety signals while watching media and exposure to advertisements promoting energy-dense foods and poor dietary habits[5,6]. A recent systematic review and meta-analysis including twenty observational studies (n 84 825) identified a positive association between television viewing during mealtime and risk of overweight/obesity in children[7]. While studies have focused primarily on television viewing during mealtime and its association with overweight and obesity, there is no evidence on the effects of media exposure during meals on other markers of cardiometabolic risk (CMR).
Non-HDL-cholesterol is an important marker of CMR in children and a significant predictor of dyslipidaemia in adulthood[8,9]. Lifestyle factors have been associated with circulating levels of non-HDL-cholesterol(10–12), and patterns of non-HDL-cholesterol identified during early childhood have been shown to persist with age[13]. In addition to non-HDL-cholesterol, other CMR markers such as blood pressure, glucose and lipids have also been shown to persist into adulthood and are associated with increased risk of developing various diseases including the metabolic syndrome, type 2 diabetes and atherosclerotic plaques[14,15]. While systematic reviews assessing the association between television viewing/screen time and markers of CMR in children, including lipids, blood pressure, glucose and insulin, have yielded inconsistent results(16–18), no studies to date have investigated the effects of mealtime media use on these CMR markers in preschool to school-aged children. The primary objective of the present study was to examine the association between total media use during mealtime and non-HDL-cholesterol levels as well as other CMR markers in children, including total cholesterol, HDL-cholesterol, TAG, glucose and insulin. Additionally, we examined whether media use during specific meals (i.e., breakfast, lunch, dinner and snacks) was associated with non-HDL-cholesterol and other CMR markers.
## Abstract
### Objectives:
To examine the association between mealtime media use and non-HDL-cholesterol as well as other markers of cardiometabolic risk (CMR) in children.
### Design:
A repeated measures study design was used to examine the association between mealtime media use and CMR outcomes. Multivariable linear regression with generalised estimating equations was used to examine the association between mealtime media use and CMR outcomes. Analyses were stratified a priori by age groups (1–4 and 5–13 years).
### Setting:
The TARGet Kids! Practice-based research network in Toronto, Canada.
### Participants:
2117 children aged 1–13 years were included in the analysis.
### Results:
After adjusting for covariates, there was no evidence that total mealtime media use was associated with non-HDL-cholesterol in 1–4 year olds ($$P \leq 0$$·10) or 5–13 year olds ($$P \leq 0$$·29). Each additional meal with media per week was associated with decreased HDL-cholesterol in 5–13 year olds (−0·006 mmol/l; 95 % CI −0·009, −0·002; $$P \leq 0$$·003) and log-TAG in 1–4 year olds (β = −0·004; 95 % CI −0·008, −0·00009; $$P \leq 0$$·04). Media use during breakfast was associated with decreased HDL-cholesterol in 5–13 year olds (−0·012 mmol/l; 95 % CI −0·02, −0·004; $$P \leq 0$$·002), while media during lunch was associated with decreased log-TAG (−0·01 mmol/l; 95 % CI −0·03, −0·002; $$P \leq 0$$·03) in children aged 1–4 years. Total mealtime media use was not associated with total cholesterol, glucose or insulin in either age group.
### Conclusions:
Mealtime media use may be associated with unfavourable lipid profiles through effects on HDL-cholesterol in school-aged children but likely not in pre-schoolers.
## Study design and population
A repeated measures study was conducted in children ≥1 year of age participating in the TARGet Kids (The Applied Research Group for Kids) cohort, a primary care practice-based research network with ongoing recruitment described previously (ClinicalTrials.gov: NCT0186953)[19]. Healthy children younger than 6 years at first visit were recruited and followed during well-child visits at eleven paediatric or primary care centres in Toronto, Canada. Participants were invited to participate in annual data collection. Children with severe developmental delay, any chronic condition (with the exception of asthma) or whose families were unable to complete questionnaires in English were not included. The present study included children with recorded visits from January 2013 to July 2018 (n 6566). Children without non-HDL-cholesterol laboratory values due to lack of blood draw or assay failure (n 4141) and those without mealtime media use questionnaire responses (n 162) were excluded. Children who were <1 year of age for their only visit (n 144) were also excluded. Data collected prior to 2013 were not utilised as questionnaires asked only about television use during mealtime, and we were interested in all forms of electronic media use.
## Mealtime media use assessment
Parents were asked to complete a detailed Nutrition and Health Questionnaire at the following well-child visits: 12 months, 18 months, 2 years and yearly thereafter. The Nutrition and Health Questionnaire administered was modified from the Canadian Community Health Survey[20] and contained questions on electronic media use during mealtime. For both a ‘typical weekday’ and ‘typical weekend day’, parents indicated typical electronic media usage during mealtime by responding to ‘Which meals did your child eat in a room with a screen device on (television, computer, tablet etc.)’, with Yes/No responses available for ‘Breakfast’, ‘Lunch’, ‘Dinner’ and ‘A snack’. The primary exposure variable of interest in the present study was total mealtime media use. This variable was created by adding all ‘Yes’ responses for each meal or snack and calculating a weighted average per week (i.e., multiplying weekday responses by 5 and weekend response by 2). Associations with total mealtime media use are reported in terms of both increasing meals per week and per day (total mealtime media use variable divided by 7). Electronic media use during individual meals (i.e., breakfast, lunch, dinner and snack) was calculated in the same way using only ‘Yes’ responses to ‘Breakfast’, ‘Lunch’, ‘Dinner’ and ‘A snack’. Associations with meal-specific media use are reported in terms of increasing meals per week.
## Outcome and covariate assessment
Non-fasting blood samples were collected by trained paediatric phlebotomists at the paediatric or primary care centres during yearly scheduled well-child visits. While blood samples were required for all participants at the time of initial recruitment, blood samples at follow-up visits were optional. Blood samples were then transported to Mount Sinai Services Laboratory, Toronto, Ontario for analysis as described previously[21]. Lipids, including total cholesterol, HDL-cholesterol and TAG, were measured using an enzymatic colorimetric assay. Non-HDL-cholesterol was calculated by subtracting HDL-cholesterol from total cholesterol. Glucose and insulin were measured using the enzymatic reference method with hexokinase and an electrochemiluminescence immunoassay, respectively. Generally, higher total cholesterol, non-HDL-cholesterol, TAG, glucose and insulin are associated with increased risk of developing various adverse health outcomes. Higher HDL-cholesterol is generally associated with a decreased risk of adverse health outcomes. Time of last meal/snack and non-water drink was recorded. Fasting time was accounted for in the current analysis, despite evidence indicating that fasting time may have little influence on serum lipid levels[21,22]. Children’s height and weight were measured by trained research assistants at clinical visits using a stadiometer (SECA) and precision digital scale (SECA model 703), respectively. Length boards were used for children under 2 years old. Age- and sex-standardised BMI z-scores (zBMI) were calculated using the WHO growth standards[23,24].
Information on relevant covariates was assessed by parent-completed questionnaire during scheduled clinic visits. Covariates were identified a priori using the published literature and included child age(25–31), sex(25–35), birth weight[32,36], total screen time(27,34–36), maternal education(29–32,34,36,37), maternal ethnicity[25,27,31,35,38], family income[25,27,33,36,38], parental history of cardiometabolic disease (high cholesterol, hypertension, heart disease and diabetes)(39–41) and breast-feeding duration[42]. Additional covariates included unstructured free play time[26,27,29,32,34,43], total screen time(27,34–36) and family meals[44]. Unstructured free play was assessed using parents’ response to the open-ended question ‘Aside from time in daycare and school, on a typical weekday, how much time does your child spend outside in unstructured free play?’. Family meals were assessed by parents’ response to the open-ended question ‘*In a* typical week, how many times does your family eat the evening meal together?’. Finally, total screen time was assessed by calculating a weighted average of typical parent-reported weekday and weekend minutes spent awake in a room with the television on, videos or DVD on, playing on the computer, playing video games or playing with handheld devices[45]. Total screen time was included as a covariate so that results would likely be independent from overall media use and specific to media use during meals.
## Statistical analysis
Descriptive analyses of the primary exposure, outcomes and covariates were examined for all subjects at their first recorded visit (Table 1). For all analyses, repeated measures of exposures and outcomes measured concurrently over time were used to investigate the association between total mealtime electronic media use and outcome variables. Linear regression modelling using generalised estimating equations was used to account for within-subject correlation. Such models can accommodate subjects with available data at both single and multiple time points, and they do not require that all subjects have repeated measures. All subjects with at least one measure of total mealtime media use and non-HDL-cholesterol were included. A first-order autoregressive covariance matrix, AR[1], with observations ordered by subjects’ age in months, was used to account for correlations among repeated measures in all analyses[46]. The AR[1] covariance matrix accounts for correlation over time for a subject with repeated measures. Under this correlation structure, correlations decline with increasing time between visits.
Table 1Baseline subject characteristicsVariableSubjects (n)Mean ± sd or n (%)Participant characteristics Age (months)211751·35 ± 31·68 Age groups2117 1–4 years1282 [61] 5–13 years835 [29] Sex2117 Male1138 [54] Female979 [46] Maternal ethnicity1853 European1181 [64] East/Southeast Asian188 [10] South Asian193 [10] Other291 [16] Maternal education2089 High school, apprenticeship/trades certificate or no degree187 [9] College/university1902 [91] Self-reported household income2061 <$30 000110 [5] $30 000–$79 999374 [18] $80 000–$149 999650 [32] ≥$150 000927 [45] zBMI (sd)21080·15 ± 1·09 Birth weight (kg)18813·25 ± 0·67 Average screen time (min/d)199186·85 ± 76·11 Average weekday free play (min/d)198850·56 ± 53·17 Maternal BMI169125·15 ± 4·98 Parental cardiometabolic disease1854 Positive history458 [25] Negative history1396 [75] Family meals (evening meals with family/week)20985·30 ± 1·93 Fasting time (h)20372·04 ± 1·51Exposure variables Total mealtime media use (meals/week)21175·91 ± 7·17 Specific meal media use (meals/week) Breakfast21171·63 ± 2·73 Lunch21170·74 ± 1·93 Dinner21171·20 ± 2·48 Snack21172·34 ± 3·11Outcome variables Non-HDL-cholesterol (mmo/l)21172·67 ± 0·70 Total cholesterol (mmol/l)21174·05 ± 0·73 HDL-cholesterol (mmol/l)21171·38 ± 0·36 TAG (mmol/l)21171·17 ± 0·66 Glucose (mmol/l)21174·60 ± 0·68 Insulin (mmol/l)209882·75 ± 70·00 In the primary analysis, the association between total mealtime electronic media use and CMR outcomes was investigated using both unadjusted and fully adjusted models. The fully adjusted model accounted for covariates that have been identified in the literature as potential confounders between mealtime media use and CMR outcomes or that have been directly associated with the outcome variables. Secondary analysis between mealtime media exposure during specific meals (breakfast, lunch, dinner and snack) and CMR outcomes was conducted using the same fully adjusted models. All analyses were stratified by a priori age groups (1–4 and 5–13 years). These age groups were selected since recent position statements on media use from both the American Academy of Pediatrics and the Canadian Paediatric Society specifically pertain to children ≥5 years[47,48]. Additionally, the majority of studies investigating mealtime television use and risk of overweight/obesity have been conducted in children aged 5 years and older[7], and associations between mealtime media use and CMR markers may differ between these age groups. Interactions between mealtime media use and age group (1–4 years and 5–13 years) on each of the outcome variables were examined in models without stratification, but P-values and estimates of main effects are reported from models stratified by age groups, as this was specified a priori. For identified associations in either age group with $P \leq 0$·05, additional adjustment for zBMI was done as an exploratory analysis to determine whether the identified associations were likely independent from changes in zBMI.
In order to facilitate the inclusion of subjects with missing covariate data, multiple imputation ($m = 20$) was performed using the MICE package[49] for all adjusted models. All covariates had missing observations <15 %, with the exception of maternal BMI (17 %). Participants with zBMI values > +5 or < −5 were excluded (n 2) in accordance with WHO guidelines[50]. The distributions of outcome variables were assessed, and both TAG and insulin were log-transformed in order to achieve normality. For all analyses, the α-error was set at 0·05 and statistical tests were two-sided. Statistical analysis was conducted using R version 3.5.1[51].
## Results
A total of 2119 children ≥1 year of age had at least one TARGet Kids! visit between 2013 and 2018 with complete data on electronic media use during mealtime and blood lipids. After excluding zBMI outliers (n 2), the remaining 2117 subjects were included in the final analysis. Of the 2117 subjects with at least one recorded visit, 27 % (n 585) had repeated exposure and outcome data measured concurrently from two visits, and 10 % (n 209) had repeated measures from three or more visits, resulting in a total of 2911 observations. There were a total of 1619 (56 %) observations in the 1–4 years age group and 1292 (44 %) observations in the 5–13 years group. Baseline subject characteristics for all children included in the final analysis are shown in Table 1. Average total mealtime media use was 5·91 ± 7·17 meals/week among all children included in the present study at first visit. Participants had an average age of approximately 51 months and 54 % of children were male. The majority of mothers were of European descent (64 %) and had a college/university degree or higher (91 %).
Results of both unadjusted and fully adjusted models examining the association between total mealtime media use and CMR outcomes are shown in Table 2. Total mealtime media use was not associated with non-HDL-cholesterol in children 1–4 or 5–13 years old in both unadjusted (1–4 years, $$P \leq 0$$·42; 5–13 years, $$P \leq 0$$·15) and adjusted models (1–4 years, $$P \leq 0$$·10; 5–13 years, $$P \leq 0$$·29). In children aged 1–4 years, there was an inverse association between total mealtime media use and log-TAG in both unadjusted ($$P \leq 0$$·003) and fully adjusted models ($$P \leq 0$$·04). In the adjusted model, each increase of one meal with media per day was associated with a decrease in log-TAG (β = −0·03; 95 % CI −0·05, −0·0008). This is approximately equivalent to a 3 % decrease in TAG levels. The association between total mealtime media use per day and log-TAG years remained significant after additional adjustment for zBMI (β = −0·03; 95 % CI −0·05, −0·0003; $$P \leq 0$$·048). In children aged 5–13 years, total mealtime media use was inversely associated with HDL-cholesterol in both unadjusted ($$P \leq 0$$·03) and adjusted models ($$P \leq 0$$·003). In the adjusted model, each increase of one meal with media per day was associated with a decrease in HDL-cholesterol by −0·04 mmol/l (95 % CI −0·06, −0·01). After additional adjustment for zBMI, the association between total mealtime media use per day and HDL-cholesterol remained significant (β = −0·04; 95 % CI −0·06, −0·01; $$P \leq 0$$·004).
Table 2Linear generalised estimating equations regression for the association between total mealtime media use per week and cardiometabolic outcomes stratified by ageUnadjustedAdjusted* Estimate95 % CI P P-int† Estimate95 % CI P P-int† Non-HDL-cholesterol (mmol/l) Age 1–4 years−0·002−0·006, 0·0030·430·10−0·004−0·01, 0·00080·100·06 Age 5–13 years0·006−0·002, 0·0140·150·004−0·004, 0·0130·29Total cholesterol (mmol/l) Age 1–4 years0·0002−0·005, 0·0050·950·68−0·004−0·010, 0·0020·170·50 Age 5–13 years0·002−0·006, 0·010·62−0·001−0·010, 0·0070·78HDL-cholesterol (mmol/l) Age 1–4 years0·002−0·0003, 0·0040·090·0050·0006−0·002, 0·0030·640·009 Age 5–13 years−0·004−0·007, −0·00040·03−0·006−0·009, −0·0020·003Log-TAG (mmol/l) Age 1–4 years−0·004−0·008, −0·0020·0030·007−0·004−0·008, −0·000090·040·02 Age 5–13 years0·003−0·002, 0·0080·170·003−0·003, 0·0090·29Glucose (mmol/l) Age 1–4 years−0·004−0·008, 0·0010·120·59−0·001−0·007, 0·0040·640·26 Age 5–13 years−0·006−0·01, 0·00040·07−0·005−0·012, 0·0010·12Log insulin (mmol/l) Age 1–4 years0·003−0·003, 0·0080·360·960·003−0·003, 0·0100·310·70 Age 5–13 years0·003−0·005, 0·010·450·0009−0·0069, 0·00860·83*Adjusted for child age, child sex, birth weight, fasting time, unstructured free play, total screen time, maternal education, maternal ethnicity, family income, parental history of cardiometabolic-related disease, breast-feeding duration and family meals.†P-values for age × total mealtime media use interactions estimated from unstratified models.
Results of the secondary analysis examining the association between media use during specific meals and non-HDL as well as other CMR markers were varied (shown in Table 3). There was no evidence for an association between media use during any specific meal and non-HDL-cholesterol in children aged 1–4 or 5–13 years ($P \leq 0$·05). In children aged 1–4 years, consuming an additional lunch with media per week was associated with decreased log-TAG (β = −0·01; 95 % CI −0·03, −0·002; $$P \leq 0$$·03). In children aged 5−13 years, consuming an additional breakfast with media per week was associated with decreased HDL-cholesterol (β = −0·012; 95 % CI −0·02, −0·004; $$P \leq 0$$·002), dinner with media was associated with increased log insulin (β = 0·02; 95 % CI 0·0007, 0·04; $$P \leq 0$$·04) and snacks with media were associated with decreased glucose (β = −0·02; 95 % CI −0·03, −0·003; $$P \leq 0$$·02).
Table 3Linear generalised estimating equations regression for the association between media use during specific meals per week and cardiometabolic outcomes stratified by age*BreakfastLunchDinnerSnackAdjusted estimate95 % CI P P-int† Adjusted estimate95 % CI P P-int† Adjusted estimate95 % CI P P-int† Adjusted estimate95 % CI P P-int† Non-HDL-cholesterol (mmol/l) Age 1–4 years−0·008−0·022, 0·0050·220·14−0·005−0·02, 0·010·590·09−0·01−0·03, 0·0020·100·19−0·005−0·017, 0·0080·470·15 Age 5–13 years0·008−0·008, 0·0240·340·03−0·01, 0·070·160·002−0·016, 0·0190·860·004−0·010, 0·0170·60Total cholesterol (mmol/l) Age 1–4 years−0·009−0·023, 0·0050·210·660·0006−0·0177, 0·01900·940·28−0·01−0·03, 0·0030·100·59−0·003−0·017, 0·0100·650·56 Age 5–13 years−0·004−0·020, 0·0120·600·02−0·02, 0·060·28−0·006−0·024, 0·0120·51−0·002−0·017, 0·0130·76HDL-cholesterol (mmol/l) Age 1–4 years−0·0007−0·007, 0·0060·840·030·006−0·003, 0·0140·200·09−0·0004−0·0071, 0·00630·900·130·002−0·005, 0·0080·620·10 Age 5–13 years−0·012−0·020, −0·0040·002−0·007−0·021, 0·0070·34−0·008−0·017, 0·0020·11−0·006−0·013, 0·0010·10Log-TAG (mmol/l) Age 1–4 years−0·004−0·014, 0·0050·350·04−0·01−0·03, −0·0020·030·29−0·01−0·02, 0·00040·060·39−0·002−0·012, 0·0070·610·03 Age 5–13 years0·010−0·002, 0·0210·12−0·0006−0·0258, 0·02450·96−0·005−0·019, 0·0100·050·006−0·003, 0·0150·19Glucose (mmol/l) Age 1–4 years−0·0002−0·0131, 0·01270·980·930·004−0·011, 0·0190·600·25−0·0007−0·0151, 0·01370·920·63−0·008−0·021, 0·0050·210·40 Age 5–13 years0·002−0·013, 0·0170·80−0·01−0·04, 0·010·33−0·005−0·025, 0·0140·60−0·02−0·03, −0·0030·02Log insulin (mmol/l) Age 1–4 years0·008−0·007, 0·0240·290·24−0·0003−0·0213, 0·02060·980·500·003−0·014, 0·0210·700·240·008−0·007, 0·0230·310·57 Age 5–13 years−0·004−0·022, 0·0120·56−0·01−0·05, 0·020·440·020·0007, 0·040·04−0·0007−0·0161, 0·01470·93*Analyses adjusted for child age, child sex, birth weight, fasting time, unstructured free play, total screen time, maternal education, maternal ethnicity, family income, parental history of cardiometabolic-related disease, breast-feeding duration and family meals.†P-values for age × media use during specific meals per week interactions estimated from unstratified models.
## Discussion
The current study provides some insight into the effects of mealtime media use on CMR markers independent of body weight. Our results suggest that total mealtime media use is likely not associated with circulating levels of non-HDL-cholesterol in pre-school (1–4 years) or school-aged children (5–13 years). In school-aged children, total mealtime media use was inversely associated with HDL-cholesterol, and this association was largely driven by media usage during breakfast. In pre-school-aged children, total mealtime media use was inversely associated with TAG, and there was evidence of an association between media use during lunch and decreased TAG in this age group.
Both the American Academy of Pediatrics and Canadian Paediatric Society recently released policy statements outlining the risks of weight gain with excessive media use in school-aged children and adolescents[47,48]. The Canadian Paediatric Society also specifically recommends that screen time be limited during family routines such as meals in pre-school-aged children[52]. In a US population of school-aged children, up to approximately 25 % of energetic intake was shown to be consumed while watching television. In our study population, children consumed an average of nearly six meals (21 % of total meals/snacks) with media per week. This may be of concern given both the positive association between mealtime media use and excess body weight[7] and the high prevalence of obesity[53]. Furthermore, it is estimated that almost 40 % of children in Canada have abnormal values for at least one cardiovascular risk factor, including BMI, blood pressure, lipids and blood glucose[54]. Such unfavourable CMR profiles have been shown to track into adulthood and lead to an increased risk of developing metabolic syndrome, atherosclerosis and type 2 diabetes[14,15].
Exposure to media during mealtime has several potentially unique effects which may make it an especially important risk factor in the development of increased CMR. Media consumption may act as a cue to stimulate eating in the absence of hunger[5,6]. Furthermore, media use during mealtime is thought to extend meal duration and ultimately increase energetic intake through reduced satiety signals[6,55]. Additionally, exposure to food advertisements on television encouraging the consumption of energy-dense foods during mealtime may promote poor dietary habits[5,56,57]. These factors likely all contribute to reported associations between mealtime media use and poor overall diet quality. Indeed, a recent systematic review identified associations between mealtime media use and lower scores on dietary quality indices. Specifically, watching television during mealtime was associated with decreased intake of fruits and vegetables, as well as an increased intake of fat- and sugar-containing foods including sugar-sweetened beverages[58]. Such dietary patterns have been shown to be associated with unfavourable CMR risk profiles[59], suggesting that changes in diet quality associated with mealtime media may mediate the association with adverse CMR profiles in children.
We observed evidence of an inverse association between total mealtime media use and levels of HDL-cholesterol in children aged 5–13 years. This suggests that increased media use during mealtime may be associated with less favourable lipid profiles in school-aged children. Evidence of an inverse association between total mealtime media use and HDL-cholesterol remained after additional adjustment for zBMI, suggesting that the effects of mealtime media use on lipid profiles may be independent from effects on zBMI[7]. Understanding which specific meals are driving observed associations is important because it has the potential to inform future interventions and it gives parents a practical target to focus on. When examining specific meals, evidence of an inverse association with HDL-cholesterol was only observed for breakfast consumed with media. A cross-sectional study of 409 children aged 6–9 years in Iran identified a similar association between breakfast consumption while watching television and increased waist circumference as well as fasting blood sugars, but not lipids[60]. Other studies have shown that skipping breakfast is associated with unfavourable cardiometabolic profiles[61], including decreased HDL-cholesterol in children[62,63]. It is therefore possible that media use during breakfast may act as a marker for irregular breakfast consumption patterns in the present study, possibly explaining its inverse association with HDL-cholesterol. Further research is necessary to determine the potential clinical significance of this association between mealtime media use and HDL-cholesterol.
The use of non-television forms of media such as smartphones and tablets was included in the current study. While the use of such devices among children has increased in recent years[64,65], it is unclear whether interacting with these devices during meals has similar effects to watching television. A randomised crossover trial found that acute energy intake was lower in children who were using a computer while eating compared with children who were watching television[66]. Other studies conducted in adults[67,68] also suggest that different forms of media may have varying effects on distraction from satiety signals and resulting energy intake. If the use of mobile media devices while eating results in less food consumption compared with watching television or no media at all, this could potentially explain the absence of associations identified between total mealtime media use and CMR markers in the present study. Such a phenomenon could also partially explain the unexpected inverse associations we observed between media use during snack time and glucose levels in children aged 5–13 years as well as media use during lunch and TAG in children aged 1–4 years. However, it must be noted that since our study was unable to differentiate between different types of media, no conclusions about possible differential effects of mobile media v. other types of media can be drawn. Further studies are necessary to determine whether mobile media use during mealtime has different effects from traditional television viewing on body weight and other CMR markers in children.
We observed some differences in the direction of associations between mealtime media use and CMR markers in our two a priori age groups. In children aged 5–13 years, total mealtime media use was inversely associated with HDL-cholesterol, suggesting that increased media use during mealtime may be associated with less favourable lipid profiles. Having more dinners with media was associated with increased insulin in this age group, although there was no evidence of an overall association between total mealtime media use (i.e., across all meals) and insulin. In children aged 1–4 years, we observed an unexpected inverse association between total mealtime media use and TAG. While TAG levels may be slightly affected by fasting time[22], it is unlikely that fasting status confounded the observed association since it was adjusted for in the final analysis. The identified inverse association in this age group may also represent a spurious finding. Alternatively, it is possible that parents of pre-school-aged children in the present study utilised media during mealtime as a distraction tactic to increase food intake in picky eaters. Indeed, this motivation for television viewing during mealtime has been described by parents of 3−5-year-old children participating in a recent qualitative study[69]. This suggests that the effects of media use during mealtime could differ between pre-school and school-aged children. Further research examining the context in which mealtime media is used in children of different ages may help to better explain apparent differences in the direction of associations observed between age groups in the present study.
While our study has a number of strengths, including a large sample size of over 2000 children, the utilisation of repeated measures, the inclusion of non-television forms of media and adjustment for a number of relevant covariates, it does possess some notable limitations. Given the disproportionate representation of participants from households with relatively high income and maternal education, the results may not be generalisable to the broader Canadian population. Although the administered questionnaire included all types of media use during mealtime (i.e., television, computer, video game console and handheld devices), it does not ask about mobile media (i.e., handheld devices including tablets and mobile phones) and non-mobile media use separately. This precluded any analysis of differences between mobile and non-mobile media use during mealtime. Furthermore, parental estimates of media use during meals consumed away from home (e.g., at school) may not be accurate for school-aged children with mobile devices. Additionally, while the identified associations were statistically significant ($P \leq 0$·05), the effect sizes were relatively small. However, these small effects may be of importance if they occur chronically as cumulative exposure to lower levels of HDL-cholesterol from childhood through to adulthood has been associated with increased atherosclerosis[70]. Nevertheless, since these associations have not yet been replicated in independent cohorts, it is possible that any of the observed associations in the present study represent spurious findings. Finally, the observational nature of the study precludes the establishment of causality in any of the identified associations.
## Conclusion
While there was no evidence that mealtime media use was associated with non-HDL-cholesterol, it may be associated with unfavourable lipid profiles through effects on HDL-cholesterol independent of body weight in children ≥5 years. This suggests that promoting media-free meals in school-aged children may have beneficial effects on minimising CMR. Additionally, the motivation for mealtime media use among children and parents may differ between pre-school and school-aged children, which may contribute to the differing direction of associations observed in our study. Further studies which examine the context for mealtime media use may help further clarify results of the present study.
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|
---
title: Relative validity of a short screener to assess diet quality in patients with
severe obesity before and after bariatric surgery
authors:
- Laura Heusschen
- Agnes AM Berendsen
- Michiel GJ Balvers
- Laura N Deden
- Jeanne HM de Vries
- Eric J Hazebroek
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991825
doi: 10.1017/S1368980022001501
license: CC BY 4.0
---
# Relative validity of a short screener to assess diet quality in patients with severe obesity before and after bariatric surgery
## Body
Obesity is reaching epidemic proportions, and bariatric surgery (BS) is proven to be one of the most effective treatments, resulting in substantial and long-term weight loss and improvement of obesity-related comorbidities(1–3). BS is performed in individuals with a BMI above 40 kg/m2 or a BMI above ≥ 35 kg/m2 with obesity-related comorbidities such as diabetes mellitus type 2, hypertension, obstructive sleep apnoea and dyslipidaemia[4]. Worldwide, the Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy are the most commonly performed bariatric procedures[5].
After BS, the amount of food that can be ingested is significantly reduced, resulting in a lower energy intake[6]. Additionally, food intolerances after surgery may lead to avoidance of food groups which in turn may impact diet quality[7]. Poor diet quality is consistently reported in patients with (severe) obesity, including those presenting for BS(8–10). This could impact their risk of developing nutritional deficiencies as well as the success of their weight loss after surgery(10–12). Therefore, monitoring diet quality is an important component in the BS programme.
Diet quality can be assessed with the Dutch Healthy Diet index 2015 (DHD2015-index)[13]. The DHD2015-index measures adherence to the Dutch food-based dietary guidelines published in 2015 by the Health Council of the Netherlands[14]. The DHD2015-index can be calculated using data from multiple food records, 24-h dietary recalls or a single FFQ. Unfortunately, these methods are time-consuming and burdensome and therefore less likely to be used in everyday clinical practice. For this reason, a short screener, the Eetscore FFQ, was developed to estimate the DHD2015-index in time-limited situations. The Eetscore FFQ showed to be acceptably correlated with the DHD2015-index derived from a full-length FFQ in a normal-weight adult population[15]. However, the Eetscore FFQ has not been evaluated in patients with (severe) obesity before or after undergoing BS.
Accurate measures of diet quality are needed to optimise nutritional care provided to these patients during the BS programme, but validated dietary assessment tools in this specific population are lacking[16]. Therefore, this study aimed to evaluate the relative validity and reproducibility of the Eetscore FFQ as a screener for diet quality in patients with (severe) obesity before and 6 months after BS.
## Abstract
### Objective:
To determine the relative validity and reproducibility of the Eetscore FFQ, a short screener for assessing diet quality, in patients with (severe) obesity before and after bariatric surgery (BS).
### Design:
The Eetscore FFQ was evaluated against 3-d food records (3d-FR) before (T0) and 6 months after BS (T6) by comparing index scores of the Dutch Healthy Diet index 2015 (DHD2015-index). Relative validity was assessed using paired t tests, Kendall’s tau-b correlation coefficients (τb), cross-classification by tertiles, weighted kappa values (k w) and Bland–Altman plots. Reproducibility of the Eetscore FFQ was assessed using intraclass correlation coefficients (ICC).
### Setting:
Regional hospital, the Netherlands.
### Participants:
Hundred and forty participants with obesity who were scheduled for BS.
### Results:
At T0, mean total DHD2015-index score derived from the Eetscore FFQ was 10·2 points higher than the food record-derived score ($P \leq 0$·001) and showed an acceptable correlation (τb = 0·42, 95 % CI: 0·27, 0·55). There was a fair agreement with a correct classification of 50 % (k $w = 0$·37, 95 % CI: 0·25, 0·49). Correlation coefficients of the individual DHD components varied from 0·01–0·54. Similar results were observed at T6 (τb = 0·31, 95 % CI: 0·12, 0·48, correct classification of 43·7 %; k $w = 0$·25, 95 % CI: 0·11, 0·40). Reproducibility of the Eetscore FFQ was good (ICC = 0·78, 95 % CI: 0·69, 0·84).
### Conclusion:
The Eetscore FFQ showed to be acceptably correlated with the DHD2015-index derived from 3d-FR, but absolute agreement was poor. Considering the need for dietary assessment methods that reduce the burden for patients, practitioners and researchers, the Eetscore FFQ can be used for ranking according to diet quality and for monitoring changes over time.
## Study design and participants
Between October 2018 and September 2019, patients with obesity who were eligible and scheduled for BS at Vitalys Obesity Clinic, part of Rijnstate hospital (Arnhem, the Netherlands), were asked to participate in this prospective cohort study. Participants were included approximately 6 weeks pre-surgery (T0) and followed up until 6 months post-surgery (T6). Exclusion criteria were a non-Dutch eating pattern, suffering from an eating disorder, inability to fill in questionnaires or food records and a previous bariatric procedure other than an adjustable gastric band.
In total, 200 participants signed the informed consent and were included in the study. Both before and after BS, we evaluated the Eetscore FFQ against 3-d food records (3d-FR) as reference method by comparing index scores of the DHD2015-index derived from both methods. At both time points, demographic information was collected and participants were asked to complete the Eetscore FFQ, followed by a 3d-FR as reference method. At T0, the Eetscore FFQ was completed twice (Eetscore FFQ1, Eetscore FFQ2) with an interval of approximately 5 weeks in order to analyse reproducibility.
From the total study sample of 200 participants, we excluded 60 participants with no Eetscore FFQ and 3d-FR (n 18), a missing Eetscore FFQ (n 5) or a missing/incomplete 3d-FR (n 37) at T0. The final study sample for data analysis at T0 consisted of 140 participants, of whom 116 completed both Eetscore FFQ1 and Eetscore FFQ2 (Fig. 1). For the study sample at T6, we additionally excluded 37 participants with no Eetscore FFQ and 3d-FR (n 22), a missing Eetscore FFQ (n 4) or a missing 3d-FR (n 11) at T6, resulting in a final study sample of 103 participants for data analysis at T6 (Fig. 1).
Fig. 1Flow chart of the study population at T0 and T6. 3d-FR, 3-d food records
## Demographic information
Socio-demographic (age, sex and educational level) and health-related information (anthropometrics, type of surgery, comorbidities and smoking status) were obtained from electronic patient records. Educational level was defined as low (primary education and prevocational secondary education), medium (senior general secondary education, pre-university education and secondary vocational education) or high (higher vocational education and university). Anthropometric measurements were performed during standard visits at the hospital. Body weight was measured to the nearest 0·1 kg with a digital weighing scale (Tanita BC-420MA), after removal of heavy clothing and shoes. Height was measured in standing position with a wall-mounted stadiometer (Seca 206). BMI was calculated as weight (kg) divided by squared height (m2). TBWL at 6 months was calculated as weight loss divided by body weight before surgery, multiplied by 100 %.
Physical activity at T0 was assessed with the validated Baecke Questionnaire[17] that evaluates a person’s habitual physical activity and separates it into three domains: work index, sports index and leisure index. Each domain could receive a score from 1 to 5 points, resulting in a total score ranging from 3 to 15. A score of 15 indicates being physically active at a high intensity.
## DHD2015-index
The development of the DHD2015-index has been previously described[13]. The DHD2015-index consists of fifteen components representing the Dutch food-based dietary guidelines of 2015[14]: vegetables, fruit, wholegrain products, legumes, nuts, dairy, fish, tea, fats and oils, coffee, red meat, processed meat, sweetened beverages, alcohol and Na. Additionally, the component ‘unhealthy food choices’ was added based on the guideline of the Netherlands Nutrition Centre[18]. Food items that contributed most to total energy, saturated fat, and mono- and disaccharide intake according to the Dutch National Food Consumption Survey (DNFCS) 2007–2010 were included in this component, such as sweet spreads, pastries, chocolate, savoury snacks, sauces and use of sugar in coffee or tea.
A complete overview of the sixteen components and their cut-off and threshold values is presented in Table 1. For every component, the score ranges from 0 (no adherence) to 10 points (complete adherence), resulting in a total score between 0 and 160 points. A graphic presentation of the scoring of the different types of components can be seen in Supplemental Fig. 1. For adequacy components (vegetables, fruit, legumes, nuts, fish and tea), no intake is awarded with 0 points and intakes between the cut-off and threshold value are scored proportionally. For moderation components (red meat, processed meat, sweetened beverages, Na, alcohol and unhealthy food choices), intakes between the cut-off and threshold value are also scored proportionally, but no intake is awarded with 10 points. Optimum components (dairy) have an optimal range of intake, and ratio components (fat and oils) reflect replacement of less preferred foods (e.g. solid fats) by more preferred foods (e.g. liquid fats and oils). The wholegrain product component is scored based on two sub-components: an adequacy component for wholegrain consumption and a ratio component to reflect replacement of refined grain products by wholegrain products. The coffee component is a qualitative component, based on the type of coffee (filtered v. unfiltered). As information on the type of coffee used was not available from the food records, this component could not be included in the validity analyses. For this reason, total score ranged between 0 and 150 for this part of the study.
Table 1Cut-off and threshold values for the calculation of the DHD2015-index components and the component ‘Unhealthy food choices’. Adapted from De Rijk et al.[15] ComponentComponent typeDutch dietary guidelines 2015Minimum score (=0 points)Maximum score (=10 points)1VegetablesAEat at least 200 g of vegetables daily0 g/d≥ 200 g/d2FruitAEat at least 200 g of fruit daily0 g/d≥ 200 g/d3Wholegrain productsAEat at least 90 g of wholegrain products daily0 g/d≥ 90 g/dRReplace refined cereal products by wholegrain productsNo consumption of wholegrain products or ratio of wholegrains to refined grains ≤ 0·7No consumption of refined products or ratio of wholegrains to refined grains ≥ 114LegumesAEat legumes weekly0 g/d≥ 10 g/d5NutsAEat at least 15 g of unsalted nuts daily0 g/d≥ 15 g/d6Dairy* OEat a few portions of dairy products daily, including milk or yogurt0 g/d or ≥ 750 g/d300–450 g/d7Fish† AEat one serving of fish weekly, preferably oily fish0 g/d≥ 15 g/d8TeaADrink three cups of black or green tea daily0 g/d≥ 450 ml/d9Fats and oilsRReplace butter, hard margarines and cooking fats by soft margarines, liquid cooking fats and vegetable oilsNo consumption of soft margarines, liquid cooking fats and vegetable oils or ratio of liquid cooking fats to solid cooking fats ≤ 0·6No consumption of butter, hard margarines and cooking fats or ratio of liquid cooking fats to solid cooking fats ≥ 1310CoffeeQReplace unfiltered coffee by filtered coffeeAny consumption of unfiltered coffeeConsumption of only filtered coffee or no coffee consumption11Red meatMLimit consumption of red meat≥ 100 g/d≤ 45 g/d12Processed meatMLimit consumption of processed meat≥ 50 g/d0 g/d13Sweetened beverages and fruit juicesMLimit consumption of sweetened beverages and fruit juices≥ 250 g/d0 g/d14AlcoholMIf alcohol is consumed at all, intake should be limited to one Dutch unit (10 g ethanol) dailyWomen: ≥ 20 g ethanol/dMen: ≥ 30 g ethanol/dWomen: ≤ 10 g ethanol/dMen: ≤ 10 g ethanol/d15NaMLimit consumption of table salt to 6 g daily≥ 3·8 g Na/d≤ 1·9 g Na/d16Unhealthy food choicesMLimit consumption of unhealthy choices≥ 7 week choices/week≤ 3 week choices/weekDHD2015-index, Dutch Healthy Diet index 2015; A, adequacy component (consume an adequate amount); R, ratio component (replace less healthy products by more healthy alternatives); O, optimum component (optimal consumption range); Q, qualitative component (choose healthier option); M, moderation component (limit consumption).*Maximum of 40 g/d cheese could be included.†Maximum of 4 g/d lean fish could be included.
## The Eetscore FFQ
The development of the Eetscore FFQ has been described in detail elsewhere[15]. Briefly, the Eetscore FFQ was developed to assess the DHD2015-index as a measure of adherence to the Dutch food-based dietary guidelines. The Eetscore FFQ assesses dietary intake over the previous month, based on fifty-five food items that account for 85 % of energy intake from the adult population of the DNFCS 2007–2010[19]. The six answer categories for questions on frequency of consumption range from ‘never’ to ‘every day’ for regularly consumed foods and from ‘not this month’ to ‘4 times a month’ for episodically consumed foods. Portion sizes are assessed in standard portions and commonly used household measures. Average daily intakes of food items are calculated by multiplying frequency of consumption by portion size in grams. The Eetscore FFQ directly reports index scores of the sixteen components of the DHD2015-index.
## Three-day food records
A 3-d estimated food record was used as the reference method. This method is considered acceptable for the assessment of usual dietary intake and is commonly used in dietary validation studies[20]. We used structured open-ended food records containing predefined food groups (including the option ‘others’) at six food occasions (breakfast, lunch, dinner and three eating occasions between the main meals). All participants received verbal instructions and were provided with a written example. They were asked to record all foods and beverages consumed over the 3 d in as much detail as possible, to describe the amounts consumed in units, household measures or provide weights when known, to report cooking methods and to include the recipes for any mixed dishes. At both time points, recorded days were randomly selected and consisted of 2 weekdays (Monday–Thursday) and 1 weekend day (Friday–Sunday) within a 1-week period. Completed food records were reviewed by the researcher for completeness with regard to portion sizes, cooking methods and description of foods. Telephone interviews with the participants were conducted in case of any uncertainties. Dietary intake data were entered in Compl-eat™, a computer-based nutrition calculation programme that is linked to the Dutch Food Composition Database (NEVO-online, version 2016)[21]. All foods and beverages from the food records were categorised into one of the fifteen DHD components (excluding coffee) to calculate the scores of the DHD2015-index. In case of missing recipes for mixed meals such as pasta or rice dishes, standard recipes of the Dutch Food Composition Database (NEVO-online, version 2016) were used[21]. Food items that did not fall into one of the DHD components (e.g. potatoes and soups) were not included. Total dietary intake of the fifteen DHD components in grams was averaged over the number of completed days before calculating corresponding index scores.
## Statistical analysis
General characteristics of the study population are reported as medians and interquartile ranges (Q1–Q3) for continuous data and as frequencies and percentages for categorical data. Total DHD2015-index score and individual component scores calculated from the Eetscore FFQ and the 3d-FR are presented as means and standard deviations.
Relative validity of the Eetscore FFQ compared to the 3d-FR was assessed by calculating Kendall’s tau-b (τb) as well as Spearman’s rho (ρ) correlation coefficients between the DHD index scores derived from both methods. At T0, we used data of the Eetscore FFQ that was completed in the same month as the 3d-FR. CI for the correlations were obtained using Fisher’s z-transformation. Correlation coefficients less than 0·20 were classified as poor, 0·20–0·49 as acceptable and ≥ 0·50 as good[22]. Additionally, total DHD2015-index scores derived from the Eetscore FFQ and the 3d-FR were categorised into tertiles. If ≥ 50 % of the participants were classified into the same tertile and/or ≤ 10 % into the opposite tertile, this was considered a good outcome[22]. Weighted kappa coefficients (k w) were calculated to further evaluate the relative level of agreement. k w coefficients less than 0·20 indicated a poor level of agreement, 0·21–0·40 fair agreement, 0·41–0·60 moderate agreement, 0·61–0·80 good agreement and greater than 0·80 a very good level of agreement[23]. Paired t tests were used to test the mean differences in the DHD index scores between the two methods. Bland–Altman plots with 95 % limits of agreement were used to visualise the differences in the total DHD2015-index score.
We additionally explored the degree of potential misreporting of dietary intake by comparing reported energy intake calculated from the food records at T0 with energy requirements as identified by the revised Goldberg cut-off method[24]. BMR was estimated using the Mifflin-St Jeor Equation[25] as this method provides the best estimation in individuals with (severe) obesity(26–28). We used a physical activity level of 1·55, reflecting a moderate active lifestyle that was in line with the median physical activity score resulting from the Baecke Questionnaire.
Reproducibility of the Eetscore FFQ was examined by calculating single-measures intraclass correlation coefficients (ICC) of absolute agreement between the DHD index scores of both FFQ at T0, using a two-way mixed model. ICC less than 0·50 indicated poor reproducibility, 0·50–0·75 moderate, 0·75–0·90 good and greater than 0·90 excellent reproducibility[29].
All analyses were conducted using SPSS statistics 25.0 (IBM).
## Participant characteristics
The study population at T0 consisted of 140 participants. The majority was female (79·3 %), never smoked (55·0 %), had a medium educational level (62·8 %) and no comorbidities (51·4 %) (Table 2). Median age was 49·0 (36·5–55·0) years, and median BMI was 41·5 (39·1–45·7) kg/m2. Median physical activity score of the Baecke Questionnaire was 8·4 (7·1–9·1).
Table 2Baseline characteristics of the study population at T0 (n 140)Study population at T0 (n 140)FrequenciesValid percentagesSex (female)11179·3Age (years) Median49·0 Q1–Q336·5–55·0BMI (kg/m2) Median41·5 Q1–Q339·1–45·7Smoking status Never7755·0 Former5337·9 Current107·1Educational level* Low2418·6 Medium8162·8 High2418·6Comorbidity None7251·4 *Diabetes mellitus* type 22316·4 Dyslipidaemia2517·9 Hypertension4330·7 OSAS2920·7Physical activity† Median8·4 Q1–Q37·1–9·1Adjustable Gastric Band1812·9OSAS, obstructive sleep apnoea syndrome.*Low education = primary education and prevocational secondary education; medium education = senior general secondary education, pre-university education and secondary vocational education; high education = higher vocational education and university. Missing for n 11.†Based on Baecke Questionnaire; total score ranging from 3 to 15. Missing for n 27.
Baseline characteristics of the study population at T6 (n 103) were similar to those of the study population at T0 (see online supplementary material, Supplemental Table 1). The majority had undergone a RYGB (80·7 %), and median BMI 6 months after surgery was 30·9 (28·5–34·3) kg/m2, resulting in a median TBWL of 25·8 (21·1–29·3) per cent.
## Relative validity of the Eetscore FFQ compared to 3-d food records
Average time difference between completing the Eetscore FFQ and the 3d-FR at T0 was 5·8 ± 7·2 d. Mean total DHD2015-index score derived from the Eetscore FFQ was 10·2 points higher than the score derived from the 3d-FR (91·8 ± 18·6 v. 81·5 ± 17·7 points, $P \leq 0$·001; Table 3a). Visual inspection of the Bland–Altman plot additionally showed relatively wide limits of agreement (–21·1 and 41·5 points, Fig. 2(a)). Index scores for the individual DHD components were significantly different for vegetables, fruit, wholegrain products, legumes, nuts, dairy, fish, tea, processed meat and Na ($P \leq 0$·05 for all).
Table 3aMean DHD2015-index scores derived from the 3d-FR and the Eetscore FFQ and corresponding validity statistics in 140 participants before BS (T0)3d-FREetscore FFQDifferenceMean sd Mean sd Mean sd P-value τb95 % CI ρ 95 % CI1.Vegetables6·72·95·42·8−1·33·3< 0·0010·230·06, 0·380·340·18, 0·482.Fruit7·53·45·93·5−1·53·1< 0·0010·410·26, 0·540·520·38, 0·643.Wholegrain products5·42·97·12·9+1·73·1< 0·0010·320·16, 0·460·420·27, 0·554.Legumes0·82·65·74·5+4·95·1< 0·0010·04−0·13, 0·200·05−0·12, 0·215.Nuts1·83·54·03·6+2·23·5< 0·0010·420·27, 0·550·500·36, 0·626.Dairy6·93·06·13·3−0·83·80·020·210·04, 0·360·280·12, 0·437.Fish2·33·85·53·4+3·24·1< 0·0010·310·15, 0·460·380·22, 0·528.Tea5·04·34·14·3−0·93·90·010·480·33, 0·600·570·44, 0·689.Fat and oils6·44·56·94·3+0·55·20·260·260·10, 0·410·300·14, 0·4510.Coffee* NA NA 7·52·7 – – – – – – – 11.Red meat8·92·78·72·7−0·23·70·590·01−0·16, 0·180·01−0·16, 0·1812.Processed meat2·23·43·33·1+1·23·5< 0·0010·340·18, 0·480·430·28, 0·5613.Sweetened beverages6·63·87·03·8+0·44·00·200·370·21, 0·510·470·32, 0·6014.Alcohol9·42·29·32·2−0·12·00·560·540·40, 0·650·550·41, 0·6615.Na7·13·27·82·5+0·73·20·010·290·13, 0·440·390·23, 0·5316.Unhealthy food choices4·64·44·84·4+0·24·70·570·340·18, 0·480·440·29, 0·57DHD2015-index score† 81·517·791·818·6+10·216·0< 0·0010·420·27, 0·550·600·47, 0·70DHD2015-index, Dutch Healthy Diet index 2015; BS, bariatric surgery; 3d-FR, 3-d food records.*The component coffee was not assessed in the 3d-FR.†The total score ranges between 0 and 150 points (excluding coffee component).
Fig. 2(a) Bland–Altman plot of the total DHD2015-index score derived from the Eetscore FFQ and 3d-FR at T0 (n 140). Middle line indicates the mean difference; upper and lower lines indicate limits of agreement based on mean difference ± 1·96 × sd (10·2 ± 31·3). ( b) Bland–Altman plot of the total DHD2015-index score derived from the Eetscore FFQ and 3d-FR at T6 (n 103). Middle line indicates the mean difference; upper and lower lines indicate limits of agreement based on mean difference ± 1·96 × sd (17·4 ± 32·0). 3d-FR, 3-d food records; DHD2015-index, Dutch Healthy Diet index 2015 Correlation of the total DHD2015-index score was acceptable (τb = 0·42, 95 % CI: 0·27, 0·55), and there was a fair level of agreement between the two methods (k $w = 0$·37, 95 % CI: 0·25, 0·49). The Eetscore FFQ correctly classified 50·0 % of the participants into the same tertile as the 3d-FR, and 5·7 % was misclassified into the opposite tertile. For the individual DHD components, a good correlation (≥ 0·50) was observed for alcohol (τb = 0·54, 95 % CI: 0·40, 0·65). Poor correlations (< 0·20) were observed for red meat (τb = 0·01, 95 % CI: –0·16, 0·18) and legumes (τb = 0·04, 95 % CI: –0·13, 0·20). Correlation coefficients of all other components ranged between 0·20 and 0·49.
At T6, average time difference between completing the Eetscore FFQ and the 3d-FR was 8·5 ± 7·4 d. Similar to T0, mean total DHD2015-index score derived from the Eetscore FFQ was higher than from the 3d-FR (mean difference of 17·4 points, $P \leq 0$·001; Table 3b) with relatively wide limits of agreement (–14·6 and 49·4 points, Fig. 2(b)). Index scores for the individual DHD components were significantly different for vegetables, fruit, wholegrain products, legumes, nuts, fish, fats and oils, processed meat, sweetened beverages and unhealthy food choices ($P \leq 0$·05 for all).
Table 3bMean DHD2015-index scores derived from the 3d-FR and the Eetscore FFQ and corresponding validity statistics in 103 participants after BS (T6)3d-FREetscore FFQDifferenceMean sd Mean sd Mean sd P-value τb95 % CI ρ 95 % CI1.Vegetables4·93·04·02·4−1·03·0< 0·010·270·08, 0·440·390·21, 0·552.Fruit7·33·26·43·4−0·92·8< 0·010·480·31, 0·620·600·45, 0·723.Wholegrain products4·43·06·93·0+2·43·5< 0·0010·240·05, 0·420·330·14, 0·504.Legumes1·43·55·54·2+4·15·4< 0·0010·07−0·13, 0·260·08−0·12, 0·275.Nuts3·04·04·93·5+2·04·1< 0·0010·320·13, 0·490·390·21, 0·556.Dairy6·23·76·73·5+0·64·20·170·210·02, 0·390·270·08, 0·447.Fish2·33·75·93·5+3·74·0< 0·0010·300·11, 0·470·360·17, 0·528.Tea4·74·44·04·1−0·73·50·060·530·36, 0·660·650·51, 0·769.Fat and oils4·94·66·24·4+1·35·70·020·17−0·03, 0·350·210·02, 0·3910.Coffee* NA NA 7·22·7 – – – – – – – 11.Red meat9·22·39·51·8+0·22·60·340·16−0·04, 0·340·17−0·03, 0·3512.Processed meat2·93·35·23·0+2·34·0< 0·0010·06−0·14, 0·250·09−0·11, 0·2813.Sweetened beverages6·54·08·32·7+1·94·0< 0·0010·250·06, 0·430·310·12, 0·4814.Alcohol9·91·09·61·7−0·31·90·180·200·00, 0·380·200·00, 0·3815.Na9·11·89·20·50·01·80·900·15−0·05, 0·330·17−0·03, 0·3516.Unhealthy food choices6·64·08·33·0+1·74·5< 0·0010·330·14, 0·500·420·24, 0·57DHD2015-index score† 83·417·2100·814·2+17·416·3< 0·0010·310·12, 0·480·440·26, 0·59DHD2015-index, Dutch Healthy Diet index 2015; BS, bariatric surgery; 3d-FR, 3-d food records.*The component coffee was not assessed in the 3d-FR.†The total score ranges between 0 and 150 points (excluding coffee component).
Correlation of the total DHD2015-index score was acceptable (τb = 0·31, 95 % CI: 0·12, 0·48), and there was a fair level of agreement between the two methods (k $w = 0$·25, 95 % CI: 0·11, 0·40). The Eetscore FFQ correctly classified 43·7 % of the participants into the same tertile as the 3d-FR, and 9·7 % was misclassified into the opposite tertile. For the individual DHD components, a good correlation (≥ 0·50) was observed for tea (τb = 0·53, 95 % CI: 0·36, 0·66). Poor correlations (< 0·20) were observed for processed meat (τb = 0·06, 95 % CI: –0·14, 0·25), legumes (τb = 0·07, 95 % CI:–0·13, 0·26), Na (τb = 0·15, 95 % CI: –0·05, 0·33), red meat (τb = 0·16, 95 % CI: –0·04, 0·34) and fats and oils (τb = 0·17, 95 % CI: –0·03, 0·35). Correlations coefficients of all other components ranged between 0·20 and 0·49.
## Misreporting
According to the revised Goldberg cut-off method, 57·1 % of the participants was classified as potential under-reporters of energy intake at T0 and 58·3 % of the participants at T6. We did not identify potential over-reporters of energy intake. Excluding potential misreporters did not markedly affect our results regarding the relative validity of the Eetscore FFQ at both time points (see online supplementary material, Supplemental Table 2a and b).
## Reproducibility of the Eetscore FFQ
Average time difference between completing the first and second Eetscore FFQ at T0 was 4·8 ± 2·3 weeks. Mean total DHD2015-index score was 100·4 ± 19·1 points for Eetscore FFQ1 and 103·3 ± 18·3 points for Eetscore FFQ2 (Table 4) with an ICC of 0·78 (95 % CI: 0·69, 0·84). Index scores of the individual DHD components were fairly similar for most components, with ICC ranging from 0·26 to 0·78. Good reproducibility (ICC 0·75–0·90) was observed for fruit (ICC = 0·76, 95 % CI: 0·67, 0·83), fish (ICC = 0·76, 95 % CI: 0·68, 0·83) and coffee (ICC = 0·78, 95 % CI: 0·70, 0·84). Poor reproducibility (ICC < 0·50) was observed for dairy (ICC = 0·26, 95 % CI: 0·08, 0·42), red meat (ICC = 0·29, 95 % CI: 0·11, 0·44), processed meat (ICC = 0·43, 95 % CI: 0·27, 0·57), fats and oils (ICC = 0·46, 95 % CI: 0·30, 0·59) and sweetened beverages (ICC = 0·46, 95 % CI: 0·30, 0·59). ICC of all other components ranged between 0·50 and 0·75 (Table 4).
Table 4Mean DHD2015-index scores derived from the first and second Eetscore FFQ and corresponding intraclass correlation coefficients (ICC) in 116 participants before BS (T0)Eetscore FFQ1Eetscore FFQ2Mean sd Mean sd ICC95 % CI1.Vegetables5·72·85·12·80·540·40, 0·662.Fruit6·03·56·53·30·760·67, 0·833.Wholegrain products7·22·87·62·70·700·59, 0·784.Legumes5·74·66·24·40·620·49, 0·725.Nuts4·33·74·13·50·710·61, 0·796.Dairy6·23·36·43·40·260·08, 0·427.Fish5·33·45·43·50·760·68, 0·838.Tea4·04·23·43·90·680·56, 0·769.Fat and oils7·24·26·54·40·460·30, 0·5910.Coffee7·42·87·52·70·780·70, 0·8411.Red meat8·82·59·02·40·290·11, 0·4412.Processed meat3·33·13·73·30·430·27, 0·5713.Sweetened beverages7·23·77·93·10·460·30, 0·5914.Alcohol9·32·69·42·30·740·65, 0·8215.Na8·02·48·51·90·550·41, 0·6716.Unhealthy food choices4·94·36·24·20·600·45, 0·72DHD2015-index score* 100·419·1103·318·30·780·69, 0·84DHD2015-index, Dutch Healthy Diet index 2015; BS, bariatric surgery.*The total score ranges between 0 and 160 points.
## Discussion
In this study, we determined the relative validity and reproducibility of the Eetscore FFQ as a screener for diet quality in patients with (severe) obesity before and after BS by comparing index scores of the DHD2015-index derived from the Eetscore FFQ to the scores derived from 3d-FR (reference method). We demonstrated an overall reasonable relative agreement between the two methods, although the Eetscore FFQ showed higher index scores in comparison with the 3-FR and absolute agreement between the two methods was poor. Correlation coefficients for the DHD component scores varied widely with best coefficients observed for fruit and tea, and worst for legumes and red meat. Reproducibility of the Eetscore FFQ was considered good.
We observed lower correlations for the total DHD2015-index score based on fifteen components (excluding coffee) between the Eetscore FFQ and 3d-FR than reported in the study of de Rijk et al., who compared the Eetscore FFQ to a full-length FFQ[15]. They reported a Kendall’s tau-b coefficient of 0·51 (95 % CI: 0·47, 0·55) for the total DHD2015-index score based on thirteen DHD components (excluding fish, fats and oils, and coffee). This could be explained by a difference in the number of DHD components included in the total score as well as a difference in reference method. The Eetscore FFQ is also an FFQ; therefore, more correlated errors might be expected with a full-length FFQ, resulting in higher correlations. Yet, a full-length FFQ might capture habitual dietary intake more accurately than three food records. Although all days of the week were equally represented across all records, foods that are not consumed on a daily basis, for example fish or legumes, could have been underestimated when recording only 3 d. This is also reflected in relative large absolute differences for these components. It has been suggested that when dietary methods assessing habitual dietary intake, such as the Eetscore FFQ, are validated against food records, a certain degree of disagreement can be expected due to the greater within-subject variations that occur over the shorter reference period of a food record[20].
In a study of Papadaki et al., Pearson’s correlation coefficient of 0·52 was observed comparing the English version of the ‘Mediterranean Diet Adherence Screener’ to 3d-FR in patients with high cardiovascular risk in the UK[30]. Schröder et al. found Pearson’s correlation coefficient of 0·61 when they compared the ‘Diet Quality Index’ derived from the ‘Short Diet Quality Screener’ to ten 24-h dietary recalls in a Spanish population[31]. In the same study, they also observed a correlation of 0·40 for the ‘Modified Mediterranean Diet Score’ derived from the ‘Brief Mediterranean Diet Screener’ compared with the score derived from ten 24-h dietary recalls[31]. These values are comparable to Spearman’s Rho correlations observed in the current study (ρ = 0·60, 95 % CI: 0·47, 0·70 at T0 and ρ = 0·44, 95 % CI: 0·26, 0·59 at T6).
In contrast to the findings on relative agreement, absolute agreement between the Eetscore FFQ and the 3d-FR was poor. According to the Bland–Altman plots, the Eetscore FFQ systematically overestimated the total DHD2015-index score compared to the 3d-FR at both time points with relatively wide limits of agreement. However, no significant proportional bias was observed. This is in line with other studies that also found higher mean index scores derived from a diet screener in comparison with food records(15,30–32).
As most FFQ, the Eetscore FFQ can be considered more appropriate for ranking patients according to their diet quality or monitoring relative differences over time, rather than assessing absolute individual scores. It is however important to note that a food record is also no golden reference method and has its own limitations with regard to assessing dietary intake. Furthermore, we evaluated the intake of food groups instead of nutrients which is more difficult because of the high day-to-day variation. This may have impacted our findings with respect to the poor absolute agreement between the two methods.
With regard to the individual DHD components, correlations varied widely with highest values found for fruit and tea, and lowest values for legumes and red meat. For legumes, we observed many participants with an extreme difference of 10 points between the index score derived from the Eetscore FFQ compared to the food record-derived score, meaning that these participants had a score of 10 for legumes according to the Eetscore FFQ, whereas their score was 0 based on the food records. This resulted in large mean differences for this component (5·7 v. 0·8 points at T0 and 5·5 v. 1·4 points at T6, $P \leq 0$·001). This could be due to the fact that food records might not accurately capture habitual dietary intake, especially for foods that are not consumed on a daily basis such as legumes, as mentioned earlier. This is in concordance with an Australian study (age ≥ 70) validating a six-item dietary screener against three 24-h dietary recalls that also observed a poor agreement for legume intake (k $w = 0$·12)[33].
For red meat, we observed poor correlations of < 0·20 at both time points, whereas mean index scores for this component were fairly similar between the two methods (8·7 v. 8·9 points at T0 and 9·5 v. 9·2 points at T6, $P \leq 0$·05). This might be explained by a low variation in the index scores for red meat. Over half of the participants scored 10 points based on the Eetscore FFQ as well as the 3d-FR. As a result, the few observations with (relatively) large differences in index score could have biased the correlation towards zero.
We also aimed to define participants who substantially under- or overreported their dietary intake by using the revised Goldberg cut-off method in which energy intake is compared with (estimated) energy expenditure. However, adequately estimating energy expenditure in subjects with (severe) obesity is challenging. In a study of Cancello et al.[26], predictive equations for resting energy expenditure were compared to indirect calorimetry in 4247 subjects with obesity (69 % women, mean age 48 ± 19 years, mean BMI 44 ± 7 kg/m2). The authors found that the Mifflin-St Jeor equation had the highest performance for both accuracy and bias but emphasise that the accuracy is still far from ideal[26]. Furthermore, the revised Goldberg cut-off method cannot be applied after BS as the condition of weight stability is violated, resulting in an invalid ratio between reported energy intake and energy requirement. We therefore assumed that participants who were identified as potential misreporters of dietary intake at T0 also misreported their intake at T6.
At both time points, the rate of potential misreporters was relatively high with 57·1 % of the study population potentially underreporting their dietary intake at T0 and 58·3 % at T6. According to a review of Poslusna et al., the percentage of under-reporters in studies using estimated food records ranged from 12 to 44 %[34], which is lower than the observed percentages in the present study. This is in line with previous research showing that a higher BMI is associated with underreporting of dietary intake[35].
Overall, excluding potential misreporters did not markedly affect our results, although caution is needed in the interpretation because of the aforementioned limitations in the use of the Goldberg cut-off method within this population.
Reproducibility of the Eetscore FFQ before surgery was considered good. The observed ICC of 0·78 was slightly lower than reported in previous research by de Rijk et al., who found an ICC of 0·91 for the total DHD2015-index score[15]. This could be due to a difference in study population as well as the multidisciplinary lifestyle programme that all participants started before undergoing BS. During this programme, patients received general information on healthy eating behaviour and dietary counselling. For most participants, the first Eetscore FFQ was administered before entering the multidisciplinary programme while they completed the second Eetscore FFQ during the programme. It is therefore plausible that participants already implemented beneficial changes with respect to their diet. This might explain the slightly higher DHD2015-index score resulting from the second Eetscore FFQ. Future studies are needed to confirm our findings while limiting the influence of such external factors. For the individual DHD components, most correlation coefficients ranged between 0·5 and 0·7 which are common in reproducibility studies of FFQ[20].
Dietary assessment is an important component in the BS programme. Currently, dietary intake of patients undergoing BS is often assessed by a dietitian with the use of food records. This assessment method is very time-consuming, might be prone to reactivity and recall bias and only reflects the intake of the past days. The Eetscore FFQ is a short, web-based tool that can be used to assess general aspects of a healthy nutrient-dense diet such as the consumption of fruits and vegetables, wholegrains and dairy. However, the Eetscore FFQ does not include additional information about patients’ eating behaviour including the distribution of food intake (e.g. few large meals or frequent smaller feedings) and the separation of food and beverages. Also, other factors affecting dietary intake may be missed by the Eetscore FFQ, such as food preparation methods and non-included food items (e.g. plant-based dairy, meat substitutes and fast food). The Eetscore FFQ can therefore be used as an additional dietary assessment tool in the BS programme rather than as a replacement for the current methodology.
Considering the need for dietary assessment methods that reduce the burden for patients, practitioners and researchers, the Eetscore FFQ can be used for ranking patients according to diet quality and for monitoring relative changes in intake over time in order to indicate an improvement or a deterioration in diet quality. This can be relevant before undergoing surgery, during annual follow-up in the late post-operative phase or in case of weight regain. Dietary assessment methods assessing actual intake may be preferred in the early post-operative phase when patients are still adapting to the new eating habits and in case of food-related complaints such as dumping syndrome or hypoglycaemia.
The main strength of this study is the validation of an existing dietary assessment tool in patients with (severe) obesity before and after BS as there is a clear lack of validated, easy-to-use tools within this patient population. Another strength is the use of multiple statistical tests to provide a comprehensive insight into various facets of validity. As Kendall’s tau-b correlation coefficients tend to be smaller, we also reported Spearman’s Rho correlations to allow for comparison with other research. Furthermore, by choosing 3d-FR as reference method, we minimised the risk of correlated measurement errors between the two methods[20].
We aimed to determine relative validity of the Eetscore FFQ both before and after BS, but thirty-seven participants dropped out between T0 and T6, resulting in two different study populations. We are aware that the study population at T0 and T6 is therefore not mutually exclusive and direct comparisons between the populations cannot be made. Nonetheless, both populations and the dropouts were similar with respect to sex, age, BMI, smoking status, education, physical activity, prevalence of comorbidities and type of surgery. Moreover, both the study population at T0 and T6 were found representative of the general Dutch bariatric patient population[36], indicating a minor risk of selection bias.
Another limitation is the lack of a golden standard reference method for dietary intake. To reduce participant burden, we chose for 3d-FR using household measures, which are prone to report bias and are not ideal for foods that are not consumed daily. For future research, we suggest to evaluate the Eetscore FFQ against dietary biomarkers that are suitable for patients after BS to provide an objective measure of dietary intake.
## Conclusion
The Eetscore FFQ is a short screener of diet quality that assesses adherence to the Dutch dietary guidelines. Based on our findings, the Eetscore FFQ was considered an acceptable screener for ranking individuals according to their diet quality and showed good reproducibility to monitor relative changes in diet quality over time. However, the tool showed poor absolute agreement and is not suitable for assessing diet quality on the individual level. Future research is needed to improve the use of the Eetscore FFQ for this purpose.
## Conflicts of interest:
There are no conflicts of interest.
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---
title: Limited implementation of California’s Healthy Default Beverage law for children’s
meals sold online
authors:
- Hannah R Thompson
- Anna Martin
- Ron Strochlic
- Sonali Singh
- Gail Woodward-Lopez
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991827
doi: 10.1017/S1368980022000039
license: CC BY 4.0
---
# Limited implementation of California’s Healthy Default Beverage law for children’s meals sold online
## Body
Rates of childhood obesity and related chronic diseases have significantly increased in recent decades, with nearly one in five US children and adolescents having obesity[1,2]. Sugar-sweetened beverages (SSB) are a significant causal contributor to overweight and poor health outcomes in youth[3,4]. Approximately 60 % of US children consume SSB on a daily basis[5], accounting for 8 % of energy intake[3]. Further, daily SSB consumption is higher for children of colour and children from low-income families, compared with White and higher income children, likely contributing to health disparities[6,7].
On average, 25 % of children’s SSB intake is consumed in restaurants[8]. This is likely due at least in part to the high availability of SSB in those settings; in 2019, 61 % of the top fifty restaurant chains in the US had SSB on children’s menus[9]. Consumption of children’s meals from quick-service restaurants (QSR) is associated with increased energetic intake[10] and increased SSB intake[11]. Moreover, non-White and low-income youth are more likely to regularly consume QSR food compared with their White and higher income counterparts, further exacerbating existing health disparities[12,13]. This could be due, in part, to a higher prevalence of QSR in low income and communities of colour[14,15], as well as disproportionate QSR advertising targeted at Black and Latinx youth[16]. Identifying interventions to decrease youth SSB consumption is necessary, and may be successful when executed in the QSR setting.
In an effort to reduce children’s SSB consumption, several states (including California, Delaware and Hawaii) and jurisdictions (including Philadelphia, PA; New York, NY; Baltimore, MD; Louisville, KY and Lafayette, CO) have enacted legislation mandating healthy beverages (i.e. water, unflavoured milk products and in some cases, 100 % fruit juice) as the default beverage options for children’s meals sold in restaurants including QSR(17–20). California’s law, Senate Bill (SB) 1192[21], known as the Healthy-By-Default Beverage law, is among the most stringent of existing legislation, allowing only plain or sparkling water with no added sweeteners, unflavoured milk or unflavoured, non-dairy milk product alternatives as default beverage options (only allowable beverages are automatically included or offered as part of a children’s meal, absence of specific request by the purchaser of the children’s meal for an alternative beverage). The law also requires that menus, menu boards and advertisements for children’s meals include only approved default options. In California, customers may still purchase SSB with children’s meals, but must specifically request those beverages.
Evidence from a broad range of fields suggests that consumers tend to select default options(22–25), with further evidence suggesting healthy default beverages are acceptable to children and parents and can result in more nutritious choices(26–28). Studies have found increased ordering of more healthful items[17,29] and reduced energetic intake[29] following the implementation of healthy default menus. Additional research indicates that cost also impacts beverage choice[30], with in-store studies demonstrating that pricing, in combination with promotion and prompting, effectively impacts purchasing behaviour[31]. However, evidence regarding the impact of healthy default policies on pricing is mixed, with some studies finding price increases[29] and others reporting no change in prices[32,33].
A study examining adherence to California’s Healthy-By-Default Children’s Meal Beverage law when ordering in-person at QSR (either inside the restaurant or via drive through) demonstrated the proportion of menu boards listing only healthy default beverages with children’s meals increased from 9·7 % to 66·1 % after SB1192 was enacted; however, few staff verbally offered beverages consistent with the legislation, with a significant decrease from 5 % to 1 %[18,34]. The incomplete implementation of the law could result in an attenuation of the intended impact to reduce SSB consumption among young children.
In recent years, there has been an increasing trend in ordering meals online (either via website or phone application) for pick-up or delivery[35]. With the onset of the COVID-19 pandemic in March 2020, which resulted in stay-at-home and social distancing orders, online ordering has further increased in popularity[36]. Whether or not QSR fully implement SB1192 when offering beverages with children’s meals sold online is therefore of increasing importance, yet remains unknown.
Collaborating with under-resourced communities, including the retail food sector, to reduce SSB consumption is a goal of CalFresh Healthy Living—the Supplemental Nutrition Assistance Program Education (SNAP-Ed) in California. The California Department of Public Health implements state-wide CalFresh Healthy Living initiatives and funds local health departments in nearly every county to implement CalFresh Healthy Living. This study was conducted to inform the work of CalFresh Healthy Living and their partners regarding the need for complementary local or state action to ensure optimal implementation and effectiveness of SB1192 in achieving the objective of reducing SSB consumption by young children. Specifically, this study examines the online ordering process for QSR located in SNAP-Ed eligible census tracts to provide specifics regarding the extent and nature of SB1192 implementation on QSR-specific and third-party online ordering platforms with the aim of informing efforts to improve policy language and provide support for policy implementation.
## Abstract
### Objective:
To reduce children’s sugar-sweetened beverage intake, California’s Healthy-By-Default Beverage law (SB1192) mandates only unflavoured dairy/non-dairy milk or water be the default drinks with restaurant children’s meals. The objective of this study is to examine consistency with this law for meals sold through online platforms from restaurants in low-income California neighbourhoods.
### Design:
This observational, cross-sectional study examines beverage availability, upcharges (additional cost) and presentation of beverage options consistent with SB1192 (using four increasingly restrictive criteria) within a random sample of quick-service restaurants (QSR) in Supplemental Nutrition Assistance Program Education eligible census tracts selling children’s meals online from November 2020 to April 2021.
### Setting:
Low-income California neighbourhoods (n 226 census tracts).
### Participants:
QSR that sold children’s meals online via a restaurant-specific platform, DoorDash, GrubHub and/or UberEats (n 631 observations from 254 QSR).
### Results:
Seventy percent of observations offered water; 63 % offered unflavoured milk. Among all beverages, water was most likely to have an upcharge; among observations offering water (n 445), 41 % had an upcharge (average $0·51). Among observations offering unflavoured milk (n 396), 11 % had an upcharge (average $0·38). No observations upcharged for soda (regular or diet). Implementation consistency with SB1192 ranged from 40·5 % (using the least restrictive criteria) to 5·6 % (most restrictive) of observations.
### Conclusions:
Based on observations from restaurant websites and three of the most popular online ordering platforms, most California QSR located in low-income neighbourhoods are not offering children’s meal beverages consistent with the state’s Healthy-By-Default Beverage law. As the popularity of online ordering increases, further work to ensure restaurants offering healthy default beverages with children’s meals sold online is necessary.
## Sample
For this observational, cross-sectional study, we sampled QSR sites from California’s thirteen largest QSR chains that sold children’s meals (defined as a combination of food items and a beverage, or a single food item and a beverage, sold together at a single price, primarily intended for consumption by a child[21]). From within each eligible chain, we randomly sampled QSR sites located in all SNAP-Ed eligible census tracts (n 1350) across the state using the 2019 Dun & Bradstreet California Retail Food Environment dataset[37]. A census tract was considered SNAP-Ed eligible if 50 % or more of households had incomes at or below 185 % of the Federal Poverty Level.
For each of the QSR chains in our sample with ≥ 79 restaurants total in the study census tracts, the number of QSR sites sampled was proportional to the size of that chain relative to the other sampled chains to achieve an error of +/– 5·15 % in the estimate of whether a QSR upcharged for default beverages (water and unflavoured milk). For chains with < 79 restaurants per chain, all QSR sites in the study census tracks were sampled. This resulted in an initial sample of 346 QSR sites from 13 chains (Fig. 1). QSR sites: that were closed (n 24); without online ordering capabilities (n 37) and without children’s meals available online (n 31) were excluded from the sample.
Fig. 1Sample restaurant and observation flow chart From the remaining 254 QSR sites, we collected data from four potential online ordering platforms (restaurant-specific platform, DoorDash, GrubHub and UberEats) per site (n 1016 possible observations). Observations without children’s meals available on a given platform were excluded (n 385 observations). The final sample included 631 observations from 254 QSR sites from thirteen chains.
The study sample of 254 QSR sites was located in 226 California SNAP-Ed eligible census tracts (Table 1). There are on average 953 children ages 0–11 living in these census tracts, 36·1 % of whom live under 100 % of the federal poverty line and 67·6 % of whom live under 185 % of the federal poverty line. On average, census tract residents were primarily Hispanic or Latinx (62·4 %) or White (non-Hispanic) (20·9 %).
Table 1Demographic characteristics of census tracts in which the sample of 254 restaurants reside, (n 226 census tracts)PopulationMean sd All ages50102106 Ages 0–11953553Proportion (%) of population under 100 % of the federal poverty line All ages27·810·1 Ages 0–1136·114·7Proportion (%) of population under 185 % of the federal poverty line All ages54·50·91 Ages 0–1167·615·0Proportion (%) of population by race/ethnicity* American Indian/Alaska Native, non-Hispanic0·52·2 Asian, non-Hispanic8·411·3 Black, non-Hispanic7·78·6 Hispanic or Latinx62·423·5 White, non-Hispanic20·919·1 Two or more races/other2·93·0*Rounded averages may not add up to 100 %.
## Data collection tool
Study data were collected from November 2020 to April 2021 using a standardised protocol and a data collection instrument adapted from an instrument previously used for assessing on-site restaurant menu compliance with SB1192[20]. Each QSR chain was assigned one pre-determined kid’s meal entrée to be ‘ordered’ during data collection for all restaurants and ordering platforms for that chain to ensure that any price differences seen within a chain were due only to differences in beverage selection. The selected entrée was usually the first entrée listed at the smallest size without an upcharge. For example, one QSR chain that offered multiple sizes and options for children’s meals entrees was assigned a hamburger as the standard entrée, and all data collected from that chain (from every restaurant and every platform) were related to a children’s meal with a hamburger.
## Beverage availability
Beverages available with children’s meals were categorised as follows: water bottle or cup; unflavoured milk (regardless of fat content); unspecified milk (unclear if flavoured/unflavoured); flavoured milk (e.g. chocolate) regardless of fat content; 100 % fruit juice with no added sugar; juice diluted with water and no added sugar; unspecified fountain/kids drink (listed as ‘fountain drink,’ ‘kids’ drink’, ‘small drink’ or something similarly non-specific, often with a link or drop-down menu with specific beverage options, including SSB); regular soda; diet soda; soda (unclear if regular or diet); other pre-sweetened beverages (e.g. sweetened iced teas, sweetened lemonades, sweetened juice drinks) and other unsweetened or artificially sweetened beverages (unsweetened iced tea, lite lemonade). Data collectors recorded whether and which beverages were offered initially when ordering online (on the first screen where beverage selection was available) and, in cases where additional options were available, on a second screen that the customer was directed to if they selected an option to see additional beverage choices. Data collectors also recorded if and which beverages were included in images of the full kids’ meal (that included all kid’s meal components). No data were collected regarding images of only beverages that were placed next to a listed beverage option on the kids’ meal ordering screen(s).
## Costs
If a beverage choice increased the total cost of the children’s meal (e.g. choosing milk increased the meal price by $1·00), then the additional cost (upcharge) was recorded. The total cost of the children’s meal with the specified entrée was also recorded. For each beverage offered with an upcharge, the upcharge amount was divided by the cost of the children’s meal to determine the upcharge as a proportion of the total children’s meal cost.
## Making healthy beverages the default/Consistency with SB1192
SB1192, as written, does not specifically mention online ordering, lacks details regarding how other (non-compliant) beverages can be offered in the online context and fails to clarify if there can be additional costs (upcharges) for default beverages. ‘ Compliance’ with the law is therefore subject to interpretation, and thus difficult to assess. To address this challenge, we developed four increasingly restrictive sets of criteria to assess the extent of implementation of the law (Fig. 2). This approach also supports the study objective by providing more nuanced information to inform improvements in policy language and implementation efforts. As written, the law clearly does not allow the initial offering of specific non-compliant beverages; therefore, all four criteria specify that one or both of the allowable default beverages (water and/or unflavoured/unspecified milk) be offered and no other specifically named beverages be offered on the first children’s meal beverage ordering screen (i.e. only initial offering of default beverages). The criteria also concern upcharges and how the purchaser can ‘request’ or access other beverages, which were not written into the law, but are directly related to the law’s intent to reduce children’s SSB consumption by making the healthy choice the easiest choice.
Fig. 2Implementation of California’s Healthy-by-Default Children’s Meal Beverage lawA using progressively more restrictive criteria (n 617 observationsB from four ordering platorms: restaurant-specific (n 221), DoorDash (n 145), GrubHub (n 106) and UberEats (n 145)) Criteria 1, the most lenient criteria (which allows for the most flexibility in the interpretation of the law as it pertains to meals sold online) allows: (a) only initial offering of default beverages, (b) upcharges for the allowable default beverages, and allows for the two ways that sites provided access to other drink options, (c) an unspecified kids’/fountain drink option on the first beverage ordering screen usually with a link or drop-down with other drink options and (d) a link with wording such as ‘other beverages’ to a second ordering screen with additional beverages. Criteria 2 allows for only (a), (c) and (d). Criteria 3 allows for only (a) and (d). Finally, the most strict criteria, Criteria 4 only allows for (a). Because of their limited number, and because not all images (such as those associated with listed beverages options) were assessed, the five observations where images of the children’s meal included a beverage other than the allowable defaults were excluded from this assessment of implementation as were the nine observations that did not include the option to select a beverage.
## Data analysis
Beverage availability, consistency of beverage availability within the same QSR site across all platforms and average beverage upcharge data were calculated using descriptive statistics. The number and proportion of observations by platform type (restaurant-specific v. third-party (DoorDash, GrubHub and UberEats) and extent of implementation based on the four criteria were also calculated. All analyses were performed in Stata/MP v16 (College Station, Texas).
## Results
Most QSR sites had online ordering capabilities on a restaurant-specific platform (n 225; 89 % of QSR sites); followed by UberEats (n 153; 60 %), DoorDash (n 147; 58 %) and GrubHub (106; 42 %).
Seventy percent of observations offered water on either the first or second screen; 62·8 % offered unflavoured milk, 24·3 % offered unspecified milk and 51·7 % offered 100 % fruit juice (Table 2). Overall, 622 observations (99 %) had beverages available on the first beverage ordering screen. Nine observations (1 %) did not enable beverage selection (presumably, only one beverage came, pre-selected, with the children’s meal or a beverage choice was possible at meal pick-up). The most common beverages offered on the first ordering screen were water (70·4 % of observations), unflavoured milk (61·8 %) and 100 % fruit juice (39·8 %). On average, QSR were least consistent in offering water on the first screen across all platforms (only offered consistently for 44·5 % of QSR). Unflavoured milk was offered consistently on the first screen across all platforms for 61·8 % of QSR sites.
Table 2Beverage availability, consistency and pricing with bundled children’s meals available to order online in fast-food restaurants in Supplemental Nutrition Assistance Program Education eligible neighbourhoods in California, (n 631 observations*, 254 restaurants, 13 restaurant chains)Observations that offered beverage with children’s meals on either first or second ordering screen†, (n 631 observations)Observations with beverage available with children’s meal on first ordering screen, (n 631 observations)Restaurants with consistent availability of beverage on first ordering screen across all platforms, (n 254 restaurants)Observations with beverage shown in an image of the children’s meal‡, (n 631 observations)Observations with beverage available with children’s meal on second ordering screen, (n 132 observations)Restaurants with consistent availability of beverage on second ordering screen across all platforms, (n 105 restaurants)Of observations that offered this beverage, number that up-charged for this beverageOf observations that upcharged for this beverage, average price of upchargeOf observations with an upcharge for this beverage, upcharge as average % of total children’s meal costBeverage n % n % n % n % n % n % n %$range%rangeBeverages allowed as defaults by California’s Healthy-by-Default Children’s Beverage law§ Water44570·544470·411344·5528·28665·22019·118341·10·510·10–1·2911·12·0–31·7Milk, unflavoured39662·839061·815761·837659·67859·12826·74210·60·380·04–1·107·40·8–17·3Milk, unspecified|| 15324·315324·315059·19915·700·01051003120·30·270·10–0·695·42·0–17·3Other beverages (not allowable defaults by California’s Healthy-by-Default Children’s Beverage law§)Milk, flavoured22335·315224·120379·900·07153·83432·4104·50·320·10–0·707·31·9–17·3Fruit juice, 100 %32651·725139·816966·510·27556·83028·69228·41·030·20–2·4023·93·2–68·6Fruit juice, diluted16626·39314·724696·900·07355·33230·5137·80·430·20–0·909·54·2–20·1Soda, regular27443·415624·719175·200·011889·41312·400·0Soda, diet27443·415624·719175·200·011889·41312·400·0Soda, unclear|| 91·420·325399·600·086·19792·400·0Other pre-sweetened beverage¶ 27543·615224·119576·800·012393·287·682·90·470·27–0·5310·28·9–11·4Other unsweetened/artificially sweetened beverage** 13020·6121·924395·700·011890·11312·400·0Unspecified fountain/kid’s drink option†† 7111·3497·823190·940·62216·88379·122·80·100·10–0·102·32·1–2·6Nine observations did not allow a beverage choice/selection with children’s meals sold online, but presumably could be selected at pick-up.*For sampled restaurants (n 254), there were four potential ordering platforms sampled per restaurant (restaurant specific platform; DoorDash, GrubHub and UberEats), resulting in a potential four observations/restaurant.†Includes observations from the first screen where beverage selection was available when ordering a children’s meal online, and in some cases, from a second screen if there was an option on the first screen to see additional beverage choices.‡Thirty-six observations did not have an image of the children’s meal that showed a beverage on any of the observed ordering screens.§California Senate Bill (SB) 1192, Healthy-by-Default Children’s Meal Beverage Law, requires restaurants that serve a children’s meal which includes a beverage make the default beverage offered with the children’s meal to be one or more of the following: 1. Water, sparkling water, or flavoured water with no added natural or artificial sweeteners; 2. Unflavoured milk (plain dairy milk); 3. Non-dairy milk alternative (example: almond, coconut or soy milk). It does not prohibit a restaurant’s ability to sell, or a customer’s ability to purchase, an alternative beverage instead of the default beverage offered with the children’s meals, if requested by the purchaser of the children’s meal.||Was not clear by online listing if beverage was flavoured or unflavoured milk or regular or diet soda.¶Other pre-sweetened beverages included sweetened iced teas, sweetened lemonades, sweetened juice drinks and milkshakes.**Other artificially or unsweetened beverages included almost exclusively unsweetened iced teas (98 %), but also included lite lemonade (2 %).††Includes items listed as ‘fountain drink,’ ‘kid’s drink,’ ‘small drink,’ or something similarly non-specific, often with a link or drop-down menu with specific beverage options, including sugar-sweetened beverages.
Only 132 observations (20·9 %) had beverages available on both a first and second screen. The most common beverages offered on the second screen were other unsweetened/artificially sweetened beverages (such as iced tea; 93·2 %), other pre-sweetened beverages (such as lemonade; 92·4 %), regular soda (89·4 %) and diet soda (89·4 %). Water and unflavoured milk were offered on the second screen for 65·2 % and 59·1 % of observations, respectively. Water was offered consistently on the second screen across all platforms for 19·1 % of QSR sites; 26·7 % of QSR sites offered unflavoured milk consistently on the second screen across all platforms.
Among all beverages, water was the most likely to have an additional cost (upcharge); among the 445 observations that offered water, 41·1 % had a water upcharge. Among the 396 observations that offered unflavoured milk, 10·6 % had an upcharge. Of the 275 observations that offered other presweetened beverages 2·9 % had an upcharge for at least one of those drinks. Of the 71 observations that offered an unspecified kids’/fountain drink option, only 2·8 % upcharged for at least one of those drinks. Juice was also frequently upcharged; of the 326 observations that included 100 % juice, 92 (28·4 %) had an upcharge and of the 166 that offered diluted juice 13 (7·8 %) had an upcharge. No observations upcharged for soda (regular, diet or unclear), or other unsweetened/artificially sweetened beverages. The average upcharge cost for water was $0·51, which on average represented an 11 % increase over the total children’s meal cost. On average, 100 % fruit juice had the most expensive upcharge (average of $1·03, representing 24 % of the average total children’s meal cost).
Using the most liberal criteria (Criteria 1), less than half of the observations (38·5 % of restaurant-specific platforms and 41·7 % of third-party platform observations; 40·5 % of all platform observations combined) implemented SB1192 (Fig. 2). If we also consider an upcharge for the allowed default beverages to be inconsistent with SB1192 (Criteria 2), then implementation rates drop considerably to only 14·0 % for restaurant-specific platform observations, 8·8 % for third-party platform observations (10·7 % for all platform observations combined). Disallowing an unspecified kids’/fountain drink option on the first beverage ordering screen (Criteria 3) lowered implementation consistency only slightly compared with Criteria 2. No observed restaurant-specific platform observations and only 8·8 % of the third-party platform observations (5·6 % of observations from all platforms combined) met the most stringent criteria (Criteria 4; only the allowed default beverages were offered on the first beverage ordering screen, with no links to additional options and no upcharges for allowable default beverages).
## Discussion
Based on observations from restaurant websites and three of the most popular online ordering services, most California QSR located in low-income neighbourhoods are not offering children’s meals that are consistent with SB1192, the state’s Healthy-By-Default Children’s Meal Beverage law, thereby diminishing the potential impacts of the legislation in reducing SSB intake among children. Further, additional costs for healthy default beverages (water and unflavoured milk) may also be discouraging families from choosing those beverages with children’s meals. Together, low consistency with SB1192 and more prevalent upcharges for default beverages, coupled with no upcharges for soda or fountain drinks (which often contain sugar), could be mitigating progress that has been made with regards to SB1192 adherence in physical QSR spaces to improve children’s diets.
Prior work examining California QSR beverage offerings with children’s meals before and after implementation of SB1192 demonstrated that the number of in-restaurant and drive-through QSR menus including only law-consistent beverages significantly increased nearly sixfold, from 10 % to 66 %[34]. However, 1-year post SB1192 implementation, one-third of sampled QSR were still not consistent with the law in regard to the menu board and only 1 % were offering beverages during in-person ordering in a manner consistent with the legislation. Interestingly, the same study found offerings on menus/menu boards did not change after similar legislation was passed in Wilmington, Delaware. This could be because no restaurant managers in Wilmington reported knowing about the law, compared with nearly one-third of California managers; or, it could be because a smaller proportion of Wilmington restaurants were chains and chains might be expected to have increased awareness and more systematic implementation of applicable legislation[34]. Online ordering platforms, however, were not examined and to our knowledge there is no published literature on the implementation of SB1192 or similar legislation on online ordering platforms.
The text of SB1192 lacks clear and specific language in several regards. First, it does not mention online ordering platforms, a concerning omission given the rise in online and on-site kiosk ordering in restaurants, which accelerated during COVID-19 restrictions beginning in March 2020. Second, no specific reference is made to upcharges for default beverages. And third, it is not clear how online platforms could present only the allowable defaults beverages while also allowing customers to request other beverage options. Given this lack of clarity in legislative language, we examined several increasingly restrictive criteria for implementation consistency with SB1192. Implementation was low (under 41 %) regardless of the criteria used, and fewer than 6 % of observations had only water or unflavoured milk available on the first online ordering screen where beverages can be chosen, with no upcharge for those beverages.
In this study, while water, unflavoured milk and 100 % fruit juice were the most frequently offered beverages on the first online ordering screen, these beverages were also the most likely to be available only at an additional cost. When there was an upcharge, choosing water with a children’s meal increased the cost of the meal by 11 %; for unflavoured milk, by 7 %. In contrast, no observations upcharged for soda (regular and diet) or fountain drinks, and very few (< 3 %) upcharged for other pre-sweetened beverages.
Upcharges for the allowable default beverages are clearly contrary to the intent of SB1192 because the upcharges likely discourage those selections among price-conscious consumers. Prior evidence shows that pricing impacts beverage choice[30], with studies conducted in stores demonstrating that cost, in combination with promotion and prompting, effectively impacts purchasing behaviour[31]. Price is an especially important factor for low-income consumers, who are significantly more conscious of cost and value than higher income consumers[38]. Higher costs for healthy default beverages sold online from QSR in low-income, majority Latinx neighbourhoods, coupled with no additional cost for SSB such as soda and pre-sweetened drinks, likely discourages consumers from making healthier selections with children’s meals sold online. Yet, this study found that 41 % of observations that offered water had an upcharge for water, and 10 % that offered unflavoured milk, and 20 % that offered an unspecified milk option, upcharged for those beverages. These upcharges not only undermine the intent of SB1192, but also likely contribute to persistent disparities in SSB consumption between lower income and higher income youth and among children of colour compared with non-Hispanic White children[39].
These findings suggest that restaurants were unaware of the legislation or uncertain as to whether, and how, to apply the mandate to online ordering; 59·5 % of the observations included options that were clearly not compliant such as SSB, artificially sweetened beverages or unsweetened tea (Fig. 2, Criteria 1). Some (nearly 8 %) had the option to choose a ‘fountain’, ‘kids’, or ‘small drink’ that often included a drop-down or link to a list that included options other than the allowable default beverages. In many instances, there was an equally prominently displayed option to click on a link to see ‘more beverages.’ This link sometimes included a photo of beverages that were clearly neither water nor unsweetened milk. One could argue whether listing these more generic options in this manner is consistent with the legislation. However, the effort involved in one click may not be a sufficient deterrent to selecting unhealthy options. Including a more inconspicuous link without photos or suggestive language as to the nature of other beverage options would be more consistent with the intent of this law.
These findings suggest the need to provide clarification to, and education for, those responsible for implementing SB1192 at QSR. Local and state agencies and their partners could work with restaurants and online ordering platforms to ensure complete implementation in a way that is most likely to reduce youth SSB consumption (the intent of the law). Changes at the restaurant chain or online platform level could impact not only the QSR and patrons in this sample, but restaurants and customers across the state. Otherwise, the opportunity presented by this legislation to influence the choices and preferences of young children will likely go largely unrealised. For those contemplating similar legislation, our findings suggest that they would be well-advised to include more specific language regarding online ordering and explicitly prohibit upcharges for allowable default beverages. No legislation can ever fully anticipate new developments or cover all possible scenarios in detail. Therefore, work by local authorities and community partners may be needed to ensure policies are fully implemented to maximise the benefit for the populations they are meant to protect and to reduce disparities.
Several study limitations necessitate mention. First, SB1192 does not contain clear language pursuant to beverages included with children’s meals sold online, making adherence to the law subject to interpretation. Second, we do not have data on QSR’ online children’s meals offerings prior to implementation of SB1192, precluding a pre-/post-examination of change. Collecting this data again, as online ordering and ordering by scanning Quick Response (QR) codes on personal devices inside of restaurants continues to increase in popularity, would provide additional important evidence on changes in consistency with SB1192 implementation over time. Third, we were not able to examine trends in consumer purchasing, nor study whether consistency with SB1192 impacted QSR’ bottom lines. However, evidence suggests that healthy default beverages are acceptable to both parents and children and do not decrease sales(33,40–48). In fact, offering healthier options at QSR does not negatively affect corporate performance[40,46] and may even have positive financial impacts[29,49]. Finally, our study sample includes QSR in low-income California neighbourhoods that were able to stay open during the COVID-19 pandemic, which could impact generalisability of these findings to QSR in higher income neighbourhoods or in other states.
For any legislation to have the intended effect, it is necessary for the legislation to be implemented. The intent of SB1192 is to reduce the consumption of SSB among young children[21] by making healthy beverage choices (i.e. unsweetened water and milk or milk alternatives) the easy (i.e. default) option when ordering a restaurant children’s meal. The strength of this behavioural economics approach is that consumer education is not necessary to influence behaviour because conscious effort on the part of the consumer is not involved[50]. In addition, the purchaser must exert effort to select an unhealthy beverage option[50]. On average, QSR located in low-income California neighbourhoods are not offering beverages with children’s meals sold online in a way that’s consistent with the state’s Healthy-By-Default Children’s Meal Beverage law, thereby diminishing the potential impacts of the legislation in reducing SSB intake among children. As web-based ordering, ordering at mobile kiosks and ordering by scanning QR codes on personal devices inside of restaurants (rather than with a person at the counter) become increasingly common, further work to ensure QSR are offering healthy default beverages with children’s meals sold online is warranted.
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|
---
title: 'Health behaviours as predictors of the Mediterranean diet adherence: a decision
tree approach'
authors:
- Joana Margarida Bôto
- Ana Marreiros
- Patrícia Diogo
- Ezequiel Pinto
- Maria Palma Mateus
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991829
doi: 10.1017/S1368980021003293
license: CC BY 4.0
---
# Health behaviours as predictors of the Mediterranean diet adherence: a decision tree approach
## Body
Dietary habits changed dramatically in modern times due to globalisation[1]. Although an abundance of food is now constantly available in developed countries, the supply of energy-dense but nutritionally poor food products has contributed to a deterioration in the quality of dietary and health habits[2]. As a consequence, the prevalence of several non-communicable diseases associated with an unbalanced diet, such as obesity, diabetes, CVD and hypertension, has increased[3]. It is estimated that non-communicable diseases account for 70 % of worldwide deaths every year[4]. Within the WHO European Region, approximately 89 % of deaths are attributed to non-communicable diseases, and one-third of these deaths occur pre-maturely (between the ages of 30 and 69 years)[5]. The onset of these diseases is currently occurring earlier in life[6]. Inadequate nutrition is related with a higher prevalence of obesity in children and adolescents, and childhood and adolescence obesity has become a major global public health concern, following rapid increases in many parts of the world in recent decades[7].
The Mediterranean diet (MD), considered as an healthy lifestyle model that contributes to preventing and reducing obesity[8,9], was recognised by UNESCO as a cultural heritage of humanity[10], and the Mediterranean food pattern is a nutritional model of excellence, scientifically reported as healthy(11–13) and sustainable[14]. Nutritionally, this food pattern is low in saturated fats, high in antioxidants, fibbers and monounsaturated fats, exhibits an adequate n-6/n-3 fatty acid balance, and is a source of phytosterols and probiotics[15,16]. Although several studies have linked adherence to the MD with a higher degree of protection against all-cause mortality, highlighting its protective role against the development of diseases(17–20), adherence to MD has decreased as a result of the incorporation of Western country habits, following globalisation[21]. The decreased adherence to MD observed in Mediterranean populations is also occurring in Portugal[22].
Among children and adolescents, epidemiological evidence suggests that dietary patterns in Mediterranean countries are changing rapidly, with an increase in the consumption of animal products, saturated fat, highly processed/energy-dense foods, ready-to-eat products and a decline in the intake of plant-based foods(23–25). Adolescence is a key period in life, and it involves multiple physiological and psychological changes that affect nutritional needs and habits[26]. Adolescents have increased energy and nutrient needs, including amino acids for growth of striated muscle, as well as Ca and vitamin D to accommodate bone growth[27,28]. Adherence to MD, it is associated with an enhanced nutritional adequacy[11] and is also recognised for preventing overweight, obesity[29] and in reducing waist circumference in adolescents[30,31] In Portugal, the latest data from the National Food and Physical Activity Survey, IAN-AF 2015–2016, reports that 78 % of adolescents (aged between 10 and 17 years) does not meet the WHO recommendation to consume more than 400 g/d of fruit and vegetables (equivalent to five or more servings/d). The daily consumption of more than 50 g of processed meat is observed in 6·3 % of the population (11·6 % in adolescents). Only 36 % of people between 15 and 21 years of age are considered physically active, complying with the current recommendations for the practice of ‘health-promoting physical activity’[32].
Machine learning is a method focused on the development of algorithms that are particularly useful for data mining[33]. Data mining is a term to describe the process of analysing large databases in search of interesting and previously unknown patterns[34]. Decision tree methodology is a commonly used data mining method to establish classification systems based on multiple covariates or for developing prediction algorithms for a target variable. The algorithm is non-parametric and can efficiently deal with large, complex datasets without imposing a complicated parametric structure[35]. Decision tree structure comprises a hierarchically organised set of data groups, called nodes, which are interconnected by tree branches. The beginning of the hierarchical structure is the root of the tree (i.e. root node). It refers to the dependent variable and includes all the observed data that are to be divided into classes during the process of model development[36]. Machine learning uses the combination of artificial intelligence algorithm with statistical analysis for decision-making, which can be useful to support dietary behaviours understanding and improvement.
This study aims to identify which health behaviours contribute to a higher adherence to MD and provide a framework for the development of food education interventions aimed at adolescents through decision-making processes using classification trees.
## Abstract
### Objective:
This study aimed to identify health behaviours that determine adolescent’s adherence to the Mediterranean diet (MD) through a decision tree statistical approach.
### Design:
Cross-sectional study, with data collected through a self-fulfilment questionnaire with five sections: [1] eating habits; [2] adherence to the MD (KIDMED index); [3] physical activity; [4] health habits and [5] socio-demographic characteristics. Anthropometric and blood pressure data were collected by a trained research team. The Automatic Chi-square Interaction Detection (CHAID) method was used to identify health behaviours that contribute to a better adherence to the MD.
### Setting:
Eight public secondary schools, in Algarve, Portugal.
### Participants:
Adolescents with ages between 15 and 19 years (n 325).
### Results:
According to the KIDMED index, we found a low adherence to MD in 9·0 % of the participants, an intermediate adherence in 45·5 % and a high adherence in 45·5 %. Participants that regularly have breakfast, eat vegetable soup, have a second piece of fruit/d, eat fresh or cooked vegetables 1 or more times a day, eat oleaginous fruits at least 2 to 3 times a week, and practice sports and leisure physical activities outside school show higher adherence to the MD ($P \leq 0$·001).
### Conclusions:
The daily intake of two pieces of fruit and vegetables proved to be a determinant health behaviour for high adherence to MD. Strategies to promote the intake of these foods among adolescents must be developed and implemented.
## Sampling and participants
This study focused on tenth-grade students, recruited through a randomised, multi-stage, stratified sample of schools, constructed from a sampling frame comprising all secondary schools in the region of the Algarve, Portugal. After following the procedures proposed by Fleiss et al. [ 37] for calculating minimum sample size, with 90 % power and 0·05 statistical significance, and considering official data provided by the Regional Directorate of Education regarding the number of registered tenth-grade students in the Algarve, we selected, in the first stage, a random sample of eight schools, stratified by their type – science or humanities curriculum schools and technological-professional schools; in the second stage, we randomly selected fourteen classes from science or humanities curriculum and nine classes from technological-professional schools. All students from these classes were invited to be a part of the study and a formal, written consent, was sent to their legal guardians.
The calculations for sample size suggested a minimum of 454 participants to achieve representativity. The classes randomly selected for recruitment had a total of 545 enrolled students. From the original sample of 545 students, 325 (59 6 %) were authorised by their guardian to be a part of the study and agreed to proceed to the data collection phase. Exclusion criteria were [1] pregnancy and [2] physical disability, mental illness, or other condition that affected the ability to fulfil the data collection questionnaire or the validity of anthropometric measurements. Data collection was conducted from April to June 2018. No participants were excluded on account of these criteria.
## Data assessment and variables
Data were collected through a self-fulfilment questionnaire regarding food, lifestyle and health. This tool has five sections: [1] dietary habits; [2] adherence to the MD (KIDMED index); [3] physical activity; [4] sleep and oral hygiene; and [5] socio-demographic characteristics. Except for section 2, the questions were developed specifically for this study, based on best nutritional epidemiology practices and also on national and international tools, namely the data collection questionnaires and protocols used in the Health Behaviour in School-Aged Children (HBSC)[38], Childhood Obesity Surveillance Initiative (COSI)[39], and the Portuguese National Food, Nutrition and Physical Activity Survey (IAN-AF)[40]. These tools were consistently found reliable in diverse settings and different populations.
The data collection tool was pre-tested in a homologous, non-random sample of 22 volunteers that were debriefed after fulfilling the questionnaire. This pre-test resulted in small edits and minor corrections to the questionnaire that were incorporated in the final version used in this study.
Anthropometric and blood pressure data were also collected, using procedures documented in a field manual and after specific training for all members of the team. Data included weight, height, waist and hip circumference, and systolic and diastolic blood pressure.
In all questions regarding dietary habits, physical activity, sleep and oral hygiene, participants were asked to consider the last 12 months as a frame of reference for their answers.
## Dietary habits
Dietary habits were assessed through close-ended questions, either multiple choice, ‘yes/no’ questions, Likert-type sentences for assessing agreement or questions for quantification (e.g. number of daily meals). As the goal for this assessment was to determine specific dietary habits, to be analysed per question and not by the means of a composite scale or index, questions were focused on partaking specific meals, their setting (e.g. ‘Do you usually have breakfast?’; ‘ Where do you usually have breakfast?’ and ‘Who usually has breakfast with you?’) and the usual foods included in each meal, selected from a list and with the ability of participants adding their own foods. The adoption of a specific kind of diet (vegan, meat-free, Halal, etc.) and the amount of water usually ingested during the day were also assessed with multiple choice questions, with the ability of participants adding other answers. The intake of alcoholic beverages was assessed using a standard, semi-quantitative, food frequency scale.
## Adherence to the Mediterranean Diet
The adherence to the MD was assessed with the Portuguese version of the KIDMED index proposed by Mateus, MP[41]. The score for this index ranges from 0 to 12 and it is based on 16 ‘yes/no’ questions. Questions denoting a negative connotation with the MD are assigned a score of -1 and those positively associated with the MD are assigned a score of +1. The sum of the scores is then categorised in three levels: level 1 – high adherence to MD (≥ 8 points); level 2 – intermediate adherence to MD (4–7 points) and level 3 – low adherence to MD (≤ 3 points), as proposed by the original authors of the KIDMED index[42,43].
## Physical activity, sleep and oral hygiene
Physical activity was assessed through nineteen multiple choice questions, aimed at gaining information regarding the usual means of transportation to and from school, the distance between home and school, the type of physical activity in school hours and as a form of leisure. We did not intend to quantify physical activity but instead identify participants who engage in any kind of physical activity and identify the usual forms of physical activity.
Participants were also asked to about their usual time of waking up, usual time of going to bed and usual frequency of brushing their teeth.
## Socio-demographic variables and anthropometrics
For the socio-demographic characterisation, we collected information regarding date and place of birth, gender, nationality, area of residence, as well as household composition, age, nationality, place of birth, education and profession of parents/guardians.
Anthropometric measurements were made using reference methodologies[44] by trained researchers. Weight was assessed to the nearest decigram (0·1 kg) with a Seca 877® digital scale. Height was measured to the nearest millimetre (0·1 cm) with a portable Seca 217® stadiometer, with the participant’s head in the Frankfurt plane. Weight and height were measured twice, with participants in bare feet and light clothes. The waist circumference was measured up to the nearest millimetre (0·1 cm), at the midpoint between the iliac crest and the lower costal margin, using a flexible and non-deformable Teflon tape. BMI was calculated using the formula Weight/Height2 (kg/m2). The BMI was used to determine the BMI/age percentile, and participants were categorised as underweight (≤5th percentile), normal weight (>5th to ≤85th percentile), overweight (>85th percentile) and obese (≥95th percentile), according to the WHO growth standards for children and adolescents[45].
## Statistical analysis
IBM-SPSS® software version 25.0 was used for statistical analysis. Data were described by absolute and relative frequencies, and mean, median (MD), standard deviation and interquartile range were computed whenever appropriate. Data were normalised by logarithmic or arcsine transformation when results were expressed as percentages. To evaluate the impact of MD adherence on each gender, while considering anthropometric data and health behaviours, we performed a one-way ANOVA multiple comparison test (Tukey, $P \leq 0$·05). For the variables that did not follow a normal distribution, non-parametric tests were computed, such as Kruskal–Wallis, Chi-square, Mann–Whitney and Spearman’s correlation coefficient ($P \leq 0$·05).
We performed a machine learning procedure as a complement to other statistical analyses in order to further study the relationship between adherence to MD and other variables. Since the variables are potentially correlated with each other, a decision tree was applied through the algorithm CHAID (Automatic Chi-square Interaction Detection)[46]. These tree models classify cases into groups or predict values of a dependent variable (criterion), based on values or categories of the independent variables (predictors). The criterion used was the classification of individuals having low, intermediate or high adherence to MD in relation to the following factors: dietary habits; items of KIDMED index; sleep and oral hygiene; school sports; out of school sports; leisure physical activity; daily hours of sedentary activities; socio-demographic data; and anthropometric and blood pressure measures. A maximum tree depth of three levels was generated by the algorithm with a minimum number of fifty initial (parent) nodes, thirty terminal (child) nodes, and Splitting *Nodes criteria* of 0·05. The algorithm was able to differentiate the groups with low, intermediate or high adherence to MD obtained by KIDMED index categories (dependent variable in node 0). All other questionnaire variables were considered independent variables. Variables were not present in the decision tree if a regression could not be generated by the algorithm; therefore, no homogenous groups were formed, and the variable was not represented in the decision tree (e.g. sleep duration). Therefore, when the algorithm can generate a new level of ramification from a node, it suggests that the new groups formed have significantly different levels of adherence to MD.
## Sample characteristics
The participants in our study (n 325) were between 15 and 19 years old, with a mean age of 16·4 years (sd 0·89). Fifty-three per cent (n 172) reported as female and 47 % (n 153) as male.
Regarding BMI/age percentile categories, 79·4 % (n 258) of the participants had normal weight and 19·7 % (n 64) were overweight. Girls had higher mean values for hip circumference than boys (Table 1). Boys were taller, heavier, with higher waist circumference and systolic blood pressure than girls (Table 1). Table 2 shows data on dietary habits. Boys reported a higher frequency for breakfast and after dinner meals ($P \leq 0$·05); girls showed a higher frequency of mid-morning meals, higher consumption of salad, vegetables and fruit at lunch and dinner, and higher median number of intermediate meals ($P \leq 0$·05). Bread is the most consumed food item at breakfast, mid-morning and at mid-afternoon meals, in both genders (Table 2).
Table 1Anthropometric and blood pressure characteristics of the sampleTotal (n 325)Female (n 172)Male (n 153) P valueM sd MDIQRM sd MDIQRM sd MDIQRWeight (kg)60·311·1358·666·3–51·856·79·9253·863·2–49·764·311·163·369·4–55·8 <0·001 Height (m)1·660·0861·661·72–1·601·610·0621·601·64–1·571·720·0671·721·76–1·68 <0·001 BMI/age percentile54285677–3156266077–3451295174–260·122Body weight status* Underweight0·718 n 312 %0·9 %0·6 %1·3 % Normal weight n 258139119 %79·4 %80·8 %77·8 % Overweight n 562927 %17·2 %16·9 %17·6 % Obese n 835 %2·5 %1·7 %3·3 %Waist circumference (cm)71·78·77075·3–66707·836973–6573·69·257278–67 <0·001 Hip circumference (cm)93·97·739398·8–8994·77·9393·599–9092·97·49297–88 0·017 Systolic BP (mmHg)11612116124–10911412114120–10712012119125–112 <0·001 Diastolic BP (mmHg)7087075–667087076–667097075–650·361BP, blood pressure; M, mean; MD, median; IQR, interquartile range.*Presented as absolute (n) and relative frequency (%).P values for gender differences computed using the Chi-square test for body weight status; Mann–Whitney’s test was used for all other comparisons; statistically significant differences ($P \leq 0$·05) are presented in bold.
Table 2Dietary characteristics of the sample and according to genderTotalFemaleMale P value n % n % n %Meals/d (n 325) Breakfast28286·814383·113990·8 0·041 Mid-morning23271·413477·99864·1 0·006 Lunch325100·0172100·0153100·0– Afternoon29691·116093·013688·90·192 Dinner32198·816998·315299·30·373 After dinner meal13140·35532·07649·7 0·001 Number of meals/d* MD5·05·05·00·275 IQR6–45–46–4Number of intermediate meals/d* MD2·02·02·0 0·005 IQR2–12–12–1The five most consumed foods at Breakfast (n 282) Bread21275·210573·410777·00·490 Milk21174·89667·111582·7 0·003 Cereals19368·49365·010071·90·212 Butter, margarine15956·48156·67856·10·929 Fruit15354·38760·86647·5 0·024 Mid-morning (n 232) Bread16470·79470·17071·40·833 Cakes, cookies12955·67757·55253·10·505 Varied pastry10444·86548·53939·80·189 Charcuterie10444·86246·34242·90·606 Butter, margarine9942·75541·04444·90·558 Afternoon (n 296) Bread24181·412880·011383·10·463 Fruit17659·510565·67152·2 0·019 Butter, margarine16555·78955·67655·90·965 Cakes, cookies16254·78955·67353·70·737 Charcuterie15753·09056·36749·30·230Food consumed at Lunch (n 325) Vegetable soup (n 325)15948·99354·16643·1 0·049 Salad (n 297)23478·513181·910374·60·129 Cooked vegetables (n 297)18261·110068·87252·2 0·003 Pulses (n 297)17358·310666·38561·60·403 Sandwich (n 325)10030·85230·24831·40·824 Dessert (n 325)19660·39555·210166·0 0·047 Fruit16584·28589·58079·2 0·049 Sweet3115·81010·52120·8 0·049 Beverage (n 325)28888·614886·014091·50·122 Water27996·914497·313596·40·672 Soft drinks13145·55839·27352·1 0·027 Fruit nectars12543·45637·86949·30·050 Beer1495·100·01410·0 <0·001 Dinner (n 321) Vegetal soup (n 321)15648·68751·56945·40·276 Salad (n 293)23379·513184·010274·5 0·044 Cooked vegetables (n 293)19265·511573·77756·2 0·002 Pulses (n 293)16355·610164·78662·80·726 Sandwich (n 321)309·3137·71711·20·283 Dessert (n 321)20262·910461·59864·50·587 Fruit15577·58480·87174·00·249 Sweet4522·52019·22526·00·249 Beverage (n 321)27886·614384·613588·80·270 Water26495·013695·112894·80·912 Soft drinks11641·75135·76548·1 0·035 Fruit nectars10638·14632·26044·4 0·035 Beer93·21.0785·9 0·014 *Presented as median (MD) and interquartile range (IQR). P values for gender differences computed with Mann–Whitney’s test for number of meals/d and number of intermediate meals/d; The Chi-square test was used in all other comparisons; statistically significant differences ($P \leq 0$·05) are presented in bold.
Table 3 presents data regarding physical activity. On the overall, when not at school, boys spend more time in sports activities than girls.
Table 3Participants’ sports activities and sedentary hours during the weekTotalFemaleMale P value n % n % n %School sport (n 325) No29390·215690·713789·5 0·727 Yes329·8169·31610·5Number of weekly hours (n 32) 1 h825·0637·5212·5 0·034 2 h1031·3743·8318·8 3 h515·600·0531·3 4 h515·616·3425·0 + 5 h412·5212·5212·5Non-academic sport (n 325) No14544·69756·44831·4 0·011 Yes18055·47543·610568·6Number of weekly hours (n 180) 1 h105·668·143·8 0·011 2 h4022·32432·41615·2 3 h2815·61216·21615·2 4 h2514·056·82019·0 + 5 h7642·52736·54946·7Leisure sports activities (n 325) No13040·08247·74831·4 0·003 Yes19560·09052·310568·6Number of weekly hours (n 195) 1 h5829·93236·02624·8 0·011 2 h5528·42730·32826·7 3 h4020·61719·12321·9 4 h2110·81112·4109·5+ 5 h2010·322·21817·1Total hours in the week watching television/computer/videogames (n 325) < 3 h4814·83520·3138·5 <0·001 3 to 6 h17553·810359·97247·1 7 to 9 h6820·92212·84630·1 ≥ 10 h3410·5127·02214·4 P values for gender differences computed with Chi-square test; statistically significant differences ($P \leq 0$·05) are presented in bold.
## Mediterranean diet and health behaviours
The distribution of the KIDMED index categories shows a low adherence to MD group in 9·0 % (n 29), intermediate adherence in 45·5 % (n 148) and high adherence in 45·5 % (n 148) of the participants (Table 4). We did not find gender differences in adherence to MD ($$P \leq 0$$·306), but some diet and health behaviours were positively associated with MD. Participants that regularly have breakfast, eat vegetable soup at lunch or dinner, and practice sports and leisure physical activities outside school show a higher median score in the KIDMED index ($P \leq 0$·001) (Table 5).
Table 4Categories of adherence to the Mediterranean Diet of the sampleTotal (n 325)Female (n 172)Male (n 153) P value n % n % n %Low adherence298·91911·0106·50·306Intermediate adherence14845·57443·07448·4High adherence14845·57945·96945·1 P value for gender differences computed with the Chi-square test.
Table 5KIDMED index categories in relation to specific health behavioursTotal (n 325)Low adherence (n 29)Intermediate adherence (n 148)High adherence (n 148)KIDMED score P value n % n % n % n %MDIQRBreakfast No4313·21655·22214·953·446–3 <0·001 Yes28286·81344·812685·114396·689–6Vegetable soup No13140·32275·97248·63725·068–5 <0·001 Yes19459·7724·17651·411175·088–6Sport outside the school No14544·62482·87047·35134·568–4 <0·001 Yes18055·4517·27852·79765·589–6Leisure physical activity No13040·02379·36644·64127·768–5 <0·001 Yes19560·0620·78255·410772·388–6MD, median; IQR, interquartile range. P values for comparisons in KIDMED score between ‘No’ and ‘Yes’ groups computed with Mann–Whitney’s test; statistically significant differences ($P \leq 0$·05) are presented in bold.
Further analyses using the KIDMED index score show a positive correlation between this variable and the number of daily meals (r spearman = 0·195, $P \leq 0$·001) and a negative correlation with sedentary hours per week (r spearman =-0·175, $$P \leq 0$$·002). No correlations between KIDMED index score and anthropometric characteristics or blood pressure were found ($P \leq 0$·05).
It is noteworthy that participants with a high adherence to MD usually consume a second piece of fruit daily (80·5 %, n 99), fresh or cooked vegetables once (59·4 %, n 133) or more than once (70·2 %, n 99) daily, and consume nuts at least 2 to 3 times a week (73·2 %, n 71) (Table 6).
Table 6KIDMED index items according to score categories of adherence to Mediterranean dietTotal (n 325)Low adherence (n 29)Intermediate adherence (n 148)High adherence (n 148) P value n % n % n % n %KIDMED index items *Takes a* fruit or fruit juice every day22368·6104·57433·213962·3 <0·001 *Has a* second fruit every day12337·800·02419·59980·5 <0·001 Has fresh or cooked vegetables regularly once a day22468·973·18437·513359·4 <0·001 Has fresh or cooked vegetables more than once a day14143·421·44028·49970·2 <0·001 Consumes fish regularly (at least 2–3 times/week)20964·331·48641·112057·4 <0·001 Goes more than once a week to a fast-food (hamburger) restaurant4915·1714·33163·31122·4 0·002 Likes pulses and eats them more than once a week20964·331·47435·413263·2 <0·001 *Consumes pasta* or rice almost every day (5 or more times/week)23772·9218·910544·311146·80·733 Has cereals or grains (bread, etc.) for breakfast27283·7165·912044·113650·0 <0·001 Consumes nuts regularly (at least 2–3 times/week)9729·800·02626·87173·2 <0·001 Uses olive oil at home30894·8247·813844·814647·4 0·001 Skips breakfast4012·31640·02152·537·5 <0·001 *Has a* dairy product for breakfast (yoghurt, milk, etc.)26982·8145·212345·713249·1 <0·001 Has commercially baked goods or pastries for breakfast7222·21013·93548·62737·50·131 Takes two yoghurts and/or some cheese (40 g) daily13040·032·35038·57759·2 <0·001 Takes sweets and candy several times every day5316·3917·03056·61426·4 0·003 KIDMED index score* MD7369– IQR8–53–17–510–8*Presented as median (MD) and interquartile range (IQR).P values for group differences in KIDMED categories according to specific dietary habits computed Chi-square tests; statistically significant differences ($P \leq 0$·05) are presented in bold.
No statistically differences between gender and adherence to the MD were found when the information is considered altogether (Table 1). However, due to the thoroughly investigated metabolic differences between genders, data were split, and an ANOVA test was performed in each gender separately by level of MD Adherence (Fig. 1), which clearly reveals significant differences in the effect of MD adherence in the analysed parameters in each gender. Males with low adherence to MD had significantly higher waist and hip circumference (Fig. 1(c) and (d)). Anthropometric variables and MD were not associated in girls ($P \leq 0$·05), but girls with high adherence to the MD showed significantly higher healthy behaviours, such as sports outside school and leisure activities (Fig. 1(e) and (f)). In both genders, higher adherence to MD (intermediate and high) is associated with higher adherence to sports outside school and leisure activities (Fig. 1(e) and (f)).
Fig. 1Effect of different levels of adherence to the MD on anthropometric measures and health behaviours (non-academic sports and other physical activities) in boys and girls. The values plotted in bars represent Mean ± sd for the graphic with anthropometric data and percentages for the graphic with physical activities. Different letters on the bars indicate significant differences (one-way ANOVA, post hoc SNK $P \leq 0$·05) between the Low (L), Medium (M) and high (H) adherence groups analysed in each gender independently To further analyse the association between adherence to MD and health behaviours, all of the variables related to health behaviours (dietary habits, sleep and oral hygiene, physical activity, socio-demographic data, anthropometric measures and blood pressure) were used in a decision tree analysis through the CHAID method (Fig. 2). This model translated 72 % of correct ratings, which reveals predictive robustness. Considering the categories of the KIDMED index (dependent variable), dietary habits were the only factors that discriminate the categories of adherence to the MD (Fig. 2). The CHAID nodes showed that the daily intake of a second piece of fruit (node 2) is more prevalent in participants with a high adherence to MD (80·5 %, n 99). All terminal nodes (6, 12 and 13) derived from node 2 showed a higher prevalence of individuals with high adherence to the MD. The intake of nuts at least 2 to 3 times/week (node 6) and the intake of fresh or cooked vegetables more than once a day (node 13) showed a prevalence of 94·3 % (n 50) and 88·3 % (n 30), respectively, in participants in high adherence to MD. In the absence of the daily intake of a second piece of fruit, only participants who simultaneously consume fresh fruit for breakfast and vegetable soup for lunch (64·5 %, n 20) show high adherence to MD (node 9). In the remaining terminal nodes, participants who do not consume a second piece of fruit daily, despite other intakes, show intermediate adherence to the MD (Fig. 2). The terminal nodes obtained by the decision tree analysis through CHAID are summarised in Table 7.
Fig. 2Decision tree obtained through CHAID method to predict which health behaviours contribute to better adherence to the MD in secondary school students. Statistical significance is represented in each tree node, when the tree ramification stops no significant differences are observed within the group. Each node is divided into a group with a significantly higher presence of the prementioned characteristic (e.g. consumption of a second piece of fruit every day) referred as ‘Yes’ or significantly lower presence of individuals with the same characteristic referred as ‘No’. When the tree does not grow from a terminal or a characteristic is not mentioned, means that there are no statistical differences among the analysed categories of KIDMED. Node 5 <absent> represent the individuals that eat a second piece of fruit daily but do not take breakfast Table 7Decision rules for the prediction of high adherence to MD and KIDMED index average scoreNodeIndependent variablesProbability of high adherence to MD (%)KIDMED index average scoreABCDEF6 Yes. Yes... 94·3 9·5313 Yes. No..No 83·3 8·819No Yes. Yes.. 64·5 7·5812 Yes. No..No 55·9 7·2410NoNo..Yes.32·06·828NoYes. No..28·66·2911NoNo..No.5·54·75A – consumption of the second piece of fruit every day; B – fruit consumption at breakfast; C – oleaginous fruits consumption at least 2 to 3 times/week; D – soup consumption at lunch; E – pulses consumption more than once a week; F – consumption of fresh or cooked vegetables more than once a day.
## Discussion
Our data show a lower prevalence for overweight (19·7 %) and obesity (2·5 %) than the data reported in the Portuguese national inquiry on food and physical activity, IAN AF 2015–2016, which suggests a national prevalence in adolescents of 23·6 % for overweight and 8·7 % for obesity[32]. On the overall, our sample showed an intermediate and high adherence to the MD, a characteristic that is associated in the literature with better nutritional adequacy[11], lower rates of overweight, obesity[29] and lower waist circumference in adolescents[30,31]. Our data and the cross-sectional nature of this study do not allow the analysis of cause and effect, but our results add to the body of evidence suggesting a positive statistical association between the MD and health.
Some of the consequences of obesity are gendered, with some evidence that the risk of mortality is higher in adult males who were obese during their adolescence[47]. Male adolescents with low adherence to MD had significantly higher waist and hip circumference ($P \leq 0$·001). As waist circumference provides a simple and practical anthropometric measure to assess central adiposity[48], these data suggest that, in our sample, heavier males have a higher abdominal adiposity and may also suggest that low adherence to a healthy diet can lead to an early onset of overweight and increase the cardiometabolic risk in male adolescents[49]. Voltas et al. [ 50] have found no relationship between anthropometric data and MD adherence during adolescence and state that this association may be reported later in life due to the low adherence to MD that may lead to negative anthropometric effects in the long term, such as higher BMI[50]. On the other hand, the MediLIFE Index study suggests that a better adherence to a Mediterranean lifestyle was associated with lower likelihood of being overweight, obese or having abdominal obesity, particularly in those with the highest adherence[51].
Physical activity must also be considered in interpreting these data. In our study, participants with higher adherence to MD (intermediate and high) were more active and these results are in accordance with other studies carried out in southern Italy and Spain, which found that greater adherence to MD was associated with a healthier lifestyle and a higher level of physical activity in adolescents[52,53].
Our results reinforce the role of adequate nutrition and regular physical activity for preventing obesity and shaping the health-related quality of life in adolescents, especially when most recently reviewed studies conducted in southern European countries report that approximately half the children and adolescents show a low adherence to MD[54].
Our study showed low adherence to MD in 9·0 % of individuals, higher than the value found by Adelantado-Renau et al. in Spanish adolescents (5·2 %) and, lower than the prevalence found in a study conducted with Sevillian adolescents (16·8 %)[55].
The use of the CHAID method allowed us to find that adherence to MD is more associated with dietary behaviours than all other health behaviours. Our results show that a daily intake of at least two pieces of fruit is the best predictor of ‘high adherence’ to the MD. When a daily second piece of fruit is not consumed, ‘high adherence’ is simultaneously associated with eating fruit at breakfast and vegetable soup at lunch.
Regarding the consumption of fruit, 31·4 % of our sample do not consume fruit daily. This rate is higher than the one found in the HBSC 2018 study, which reports a prevalence of 11·5 % for rarely or never consuming fruit[56], but lower than the prevalence of 78 % for low consumption of fruit and vegetables by Portuguese adolescents, reported in the IAN-AF[32].
The daily consumption of a second piece of fruit, the variable with the highest differentiating power in our CHAID analysis, was reported by 37·8 % of our sample (n 123). This prevalence was lower than the one reported by Rito et al. ( 47·9 %) for the same variable, in another recent study also developed in Portugal[57].
Our study shows that 54·3 % (n 153) of the individuals eat fruit at breakfast and 48·9 % (n 159) eat vegetable soup at lunch. Fruit consumption is one of three elements, together with consumption of cereals and dairy products, that constitute a healthy breakfast[58] which is associated with higher adherence to the MD[59]. Vegetables have a strong presence in MD and vegetable soup, a tradition in Portuguese food, has an important contribution to the daily intake of vegetables[60], allowing to achieve the WHO recommendation for daily consumption of 400 g of fruit and vegetables to help prevent chronic disease and nutritional deficiencies[12].
Our results are in accordance with the ones from other studies, which suggest that the promotion of healthy eating habits among adolescents should be a priority. In a study by Chacón-Cuberos et al. [ 52], adolescents who followed healthy eating habits showed academic benefits, such as better organisational habits, critical thinking, effort and study habits. Boing et al. [ 61] highlight that school environment interventions may be effective for promoting healthy behaviours and reinforce the importance of the school context on adolescent health. School can facilitate the engagement in healthy behaviour by offering and stimulating opportunities to be healthy (e.g. offer healthy options in the cafeteria, etc) and by promoting barriers to unsafe/unhealthy behaviour (e.g. not selling energy-dense foods in the cafeteria)[61,62]. Aarestrup AK et al. [ 63] found that schools that promote barriers to unsafe/unhealthy behaviours, while having teachers and students who value that policy, show higher rates of programme implementation and acceptance for change. In Portugal, the results of the Eat Mediterranean programme showed a better adherence to the MD after an intervention of educational sessions promoting MD, which proved successful in changing dietary patterns among adolescents. This programme showed an increase in the number of individuals who eat a second piece of fruit every day, as well as an increase in intake of fresh or cooked vegetables and oleaginous fruits[57].
## Strengths and limitations of this study
Our aim was to identify behaviours associated with the MD through a decision tree model, in a representative sample of Algarve adolescents. We consider the strength of this work to be the use of the CHAID method and our results suggest that CHAID can be used in risk analysis and target segmentation for the pre-detection and management of low adherence to MD. According to Song and Lu[35] ‘the decision tree method is a powerful statistical tool for classification, prediction, interpretation and data manipulation that has several potential applications in health research’. The use of decision tree models based on machine learning techniques is a powerful and robust statistically tool for big data analysis, and to support decision-making in the health and human nutrition field.
The main limitations of our study are related with the self-report nature of dietary behaviours and with the limited dietary data collected besides the KIDMED index. Our methods for assessing dietary data do not allow for a proper food frequency or quantitative analysis, and we did not collect information regarding other variables widely known to determine food choice in adolescents, such as parental and peer social support, or other home environment variables.
Unfortunately, we also did not achieve sample size representativity. Our calculations for sample size suggested a minimum of 454 participants and 545 students were invited to be a part of the study. We collected written authorisations from legal guardians of the potential participants in a number far exceeding the minimum sample size. Nevertheless, on the day that data collection was scheduled, a significant number of adolescents, despite having written authorisation from their guardian and having previously agreed to be a part of the study, declined the procedure needed to collect anthropometric data. An analysis (not included in this paper) on the socio-demographic characteristics of the students that declined anthropometric assessment, showed that there were no statistically significant differences between these students and those in the final sample. As per our study protocol, students that did not complete the anthropometric assessment were excluded from the final sample, which totalled 325 participants. This can limit the generalisation of our data, but the non-parametric statistical decision tree approach in this paper can safely be used with our final sample size and variables. Furthermore, the similarities in socio-demographic profile between our final sample and both the non-participants and the overall adolescent population in the Algarve (based on census data from the National Statistics Institute, not show, as it is not within the scope of this paper) suggest that, despite the limitation, our data can contribute to an overall assessment of this region’s adolescents.
## Conclusions
The consumption of nuts and fresh or cooked vegetables was associated with a high adherence to the MD. Through the decision tree methodology, the daily consumption of two portions of fruit and vegetables proved to be the best determinant for high adherence to the MD.
Although adherence to a MD can also be influenced by factors such as parental and peer social support, or by the overall healthfulness of home food environment, schools can provide the framework to a healthy, Mediterranean way of eating, if adequate interventions can be put in place.
The use of the CHAID multivariate tree analysis technique, when accompanied by other statistical analyses, is a promising tool in nutrition-related studies.
The *Algarve is* a region with traditionally Mediterranean eating habits, and our results allowed us to identify specific habits of MD adherence that should be encouraged in adolescents. Future interventions can be tailored considering the promotion of the daily consumption of two portions of fruit, and vegetables, in school’s settings, to promote a higher adherence to MD and contribute to improving the health and quality of life of adolescents.
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|
---
title: 'Electoral campaign contributions: an obstacle to sugary drink industry regulation
in Brazil?'
authors:
- Aline Brandão Mariath
- Larissa Galastri Baraldi
- Ana Paula Bortoletto Martins
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991833
doi: 10.1017/S1368980021005036
license: CC BY 4.0
---
# Electoral campaign contributions: an obstacle to sugary drink industry regulation in Brazil?
## Body
There is now a wealth of evidence on the negative impact of sugary drinks on human health. Sugary drink intake has been associated with increased risk of dental caries and tooth decay, weight gain and higher body adiposity, type 2 diabetes, high blood pressure and other health problems(1–5). As a result, it is widely accepted that efforts to tackle obesity and diet-related non-communicable diseases must include government regulation of sugary drink industry activities and practices, particularly because industry’s voluntary self-regulation initiatives have been shown insufficient to achieve this goal(6–10). Frequently recommended public policies include sugary drinks taxation, restrictions to marketing targeted at children, adoption of warning front-of-package nutrition labels in industrialised products, among others(11–15).
However, government regulation expectedly faces strong opposition from the private sector as it might impact production practices, sales and profit[7,16,17]. In order to prevent or postpone regulation, sugary drink industries use a considerable variety of corporate political activity (CPA) strategies and practices. Studies have shown that this industry sector is highly engaged in CPA. Practices such as sponsoring health-related organisations, funding and influencing health-related scientific research, disseminating messages in the media to shift the blame away from the sugary drink industry in the obesity epidemic, casting doubt on scientific evidence linking sugary drinks to obesity and other diet-related non-communicable diseases, using corporate social responsibility actions, lobbying against government regulation and threatening to use legal measures against government regulation have been reported(18–24). CPA strategies and practices of the ultra-processed food and drink industry can be undertaken individually by companies, but when it comes to publicly opposing regulation, the literature has provided evidence that collective action through industry associations is often preferred(17,25–27), as this could help mitigate reputational risks for individual companies.
Despite the recent increase in the research field addressing these issues, especially following the publication of the framework developed by Mialon et al.[16] to identify and monitor the CPA of the ultra-processed food industry, very little is known with respect to electoral campaign contributions, which is a practice included in the ‘Financial incentive’ strategy. This strategy refers to the provision of any gifts, funds or financial incentives to politicians, political parties and other policymakers.
Corporate electoral campaign contributions can be pragmatic, with the aim of pursuing financial returns, or ideological, as a means of influencing election results. Both types of motivation can have political and economic consequences because campaign contributions are acknowledged to facilitate access to elected officials and can be used to influence political decisions in policy-making processes. In fact, campaign contributions are regarded as part of lobbying strategies because they increase the chances of lobbying success[28,29].
In this paper, we aim to assess corporate electoral campaign contributions from industries related to sugary drinks production to the candidates to the 55th Congress (February 2015 to February 2019) of the Chamber of Deputies, Brazil, as well as the characteristics of the elected officials financed by the sector. This research represents a unique opportunity to look into this corporate political practice in detail in the country because corporate campaign contributions – which in the 2014 election cycle represented 75 % of total electoral funds(30–32) – have been forbidden since September 2015[33]. In addition to that, contributions from food and drink companies were the second highest prevalent in this election, running behind only the contributions from construction companies[34].
## Abstract
### Objective:
To assess corporate electoral campaign contributions from industries related to sugary drinks production and the characteristics of the elected officials financed by the sector.
### Design:
Cross-sectional analysis of electoral campaign contributions from corporations related to sugary drinks production (sugary drink industries and sugary drink input industries) to candidates to the Chamber of Deputies, Brazil.
### Setting:
Elections to the 55th Congress (2015–2019), held in October 2014.
### Participants:
Candidates to the Chamber of Deputies, Brazil.
### Results:
Forty-nine companies or corporate groups that produce sugary drinks and fifty-two corporations that produce inputs for sugary drinks manufacturing contributed to electoral campaigns of candidates in the 2014 Election. Contributions from this industry sector represented 7·3 % of all corporate contributions and helped finance 11·7 % of the candidates and 46·2 % of the elected officials. The transnationals Ambev and Coca-Cola were the first and second biggest donors, respectively. Revenues mediated by political parties, from sugary drink industries and from corporate members of some industry associations (Abir, Unica and CitrusBR), were more prevalent. Among elected officials, a significant association was found between being financed by the sector and representing the south-east region, having higher education level and referring themselves as being professional politicians. In the multivariate model, financed candidates were 27 % more likely to be elected.
### Conclusions:
Corporations related to sugary drinks production have contributed to the electoral campaigns of almost half of the Federal Deputies in Brazil in 2014. This possibly facilitates access to decision-makers and could help buy influence on legislative proposals, including health-related food policies.
## Methods
Brazil is a democratic multi-party presidential Republic which adopts a bicameral system for lawmaking process at the national level. The National *Congress is* comprised of the Chamber of Deputies (the lower House) and the Federal Senate (the upper House). Every 4 years, 513 representatives (Federal Deputies) are elected across each of the twenty-six States and the Federal District for a 4-year mandate in the Chamber of Deputies. Each State and the Federal District elect between eight and seventy-two representatives – the number of seats varies according to the local population. The number of candidates also varies across States and the Federal District[35].
The law that regulates the elections in Brazil requires political parties and candidates to report all campaign revenues and payments. With respect to the revenues of candidates and political parties, until September 2015 corporations could contribute up to 2 % of their gross revenues in the year before the election cycle. These corporate contributions were added to government funds to political parties, personal financial resources from the candidates and contributions from citizens[36]. Corporations could channel their contributions directly to candidates (herein called ‘direct contributions’) or to political parties, which then would distribute the financial resources to their candidates, according to unknown criteria (herein called ‘indirect contributions’, as they were mediated by the political parties). In addition, candidates could channel their own financial resources, from any source (corporations, political party or citizens) to other candidates. This type of contribution was also called ‘indirect contribution’ in this analysis. Because most of the indirect contributions were mediated by the political parties, we opted not to distinguish the intermediary source (if the political party or other candidate).
Data on campaign contributions are publicly available from the Superior Electoral Court website[37], and the dataset from the 2014 Election (held in October 2014) was downloaded in February 2020. Variables related to the candidates (name, CPF – ‘Individual Taxpayer Registration number’ – represented State, political party, date of birth, sex, ethnicity, marital status, schooling, occupation and electoral result) and the revenues (amount, name of the donor, registration number of the donor at the Brazilian Internal Revenue Service, and economic sector of the donor) were used. When campaign contributions were indirect (intermediated by political parties or other candidates), variables related to the original donor (name of the original donor, registration number of the original donor at the Brazilian Internal Revenue Service and economic sector of the original donor) were also collected. Contributions were converted from Reais (the Brazilian currency) to US$ using the mean exchange rate calculated from the election period (between July and October 2014).
After an initial exploratory analysis, the variables related to the candidates were organised as follows: (a) States and the Federal District were grouped into the regions north, north-east, center-west, south-east and south; (b) age (calculated based on the election day) was categorised as <35 years old, ≥35 and <50 years old, ≥50 and <65 years old, and ≥ 65 years old; (c) ethnicity was grouped into Caucasians and non-Caucasians (pardos, blacks, Asians and indigenous); (d) as for marital status, married and widowed were group, as well as separated and divorced candidates; (e) schooling was classified dichotomously according to the completion of higher education level; (f) occupation was classified dichotomously, and those who referred already being an elected official were regarded as professional politicians; (g) political parties were classified according to their coalitions as opposing the Federal Executive branch, supporting the Federal Executive branch or no coalition; and (h) the result of the election was classified dichotomously as elected and non-elected (this category included those who figured in the list of substitutes and although not having been elected could gain a seat in the Chamber of Deputies sometime during the 55th Congress).
As for the variables related to revenues, they were used to identify the two groups of corporations of interest for this study: [1] sugary drink industries, which comprised manufacturers that had at least one type of sugar-sweetened beverage in their product portfolio (i.e. sodas, energy drinks, sports drinks, 50 % per cent fruit juices, soya-based drinks, powdered drink mixes, syrups, ready-to-drink teas, yogurts and other dairy drinks, chocolate milk powder, powdered coffee). Distribution companies that belonged to major sugary drink manufacturers were also included in this group. Corporations that only produced artificially sweetened beverages or non-sugar-added 100 % fruit juice were not included; and [2] sugary drink input industries, which comprised manufacturers that produce the main inputs for sugary drinks (sugars and fruit concentrates). Although food additives are largely used for manufacturing sugary drinks, companies that produce this type of input were not included in this study because of the inability to determine precisely which additives are used specifically for producing sugary drinks.
In Brazil, medium and large corporations often hold several registry numbers at the Brazilian Internal Revenue Service, especially if they operate in more than one State. This is the case, for instance, of corporations that produce sugary drinks such as Coca-Cola and Ambev. Each of their factories and distributors holds a unique registry number. Another example is that of larger sugarcane industries that own several sugarcane farms – each farm is considered a unique company and therefore holds a unique registry number, regardless of being located in the same city or not. As a consequence, corporations such as these could use more than one registry number to make their campaign contributions. In fact, this used to be a common practice. Widely distributing campaign contributions among several of their registry numbers was a strategy used, especially by large corporations to divert attention from the high amounts of money they contributed to electoral campaigns[34]. In addition, each company record (represented by its registry number) at the Brazilian Internal Revenue Service describes their economic activities. However, because medium and large corporations can have multiple registry numbers, economic activities listed for a single registry number at the Brazilian Internal Revenue Service might not reflect all those activities the corporation is involved in. For instance, a corporation that produces sugarcane sugar could have contributed using a registry number whose main activity was ‘sugarcane crop production’; an ultra-processed food industry which has sugary drinks among its portfolio of products could have contributed using a registry number whose main activity was ‘biscuit production’.
Taking all this into account, so as to reduce the chances of not including companies or corporations related to sugary drinks and sugary drink inputs production, we decided to adopt a broader approach when identifying corporations of interest for this analysis. The dataset from the Superior Electoral Court provides a variable that identifies the economic sector of the donor, which is based on the codes and descriptions of the National Classification of Economic Activities (NCEA) and represents the main economic activity related to its registry number at the Brazilian Internal Revenue Service (only one code from the NCEA). Firstly, we selected a wide variety of codes related to the economic sector that could potentially include a corporation of interest, regardless of whether or not they were strictly related to sugary drinks or their inputs (i.e. ‘fruit juice production’, ‘ethanol production’, etc.).
Secondly, the corporations classified under the selected codes were individually checked on the Brazilian Internal Revenue Service website[38]. For those that were active at the time of this research, we based inclusion on their main and secondary economic activities available at the Brazilian Internal Revenue Service website (and not on the activity reported to the Superior Electoral Court). Decision-making was also supplemented by information available at the websites of the corporations, whenever they were available. In the case of inactive corporations, classification was based preferably on the economic activity informed at the Brazilian Internal Revenue Service website. When this piece of information was not available, economic activity reported to the Superior Electoral Court was used. Registry numbers that belonged to the same corporation were clustered.
Next, corporations were classified according to the type of sugary drink or input they produced, identified from their websites. Categories of sugary drink industries were as follows: [1] dairy drinks; [2] non-dairy drinks; and [3] dairy and non-dairy drinks. These industries were also classified in non-exclusive categories related to the subtype of sugary drinks they produced, as some corporate groups have more than one type of sugary drinks in their product portfolio. Sugary drink input industries were categorised as [1] sugars or [2] fruit concentrates.
Finally, corporations were classified according to their participation in industry associations, as they often have seats in public hearings in the Brazilian Legislature and also have a history of participation and interference in public policy processes related to food and nutrition[25,26,39]. The associations were identified from a registry of private sector and civil society representatives kept by de 1st Secretariat of the Chamber of Deputies, requested by FOI. These representatives have a facilitated access to Federal Deputies and are often invited to take part in public hearings. Selected trade associations were as follows: the Brazilian Association of Soft Drinks and Non-Alcoholic Beverages Industry (Abir), which represents Big Soda and major soft drink industries in Brazil; Associação dos Fabricantes de Refrigerantes do Brasil (Afrebras) and Sindicato *Nacional da* Indústria de Refrigerantes (Sindirefri), both representing small- and medium-sized soda industries; the Brazilian Association of Citrus Exporters (CitrusBR), which represents producers and exporters of citrus juices; and the Brazilian Sugarcane Industry Association (Unica), which represents the main sugar, ethanol and bioelectricity producers in the south central of the country. Additionally, we decided to include Viva Lácteos – which represents dairy manufacturers and is not in the registry kept by the 1st Secretariat of the Chamber of Deputies, but has recently participated in a voluntary agreement to reduce sugar content of industrialised foods[40,41]. Associated companies and corporate groups were identified from the industry associations’ websites.
The relevance of contributions from associated corporations was assessed as follows. First, contributions from corporations belonging to each of the selected industry associations were added up. Secondly, for each of the selected industry associations, we selected all non-affiliated corporations whose activities were strictly related to their scope and, therefore, could be potential affiliates. Next, for each association, we calculated the proportion of contributions from affiliated corporations in relation to the sum of all contributions from corporations whose activities were strictly related to their scope.
The association of categorical independent variables was analysed with chi-square test or one-tailed Fisher’s exact test, depending on their frequencies. In Brazil, both campaign revenues and payments are highly heterogeneous. The cost per vote varies a lot across States and can be influenced by the political capital of the candidate, the number of running candidates and seats in a given State, geographic and demographic characteristics of the region where a candidate’s base voters are registered, as well as the fact that elections to the Chamber of Deputies are held under a proportional representation system. As a result, in order to assess whether contributions from sugary drink and/or sugary drink input industries increased the chances of being elected, we decided not to use face values of contributions, but rather a dichotomous variable to describe whether candidates had been financed by this industry sector or not. As there was a large number of individuals who did not receive any contribution from this industry sector, a generalised linear model analysis using negative binomial regression was performed. In this analysis, only candidates who had reported at least one corporate campaign contribution were included, because being financed by the private corporate sector per se already increases the chances of being elected. Being financed by sugary drink and/or sugary drink input industries (yes or no) was the outcome, and the result of the election (elected or non-elected) was the exposure. The multivariate model was adjusted for sex, ethnicity, represented region, higher education level, being a professional politician, political party coalition and total campaign revenues. Data organisation and analyses were carried out using Stata IC 15[42], and differences were considered statistically significant at the level of $P \leq 0$·05.
## Results
Forty-nine corporations that produce sugary drinks and fifty-two corporations that produce inputs for sugary drinks manufacturing contributed to electoral campaigns of candidates to the Chamber of Deputies in the 2014 Election. Among corporations that produce sugary drinks, 46·9 % only produce non-dairy drinks (n 23), 42·9 % produce only dairy drinks (n 21) and 10·2 % produce both types of drinks (n 5). With respect to the subtypes of sugary drinks, ready-to-drink dairy drinks are produced by 38·8 % of the corporations (n 19); fruit or fruit-flavoured drinks, by 24·5 % (n 12); sodas, by 22·4 % (n 11); energy drinks, by 20·4 % (n 10); fruit-flavoured powdered drinks and powdered dairy drinks, by 14·3 % (n 7); ready-to-drink teas and sports drinks, by 6·1 % (n 3); and fruit or fruit-flavoured concentrates, by 4·1 % (n 2). As for corporations that produce inputs for sugary drinks manufacturing, 96·2 % only produce sugars (n 50), and 3·8 % produce fruit concentrates (n 2) (data not shown).
Contributions from sugary drink industries and sugary drink inputs industries totalled US$ 25,131,856·92 (7·3 % of all corporate contributions) and helped finance the electoral campaigns of 11·7 % of the candidates (585 out of 4985 candidates). The distribution of the financial resources is shown in Table 1. Among sugary drink industries, contributions from non-dairy drinks industries were the most prevalent (59·9 % of the candidates), while among sugary drink inputs industries, the most prevalent contributions were those from corporations which produced sugars (85·6 % of the candidates). With the exception of contributions from corporations related to sugar production, indirect contributions to the candidates were the most prevalent.
Table 1Absolute (US$) and relative (%) electoral campaign contributions from corporations that produce sugary drinks and their inputs, as reported by the candidates to the 55th Congress of the Chamber of Deputies. Brazil, 2014Industry sectorCorporations (n)Candidates (n)Direct contributions (US$)Indirect contributions (US$)Total contributions (US$)%Sugary drink industries Dairy drinks2144817 232·441 275 833·532 093 065·9712·5 % Non-dairy drinks233201 006 536·389 022 004·7510 028 541·1359·9 % Dairy and non-dairy drinks583912 280·693 709 097·634 621 378·3227·6 %Total:493992 736 049·5114 006 935·9116 742 985·42100 %Sugary drink input industries Sugars501845 461 723·001 721 872·397 183 595·3985·6 % Fruit concentrates2129405 988·56799 287·551 205 276·1114·4 %Total:522895 867 711·562 521 159·948 388 871·50100 %Total:1015858 603 761·0716 528 095·8525 131 856·92Note: Some candidates have been financed by more than one corporation related to the industry sectors hereby analysed. Therefore, the sum of candidates in total values does not necessarily represent the sum of the subgroups.
The top five biggest donors among sugary drink industries were as follows: [1] Ambev (US$ 6 010 392·98); [2] Coca-Cola (US$ 4 583 871·93); [3] Cervejaria Petrópolis (US$ 3 007 856·14); [4] Vigor Alimentos (US$ 877 175·43); and [5] Brasil Kirin (US$ 548 042·10). Among sugary drink inputs industries, the top five donors were as follows: [1] Atvos (US$ 1 725 913·04); [2] Coopersucar (US$ 1 534 950·00); [3] Tereos (US$ 844 250·00); [4] Cutrale (US$ 839 064·69); and [5] Grupo São Martinho (US$ 403 923·46).
Contributions from associated corporations are described in Table 2. Indirect contributions from corporations associated with Viva Lácteos, Abir and CitrusBr were more prevalent than those from non-associated groups. As for corporations associated with Unica, direct contributions were the most prevalent. No indirect contributions from companies associated with Afrebras and Sindirefri were identified. Contributions from corporations associated with Abir, Unica and CitrusBR were always more prevalent in comparison to non-associated ones, representing 60·6 %, 73·3 % and 69·6 % of the total contributions from corporations whose activities were strictly related to the scope of the associations. Contributions from companies associated with Afrebras and Sindirefri represented only 1·0 % and 0·1 %, respectively, of all contributions from corporations that produce soft drinks.
Table 2Absolute (US$) electoral campaign contributions from associated corporations that produce sugary drinks and their inputs and their participation in relation to non-associated corporate groups, as reported by the candidates to the 55th Congress of the Chamber of Deputies. Brazil, 2014Industry associationCorporations (n)Candidates (n)Direct contributions (US$)Indirect contributions (US$)Total contributions (US$)Participation in relation to non-associated corporationsSugary drink industries Viva Lácteos817528 508·751 349 339·651 877 848·4014·9 % Abir13* 308903 036·389 235 179·5010 138 215·8860·6 % Sindirefri1314 254·39014 254·390·1 % Afrebras36146 438·590146 438·591·0 %Sugary drink input industries Unica111373 629 360·751 517 766·855 147 127·6073·3 % CitrusBR129404 824·56434 240·17839 064·7369·6 %*Eight out of the thirteen corporations were part of the Coca-Cola System in Brazil.
As for the profile of candidates and elected Federal Deputies financed by corporations related to sugary drinks and sugary drink input industries, these are shown in Table 3. The percentages of candidates who were financed were higher among men, Caucasians, in the age group ≥ 65 years, who were married/widowed or separated/divorced, who represented the north-east region of the country, who had higher education level, who referred being professional politicians and whose political party coalition was aligned with the Federal Executive branch. Regarding the elected Federal Deputies, 46·2 % were financed by this industry sector (n 237), and a significant association was found for those who represented the south-east region, who had higher education level and who referred being professional politicians.
Table 3Distribution of candidates and elected Federal Deputies to the 55th Congress of the Chamber of Deputies according to reported electoral campaign financing from sugary drink and sugary drink input industries. Brazil, 2014VariablesCandidatesElected Federal deputiesFinanced % n Non-financed % n P Financed % n Non-financed % n P Sex Males13·648986·43110<0·00192·421988·02430·10 Females6·99693·112907·61812·033Ethnicity Caucasians14·042786,02616<0·00183·119777·52140·11 Non-Caucasians8·115891·9178416·94022·562Age <35 years old9·96990·1625<0·00110·12410·9300·54 ≥35 and <50 years old10·020690·0185332·57729·381 ≥50 and <65 years old13·124486·9161845·210750·4139 ≥ 65 years old17·86682·230412·2299·426Marital status Single7·710592·31264<0·00113·93315·6430·86 Married or widowed13·540086·5255473·817572·8201 Separated or divorced12·18087·958212·32911·632Region North6·63693·4509<0·0015·51318·852< 0·001 North-east15·815884·284330·87328·378 Center-west12·44687·63258·4207·621 South-east11·026989·0218540·59630·183 South12·47687·653814·83515·242Higher education No7·316692·72094<0·00116·03823·665< 0·05 Yes15·441984·6230684·019976·4211Professional politician No8·738091·34003<0·00140·59656·5156< 0·001 Yes34·120565·939759·514143·5120Party coalition No coalition4,54995·51033<0·0013·897·2200·23 Situation17·629282·4136559·914258·7162 Opposition10·924489·1200236·38634·194 The regression analysis comprised a subset of 2803 candidates who reported having received at least one corporate contribution. The results showed that being financed by corporations related to sugary drinks and/or sugary drink input industries increased the chances of being elected. In the multivariate model (adjusted for sex, ethnicity, represented region, higher education level, being a professional politician, political party coalition and total campaign revenues), those who had been financed by this industry sector were 27 % more likely to be elected (prevalence ratio: 1·27; $$P \leq 0$$·008; (95 % CI 1·06, 1·52)) than those who had not been supported (data not shown).
## Discussion
To the best of our knowledge, this study represents the first systematic attempt worldwide to address electoral campaign contributions from corporations related to the sugary drink industry tracking both donors and benefited candidates. Therefore, it fills an important knowledge gap with respect to this CPA practice. Undoubtedly, research like this can only be carried out due to the transparency of accountability reports in Brazil. Although this study only points to the tip of the iceberg with regard to corporate strategies to influence public policies in the Brazilian Legislature, it provides important insights for the understanding of this practice. In addition, transnational corporations have been identified among electoral campaign donors, and it is likely that this CPA practice could also be found internationally, especially in countries where corporate campaign contributions are allowed.
Some of the corporations identified in this study are among the biggest corporate donors of all electoral campaigns at the national level in Brazil from 2002 to 2014: Construtora Queiroz Galvão, a conglomerate that includes Queiroz Galvão Alimentos (a fruit concentrate producer), in the fifth position; Construtora Norberto Odebrecht S.A., a conglomerate that holds sugarcane sugar producers, in the ninth position; Cervejaria Petrópolis, which produces beers and soft drinks, in the eleventh position; Sucocítrico Cutrale Ltda., which produces fruit concentrates, in the fifteenth position; and Recofarma Indústrias do Amazonas Ltda., which is part of the Coca-Cola System in Brazil, in the twentieth position[34].
The findings that a modest number of candidates were financed by this industry sector despite the presence of corporate groups that figure among the biggest donors in national elections, as well as the predominance of indirect contributions, are in line with previous political science research analysing corporate campaign contributions in Brazil. This used to be the big picture until 2015: corporate contributions were the main source of funding for electoral campaigns; a few companies and corporate groups donated significant amounts of money which were allocated to a few candidates, who consequently received high sums for their campaigns; political parties had a central role as mediators of corporate contributions, especially those from big donors(30–32,43–45).
The reasons why corporations contribute to elections through political parties and the strategies used to distribute financial resources to political parties and candidates are unknown in this study. It is possible that contributing to the political party could create bonds with the institution, which has more power to develop, shape and introduce public policies. If the choice regarding which candidates were to be financed was made by the political party, resources could have been distributed to those with more chances of being elected. If the choice was made by the corporations, contributions mediated by political parties or other candidates could have been a means to hide a possible relationship with the elected officials.
It is likely that both direct and indirect contributions could create bonds between the corporations and elected officials. Yet, we are not aware of any study addressing whether there are differences in the effect of these types of contribution on the behaviour of elected officials in Brazil. Theoretically, one could say that direct contributions could have a stronger influence on how an elected official votes or defends private interests. However, two hypothetical scenarios must be taken into account. First, if the final destination of the money (candidate) was defined in its origin (corporation), the effect of an indirect contribution in establishing a relationship between the elected official and the corporation could have been the same as that produced by a direct contribution. Second, even if the candidates to be financed were not defined a priori by the corporations, both recipients and donors knew who had benefited from the contributions. As a result, although most contributions from sugary drink and sugary drink input industries were made indirectly to candidates, we cannot rule out the possibility that these contributions could indeed buy influence of elected officials on decision-making processes.
The participation from sugary drink industries was greater than that of sugary drink input industries. This result could reflect the considerable economic power of some corporations that produce soft drinks, including the transnational Coca-Cola and Ambev, as well as Cervejaria Petrópolis, which was used as a front company by Construtora Norberto Odebrecht to hide the origin of part of its contributions. In addition, corporations related to agribusiness – in which sugary drink input industries are included – frequently have their owners running up to elections, thus reducing the number of candidates they finance[34]. In the case of Viva Lácteos, as the association was initiated in 2014, the results of the present analysis could have reflected a period when their associates were not articulated enough for collective action, especially through electoral campaign contributions[46]. Also, we cannot rule out that the focus of the CPA from this particular association might be outside the Brazilian Legislature, as by February 2020 no representative had been registered at the 1st Secretariat of the Chamber of Deputies.
The results of the present study also demonstrate that contributions from corporations that are organised in industry associations for collective action – especially those associated with Abir, Unica, and CitrusBR – were more prevalent than those from non-associated ones. This result reflects both the economic power of the associated corporations and their organisation for political action. Attention should be drawn to the fact that industry associations often have a seat in public hearings aimed at discussing public health policies. The most recent one was held in December 2018 to discuss PL $\frac{8541}{2017}$ and its attached bills (PL $\frac{8675}{2017}$ and PL 10,$\frac{075}{2018}$), which aimed at increasing taxes on sugary drinks, and in which both Abir and Unica participated. Certainly, the participation of interest groups in electoral processes is part of democratic systems. What is at stake is the imbalance of forces created by the participation of such highly organised special interest groups, which can undermine public interests – including the formulation and implementation of health-related public policies[39,47].
The characteristics of the contributions from sugary drink and sugary drink input industries are aligned with those from the private corporate sector as a whole, as reported in the Brazilian political science literature. They show a preference for male candidates, Caucasians, with a high level of education, who are professional politicians and affiliated to more organised political parties[32,48,49].
Money is a very important resource for electoral success[32,43,48,50]. Total revenues of candidates positively correlate with the number of votes, and elected officials usually report having received and spent a significantly higher amount of money in comparison to non-elected candidates. If on the one hand it is true that campaign financing influences the result, on the other expectations related to the chances of victory of the candidates might help increase contributions to their campaigns(43,48,50–52). In an analysis of the 2010 Elections to the Chamber of Deputies, Cervi et al.[48] have found that variables such as ‘occupation’, ‘type of political party’ and ‘campaign financing’ mutually reinforce each other. Candidates who referred themselves as being professional politicians, in special those who were seeking re-election, were more likely to be elected. These professional politicians also tend to concentrate on bigger political parties and to get more revenues for their campaigns. Therefore, money is only one among a set of variables that can influence electoral results and which are not always easily measurable.
Despite not being possible to establish a causal relationship between the electoral campaign contributions and the electoral result, those who have received contributions from sugary drink and/or sugary drink input industries are significantly more likely to be elected than their counterparts. As a result, almost half of the elected Federal Deputies have a history of campaign contributions from at least one corporation that is likely to oppose health-related regulation of sugary drinks. This can have several implications for public health policies. The effects of past electoral campaign contributions are likely not to be limited to how elected officials vote. They could also influence intermediate stages of the decision-making process, such as the sponsorship and reporting of bills, the inclusion of a bill in the voting calendar, the call for public hearings, etc. Indeed, there is evidence that past campaign contributions from Coca-cola and Ambev could have influenced three legislators in favour of private interests in the legislative process of PL $\frac{8541}{2017}$ and its attached bills (PL $\frac{8675}{2017}$ and PL 10,$\frac{075}{2018}$). They were involved in a chain of events that clearly helped postpone deliberation in the Committee of the Chamber of Deputies where health-related bills are considered[39].
The main strength of this study was the approach used to identify the corporations related to sugary drink and sugary drink input production that contributed financially for this election. The search of a wide array of economic activities and individual checking of the companies have certainly helped increase the accuracy of the estimated participation of this industry sector. Despite this, there are still three limitations for the selection criteria adopted. The first relates to the use of the current economic activity classification. Information on corporations that had been dissolved could not be checked carefully. Also, a few corporations have changed their economic activity completely, possibly indicating they were front corporations used for money laundry or disguising illegal contributions. In such case, we believe these contributions were not likely to have been used to buy future influence on public policies related to the industry sector herein analysed. The second relates to corporations with multiple economic activities. In such cases, only the contributions made under the registries strictly related to the scope of this analysis were accounted. The third relates to corruption scandals which involved two corporations included in the scope of this analysis. Construtora Norberto Odebrecht S.A. has strategically distributed their campaign contributions throughout the several smaller companies that compose the group, including those that grow sugarcane and produce sugar, in order to conform to the financial limit imposed by law and to divert attention from these contributions. And Cervejaria Petrópolis was used as a front company to distribute both legal and illegal financial resources from Construtora Norberto Odebrecht S.A. in exchange for future contracts between the two corporations[34]. The effects of these contributions on the future behaviour of elected officials are unknown. This study has also limitations intrinsic to any analysis of electoral campaign contributions in Brazil. First, slush funds cannot be accounted. Second, it is not possible to identify whether regular citizens who contributed to electoral campaigns had any type of relationship with sugary drink or sugary drink input industries. However, this would not impact results significantly, as financial resources from regular citizens represent the smallest share of campaign contributions. Overall, we believe all the limitations of this study could result in an underestimation of the participation of sugary drink and sugary drink input industries, and not the opposite.
In conclusion, this study has shown substantial electoral campaign contributions from the sugary drink and sugary drink input industries to candidates to the Chamber of Deputies in the 55th Congress. As a result, this sector has contributed to the electoral campaigns of almost half of the elected Federal Deputies. We highlight the predominance of revenues mediated by political parties, from sugary drink industries and from corporations organised in some industry associations. This scenario is worrisome because possibly facilitates access to lobby decision-makers and can help buy influence on legislative outcomes. It should be borne in mind that identifying campaign contributions is not sufficient for understanding the influence of corporations on public health policy processes, as lobbying also plays an important role. Moreover, despite the accountability of electoral campaign contributions in Brazil, there is a lack of transparency in decision-making processes, especially because lobbying activities are not regulated in the country and public consultations are not always held. Finally, although corporate electoral campaign contributions are no longer allowed in the country, medium- and long-term effects of previous contributions on the behaviour of elected officials are unknown. Further research must address whether campaign contributions might have influenced decision-making on legislative proposals that could somehow impact sugary drink industry manufacture and commercial practices.
## Conflict of interest:
We have no conflicts of interest to disclose.
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|
---
title: Dietary changes based on food purchase patterns following a type 2 diabetes
diagnosis
authors:
- Anna Kristina Edenbrandt
- Bettina Ewers
- Heidi Storgaard
- Sinne Smed
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991834
doi: 10.1017/S1368980022001409
license: CC BY 4.0
---
# Dietary changes based on food purchase patterns following a type 2 diabetes diagnosis
## Body
Due to unhealthy eating, sedentary lifestyles and ageing populations, the proportion of individuals with lifestyle-related diseases and obesity is increasing[1,2]. In 2016, more than 1·9 billion adults where overweight. Of these, 650 million were obese[3]. Type 2 diabetes (T2D) is a potential consequence of poor diet and obesity[4], since obesity and inactivity cause accumulation of visceral fat leading to metabolic changes, which results in insulin resistance and over time impaired in insulin secretion, resulting in impaired glucose tolerance, prediabetes and eventually T2D[5]. With T2D, the risk of severe and life-threatening microvascular complications (retinopathy, nephropathy and neuropathy) and macrovascular complications (CVD) markedly increases[6].
Globally, approximately 463 million individuals are diagnosed with diabetes, and it is the direct cause of 4·2 million deaths per year[2]. This induces a huge burden on the health care system, and the total costs related to diabetes constitutes 10 % of global health expenditures, with the major part being treatment of complications[2,7]. In Europe alone, 60 million individuals are diagnosed with diabetes, and the prevalence is increasing in all age groups[4]. It is estimated that in Denmark, the prevalence of individuals at increased risk of diabetes (prediabetes) is approximately 360·000[8], and that at least 250·000 individuals have overt T2D[9] and the costs related to T2D is estimated to 87 million DKK a day[10]. Both prevalence and extent of obesity and thereby diabetes continuously increase worldwide, and projections point to catastrophic consequences for health care systems and societies[2,3]. There is thus a need of increased focus on prevention and treatment[10].
According to both international[11] and Danish treatment guidelines[12], the key components in both prevention and treatment of T2D are lifestyle modifications and pharmacological treatment. The impact of adhering to the established food-based dietary guidelines on morbidity and mortality in the general population has been studied extensively in several primarily prospective cohorts(13–19). All pointing at a reduced risk of T2D, CVD and all-cause mortality among individuals with higher adherence to the established dietary guidelines. Similarly, there is a reduced risk of CVD and mortality following a T2D diagnosis due to lifestyle modifications(19–23). Thus, eating healthily is essential, especially following a T2D diagnosis. Accordingly, healthy dietary habits focusing on carbohydrate quality and quantity (e.g. high in dietary fibre and with limited added sugar) and fat quality (with limited intake of saturated fat and high intake of monounsaturated fat) are recommended in both national and international guidelines for the management of diabetes(24–26).
Despite dietary guidelines for the management of T2D based on evidence of the effects on glycaemic and metabolic control, and reductions in diabetes-related complications, studies show that adherence to the dietary guidelines is rather poor among individuals with diabetes(27–30). However, prevalent research examining dietary intake among individuals diagnosed with T2D is commonly based on proxies for diagnosis status such as self-tests[31,32], self-reported diagnosis status(33–35) and/or self-reported dietary intake(34–37). Yet, self-reported data are associated with uncertainty, and underreporting is a major problem in self-reported dietary assessment methods, especially among obese including individuals with T2D[27,38,39]. Furthermore, much of the analysis is based on cross-sectional or repeated cross-sectional analyses, comparing diagnosed individuals with undiagnosed individuals. An exception is the seminal study on dietary changes following T2D by Oster[32], who found a small average reduction in energy intake, but with substantial variation between individuals based on a panel dataset on observed food purchases. Oster uses the purchase of diabetes-related products (products such as testing strips and glucose monitors) and a machine learning approach to identify diabetes diagnosis.
There are three main contributions of this paper. First, we extend on the work by Oster[32] by utilising an identification strategy for the T2D diagnosis of significantly higher precision. We use data on diagnosis for a sample of Danish adults registered in the official patient register. Second, in contrast to many of the existing studies, we use detailed observed data on food purchases as a proxy for dietary intake. The detailed purchase data also allow us to investigate which dietary changes are most common among individuals following a T2D diagnosis. Changes are investigated for several energy-adjusted nutrients and food groups as well as overall adherence to dietary guidelines. Importantly, the panel structure of our data enables us to explore if the potential changes in purchase patterns are of short- or long-term duration. Most of the existing literature are based on cross-sectional observations or shorter timer periods. Third, we explore differences between sociodemographic profiles in relation to changes in food consumption patterns following a T2D diagnosis.
## Abstract
### Objective:
The study explores whether type 2 diabetes (T2D) diagnosis affects food consumption patterns in line with the dietary recommendations provided to individuals in relation to a diagnosis.
### Design:
Based on detailed food purchase data, we explore which dietary changes are most common following a T2D diagnosis. Changes are investigated for several energy-adjusted nutrients and food groups and overall adherence to dietary guidelines.
### Setting:
We use data on diagnosis of T2D and hospitalisation in relation to T2D for a sample of adult Danes registered in the official patient register. This is combined with detailed scanner data on food purchases, which are used as a proxy for dietary intake.
### Participants:
We included 274 individuals in Denmark who are diagnosed during their participation in a consumer panel where they report their food purchases and 16 395 individuals who are not diagnosed.
### Results:
Results suggest some changes in dietary composition following diagnosis, as measured by a Healthy Eating Index and for specific food groups and nutrients, although the long-term effects are limited. Socio-economic characteristics are poor predictors of dietary changes following diagnosis. Change in diet following diagnosis vary with the pre-diagnosis consumption patterns, where individuals with relatively unhealthy overall diets prior to diagnosis improve overall healthiness more compared to individuals with relatively healthy diets prior to diagnosis.
### Conclusions:
Adherence to dietary advice is low, on average, but there is large variation in behavioural change between the diagnosed individuals. Our results stress the difficulty for diagnosed individuals to shift dietary habits, particularly in the long term.
## Materials
We combine several sources of data, which enables us to explore dietary effects from T2D diagnosis. The data include detailed information on food purchases of the households in a large consumer panel. Further, we include information about the point in time for T2D diagnosis for individuals in each household in the consumer panel. Finally, the data include information about the socio-economic profile of the individuals within the households.
## Food purchase data
The analysis is based on a dataset provided by GfK ConsumerScan Denmark that covers the period 2006–2017. The dataset consists of approximately 1500 households per week that register all food products purchased on a daily basis with a home scanner device, providing information on product level. Each month some households leave the panel and others enter. Gfk seeks to hold a representative sample of households with respect to age, education, family size and geographical location, so leaving households are replaced by households with similar characteristics. A total of 8524 households and 16 395 adult individuals participated in the panel during all or some of or the period included in this study.
The purchase data are combined with nutrient content data based on the Danish Food Composition Databank managed and updated by the National Food Institute[40]. This databank includes information about the content of energy and macronutrients (e.g. carbohydrates, protein and fat) as well as subcategories of macronutrients (e.g. sugar, fibre, saturated, monounsaturated and polyunsaturated fat) content per 100 g of each of the 1049 food products included in the databank at the point of establishment of the dataset. The database is continuously updated to include more foods[40]. For the type of foods that do not exist in the databank, the nutritional contents are calculated based on an average recipe for the food in question.
Since we only have information about food purchases for the entire household, we construct individual consumption for each member of the household based on standard individuals. Each household member is given a weight relative to the standard individual, dependent on gender and age. The recommended daily energy intake is taken from the Danish National Survey of Diet and Physical Activity in Denmark and Becker with colleagues[41,42]. The survey consists of energy intake for sedentary and active females and males in various age groups. The standard person chosen here is a woman at the age of 30–60 years with average exercise levels. She has a recommended energy requirement at 9900 kJ/d. That is, a household consisting of a female and a male both aged 30–60 years will have a family energy requirement of 11 000 kJ + 9900 kJ = 20 900. This household hence consists of 2·1 standard persons. As a robustness test, we also estimate models based on a sample consisting of only single households.
We employ two types of outcome measures for dietary healthiness. The first type of measure is a composite measure in the form of a Healthy Eating Index (HEI), evaluating the adherence to the official dietary guidelines of the Danish Ministry of Family and Consumer Affairs, taking the composition of the household’s diet into account. The HEI, developed by Smed[43] is similar in set-up as the HEI originally developed by Kennedy et al.[44] but adjusted to the Danish dietary recommendations, gives an indication of the dietary healthiness of the household covering eight aspects of the diet, including the amount of fruit and vegetables, fish and the sugar, fat and fibre content. The original HEI ranges from 0 to but is rescaled to range from 1 to 100 for ease of interpretation. More details on the criteria that are included in the construction of the HEI are available in Appendix A.1.
The second category of dietary outcome measure is a set of variables based on energy percentages from specific nutrients or product types. The analysed nutrients were selected based on the recommendations to individuals with diabetes in Denmark and include the energy percentages from fruit and vegetables, fish, meat and three different unhealthy food products (sugar-sweetened beverages (SSB), cakes and candy). Moreover, we use the energy percentages from protein, unsaturated fat, saturated fat, added sugar, carbohydrates and fibre. These variables are calculated as grams of the nutrient consumed times the energy density in the nutrient in kJ/g and then divided by total energy consumption in the household. Each of the dietary measures is calculated on a monthly basis.
In addition, as a robustness test, we estimate models for the specific food and nutrient categories, where the unit is the absolute intake of energy rather than the percentage of energy. The energy from each category was adjusted to follow standard individual units for each individual.
## Diagnosis data and identification
The identification of T2D diagnosis accommodates individuals who have been diagnosed at a hospital and/or had a complication related to T2D, treated at a hospital. The national patient register provides these data. The time of the diagnosis is registered in the data. For individuals who do not have a diagnosis registered, we use the first occurrence of a T2D-related complication as identification of diagnosis. This is made possible from the detailed data on treatments at hospitals. For example, when an individual is treated for a foot or eye complication, the medical doctor at the hospital registers it as ‘foot/eye complication in relation to T2D’. These data cover the period 1990–2017. Moreover, we obtain information about prescription of medication for T2D treatment provided by the Danish Medicines Agency. For individuals treated with T2D medicine, but without a diagnosis or complication registered in our data, we use first occurrence of prescribed T2D medicine as identification of a diagnosis. The medicine that is used as identifier is insulin (all types). This includes all medicines coded as A10 in the medicine register[45]. Individuals who are diagnosed with type 1 diabetes (T1D) are not included in this study. We test the robustness of this identification strategy by also estimating the model with a more strict definition of T2D diagnosis, where those who are only medicated are excluded from analysis. Diagnosis made by general practitioners (GP) are not registered in our data. However, the GPs’ medical prescription is included. For the purpose of identification, it is important to note that individuals who are diagnosed by a GP, and neither are treated for any complication nor prescribed any medicine, are not identified as individuals with T2D in our sample. This implies that if such individuals change behaviour in a systematically different way compared to those identified in our sample, this would cause bias in our estimations. We expect that individuals who are not treated with T2D medicine or for complications are more successful in adapting their lifestyle, including their diet, in accordance with the recommendations. This implies a downward bias in the estimates of behavioural change in our data.
In Denmark, lifestyle intervention programmes for individuals are offered by both the health care system and the municipalities in conjunction a with T2D diagnosis. These include education regarding the disease and self-management, diet, physical activity/exercise training and smoking cessation[12]. Thus, a T2D diagnosis in *Denmark is* associated with the information treatment from the diagnosis itself, where the diagnosed individual is made aware of the medical status. Further, a T2D diagnosis also implies that the individual is offered more detailed information from dietarians on practical guidelines to target the disease. This study explores the effects from the information treatment from the diagnosis itself, while we do not have information about if the individual in addition participate in follow-up sessions with dietarians.
## Coupling of food purchase data and register data
The food purchase data were connected with the official register data by Statistics Denmark based on a search for CPR numbers (personal identity number from the Central Person Register) of the individuals in the household panel. GfK provided names, addresses and ages of the panel members, and Statistics Denmark used this information to retrieve the CPR numbers and thereafter to match with register data. All CPR numbers, names and addresses were replaced with a unique identification number, ensuring that the households are anonymous in the dataset that we access. Data are only available through the server at Statistics Denmark, and only estimation results and average statistics can be exported from the server.
Our final dataset includes 1296 individuals who have T2D (about 8 % of the individuals in the panel), which is comparable to the rate of T2D in the Danish population (about 5 % of total population), when we take into account that our panel members are older than the average Danish population. In the main analysis, we only include individuals who are diagnosed during their participation in the consumer panel, such that they are included if they register purchases at least one of the 3 months prior to the diagnosis, and at least one of the 3 months following their diagnosis. This gives us 274 individuals who were diagnosed with T2D. Individuals diagnosed prior to their participation in the food panel are excluded from the main analysis.
## Registry data on personal information
To explore heterogeneity in dietary changes upon a diagnosis, we link the purchase data and diagnosis data with registry data from Statistics Denmark on personal characteristics such as age, gender, family composition and income.
According to the literature concerning information provision in the general population, women are more prone to use and respond to health labels and claims, and they are generally more interested in healthy eating and information about healthy eating[46,47]. Hence, we expect that women have a larger response to the information provided in relation to a T2D diagnosis compared to males.
*In* general, income and higher education are associated with healthier diets[48], and these groups are also found to be more responsive to information and to a larger degree use and understand labels[46]. We therefore expect a positive relationship between the reaction to a diagnosis and income.
We also include age, although there is not clear evidence to suggest any particular effect of age on the response to information[46] and therefore not on the reaction to a diagnosis. Further, we explore whether the reaction to a T2D diagnosis varies between individuals in single and multiple individual households. Food consumption is an important part of an individual’s social life. A diagnosed individual may make changes in diet, but if the rest of the household does not make such changes, the dietary changes measured on the household basis are less clear.
In sum, since we only have weak prior expectations that are based on the general publics’ reaction to information, we choose an exploratory approach, as we do not have clear evidence to guide expectations regarding the influence of socio-economic characteristic on the response to the information given in relation to a diagnosis.
Summary statistics for the sample are presented in Table 1. The sample of individuals who are diagnosed with T2D during their participation in the panel are compared with individuals without a known T2D diagnosis. Individuals with T2D are significantly older with a mean age of 60 years compared with 45 years for individuals without known diabetes. Moreover, individuals with T2D consists of smaller households, which presumably is related to age, where fewer have children living in the household. We do not see statistically significant differences in gender distribution or income levels between individuals with and without known diabetes.
Table 1Summary statisticsT2DNo known T2DDifferenceIndividuals27415 099Personal characteristicsFemale (%)55·153·7insAge (mean)59·944·8 *** Household size (pers)1·92·5 *** Single household (%)34·317·9 *** Annual personal income (DKK)257 645262 934insFood purchase patternsHEI (running from 0 to 100)75·776·3insFood categories, percent of total energy consumed Fruit and vegetables6·48·2 * Fish1·31·1ins Meat13·510·8 *** Sugar-sweetened beverages2·42·5ins Cakes2·52·4ins Candy2·23·1 * Nutrients, percent of total energy consumed Protein14·914·8ins Unsaturated fat19·318·1ins Saturated fat16·314·9 *** Added sugar4·55·4 ** Carbohydrates40·744·5 *** Fibre2·12·2insT2D, type 2 diabetes; HEI, Healthy Eating Index. Food purchase patterns for the T2D sample are calculated based on the pre-diabetes period only. Tests for differences between samples (t tests and tests):*indicates if $P \leq 0$·05,**if $P \leq 0.01$, and***if $P \leq 0.001$, ins = insignificant. ( 10 DKK∼1.6 USD).
We further compare the purchase patterns between individuals with T2D and without known T2D (bottom panel in Table 1). The purchase patterns are based on household basis. This implies that non-T2D-individuals who are in a household with a T2D individual are included in the non-T2D sample, when they have the same purchase patterns as their T2D household members. However, these individuals constitute a minor share of the total number of individual in the non-T2D sample. Only the months of purchases prior to the diagnosis for individuals with T2D are included to exclude any effect on purchases from information provided when diagnosed. Surprisingly, there is not a statistically significant difference in the overall dietary healthiness, as measured by the HEI. However, some purchase patterns vary significantly between those with and without known T2D. Individuals with T2D purchase less fruit and vegetables and more meat. Their purchases also contain a higher share of saturated fat, while less carbohydrates and added sugar.
## Empirical framework
To explore the dietary changes in relation to a diagnosis, we apply an event study analysis using the panel structure of the data. This allows us to examine whether T2D individuals change their food purchases in accordance with the dietary recommendations provided when diagnosed (hereafter referred to as ‘information treatment’). The analysis only includes individuals who were diagnosed (following the identification strategy in section 3·2) during their participation in the food panel and individuals with no diagnosis as a control group. Individuals who are diagnosed prior to joining the panel are excluded from analysis. We test if the assumption of parallel trends pre-diagnosis hold.
We test for information treatment effects on a number of diet-related measures, as described in section 3·1. For this reason, equation [1] describes a general model, where the dependent variable y represents the different measures investigated. We estimate the following specification:[1] Let equal the dietary measure for individual i during month t. We include yearly (and monthly) fixed effects, to control for shifts in diet over the time span of the data, as well seasonal effects. Several new dietary ‘trends’, for example, the New Nordic Diet, are observed during our data period. These might lead to systematic changes in dietary composition, which is captured through the yearly dummies. Monthly dummies are introduced in order to capture seasonality in healthiness of diets[49,50]. The term a i is an individual fixed effect and are regression disturbances with mean zero that are independently distributed. To account for general forms of autocorrelation in across months for an individual, and the possibility that this pattern differs across individuals, we report test statistics based on standard errors that are robust to this form of heteroscedasticity and autocorrelation.
The variable of main interest is the information treatment effects variables (β). The information treatment is the diabetes diagnosis. The treatment variable is constructed as a dummy variable, taking the value one from the month an individual was diagnosed and onwards, and zero otherwise. To test for delayed or reverse effects following diagnosis, we include lagged variables such as. Hence, the lagged treatment variable takes the value 1 if the individual was diagnosed k months prior. We initially include $K = 12$ lagged variables, since long-term follow-up of dietary changes for more than 12 months is important in trials examining the dietary impact on clinical outcomes. Significant improvements in, for example, glycaemic control among individuals with T2D have been difficult to find after 12 months, possibly due to lack of long-term dietary adherence[51]. We check robustness of our results by artificially setting the treatment date to be 1, 2, 3 and 6 months before the diagnosis. This is to test if diagnosed individuals are aware of their condition before the diagnosis, as identified with our identification strategy, and adjust their diet prior to their diagnosis in conjunction with a hospital visit.
In a second set of analysis, we test for heterogeneity in purchase behaviour following diabetes information treatment related to personal characteristics. For this purpose, we included the diagnosed individuals only and generate a variable for each diagnosed individual who described the individual’s diet before and after the diagnosis. For each of the dietary outcome variables (HEI, food and nutrient-specific energy shares), we calculate the average value over 6 months prior to the information treatment, as well as the average value over the 6 months after the information treatment. *We* generate the individual change variable [] from the change in these averages for each individual:[2] where $t = 0$ is the month of the diagnosis. For individuals with missing information for months prior or following the diagnosis, we calculate the average for the non-missing months. We do not include data based on the month of the diagnosis, since this month may not have been representative of the purchase behaviour. For example, individuals receiving their diagnosis during hospitalisation will likely do less grocery shopping during this month. We estimate the following specification to test for heterogeneity in the change in purchase patterns following a diagnosis:[3] We estimate equation [3] by ordinary least squares. While the previous analysis based on equation [1] includes both diagnosed and non-diagnosed individuals, equation [3] includes diagnosed individuals only. We include the individuals purchase behaviour prior to diagnosis (baseline purchase behaviour), to investigate how the purchase behaviour prior to diagnosis relates to the behavioural change following the diagnosis. Hence, the terms denoted with estimate the effect of the baseline purchase behaviour []. As robustness test, we also estimate equation [3] where the baseline consumption is divided into quartiles. *We* generate a q25-dummy variable that take the value 1 if the individual is among the lowest 25 % and 0 otherwise and correspondingly q75-dummy variable for the top 25 %. Furthermore, the results that we retrieve from estimating equation 3 could have been derived from estimating equation [1] with interactions between the treatment dummies and the sociodemographic variables. However, as we have a relatively small number of diagnosed individuals, this leads to many insignificant variables.
## Event study on effect of type 2 diabetes diagnosis
Figure 1 reports the association between diagnosis and dietary measures, where each of the dietary measures are regressed on the T2D diagnosis (equation 1), while Table A2 in appendix reports full results. The main variables of interest are the immediate effect of the diagnosis (Diagnosis) and the longer-term effect (the 12-month lagged diagnosis variable Diagnosis12m). Initially, we included lags for up to 12 months following diagnosis ($K = 12$ in equation 1). However, sensitivity analysis suggested that only including one lag where $k = 12$ did not decrease model fit, wherefore we proceed with only including Diagnosis12m. To investigate if the effects on the dietary outcomes holds in the longer run, we test if the sum of the immediate effect and the lagged effect are different from zero. Test results are displayed as asterix (*) in Fig. 1 and in the last row of Table A2 in appendix. The overall results reveal a statistically significant effect from diagnosis on the overall dietary healthiness (HEI) in the months following diagnosis, although the change is a relatively small improvement (1 percentage point increase). However, the immediate improvement in HEI does not hold in the longer term, as revealed by the negative and significant lagged effect from the diagnosis. The results rather suggest a worse HEI-value after 12 months.
Fig. 1Effect of diagnosis on dietary health. * *The sum* of immediate and after 12 months changes is significant. Labels show aggregated effect after 12 months. SSB, sugar-sweetened beverages While there exists regional variations in the dietary recommendations delivered by dietitians in Denmark, a number of key elements are consistently provided to T2D-diagnosed individuals. Such key elements in dietary management of T2D include improvement of overall carbohydrate quality (and quantity) by increasing the intake of dietary fibre from wholegrain products, eating more vegetables and some fruits, and limiting the intake of added sugar. In addition, emphasis is made on improving fat quality by increasing the intake of primarily plant-based MUFA and limiting the intake of SFA(24–26).
In line with such dietary guidelines, we observed that the share of energy originating from fruit and vegetables increases following a diagnosis. Although these changes are somewhat reversed after the first 12 months, there is still a significant long-term increase in fruit and vegetable consumption. Intake of fish and meat declines on a long-term basis following diagnosis. The effects on unhealthy food groups show that while intake of candy and snacks decline in the short run, these effects are reversed in the longer run. However, the joint effect is nonsignificant, so the intake of SSB, cakes and candy are unchanged following diagnosis in the long run.
The energy share deriving from saturated fat decreased following diagnosis, which is in line with the recommendations. However, these changes are reversed after 12 months, resulting in insignificant long-term effects. We find the same pattern for dietary fibre, with an immediate increase, which is not maintained in the longer term. No significant change is found for the energy share from unsaturated fat. In contrast, we find that the short- and long-term shares of energy consumption from added sugar and carbohydrates increased following a diagnosis, which is in contrast to the dietary guidelines. While the effects on specific unhealthy food categories do not display an increase, the increased intake of added sugar and carbohydrates can be related to increased intake of other food categories, perhaps food groups where the added sugar content is less salient.
## Heterogeneity in dietary outcomes following diagnosis
We proceed by analysing variation in dietary change following a diagnosis. Change variables are generated for each dietary outcome, which is the difference between the diet in the months following the diagnosis and the months prior to the diagnosis, as specified in equation [3]. Summary statistics for these change variables show that on average, none of the dietary measures change (Appendix Table A3). However, each of these change variables have wide distributions, suggesting that there is large heterogeneity in the change in diet following a diagnosis. For example, the average individual increases the energy share from fruits and vegetables with 0·95 percentage points, while individuals in the high end (95th percentile) has increased it with 6·4 percentage points, and in the low end (5th percentile) reduced it with 4·9 percentage point.
To gain insights into this heterogeneity, we first display scatterplots with the diagnosis-induced change on the pre-diagnosis consumption level including simple linear and quadratic regression lines (Figure A1 in appendix). The plots confirm the large heterogeneity in the dietary change due to diagnosis, but also for most food and nutrient groups a negative relation in pre-diagnosis consumption level and some rather large outliers. We estimate equation [3] for each of the dietary outcome variables to investigate if there are systematic differences in the reaction to a T2D diagnosis, as explained by socio-economic characteristics and on the purchase behaviour prior to diagnosis (baseline consumption). Results for these estimations are displayed in Table 2 with baseline consumption in a quadratic specification (Panel A) and in quartiles (Panel B). We note that while the models with quadratic specifications have better model fit, these are more sensitive to outliers. For this reason, we present both specifications and focus on results that hold across both specifications.
Table 2Change in diet 6 months after diagnosis compared to 6 months prior to diagnosisEnergy shares from food groupsHEIF&VFishMeatSSBCakesCandyPanel A: Continous specification Baseline−1415* −0·354* −0·467*** −0·691*** −0·260*** −0·221** −0·032 (|t-ratio|)2120205039505920255032600·450 Baseline2 0·7581800** 4291*** 0·712*** −0·589−2398*** −1948*** (|t-ratio|)1700309040402670105080305790 R2 0·190·060·080·280·180·600·40 F-statistica 7·232·112·8712·336·7846·6221·32Panel B: Categorical specification Baseline_q250·022*** 0·0040·0010·038*** 0·008* 0·008* 0·003 (|t-ratio|)2·7100·3800·2804·5701·8301·9500·680 Baseline_q75−0·030*** 0·000−0·003−0·045*** −0·014*** −0·023*** −0·018*** (|t-ratio|)−3·850−0·020−1·230−5·380−3·090−4·950−4·480 R2 0·140·010·030·230·070·140·10 F-statistic6·620·431·2512·933·236·824·76HEI, Healthy Eating Index, F&V, fruit and vegetables; SSB, sugar-sweetened beverages.* $P \leq 0.05$,** $P \leq 0.01$,*** $P \leq 0.001.$Only variables of interest is displayed here. Full regression tables are shown in supplementary material Table S11 and S12. t-Ratios are based on robust standard errors. For F-statistic of the null hypothesis, all coefficients are equal to zero.$$n = 261$.$
Table 2(Continued)Energy share from nutrientsProteinUnsat. fatSat. fatCarbo. FiberAdded sugarPanel A: Continuous specification Baseline0·0050·016*** 0·0070·018** 0·001* 0·003 (|t-ratio|)1·1603·1801·5801·9901·6600·640 Baseline2 −0·012*** −0·021*** −0·022*** −0·028*** −0·004*** −0·011*** (|t-ratio|)−3·070−4·020−4·730−3·070−5·340−2·450 R2 0·070·140·130·100·150·05 F-statistica 3·066·866·354·767·522·09Panel B: Categorical specification Baseline_q250·0050·016*** 0·0070·018** 0·001* 0·003 (|t-ratio|)1·1603·1801·5801·9901·6600·640 Baseline_q75−0·012*** −0·021*** −0·022*** −0·028*** −0·004*** −0·011*** (|t-ratio|)−3·070−4·020−4·730−3·070−5·340−2·450 R2 0·070·140·130·100·150·05 F-statistic3·066·866·354·767·522·09Unsat. fat, unsaturated fat; Sat. fat, saturated fat; Carbo, carbohydrates.* $P \leq 0$·05,** $P \leq 0$·01,*** $P \leq 0$·001.Only variables of interest are displayed here. Full regression tables are shown in Supplementary Tables S11 and S12. t-Ratios are based on robust standard errors. For F-statistic of the null hypothesis, all coefficients are equal to zero. $$n = 261$.$
Notably, socio-economic characteristics are poor predictors of dietary behaviour following a T2D diagnosis. With few exceptions, there are no statistically significant differences in dietary change following a diagnosis between the included socio-economic characteristics. Full results are presented in Supplementary Tables S11 and S12. As could be expected, diagnosed individuals who are in single households show a larger improvement in overall healthiness (HEI). This does not necessarily imply that single-household individuals are more successful in improving their diets. If diagnosed individuals in larger households make changes in their diet, while the rest of the household does not make such changes, the dietary changes measured on the household basis are less clear compared to a single household.
Contrary to the socio-economic characteristics, the baseline diet is an important explanatory variable for the change in diet following diagnosis. Individuals with a relatively poor HEI prior to their diagnosis improve their HEI compared to those with a higher HEI prior to diagnosis. In other words, individuals with relatively poor overall diet prior to diagnosis changed more towards a healthy diet.
The pattern that baseline consumption is correlated with changes in consumption following diagnosis holds for most food and nutrition categories. For example, individuals with a relatively high share of saturated fat in their consumption reduced their energy percentage from saturated fat more compared to those individuals with a lower baseline consumption. Similar conclusions hold for unsaturated fat and added sugar; individuals in households with high shares from these nutrients decrease their percentages from these nutrients compared with the central group. The HEI show that largest improvements are found for those with a lower HEI value, which might have the natural interpretation that those with the worst diet have more to improve to follow the recommendations. For meat, we see that those who have a larger energy share in the first place decrease consumption most, and the same holds for the unhealthy food groups, SSB, cake and candy.
## Robustness in findings
In our analysis, consumption measures for specific food and nutrient categories are expressed as percentages of total energy consumption. An individual whose diet consists of a high share of saturated fat may have adjusted their diet by reducing the total saturated fat consumption. Even if the individual makes no other changes to the diet, the energy share for the rest of the categories are then affected. The energy share from sugar will increase, even if the total amount of sugar consumed is unchanged. For robustness, we therefore re-estimate all models presented in Fig. 1 (equation 1), while measuring the outcome variables in absolute energy intake rather than in energy shares. Results from these models are presented in Supplementary Table S1. Overall, the main results from these models are similar. Fruit and vegetable consumption increases, while meat and fish consumption decreases in the long term. While there are no significant long-term effects on the unhealthy products (SSB, candy and cakes) when measured in shares, there are significant increases in the total energy from these food groups. Further, added sugar and carbohydrates increase in the long term for both share and total energy. For protein and fat (unsaturated and saturated), only the total energy displays statistically significant long-term changes. Interestingly, we find that total energy consumed decrease significantly.
Moreover, we also re-estimate the models presented in Table 2, where the dependent variable is the change following diagnosis (equation 3) with the real changes instead of the changes in energy shares. Results from these estimations are presented in Supplementary Table S2. For the nutrients, those with a high consumption of protein, unsaturated and saturated fat as well as fibre and added sugar decrease consumption the most. For the unhealthy foods, such as SSB and candy, we see the same tendency. Hence, the main conclusion is unchanged; larger room is found for improvement in the diet of those with the most unhealthy diet and no effects are found from sociodemographic variables. There are some minor differences in the results compared to the estimations in shares. These can be explained by the fact that we find that total energy consumed decreases most for those with largest energy consumption prior to diagnosis of T2D.
The identification of T2D diagnosis accommodates individuals who have been diagnosed at a hospital and/or had a complication related to T2D, treated at a hospital. We further use first occurrence of prescribed T2D medicine as identification of a diagnosis for individuals treated with T2D medicine, but without a diagnosis or complication registered in our data. For robustness, we estimate the models presented in Fig. 1 (and Table A2) based on a stricter identification of diagnosis. We then only include individuals who are diagnosed or have had a complication, such that individuals who are identified based on their medical prescription are excluded. We thereby test the results if they are sensitive to potential inclusion of individuals treated with antihyperglucemic drugs for other diagnoses than T2D (e.g. polycystic ovarian syndrome, gestational diabetes mellitus and type 1 diabetes). This gives a smaller sample of T2D individuals (n 60 v. n 274). Overall, the results from this smaller sample provide similar results, with fewer statistically significant estimates, which is to be expected given the smaller sample size. The results for this stricter definition are provided in Supplementary Table S3. We also re-estimated equation 3 with this stricter definition, but due to the small sample size most parameters were insignificant. We have omitted these results from the material.
One limitation of the study is that we have purchase data on the household level, while the diagnosis data are on the individual level. This implies that there may be diagnosed individuals living in a household with someone who continue to eat as before. To test the robustness of our results to the results that might appear from some kind of household bargaining over diets, we re-estimate the results from Table A2 and Table A3 with only single households. The results of these estimations are shown in Supplementary Table S4. Main results are unchanged. We also re-estimated equation 3 with only singles, but since we only have eighty individuals, the majority of parameters are insignificant whereas this is omitted from the supplementary material.
To test if the diagnosis variable in our regressions in Table A2 actually measures an effect of a diagnosis, we estimate a set of regressions for robustness, where we include lead variables for the diagnosis. Hence, a variable indicating the months prior to the diagnosis is included in equation 1. If the diagnosis effect in equation 1 is indeed the effect of a diagnosis, the lead variable should be zero (there should not be an effect on diet prior to the diagnosis). We estimate regressions including 1-, 2-, 3- and 6-month lead variables. We present the results in Supplementary Tables S5–S8. For all models, the lead variables are statistically insignificant.
A main assumption in the event study analysis is that of parallel trends, implying that the diagnosed individuals follow the same trend as the undiagnosed individuals prior to their diagnosis. To test if this assumption holds, we re-estimate equation 1 (displayed in Fig. 1 and Table A2) while including interaction terms between the time variables (year and month dummies) and the diagnosed individuals in the time periods prior to their diagnosis. If the parallel trend assumption holds, the interactions between the time dummies and the diagnosed individuals prior to their diagnosis should be zero. The results support the parallel trend assumption, as revealed in the last row of Supplementary Table S9 for some models, but not for all. We therefore re-estimate equation 1 with only the diagnosed individuals (before and after their diagnosis). Results are displayed in Supplementary Table S10.
## Discussion
We explore the dietary effects following T2D diagnosis by combining several high-quality data sources. First, we include detailed medical records, as registered by the official national registry in Denmark. Diagnosis is identified based on either diagnosis, treatment of complication related to T2D or medical prescription for treating T2D. These data are merged with data on individuals’ socio-economic characteristics. Finally, we combine these data with the individuals’ household food purchase data, as registered in a consumer panel where all purchases are scanned and reported on a daily basis. Our sample includes 274 individuals who are diagnosed with T2D while reporting food purchases in the consumer panel. We have additional data on 15 099 individuals not identified with T2D. Event study regression analysis indicates some changes in dietary composition following diagnosis. In particular, at the positive side, we see an immediate increase in the overall dietary healthiness, as measured by a HEI, increase in fruit and vegetable consumption, and decrease in meat consumption, saturated fat as well as candy and cake consumption. However, we also see increase in consumption of added sugar, and the majority of the positive immediate changes are reversed when we consider the 12 months ahead. Our main findings are hereby consistent with findings from other studies, which suggest that diagnosis on lifestyle-related diseases have limited impact on food consumption[32]. Our results hold across a range of robustness tests.
We further explore heterogeneity in dietary changes following diagnosis. Two main conclusions can be drawn from this analysis. First, socio-economic characteristics are poor predictors of dietary changes following diagnosis. Evidence from studies on general populations suggest that responses to public health information vary with gender and income levels. However, we do not find differences in dietary change between men and women, or between income levels. These findings are supported by Oster[32], who concludes that personal characteristics are not strongly correlated with dietary changes following a diagnosis. Our findings suggest that behavioural changes based on health promotion in the general population cannot be generalised to T2D individuals, as these are not a random sample of the population.
Second, changes in purchase patterns following diagnosis vary with the pre-diagnosis consumption patterns. *In* general, individuals with relatively poor diet prior to diagnosis make relatively more health-beneficial changes compared to individuals with a relatively healthier diet prior to the diagnosis. A possible explanation to this pattern could be that these individuals have more room for improvement as pre-diagnosis consumption levels are lower, an explanation in line with Oster[32]. It is also possible that individuals with healthier purchase patterns prior to a diagnosis make other types of changes such as quitting smoking, being more physically active or consuming less alcohol. It may also be that those with relatively healthier diet are less interested in participating in the offered nutrition support and education. The latter explanation might be an interesting route for further research. On the positive note, we find that for unhealthy nutrients and foods also that those with the largest pre-diagnosis level of consumption have the largest decrease in consumption.
A central strength with this study compared to previous studies is that we use registry data. Existing evidence on the impact of a T2D diagnosis on food consumption is based on self-reported diagnosis status(31–34). In addition, detailed results are available in this study on food purchases, as reported daily in a consumer panel. This has the advantage compared to self-reported dietary intake measurements[34,36,37], where memory and selective reporting are potential sources of measurement error.
We note that individuals, who are diagnosed by their GP with no medical prescriptions or any treated complications, are not identified as T2D individuals in our data. These individuals may make the largest dietary improvements, since they are successful in avoiding complications and medication. If so, the estimated changes in diet in our sample are downward-biased; the true average effects from a diagnosis are larger than we see.
## Limitations
While the quality of the combined data sources used in this study is an important strength compared to previous studies, some potential misclassifications of the diagnosis and food purchases must be taken into consideration when interpreting the results.
First, while participants in the consumer panel are asked to report all their daily purchases, some purchases may be more likely to be underreported. It can be expected that unhealthy products, such as spontaneous purchases of snacks and sweets, are un- or under-reported. Such misreporting is mainly a concern if the likelihood of misreporting is affected by a diagnosis. We do not have reasons to expect this, but it should be considered an uncertainty when interpreting our results.
Further, the reporting of purchases is made on a household basis. We assume that the distribution of the purchased food is even, based on standard individual measures which were constructed based on recommended nutrient requirements. If the consumption distribution changes in the household following the diagnosis of an individual, this is a source of bias. However, we do not have any expectations that this takes place in a systematic way. Finally, while our data sources are of high quality, the sample of individuals included in this study is limited [274]. Despite these limitations, our study contributes with insights based on reliable data sources.
Another limitation with the present study is that we cannot disentangle the effect of the information from the effect of a diagnosis itself and the effect of dietary guidance offered to the diagnosed individual. Unfortunately, our data do not indicate if the individual accepted the offer of dietary guidance by dieticians. What we do know is that a diagnosis made in conjunction with a hospital visit, which is the diagnosis that is included in our data, is always accompanied by an offer to obtain professional dietarian guidance. Those that have mild T2D will often get the offer via their own GP and possibly a referral to the municipality. Those with more complicated T2D will in many cases be referred to diabetes control by a specialist at a hospital or a T2D centre and receive dietitian guidance there. Thus, in this study, we test the effect of a diagnosis itself and cannot control if additional dietary support occurs.
## Implications for policy and future research
The main finding in this study is that a diagnosis of the lifestyle-related disease T2D has limited impact on food consumption. The low dietary adherence following a diagnosis might partially be explained by the fact that those who are able to change their dietary behaviour have already done so since body weight management, treatment of high cholesterol levels and hypertension are often preceding a T2D diagnoses. Hence, the diagnosed individuals are not a random sample of the population, but mainly self-selected individuals who have failed to react to earlier warning messages. If possible, future studies should compare the socio-economic profiles of those reacting to these pre-warnings with the general panel and the diabetic sample.
Importantly, our findings emphasise the difficulty for diagnosed individuals to change their dietary habits, especially on a long-term basis. Hence, the low adherence to dietary guidelines following a T2D underlines the need for additional measures and policies to support and induce dietary improvements among diagnosed individuals, preferably some months after diagnosis to maintain healthy dietary changes. More research is needed to identify successful methods for supporting T2D diagnosed individuals in changing food consumption in line with dietary guidelines.
Another area that future research should investigate is the effects of medication. If the diet is changed in accordance with the guidelines, the need for T2D medication may be significantly reduced or postponed. However, the use of traditional medication reduces the symptoms even without dietary changes and newer medication types (GLP1 receptor agonists) help to regulate food intake, increasing preferences for healthier diets. An important question is if traditional pharmaceutical treatment actually induces worse dietary behaviour as it reduces the present utility costs of disease by reducing some of the potential disease symptoms associated with having a poor diet in the short run and if newer treatment varieties actually lead to improvements in dietary quality. Future research should explore these relationships.
## Conflict of interest:
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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|
---
title: 'Nutrition knowledge among university students in the UK: a cross-sectional
study'
authors:
- Katerina Belogianni
- Ann Ooms
- Anastasia Lykou
- Hannah Jayne Moir
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991837
doi: 10.1017/S1368980021004754
license: CC BY 4.0
---
# Nutrition knowledge among university students in the UK: a cross-sectional study
## Body
The transition to university can be a turbulent period of a young persons life, characterised by increased independency, socialising, self-regulation and self-organisation[1]. Qualitative research among university students have shown that dietary habits are driven by a cluster of personal, societal, environmental and academic factors[2]. Among societal and environmental parameters are the influence of peers and the availability and affordability of foods. Nutrition knowledge (NK) and perceived health benefits of food, together with other individual factors (e.g. cooking skills), are also factors affecting dietary behaviour according to students[2]. NK in university students has been found to positively associate with an increased intake of fruit, dairy, protein and wholegrain foods[3] or other dietary behaviours (i.e. reading food labels)[4]. Findings from existing cross-sectional studies in university students suggest an inadequate knowledge of various nutritional topics. In particular, students failed to correctly answer more than 50 % of the questions in relation to fruit and vegetables[5], milk or their alternatives and fermented dairy products[5], vitamin D[6,7], food labels[8] and the impact of diet on chronic diseases[4,9,10].
The General Nutrition Knowledge Questionnaire (GNKQ) developed by Parmenter & Wardle [1999][11] in the UK is a validated tool to assess NK in adults and has also been used previously in studies with university students[4,9,12,13]. Studies among university students, which assessed knowledge using the GNKQ tool, found that the mean scores of correct answers ranged from 51 % to 67 %, suggesting a moderate level of overall knowledge[4,9,12,13]. Kliemann et al.[14] published in 2016 a revised version of the GNKQ including updated evidence-based information on nutritional facts and dietary recommendations. The revised version included questions on dietary recommendations according to the UK Eatwell Guide published in 2016[15], hidden sources of salt and added sugars, food labelling and cooking methods, as well as glycaemic index of foods, body shape and optimal practices to maintain a healthy body weight.
Academic discipline, sex, age and socio-economic parameters are factors that might affect the level of NK. Having received nutritional education[16] or studying a health-related course[17] has also been associated with increased knowledge in some students. An increased level of knowledge has also been reported in older students when compared to their younger counterparts[18,19] as well as female students[20,21]. Additional studies found that high socio-economic status[10], healthy BMI[22], non-Hispanic White race[20] and living alone[12] were positively associated with greater NK in university students. These studies were not undertaken in Europe, which highlights the gap in the European literature on this topic. In the UK, however, a similar study among university students assessed NK as a predictor of diet quality, using the initial version of the GNKQ[4]. The study showed that socio-demographic characteristics had an impact on NK, while NK was a significant predictor of diet quality.
By increasing the knowledge of students with regard to nutrition and healthy eating, students are given the opportunity to personalise this knowledge to improve their diet quality. Considering that students from non-health academic disciplines (e.g. Political Sciences, Mathematics) might never have the chance to receive evidence-based nutritional information via their courses, it is important to include nutrition information in any health-promoting strategy. The objectives of this study were to explore the level of NK in a sample of university students in the UK from various academic disciplines, investigate potential factors affecting knowledge and explore predictors of good NK. Understanding the current level of students’ knowledge contributes towards the design of targeted and more successful interventions within university settings.
## Abstract
### Objective:
To investigate nutrition knowledge (NK) in university students, potential factors affecting knowledge and predictors of good NK.
### Design:
A cross-sectional study was conducted in 2017–2018. The revised General Nutrition Knowledge Questionnaire was administered online to assess overall NK and subsections of knowledge (dietary recommendations, nutrient sources of foods, healthy food choices and diet–disease relationships). The Kruskal–Wallis test was used to compare overall NK scores according to sex, age, ethnicity, field of study, studying status, living arrangement, being on a special diet and perceived health. Logistic regression was performed to identify which of these factors were associated with a good level of NK (defined as having an overall NK score above the median score of the sample population).
### Setting:
Two London-based universities.
### Participants:
One hundred and ninety students from various academic disciplines.
### Results:
The highest NK scores were found in the healthy food choices (10 out of 13 points) and the lowest in the nutrient sources of foods section (25 out of 36 points). Overall NK score was 64 out of 88 points, with 46·8 % students reaching a good level of knowledge. Knowledge scores significantly differed according to age, field of study, ethnicity and perceived health. Having good NK was positively associated with age (OR = 1·05, (95 % CI 1·00, 1·1), $P \leq 0$·05), White ethnicity (OR = 3·27, (95 % CI 1·68, 6·35), $P \leq 0$·001) and health rating as very good or excellent (OR = 4·71, (95 % CI 1·95, 11·4), $P \leq 0$·05).
### Conclusions:
Future health-promoting interventions should focus on increasing knowledge of specific nutrition areas and consider the personal and academic factors affecting NK in university students.
## Study population and design
This cross-sectional study took place in two London-based UK Higher Education Institutions which provide both health (e.g. Medicine, Nursing, Midwifery, Physiotherapy) and non-health courses (e.g. Engineering, Art and Design, Business), giving the opportunity to recruit students across different academic disciplines. The study was approved by the two universities’ Ethics Committee. Eligible participants were students from all ages (above 18 years old), independent of their mode of attendance (part-time or full-time) or studying status (undergraduate or postgraduate) with no further exclusion criteria applied. The link to the online survey was circulated via both universities’ electronic mail systems and the online survey was open during one academic year (2017–2018). The survey was anonymous, participation was voluntary and no survey questions were mandatory. Informed consent was obtained by clicking to ‘agree’ with consent statements prior to entering the survey. Power sample size was calculated based on Kliemann et al.[14], which found that the standard deviation of the NK score of non-dietetic students is 9·2. Assuming a mean score of 65 in our sample (scores range from 0 to 88) and a sd of 9·2, a sample size of 180 participants was considered sufficient.
## Nutrition knowledge
NK was assessed using the revised General Nutrition Knowledge Questionnaire (GNKQ-R)[14]. The GNKQ-R assesses the following four sections of NK: [1] dietary recommendations (eighteen items); [2] nutrient sources of foods (thirty-six items); [3] healthy food choices (thirteen items); and [4] diet–disease relationships and weight management (twenty-one items) and overall knowledge (sum of all sections, totalling eighty-eight items). Examples of questions in the first section included whether experts recommend eating more or less foods from various groups (e.g. fruits, vegetables, wholegrains, oily fish and fats) as well as the recommended servings according to the UK Eatwell Guide. Examples of questions in the second section included whether specific foods (breakfast cereals, ketchup, cheese, etc.) are high or low in added sugars, salt, fibre, protein or specific type of fats. In the third section, participants were asked to choose the healthiest option from a list of meals, desserts or foods cooked in different ways. Examples of questions in the fourth section included whether the intake of specific foods and nutrients such as red meat, sugar and fibre increases or decreases the risk of diseases such as cancer and type 2 diabetes. This section also included questions about good practices to maintain a healthy body weight, such as reading food labels and avoid grazing throughout the day. Each question had only one option and the correct answer was given one point (otherwise null). The GNKQ-R has high internal consistency (Cronbach’s α > 0·7) and external reliability (intraclass correlation coefficient >0·7) in all sections[14]. Before administration, a pilot study was undertaken to estimate the feasibility of the survey.
Demographic- and academic-related questions were included at the end of the GKNQ-R survey. Students were asked about their sex (i.e. male and female), age (years), university enrolled, Faculty enrolled (i.e. Arts and Social Sciences; Health, Social Care and Education; Business and Law; Art, Design and Architecture; Science, Engineering and Computing and Medicine; Biomedical Sciences), studying status (i.e. undergraduate and postgraduate), having any nutrition qualification (i.e. yes and no), current living arrangement (i.e. living with parents/carer/family, sharing a house or flat, living in a student accommodation and living alone in a house or flat), ethnicity (i.e. White, Black, Asian, other or mixed background), perceived health (e.g. poor, fair, very good and excellent), being on a special diet (e.g. yes or no with further clarification on the type of the diet), body weight, stature and whether being a smoker (i.e. yes and no).
## Data analysis
Initially, 301 participants entered the study, of which 249 participants provided consent and 190 completed at least 90 % of the questionnaire and were included in the final analysis. Multiple imputation was performed to account for the missing values, under the missing at random assumption. Missing values of scores, along with students’ demographic and academic characteristics, were replaced with imputed values (five imputed values were selected for each missing cell) and the analysis was performed for all five datasets. Statistical analysis was performed using the statistical program R and the package Amelia II[23] (for missing values imputation) and the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, Version 24.0). The statistical significance level was set at P ≤ 0·05.
The Shapiro–Wilk test as well as Q-Q (quantile-quantile) probability and cumulative frequency plots were used to determine the normality of data distribution. The null hypothesis of the test was rejected for GKNQ-R ($P \leq 0$·001); therefore, non-parametric tests were used in the data analyses. Descriptive characteristics of the participants are presented as means and standard deviations or as absolute and relevant frequencies. Descriptive statistical analyses were performed to calculate the median scores and interquartile ranges of each knowledge section and overall NK.
The Kruskal–Wallis test was used to compare the median values of overall GNKQ-R scores in the various groups of students. The categorical variable ‘field of study’ was created based on the Faculty of study to group students into healthcare and non-healthcare field of study. The healthcare field of study included students from the Faculties of Health, Social Care and Education, Medicine and Biomedical Sciences and those from other Faculties holding a nutrition qualification. Students from the remaining Faculties and with no nutrition qualification were included in the non-healthcare field of study. The median score of overall NK of the sample population was used as a cut-off point to indicate the level of NK, as suggested in similar studies[18,24]. Students with scores equal to or above than this value were categorised as having ‘good’ and those with lower values were categorised as having ‘poor’ NK. Chi-square (χ2) tests were used to examine the level of NK (‘poor’/‘good’) according to sex, age, ethnicity, field of study, studying status, living arrangement, being on a special diet and perceived health.
Binary logistic regression analysis was performed to identify significant predictors of good NK (dependent variable). A stepwise forward variable selection was used to identify all independent variables with a significant bivariate crude association with the dependent variable. Prior nutrition qualification was excluded from the analysis as it interacted with the ‘field of study’ variable, while sex and BMI variables were included, despite no significant association being found, as evidence suggests they are predictors of NK [4,21]. The analysis was performed for all five imputed datasets, returning similar results. For simplicity reasons, the findings of a single dataset are presented.
## Results
The characteristics of the study population are presented in Table 1. The majority of students were female (68·9 %), of White ethnicity (59·5 %), undergraduate (78·4 %) and younger than 25 years old (62·6 %). The final sample had a mixed population, with 41·1 % of students enrolled in a healthcare course and 58·9 % enrolled in a non-healthcare course. About one-third of students (33 %) were living with their family or sharing a house and one-quarter of students (24·7 %) were living in student accommodation. When asked to rate their health, 27·4 % perceived their health as ‘very good’ or ‘excellent’. The majority of students (64·2 %) had a normal BMI with a mean value of 24·6 ± 5·7 kg/m2 and about one-third (35·8 %) belonged to the overweight/obese BMI category (i.e. BMI ≥ 25 kg/m2). Finally, very few students (14·2 %) reported being on a special diet (e.g. vegetarian and vegan).
Table 1Description of demographic and academic characteristics of the sample population (n 190)VariableNumber of participants (n)Percentage of cohort (%)Age (years) ≤ 2511962·6 >257137·4 Mean25·7 sd 8·1Sex Female13168·9 Male5931·1Ethnicity White* 11359·5 Black†, Asian‡, Mixed/Other§ 7740·5Studying status Undergraduate14978·4 Postgraduate4121·6Field of study Healthcare|| 7841·1 Non-healthcare11258·9Living arrangement With parent(s)/carer/family6333·2 *Sharing a* house or flat6433·7 Student accommodation4724·7 Alone (in house/flat)168·4Perceived health Poor/fair5830·5 Good8042·1 Very Good/excellent5227·4Being on a special diet No16385·8 Yes2714·2Being a smoker No16687·4 Yes2412·6BMI (kg/m2) Underweight/normal weight (<25·0)12264·2 Overweight (≥25)6835·8 Mean24·6Min = 16, max = 53 sd 5·7*White British, White Irish or other White ethnic background.†Black British, Black Caribbean, Black African or other Black ethnic background.‡Indian, Pakistani, Bangladeshi, Chinese or other Asian ethnic background.§White and Black Caribbean, White and Black African, White and Asian or other mixed ethnic background.||The healthcare field included students from the Faculty of Health, Social Care and Education and the Medicine/Biomedical Sciences and those from other Faculties with a nutrition qualification. All other students were included in the non-healthcare field.
Students had an overall NK median score of 64 out of 88 points (72·7 %) (Table 2). With regard to the subsections of knowledge, students had a median score of 14 out of 18 points on the section of dietary recommendations; a median score of 25 out of 36 points on the section of nutrient sources of foods; a median score of 10 out of 13 points on the section of healthy food choices and a median score of 15 out of 21 points on the section of diet–disease relationships and weight management. Students’ responses to individual questions were further investigated to gain a better understanding of their knowledge within each section (data not shown). With regard to dietary recommendations, about half of the students were not aware of the recommendations of increasing wholegrain intake, reducing alcohol intake to one drink both for men and women, two glasses of fruit juice count only as one serving of fruit and starchy foods should make up a third and not a quarter of our diet. With regard to food groups and the nutrients they contain, less than half of the participants identified that breakfast cereals and bread are hidden sources of salt and about half of them were not aware that regular pasta has a low fibre content opposed to plantains which have a high fibre content. When asked about the type of fat contained in various foods, only one in five students reported that sunflower oil is rich in polyunsaturated fat, one in four reported that olive oil is rich in monosaturated fat and one in three reported that eggs are rich in cholesterol, with many students choosing ‘not sure’ as an option to these questions. In the section of healthy food choices, students performed well in general and managed to select the healthiest option when asked about different types of meals, drinks and desserts. However, their knowledge was not as strong when asked which cooking method, that is, sauteing, grilling or baking, requires fat to be added, with only one-third of participants choosing sauteing as the correct answer. In the last section of diet and disease relationships and weight management, about half of the students reported correctly that eating less red meat helps prevent cancer and that a high protein diet does not help to maintain a healthy weight.
Table 2Nutrition knowledge of the sample population (n 190)Nutrition knowledgeMaximum knowledge scoreMedian scoreIQRDietary recommendations1813·53Nutrient sources of foods3625·06Healthy food choices1310·03Diet–disease relationships and weight management2115·04Overall nutrition knowledge8864·012Level* n %Good8946·8Poor10153·2GNKQ-R, General Nutrition Knowledge Questionnaire-Revised.*Good nutrition knowledge is defined as having an overall median GNKQ-R score ≥ 64 points and poor knowledge as having a score <64 points.
The median scores of overall NK among the different groups of students as well as the number of students with ‘poor’ or ‘good’ levels of NK for each group are presented in Table 3. In particular, the median scores of knowledge were higher for students in the healthcare field of study compared to students in the non-healthcare field of study (66·0 v. 62·0, $P \leq 0$·05). However, the number of students with a ‘good’ or ‘poor’ level of NK did not differ significantly within each group ($$P \leq 0$$·106). Students of White ethnicity also had higher median scores of NK than students of Black, Asian or other/Mixed ethnicity (66·0 v. 61·0, $P \leq 0$·001), with 70·1 % of students of the Black, Asian and other or Mixed ethnic groups demonstrating ‘poor’ level of NK and 29·9 % demonstrating ‘good’ level of NK ($P \leq 0$·001). Similarly, students with a ‘good’/‘excellent’ perceived health had higher median scores of NK compared to students who perceived their health as ‘good’ or ‘poor’/‘fair’ (68·0 v. 63·5 v. 61·0, $P \leq 0$·05), with 70·7 % of students in the ‘poor’/‘fair’ category demonstrating ‘poor’ level of NK and 29·3 % demonstrating ‘good’ level of NK ($P \leq 0$·001). A marginally significant trend was found for age, with students aged 25 years or above 25 years having higher median NK scores compared to their younger counterparts ($$P \leq 0$$·049); however, no significant differences were found for the level of knowledge within each group. No significant differences in median scores or level of knowledge were found according to sex ($$P \leq 0$$·145), BMI ($$P \leq 0$$·846), studying status ($$P \leq 0$$·460), living arrangements ($$P \leq 0$$·229) and being on a special diet ($$P \leq 0$$·134).
Table 3Number of students with poor or good level of nutrition knowledge and the median scores of overall nutrition knowledge by socio-demographic and other categorical variables in the student population (n 190)VariableOverall nutrition knowledgePoor nutrition knowledgeGood nutrition knowledge* Median scores n within group variable% n within group variable%Age category ≤25 years62·06857·15142·9 >25 years66·03346·53853·5 $$P \leq 0$$·049 X2 = 2·03, $$P \leq 0$$·154Field of study Healthcare66·03646·24253·8 Non-healthcare62·06558·04742·0 $$P \leq 0$$·042 X2 = 2·61, $$P \leq 0$$·106Ethnicity White66·04741·66658·4 Black, Asian, Mixed/Other61·05470·12329·9 $$P \leq 0$$·000 X2 = 15·0, $$P \leq 0$$·000Perceived health Poor/fair61·04170·71729·3 Good63·54353·83746·3 Very Good/excellent68·01732·73567·3 $$P \leq 0$$·001 X2 = 16·0, $$P \leq 0$$·000GNKQ-R, General Nutrition Knowledge Questionnaire-Revised.*Good knowledge is defined as having an overall median GNKQ-R score ≥ 64 points.
The logistic regression analysis showed that age, perceived health and ethnicity were significant predictors of good NK (Table 4). In particular, students who rated their health as ‘very good’ or ‘excellent’ were 4·7 times more likely to have ‘good’ NK compared to students who rated their health as ‘poor’ or ‘fair’ (OR = 4·71, (95 % CI 1·95, 11·37), $P \leq 0$·05). Similarly, those of White ethnicity were three times more likely to have ‘good’ NK compared to students of ethnicity other than White (OR = 3·27, (95 % CI 1·68, 6·35), $P \leq 0$·001). An association was also found between age and knowledge, as a 1-year increment in age could increase the level of ‘good’ NK by 5 % (OR = 1·05, (95 % CI 1·00, 1·1), $P \leq 0$·05). No significant association was found between sex ($$P \leq 0$$·672), BMI ($$P \leq 0$$·733) and field of study ($$P \leq 0$$·609) and ‘good’ NK. The overall model predicted 17·1 % of the dependent variable (Model Summary R2 = 0·171, $P \leq 0$·001).
Table 4Predictors of good nutrition knowledge among university students (n 190)Dependent variablePredictorsOR95 % CI P-valueLowerUpperGood nutrition knowledgeAge (years)1·051·001·10·041Perceived health Poor/fair (RG) Good1·790·823·910·144 Very good/excellent4·711·9511·40·001Ethnicity Black/Asian/Mixed/Other (RG) White3·271·686·350·000RG, reference group. The full model included age, BMI, perceived health, field of study, ethnicity and sex.
## Discussion
The current study aimed to investigate the level of NK, factors affecting knowledge and predictors of good NK in a sample of university students in the UK. The majority of participants were White, female, undergraduate students, younger than 25 years old, which is comparable to populations in similar studies[4,8], suggesting that specific groups of students might be more interested in participating in health-related surveys. Most students had a normal BMI, and about one-third were overweight or obese (Table 1). These numbers are consistent with those found in a large cross-sectional study in the USA, where about one-third of students were overweight or obese[8]. In the UK, the study by Cooke & Papadaki[4] found a slightly lower number of overweight and obese students (24 %). Although both studies used self-reported anthropometric measures, Cooke & Papadaki[4] included a larger sample size (n 500) across thirty-seven universities in the UK (outside the London area), which might explain the differences found in the prevalence of obesity. In this study, the majority of students seemed to follow a healthy lifestyle in terms of not smoking and maintaining a healthy body weight even though only 24·7 % rated their health as ‘very good’ or ‘excellent’ (Table 1). Due to the specific characteristics of this population, it was expected that students would be more aware of healthy nutrition. Existing evidence suggests that adults with an increased education level in the UK have higher levels of NK compared to those with lower or no qualifications[25]. Also, about half of the participants (41·1 %) were from a healthcare field of study, such as Midwifery, Nursing, and Medicine, and were expected to have had some previous exposure on nutrition education during their courses, although the year of study was not included as an independent variable.
In the current study, students had a median NK score of 64 out of 88 (72·7 %) (Table 2). These scores were higher than the ones reported in studies previously undertaken in the UK (65·5 %)[4] and Croatia (67·4 %)[12] which used the same questionnaire (old version) to assess knowledge. The findings reported in the UK in 2013[4] and the current study suggest that the level of knowledge of students in the UK has slightly improved over the last 5 years, although it is not clear to what extent the assessment method and characteristics of the sample population of the two studies (field of study and geographical differences) affected overall NK.
A closer investigation of individual responses showed that very few participants answered correctly the questions related to fat, salt and fibre, indicating gaps in knowledge about these nutrients and the foods containing them. This lack of knowledge could further explain the findings of previous studies reporting that university students consume high amounts of fats[26,27] and salt[28] and low amounts of dietary fibre[27]. A high number of students in this current study were aware of the healthiest meals and desserts from a list of options, which might be consistent with the fact that 64·2 % of students had a healthy BMI; however, the dietary habits and physical activity levels were not investigated in this study which means that valid conclusions cannot be made. Although the current study demonstrated an adequate level of knowledge in the section of diet–disease relationship and weight management questions, many students failed to answer correctly the questions about optimal practices to maintain a healthy body weight or prevent weight gain with some students answering that following a high protein diet, taking nutritional supplements or avoiding fat from diet are orthodox practices. Fad diets, which usually include the elimination of food groups from diet, are popular and common practices to lose weight, especially in females[29] and overweight young adults[30] due to their ‘promising’ quick and easy outcomes. Trying to lose weight is a concern occupying not only overweight, but also many students with a healthy body weight[31]. A study among 38 204 university students in the USA demonstrated that students with false perceived body weight were more likely to engage in unorthodox weight loss practices, and only one-third of those trying to lose weight did so by following a balanced diet and exercise[31].
The current study found that less than half of the students (46·8 %) reached a ‘good’ level of NK (median score ≥ 64 points) (Table 2). Although participants studying a healthcare discipline had significantly higher median scores than students from non-healthcare disciplines ($P \leq 0$·05), ‘good’ level of NK was not positively associated with field of study ($$P \leq 0$$·555) or differed between students in healthcare and non-healthcare courses ($$P \leq 0$$·154). These findings are consistent with other studies demonstrating that students from Theoretical and non-Medical Practical Sciences had lower NK compared to students from Medical or other Health Sciences or those with a nutrition qualification[17,20,21], justifying the initial speculation that students from a healthcare course had been somehow exposed to and were more knowledgeable about good nutritional practices. However, it is not clear whether this increased knowledge reaches a satisfactory level that could affect the dietary habits of students. Other studies have found that prior nutrition education or studying a health-related course did not significantly impact knowledge[5,32], indicating that nutrition education interventions should be applied to students across all academic disciplines.
Age, ethnicity and perceived health were significant predictors of ‘good’ NK (Table 4). Students who rated their health as ‘very good’ or ‘excellent’ were more likely to have ‘good’ knowledge compared to students who rated having ‘poor’, ‘fair’ or ‘good’ health, implying that feeling healthier is related to better NK. These findings are in line with the study by Matthews et al.[5] which found that students from Health Sciences felt more confident to claim that they have good knowledge of nutrition topics. Senior students were also found to have greater knowledge compared to junior students. This is consistent with the findings of similar studies[18,19] and might partially explain the so-called phenomenon of ‘freshmen fifteen’, which refers to the belief that students gain fifteen pounds (6·8 kg) during their first year of studies[33]. Although pooled evidence from meta-analyses showed that the actual weight gain is much less (1·36 kg)[34], body weight and dietary habits seem to start changing unfavourably during the first year of studies[35,36], highlighting the importance of implementing interventions to increase knowledge and awareness of healthy eating in early academic years. The study also found that White students had higher levels of knowledge compared to students with Black, Asian or other/mixed ethnicity. This might be due to the different cultural and culinary traditions of the different ethnic groups. What is further alarming is that even dietetic students seem not to be knowledgeable of the food habits and health beliefs of individuals from different ethnic groups, as reported in McArthur et al.[37]. These findings suggest that the cultural background of students might play an important role on their dietary knowledge and behaviour, which may be overlooked in current health-promoting strategies.
Sample limitations of the current study include a non-stratified sample with a high dropout rate (37 %), as 190 out of 301 participants who entered the study and provided consent completed at least 90 % of the questions. BMI was calculated based on self-reported data, providing less accurate values (underreporting) when compared with data assessed with objective methods[38]. However, self-reported measures of BMI seem to have a small effect on associations observed in epidemiological studies and can still provide important information[38]. To the best of our knowledge, this is the first study that used the revised version of the GNKQ published in 2016, exploring the knowledge on updated nutritional information, such as oily fish, hidden sources of salt, alcohol intake, cooking methods, food labelling and optimal practices to maintain a healthy body weight. Besides Cooke & Papadaki[4], who investigated NK as a predictor of food label use in a sample of university students in the UK, no similar studies have been conducted in the UK to investigate gaps in NK and factors affecting this knowledge in students. It should be noted though that the GNKQ-R included only multiple choice questions, which allowed students to guess the right answer or choose it by excluding the obvious wrong answer. Also, the relationship between knowledge and dietary habits as well as the impact of the environment and social support on dietary behaviour was not explored in this study. Students were recruited from two London-based universities, one of which provided only health-related academic disciplines. This resulted in having a high number of participants from healthcare courses which reduces the generalisability of our findings, although, concurrently, it provided the opportunity to explore the impact of the field of study on NK. It is important to note that both universities attract a high number of diverse students and international students, which is very common for London-based universities and might explain the differences in knowledge found between the different ethnic groups[39]. It may also imply that the lifestyle challenges and difficulties students face during the transition from high school to university might be more intense for the students who just relocated to the UK for studying. However, these are speculations and are not addressed by this research.
## Conclusions
Students demonstrated good knowledge in the section of healthy food choices; however, their knowledge about the nutrient sources of foods was inadequate. Gaps in knowledge were found regarding the intake of fats, salt and good practices of weight management, indicating areas for improvement when designing nutrition education interventions. When assessing knowledge, using a mixed-methods research design or enhancing the quantitative data with open-ended questions might help to elaborate and gain an in-depth understanding of students’ knowledge. When investigating NK, researchers should consider the academic discipline but also the different cultural and ethnic backgrounds of students, as this study found that White students and students from a healthcare field of study demonstrated higher levels of NK. Among students from a healthcare field of study, the majority did not manage to demonstrate a good level of NK, suggesting that nutritional education interventions would be beneficial to all students, irrespective of their course. Finally, more research is needed to investigate the reliability and validity of the sources of information that students use to gain knowledge on nutrition and weight management practices. In order to inform policy actions, future research needs to investigate to what extent NK affects students’ dietary habits, alongside the impact of the environmental and social factors on dietary behaviour.
## Conflict of interest:
All authors declare no conflict of interest.
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|
---
title: 'Nutrient intake in low-carbohydrate diets in comparison to the 2020–2025 Dietary
Guidelines for Americans: a cross-sectional study'
authors:
- Maximilian Andreas Storz
- Alvaro Luis Ronco
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC9991840
doi: 10.1017/S0007114522001908
license: CC BY 4.0
---
# Nutrient intake in low-carbohydrate diets in comparison to the 2020–2025 Dietary Guidelines for Americans: a cross-sectional study
## Body
Low-carbohydrate diets (LCD) are a matter of controversy[1], but have recently been promoted for a variety of health conditions, including type 2 diabetes and obesity[2]. A reduced overall intake in carbohydrates is common to all low carbohydrate approaches, but a clear consensus on what defines an LCD is missing[3]. Traditionally, LCD derive < 26 % of total energy from carbohydrates or include < 130 g of carbohydrates per day (Fig. 1)[3].
Fig. 1.Definitions of ‘low-carbohydrate diet’. Several definitions for low-carbohydrate diets (LCD) exist, either based on percentage of total energy from carbohydrate or based on total daily carbohydrate load. Modified from Oh et al.[3]. Modified from Servier Medical Art database by Servier (www.smart.servier.com Creative Commons 3.0).
Very LCD derive < 10 % of total energy from carbohydrates[3]. A special form of very LCD is the ketogenic diet, which generally limits carbohydrate intake to 50 g/d[4]. The so-called ‘classic’ ketogenic diet is characterised by a 4:1 ratio of fat to carbohydrates[2,4]. As such, fat may provide more than 90 % of total energy intake[4].
Unfortunately, the large heterogeneity in definitions of LCD makes comparison of clinical trials using that particular dietary approach difficult. Furthermore, LCD often differ in their diet composition (e.g. with regard to carbohydrate quality), which may also lead to conflicting results in clinical studies[5].
While occasionally recommended for weight loss[6], reliable long-term data indicating sustainable dietary effects are scarce[1]. One of the frequently mentioned potential short-term benefits of LCD is improved glycaemic control in individuals with type 1 diabetes and in individuals with overweight[7,8]. Ketogenic diets are probably best known for their potential benefits in children with drug-resistant epilepsy, although evidence for their effectiveness in adults remains uncertain[9].
Yet, there are various studies which associated carbohydrate-restricted dietary patterns with adverse health outcomes, including (but not limited to) an increased risk of type 2 diabetes in men[10], weight gain[11] and cardiac arrhythmias[12]. LCD have also been associated with an increased overall and cause-specific mortality in two large independent studies[13,14].
Nevertheless, the percentage of US adults following an LCD more than doubled between $\frac{2007}{2008}$ and $\frac{2017}{2018}$, and increased from 0·9 % to 2·2 %[15]. Some researchers observed this trend with great concern and highlighted potential nutritional deficiencies and impaired overall diet quality when following LCD[2,16].
LCD are typically high in saturated fat and cholesterol[17], while often low in certain vitamins (A, E and B6) and micronutrients (such as Mg and potassium)[16,17]. Freedman, King and Kennedy emphasised that high-fat, low-carbohydrate diets are nutritionally inadequate[16], whereas other authors provided opposite results[18]. Despite these persistent controversies, positive media support for low-carbohydrate-high-fat diets may be tempting for many individuals[11], who are often unaware of potential nutritional deficiencies when restricting carbohydrates. Unawareness may also be present with regard to the excessive intake of potentially harmful nutrients (e.g. saturated fat).
To raise awareness, the present study sought to re-visit diet quality and nutrient intake in a nationally representative sample of US adults following an LCD. Using data from the National Health and Nutrition Examination Surveys (NHANES), we compared nutrient intake profiles of self-identified low-carbohydrate dieters with the daily nutritional goals (DNG) specified in the current 2020–2025 Dietary Guidelines for Americans (DGA)[19]. We sought to investigate for which nutrients the DNG were met, and whether an insufficient (or excessive) nutrient intake occurred. Moreover, we compared the intake profiles of LCD consumers with the US general population denying a special diet.
## Abstract
The percentage of US adults following low-carbohydrate diets (LCD) doubled in the last decade. Some researchers observed this trend with concern and highlighted the potential for nutritional deficiencies and impaired overall diet quality with LCD. The present study investigated nutrient intake in a nationally representative sample of 307 US adults following an LCD. Using cross-sectional data from the National Health and Nutrition Examination Surveys, we compared nutrient intake profiles in said individuals with the daily nutritional goals specified in the current 2020–2025 Dietary Guidelines for Americans (DGA). Results were then compared with the general population consuming a standard American diet. Almost 57 % of low-carbohydrate dieters were female, and the mean age was 48·67 (1·35) years. Individuals consuming LCD exceeded the recommendations for saturated fat, total lipid and sodium intake (both sexes). An insufficient intake was observed for fibre, Mg, potassium and several other vitamins (vitamins A, E, D in both sexes as well as vitamin C in men and folate in women). Neither men nor women met the recommendations for fibre intake. A comparable picture was found for the general population. The potentially insufficient intake of several essential nutrients in LCD warrants consideration and a careful assessment with regard to the current DGA.
## The Health and Nutrition Examination Surveys
The NHANES is a cross-sectional and nationally representative US-based survey designed to assess the health and nutritional status of noninstitutionalised adults and children in the USA[15,20]. NHANES is conducted by the National Center for Health Statistics, which is part of the Centers for Disease Control and Prevention. The survey examines a sample of approximately 5000 persons per annum and includes demographic, socio-economic, dietary, and health-related interview questions.
Interviews are standardised and conducted in participants’ homes[20]. Health measurements are performed in specially equipped and designed mobile examination centres, which travel to locations throughout the entire country[15,20]. A detailed description of the NHANES may be obtained from the official NHANES homepage[21].
## Population
We merged and appended multiple NHANES modules, including the dietary interview module and the demographics public release file[22,23], which contains demographic data (age, sex, race/ethnicity, marital status, etc.) and the sampling weights. The dietary interview component included detailed dietary intake information from NHANES participants. We obtained estimates of energy and nutrient intake from the first day of the dietary recall and extracted information on all nutrients, vitamins and minerals included in the DNG Table A1–2 in the 2020–2025 DGA[19]. To calculate the percentage of total energy from each macronutrient, we used Atwater’s values for the metabolisable energy of macronutrients[24,25].
As part of the dietary interview, participants were asked ‘Are you currently on any kind of diet, either to lose weight or for some other health-related reason?’[22]. Those who answered with ‘yes’ were subsequently asked ‘What kind of diet are you on?’. No list of diets or standardised definitions were provided. Responses were categorised to ‘weight loss or low energy diets’, ‘low fat/low cholesterol diet’, ‘low salt/low sodium diet’, ‘sugar-free/low-sugar diet’, ‘low-fibre diet’, ‘high-fibre diet’, ‘diabetic diet’, ‘low-carbohydrate diet’, ‘weight gain/muscle building diet’, ‘high protein diet’ or ‘other special diet’. From 2009 onwards, additional diets (e.g. ‘gluten-free or celiac diet’) were added. For the present study, we investigated nutrient intake in those individuals following an LCD. In addition, we investigated nutrient intake in the general population that denied consumption of a special diet (answering ‘no’ to the aforementioned question on special diets) and consumed the average American diet. Nutrient intake profiles of both groups were then descriptively compared with the DNG in the current DGA (without statistical calculations).
## Dietary Guidelines for Americans
The DGA is the cornerstone of US Federal nutrition policy and nutrition education activities[26]. A major aim of the DGA is to provide food-based recommendations to promote health and to help prevent diet-related diseases. The DGA is published jointly by the US Department of Health and Human Services and the US Department of Agriculture every 5 years.
Designed for nutrition and health professionals to support all individuals consume a healthy, nutritionally adequate diet, the current DGA encompasses 164 pages. Given the many dietary components of public health concern for the general US population (potassium, dietary fibre, vitamin D, etc.), the DGA also include age–sex-specific nutritional goals which can be found in the appendix (page 131)[19].
DNG are available for both sexes and stratified by age groups (e.g. 19–30 years, 31–50 years and 51+ years). We made use of this classification and descriptively compared nutrient intake in LCD consumers (and the general population denying a special diet) with the DNG stratified by age–sex groups. The case number of individuals aged 18 years or younger following an LCD was severely limited (< 30 individuals). Thus only individuals aged 19 years or older were considered eligible for this study.
The DNG in the current DGA stem from various sources. Sources and concepts include adequate intake, Acceptable Macronutrient Distribution Range (AMDR), Chronic Disease Reduction Level, DGA and the Recommended Dietary Allowance (RDA). The adequate intake is a dietary recommendation used when there is not enough available scientific data to calculate an average nutrient requirement[27]. An adequate intake is the ‘average nutrient level consumed daily by a typical healthy population that is assumed to be adequate for the population’s needs’. RDA are the ‘levels of intake of essential nutrients that […] are judged by the Food and Nutrition Board to be adequate to meet the known nutrient needs of practically all healthy persons’[28]. The Chronic Disease Reduction Level represents the lowest level of intake of a nutrient for which there is sufficient evidence to characterise a chronic disease risk reduction. In this analysis, it is used only for sodium and reflects the intake above which intake reduction is expected to reduce chronic disease risk within an apparently healthy population[29]. The AMDR describes recommendations for macronutrient intake in the context of a complete diet and expresses intake recommendations as a percentage of total energetic intake[30]. Epidemiological evidence suggested that consumption within these ranges may also play a role in reducing risk of chronic diseases[31].
Our analysis covered all nutrients, vitamins and minerals included in the DNG Table A1–2 of the current DGA (2020–2025)[19]. Nutrients included carbohydrate, protein and fat (reported in g/d and in %/total kcal intake), saturated fat intake, linoleic acid intake and linolenic acid. Minerals included Ca, iron, Mg, phosphorus, potassium, sodium and Zn. Vitamins included vitamins A, E, D, C, B1, B2, niacin, B6, B12, K and folate. Finally, we analysed fibre intake in all groups.
## Statistics
The NHANES sample is selected through a complex, multistage probability design[15], and we used the ‘svyset’ and ‘svy’ commands for all statistical procedures to properly account for population weights and the NHANES survey design characteristics. We conducted all statistical analyses with STATA software version 14 (StataCorp.)[32]. We performed unconditional subclass analyses (preserving the main survey design and providing larger standard errors) to estimate nutrient intake in self-identified LCD followers[33]. To increase the sample size for analyses stratified by population subgroups, we appended six NHANES survey cycles (2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016 and 2017–2018). We only included individuals with a full data set.
Normally distributed variables were described with their mean and standard error in parentheses. For categorical variables, we reported the number of observations (n) as well as weighted proportions (with their corresponding standard error) in parenthesis.
To facilitate comparison between estimated nutrient intake in low-carbohydrate dieters and the recommendations found in the DGA, we employed colour coding for all tables. Green colour indicates that the DNG were met, whereas red colour indicates a violation of the recommendations. This could be either an excessive intake (e.g. excessive sodium intake) or an insufficient intake (for example, a lack of potassium in diet), as indicated by the arrow direction. For energy intake (in kcal/d), orange colour coding was used. All comparisons were performed in an entirely descriptive way, without testing for statistical significance.
## Sample characteristics
After removal of individuals aged 18 years or younger and after removal of individuals with an incomplete data set (n 27 in the LCD group and n 19 086 in the general population), the analysed sample included n 307 individuals on a LCD. This may be extrapolated to represent 3 089 597 US Americans on an LCD. Sample characteristics are presented in Table 1.
Table 1.Sample characteristics(Numbers and percentages; standard errors)Individuals on an LCDGeneral populationNumber of observations (n)Weighted proportion (%) se Number of observations (n)Weighted proportion (%) se Age 19–30 years n 4617·113·15 n 572923·340·66 31–50 years n 11633·513·82 n 886635·550·62 51 years and older n 14549·394·80 n 12 19841·110·63Sex Male n 11942·724·18 n 13 43149·760·38 Female n 18857·284·18 n 13 36250·240·38Race/Ethnicity Mexican American n 324·811·14 n 41118·960·78 Other Hispanic n 213·100·98 n 27705·910·47 Non-Hispanic White n 15874·412·85 n 11 00165·511·48 Non-Hispanic Black n 435·391·02 n 585211·670·78 Other Race - Including Multi-Racial n 5312·292·14 n 30597·950·47LCD, low-carbohydrate diets. The sample included n 307 individuals on a low-carbohydrate diet and n 26 793 denying a special diet.
Mean age of the entire LCD cohort was 48·67 (1·35) years. Males who consumed an LCD were slightly older (48·71 (1·58) years) than females (48·64 (1·58) years). Our data also suggested that LCD were more popular among females. More than 74 % of the LCD cohort were of Non-Hispanic White origin. *The* general population denying any special diet included 26 793 individuals, which may be extrapolated to represent 194 297 552 US Americans. Sample characteristics may be obtained from Table 1.
## Macronutrient and fibre intake
Table 2 compares (macro-)nutrient and fibre intake in males following an LCD with the DNG in the 2020–2025 DGA, stratified by age group. In a similar style, Table 3 compares nutrient intake and fibre intake in females following an LCD with the current DGA.
Table 2.Macronutrient and fibre intake in males following a low-carbohydrate diets (LCD) compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)AMDR, Acceptable Macronutrient Distribution Range; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
Table 3.Macronutrient and fibre intake in females following a low-carbohydrate diet (LCD) compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)AMDR, Acceptable Macronutrient Distribution Range; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
Males on an LCD had a higher energy intake than females on an LCD, except in the group aged 19–30 years. Energy intake was below the recommended daily nutritional goal in both sexes, except in males and females aged 51 years or older (Table 2).
Individuals consuming an LCD exceeded the daily nutritional goals for protein and fat (in g/d). Males aged 31–50 years consumed more than twice as much total protein (113·53 (7·46) g) as recommended (56 g). Both sexes obtained < 41 % of total energy from carbohydrates. By the definition of Oh, Gilani, and Uppaluri, both groups moderately restricted carbohydrates [3]. The lowest carbohydrate intake (expressed as a percentage of total energy intake) was observed in males aged 19–30 years (31·94 %). Carbohydrate restriction was generally less pronounced in females (39·94 %, 40·20 % and 38·92 %) as compared with men (31·94 %, 33·26 % and 37·98 %).
Males aged 31–50 years and 51+ years consumed significantly more total fat (106·05 (8·61) g/d and 103·34 (10·06) g/d, respectively) than females (69·48 (4·96) g/d and 76·43 (4·68) g/d, respectively). In participants aged 19–30 years, females consumed slightly more total fat (81·41 (11·97) g/d) than males (80·67 (8·58) g/d). This pattern was also found with regard to saturated fat intake, monounsaturated fat intake and polyunsaturated fat intake. Males aged 31–50 years consumed, on average, 34·39 (3·28) g of saturated fat per day, whereas males aged 51+ years consumed 30·40 (2·52) g/d. In females, average consumption of saturated fat was lower in both age groups (22·38 (1·86) and 24·12 (1·94) g/d, respectively). Saturated fat intake (expressed as a percentage of total kcals) in both sexes exceeded the recommendations in the current DGA. Moreover, our data suggest that both sexes failed to meet the daily nutritional goals for fibre. Total fibre intake in g/d is reported in Tables 2–5. The lowest fibre intake was found in males aged 19–30 years on an LCD (11·01 g (2·46)). Notably, intake of 18:2 linoleic acid was also below the recommendations in males aged 19–30 years adhering to an LCD.
Table 4.Macronutrient and fibre intake in males denying a special diet compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)AMDR, Acceptable Macronutrient Distribution Range; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
Table 5.Macronutrient and fibre intake in females denying a special diet compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)AMDR, Acceptable Macronutrient Distribution Range; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
In a similar style, Tables 4 and 5 display macronutrient and fibre intake in males and females in the general population denying a special diet. When comparing energy intake in males on an LCD with males in the general population, Tables 2 and 4 suggest substantial differences. In all three age groups, energy intake in LCD consumers was well below the intake in the general population. Carbohydrate intake (expressed as a percentage of total energy intake) in the general population was consistently within the acceptable macronutrient distribution range. The same applied for total lipid intake (AMDR, Table 4). The male general population exceeded the DGA recommendation for SFA intake; however, compared with LCD consumers, the difference was less pronounced. When comparing females denying a special diet (Table 5) with females on an LCD diet (Table 3), we observed a comparable picture. Across all three age groups, females in the general population met the recommendations for carbohydrate intake (AMDR) and total lipid intake (AMDR).
## Mineral and vitamin intake
In a similar style, Tables 6–9 display mineral and vitamin intake, across all age categories. Males on an LCD did not meet the recommendations for potassium intake (Table 6) and Mg intake (except the group aged 19–30 years). Sodium intake exceeded the DGA recommendations, particularly in males aged 31–50 years. Ca intake in males aged 51+ years was also below the RDA in the current DGA. A comparable picture was found in the general population denying a special diet (Table 8).
Table 6.Mineral and vitamin intake in males following low-carbohydrate diets (LCD) compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)CDRR, Chronic Disease Reduction Level; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
Table 7.Mineral and vitamin intake in females following low-carbohydrate diets (LCD) compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)CDRR, Chronic Disease Reduction Level; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
Table 8.Mineral and vitamin intake in males denying a special diet compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)CDRR, Chronic Disease Reduction Level; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
Table 9.Mineral and vitamin intake in females denying a special diet compared with the daily nutritional goals (DNG) in the 2020–2025 Dietary Guidelines for Americans (DGA) stratified by age group(Mean values and standard errors of the mean)CDRR, Chronic Disease Reduction Level; RDA, Recommended Dietary Allowance; AI, adequate intake; based on[19].
Females aged 19–50 years on an LCD did not meet the recommendations for iron (Table 7). Intakes below the recommendations were also found for Ca (particularly in female LCD consumers aged 51+ years) and Mg. Again, a comparable picture was found in the general population (Table 9).
Moreover, both sexes failed to meet the recommendations for vitamins A and E. Males on an LCD also failed the recommendations for vitamin C (Table 6) across all age groups. A comparable picture was found in females aged 31–50 years on an LCD.
Notably, vitamin A intake was below the RDA in males aged 51+ years on an LCD (Table 6). In females on an LCD, this pattern was observed across in age categories. For intakes in the general population, the reader is referred to Tables 8 and 9.
NHANES reports vitamin D intake in μg/d (see Tables 6–9). One μg of vitamin D equals 40 IE, and as such both sexes were well below the RDA of 600 IE. An insufficient intake was also observed with regard to choline (in both sexes) and folate (in females reporting an LCD aged 31+ years only).
Intake of minerals and vitamins in the general population denying a special diet stratified by age groups may be obtained from Tables 8 and 9. Notable differences in males were found with regard to vitamin K and vitamin A. In females, there were notable differences with regard to folate,
## Discussion
The present study sought to investigate diet quality and nutrient intake in a nationally representative sample of US adults following an LCD. Individuals on LCD exceeded the recommendations for SFA intake, total lipid intake and sodium intake (both sexes). An insufficient intake was observed for fibre, Mg, potassium and several other vitamins (vitamins A, E, D in both sexes as well as vitamin C in men and folate in women aged 31 years or older). Our findings are of great concern, given the growing interest in LCD in the US general population within the last decade[15].
The ingestion of excessive amounts of SFA (as observed in individuals on an LCD in our study but also in the general population denying a special diet) is considered to be a risk factor for CVD, insulin resistance, dyslipidaemia and obesity[34]. A high intake of SFA favours a pro-inflammatory status that contributes to the development of insulin resistance and exerts lipotoxic effects on human tissues[34,35]. Furthermore, a high SFA intake has been associated with diabetes[36], coronary heart disease[37], hepatic and visceral fat storage[38] and cognitive decline[39]. A high intake of saturated or trans-unsaturated (hydrogenated) fats has also been associated with an increased risk for Alzheimer disease[40].
As such, researchers emphasised that dietary recommendations should continue to focus on replacing total saturated fat with more healthy sources of energy[37]. This has recently been confirmed by the WHO, which recommends limiting SFA intake to < 10 % of total energy intake[41]. The type and proportion of dietary fatty acids should also be considered. An increased intake of PUFA might attenuate the pro-inflammatory effects of a high SFA intake, in particular when taking n-3 PUFA into consideration[42]. On the other hand, reduced levels of vitamins A, C and E may enhance oxidative stress, and within such a context PUFA could be even more prone to oxidation (and thus favour inflammation)[43]. As such, it is advisable to reduce SFA intake overall.
Apart from not meeting the recommendations for SFA, individuals on an LCD in our sample also demonstrated an excessive sodium intake. Excess dietary sodium has been linked to elevations in blood pressure and other health repercussion[44]. A reduced sodium intake, in contrast, reduces blood pressure in adults and has also been associated with a reduced risk of stroke and fatal coronary heart disease[45]. In light of these findings, sodium intake in LCD consumers in our cohort appears of particular concern. The reservation must be made, however, that sodium intake in the general population consuming a Western diet showed a comparable picture.
Another point worth mentioning is the low fibre intake in our sample (both groups). Fibre-deficient diets have been associated with colon cancer and other bowel diseases as well as with obesity and type 2 diabetes[32,46]. Increased consumption of fibre-rich plant-based foods (up to a dietary fibre intake of 50 g/d) has been suggested as a potential strategy to extend lifespan and to improve the quality of the years gained by reducing the effects of diseases associated with high-income lifestyles[46]. Of note, LCD eaters in our sample did not meet the Institute of Medicine’s recommendations for fibre intake (suggesting an AI of 14 g/1000 kcal per day), highlighting once more the great risk for fibre deficiency with an LCD[47]. Again, the reservation must be made that fibre deficiency is a nutritional public health concern in the USA, as reflected by the intake data in the general population on a Western diet in our sample.
Additional deficiencies worth discussing include Mg, potassium and vitamins A, E and C (in males only). Vitamin A deficiency can lead to a series of ocular and dermatological symptoms and anaemia[48,49]. It has also been associated with a weak resistance to infection, which can increase the severity of infectious diseases and the risk of death[48]. The recommended dietary allowance (RDA) of vitamin A in the DGA in healthy adults is 900 μg/d for men and 700 μg/d for women. Individuals on an LCD (females in all age groups and males aged 51 years or older) in our sample failed to reach these targets. The same applies for vitamin E, where deficiencies have been associated with ataxia, neuropathy, anaemia and other health conditions[50].
Many issues regarding benefits and risks of LCD remain controversial or unresolved[51]. Some authors emphasised that advocates of carbohydrate restriction are often in open disagreement with nutritional authorities[52]. A prominent example is Sweden, where advocates of LCD dedicated themselves to achieving an overwhelming public presence in the propagation of simplified accounts of dietary science[53]. During these debates, potential health risks of LCD have often been pushed into the background.
The present study sought to re-visit diet quality and nutrient intake in a nationally representative sample of US adults following an LCD. By investigating reliable estimates of nutrient intake in LCD followers, the authors aim was to compare actual nutrient intake in low-carbohydrate eaters with up-to-date established dietary guidelines (DGA). This simple (but nationally representative) comparison revealed a series of nutrients of concern and emphasised once more that long-term adherence to LCD could potentially result in nutritional deficiencies[2]. Our findings are in accordance with previous studies and reviews, summarising potential deficiencies occurring upon consumption of an LCD. As suggested by Freedmann, King and Kennedy, we also observed a low intake in vitamins E, A, folate, Ca, Mg, potassium and dietary fibre[16]. Our findings also confirm the aforementioned study with regard to the high total fat and saturated fat intake.
Monitoring (and supplementation) of those critical nutrients in LCD consumers might thus be warranted. Nevertheless, our results should not distract from the fact that we observed a comparable picture in the general population. The current DGA emphasise many nutrients of public health concern (fibre, Ca, etc.) and our data somewhat confirm that[19].
Although cross-sectional (and thus with inherent limitations), our data allow for important descriptive insights into nutrient intake in LCD consumers. The overall picture is somewhat comparable to the general population consuming a typical Western diet and denying a special diet. An insufficient intake for many critical nutrients was observed in both groups, and the key features (e.g. the insufficient folate intake in females reporting an LCD) have been described in the Results section.
The present study draws upon a number of additional strengths. We present a nationally representative and large data set from the National Health and Nutrition Examination Survey with 307 individuals on an LCD. To the best of our knowledge, we also present the first comparison of nutrient intake in this group with the current DGA. Our sample may be extrapolated to represent 3 148 420 US Americans on an LCD and includes all nutrients presented in the DNG Table A1–2 in the 2020–2025 DGA. An additional asset is the analysis stratified by age–sex groups in full accordance with the current DGA.
Weaknesses included the cross-sectional nature of our data and the lack of other demographic variables describing our sample. Adding additional demographic variables of interest (such as marital status or income), however, would have reduced our total sample size, and as such we refrained from that step. The same applied for anthropometric data describing our sample and for information on carbohydrate quality. Furthermore, some participants reported consumption of more than one special diet at the same time. Thus, we must acknowledge that there was a certain overlap. LCD consumers also reported concurrent adherence to the following dietary patterns: weight loss diet, n 43; low sugar diet, n 31; low sodium diet, n 20; diabetic diet, n 17; high protein diet, n 28 and renal diet, n 1 and gluten-free diet, n 3. Simply excluding the aforementioned participants from the analysis would have led to a significant decrease in sample size, and as such we decided against that step. Given that LCD may be used for weight loss and are naturally higher in protein intake (subsequent to a shift in macronutrient distribution), one may also argue that adherence to certain combinations of special diets (e.g. LCD and weight loss diet or LCD and high-protein diet) may not be regarded as necessarily problematic. Of note, general aspects of special diets among adults in the USA have been analysed in detail by Stierman et al., and the reader is referred to their data report for additional information on that topic[15]. Whether LCD were prescribed by healthcare professionals (e.g. for the treatment of an underlying medical condition) or were self-initiated was not ascertainable from our data.
All comparisons were performed in a descriptive way without testing for statistical significance. The lack of weighted proportions of individuals meeting the recommended intakes in the DGA is another potential limitation of our analysis. Then again, the descriptive approach (comparing mean intakes in a large sample) also has its advantages and the employed colour coding may facilitate this process. Given that the violations for many nutrients appeared to be significant from a clinical perspective (e.g. the low fibre intake of only 12·69 g/d in female LCD consumer aged 31–50 years), we refrained from calculating additional p-values.
As with all dietary recall studies, NHANES dietary interviews are also subject to recall bias. Notably, some of the DNG found in the current DGA are meant to be met over time. Usage of a single 24-h-dietary recall to assess nutrient intake at a single point could thus be interpreted as problematic, although the recall method itself is considered reliable[54]. Finally, LCD status was self-reported, which may also have contributed to the aforementioned bias. The same may apply to individuals that denied consumption of a special diet; here again reporting and recall bias may not be fully excluded.
## Conclusion
Individuals following an LCD in this sample exceeded the recommendations for SFA, lipid and sodium intake while not meeting the intake recommendations for fibre, potassium and several other vitamins. Comparable intake violations were found in the general population. Given the continuously increasing interest in LCD in the US general population, our findings are of high importance. Although cross-sectional in nature, our data raise the possibility of inadequate nutrient intakes in LCD. Furthermore, given the continuously increasing interest in LCD in the US general population, we believe that our findings are important as evidence that does not support this dietary style as an entirely healthy one because of its potential deficiencies. Regular monitoring of critical nutrients (e.g. fibre, vitamins A and E, folate and iron) might thus be advisable.
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|
---
title: 'Characterisation of meat consumption across socio-demographic, lifestyle and
anthropometric groups in Switzerland: results from the National Nutrition Survey
menuCH'
authors:
- Linda Tschanz
- Ivo Kaelin
- Anna Wróbel
- Sabine Rohrmann
- Janice Sych
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991843
doi: 10.1017/S136898002200101X
license: CC BY 4.0
---
# Characterisation of meat consumption across socio-demographic, lifestyle and anthropometric groups in Switzerland: results from the National Nutrition Survey menuCH
## Body
Meat is known to contribute significant amounts of protein, minerals and vitamins to the human diet[1]. However, in large meta-analyses of prospective studies, certain meat categories, in particular processed meat (PM) and unprocessed red meat, have been associated with increased risk of several non-communicable diseases such as CVD[2,3], specific cancer types such as colorectal cancer[4] and type-2 diabetes[3].
In addition, these results indicate that a consumption level of more than 50–100 g/d of red meat is considered to be associated with health risks[2,4]. Several nutritional societies have revised their recommendations to limit the consumption of PM and red meat types(5–7). This information presents an interesting contrast to current dietary recommendations in Switzerland where the Swiss Food Pyramid presents meat alongside with fish and plant-based protein sources, such as tofu, on the fourth pyramid level[8]. Since 2011, adults have been advised to consume 100–120 g of these products daily. To meet this recommendation, consumption of two to three portions of meat (including poultry) weekly, but no more than one portion of PM per week, are recommended. Conducted every five years and since 1992, the Swiss Health Survey assesses the consumption frequency of selected food items using an abbreviated food list that includes the combined intakes of meat and meat products. According to this survey, from 1992 to 2017, the percentage of the Swiss population adhering to the recommendation of only consuming meat two to three times per week increased from 39 % to 47 %.[9]. Additionally, food balance sheet data from 2007 to 2018 have shown a declining trend in annual consumption of all meat types except poultry-based products[10]. The only other sources of quantitative meat consumption data in Switzerland are reports about individual regions and selected demographic groups[11,12].
In Switzerland, there is also a lack of national data that connect socio-demographics, lifestyle and meat consumption. The drivers behind meat consumption are complex and vary considerably between countries. From a public health and sustainability perspective, this knowledge is valuable for the development of more effective strategies to influence the habits of population subgroups with high meat consumptions[13]. We have previously shown differences in the consumption of PM[14] and in types of meat consumers (i.e. non-consumers v. low, moderate and high consumers)[15] in the Swiss population based on socio-economic, lifestyle and anthropometric factors. To date, no study has addressed whether and how these factors influence the consumption of unprocessed red meat and white meat in subgroups of the Swiss population.
In light of the above discussions, the aim of the current study was to examine the meat consumption of the Swiss population, focusing on total meat consumption, and in particular unprocessed red and white meat. Additionally, it will investigate how meat consumption is associated with selected socio-demographic, lifestyle and anthropometric factors.
## Abstract
### Objective:
Characterising meat consumption in Switzerland across socio-demographic, lifestyle and anthropometric groups.
### Design:
Representative national data from the menuCH survey (two 24-hour dietary recalls, anthropometric measurements and a lifestyle questionnaire) were used to analyse the total average daily intake of meat and main meat categories. Energy-standardised average intake (g/1000 kcal) was calculated and its association with 12 socio-demographic, lifestyle and anthropometric variables was investigated using multivariable linear regression.
### Setting:
Switzerland.
### Participants:
Totally, 2057 participants aged 18–75 years.
### Results:
Average total meat intake was 109 g/d, which included 43 g/d of processed meat, 37 g/d of red meat and 27 g/d of white meat. Energy-standardised meat intake was highest for men, the Italian-language region and the youngest age group (18–29 years). Regression results showed significantly lower total meat and red meat consumption (g/1000 kcal) for women than men. However, there were no sex-specific differences for white meat. Total meat and white meat consumption were positively associated with the 18–29 age group, compared with 30–44 years, non-Swiss compared with Swiss participants and one-parent families with children compared with couples without children. Consumption of all categories of meat showed positive associations for BMI > 25 kg/m2 compared with BMI 18·5–25 kg/m2 and for French- and Italian-language regions compared with German-language region.
### Conclusion:
The current study reveals that there are significant differences in the amounts and types of meat consumed in Switzerland, suggesting that evidence-based risks and benefits of these categories need to be emphasised more in meat consumption recommendations.
## Materials and methods
In the current analysis, data from menuCH, a population-based, cross-sectional National Nutrition Survey, were analysed to characterise meat consumption in Switzerland. The menuCH study included 2057 participants (response rate of 38 %), who were interviewed about their dietary habits in $\frac{2014}{2015.}$ Adults between 18 and 75 years of age were randomly drawn from a stratified sample intended to be representative of the seven Swiss regions (Lake Geneva Regions, Mittelland, Northwestern Switzerland, Zurich, Eastern Switzerland, Central Switzerland and Ticino) and of sex and age of the population (five age groups: 18–29, 30–39, 40–49, 50–64 and 65–75)[16]. Diet was assessed using two 24-hour dietary recalls (24HDR). The first 24HDR interview was conducted in person and the second was completed by telephone. During the first interview, anthropometric measurements (height, weight and waist circumference) were recorded according to the MONICA protocol from the WHO and used to calculate the BMI[17]. When body measurements were not possible (fourteen pregnant women, thirteen lactating and seven other participants), self-reported weight or height was used. Food consumption of the participants was recorded using the trilingual Swiss version (0·2014·02·27) of GloboDiet® software (formerly EPIC-Soft®, International Agency for Research on Cancer IARC, Lyon, France[18] adapted for Switzerland by the Federal Food Safety and Veterinary Office, Bern, Switzerland). Data cleaning was carried out using an updated version of the software (2015·09·28). The consumption data were linked to the Swiss Food Composition Database[19]. The menuCH questionnaire contained questions on nutrition and dietary habits as well as socio-demographic and economic factors. The short version of the International Physical Activity Questionnaire was used to assess the physical activity[20].
We used the consumption data from the two 24HDR interviews and selected socio-demographic and lifestyle information from the menuCH questionnaire to quantify and characterise meat consumption. According to a model used in our earlier work on menuCH data,[14],[15] the following socio-demographic, anthropometric and lifestyle variables were selected a priori and investigated in relation to meat consumption: sex (male, female), language region (German, French and Italian), age group (18–29, 30–44, 45–59 and 60–75 years), BMI category (< 18·5, 18·5–< 25·0, 25·0–< 30·0 and ≥ 30 kg/m2), nationality (Swiss only, Swiss with dual citizenship and non-Swiss), gross household income (< 6000, 6000–13 000 and > 13 000 Swiss Francs/month), education (primary-, secondary- and tertiary-level education), smoking status (never, former and current), household status (living alone, adult living with parents, one-parent family with children, couple without children, couple with children and others), physical activity level (low, medium and high), health status (very poor to medium, good to very good) and currently following a weight-loss diet (yes, no).
In our analysis, we differentiated between unprocessed red meat and unprocessed white meat, which corresponds to meat from mammals and poultry, respectively. All meat refers to ‘meat and meat products’ as defined in Globodiet® with the addition of meat from bolognaise sauce and the removal of meat substitutes (Fig. S1) and represents all meat consumption by menuCH participants. The groups ‘PM and sausages’, ‘bolognaise sauce’ and ‘meat skewer’ from Globodiet® were categorised as PM[14]. Unprocessed meat (UPM) included all meat not categorised as PM, and UPM was categorised into sub-categories of white, red and other UPM. Offal was included with the respective meat type: red meat included meat from mammals and its offal; white meat included poultry and its offal and other UPM included meat that was not specified by the participants and mixed meat. The total consumption of the most consumed UPM types (see online supplementary material, Supplemental Fig. S2) was also quantified according to the following sub-categories: beef, pork, lamb and veal from the red meat category and poultry from the white meat category. In this categorisation, all quantities of offal were combined to form a separate meat type (offal). The remaining UPM consisted of rarely consumed meat types such as game, horse, rabbit and goat as well as any meat consumption, which was not clearly specified by study participants, i.e. unspecified meat types.
Only participants who completed both 24HDR interviews were included in the analysis (n 2057). Mean daily meat consumption is presented in grams per day with the standard error of the mean (g/d; sem). To correct for sampling design and nonresponse, survey weighting factors such as sex, age, major areas of Switzerland, marital status, household size and nationality were applied to the means of all results. The means for consumption data were also weighted for season and weekday to account for uneven collection of 24HDR over the year and week.[21]. To facilitate comparisons between demographic groups, energy-standardised meat consumption (g/1000 kcal, sem) was calculated for the main meat categories and used to investigate associations between meat consumption and socio-demographic, anthropometric and lifestyle factors (sex, language region, age, BMI, nationality, education, household status, income, physical activity, smoking, health status and following a weight-loss diet) using multivariable linear regression. Results were presented as coefficients with a 95 % CI of energy-standardised meat consumption. All analyses were performed with R-4·0·2, using the ggplot2, dplyr and plyr packages. To estimate missing values from the questionnaire, multivariate imputation by chained equations was performed using the R-package mice. The results were reported according to the STROBE-nut guidelines[22].
## Results
All 2057 of the participants in our analysis lived in Switzerland, 933 were men and 1124 were women (Table 1). Once weighting factors had been applied to the survey data, the sample represented 4 627 878 residents of Switzerland, which were balanced between the sexes and of which 60 % were between 30 and 59 years old. The majority was German-speaking (69 %) and of Swiss nationality (61 %). Regarding lifestyle, 23 % of the study population were current smokers and 44 % were overweight or obese. In terms of education, 53 % of the participants had a tertiary degree.
Table 1Description of the menuCH population and its processed meat, unprocessed meat, red meat and white meat consumers (n 2057)Total samplePM consumersUPM consumersRed meat consumersWhite meat consumersCrudeWeighted* Weighted* Weighted* Weighted* Weighted* %%%%%%Sex Men45·449·854·952·955·150·2 Women54·650·245·147·144·949·8Language region† German65·269·271·067·967·063·8 French24·425·223·526·627·730·1 Italian10·45·65·55·55·36·1Age group‡ 18–29 years19·418·818·019·315·924·2 30–44 years25·929·831·030·530·931·7 45–59 years30·429·830·129·529·628·9 60–75 years24·321·620·920·723·615·2BMI§ Underweight (BMI < 18·5 kg/m2)2·42·42·01·61·61·9 Normal (18·5 ≤ BMI < 25·0 kg/m2)54·354·153·051·349·353·9 Overweight (25·0 ≤ BMI < 30·0 kg/m2)30·530·631·532·333·531·2 Obese (BMI ≥ 30·0 kg/m2)12·812·913·514·815·613·0Nationality Swiss72·561·462·759·462·453·0 Swiss binational14·413·823·614·814·616·4 Non-Swiss13·024·813·725·823·030·6Education level Primary4·34·74·45·04·56·0 Secondary47·142·643·142·945·837·9 Tertiary48·552·752·552·149·756·1Household status Living alone16·018·216·417·816·319·2 Adult living with parents7·77·17·38·07·98·8 One-parent family with children4·54·44·24·64·84·8 Couple without children33·531·731·430·233·124·8 Couple with children33·032·934·833·632·736·1 Others5·35·75·95·85·26·3Income< 600016·817·716·316·816·915·3 6000–13 00040·939·840·839·739·538·4 > 13 00013·914·916·014·814·816·6 Imputed || 28·427·626·928·728·829·7Physical activity Low14·712·917·015·716·714·4 Medium22·122·721·221·821·723·4 High40·240·340·640·138·241·4 Imputed || 23·024·221·222·423·420·8Smoking status Never44·542·940·942·041·042·9 Former smoker33·533·734·533·233·631·7 Current smoker22·023·424·624·825·425·4Health status Very poor to medium13·312·813·113·013·711·2 Good to very good86·787·286·987·086·388·8Currently on a diet Yes5·65·54·76·46·27·1 No94·494·595·393·693·892·9PM, processed meat; UPM, unprocessed meat; CHF, Swiss francs.*Weighted for sex, age, marital status, major area of Switzerland, household size and nationality.†German-language region: cantons Aargau, Basel–Land, Basel–Stadt, Bern, Lucerne, St. Gallen, Zurich; French-language region: Geneva, Jura, Neuchâtel, Vaud; Italian-language region: Ticino.‡Self-reported age on the day of the first 24-hour dietary recall interview.§BMI was based on measured height and weight, or on self-reported estimations when measurements were not possible.|| Multivariate imputation by chained equations was used for missing values; imputed values of < 0·4 % are not shown.
Eighty-nine percent of the menuCH study population reported that they consumed meat. When looking specifically at PM, UPM, red meat and white meat, the respective rate of consumption was 72 %, 69 %, 46 % and 35 %. The demographics of processed, unprocessed and red meat consumers were similar to the entire study population, whereas the demographics of white meat consumers showed some differences to other meat categories: there were equal numbers of female and male study participants, but there were higher numbers of participants from the youngest age group, with non-Swiss nationality, a tertiary-level education and with children (Table 1).
Table 2 shows that the mean daily meat consumption was 109 g/d, including 66 g/d UPM. The actual and energy-standardised meat consumption of men was higher than that of women by 58 g/d and 13 g/1000 kcal, respectively. In the French- and Italian-language regions, all meat and UPM consumption (actual and energy-standardised) was higher than in the German-language region. Actual and energy-standardised daily meat consumption decreased slightly over the age groups, while UPM consumption was highest in the 30–44 age group.
Table 2Mean daily consumption of all meat and main sub-categories of unprocessed meat, red, white and other meat by total population, sex, language region and age group based on two 24-hour dietary recalls, n 2057 (g/d, g/1000 kcal)* All MeatAll Unprocessed MeatRed MeatWhite MeatOtherMean sem MeanMean sem MeanMean sem MeanMean sem MeanMean sem Mean(g/d)(g/1000 kcal)(g/d)(g/1000 kcal)(g/d)(g/1000 kcal)(g/d)(g/1000 kcal)(g/d)(g/1000 kcal)Total sample108·92·049·966·21·631·236·91·217·126·81·113·02·50·31·1Sex Men138·23·356·583·02·834·648·22·219·831·31·913·53·50·61·4 Women79·81·943·249·51·627·825·61·214·522·31·112·61·60·30·8Language region† German105·62·44760·51·927·633·11·514·924·31·311·53·10·41·2 French115·94·155·279·13·538·845·32·722·332·22·515·71·60·50·8 Italian116·76·561·477·45·641·344·74·921·532·23·619·60·50·30·2Age group‡ 18–29 years123·85·053·973·84·133·532·02·814·138·23·117·43·50·71·9 30–44 years117·44·151·774·63·533·441·42·718·229·92·314·03·20·81·1 45–59 years103·83·448·763·32·831·037·02·218·023·91·812·12·50·60·9 60–75 years91·03·345·451·92·526·534·52·217·116·61·59·00·80·20·4 sem, standard error of the mean.*Weighted for sex, age, marital status, major area of Switzerland, household size, nationality, season and weekday.†German-language region: cantons Aargau, Basel–Land, Basel–Stadt, Bern, Lucerne, St. Gallen, Zurich; French-language region: Geneva, Jura, Neuchâtel, Vaud; Italian-language region: Ticino.‡Self-reported age on the day of the first 24-hour dietary recall. Other unprocessed meat corresponds to unprocessed meat that was not further specified.
UPM consumption by sex, language region and age group showed similar variations between the demographic groups as for all meat (Table 2). An exception was that energy-standardised all-meat consumption seemed to decrease progressively over the age groups, whereas UPM consumption was similar for the three age groups (18–59 years) and slightly lower in the oldest age group. In the French- and Italian-language regions, higher amounts of UPM were consumed, 18–19 g/d and 11–13 g/1000 kcal, respectively, than in the German-language region. The histograms for all meat and UPM consumption, showing the distribution of daily mean consumption by the population, had a similar skewed shape but compared with all meat, higher number of participants did not consume UPM (Fig. 1).
Fig. 1The histograms present the number of study participants and the consumption of all meat (a) and of unprocessed meat (b) by participants, using the mean of two 24-hour dietary recalls (g/d). For the sample, the dark green bar indicates no meat consumption; the red line indicates mean intake; and the blue lines are first, second and third quartiles. All data were weighted for sex, age, marital status, major area of Switzerland, household size, nationality, season and weekday (n 2057) Mean daily red meat consumption was 37 g/d, corresponding to 34 % of all meat consumption (Table 2). Men consumed approximately twice the amount of red meat as women. Absolute and energy-standardised red-meat consumption were similar in the French- and Italian-language regions, and both were higher than in the German-language region. The energy-standardised consumption of red meat did not suggest a decrease over the age groups, unlike all meat (Table 2).
The mean daily consumption of 27 g/d of white meat contributed 25 % to all meat consumption (Table 2). Mean energy-standardised white meat consumption was the same for men and women. White meat consumption in the French- and Italian-language regions was higher than that in the German-language region. The mean daily as well as the mean energy-standardised white meat consumption decreased over the age groups.
Table 3 and Supplemental Table S1 detail the consumption of the most common UPM types. The meat type consumed in highest quantities by the menuCH population was poultry (27 g/d – without poultry offal) followed by beef (16 g/d), pork (11 g/d), veal and lamb (3 g/d). Only a small amount of offal was consumed (1 g, all offal types combined). The remaining 6 g/d of UPM consisted of rarely consumed meat types such as game, horse, goat, rabbit and mixed meat products of UPM. Men consumed more of every UPM type than women (actual and energy-standardised consumption) except for poultry. Beef consumption in the Italian and the French-language regions was almost twofold higher than that of the German-language region. Pork consumption was lowest in the French-language region and a decrease was observed in the 30–44 age group and across the older age groups. The highest veal consumption was reported in the Italian region and the lowest consumption occurred in the French-language region. The highest lamb consumption was reported in the French-language region and in the oldest age group. Offal was also consumed the most in the French-language region and by study participants aged 45–59 years.
Table 3Mean daily consumption of unprocessed meat sub-categories by total population, sex, language region and age group based on two 24-hour dietary recalls, n 2057 (g/d)* PoultryBeefPorkVealLambOffalRemaining unprocessed meatMean (g/d) sem Mean (g/d) sem Mean (g/d) sem Mean (g/d) sem Mean (g/d) sem Mean (g/d) sem Mean (g/d) sem Total sample26·61·115·60·811·30·73·30·32·70·31·00·25·70·5Sex Men31·11·919·41·314·61·24·30·63·40·61·50·48·71·0 Women22·11·111·70·98·00·72·40·31·90·30·60·22·80·4Language region† German24·31·312·40·812·40·93·70·42·30·40·50·24·90·5 French31·42·422·71·88·61·11·80·54·20·82·50·67·81·3 Italian32·23·621·33·610·32·76·31·60·40·40·40·46·51·8Age group‡ 18–29 years38·03·114·11·810·41·72·60·62·10·60·20·16·31·1 30–44 years29·52·218·11·713·71·53·40·72·60·61·20·56·01·0 45–59 years23·71·814·71·311·11·23·90·71·70·61·50·46·81·1 60–75 years16·41·514·51·59·01·23·20·64·60·80·80·33·50·7 sem, standard error of the mean.*Weighted for sex, age, marital status, major area of Switzerland, household size, nationality, season and weekday.†German-language region: cantons Aargau, Basel–Land, Basel–Stadt, Bern, Lucerne, St. Gallen, Zurich; French-language region: Geneva, Jura, Neuchâtel, Vaud; Italian-language region: Ticino.‡Self-reported age on the day of the first 24-hour dietary recall. Remaining unprocessed meat includes unspecified unprocessed meat and meat from animals not specifically listed in this table. Offal includes offal of poultry, beef, pork, veal, lamb and remaining unprocessed meat.
Taking confounding factors into account, the energy-standardised meat consumption of men was significantly higher than that of women, with the exception of white meat consumption (Table 4). Consumption of all meat categories differed significantly between the language regions and between the BMI groups. Higher energy-standardised meat consumption (all categories) was observed in the French and the Italian language regions than in the German-language region; in obese and overweight participants compared with those with a normal BMI, and in current smokers compared with those who have never smoked (only in the category of all meat). Additionally, factors such as age, nationality, education degree, household status and following a weight-loss diet were identified as possible determinants for meat-category consumption amounts.
Table 4Associations of energy-standardised consumption of all meat, red meat and white meat with socio-demographic and lifestyle factors, by multivariable linear regression analysis, n 2057 (g/1000 kcal)* All meat (g/1000 kcal)Red meat (g/1000 kcal)White meat (g/1000 kcal)Coefficients95 % CI† Coefficients95 % CI† Coefficients95 % CI† SexMen0ref.0ref.0ref. Women−10·12−13·60, –6·64−3·90−6·35, –1·51−0·81−3·01, 1·39Language regions‡ German0ref.0ref.0ref. French6·973·14, 10·797·404·74, 10·062·910·49, 5·33 Italian11·904·70, 19·115·580·58, 10·597·022·45, 11·59Age groups§ 18–29 years7·051·41, 12·68−2·80−6·73, 1·088·374·81, 11·93 30–44 years0ref.0ref.0ref. 45–59 years−4·64−8·96, –0·31−1·00−3·99, 2·01−1·52−4·25, 1·22 60–75 years−5·29−10·63, 0·05−0·80−4·51, 2·89−2·11−5·49, 1·27BMI categories|| Underweight−7·40−18·31, 3·51−4·90−12·50, 2·65−2·79−9·69, 4·11 Normal0ref.0ref.0ref. Overweight12·959·09, 16·815·302·63, 8·003·931·48, 6·37 Obese18·2112·80, 23·635·802·04, 9·566·152·72, 9·58Nationality Swiss only0ref.0ref.0ref. Swiss binational5·030·21, 9·872·03−1·32, 5·371·97−1·08, 5·02 Non-Swiss6·862·24, 10·892·02−0·85, 4·885·983·37, 8·59Education level Primary0·21−7·89, 8·31−8·04−13·63, –2·454·63−0·50, 9·76 Secondary0ref.0ref.0ref. Tertiary−8·60−12·23, –4·98−5·59−8·09, –3·08−0·86−3·16, 1·44Household status Living alone2·79−2·61, 8·19−3·49−7·20, 0·215·652·26, 9·05 Adult living with parents−2·19−9·99, 5·61−1·61−7·01, 3·78−4·85−9·79, 0·09 One-parent family with children9·040·43, 17·654·68−1·29, 10·656·130·68, 11·58 Couple without children0ref0ref.0ref. Couple with children5·030·67, 9·390·77−2·26, 3·793·640·89, 6·40 Others−0·21−7·96, 7·55−1·12−6·50, 4·25−2·03−6·95, 2·89Income (CHF/month) < 6000−0·11−5·87, 5·660·77−2·84, 4·38−1·15−4·30, 2·00 6000–13 0000ref.0ref0Ref. > 13 000−0·36−5·44, 4·72−1·22−5·08, 2·641·03−2·69, 4·74Physical activity level Low0ref0ref.0ref Moderate−3·61−9·32, 2·09−1·12−5·34, 3·100·02−3·28, 3·32 High−3·26−8·53, 2·01−1·70−5·76, 2·360·86−2·44, 4·16Smoking status Never smoked0ref.0ref.0ref. Former smoker0·69−3·06, 4·44−0·97−3·55, 1·62−0·33−2·70, 2·03 Current smoker4·400·04, 8·762·15−0·87, 5·17−0·64−3·40, 2·11Health status Very poor to medium0·12−5·13, 5·360·35−3·29, 3·98−0·82−4·14, 2·49 Good to very good0ref.0ref.0ref. Currently on a diet Yes0·09−7·02, 7·201·46−3·48, 6·396·321·81, 10·82 No0ref.0ref.0ref. CHF, Swiss francs.*Results of the multivariable linear regressions were adjusted for all variables presented in this table and weighted for sex, age, marital status, major area of Switzerland, household size, nationality, season and weekday.†CI: confidence interval.‡German-language region included cantons: Aargau, Basel–Land, Basel–Stadt, Ben, Lucerne, St. Gallen, Zurich; French-language region: Geneva, Jura, Neufchâtel, Vaud and Italian-language region: Ticino.§Age corresponds to self-reported age on the day of the first 24-hour dietary recall.||BMI was based on measured height and weight, or on self-reported estimations when measurements were not possible.
## Discussion
The menuCH survey provides the first national meat consumption data, showing that the vast majority (90 %) of the Swiss population consumes meat. The mean amount of 109 g/d for all meat is lower than the published meat consumption of 131 g/d (48 kg/year) estimated from food balance sheets[23], which often overestimate the consumption of many food groups. In food consumption surveys, individual food consumption is assessed. In contrast to food balance sheets, the available amount of food based on production and trade is calculated per capita of the population. Furthermore, food balance sheets do not account for food losses due to preparation, spoilage or food waste and do not consider meals eaten outside of the home[24].
According to menuCH, the male population consumed almost twice as much meat as women, which was observed for all meat types except white meat. This sex-specific difference in meat consumption was significant and independent of total energy intake as reported in previous studies(25–28). Lower meat consumption among women has been attributed to their greater interest in personal health, body weight maintenance and animal welfare concerns[29]; nevertheless, the perception of meat as a masculine food might be decreasing[30].
Mean energy-standardised daily meat consumption was significantly higher in the French and Italian regions than in the German-language region. In recent studies using menuCH survey data, language region differences were also described for the consumption of other food groups and for overall diet quality[16,31], and this is possibly linked to the influences of culinary habits in the neighbouring countries Germany, France and Italy. Nevertheless, energy-adjusted all meat consumption in France (absolute consumption not reported) and absolute mean daily all meat consumption in Germany and Italy were higher than mean daily all meat consumption in Switzerland[26,32,33].
Our results show that UPM consumption was responsible for the higher all meat consumption in the French and Italian regions compared with the German-language region. Interestingly, in earlier Swiss Health Surveys, red meat was consumed more frequently in the German and in the French regions compared with the Italian language region[34]. However, the results are difficult to compare due to differences in the assessment methods. Comparisons of red, white and PM consumption amounts between countries are limited by differences in definitions and categorisations[25]. However, patterns of UPM and red meat consumption in the EPIC centres of France, Italy and Germany[27] were indeed similar to those observed in the corresponding Swiss-language regions.
Poultry (without offal) was the highest consumed type of UPM by the Swiss population followed by beef, pork, veal, lamb and offal. According to food balance sheets, poultry consumption has increased since 2015, when the menuCH study was conducted[10]. Our result for poultry consumption (27 g/d) is comparable with that reported in France (26 g/d) but higher than amounts consumed in Italy (21 g/d) and Germany (18 g/d)[26,35,36]. Poultry is positively viewed for its high-quality protein, favourable lipid profile, as well as its vitamins and minerals[37]. Furthermore, it has neutral or protective associations with the major chronic diseases including obesity[2,38]. EPIC data show that beef, veal and lamb consumption was higher in the French and Italian centres than in the German centres, where pork consumption was higher[27]. In our results, pork consumption was highest in the German-language region, whereas beef, veal and lamb consumption was highest in the French and Italian-language regions. This could be due to the fact that traditional Italian and French meat dishes are prepared with different types of meat, for example, veal is widely used in Italian cuisine[39], and lamb and offal are commonly used in French cuisine[40].
The current study shows that the overweight and obese population in Switzerland consumed significantly higher amounts of all meat categories compared with the normal-weight population, independent of their energy intake, confirming results from Germany and France[26,32]. In an earlier study using menuCH Survey data, age, language region, education, income, household status, smoking, health status and physical activity were reported as determinants of meat consumption as well as for being overweight and obese[41]. Hence, high meat consumption could be an indicator of an unbalanced diet. Our results suggested one-parent families with children had higher meat consumption compared with couples without children, which did not agree with an earlier study in the USA[42]. In the German National Survey, the likelyhood of non-meat consumption was higher for smaller households[26]. Recent reports indicate that people with low or no meat consumption, not only in Germany and France but also in Switzerland, choose considerably healthier foods with regard to their nutrient and energy balance[26,32,43].
Reductions in meat consumption are a concern in the ageing population, largely due to increased risk of nutritional inadequacy and higher requirements for some nutrients such as protein[11]. However, our analysis only shows reduced meat consumption (all meat, g/1000 kcal) in the 45–59 age category compared with the reference age group (30–44 years). In contrast, all meat and white meat consumption was higher in the youngest age group compared with the reference group, even though this age group was more likely to consume no meat in other studies[26,44].
A significantly higher meat consumption, in particular white meat, was observed in the non-Swiss group, which is a highly diverse population group in Switzerland. Our results showed that participants with a tertiary-level education consumed less meat in general, which was consistent with results from Germany and France[26,32], but this differed from the Swiss Health Survey findings that were based on meat consumption frequency[34]. We found that decreased red meat consumption was associated not only with a tertiary-level education, as reported in the French population[45], but also with a primary level of education, most probably for different reasons[13]. For example, recent media attention on the risks of red meat would likely influence the meat choices of both low and highly educated participants, while the price of meat might have a greater influence on participants with a lower education. In our study, however, the association between meat consumption and income was NS. PM consumption was shown to be negatively associated with education in the Swiss population[14], which is consistent with reports from Germany and France[26,32]. Participants living with their children consumed significantly more meat in general and more white meat compared with participants living with a partner but without children.
Dietary recommendations for meat consumption must consider both the benefits and risks of this food group and must address different demographic groups of the population. In Switzerland, it is recommended not to exceed more than 100–120 g of UPM, two to three days a week. The meat consumption data from the two 24HDR interviews provide no information about meat consumption frequency. However, 19 % of the menuCH participants exceeded the recommended UPM consumption amount of 120 g/d[8]. According to the Swiss Health Survey from 2017, 53 % of the population consumed an undefined amount of all meat including PM more than three times a week and therefore exceeded the frequency recommendations for meat consumption[9].
A major strength of our study is the quantitative data derived from the two 24HDR interviews, which allowed the meat consumption (all types) of a representative national sample from the three main language regions of Switzerland to be assessed. Two 24HDR dietary recalls can be used to describe the habitual dietary intake distribution in food consumption surveys (i.e. for a population) given that they were collected on non-consecutive sampling days and cover all seasons and days of the week[46]. However, to accurately describe habitual meat consumption, the 24HDR interviews should be combined with a short FFQ to capture consumption of rarely eaten foods,[47] in particular in combination with statistical models that take into account within-person variation, such as the Statistical Program to Assess Dietary Exposure[48]. Since the menuCH survey only included two 24HDR interviews, the assessment of meat consumption of those who only consume meat infrequently, e.g. flexitarians, is very limited. To better evaluate the links between meat consumption and potential health risks in Switzerland, future dietary surveys should also include markers of health such as blood pressure levels, blood samples and urine samples as collected in previous investigations in other countries[49].
In conclusion, the current study is a comprehensive description of the first national data set on meat consumption in Switzerland, revealing that the mean daily consumption of red meat (37 g/d) was below the consumption level (50–100 g/d) that is considered to be associated with health risks[2,4]. However, the consumption of red meat differs between subgroups of the population and a considerable proportion of the population exceeds this recommended consumption level (29 % and 13 % of the population consumed more than 50 g/d and 100 g/d, respectively). Given the differences in health risks associated with red and white meat and their very different environmental impacts, it might be worthwhile to develop separate dietary recommendations for these meat categories, which take into account the latest findings on their effects on nutrition and health.
## Conflicts of interest:
The authors declare no conflicts of interest.
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|
---
title: 'Adherence to the South African food based dietary guidelines may reduce breast
cancer risk in black South African women: the South African Breast Cancer (SABC)
study'
authors:
- Inarie Jacobs
- Christine Taljaard-Krugell
- Mariaan Wicks
- Jane M Badham
- Herbert Cubasch
- Maureen Joffe
- Ria Laubscher
- Isabelle Romieu
- Carine Biessy
- Marc J Gunter
- Sabina Rinaldi
- Inge Huybrechts
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991845
doi: 10.1017/S1368980021004675
license: CC BY 4.0
---
# Adherence to the South African food based dietary guidelines may reduce breast cancer risk in black South African women: the South African Breast Cancer (SABC) study
## Body
South Africa, an upper-middle income country, is undergoing a rapid nutrition transition characterised by shifts in dietary-and-lifestyle patterns. Nutritious traditional meals and active lifestyles are being replaced with frequent consumption of highly processed foods, often being energy dense and nutrient poor, and high levels of physical inactivity and sedentary behaviour[1]. Concurrent to these dietary-and-lifestyle shifts is the continuously increasing rate of obesity, especially among South African women[2]. Obesity and overweight are important risk factors for different cancer types, especially postmenopausal breast cancer[3]. Breast cancer is the leading diagnosed cancer among South African women and contributes to current public health challenges in South Africa[4]. The age-standardised incidence rate of breast cancer in South *Africa is* 52·6 per 100 000 women, while the age-standardised mortality rate is 16·0 per 100 000 women[4]. Incidence rates of breast cancer are lower in black South African women (age-standardised rate of 20·8) compared with other ethnic groups (white, coloured and Indian)[5], but evidence suggests that black women have higher mortality rates compared with other races[6]. Based on data from 2020, incidence rates of all cancer cases, including breast cancer, are predicted to rise with 47 % by 2040[7]. This is worrisome since higher incidence rates may simultaneously increase mortality rates, especially in resource poor countries such as South Africa[4]. It is therefore important to identify specific, modifiable risk factors that could be used to implement preventive actions, especially for black South African women.
The FAO of the UN and WHO recommend country-specific food based dietary guidelines (FBDG) to promote healthy dietary patterns[8]. The aim of country-specific FBDG is to reduce nutrition deficiencies and to assist in preventing the development of non-communicable diseases[8]. The South African food based dietary guidelines (SAFBDG) were initially published in 2003 and revised in 2012[9]. These recommendations consist of eleven short, simple and clear nutrition messages that promote a varied and adequate diet and consider foods that are available, culturally sensitive, affordable and environmentally sustainable[9]. However, the level of adherence to the SAFBDG and the association thereof with breast cancer in black women from Soweto are not yet known.
A similar study, conducted in black women from Soweto, South Africa, recently showed that higher adherence to the 2018 World Cancer Research Fund/American Institution for Cancer Research’s (WCRF/AICR) Cancer Prevention Recommendations was associated with a lower breast cancer risk in this population[10]. Although the SAFBDG promote similar dietary guidelines as the 2018 WCRF/AICR Cancer prevention guidelines (i.e. increase consumption of fruit, vegetable, beans, lean meats and limit foods high in saturated fat and added sugar), there are some important differences between the two sets of recommendations to consider. Compared with the 2018 WCRF/AICR Cancer Prevention Recommendations, the SAFBDG are guidelines specifically developed for South Africa based on prevailing dietary patterns which aim to address current nutrition-related health problems within South Africa[9]. On the other hand, the 2018 WCRF/AICR Cancer Prevention Recommendations are more lifestyle orientated and include cancer-specific recommendations, based on robust evidence from mostly higher income countries[3]. Investigating the association between adherence to the SAFBDG and breast cancer risk will complement the previous work of Jacobs and colleagues [2021] by providing additional country-specific or context-specific insight into the diets (and variety thereof) of black women from Soweto, South Africa. The current study aims to first investigate the level of adherence to the SAFBDG (overall and for each individual SAFBDG recommendation) and second to assess whether higher adherence to the SAFBDG (overall and for each individual SAFBDG recommendation) is associated with a reduced breast cancer risk in black South African women from Soweto.
## Abstract
### Objective:
To determine the level of adherence and to assess the association between higher adherence to the South African food based dietary guidelines (SAFBDG) and breast cancer risk.
### Design:
Population-based, case–control study (the South African Breast Cancer study) matched on age and demographic settings. Validated questionnaires were used to collect dietary and epidemiological data. To assess adherence to the SAFBDG, a nine-point adherence score (out of eleven guidelines) was developed, using suggested adherence cut-points for scoring each recommendation (0 and 1). When the association between higher adherence to the SAFBDG and breast cancer risk was assessed, data-driven tertiles among controls were used as cut-points for scoring each recommendation (0, 0·5 and 1). OR and 95 % CI were estimated using multivariate logistic regression models.
### Setting:
Soweto, South Africa.
### Participants:
Black urban women, 396 breast cancer cases and 396 controls.
### Results:
After adjusting for potential confounders, higher adherence (>5·0) to the SAFBDG v. lower adherence (<3·5) was statistically significantly inversely associated with breast cancer risk overall (OR = 0·56, 95 % CI 0·38, 0·85), among postmenopausal women (OR = 0·64, 95 % CI 0·40, 0·97) as well as for oestrogen-positive breast cancers (OR = 0·51, 95 % CI 0·32, 0·89). Only 32·3 % of cases and 39·1 % of controls adhered to at least half (a score >4·5) of the SAFBDG.
### Conclusions:
Higher adherence to the SAFBDG may reduce breast cancer risk in this population. The concerning low levels of adherence to the SAFBDG emphasise the need for education campaigns and to create healthy food environments in South Africa to increase adherence to the SAFBDG.
## Methods and study population
The database from the South African Breast Cancer (SABC) study, a population-based, case (n 396) control (n 396) study conducted among black urban women from the greater Soweto population from 2014 to 2017, was used to conduct the current study. Breast cancer cases were newly diagnosed, prior to any cancer treatment from the Chris Hani Baragwanath Academic Hospital. Cases were recruited as soon as possible after the cancer diagnoses. Controls were healthy and unrelated to the breast cancer cases with no history of cancer diagnoses and matched by age (± 5 years) and area of residence to the cases. Information regarding inclusion and exclusion criteria of breast cancer cases and controls and recruitment of breast cancer cases was previously described elsewhere[11]. The sample size had a sufficient power of 80 % (when type II error rate = 10 %) for OR ≥ 1·5 and type I error set at 5 %[12]. “ This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the International Agency for Research on Cancer, the University of the Witwatersrand and North-West University. Written informed consent was obtained from all subjects/patients.”
## Determining habitual dietary intake
A validated and reproducible culture-specific Quantitative FFQ (QFFQ) was used together with household utensils, food portion pictures and food models to determine habitual dietary intake[13,14]. For validation/reproducibility of the QFFQ (in previous studies), spearman rank correlation coefficients of 0·14–0·59 were obtained when macro- and micronutrients intakes and food groups were compared with a 7 d weighed food record and foods captured by a QFFQ[13,14]. In 2011l, reproducibility of the QFFQ was evaluated again in the Prospective Urban and Rural Epidemiological-SA study, and correlations for energy, macro- and micronutrients were good (between 0·59 and 0·76), which indicated that the validity of the QFFQ stayed consistent over time[15]. The QFFQ was administered by registered dietitians, a nutritionist and registered nurse who all received training by nutritionists with over 30 years of experience in dietary assessment to ensure consistency among the different interviewers. Nutrient content information (macro- and micronutrients) was obtained using the South African Food Composition Tables[16]. The dietary intake frequency included the amount of times foods were consumed per d/week/month or never. Life size colour photographs of thirty-seven foods (in three portion sizes) were displayed in the food portion picture booklet. Participants were asked about their habitual dietary intake over the past month. Daily intakes of the different foods included in the QFFQ were calculated by two nutritionists to ensure accurate and quality processing of the QFFQ, using a stepwise approach. A stepwise approach was used to calculate daily intakes. Consumption frequencies were converted into number of days per month, and the amount of each portion consumed (for each individual) was converted into grams, using standardised tables to convert household measurements into grams. The daily consumption was calculated by multiplying the frequency of consumption (d/month) by the portion sizes (converted to grams) divided over 30 d. The daily energy and nutrient intakes were determined by multiplying the daily intake of each food item (as consumed) by the nutrient and energy content (per 100 g), derived from the South African Food Composition tables, and then adding together the contributions from all food items[16].
## Non-dietary assessments
Trained investigators and fieldworkers conducted face-to-face interviews. Detailed information on demographic factors and socio-economic status – ethnicity, history of health, family history of breast cancer, reproductive risk factors (age/year at full-term pregnancy, breastfeeding history, age at menarche and at menopause for postmenopausal women, use of oral contraceptives and hormone replacement therapy), family history of cancer, breast health (previous breast lumps by breast laterality and breast pains), smoking habits and physical activity (recreational, transportation, occupational and household) – were collected. Weight, height, sitting height, hip circumference and waist circumference were measured according to standardised procedures. Measurements were always done in duplicate and redone if there was discordance. Measurements were done by the same person throughout the study to avoid inter-user variation, and all equipment was calibrated regularly. All questionnaires used to collect the anthropometry and lifestyle information were validated and proven reproducible in studies conducted in South Africa and elsewhere[17,18].
## To determine the level of adherence to the South African food based dietary guidelines (overall and for each individual South African food based dietary guideline recommendation)
To determine the level of adherence to the SAFBDG, an adherence score was developed since no standardised scoring algorithm currently exists to measure adherence to the SAFBDG. Table 1 provides a detailed layout of the construction of the adherence score with recommended intakes/adherence levels, methods and definitions used for each recommendation to determine adherence to the SAFBDG. Each of the participants’ adherence to the SAFBDG was calculated for overall adherence as well as for each individual SAFBDG recommendation. Recommendations regarding salt (use salt and foods high in salt sparingly) and water (drink lots of clean, safe water) consumption were excluded from the overall adherence score as the required information to assess adherence to these recommendations were not gathered during data collection.
Table 1A detailed layout of suggested food intakes, methods used and foods included in each food groups to construct food groups for measuring adherence to the South African food based dietary guidelines (SAFBDG)RecommendationSuggested intake/measure of adherenceMethod used to measure adherence to the individual SAFBDG recommendationFoods included in food groups to measure adherence to the guideline/Exclusions and definitions1) *Enjoy a* variety of foodsDDS > 4 (out of 9)A DDS score <4 was considered a low dietary diversity. Calculating a DDS with nine food groups, using the FAO guidelines, with nine being the highest DDS and zero [0] the lowest DDS[28].1·1) Cereals, roots and tubers: rice, corn, sorghum, oats, samp, sweet potatoes, potatoes, fortified and unfortified maize meal and bread, products made with wheat flour or any other grain flour such as pasta, porridges and tortillasExcluded: sweet biscuits and cakes as they are classified under “sweets” (not measured as part of the DDS in the current study).1·2) Meat, poultry and fish: organ meat, chicken (fresh and frozen), fish and seafood (fresh, frozen and canned), red meat (beef, pork, goat, mutton, venison), processed meat (salami, sausage/vienna, ham, polony, corned beef, bacon) and dried meat (biltong, mopani worms).1·3) Dairy: milk (whole, low-fat and skimmed milk) and milk products (custard, hard and soft cheese, processed cheese and yoghurt).1·4) Eggs: scrambled, fried and boiled/poached.1·5) Vitamin A-rich fruit and vegetables (for plant foods – 60 RAE per 100 g & for liquids- 30 RAE per 100 g)[28]: carrots, pumpkin, red pepper, sweet potato (yellow flesh), spinach, apricots, melon (orange flesh), mango, pawpaw, apricot juice and peaches. Vegetables mixed with potatoes were separated according to standardised recipes from the Condensed Food Composition Tables of South Africa.1·6) Legumes/pulses: beans (dried, cooked, raw and canned), peanuts, peanut butter, lentils and soya products.1·7) Other fruit: banana, fig, grapes, guava, raisins, naartjie/tangerine, orange, blueberry, plum, strawberry, melon (green flesh), watermelon, pineapple, pear, apple, litchi, pomegranate, avocado and kiwifruit.1·8) Other vegetables: beetroot, onions, broccoli, cauliflower, lettuce, cabbage, green beans, brinjal, cucumber, mushroom, tomato, gem squash, peppers and okra. Mixed vegetable dishes were separated according to standardised recipes from the Condensed Food Composition Tables of South Africa.1·9) Fats and oils: butter, tallow, margarine, mayonnaise, vegetable oil, shortenings and sour cream.2) Be active. At least 150 min of moderate and vigorous physical activity/week[19] Calculating the sum of light, moderate and vigorous physical activity (min/week).Due to low levels of moderate and vigorous physical activities in both breast cancer cases and controls, light physical activities were included in the analysis.3) Make starchy food part of most meals* No specific suggestion. General guideline of ten minimally processed/whole grains/root vegetables exchanges/units/day (based on 8 500 kJ intake/d)[27].Exchange quantities for one unit:+ maize meal porridge, + soft/maltabella/oats = 125 g+ maize meal porridge, stiff = 60 g+ maize meal porridge, crumbly = 45 g+ bread = 35 g+ potatoes/sweet potatoes = 100 g +cooked, pasta/samp/whole grains = 75 g, +unsweetened breakfast cereals = 25 g+ cooked rice = 65 gIncluded: carbohydrates in the form of minimally/unprocessed or whole grains and root vegetables such as potatoes and white fleshed sweet (roasted, boiled, mashed, sautéed in sunflower oils, no added sugar or saturated fats) potatoes, fortified white and brown bread, whole wheat bread, fortified and unfortified maize meal, white and brown rice, plain pasta (macaroni, spaghetti), sorghum, bulgur wheat, oats and minimally processed breakfast cereals without added sugar (see definition of added sugar in table below).Excluded: highly processed/refined starchy foods (provita’s, biscuits, cookies, cakes, tarts, pies), deep fried potatoes/French fries, vetkoek (deep fried dough) and legumes (legumes are included in the individual SAFBDG recommendation “Eat dry beans, split peas, lentils and soya regularly”).4) Eat plenty of vegetables and fruit every day.5 portions or 400 g/d[21].Calculating the sum of all fruit and vegetables consumed in g/d, using the food composition tables of South Africa[16] Vegetables: spinach, green beans, green leafy vegetables, beetroot, carrot, onions, broccoli, cauliflower, lettuce, cabbage, brinjal, baby marrow, okra, cucumber, corn, mushrooms, tomato, gem squash, green pepper. Excluded: potato and white fleshed sweet potato (included in the individual SAFBDG recommendation “Make starchy foods part of most meals”. Fruit: banana, apple, apricot, melon (orange and green flesh), figs, guava, raisins, mango, tangerines/naartjies, orange, pawpaw, peach, blueberry, peach, plum, strawberry, watermelon, pineapple, pear, fruit salad, prune, litchi, pomegranate, avocado, kiwifruit and 100 % pure fruit juice.5) Eat dry beans, split peas, lentils and soya regularly† No specific indication. At least 2 to 3 portions/week. One portion equals ½ cup or 75 g of dry beans/peas/cooked lentils or 30 g of soya mince. Calculating the sum of all legumes consumed in g/d, using the food composition tables of South Africa[16].Beans, split peas, lentils (dried, canned, raw and cooked) and soya products.6) Have milk, maas or yoghurt every day. Milk, maas or yoghurt at least >400 ml/d[26].Calculating the sum of milk, maas or yoghurt in ml/d, using the food composition tables and food quantities table of South Africa[16,48].Milk (either fresh or powdered), unsweetened yoghurt and maas (traditional fermented milk).Excluded: sweetened, full-fat and processed milk products and cheeses to prevent intake of SFA, Na and sugar[35].7) Fish, chicken, lean meats or eggs can be eaten daily.2 to 3 fish servings/weekApproximately 4 Eggs/weekLean meat ≤90 g/d[22] Calculating the sum of fish, chicken, lean meats and eggs consumed in g/d, using the food composition tables of South Africa[16] Fish and seafood, especially oily fish such as sardines, pilchards, tuna, anchovies and mackerel (fresh, frozen and canned), chicken (white and dark meat), lean meat and eggs (scrambled, fried, boiled/poached).Lean meat is defined as meat with a fat percentage between 5 % and 10 % can be labelled as lean or trimmed[46].8) Use fats sparingly. Choose vegetable oils rather than hard fats.≥20 % Total fat intake ≤30 % of total energy intake/d.Saturated fat <10 % of total energy intake/d[24].Using the total amount of fat as a percentage of total energy intake and total amount of saturated fat as a percentage of total energy intake. Measured by the food composition tables of South Africa[16] Total fat measured in all single foods consumed in breast cancer cases and controls. Total fat includes trans fatty acids, SFA, MUFA and PUFA (including essential fatty acids such as n-3, n-6).Hard fats refer to all animal fats and vegetables oils such as palm kernel and coconut oil.9) Use sugar and foods and drinks high in sugar sparingly. Added sugar intake <10 % of total energy intake. Added sugar intake <6 % for further health benefits[25].Total amount of added sugar as a percentage of total energy intake, measured by the food composition tables of South Africa[16] Added sugar measured in all single foods consumed in breast cancer cases and controls. ‡Added sugar refer to “any sugar added to foodstuffs during processing and includes but is not restricted to sugar as defined by Regulations Relating to the Use of Sweeteners in Foodstuffs under the Act, honey, molasses, sucrose with added molasses, coloured sugar, fruit juice concentrate, deflavoured and/or deionized fruit juice and concentrates thereof, high-fructose corn syrup and malt or any other syrup of various origins”[47].DDS, dietary diversity score; RAE, retinol activity equivalent.*No specific indication of portion size or frequency of consumption by the SAFBDG. General guideline is to consume ten starchy food guide units per day (based on 8500 kJ intake/d)[27] (one food guide unit: maize meal porridge, soft/maltabella/oats = 125 g, maize meal porridge, stiff = 60 g, crumbly = 45g bread = 35 g, potatoes/sweet potatoes = 100 g, cooked pasta/samp/whole grains = 75 g, unsweetened breakfast cereals 25 g and cooked rice = 65 g) of the study population(48–50).†To establish the recommended portion size to measure adherence to this guideline, legume consumption was compared with the legume consumption in other South African studies (PURE, SANHANES), and guidance from the Nutrition Information Centre of the University of Stellenbosch was followed(29–31).‡Definition of added sugar as described in the Regulations Relating to the Labelling and Advertising of Foodstuffs, No. R. 146 of 1 March 2010. Foodstuffs, Cosmetics and Disinfectants Act (Act 54 of 1972)[47].
A maximum overall adherence score of nine (to the SAFBDG) was therefore possible. To determine the level of adherence to the SAFBDG, overall and for each individual SAFBDG recommendation, suggested portions sizes/d or week and percentages of total energy intake per day were used as cut-points (as currently suggested in the technical support papers of the SAFBDG)(19–27). As an example, it is advised to consume 400 g fruit and vegetables and to have <10 % of total energy from saturated fat intake/d[21,24].
To calculate adherence to the individual SAFBDG recommendation “*Enjoy a* variety of foods”, a Dietary Diversity Score (DDS), based on nine food groups, was used. The nine food groups were based on the FAO of the UN guidelines and included [1] cereals, roots and tubers; [2] meat, poultry and fish; [3] diary; [4] eggs; [5] vitamin A-rich vegetables and fruit; [6] legumes; [7] other vegetables than vitamin A-rich; [8] other fruits than vitamin A-rich and [9] fats and oils[28]. The lowest possible DDS score was zero [0] and the highest possible DDS nine [9]. A DDS score below four [4] was considered as having a low dietary diversity[28].
No specific portion size was stated in the SAFBDG to measure adherence to the individual SAFBDG recommendation “Make starchy foods part of most meals”[27]. To assess adherence to this recommendation, a general guideline (consume ten starchy exchanges/units/d, based on 8500 kJ intake/d) was used[37]. Starchy exchanges/units included minimally processed starches, whole grains and root vegetables (potato, white fleshed sweet potato)[27]. With regard to measuring adherence to the SAFBDG recommendation “Eat dry beans, spilt peas, lentils and soya regularly”, no specific amount (percentage of total energy) or portion size was stated in the SAFBDG[19]. To establish the recommended portion size to measure adherence to this guideline, legume consumption was compared with the legume consumption in other South African studies (Prospective Urban and Rural Epidemiologica, SANHANES) and guidance from the Nutrition Information Centre of the University of Stellenbosch was followed(29–31). Sensitivity analysis was conducted without the recommendation “Eat dry beans, spilt peas, lentils and soya regularly” and “Make starchy foods part of most meals” but did not change the overall adherence result (results not shown).
Adherence scores to each individual SAFBDG recommendation, using recommended portion sizes, were calculated as follows: one [1] point when adhering to the recommendation and zero [0] points for non-adherence to the recommendation. Two sub-categories were developed to measure adherence to the recommendations “Fish, chicken, lean meat or eggs can be eaten daily” and “Use fats sparingly. The two sub-categories for the recommendation “Fish, chicken, lean meat or eggs can be eaten daily” included: [1] fish, chicken and lean meat consumption can be eaten daily and [2] egg consumption can be eaten daily. The two sub-categories to measure adherence to the recommendation “Choose vegetable oils rather than hard fats” included [1] keep total fat intake between the recommended range (≥20 % and ≤30 % of total energy intake) and [2] limit saturated fat intake <10 % of total energy intake. Adherence to these individual SAFBDG recommendations with two sub-categories, mentioned above, was scored as follows: zero [0] points for non-adherence and half a point (0·5) for adherence. To measure adherence to the individual SAFBDG recommendation “Use sugar & food & drinks high in sugar sparingly”, three sub-categories, non-adherence (0 points), partial adherence (0·5 point) and adherence (1 point) were developed. This was based on the fact that the SAFBDG, in accordance with the WHO’s healthy diet indicator, advise an added sugar intake of <10 % of total energy intake, while an added sugar intake of <6 % of total energy may have additional health benefits[25].
## Assessing the association between adherence to the South African food based dietary guidelines (overall and for each individual South African food based dietary guideline recommendation) and breast cancer risk
The distribution of adherence to five (out of nine) of the individual SAFBDG recommendations resulted in highly skewed categories (adherence/non-adherence to an individual SAFBDG recommendation ≥73 % for both breast cancer cases and controls). Therefore, data-driven tertiles (33rd and 66th percentiles) were used to determine the cut-off points for assessing the association between adherence to the SAFBDG (overall and for each individual SAFBDG recommendation) and breast cancer risk. Cut-off points for each individual SAFBDG recommendation, using data-driven tertiles, were calculated as follows: one [1] point when adhering to the recommendation (highest tertile); half (0·5) a point for partial adherence to the recommendation (middle tertile) and zero [0] points for non-adherence to the recommendation (lowest tertile) (see online Supplemental Table 1). Each individual SAFBDG recommendation contributed equally to the total adherence score. The recommendations, “Use fats sparingly” and “Choose vegetable oils rather than hard fats” had two sub-recommendations that were scored individually and were divided by two to determine an average score (0; 0·25 and 0·5). Finally, tertiles of control participants (33rd and 66th percentiles) were used to determine adherence to the overall score and the association with breast cancer risk, with ≤3·5 being the lowest adherence tertile and >5·0 the highest adherence tertile.
## Statistical analysis
A total of 399 breast cancer cases and 399 matched controls were recruited in the SABC study. Of those, three breast cancer cases and three matched controls were excluded due to missing dietary data information. Descriptive analyses were performed, and differences between cases and controls were assessed using paired sample t-test (normal distributed data presented as mean ± s d) and Wilcoxon signed rank test (not normal data, presented as median, 25th and 75th percentiles) for continuous variables and paired χ2 test for categorical variables (presented as percentages). Specifications of the WHO were used to calculate BMI, using measured height and weight (kg/m2).
## Assessing the association between adherence to the South African food based dietary guidelines and breast cancer risk: overall and individual guidelines
Conditional logistic regression models were used to compute OR and associated 95 % CI to determine the association between breast cancer risk and adherence to the SAFBDG (overall and each individual SAFBDG recommendation). Adherence scores (overall and for each individual SAFBDG recommendation) were stratified by hormonal breast cancer receptor subtypes, menopausal status (pre v. post) and obesity (BMI ≥ 30 kg/m2). For the two latest variables, unconditional logistic regression was used. Additional analysis was conducted to determine significant interactions among strata (menopausal status, hormonal breast cancer receptors and obesity).
The following confounders were examined for adherence to the overall- and individual SAFBDG recommendation (chosen a priory from known breast cancer risk factors): individual income (R1–R3000, R3001–R6000 and R6001+), ethnicity (Zulu/Pedi/Swazi, Xhosa, Sotho, Tshwane, Venda, Tsonga and Ndebele), level of education (none/primary school, high school and college/postgraduate/diploma), smoking (smokers and non-smokers), height (continuous), waist circumference (continuous data), age at menarche (continuous), full-term pregnancy (yes/no), age at first pregnancy (<24 v. >24 years of age), age at menopause (<48 v. >48 years of age), parity (≤ three children v. > three children), duration of exclusive breast-feeding (months), use of exogenous hormones (hormonal birth control to avoid pregnancy: oral contraceptives and injections), or hormone replacement therapy after menopause), family history of breast cancer (yes/no), HIV status (positive v. negative), total energy intake in kJ (continuous), alcohol intake in gram (continuous) and under reporting (under reporting, plausible reporting and over reporting). Under reporting (13·1 % of breast cancer cases and 11·6 % of controls) and over reporting (24·0 % of breast cancer cases and 27 % of controls) cut-off points were calculated using the Goldberg and Black principle to determine over-and-under reporting of energy intake[32]. Menopausal status, ethnicity, total energy intake, alcohol intake, individual income/month and waist circumference altered the crude OR by more than 10 % when assessing adherence to overall and individual SAFBDG recommendations and were included in our final model.
An additional confounder, habitual physical activity per day (active v. less active), was examined when adherence to the individual SAFBDG recommendations was assessed. This was done as physical activity was part of the overall score and as such not included as a confounder in the overall score analyses.
## Results
Selected descriptive characteristics amongst cases and controls are reported in Table 2. Ethnicity differed significantly between cases and controls with breast cancer cases having more Ndebele-speaking people and controls having more Sotho-speaking people. Breast cancer cases had a significant lower waist circumference (93·3 cm ± 13·8 cm) compared with controls (95·8 cm ± 13·7 cm) and had less HIV-positive (16·5 %) cases than controls (22·6 %). Controls had a higher percentage of alcohol consumers but consumed less ethanol (4·6 g/d) on average than cases (5·4 g). Hormone-responsive breast cancers, ER+ (75·3 %) and PR+ (66·4 %) were the dominant breast cancer subtypes, while triple negative breast cancer accounted for 16·2 % (not stratified by menopausal status).
Table 2Distribution of characteristics between breast cancer case and control participants (means ± sd, median and 25th; 75th percentiles, based on distribution of variables)CharacteristicsBreast cancer cases (n 396)Controls (n 396) P-value n % n %Socio-demographic factors *Age (years) Mean54·754·60·980 sd 12·912·9 Ethnicity0·041 Zulu/Pedi/Xhosa/Tswana/Swazi6716·96616·6 Sotho10827·314436·4 Venda/Tsonga10526·59123·0 Ndebele11629·39524·0 Level of education0·078 None/primary9724·57117·9 High School25764·927970·5 College/university/postgraduate4210·64611·6 Individual income/month0·350 R012531·610827·3 R1–R300021955·322757·3 R3001–R6000+5213·16115·4Anthropometry *BMI (kg/m2) Mean31·431·80·317 sd 7·06·9 *WC (cm) Mean93·395·80·011 sd 13·813·7Lifestyle factors †*Total vigorous* and moderate PA min/week Median39·432·10·303 25th percentile, 75th percentile7·8, 85·89·1, 70·8 Current smokers358·84411·10·286 HIV positivity6516·49022·70·025Dietary factors †TE (kJ/d) Median914689900·239 25th percentile, 75th percentile6812, 97597184, 10 284 †Protein (g/d) Median63·863·50·073 25th percentile, 75th percentile47·4, 82·749·2, 93·1 % of TE11·812·0 †Total fat (g/d) Median64·864·40·125 25th percentile, 75th percentile42·4, 91·947·2, 95·7 % of TE26·927·2 †Saturated fat (g/d) Median17·919·20·044 25th percentile, 75th percentile11·5, 26·112·7, 27·9 % of TE7·48·1 *CHO (g/d) Mean330·8338·70·445 sd 143·5147·3 % of TE61·464·0 *Dietary fibre (g/d) Mean24·925·30·616 sd 11·0311·4 †Added sugar (g/d) Median65·367·90·313 25th percentile, 75th percentile38·4, 105·539·9, 109·7 % of TE12·112·0 Non-alcohol consumers35088·432181·10·004 †Ethanol intake in alcohol consumers (g/d) Median5·44·60·005 25th percentile, 75th percentile2·8, 13·82·5, 14·7 †Dietary diversity score Median3·04·0<0·001 25th percentile, 75th percentile2·0, 4·52·5, 7·0 *Starchy staple foods (g/d) Mean543·1574·50·095 sd 270·0258·8 †Fruit, fruit juice and non-starchy vegetables (g/d) Median342·5418·30·001 25th percentile, 75th percentile169·1, 627·3208·9, 1098·1 †Legumes (g/d) Median14·318·70·171 25th percentile, 75th percentile6·0, 49·66·0, 101·1 †Milk, maas or yoghurt (g/week) Median91·7100·00·004 25th percentile, 75th percentile22·9, 141·543·1, 195·0 †Fish, chicken, lean meat or eggs (g/d) Median43·956·80·061 25th percentile, 75th percentile17·1, 100·021·4, 145·5Breast cancer risk factors Full-term pregnancy in parous women37795·238296·50·374 Ever breast fed in parous women33991·434989·90·496 †,‡Duration of breast-feeding (months) Median35410·187 25th percentile, 75th percentile20, 6224, 62 §Premenopausal13333·613433·80·852 §Postmenopausal24865·125765·70·852 †Age at menarche Median15150·537 25th percentile, 75th percentile13, 1613, 16 †,||Age at menopause (years) Median47480·331 25th percentile, 75th percentile42, 5044, 50 Family history of breast cancer256·3174·30·205 Use of birth control (contraceptives)22957·821554·30·238Breast cancer case characteristics *Receptor status* ER+29875·3– PR+26366·4– HER211428·8– ¶Breast Cancer case subtype HER2 enriched215·3– Luminal A4010·1– Luminal B26967·9– TNBC6416·2–WC, waist circumference; TE, total energy; CHO, carbohydrates; PA, physical activity; ER+, oestrogen receptor positive; PR+ progesterone receptor positive; HER2, Human-Epidermal Growth Factor-2; TNBC, triple negative breast cancer; HRT, hormone replacement therapy.*Data are presented as means and standard deviations (sd).†Data are presented as median (25th percentile, 75th percentile).‡In breast feeding women only.§Twenty missing values for menopausal status (fifteen cases and five controls) Missing values were excluded from percentage calculations.||Among postmenopausal women only.¶Defined using Allred scores.
Table 3 presents the level of adherence to the SAFBDG (overall and for each individual SAFBDG recommendation) between cases and controls, using suggested portion sizes or percentages of total energy intake as cut-points for all the SAFBDG recommendations. Regarding overall adherence to the SAFBDG, only 32·3 % of breast cancer cases and 39·1 % of controls adhered to at least half (4·5) of the SAFBDG (nine out of the eleven were measured).
Table 3Measuring the level of adherence to the South African food based dietary guidelines (SAFBDG) between breast cancer cases and controls, using recommended portion sizes or percentages of total energy intakeSAFBDG recommendationsRecommended portion or % of TEI for a healthy dietScore criteriaCases n %Controls n % P-value1) *Enjoy a* variety of foodsDDS score <4024160·919448·90·001DDS score ≥4115539·120251·12) Be activeModerate PA < 150 min/week039599·739399·20·563Moderate PA ≥ 150 min/week110·320·83) *Make starchy foods part of most mealsLess than ten starch exchanges/d024762·422857·60·168At least ten starch exchanges/d114937·616842·44) Eat plenty of vegetables and fruit everydayFruit & vegetables <400 g/d022155·819649·50·075Fruit & vegetables ≥400 g/d117544·220050·55) †Eat dry beans, split peas and lentils regularly ‡Less than 2 or 3 times/week032882·830677·30·050 ‡At least 2 or 3 times/week16817·29022·76) Have milk, maas or yoghurt everydayMilk, maas or yoghurt <400 ml/d039198·739098·50·761Milk, maas or yoghurt ≥400 ml/d151·361·57) Fish chicken, lean meat or eggs can be eaten daily‖ Lean meat or fish >90 g/d07117·910727·00·002Lean meat or fish ≤90 g/d0·532582·128973·0 §Eggs <4/week024160·821153·30·031 §Eggs ≥4/week0·515539·218546·78) Drink lots of clean safe waterNot included for analysis9) Use fats sparingly. Choose vegetable oils rather than hard fats (divided into two subgroups)‖ Total fat <20 % or >30 %018847·521754·80·039Total fat ≥20 % & ≤30 %0·520852·517945·2Saturated fat ≥10 % of TEI032381·631880·30·651Saturated fat <10 % of TEI0·57318·47819·710) Use sugar & foods & drinks sparingly¶ Added sugar ≥10 % of TEI025664·624862·60·719Added sugar <10 % & ≥5 % of TEI0·59724·59824·7Added sugar <6 % of TEI14310·95012·711) Use salt & food high in salt sparinglyNot included for analysisOverall adherence score Total adherence score Median44 25th and 75th percentiles2·75, 5·03·0, 5·75 Adherence score >4·512832·315539·10·045SAFBDG, South African food based dietary guidelines; TEI, total energy intake; DDS, dietary diversity score.*No specific indication of portion size or frequency of consumption by the SAFBDG. General guideline is to consume ten starchy food guide units per day (based on 8500 kJ intake/d)[27] (one food guide unit: maize meal porridge, soft/maltabella/oats = 125 g, maize meal porridge, stiff = 60 g, crumbly=45g bread=35 g, potatoes/sweet potatoes = 100 g, cooked pasta/samp/whole grains = 75 g, unsweetened breakfast cereals 25 g and cooked rice = 65 g) of the study population[49,50].†No specific indication of portion size or frequency of consumption by the WHO or SAFBDG. Current frequency is based on estimate recommendation from global food based dietary guidelines and national recommendations[30,50].‡One serving equals 75 g of dry beans, peas, cooked lentils or 30 g of soya mince or 21·4 g/d[50].§The current recommendation suggest up to 4 eggs/week and equals 29 g/d (based on the weight of one large egg, 50 g).‖*Guideline is* divided into two subgroups.¶Guideline has three categories to measure adherence.
Both cases and controls showed adherence levels <50 % to the following individual SAFBDG recommendations: “Be active”, “Make Starchy foods part of most meals”, “Eat plenty of fruit and vegetables every day”, “Eat dry beans, spilt peas and lentils regularly”, “Have milk, maas or yoghurt every day” and “Use sugar & foods & drinks sparingly”. Fewer cases adhered to the individual SAFBDG recommendation “*Enjoy a* variety of foods” (measured by a DDS score), with only 39·1 % of cases having a dietary diversity score above four (out of nine) compared with 51·1 % of controls. Both cases (17·2 %) and controls (22·7 %) showed low adherence to the individual SAFBDG recommendation “Eat dry beans, spilt peas and lentils regularly”. In addition, both cases (82·1 %) and controls (73 %) showed high adherence to the sub-category for fish, chicken and lean meat consumption (<90 g/d), while controls (46·7 %) were more likely to adhere to the sub-category on egg consumption (at least four eggs/week) than breast cancer cases (39·2 %). Although adherence to the individual SAFBDG “Use fats sparingly. Choose vegetable oils rather than hard fats” was low, breast cancer cases (52·5 %) were more likely to adhere to the total fat sub-recommendation (total fat >20 % and <30 % of total energy intake) than controls (45·2 %).
Table 4 provides results on the association between overall adherence to the SAFBDG, using data-driven tertiles for each SAFBDG recommendation, and the association with breast cancer risk. After adjusting for potential confounders, higher adherence (>5·0) v. lowest adherence (≤3·5) to the SAFBDG showed a significant inverse association with breast cancer risk overall (OR = 0·56, 95 % CI (0·38, 0·85), $$P \leq 0$$·006), among postmenopausal women (OR = 0·64, 95 % CI (0·40, 0·97), $$P \leq 0$$·034) as well as in oestrogen receptor positive (ER+) breast cancer (OR = 0·51, 95 % CI (0·32, 0·89), $$P \leq 0$$·004). No significant association with breast cancer risk was observed in premenopausal or obese women.
Table 4The association between overall South African food based dietary guideline (SAFBDG) adherence and breast cancer risk, using data-driven tertiles (33rd and 66th percentiles)Adherence scoreCasesControlCrude outputAdjusted model 2 || P-value for stratification interactions n % n %OR95 % CIOR95 % CIOverall (cases n 396; controls n 396)≤3·518847·515338·611>3·5; ≤5·011729·512030·30·840·60, 1·160·810·57, 1·16n/a>5·09123·012331·10·580·39, 0·850·560·38, 0·85n/a § P trend 0·0050·006 *,†Premenopausal (n 267) (cases n 133; controls n 134)≤3·55843·65037·311>3·5; ≤5·04533·84332·10·840·45, 1·570·890·49, 1·630·880>5·03022·64130·60·530·25, 1·120·710·37, 1·360·782 § P trend 0·0950·303 *,†Postmenopausal (n 505) (cases n 248; controls n 257)≤3·512249·210340·111>3·5; ≤5·06927·87529·20·720·51, 1·200·750·48, 1·160·880>5·05723·07930·70·540·33, 0·880·640·40, 0·970·782 § P trend 0·0140·034ER+ (n 298)≤3·59531·9–11>3·5; ≤5·013344·6–0·660·31, 1·440·750·49, 1·130·753>5·07023·5–0·780·34, 1·790·510·32, 0·80·516 § P trend 0·5630·004PR+ (n 263)≤3·59536·1–11>3·5; ≤5·010941·4–0·540·27, 1·100·820·53, 1·270·883>5·05922·4–0·380·18, 0·780·650·40, 1·050·782 § P trend 0·0080·077 *,‡Obese (n 466) (cases = 231; controls = 235)≤3·510846·89339·611>3·5; ≤ 5·07030·37833·21·020·60, 1·750·720·46, 1·150·874>5·05322·96427·20·850·43, 1·660·740·45, 1·220·678 § P trend 0·6240·239ER+, estrogen receptor positive; PR+, progesterone receptor positive.*Unconditional logistic regression.†Twenty missing values for menopausal status (fifteen cases and five controls).‡Obesity defined as BMI ≥ 30 kg/m2.§Indicating significance for OR to determine the association with breast cancer risk (trend analysis comparing highest v. lowest tertiles).||Indicating significance for stratification interactions. Adjusted Model 2: Adjusted for ethnicity, total energy intake, alcohol intake, individual income per month, waist circumference (not adjusted for waist circumference when stratified by obesity status) and menopausal status (not adjusted for menopausal status when stratified by menopausal status).
The association between higher adherence to individual SAFBDG recommendations, using data-driven tertiles for each individual SAFBDG recommendation, with breast cancer risk is presented in Table 5. After adjustment for potential confounding factors, higher adherence to the recommendation “*Enjoy a* variety of foods” (measured by a dietary diversity score) showed an inverse association with breast cancer risk overall (OR = 0·46, 95 % CI (0·29, 0·70), $P \leq 0$·001), in postmenopausal women (OR = 0·48, 95 % CI (0·30, 0·77), $$P \leq 0$$·003) and in participants with ER+ and progesterone receptor positive (PR+) breast cancers (OR = 0·42, 95 % CI (0·26, 0·68), $P \leq 0$·001 and OR = 0·51, 95 % CI (0·31, 0·85), $$P \leq 0$$·010, respectively). With regard to the recommendation “Make starchy food part of most meals”, higher adherence showed an inverse association with ER+ breast cancer (OR = 0·65, 95 % CI (0·42, 0·99), $$P \leq 0$$·047). Furthermore, higher adherence to the recommendation “Eat plenty of vegetables and fruit everyday” showed an inverse association with breast cancer risk overall (OR = 0·58, 95 % CI (0·38, 0·86), $$P \leq 0$$·008), in postmenopausal women (OR = 0·62, 95 % CI (0·39, 0·99), $$P \leq 0$$·046) and in participants with ER+ and PR+ breast cancers (OR = 0·51, 95 % CI (0·32, 0·81), $$P \leq 0$$·005 and OR = 0·59, 95 % CI (0·37, 0·94), $$P \leq 0$$·028, respectively). Higher consumption of milk, maas or yoghurt showed an inverse association with breast cancer risk overall (OR = 0·69, 95 % CI (0·47, 0·97), $$P \leq 0$$·025), in postmenopausal women (OR = 0·69, 95 % CI (0·43, 0·98), $$P \leq 0$$·039) and in participants with ER+ breast cancers (OR = 0·54, 95 % CI (0·35, 0·84), $$P \leq 0$$·006). Higher adherence to the recommendation “Fish, chicken, lean meat or eggs can be eaten daily” showed an inverse association with breast cancer risk overall (OR = 0·67, 95 % CI (0·46, 0·95), $$P \leq 0$$·036) and in participants with ER+ breast cancer (OR = 0·56, 95 % CI (0·36, 0·87), $$P \leq 0$$·010).
Table 5The association between adherence to individual South African food based dietary guideline (SAFBDG) recommendations and breast cancer risk, using data-driven tertiles (33rd and 66th percentiles)South African food based dietary guidelinesScore adherenceOverall* (cases n 396; controls n 396) **,‡Premenopausal (cases = 133; controls = 134) **,‡Postmenopausal (cases = 248; controls = 257) †ER+ (n 298) †PR+ (n 263 *,§Obese (cases = 231; controls = 235)OR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CIOR95 % CIEnjoy a variety of foods01111110·50·840·57, 1·230·730·40, 1·320·900·57, 1·440·830·54, 1·260·810·52, 1·251·020·64, 1·6410·460·29, 0·700·600·30, 1·190·480·30, 0·770·420·26, 0·680·510·31, 0·850·630·38, 1·04 P trend <0·0010·1470·003<0·0010·0100·069Be active01111110·50·940·61, 1·450·580·27, 1·281·200·73, 1·950·810·40, 1·330·840·50, 1·430·880·52, 1·4910·910·48, 1·720·560·18, 1·801·230·64, 2·340·770·37, 1·630·820·37, 1·811·270·65, 2·48 P trend 0·7710·3320·5310·5010·6200·485Make starchy food part of most meals.01111110·51·000·69, 1·461·210·64, 2·320·890·58, 1·350·940·62, 1·441·090·71, 1·710·960·62, 1·5010·860·59, 1·261·070·56, 2·070·800·50, 1·290·650·42, 0·990·840·53, 1·330·790·47, 1·30 P trend 0·4450·8320·3630·0470·4510·354Eat plenty of vegetables and fruit every day01111110·51·030·71, 1·460·730·40, 1·321·250·81, 1·931·110·75, 1·671·060·70, 1·591·140·73, 1·7810·580·38, 0·860·600·30, 1·190·620·39, 0·990·510·32, 0·810·590·37, 0·940·770·47, 1·26 P trend 0·0080·0890·0460·0050·0280·291Eat dry beans, split peas, lentils and soya regularly01111110·50·830·53, 1·301·580·68, 3·640·640·38, 1·080·830·51, 1·370·900·52, 1·560·750·43, 1·3110·820·59, 1·151·040·60, 1·800·830·55, 1·260·950·65, 1·370·960·65, 1·410·690·45, 1·06 P trend 0·2550·8890·3900·7740·8210·088Have milk, maas or yoghurt every day01111110·50·780·54, 1·130·870·47, 1·600·790·52, 1·230·740·49, 1·130·930·59, 1·471·060·68, 1·6810·690·47, 0·970·750·39, 1·440·690·43, 0·980·540·35, 0·840·660·41, 1·050·780·48, 1·23 Ptrend0·0250·3920·0390·0060·0800·319Fish, chicken, lean meat or eggs can be eaten daily01111110·50·820·58, 1·160·640·35, 1·170·960·63, 1·450·740·49, 1·110·850·56, 1·280·880·57, 1·3710·670·46, 0·950·720·36, 1·450·740·46, 1·200·560·36, 0·870·700·44, 1·110·930·56, 1·54 P trend 0·0360·3550·2230·0100·1330·773Use fats sparingly – choose vegetable oils, rather than hard fats: total fat‖ 0·51111110·250·790·55, 1·150·730·40, 1·330·840·53, 1·340·760·51, 1·140·770·50, 1·190·760·48, 1·2101·050·71, 1·541·410·72, 2·760·920·59, 1·440·960·62, 1·490·970·62, 1·551·100·68, 1·77 P trend 0·8250·3140·7170·8710·9190·697Use fats sparingly – choose vegetable oils, rather than hard fats: total saturated fat‖ 0·51111110·250·990·68, 1·461·280·70, 2·350·840·53, 1·341·000·66, 1·520·970·62, 1·520·900·56, 1·4501·340·92, 1·971·260·67, 2·381·290·82, 2·041·260·82, 1·931·150·74, 1·811·300·82, 2·07 P trend 0·1320·4760·2650·2850·5340·269Use sugar and foods and drinks high in sugar sparingly11111110·51·060·74, 1·521·210·65, 2·251·100·72, 1·700·990·66, 1·491·030·67, 1·581·280·81, 2·0101·010·70, 1·461·430·74, 2·730·900·58, 1·390·960·63, 1·431·070·70, 1·640·940·58, 1·50 P trend 0·9320·2850·6260·8320·7650·784ER+, estrogen receptor positive; PR+, Progesterone receptor positive.*Adjusted for total energy intake, alcohol intake, individual income/month, ethnicity, waist circumference, physical activity, (unless the variable was part of the recommendation under investigation) and menopausal status (not when stratified by menopausal status).†Stratified by estrogen receptor status or progesterone receptor status.‡Twenty missing values for menopausal status (fifteen cases and five controls).§Stratified by obesity defined as BMI ≥ 30 kg/m2 and using unconditional logistic regression.‖Adherence to the guideline “Use fats sparingly. Choose vegetable oils, rather than hard fats” are measured by two subcategories, total saturated fat and total saturated fat intake.**Stratified by menopausal status or obesity and using unconditional logistic regression.0 indicates the lowest and 1 the highest adherence to the specific recommendation. No significant P-values were observed when interactions among strata were assessed.
## Discussion
To our knowledge, this is the first study investigating the relationship between adherence to the SAFBDG and breast cancer risk. The results indicate that higher adherence to the SAFBDG, using data-driven cut-points, may reduce the risk of developing breast cancer in this population overall, in postmenopausal women and for women with ER+ breast cancer. The strongest inverse associations with breast cancer risk were seen for higher adherence to the following individual SAFBDG recommendations “*Enjoy a* variety of foods”, “Eat plenty of fruit and vegetables every day”, “Have milk, maas or yoghurt every day” and “Fish, chicken, lean meat or eggs can be eaten daily”. The study results further indicate a concerning low level of adherence to the SAFBDG, whether adherence was measured as overall adherence or individual recommendation adherence.
Higher adherence to the SAFBG (measured with data-driven cut-points) may protect against the development of breast cancer in this population. Studies investigating the association between adherence to the SAFBDG (and other dietary guidelines) and nutrition-related diseases in this South African population are still limited. However, our findings are in line with results from international studies investigating adherence to national Food Based Dietary Guidelines in association with non-communicable disease and obesity risk. Although national Food Based Dietary Guidelines may differ, based on the population, Food Based Dietary *Guidelines* generally promote similar healthy eating behaviour/patterns globally. A Danish Cohort study of 54 305 participants showed that higher adherence to the Danish Food Based Dietary Guidelines had an inverse association with Type 2 diabetes and CVD[33]. A Dutch Cohort study showed that higher adherence to the Dutch Food Based Dietary Guidelines was inversely associated with overall mortality and non-communicable diseases such as Type 2 diabetes and colorectal cancer[34]. The European Prospective Investigation into Cancer and Nutrition study (EPIC-Granada study conducted in Spain) showed that higher adherence to the Spanish Dietary Guidelines was associated with a lower risk of being obese[35]. These global findings highlight the importance of adhering to national Food Based Dietary Guidelines to reduce non-communicable diseases such as breast cancer.
## “Enjoy a variety of foods”
Higher adherence to the individual SAFBDG recommendation “*Enjoy a* variety of foods”, using data-driven cut-points, showed an inverse association with breast cancer risk. This indicate that consumption of a variety of foods may play an important role in breast cancer prevention. However, more than 60 % of cases and nearly half of controls did not adhere to this recommendation when suggested adherence cut-points were used. Similar results from the South African National Health and Nutrition Examination Survey showed that 50 % of the black South African population had a DDS below four[31]. This is a concern as the low DDS in the current study indicates that a variety of foods and thus a variety of micronutrients, which may protect against breast cancer, are not consumed[3,31].
## “Eat plenty of fruit and vegetables every day” and “eat dry beans, split peas, lentils and soya regularly”
Higher adherence to the individual SAFBDG recommendation “Eat plenty of fruit and vegetables every day”, using data-driven cut-points, was inversely associated with breast cancer risk in the current study. Worrisome, however, is that adherence to the SAFBDG recommendation “Eat plenty of fruit and vegetables every day” (using suggested adherence cut-points) was low in both cases and controls in our study. The Prospective Urban and Rural Epidemiology cohort study, conducted in the North-West Province of South Africa, showed that only 31·5 % of urban women (n 355) adhered to this recommendation[29]. Low fruit and vegetable consumption was also observed in other South African regions[21,36]. In addition, concerning low levels of adherence to the individual SAFBDG recommendation “Eat dry beans, split peas, lentils and soya regularly” was observed when suggested adherence cut-points were used in the analysis and is in line with other studies reporting low legume consumption in South Africa[29]. Low adherence to these respective recommendations is of concern as high fibre foods such as fruit, vegetables and legumes have been linked to a decreased risk of colorectal cancer and can help maintain a healthy weight necessary to decrease the risk for postmenopausal breast cancer[3].
## “Having milk, maas or yoghurt every day and “fish, chicken, lean meats or eggs can be eaten daily”
Higher adherence to the individual SAFBDG recommendations “Having milk, maas or yoghurt every day and “Fish, chicken, lean meats or eggs can be eaten daily” showed inverse associations with breast cancer risk when data-driven cut-points were used. However, both cases and controls showed worrying low levels of adherence to the SAFBDG recommendation “Have milk, maas or yoghurt everyday” (using suggested adherence cut-points). The Prospective Urban and Rural Epidemiologica study, mentioned above, showed similar low adherence levels (11·5 %) to this recommendation amongst urban women (400 g of milk, maas or yoghurt or 50 g hard cheese/d)[29]. Low adherence to this recommendation is concerning since the evidence suggests that dairy products and diets high in Ca may decrease the risk of premenopausal breast cancer[3].
## “Make starchy foods part of most meals”
Almost 60 % of both cases and controls did not adhere to the SAFBDG recommendation “Make starchy foods part of most meals” when suggested adherence cut-points were used. This finding was unexpected since staple foods, such as fortified maize meal, bread, rice, etc., promoted in this recommendation, cost less per unit of energy than animal products, fruit and vegetables and is therefore considered affordable[37]. Despite the low consumption of minimally processed starchy foods, total carbohydrate intake for both cases (61·4 %) and controls (64·0 %) was within the recommended macronutrient distribution range (45–65 % of total energy intake). This finding indicates that not all starchy foods consumed are of high nutritional value and reflect the changes in dietary carbohydrate consumption, from high fibre and nutrient dense starchy staple food intakes to higher consumption of refined starches, lacking nutrients and having a high added sugar content.
## “Use fats sparingly. Choose vegetable oils rather than hard fats” and “use sugar and foods and drinks high in sugar sparingly”
Adherence to the SAFBDG recommendations: [1] “Use fats sparingly. Choose vegetable oils rather than hard fats” and [2] “Use sugar & foods & drinks sparingly” also showed low levels of adherence in both cases and controls when suggested portions were used as adherence cut-points. Similar, low levels of adherence to the SAFBDG recommendation regarding total fat intake were observed in Cape Town, South Africa[38]. With regard to added sugar intake, a review of dietary surveys in the adult South African population from 2000 to 2015 and cross-sectional studies showed that added sugar intake was greater than the recommended 10 % of total energy intake in various provinces of South Africa (North-West, KwaZulu-Natal, Western Cape, Free state)[1,38]. Diets high in saturated fat and added sugar are concerning because diets high in saturated fat and added sugar have been linked to a higher risk of being overweight or obese, which is a known risk factor for postmenopausal breast cancer[4].
## “Be active”
Apart from low adherence to the nutrition-related SAFDG recommendations, adherence to the SAFBDG recommendation “Be active” also showed worrying low adherence levels in both breast cancer cases and controls in the current study. This low physical activity level is not a new finding and is in line with several studies conducted amongst black South African women[39,40]. The finding is alarming as physical activity may protect against breast cancer development and being overweight or obese[3].
## Level of adherence to the overall South African food based dietary guidelines
A modelling study, reviewing global adherence to national food based dietary guidelines in 85 countries, showed that South *Africa is* among the countries with the lowest adherence to the national food based dietary guidelines[41]. In line with the above review, the results of our study clearly showed low adherence to the overall and individual SAFBDG recommendations.
Poverty, influencing purchasing power, and a growing obesogenic food environment are considered key barriers to healthier eating patterns in South Africa[37]. Additionally, the Strategy for the Prevention and Control of Obesity in South Africa states that high crime rates and gender-based violence, especially in urban South African areas, contribute to the perception of it being unsafe to exercise outdoors and could also contribute to low physical activity levels (all ethnicities)[42]. A lack of exercise space due to small plots and size of physical buildings in low-income households may also be considered a potential limitation to physical activity[43]. Also, the many challenges associated with measuring self-reported physical activity levels in epidemiological studies such as recall bias of participants may further contribute to low physical activity levels. Furthermore, a lack of knowledge (especially in resource poor settings), education and skills regarding food preparation methods (especially for beans/lentils); length of preparation time; taste preferences (unsweetened products v. sweetened products) and perceptions towards a healthy diet and social and cultural influences may also contribute to the low adherence levels observed[1]. Given the effect of the COVID-19 pandemic, resulting in a higher unemployment rate and higher food prices, adherence to the SAFBDG may decrease even further[44]. Low adherence to the recommendation “Eat dry beans, split peas, lentils and soya regularly” is not likely to be influenced by economic status since beans and other legumes are affordable in South Africa[37]. However, the cost occurred as a result of the length of preparation of beans and other legumes may influence the consumption thereof. Therefore, more research is required to understand the drivers of consumption of specific food groups such as beans and other legumes in this population.
Results of our study suggest that dietary intake that is not well aligned with the SAFBDG recommendations is associated with an increased risk of developing breast cancer in this black female population of Soweto, South Africa. It is therefore critical to also create sustainable and healthy food environments that support the affordability, availability and accessibility of healthy foods and safe environments that support physical activity in order to enable adherence to the SAFBDG in this population. However, creating sustainable and healthy food environments in South *Africa is* not an easy task and requires multi-sectoral and transdisciplinary public health engagement, beyond those already in place[45].
Furthermore, results of our study are in line with a recent study, conducted in the same black female population of Soweto, South Africa, which investigated the association between adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and breast cancer risk[10]. Both sets of recommendations showed that higher consumption of fruit and vegetables may reduce breast cancer risk in this population. The 2018 WCRF/AICR Cancer Prevention Recommendations emphasised the importance of following an overall healthy lifestyle (being physically active, a healthy weight and following healthy diet) for breast cancer prevention. Results of the current study complement these findings of Jacobs and colleagues [2021] by [1] highlighting the potential benefits of specific foods (milk, maas, yoghurt, lean meats and eggs) and [2] emphasising the importance of following a diverse diet for breast cancer prevention in this population. While the findings of both studies provide valuable insights in the lifestyle and diets of black women from Soweto, more research is required to understand how the 2018 WCRF/AICR Cancer Prevention Recommendations and the SAFBDG could be used as key nutrition intervention/tool in breast cancer prevention studies.
Strengths of the current study include the fact that cases were recruited prior to any breast cancer treatment and that the questionnaires used to obtain data were proven to be validated and data used in the analysis were standardised and administered by trained personnel. Limitations include the relatively limited sample size of the current study, the nature of the case–control study design which is prone to differential biases of cases and the use of a QFFQ to collect dietary data which relies on the memory of participants and is therefore more prone to recall bias. Dietary intake and physical activity were measured over the past month when habitual dietary intake of case participants could have changed due to illness and may contribute to random misclassification and under estimation of dietary intake. In addition, although dietary intakes were captured throughout the year (in different participants) and that breast cancer cases and controls were recruited little time apart, seasonal variability of foods (not adjusted for) may have influenced usual reporting of dietary intakes. It is also noteworthy that QFFQ are not ideal for measuring absolute dietary intakes as their main goal is to measure relative dietary intakes, allowing the ranking of people according to their dietary intakes. Therefore, comparison with fixed cutoffs is not recommended, especially in a population with highly skew data, which motived the use of data-driven tertiles for dividing the participants according to their dietary intakes and to measure adherence to the SAFBDG. Using such an alternative method may have influenced the overall results.
The methodology used in the current study is one of the first attempts to measure quantitative adherence to the SAFBDG and requires more investigation to establish a standardised adherence algorithm. We followed the exact recommendations as stated in the technical support papers of the revised SAFBDG. But, precise recommendations were not always stated such as in the case of measuring adherence to the recommendations “Make starchy foods part of most meals” and “Eat dry beans, split peas, lentils and soya regularly”. To measure adherence to these two recommendations, assumptions were made, based on previous studies in South Africa or consulting with nutrition experts in South Africa. Such assumptions made the operationalisation of the current study challenging.
In conclusion, higher adherence to the SAFBDG showed inverse associations with breast cancer risk overall, in postmenopausal women and for women with ER+ breast cancer. In particular, the increased consumption of a variety of foods, fruit and vegetables, milk, maas or yoghurt and lean meat or eggs showed strong inverse associations with breast cancer risk. However, the black female population included in the current study showed concerning low levels of adherence to the SAFBDG. It is therefore necessary to promote adherence to the SAFBDG in both preventative education campaigns/actions and the creation of sustainable and healthy food environments that enhances the affordability, availability and accessibility of healthier foods, together with safe environments that support increased physical activity in order to enable adherence to the SAFBDG.
## Conflict of interest:
The authors declare that there are no conflicts of interest.
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---
title: The psychosocial antecedents of the adherence to the Mediterranean diet
authors:
- Valentina Carfora
- Maria Morandi
- Anđela Jelić
- Patrizia Catellani
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991846
doi: 10.1017/S1368980022000878
license: CC BY 4.0
---
# The psychosocial antecedents of the adherence to the Mediterranean diet
## Body
The Mediterranean Diet (MeDiet) is a traditional dietary pattern distinctive of the Mediterranean olive cultivation areas[1]. It received the recognition of UNESCO Intangible Cultural Heritage in 2010, as it contributes to transmit a set of knowledge, symbols and rituals regarding food production, conservation, cooking and consumption, which is the basis of the cultural identity and continuity of the Mediterranean communities[2]. The MeDiet is characterised by the consumption of a variety of fresh, local and seasonal food products. Its recommended dietary pattern is represented by the MeDiet Pyramid, which is divided into foods that should be consumed on a daily, weekly and monthly basis. Specifically, plant foods (i.e. cereals, fruit, vegetables, legumes and nuts) and olive oil should be eaten daily. Dairy products, fish, seafood, eggs, cheese, yogurt and poultry should be consumed weekly. Finally, sweets, red meat and processed meat should be preferably consumed monthly[1].
An increasing number of studies supports the beneficial effects of the MeDiet on a range of physical and mental health outcomes. For instance, the MeDiet shows a general preventive effect against CVD, obesity, metabolic syndrome, diabetes, several types of cancer, osteoporosis and premature mortality[3,4]. The MeDiet is also beneficial for brain functioning, as it is a protection factor against cognitive decline and dementia, Parkinson’s disease and depression[3]. Furthermore, as the MeDiet is related to positive health outcomes and quality of life[5], an increase of the adherence to the MeDiet might reduce private and societal health-related costs[6]. For all these reasons, many national and European organisations and institutions are currently trying to support and pursue the values and benefits of the MeDiet.
Despite this public effort, an increase of the westernisation of nutritional habits is leading to a lower adherence to the MeDiet in both Mediterranean and Non-Mediterranean countries[7]. In Italy, for example, nutritional habits are scarcely consistent with the MeDiet recommendations, with more than half of Italians having a low consumption of plant foods and a high consumption of sweets, red and processed meat[8].
So far, research on the psychosocial antecedents that might lead people to have a higher adherence to the MeDiet recommendations have been scarce. To overcome this gap in the current literature, in the present study, we aimed to propose and test the plausibility of an integrated theoretical model to assess the role of different psychosocial (rational, social, emotional, motivational and behavioural) antecedents in increasing the intention to adhere to the MeDiet. The identification of these psychosocial antecedents and their relationships is important to design effective public and private promotion campaigns aimed at increasing the adoption of the MeDiet.
## Abstract
### Objective:
Most previous research on the antecedents of healthy food choice has not investigated the links between these antecedents and has focused on specific food choice rather than on an overall diet. In the present study, we tested the plausibility of an integrated theoretical model aiming to explain the role of different psychosocial factors in increasing the intention to adhere to the Mediterranean Diet (MeDiet).
### Design:
An online survey measured participants’ attitude and perceived behavioural control (i.e. rational antecedents), subjective norm (i.e. social antecedent), positive and negative anticipated emotions (i.e. emotional antecedents), food choice health and mood motives (i.e. motivational antecedents), past adherence to the MeDiet (i.e. behavioural antecedent), and intention to adhere to the MeDiet.
### Setting:
Italy.
### Participants:
1940 adults: 1086 females; 854 males; mean age = 35·65; sd = 14·75; age range = 18–84.
### Results:
Structural Equation Modelling (sem) analyses confirmed the plausibility of the proposed model. Perceived behavioural control was the strongest rational antecedent of intention, followed by the emotional (i.e. anticipated emotions) and the social (i.e. subjective norm) antecedents. Mediation analysis showed that motivational antecedents had only an indirect impact on intention via emotional antecedents. Finally, multigroup sem analysis highlighted that past adherence to the MeDiet moderated the hypothesised paths among all the study variables.
### Conclusions:
The above findings advance our comprehension of which antecedents public communication might leverage to promote an increase in the adherence to the MeDiet.
## Theoretical background
Previous research on the antecedents of healthy food choice has mainly focused attention mainly on the rational components of this choice, such as attitude, perceived behavioural control or self-efficacy[9,10]. A more limited number of studies have instead investigated the motivational, emotional and behavioural components of this choice, such as health motives or habits[11,12]. So far, however, the links between these antecedents have been scarcely investigated, and this is likely due to the lack of a common conceptual framework to understand the predictors of food choice and diet[13]. Moreover, most of the past studies focused on the reasons for choosing individual foods (e.g. fruit and vegetable, meat, sugary snack or beverage) rather than considering the difficult issue of predicting adherence to a healthy diet, which indeed is the key to obtaining long-term health benefits[14]. In the present study, we proposed and tested an integrated model of the different antecedents that influence the adherence to the MeDiet, by considering rational, social, emotional, motivational and behavioural antecedents. These antecedents are presented in Fig. 1 and described below, together with the hypotheses about their interrelationships.
Fig. 1Integrated theoretical model to explain the intention to adhere to the Mediterranean diet To consider the rational and social antecedents of people’s intention to adhere to the MeDiet, we assumed the Theory of Planned Behaviour (TPB) as a frame of reference[15]. The TPB postulates that the personal intention to perform a behaviour is determined by both rational and social factors. The rational factors include attitude and perceived behavioural control, where attitude is related to the personal positive or negative evaluation of a behaviour, while perceived behavioural control is the perception of personal ability and external possibility to perform a specific behaviour. The social component of the TPB model is represented by subjective norm, that is the perceived social pressure to perform a certain behaviour.
In the domain of the studies on eating behaviours, several meta-analyses confirmed that the TPB is a robust theoretical model to predict people’s intentions related to food choice[16], as well as the intention to follow a healthy dietary pattern[9]. To the best of our knowledge, however, only few studies have explored and confirmed the predictive power of the TPB on the adherence to the MeDiet[13,17]. In these studies, perceived behavioural control appeared to be the strongest predictor of the intention to adhere to the MeDiet, followed by a positive attitude towards the MeDiet. Subjective norm had instead the lowest role in predicting people’s intention. Based on the above, in the present study, we expected that: H1a: Positive attitude towards the adherence to the MeDiet influences the intention to adhere to it. H1b: Perceived behavioural control influences the intention to adhere to the MeDiet. H1c: Subjective norm related to the adherence to the MeDiet influences the intention to adhere to it.
Over and above the rational and social factors, emotional antecedents are likely to play a relevant role in orienting diet habits. Emotions are essential elements in the decision-making process, as they influence information processing, responses to persuasive stimuli, goal setting and goal-directed behaviour implementation[18]. Among the different types of emotions, anticipated emotions have aroused considerable interest given their strong predictive power. They can be defined as the positive or negative emotional reactions elicited by the anticipation of the consequences of acting or not acting a certain behaviour[19].
Many researchers have already included anticipated emotions in the TPB model, even if most of them have paid attention only to anticipated emotions with a negative valence (e.g. anticipated regret, guilt or shame), without considering those with a positive valence (e.g. satisfaction, happiness or proudness)[20]. Moreover, so far, only few studies have included anticipated emotions in theoretical models aimed to explain food choice(21–23), and again they focused predominantly on negative anticipated emotions. To the best of our knowledge, only Mari et al.[13] have investigated the role of anticipated emotions in the prediction of the adherence to the MeDiet. This study has shown that both positive and negative anticipated emotions influence the desire to follow the MeDiet. Thus, in our study, we hypothesised that: H2a: Positive anticipated emotions related to following the MeDiet influence the intention to adhere to the MeDiet. H2b: Negative anticipated emotions related to following the MeDiet influence the intention to adhere to the MeDiet.
Motivational antecedents also play a relevant role in determining people’s food choices, and these antecedents include health and mood motives[11]. On the one side, health motive leads individuals to choose healthy and nutritious food (e.g. rich in vitamins and minerals) or food that improves physical appearance (e.g. promote skin, teeth, hair, nails appearance). On the other side, mood motive is centred on the emotional well-being and highlights individuals’ interest to eat food to reduce stress and relax, or to cheer up and feel good[11]. These motives play a decisive role in influencing food choice insofar as they determine people’s attitude towards the selection of certain foods. For instance, they have been found to influence attitude towards healthy eating and personalised dietary advice[24,25]. Although the role of these motives in the case of people’s intention to adhere to the MedDiet has not been analysed, starting from the results of past studies on motivational antecedents of other healthy diets in the present study we expected that: H3a: Health motive influences positive attitude towards the MeDiet. H3b: Mood motive influences positive attitude towards the MeDiet.
When evaluating a behaviour, people feel positive or negative emotions as a consequence of the perception of consistency or inconsistency of this behaviour with their inner motivations. This emotional experience improves the ability to anticipate similar emotions when deciding how to act in the future and, in turn, influences intentions[26]. Similarly, when making a food choice, people are likely to mentally anticipate the emotions they will experience if such choice is either consistent or not consistent with their health or mood motives. Accordingly, in the present study, we tested the hypotheses below. H4a: Health motive elicit positive anticipated emotions towards adhering to the MeDiet. H4b: Health motive elicit negative anticipated emotions towards adhering to the MeDiet. H4c: Mood motive elicit positive anticipated emotions towards not adhering to the MeDiet. H4d: Mood motive elicit negative anticipated emotions towards not adhering to the MeDiet.
Motivational antecedents have been shown to have also a direct impact on intention related to food choice. For example, health motive has been found to directly predict the intention to purchase both healthy and unhealthy food[27,28]. Similarly, mood motive has been found to directly influence the intention to follow personalised dietary advice[24]. To the best of our knowledge, no previous research has investigated the role of health and mood motives to predict the adherence to the MeDiet. In the present study, we tested the following hypotheses. H5a: Health motive influences the intention to adhere to the MeDiet. H5d: Mood motive influences the intention to adhere to the MeDiet.
Finally, in our study, we considered the behavioural antecedents of the intention to adhere to the MeDiet. Several studies have shown that past behaviour is a powerful predictor of behaviour intention[12,29,30]. Actually, several researchers have included past behaviour in the TPB model, showing that it is often the strongest predictor of intention, over and above the original TPB variables. This evidence has been confirmed also in the case of frequently repeated behaviours[31]. Previous scholars have shown that the inclusion of past behaviour increases the predictiveness of the TPB model to explain people’s intention related to frequent food choices[32,33], also in the case of people’s intention to follow the MeDiet[13]. Accordingly, we hypothesised that: H6: Past adherence to the MeDiet influences the intention to follow the MeDiet.
Previous researchers have also found that past behaviour is a moderator within the framework of the TPB. For example, past behaviour was shown to moderate the attitude-intention and the anticipated emotion-intention relationships[31,34]. Consistently, in the present study, we aimed to evaluate whether different levels of past adherence to the MeDiet (low, medium and high adherence) would produce a different impact of the other study variables on participants’ intention to adhere to the MeDiet. Hence, we investigated the Research Question (RQ) below. RQ: Past behaviour moderates the impacts of the other study variables on the intention to adhere to the MeDiet.
## Participants and measures
Ethical approval for this study was obtained from the Catholic University of the Sacred Heart (Milan). Using the A-priori Sample Size Calculator for Structural Equation Models created by Daniel Soper[35], we computed the minimum sample size required for a structural equation model study. Results recommended to involve at least 184 participants to test our integrated model (nine latent variables and thirty observed variables; expected effect size = 0·30; P-value = 0·05; statistical power level = 0·80) and 243 participants to test the moderation of the three levels of past adherence to the MeDiet (twenty-seven latent variables and ninety observed variables; expected effect size = 0·30; P value = 0·05; statistical power level = 0·80). However, we opted to increase our sample size, following Jackson’s recommendation[36] to have a sample size to parameters ratio of 20:1 or at least 10:1.
In October 2020, about 2100 Italian adults were invited by the students of the Department of Psychology to fill in an online questionnaire. Each student was asked to invite three females (one between the age of 18 and 25, one between the age of 26–45, one between the age of 46–75) and three males (same criteria used for females) via e-mail or text message. At the beginning of the questionnaire, we provided participants with instructions on how to properly fill out the online questionnaire, as well as information on how to employ the different types of measurement scales. A control question to verify if participants’ replies were reliable was also included in the questionnaire. A total of 1940 participants correctly and fully completed the questionnaire (1086 females; 854 males; mean age = 35·65; sd = 14·75; age range = 18–84). A description of the measures collected through the questionnaire follows below. The full list of the items of each measure is reported in Table 1.
Table 1Results of the measurement modelConstructItems λ CRAVEHealth motiveIt is important that the food I eat…0·880·71… contains vitamins and minerals0·77… is healthy0·86… is nutritious0·63Mood motiveIt is important that the food I eat…0·910·77… helps me relax0·78… cheers me up and makes me feel good0·77… helps me cope with stress0·89Attitude towards the MeDietIn the next month, following the MeDiet will be/would be…0·950·70… disgusting – tasty0·74… sad – joyful0·69… unpleasant – pleasant0·74… boring – funny0·61… disadvantageous – advantageous0·88… fool – wise0·91… uneffective – effective0·91… useless – useful0·91Perceived behavioural controlIn the next month…0·860·67… following the MeDiet will be entirely up to me0·50… I think I have enough opportunities to follow MeDiet0·82… I feel capable of following the MeDiet0·80Subjective normMost of the people who are important to me…0·890·73… think that I should follow the MeDiet0·93… would approve if I followed the MeDiet in the next month0·94… would like me to follow the MeDiet in the next month0·47Positive anticipated emotionsIn the next month, if I will follow MeDiet …0·930·82… I will be proud of myself0·85… I will feel secure0·82… I will be satisfied0·89Negative anticipated emotionsIn the next month, if I will not follow the MeDiet …0·940·84… I will regret it0·84… I will feel worried0·88… I will feel dissatisfied0·88Intention to adhere to the MeDietIn the next month…0·970·92… I intend to follow the MeDiet0·92… I plan to follow the MeDiet0·94… I will follow the MeDiet0·95 λ, standardised factor loading; CR, composite reliability; AVE, average variance extracted; MeDiet, Mediterranean diet. Attitude was assessed with a seven-point semantic differential scale. All other scales were measured with a response scale varying from ‘strongly disagree’ [1] to ‘strongly agree’ [7].
## Health motive
Participants’ health motive regarding food choice was measured with three items on a seven-point Likert scale (e.g. ‘It’s important that the food I eat contains vitamins and minerals… strongly disagree [1] – strongly agree [7]’; adapted from Naughton et al.[11]). High scores indicated a strong health motive regarding food choice (α = 0·79).
## Mood motive
Participants’ mood motive regarding food choice was measured with three items on a seven-point Likert scale (e.g. ‘It’s important that the food I eat helps me relax… strongly disagree [1] – strongly agree [7]’; adapted from Naughton et al.[11]). High scores showed a high mood motive regarding food choice (α = 0·76).
## Attitude
Using a seven-point semantic differential scale with eight items, we measured participants’ attitudes towards the MeDiet (e.g. ‘Following the Mediterranean Diet is…unpleasant – pleasant’; adapted from Mari et al.[13]). High values indicated a greater positive attitude towards the MeDiet (α = 0·94).
## Subjective norm
Participants’ subjective norm was measured using three items on a seven-point Likert scale (e.g. ‘Most people who are important to me think that I should follow the Mediterranean Diet… strongly disagree [1] – strongly agree [7]’; adapted from Mari et al.[13]). High scores showed a elevate perception of a social expectation towards following the MeDiet (α = 0·81).
## Perceived behavioural control
Participants’ perceived behavioural control over following the MeDiet was assessed using three items on a seven-point Likert scale (‘In the next month, following the Mediterranean Diet will be entirely up to me … strongly disagree [1] – strongly agree [7]’; adapted from Mari et al.[13]). High scores indicated a high perception of control over adhering to the MeDiet (α = 0·94).
## Anticipated positive emotions
Participants’ anticipated positive emotions for following the MeDiet were measured using three items (e.g. ‘In the next month, if I will follow the Mediterranean Diet I will be proud of myself… strongly disagree [1] – strongly agree [7]’; adapted form Carfora et al.[37]). High score indicated strong anticipated positive emotions (α = 0·94).
## Anticipated negative emotions
Participants’ anticipated negative emotions for not following the MeDiet were measured using three items (e.g. ‘In the next month, if I will not follow Mediterranean Diet I will regret it… strongly disagree [1] – strongly agree [7]’; adapted form Carfora et al.[37]). High values indicated strong anticipated negative emotions (α = 0·81).
## Past adherence to the MeDiet
Participants’ past adherence to the MeDiet was assessed using the ‘Short Questionnaire to Assess Adherence to the Mediterranean Diet’[38] composed of fourteen items (e.g. ‘How many fruit portions (including natural fruit juices) do you consume per day?… less than a portion [1] – more than five portions [7]’). The final score ranged from 1 to 14 and was obtained by recoding the responses in 0 points or 1 point following the criteria of the original scale.
## Intention to adhere to the MeDiet
Participants’ intention to adhere to the MeDiet was assessed with three items on a seven-point Likert Scale (e.g. ‘In the next month, I intend to follow the Mediterranean diet … strongly disagree [1] – strongly agree [7]’; adapted from Mari et al.[13]). High scores showed a high intention to adhere to the MeDiet (α = 0·96).
At the beginning of the online questionnaire, participants provided written consent. Then, they completed two scales assessing health and mood motives related to their food choice and self-reported their adherence to the MeDiet. After that, they read a definition of the MeDiet (see Appendix) and filled out the TPB scales (attitude, subjective norm, perceived behavioural control) plus the scales to assess their positive and negative anticipated emotions regarding adherence to MeDiet. Finally, participants answered socio-demographic questions.
## Data analyses
We ran all analyses using MPLUS 7. As preliminary analyses, we tested the measurement model with confirmatory factor analysis. We verified the internal consistency among the observed variables using Cronbach’s α and composite reliability. We then tested convergent and discriminant validities of our data using average variance extracted (AVE) values.
Then, we verified our hypotheses (H1–H6) by testing the goodness-of-fit of four nested sem models. We compared the nested models with the χ2 difference test (Δχ2). Each nested model is described below.
Model 1 tested our H1a–H1c about the role of rational and social antecedents and included the paths from attitude, perceived behavioural control and subjective norm to intention as free parameters. In this Model 1, the regression weights of the paths among the other variables were fixed to 0.
Model 2 tested our H2a and H2b on the emotional antecedents, by including the paths from positive and negative anticipated emotions to intention. The regression weights of the other hypothesised paths were fixed to 0.
Model 3 tested our H3–H6 related to the inclusion of motivational antecedents. Thus, we inserted the following paths: (a) the path from both health motive (H3a) and mood motive (H3b) to attitude; (b) the paths from both health motive (H4a) and mood motive (H4b) to positive anticipated emotions; (c) the paths from both health motive (H4c) and mood motive (H4d) to negative anticipated emotions intention; and (d) the paths from both health motive (H5a) and mood motive (H5b) to negative anticipated emotions intention.
The regression weights of the paths related to past adherence to the MeDiet were fixed to 0.
Model 4 verified our H6 about the inclusion of a behavioural antecedent by including path from past adherence to the MeDiet to intention.
Finally, to test our RQ, we run a multigroup sem analysis to verify if our integrated model (Model 4) would differ according to the past adherence to the MeDiet. We created a group variable to distinguish among the low-adherence group, the medium-adherence group and the high-adherence group[38]. The low-adherence group included participants with a score equal to or < 5 on the past adherence variable. The medium-adherence group included participants with a score from 6 to 9 on the past adherence variable. The high-adherence group included participants with a score equal to or greater than 10 on the past adherence variable. Then, to disconfirm the invariance of the paths among the study variables across the above groups, we constrained the paths of each group to be invariant in the other groups, and then we used Wald tests to disconfirm the invariance of the paths. These analyses allowed us to verify if the past adherence to the MeDiet moderated the relationship among the psychosocial antecedents and the participants’ intention to follow the MeDiet in the following month.
In all the above analyses, the goodness of fit of all models was tested using χ2 and incremental goodness-of-fit indexes: root mean square error of approximation (RMSEA) < 0·05, comparative fit index (CFI) < 0·90, Tucker-Lewis index (TLI) < 0·90 and standardised root mean squared residual < 0·08[39,40]. Models with significant χ2 test results were accepted on the condition that the CFI or TLI value reaches 0·95 or more, and the value of RMSEA was fewer than 0·08[41].
## Preliminary analyses
All socio-demographic data are reported in Table 2. Confirmatory factor analysis showed that the measurement model fit the data satisfactorily (χ 2 [349] = 4320·16, $$P \leq 0$$·001; RMSEA = 0·07, CFI = 0·91, TLI = 0·90, standardised root mean squared residual = 0·05). Composite reliability values were all greater than the minimum threshold of 0·60[42]. Thus, we confirmed the reliability of the measurement model. The standardised factor loadings and the AVE values were all above the recommended threshold[43,44], showing that all constructs had a high convergent validity. Finally, all AVE were higher than correlations between latent constructs, confirming the discriminant validity of the study variables[44]. Table 1 shows the results of the measurement model. Table 3 reports means, SD and AVE of our study variables and correlations among them.
Table 2Demographics of study sampleFemaleMaleTotalAge % 18–24 years (young)34·729·432·4 % 25–34 years (young adults)22·624·923·6 % 35–54 years (adults)29·930·029·9 & 55+ (seniors)12·815·714·1Number of residents in your municipality % Less than 5·00018·517·918·2 % Between 5000 and 30 00042·641·842·3 % Between 30 000 and 250 00012·010·111·1 % Between 250 000 and 500 00021·023·922·3 % More than 500 0005·96·36·1Marital status % Single18·517·950·6 % Married42·641·831·8 % Cohabiting couple12·010·111·9 % Separated/divorced21·023·94·3 % Widow5·96·31·5 Table 3Means, standard deviations, average variance extracted values and correlations among the study variablesStudy variables1.2.3.4.5.6.7.8.9.Mean sd 1. Health motive0·715·451·062. Mood motive0·340·775·261·143. Attitude towards the MeDiet0·21* 0·10* 0·705·551·324. Perceived behavioural control0·29* 0·15* 0·32* 0·675·391·115. Subjective norm0·13* 0·11* 0·20* 0·24* 0·731·403·986. Positive anticipated emotions0·26* 0·24* 0·43* 0·31* 0·44* 0·825·501·117. Negative anticipated emotions0·21* 0·17* 0·27* 0·46* 0·30* 0·42* 0·921·524·228. Past adherence to the MeDiet0·27* 0·08* 0·19* 0·05* 0·25* 0·13* 0·11* –6·822·089. Intention to adhere to the MeDiet0·31* 0·17* 0·40* 0·44* 0·56* 0·57* 0·50* 0·25* 0·924·731·50 sd = standard deviation.* $$P \leq 0$$·001.In the diagonal row, the bold values are the average variances extracted for the latent construct. The numbers below diagonal are the correlation coefficients among the study variables.
## Model comparisons
The results of the comparisons among the four nested models showed that only model 4 (i.e. the model including attitude, perceived behavioural control, subjective norm, positive and negative anticipated emotions, health and mood motives and past adherence to the MeDiet as predictors of participants’ intention to adhere to the MeDiet) had an acceptable goodness of fit (Model 4: χ 2 [369] = 2837·460, $$P \leq 0$$·001; RMSEA = 0·05, CFI = 0·92, TLI = 0·91, standardised root mean squared residualc = 0·05). The comparison between model 1 and model 2 supported the addition of positive and negative anticipated emotions, Δχ2 [9] = 1364·21, $$P \leq 0$$·001. The comparison between model 2 and model 3 confirmed the inclusion of health and mood motives, Δχ2 [13] = 407·70, $$P \leq 0$$·001. Finally, the comparison between model 3 and model 4 supported the inclusion of past adherence to the MeDiet, Δχ2 [8] = 234·75, $$P \leq 0$$·001. Therefore, as expected, the more comprehensive model 4 was the model that best predicted participants’ intentions to adhere to the MeDiet. Table 4 shows the goodness of fit and the standardised coefficients of each tested model.
Table 4Goodness of fit and standardised coefficients for each nested modelModel 1 (TPB)Model 2 (model 1 plus anticipated emotions)Model 3 (model 2 plus motivations)Model 4 (model 3 plus past adherence) χ2 (df)4844·12 [399]; $$P \leq 0$$·0013479·91 [390]; $$P \leq 0$$·0013072·21 [377]; $$P \leq 0$$·0012837·46 [369]; $$P \leq 0$$·001RMSEA0·090·060·060·05CFI0·870·900·910·92TLI0·860·890·900·91SRMR0·190·110·060·05Attitude towards the MeDiet → intention to adhere to the MeDiet0·12** 0·04* 0·030·03Perceived behavioural control → intention to adhere to the MeDiet0·55** 0·43** 0·42** 0·41** Subjective norm → intention to adhere to the MeDiet0·28** 0·18** 0·18** 0·18** Positive anticipated emotions → intention to adhere to the MeDietFixed to 00·21** 0·21** 0·21** Negative anticipated emotions → intention to adhere to the MeDietFixed to 00·18** 0·18** 0·18** Health motive → attitude towards the MeDietFixed to 0Fixed to 00·17** 0·17** Health motive → positive anticipated emotionsFixed to 0Fixed to 00·23** 0·23** Health motive → negative anticipated emotionsFixed to 0Fixed to 00·20** 0·20** Health motive → intention to adhere to the MeDietFixed to 0Fixed to 00·06* 0·04Health motive → attitude towards the MeDiet → intention to adhere to the MeDietFixed to 0Fixed to 00·010·00Health motive → positive anticipated emotions → intention to adhere to the MeDietFixed to 0Fixed to 00·05** 0·05** Health motive → negative anticipated emotions→ intention to adhere to the MeDietFixed to 0Fixed to 00·03** 0·03** Mood motive → attitude towards the MeDietFixed to 0Fixed to 00·030·03Mood motive → positive anticipated emotionsFixed to 0Fixed to 00·19** 0·19** Mood motive → negative anticipated emotionsFixed to 0Fixed to 00·13** 0·13** Mood motive → intention to adhere to MeDietFixed to 0Fixed to 00·020·02Mood motive → attitude towards the MeDiet → intention to adhere to the MeDietFixed to 0Fixed to 00·000·00Mood motive → positive anticipated emotions → intention to adhere to the MeDietFixed to 0Fixed to 00·04** 0·04** Mood motive → negative anticipated emotions→ intention to adhere to theMeDietFixed to 0Fixed to 00·02** 0·02** Past adherence to the MeDiet → intention to adhere to the MeDietFixed to 0Fixed to 0Fixed to 00·06** R2 attitude towards the MeDiet––0·03** 0·03** R2 positive anticipated emotions––0·12** 0·12** R2 negative anticipated emotions––0·07** 0·07** R 2 intention to adhere to the MeDiet0·57** 0·62** 0·63** 0·64** MeDiet, Mediterranean diet; χ2, goodness-of-fit statistics; df, degrees of freedom of χ2 statistics; CFI, comparative fit index; TLI, Tucker–Lewis fit index; RMSEA, root mean square error of approximation.*$P \leq 0$·05.** $P \leq 0$·001.
Regarding the predictiveness of the rational and social antecedents, Model 4 (Table 4 and Fig. 2) showed that participants’ attitude did not predict the intention to adhere to the MeDiet, disconfirming our H1a. It should be noted that before the addition of the health and mood motivations, attitude was a significant predictor of participants’ intention (model 1: β = 0·12, $$P \leq 0$$·001; model 3: β = 0·04, $$P \leq 0$$·05). Our H1b and H1c were instead confirmed, with both perceived behavioural control and subjective norm having a significant effect on participants’ intention.
Fig. 2Results of the integrated model to explain the intention to adhere to the Mediterranean diet (model 4) As regards the contribution of the emotional antecedents in explaining the intention to adhere to the MeDiet, both positive and negative anticipated emotions explained participants’ intention. Therefore, we confirmed our H2a and H2b.
We then tested the role of the motivational antecedents and found that health motive influenced attitude, positive anticipated emotions and negative anticipated emotions. Thus, our H3a, H4a and H4c were confirmed. Instead, health motive did not explain participants’ intention, disconfirming our H5a. Additional mediation analyses showed that health motives had an indirect impact on intention via both positive and negative anticipated emotions, but not via attitude.
Regarding the effect of participants’ mood motive, this variable did not predict participants’ positive attitude, thus we rejected our H3b. However, mood motive determined both positive and negative anticipated emotion, confirming our H4b and H4d. Similar to health motive, mood motive did not explain participants’ intention, and thus H5b was not supported. As for health motive, mood motive had an indirect impact on intention via both positive and negative anticipated emotions, but not via attitude.
Finally, the inclusion of the path of past adherence to the MeDiet on participants’ intention was supported, confirming H6 and the importance of considering behavioural antecedents related to past adherence to the MeDiet.
In model 4, the variances of attitude (R2 = 0·03), positive anticipated emotions (R2 = 0·12), negative anticipated emotions (R2 = 0·07) and intention (R2 = 0·64) were all significantly explained.
In sum, the above results confirmed the importance of integrating rational, social, emotional, motivational and behavioural factors to explain intention to adhere to the MeDiet. Moreover, they suggested that participants’ perception of control over following the MeDiet was the rational antecedent that most influenced participants’ intention, followed by both emotional and social antecedents. Finally, the motivational antecedents only indirectly determined participants’ intention by increasing their anticipation of positive and negative emotions deriving from adherence or missed adherence to the MeDiet.
## Comparison of the integrated model across low, medium and high levels of adherence to the MeDiet
Multigroup sem analysis was used to investigate differences in the impact of the study variables on participants’ intention to adhere to the MeDiet across each group of MeDiet adherence (low, medium and high adherence). The paths among the study variables were the same of those tested in model 4. The multi-group models obtained an acceptable fit (χ 2 = 4007·12, df = 1191; χ 2 contribution of the Low-Adherence Model = 1332·49; χ 2 contribution of the Medium-Adherence Model = 1892·23; χ 2 contribution of the High-Adherence Model = 782·40; RMSEA = 0·06; CFI = 0·91; TLI = 0·90; Table 5).
Table 5Standardised factor loadings in the case of low, medium and high adherence to the MeDietLow-adherence groupMedium-adherence groupHigh-adherence groupAttitude towards the MeDiet → intention to adhere to the MeDiet0·09* 0·020·00Perceived behavioural control → intention to adhere to the MeDiet0·48** 0·40** 0·30** Subjective norm → intention to adhere to the MeDiet0·23** 0·14** 0·28** Positive anticipated emotions → intention to adhere to the MeDiet0·020·28** 0·36** Negative anticipated emotions → intention to adhere to the MeDiet0·21** 0·18** 0·17** Health motive → attitude towards the MeDiet0·17** 0·10* 0·19* Health motive → positive anticipated emotions0·22** 0·22** 0·14Health motive → negative anticipated emotions0·18** 0·17** 0·12Health motive → intention to adhere to the MeDiet0·10* 0·020·02Health motive → attitude towards the MeDiet → intention to adhere to the MeDiet0·010·000·00Health motive → positive anticipated emotions → intention to adhere to the MeDiet0·000·06** 0·05Health motive → negative anticipated emotions → intention to adhere to the MeDiet0·04* 0·03** 0·02Mood motive → attitude towards the MeDiet0·010·050·04Mood motive → positive anticipated Emotions0·17** 0·20** 0·21* Mood motive → negative anticipated emotions0·090·14** 0·16* Mood motive → intention to adhere to MeDiet0·040·040·05Mood motive → attitude towards the MeDiet → intention to adhere to the MeDiet0·020·000·00Mood motive → positive anticipated emotions → intention to adhere to the MeDiet0·000·05** 0·07** Mood motive → negative anticipated emotions → intention to adhere to the MeDiet0·020·03** 0·03Past adherence to the MeDiet → intention to adhere to the MeDiet−0·040·050·05MeDiet, Mediterranean diet.* $P \leq 0$·05.** $P \leq 0$·001.
In the case of low past adherence to the MeDiet (Table 5, Fig. 3), participants’ perception of behavioural control and societal expectations about the adherence to the MedDiet were the most important antecedents of their intention to adhere to it, followed by negative anticipated emotions and attitude. Health motive had a direct effect on intention. Moreover, this motivational determinant indirectly increased participants’ intention by leveraging on their anticipation of negative emotions deriving from missed adherence to the MeDiet. Mood motive, instead, had only an effect on the anticipation of positive emotions deriving from adhering to the MeDiet. However, this motive did not impact on intention to adhere to the MeDiet. Finally, participants’ past adherence did not explain their intention.
Fig. 3Low-adherence group: results of the integrated model to explain the intention to adhere to the Mediterranean diet In the case of medium past adherence to the MeDiet (Table 5, Fig. 4), participants intended to follow it when they had a high perception of control and high positive and negative anticipated emotions. In this group, the role of subjective norm was low, and the role of attitude was NS. In addition, the health and mood motives exerted an impact on participants’ intention only if they elicited the anticipation of positive and negative emotions related to adherence or not adherence to the MeDiet. Finally, past adherence to the MeDiet increased participants’ intention to adhere to the MeDiet in the next month.
Fig. 4Medium-adherence group: results of the integrated model to explain intention to adhere to the Mediterranean diet Finally, in the case of participants with a high past adherence to the MeDiet (Table 5, Fig. 5), emotional antecedents played a decisive role. These participants intended to keep following this diet mainly because they anticipated positive and negative emotions if they would engage (or not) in this behaviour in the future. They were also influenced by high perceptions of control over the behaviour and social expectations about it. Again, participants’ attitude towards adhering to the MeDiet did not influence their intention. Interestingly, among the motivational antecedents only the mood motive had an important influence on intention, given its ability to elicit the anticipation of positive/negative emotions. Health motive, instead, did not play a relevant role.
Fig. 5High-adherence group: results of the integrated model to explain intention to adhere to the Mediterranean diet
## Tests of invariant paths in the multigroup SEM model
Table 6 reports the findings of the Wald tests for each comparison used to disconfirm the invariance of the paths among study variables across groups. In these analyses, we run the Wald only when a path was significant in at least one group. The main results are discussed below.
Table 6Results of the comparisons of the main paths among participants’ levels of adherence to the Mediterranean diet (MeDiet)Low adherence v. medium adherenceLow adherence v. high adherenceMedium adherence v. ‘rencea. Attitude towards the MeDiet → intention to adhere to the MeDiet χ2 [1] = 1·83, $$P \leq 0$$·17 χ2 [1] = 1·73, $$P \leq 0$$·19 χ2 [1] = 0·20, $$P \leq 0$$·67b. Perceived behavioural control → intention to adhere to the MeDiet χ2 [1] = 3·09, $$P \leq 0$$·05 χ2 [1] = 3·98, $$P \leq 0$$·05 χ2 [1] = 0·66, $$P \leq 0$$·42c. Subjective norm → intention to adhere to the MeDiet χ2 [1] = 4·06, $$P \leq 0$$·05 χ2 [1] = 0·02, $$P \leq 0$$·88 χ2 [1] = 2·69, $$P \leq 0$$·10d. Positive anticipated emotions → intention to adhere to the MeDiet χ2 [1] = 14·00, $$P \leq 0$$·001 χ2 [1] = 12·83, $$P \leq 0$$·001 χ2 [1] = 2·79, $$P \leq 0$$·05e. Negative anticipated emotions → intention to adhere to the MeDiet χ2 [1] = 0·49, $$P \leq 0$$·48 χ2[1] = 0·78, $$P \leq 0$$·38 χ2[1] = 0·11, $$P \leq 0$$·73f. Health motive → attitude towards the MeDiet χ2 [1] = 1·39, $$P \leq 0$$·24 χ2 [1] = 0·14, $$P \leq 0$$·71 χ2 [1] = 1·26, $$P \leq 0$$·26g. Health motive → intention to adhere to the MeDiet χ2 [1] = 1·85, $$P \leq 0$$·17 χ2 [1] = 1·92, $$P \leq 0$$·16 χ2 [1] = 0·35, $$P \leq 0$$·56h. Health motive → positive anticipated emotions χ2 [1] = 0·37, $$P \leq 0$$·54 χ2 [1] = 0·37, $$P \leq 0$$·54 χ2 [1] = 0·47, $$P \leq 0$$·49i. Health motive → positive anticipated emotions → intention to adhere to the MeDiet χ2 [1] = 8·08, $$P \leq 0$$·00 χ2 [1] = 1·13, $$P \leq 0$$·29 χ2 [1] = 0·06, $$P \leq 0$$·81l. Health motive → negative anticipated emotions χ2 [1] = 0·07, $$P \leq 0$$·79 χ2 [1] = 0·07, $$P \leq 0$$·80 χ2 [1] = 0·02, $$P \leq 0$$·89m. Mood motive → negative anticipated emotions → intention to adhere to the MeDiet χ2 [1] = 0·32, $$P \leq 0$$·57 χ2 [1] = 0·35, $$P \leq 0$$·55 χ2 [1] = 0·06, $$P \leq 0$$·81n. Mood motive → positive anticipated emotions χ2 [1] = 0·72, $$P \leq 0$$·40 χ2 [1] = 0·15, $$P \leq 0$$·70 χ2 [1] = 0·06, $$P \leq 0$$·80o. Mood motive → positive anticipated emotions → intention to adhere to the MeDiet χ2 [1] = 9·93, $$P \leq 0$$·00 χ2 [1] = 3·98, $$P \leq 0$$·05 χ2 [1] = 0·08, $$P \leq 0$$·77p. Mood motive → negative anticipated emotions χ2 [1] = 2·24, $$P \leq 0$$·13 χ2 [1] = 1·31, $$P \leq 0$$·25 χ2 [1] = 0·05, $$P \leq 0$$·81q. Mood motive → negative anticipated emotions→ intention to adhere to the MeDiet χ2 [1] = 1·18, $$P \leq 0$$·28 χ2 [1] = 0·67, $$P \leq 0$$·41 χ2 [1] = 0·00, $$P \leq 0$$·96 Considering the rational antecedents of the adherence to the MeDiet, the Wald test confirmed that a positive attitude towards the MeDiet had a direct effect on participants’ intention only in the case of low adherence to the MeDiet (Table 6, a). The impact of perceived behavioural control was instead higher when people had a low adherence to the MeDiet, as compared with people with a medium or high adherence (Table 6, b). As to social antecedents, Wald results confirmed that in the medium-adherence group, the societal expectation had a lower impact on intention than in the low-adherence group, while the other comparisons were not significant (Table 6, c).
As for the role of the emotional antecedents, positive anticipated emotions did not influence intention when participants had a low past adherence, while their effect increased as past adherence increased (Table 6, d). Differently, negative anticipated emotions had the same role in determining participants’ intention in all three groups (Table 6, e).
If we now consider the motivational antecedents, we find that participants’ health motive had a greater indirect effect on intention through anticipated positive emotions in the case of the medium-adherence group, compared with the high-adherence group (Table 6, i). Meanwhile, the effect of mood motive on positive anticipated emotions was lower in the low-adherence group, compared with both the medium- and high-adherence groups (Table 6, n). Consistently, also its indirect effect on intention through positive anticipated emotions was absent in the low-adherence group but present in the other two groups (Table 6, o). The other Wald tests did not reveal further differences in the effect of motives on the other study variables (Table 6, f, g, h, l, m, p, q).
Finally, we did not find difference in the behavioural antecedent, given that it was not a significant factor in all three models.
## Discussion
The current study contributes to our understanding of the psychosocial antecedents determining Italians’ intention to adhere to the MeDiet. Specifically, we validated an integrated psychosocial framework to analyse the role of rational, social, emotional, motivational and behavioural antecedents of the adherence to the MeDiet and to verify if baseline adherence to MeDiet moderates the relationships among these antecedents. Our results add to previous literature in many respects.
As to the rational and social antecedents of the intention to adhere to the MeDiet, similarly to what found by Mari et al.[13], our model showed that perceived behavioural control was the most important factor of the intention to adhere to the MeDiet. This result suggests that people need to feel sure about their own capability and possibility to follow the MeDiet to behave accordingly. Differently from the results of past research on individual food choice, in our study a positive attitude towards MeDiet had only no influence on intention and a lower impact as compared with the impact of subjective norm[9]. This result highlights the role of social context in determining Italians’ willingness to follow the MeDiet. Likely, the higher effect of the social expectation in the case of the MeDiet can be attributed to the important cultural heritage connected with this diet in Italy.
Our results add to the current literature on the role of emotional antecedents in the adherence to a healthy diet. Only few past studies have considered the role of both positive and negative anticipated emotions in developing food choice intention[23] and these studies were focused on specific food choices, such as eating filled chocolate[23] or hamburgers[42]. Therefore, our results add to the current literature showing that both positive and negative anticipated emotions are important antecedents of the intention not only in the case of specific food choices but also in the case of overall healthy dietary choices. Moreover, our findings show that positive anticipated emotions have a greater impact than negative anticipated emotions on Italians’ intention to adhere to the MeDiet.
In our study, anticipated emotions had a higher impact than attitude on intention. In line with Ajzen and Fishbein[45], we considered attitude as the rational evaluation of both the experiential and instrumental consequences of the MeDiet. The former refers to the benefits and costs associated with this behaviour (e.g. healthy or unhealthy, foolish or wise). The latter is related to emotion-laden judgments about the consequences of this behaviour (e.g. pleasant or unpleasant, enjoyable or unenjoyable). The difference between the experiential component of attitude and the anticipated emotions lies in at least two aspects[46]. First, anticipated emotions focus on the affects that are expected to follow an action or inaction, rather than those expected to occur while the action is being performed. Second, anticipated emotions are self-conscious emotions (e.g. pride, regret…), while experiential attitudes are focused on hedonic emotions (e.g. pleasure, enjoyment…). Therefore, the Italian adherence to the MeDiet seems to be driven more by self-conscious emotions that are expected to follow the adhesion to it, rather than by contextual experiential or instrumental consequences.
As to motivational antecedents, in our study health motive determined both emotional and rational antecedents. Specifically, our study offers the first evidence that health motive influences both positive and negative anticipated emotions and has an indirect effect on intention through them. These results underline that Italians with health motive intend to adhere to the MeDiet to experience future self-conscious positive emotions and avoid negative self-conscious emotions, rather than to obtain experiential or instrumental consequences from adherence. Mood motive also influenced both positive and negative anticipated emotions related to the MeDiet and had an indirect effect on intention to adhere to it. Again, people who prefer food that increases their positive mood apparently do not adhere to the MeDiet because they evaluate its experiential or instrumental benefits. They are more likely to adhere because they anticipate that they will feel proud, relaxed or satisfied and will avoid experiencing regret, worry or dissatisfaction.
Finally, as regards behavioural antecedents, our results show that the addition of past behaviour increased the explanation of intention significantly. In the model including behavioural antecedents, rational, social and emotional antecedents had the same effect as the previous Model 3. However, health motive was no longer a significant predictor of intention, suggesting that in the case of recurrent behaviours the recall of the health motivational antecedent is not needed anymore.
Besides having a direct effect on intention, past behaviour moderated the relationships among the other study variables determining intention. First of all, as to rational antecedents perceived behavioural control influenced the intention to adhere to the MeDiet in all three groups. This suggests that, independently from the level of adherence, people intend to follow the MeDiet if they perceive that this choice is possible and easy to perform. As to positive attitude, in our study it influenced intention only in the case of a low adherence to the MeDiet, that is when individuals were not engaged in the behaviour yet. As regards social antecedents, subjective norm determined the intention to adhere to the MeDiet in all three groups, suggesting that Italians intend to follow the MeDiet if they perceive that the important others will approve this choice. As to emotional antecedents, they also impacted on the intention to adhere to the MeDiet in all three groups, with an exception for positive anticipated emotions in the low-adherence group. This latter result could be explained considering that individuals with a low adherence to the MeDiet have probably little experience of positive emotions for having followed the MeDiet. For this reason, they may not expect to experience them in the future, and thus they do not anticipate these emotions as a consequence of the adherence to the MeDiet. In contrast, individuals with medium or high adherence to the MeDiet have previously experienced positive emotions associated to their behaviour and therefore anticipate them during the decision-making process.
If we now turn our attention to motivational antecedents, we notice that health motive influenced attitude towards the MeDiet in all three groups. However, only in the low-adherence group health motive had an indirect impact on intention via attitude and negative anticipated emotions. Therefore, when people have followed the Mediterranean diet very little in the past, they are pushed to do so in the future for rational reasons and to avoid negative emotions. On the contrary, in the high-adherence group health motive had no impact on intention. Unlike health motive, mood motive increased its relevance with increasing participants’ adherence. Specifically, it had an indirect impact on intention through positive and negative anticipated emotions in the medium-adherence group, and through positive anticipated emotions in the high-adherence group. The effects of the MeDiet on mental wellbeing are less known than those on physical health, and individuals who follow the MeDiet constantly may be more informed about the psychological effects of following the MeDiet, or have previously experienced them.
In sum, the results of the present study suggest that rational antecedents are the most relevant predictors of the intention to adhere to the MeDiet in the case of low adherence to it, followed by negative anticipated emotions and health motive. Instead, positive anticipated emotions and mood motive play a more central role in maintaining the intention to follow the MeDiet when people already do so.
## Limitations
Although this study offers several insights on what the main antecedents of the MeDiet are and how they are influenced by past adherence, it is not exempt from some limitations. First, although our sample was large, distributed in a sizeable age range, and enough balanced for gender, it was not representative of the general Italian population, because it was not fully balanced in terms of other sociodemographic variables (e.g. the Italian region of residence, the level of education, the average income). Thus, our data should be generalised with caution and future studies could usefully test the explanatory power of this model in other populations. They could also usefully evaluate whether the relationships between the antecedents of the intention to adopt the MeDiet are different in populations living far from the Mediterranean basin.
Second, in our integrated model, we did not control for the role of socio-demographic variables, as well as other variables about sensory aspects (e.g. taste), and medical conditions. This limitation depended on the size of our sample, which limited the number of paths that we could include and led us to focus only on the most relevant factors within a psychosocial theoretical framework. Moreover, in this study we collected data in a single time, limiting our analyses on future intentions and behaviour of the previous week. Future studies could consider adopting a longitudinal design with two times, which would include a behavioural measurement at time 2 and allow considering to what extent the intention to adopt the MeDiet is translated into actual behaviour. This could also help understanding whether some of the considered antecedents at time 1 moderate the relationship between intention at time 1 and behaviour at time 2. Furthermore, in this study we did not include a measurement of the moral antecedents of participant’s intention to adhere to the MeDiet. People’s adherence to the MeDiet might be guided by pro-environmental values and the awareness of the environmental consequences of their food choices[47,48]. In a more extensive model of antecedents of the MeDiet adherence future studies might therefore include a moral dimension.
Third, in our study we did not consider participants’ knowledge of the MeDiet. Although the study was conducted in a Mediterranean country and we clearly defined the MeDiet at the beginning of the study (Appendix 1), participants’ knowledge of the MeDiet could influence the observed relationships among variables. Future studies could consider if participants’ knowledge of the MeDiet covariates with both socio-demographic and psycho-social variables in determining the intention to adhere to the MeDiet.
Last, but not least, this study has the common limitation of quantitative research in psychology. Statistical analysis not always allows a meaningful theoretical interpretation, given that participants may have differently interpreted the same items[49]. Using a mixed methods approach, future studies on MeDiet could integrate quantitative measures with qualitative ones (i.e. interview, focus group) to better investigate how people, characterised by different sociodemographic and psychosocial characteristics, subjectively interpret the determinants of the MeDiet investigated in this study.
## Practical implications
As to the practical implications deriving from our findings, the present study offers at least three important insights for future public actions and campaigns aimed at promoting greater adherence to the MeDiet. First, we found that perceived behavioural control was the most important antecedent for Italian participants, regardless of their past adherence to the MeDiet. In light of this result, policy-makers should focus on how to increase Italians’ perceptions of control towards selecting Mediterranean food. This could be achieved by providing information on how to recognise and include it in a balanced diet. For example, future public campaigns might propose alternative dishes and recipes to reduce unhealthy foods and replace them with healthier choices. Moreover, Italian institutions might work on making the MeDiet more accessible by reducing the taxation on local products or recommending their introduction in school or work canteens. Second, we found that the social context and related expectations induce Italians to choose a MeDiet. This is linked to two of the pillars of the MeDiet, which are commensality (i.e. the act of eating with other people) and conviviality (i.e. the pleasure associated to shared meals)[50]. Policy makers could enhance the perceived benefits of eating together, also by trying to reduce the economic, time and social pressures that may inhibit this pleasurable activity.
Third, our results demonstrate that, according to the degree of past adherence to the MeDiet, different psychosocial factors drive the choice of further adhering to it. Interventions and communications should therefore be tailored accordingly, to maximise their effect on promoting the MeDiet. Specifically, our findings suggest that enhancing positive attitudes towards the MeDiet could be a promising ‘blanket approach’ only with people who have a low past adherence to it. In this case, communication should highlight the positive instrumental and experiential consequences that can derive from Mediterranean food, such as the pleasant taste of it or its healthy properties. In the case of people who already widely adhere to the MeDiet, communication should instead leverage self-conscious positive emotions (such as pride and satisfaction), thus increasing the possibility that this healthy eating practice will be maintained over time.
Finally, future studies might explore the possibility of applying this theoretical model to the development of communication strategies useful for promoting the adherence to the MeDiet using machine learning. Based on our findings, social psychologists and engineers might build together a model of a dialogue manager capable of fast profiling recipients and selecting the messages that are potentially most persuasive according to recipients’ profiles[51,52].
## Conclusion
The present research contributes to our understanding of the psychosocial antecedents associated with the Italians’ intention to adhere to the MeDiet. At first glance, such intention seems to be mostly influenced by perceived behavioural control and positive anticipated emotions, with some further indirect effects of motivational antecedents. However, we have seen that a more complex picture appears when considering participants’ different degrees of past adherence to the MeDiet. Our results therefore confirm recent arguments according to which a ‘one-size fits all’ strategy might not be the most effective approach for encouraging healthy eating behaviour.
## Conflict of interest:
There are no conflicts of interest.
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|
---
title: Differences in energy and nutrient content of menu items served by large chain
restaurants in the USA and the UK in 2018
authors:
- Yuru Huang
- Thomas Burgoine
- Dolly RZ Theis
- Jean Adams
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991847
doi: 10.1017/S1368980022001379
license: CC BY 4.0
---
# Differences in energy and nutrient content of menu items served by large chain restaurants in the USA and the UK in 2018
## Body
Eating food prepared out of the home has become increasingly common worldwide. The past decade has seen a sharp increase in ‘out-of-home’ food expenditure. Americans spent 45·2 % of total food expenditures on out-of-home food in 2020[1]. In the UK, 31·0 % of total food and drink expenditure was spent on eating out in 2018–2019[2]. Restaurant sales were projected to increase by 4 % in the USA in 2020 – despite the COVID pandemic – to a total of $899 billion[3].
Out-of-home food is consistently more energy dense and lower in nutritional quality compared with food prepared at home[4]. Studies have indicated that more frequent consumption of restaurant food is associated with higher intakes of energy and nutrients detrimental to health, poorer overall diet quality and greater BMI(5–7). Restaurant meals in both the UK and USA contain high levels of energy and macronutrients, such as total fat, carbohydrates and Na(8–10). Only 8 % of meals offered at large USA chain restaurants met all seven healthy criteria set by the American Heart Association, and only 9 % of meals offered at large UK chain restaurants met guidelines for energy set by Public Health England[8,11].
Consumption of restaurant foods is also an important contributor to childhood obesity[12,13]. Previous research indicates that children’s menu items from chain restaurants are high in energy, fat, saturated fat and Na[14,15]. Approximately one-third of children’s main dishes at fast-food restaurant chains in the USA, and the majority of those at sit down restaurant chains exceeded the 2010 Dietary Guidelines for children in 2010–2014[15], with little evidence of improvement over time[10]. Likewise, a study in the UK found that 68 % of younger children’s (aged 2–5 years) and 55 % of older children’s (aged 6–12 years) meals contained more total fat than recommended[14].
One potential explanation for the lack of improvement in the nutritional content of both adult and children’s menu items may be challenges of food technology. For example, ingredients that contain Na act as preservatives, leavening agents or emulsifiers and also help maintain the taste of foods[16]. Reducing Na content, therefore, requires Na alternatives to address food safety concerns while sustaining taste. Reducing other nutrients in food poses similar food science challenges[17,18]. However, there is substantial variability of menu item nutritional profiles in different countries, even for the same menu item at the same chain restaurant, indicating that there may be room for improvement despite food technology challenges[19].
Yet while there is extensive research on the restaurant food environment in the USA, there is limited evidence on how it compares to other countries[10,11,20]. This includes the UK, which is generally thought to be culturally similar and has many chain restaurants in common. However, the UK and the USA differ in both obesity prevalence (42 % in the USA and 28 % in the UK in 2018), frequency of use of OOH food sources and policy context(21–24). Previous studies of restaurant foods in both countries have highlighted individual product-level differences. As an example, the energy density of a Big Mac was 958 kcal/100 g in the UK and 1054 kcal/100 g in the USA in 2012[19]. These studies, however, focused on fast-food restaurant chains only and included relatively small samples of items. To date, no study has quantified differences in energy and nutrient content of whole menus, including those for adults and children, across all types of large chain restaurants and across countries.
In this study, we aimed to capture, quantify and compare the nutritional landscape of restaurant foods in the UK and USA, by comparing nutritional profiles of chain restaurant menu items in these two countries. We examined the energy and nutritional differences of both adult and children’s menu items separately.
## Abstract
### Objective:
To quantify the sector-wide energy and nutritional differences of both adult and children’s restaurant menu items in the UK and the USA in 2018.
### Design:
Cross-sectional study.
### Setting:
Energy and nutritional information provided on restaurant websites.
### Participants:
Menu items (n 40 902) served by forty-two large UK chains and ninety-six large USA chains.
### Results:
Mean absolute energy, fat and saturated fat values were higher in USA menu items. For example, the mean adjusted per-item differences of adult menu items between the USA and the UK were 45·6 kcal for energy and 3·2 g for fat. Comparable figures for children’s menu items were 43·7 kcal and 4 g. Compared with UK menu items, USA adult menu items also had higher sugar content (3·2 g, 95 % CI (0·5, 6)), and children’s menu items had higher Na content (181·1 mg, 95 % CI (108·4, 253·7)). Overall, 96·8 % of UK and 95·8 % of USA menu items exceeded recommended levels for at least one of Na, fat, saturated fat or sugars.
### Conclusions:
Menu items served by large chain restaurants had higher mean absolute levels of energy, fat and saturated fat in the USA compared with the UK. UK adult menu items were also lower in sugars compared with the USA ones and children’s items lower in Na. As more than 95 % of all items were considered to have high levels of at least one nutrient of public health concern in the USA and the UK, improvements in restaurant menu items are needed in both countries.
## Methodology
This is a cross-sectional study. We collated energy and nutrient information published online for menu items served by large chain restaurants in the UK and the USA, in 2018. We used the data to examine energy and nutritional differences in menu items in these two countries and estimated the proportion of menu items exceeding recommended levels of Na, total fat, saturated fat and free sugars.
## Data collection
We acquired the 2018 USA data from the MenuStat project, a publically available nutritional database of food served by the largest chain restaurants in the USA[25]. We collected 2018 UK data as part of the UK MenuTracker project. Data collection methodologies of MenuStat (USA) and MenuTracker (UK) are comparable, and details of data collection are reported elsewhere[26,27]. Briefly, the largest 100 chains (based on total sales volume) in each country that posted online nutritional information in 2018 were included. Item-level nutritional information was manually transcribed from chain websites into Excel spreadsheets.
## Data standardisation
We standardised pizza portions because restaurants presented the information for this type of food differently. For example, some restaurants presented energy and nutritional information based on one slice of pizza (not always clarifying how many slices were in a pizza), while others presented information based on the whole pizza. For pizzas described in menus as medium, large, family sized or for sharing, we calculated the energy and nutrient content of three slices of pizza. For pizzas described as small or for individual consumption, the energy and nutrient content were calculated based on the whole pizza. This was in accordance with how Domino’s, a leading pizza chain, presented the energy and nutritional information on their pizzas. Salt (g) content was converted to Na (mg) with a conversion factor of 400. All column names were standardised before combining data from the two databases.
## Item- and restaurant-level characteristics
Restaurant chains were categorised as ‘fast-casual’, ‘fast-food restaurant’ or ‘full-service’, based on criteria defined previously[20]. Briefly, full-service restaurants were those that provide table service (e.g. Applebee’s in the USA, Zizzi in the UK). Fast-casual restaurants were those that self-identified as fast-casual, or met at least two of the following criteria, as defined previously[28]: no table service, food preparation onsite, commitment to higher quality/fresher ingredients or sustainability and reusable utensils (e.g. Starbucks). The commitment to higher quality/fresh ingredients or sustainability was identified through the company’s official website. Fast-food restaurants were those that provided no table services and met fewer than two of the criteria above (e.g. McDonald’s). In both data sets, MenuTracker and MenuStat, the following binary encoded features were available for every menu item: children’s menu items, limited time offer, regional and shareable status. Descriptions and examples for these features are described elsewhere[20,26,27]. In short, menu items were categorised based on descriptions provided by restaurant websites. Children’s menu items were items on ‘children’s’ or ‘kid’s’ menus or labelled in other ways as for ‘children’ or ‘kids’, and adult menu items were those not identified as children’s menu items. Each menu item was also coded into one of the twelve food categories: Appetisers & Sides, Baked Goods, Beverages, Burgers, Desserts, Fried Potatoes, Main Courses, Pizza, Salads, Sandwiches, Soup and Toppings & Ingredients.
## Pan American Health Organisation nutrient profile model
There are few recommended dietary guidelines for restaurant menu items. Those that exist typically focus on whole meals rather than individual menu items[29]. Instead, we used the Pan American Health Organisation (PAHO) Nutrient Profile Model (NPM) to calculate which menu items contained excess levels of Na, total fat, saturated fat or free sugars (Table 1)[30]. These criteria are largely similar to the WHO’s healthy diet guidelines[31]. We applied these criteria to items from menus in both countries. As information on free sugar content was not available in our data, we made assumptions about the relationship between free sugars and total sugars (see online Supplemental Appendix Table S1). The assumptions were also based on the PAHO NPM, with modifications to accommodate restaurant menu items, as ingredient information is typically not provided by restaurants[30].
Table 1PAHO NPM criteria: items high in Na, total fat, saturated fat and free sugarsNutrientNaTotal fatSaturated fatFree sugarsCriteria≥1 mg Na/kcal≥30 % of energy from total fat≥10 % of energy from total fat≥10 % of energy from free sugars
## Statistical analysis
We used linear mixed models with random intercepts for adult and children’s menu items separately, to predict differences between countries in energy (kcal), fat (g), saturated fat (g), carbohydrates (g), sugars (g), protein (g) and Na (mg) per item served by chain restaurants. Random intercepts were used to account for restaurant- and country-level clustering. We controlled for restaurant type, food category, limited time offer, regionally offered items and shareable status. We also calculated the proportions of adult and children’s menu items that exceeded recommended levels of Na (mg), total fat (g), saturated fat (g) or free sugars (g) in both countries based on the PAHO NPM criteria.
In sensitivity analyses, we first tested if the results were robust to differences in what restaurants were present in the UK and the USA. We restricted our analyses to chain restaurants operating in both countries to provide a like-for-like rather than full landscape comparison (see online Supplemental Appendix Fig. S2). Second, we calculated the odds ratio of a USA item being high in Na, total fat, saturated fat or free sugars compared with a UK item using mixed effect logistic regressions, adjusted for restaurant – and item-level covariates (see online Supplemental Appendix Table S3). This was to tease out the potential effect of these covariates on proportions of items high in Na, total fat, saturated fat or free sugars.
All statistical analyses were performed in R (version 4.0.2; R Foundation for Statistical Computing).
## Results
Out of the 100 largest chains, forty-two chains in the UK and ninety-six chains in the USA provided some form of energy and nutrition information online in 2018. Energy and nutritional information of menu items served by these chains were included. Across these chains, 10 782 menu items were served in the UK and 30 120 in the USA. Compared with the UK, adult menu items in the USA were more likely to be served by fast-food restaurants, described as shareable, or were regionally offered (Table 2). Similarly, children’s menu items in the USA were more likely to be served by fast-food restaurants and described as limited time or regionally offered. The distribution of items across food categories also varied. For example, 30·5 % and 40·5 % of USA adult and children’s menu items were beverages, while only 21·9 % and 3·9 % were beverages in the UK. The availability of energy and nutrient information of these menu items has been described elsewhere[25,32].
Table 2Restaurant- and item-level characteristics. Adult menu itemsChildren’s menu itemsUK n 9852USA n 28 229 P-value* UK n 930USA n 1891 P-value* n % n % n % n %Restaurant typeFast-casual287829·2497217·6101·153328·2Fast-food255225·914 34550·8<0·00110511·353328·2<0·001Full-service442244·9891231·681587·682543·6ShareableNon-shareable960397·527 35196·90·004930100188499·60·146Shareable2492·58783·10070·4Food categoryAppetizers and sides114111·619106·833435·91769·3Baked goods5395·59383·3353·820·1Beverages215521·9860930·5363·976540·5Burgers3643·77012·5374653·4Desserts6776·913404·7<0·001929·9874·6<0·001Main courses120612·2354312·617318·636719·4Fried potatoes2062·13161·1778·3462·4Pizza151415·416105·7283251·3Salads2842·98683·1262·8341·8Sandwiches6686·8332611·8303·21146Soup1921·9570230·3191Toppings and Ingredients9069·2449815·9596·319110·1Regionally offeredNon regionally offered item980199·527 16196·2<0·00192999·9186398·50·001Regionally offered item510·510683·810·1281·5Limited time offerNon limited time offer964297·927 59397·70·506930100187799·30·019Limited time offer2102·16362·300140·7* P-value derived from χ2 tests.
## Differences in mean absolute energy and nutrient values of adult menu items
Figure 1 shows the predicted mean absolute energy and nutrient values per item for adult menu items in each country after adjusting for restaurant and menu-level covariates. Mean absolute predicted energy and nutrient values were higher in USA than UK for energy, fat, saturated fat and sugars. The mean absolute per-item differences between the USA and the UK were 45·6 kcal for energy (95 % CI (1·9, 89·3)), 3·2 g for fat (95 % CI (0·8, 5·6)), 1·2 g for saturated fat (95 % CI (0·2, 2·1)) and 3·2 g for sugars (95 % CI (0·5, 6·0)). Differences in mean absolute per-item differences between the USA and UK were not statistically significant for carbohydrates, protein and Na. Results from unadjusted analyses are shown in the Supplemental Appendix Table S4.
Fig. 1Predicted energy and nutrient values per item in adult menu items, by country. Adjusted for restaurant type, food group, limited time offer, regionally offered status and shareable status. * $P \leq 0$·05. NS, not statistically significant
## Differences in mean absolute energy and nutrient values of children’s menu items
As shown in Fig. 2, among children’s menu items, after adjustment for restaurant and menu-level covariates, mean absolute predicted energy, fat, saturated fat and Na values were higher in USA than UK. Compared with an average children’s menu item from a large chain restaurant in the UK, an average children’s menu item from a large chain restaurant in the USA contained more energy by 43·7 kcal (95 % CI (6·3, 81)), more fat by 4·0 g (95 % CI (1·9, 6·0)), more saturated fat by 1·2 g (95 % CI (0·3, 2·0)) and more Na by 181·1 mg (95 % CI (108·4, 253·7)). Results from unadjusted analyses are shown in the Supplemental Appendix Table S4.
In the sensitivity analysis, we analysed nutritional differences of menu items served by chains operating in both countries and found that the results were largely similar. Across these chains, energy and nutrient values were higher in the USA, except for protein and saturated fat (Supplementary Appendix Figure S2).
Fig. 2Predicted energy and nutrient values per item in children’s menu items, by country. Adjusted for restaurant type, food group, limited time offer, regionally offered status and shareable status. ** $P \leq 0$·001, *$P \leq 0$·05. NS, not statistically significant
## Proportions of menu items in excess of recommended nutrient levels
As shown in Fig. 3, in the USA, higher proportions of children’s menu items contained excess levels of fat, saturated fat, Na or sugars according to the PAHO recommendations compared with that in the UK. In contrast, adult menu items from the USA had lower proportions of items that exceeded nutritional recommendations, except for Na. Overall, 96·8 % of UK and 95·8 % of USA menu items exceeded recommended levels for at least one of Na, fat, saturated fat or sugars according to the PAHO NPM. In the sensitivity analysis, we estimated the odds of a menu item being high in nutrients to limit in the two countries, adjusted for restaurant- and item-level characteristics (see online Supplemental Appendix Table S3), and results were consistent.
Fig. 3Proportion of children’s and adult’s menu items high in fat, saturated fat, sodium and sugars, by country
## Summary of findings
Our study was the first to describe and compare the energy and nutritional composition of adults and children’s menu items served by large chain restaurants in the UK and the USA. We sourced information on more than 40 000 menu items served by 138 large restaurant chains. After adjustment for restaurant- and item-level characteristics, adult menu items in the USA had higher absolute energy, fat, saturated fat and sugar levels on average than those in the UK. USA children’s items had higher absolute energy, fat, saturated fat and Na than those in the UK. USA children’s items contained 80 % more Na and about 50 % more saturated fat than the UK ones. We used a recognised international nutrient profiling model to assess nutrient content (relative to energy) and identify items high in nutrients to limit. More than 95 % of items were high in at least one nutrient to limit and 39–67 % of adult and children’s items from each country were high in each nutrient. Higher proportions of children’s menu items contained excess levels (relative to energy) of Na, fat, saturated fat and free sugars in the USA, compared with the UK. The reverse was seen for adult items: higher proportions of adult menu items in the UK had excess (relative to energy) levels of fat, saturated fat and free sugars, compared with those in the USA.
## Strength and limitations
Our study is the first to assess energy and nutritional differences across whole menus of all types of large chain restaurants in the UK and the USA. To the best of our knowledge, it is also the largest to date to investigate the nutritional differences between restaurant menu items across countries.
In terms of limitations, we only examined menu items served by restaurants in the top 100 for sales in each country that published nutritional information online in 2018. Meals from smaller chains and independent restaurants may also contain high levels of energy and other nutrients to limit; however, they were not included in our study[33,34]. Smaller chains and independent restaurants typically do not provide nutritional information online. This (and the proportion of large restaurants providing information on the websites in the UK v. USA) may reflect differences in menu labelling policy – in the USA chains with twenty or more outlets have had to provide menu labelling since 2014; in the UK chains with 250 or more employees have had to do so since April 2022[35,36]. However, the results from the sensitivity analysis where we compared restaurants that operate in both countries indicate the nutritional differences were robust regardless of whether we included the same or all restaurants from each country.
Furthermore, the primary outcomes (e.g. the mean energy/nutrient per item) were not weighted by sales, due to the lack of restaurant sales data. Therefore, the averages of energy and nutritional values focused on what was available on the menu, rather than what was purchased and/or consumed. As portion sizes were largely missing (∼67 %), we also could not investigate between-country differences in portion sizes.
Moreover, we standardised pizza portions based on the approach used by one chain, which may not accurately reflect actual pizza portions. However, as the same set of standardisation rules were applied to both USA and UK pizza items, we anticipated the results to be robust against different definitions of a pizza portion.
Lastly, the PAHO NPM includes criteria for identifying items high in free sugars, regarding which we had no data. The assumptions we used to convert reported total sugars into estimated free sugars were based on the PAHO NPM. However, their method for estimating free sugars from total reported sugar requires a list of ingredients for each product and such data are not available for restaurant foods. Our modified method did not take into account the sophisticated nature of the relationship between free sugars and total sugars, and there could be an overestimation of proportions of items that contained excess levels of free sugars. While there were many dietary guidelines to choose from, the PAHO guideline is designed for individual food and drink products (rather than whole diets) and allowed menu item-level comparison. It is broadly consistent with the WHO international dietary guideline provided for whole diets[31].
## Proportion of menu items with excessive levels of Na, total fat, saturated fat or free sugars
In both the UK and USA, government interventions designed to improve public health through affecting change in the food system have been and are being introduced. These include voluntary targets (in both countries), such as salt or Na content reduction, and mandatory regulations, such as menu calorie labelling (currently mandatory in the USA, and implemented in the UK in April 2022) and advertising restrictions on less healthy foods and menu items on television (proposed in the UK)(37–41). Despite these efforts, our study found over 95 % of menu items to have excess levels of Na, total fat, saturated fat or free sugars in both the USA and the UK, as of 2018. This is broadly consistent with previous findings using different dietary recommendations[8,11]. These findings suggest that the current policies designed to improve the healthfulness of restaurant foods may not yet be achieving their intended effects. Policymakers should consider additional ways to ensure it is easy to choose healthy out-of-home options. For example, the UK’s National Food Strategy recommends introducing a sugar and salt reformulation tax in restaurants, to incentivise recipe reformulation[42]. Other fiscal policies, such as the UK’s Soft Drinks Industry Levy, have shown high effectiveness in promoting reformulation[43].
## Variations in energy and nutrient content of restaurant menu items
In this study we found considerable variation in the energy and nutrient content of restaurant menu items, which is in line with previous multi-country studies[19,44,45]. Large chain restaurants in the UK tended to offer food and beverages that were lower in absolute energy, fat and saturated fat compared with those in the USA. UK adult menu items were also lower in sugars compared with USA ones and children’s items lower in Na. This might partly be explained by differing portion sizes between the two countries. However, if different portion sizes were the full explanation, differences in other nutrients would be proportional to those in energy and they were not. Differences in food composition or preparation may also play a role in the between-country differences we found. In terms of macro-level differences in composition, it may be, for example, that main courses in the USA typically include a side, whilst in the UK they do not. At a more micro-level, chain restaurants appear to use different ingredients, even for the same menu item in different countries, with, for example, the USA ingredient list for a McDonald’s Big Mac being considerably longer than the UK equivalent (see online Supplemental Appendix Table S5). There may also be systematic differences in reliance on processed and ultra-processed foods.
Our results set a benchmark for future monitoring of restaurant menu items and highlight room for improvement for large chain restaurants in the USA in particular. Subject to consumer acceptability, the energy, fat and saturated fat content of menu items in the USA could potentially match the equivalent UK energy and nutrient levels. Future studies exploring category-specific differences could also shed light on which food categories to prioritise.
## Difference in energy and nutrient content of children’s menu items
Between-country differences in energy and nutrients were evident among children’s menu items, with an average children’s menu item in the USA having an additional 43·7 kcal and 181·1 mg of Na in the USA than in the UK. As the frequency of eating outside the home is also associated with less healthy dietary intake in children (as well as in adults), children’s meals are a target for reducing childhood obesity[46,47]. Previous modelling studies predicted that a relatively small reduction in daily energy intake could be sufficient to reverse the trend of increasing body weight among children[48,49]. As such, small, gradual and consistent improvements in children’s menu items may help tackle childhood obesity. This seems particularly achievable in the USA if item composition moves further towards that seen in the UK.
## Restaurant menu items exceeding Pan American Health Organisation Nutrient Profile Model nutrient levels
Despite the lower absolute values of energy and nutrients studied in UK items, our results indicate that UK adult menu items were more likely to exceed recommended fat, saturated fat and sugar levels, based on PAHO NPM criteria than those in the USA. At face value, these results seem to contradict one another. However, the PAHO NPM criteria examine nutrient content relative to energy content. It seems that a greater proportion of energy in adult menu items come from fat, saturated fat and sugars in the UK than in the USA. In contrast, non-sugar carbohydrates might make up a higher proportion of energy in the USA. Although a few national initiatives (e.g. sugar reduction program in 2016) have been introduced in the UK to incentivise reformulation in the chain restaurant sector, the effect of these programmes may have not been fully realised at the time of our data collection[50]. Continued monitoring of restaurant foods would help understand the potential effect of existing and any new government programmes, and build the evidence for how these types of population-level interventions work in practice[27,51].
## Conclusions
In this study, we found that menu items served by large chain restaurants had higher absolute levels of energy, fat and saturated fat in the USA than in the UK. USA adult menu items also had higher sugar content compared with the UK. Between-country differences were prominent in children’s menu items, especially for Na and saturated fat. However, as more than 95 % of all items in both countries were considered to have high levels of at least one nutrient to limit in the PAHO NPM, improvements in the nutritional composition of restaurant menu items are needed in both countries.
## Conflicts of interest:
There are no conflicts of interest.
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|
---
title: 'Putting nutrition education on the table: development of a curriculum to meet
future doctors’ needs'
authors:
- Glenys Jones
- Elaine Macaninch
- Duane D. Mellor
- Ayela Spiro
- Kathy Martyn
- Thomas Butler
- Alice Johnson
- J. Bernadette Moore
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC9991850
doi: 10.1017/S0007114522001635
license: CC BY 4.0
---
# Putting nutrition education on the table: development of a curriculum to meet future doctors’ needs
## Body
The relationship between malnutrition (which includes undernutrition, overnutrition and micronutrient deficiencies) and ill health is unequivocal[1]. Globally suboptimal diets are now the leading preventable cause of morbidity and mortality from a range of chronic diseases, in particular CVD, cancers and type 2 diabetes[2]. Specifically, in 2017, inadequate dietary patterns (high in Na; low in wholegrains, fruit, vegetables, nuts and seeds, and n-3 fatty acids) were responsible for more deaths globally than tobacco smoking. Notably however, evidence suggests that making dietary changes in line with those of public health recommendations across the globe (e.g. increased wholegrains, fish, vegetables and decreased processed meats, sugar and salt) may be associated with increases in life expectancy[3]. Modelling of data from the Global Burden of Disease Study has suggested sustained dietary change, from a Western diet to an optimised healthy diet made at the age of 20 years and upheld through life, could increase life expectancy by over a decade[4]. Although such modelling studies have their caveats, these data are in line with numerous population studies that show following healthier dietary practices to be correlated with reductions in mortality risk from chronic diseases such as hypertension, obesity, diabetes, CVD and some cancers(3,5–7).
Undernutrition is a poorly recognised public health problem, associated with many adverse outcomes including increased hospital admissions, longer lengths of hospital stay clinical complications and increased mortality[8]. In the UK, prior to the pandemic, undernutrition was estimated to affect over 3 million people at a cost of £23·5 billion[8]. Simultaneously, the prevalence of overweight and obesity in the UK has never been higher and, in England alone, costs the NHS £6·1 billion and wider society £27 billion annually[9]. Almost two-thirds of English adults (63 %; 67 % of men and 60 % of women) are overweight (a BMI ≥ 25 kgm–2) and more than one in four (27 %; 26 % of men and 29 % of women) are living with obesity (a BMI ≥ 30 kgm–2)[10]. New data from the National Child Measurement Programme in England suggests a dramatic increase in both the percentage of children at reception (ages 4–5 years) and year 6 (ages 11–12 years) living with overweight and obesity[11]. Moreover, the deprivation gap has widened. For instance, at reception the prevalence of obesity is 20·3 % in children from the most deprived families, in contrast to 7·8 % in children from the least deprived families[11].
While excess weight has been a major focus of public health, it is important to recognise the significance of micronutrient deficiencies arising from poor-quality diets and suboptimal dietary patterns. UK dietary survey data suggest that substantial proportions of some population groups have low intakes of various essential vitamins and minerals[12,13], and income analysis suggests that intakes of most micronutrients tend to increase with income[14]. The pandemic has shone harsh light on persistent and widening health, socio-economic and geographical inequalities, as well as inequality in the prevention, management and treatment of malnutrition[15,16]. For example, in a retrospective longitudinal study exploring UK and US pandemic diet and lifestyle behaviours, disruption of healthy lifestyle behaviours was higher in younger, female and socio-economically deprived participants[17]. Separately, NHS *Digital data* showed an almost 50 % increase in the number of under-20s admitted to hospital with an eating disorder in 2020–21 compared with the previous year, with the number exceeding 3200[18]. This was reported to be a result of insufficient community support provision to meet demand, leading to an increase in the number reaching the point of hospitalisation[18].
Adults aged over 60 years are more likely than younger age groups to suffer from malnutrition. A recent Age UK survey reported that since the beginning of the pandemic, 1·4 million older people in England aged 60+ years have been eating less and 3·7 million reported that they, or others in their household, have been unable to eat nutritious food. Therefore, pandemic-related stressors have further increased the risk of undernutrition and malnutrition in this already vulnerable age group[19]. Socio-economic challenges that decrease food access, including food poverty and food insecurity, negatively impact on physical and mental health and quality of life and underscore the complex biological, social and environmental determinants of malnutrition[16,20,21]. This has been recognised in public health policy and white papers such as the National Food Strategy and Levelling Up the UK[22]. The National Food Strategy aimed to help widen focus on the interplay between food and health systems, climate change, and economic and political drivers[23]. The combination of stressors (COVID-19 and obesity pandemics as well as climate change) perhaps means that the need for effective and evidence-based nutrition education for medical doctors and healthcare professionals has never been so critical.
The specific role of medical doctors in addressing nutrition in clinical practice has been acknowledged by multiple authoritative professional bodies[24]. The GMC’s Outcomes for Graduates sets out the following for what newly qualified doctors must be able to do with respect to nutrition: recognise where poor nutrition is contributing to ill health; take action by seeking advice from colleagues and making appropriate referrals; apply principles and knowledge relating to nutrition to medical practice and integrate these into patient care; and lastly, discuss the role and impact of nutrition to the health of individual patients and societies[25]. Moreover, in the 2019 NHS Long Term Plan, which has a major focus on the prevention of disease and health inequalities, a commitment was set to ensure nutrition has a greater place in professional education training. This was specific, so that doctors would be encouraged to address the role of nutrition in health in an informed and sensitive way and refer cases appropriately where nutrition support is required[26].
Intuitively, equipping the next generation of medical doctors with appropriate nutrition competencies should support disease prevention and improve clinical outcomes[27,28]. Yet while medical students and trainees acknowledge they need to develop their skills and knowledge in nutrition, there are widespread reports of insufficient nutrition education during medical training in UK and globally(27,29–32). Pooled survey and evaluation data suggest most UK medical students and doctors feel their nutrition training was inadequate, with more than 70 % reporting they could identify less than 2 h across their academic and clinical training[29]. Separate research has suggested that in fact students underestimate the amount of nutrition content in their medical education[30,33,34]. This documented divergence between medical students’ perception of nutrition content and actual teaching hours perhaps highlights a need for nutrition teaching to be explicitly flagged to students[30,33,34].
This will inevitably not be without challenges. How to best incorporate nutrition knowledge and clinical skills as components of medical education and clinical practice in an already complex and demanding medical curriculum may require new integrative approaches[35]. However, tackling these challenges is critical to provide the effective nutrition care required given the current levels of malnutrition and chronic disease patterns. Doctors do not need to be nutritionists, but they can play a critical role in reducing the health impacts of poor nutrition. Namely, by recognising the contribution of nutrition in clinical and population health, and developing the knowledge, skills and confidence to either offer advice or refer on appropriately depending on the context[36]. Knowledge and practice in appropriate referrals to specialists are important, because if doctors or healthcare professionals are not providing nutrition information and advice, patients will seek information elsewhere. Evidence-based nutrition and accurate information on food and health can be difficult to find in a contested space filled with commercial interests, social media and influencers[36,37].
## Abstract
COVID-19 has further exacerbated trends of widening health inequalities in the UK. Shockingly, the number of years of life lived in general good health differs by over 18 years between the most and least deprived areas of England. Poor diets and obesity are established major risk factors for chronic cardiometabolic diseases and cancer, as well as severe COVID-19. For doctors to provide the best care to their patients, there is an urgent need to improve nutrition education in undergraduate medical school training.
With this imperative, the Association for Nutrition established an Interprofessional Working Group on Medical Education (AfN IPG) to develop a new, modern undergraduate nutrition curriculum for medical doctors. The AfN IPG brought together expertise from nutrition, dietetic and medical professionals, representing the National Health Service (NHS), royal colleges, medical schools and universities, government public health departments, learned societies, medical students, and nutrition educators. The curriculum was developed with the key objective of being implementable through integration with the current undergraduate training of medical doctors.
Through an iterative and transparent consultative process, thirteen key nutritional competencies, to be achieved through mastery of eleven graduation fundamentals, were established. The curriculum to facilitate the achievement of these key competencies is divided into eight topic areas, each underpinned by a learning objective statement and teaching points detailing the knowledge and skills development required. The teaching points can be achieved through clinical teaching and a combination of facilitated learning activities and practical skill acquisition. Therefore, the nutrition curriculum enables mastery of these nutritional competencies in a way that will complement and strengthen medical students’ achievement of the General Medical Council (GMC) Outcome for Graduates.
As nutrition is an integrative science, the AfN IPG recommends that the curriculum is incorporated into initial undergraduate medical studies before specialist training. This will enable our future doctors to recognise how nutrition is related to multiple aspects of their training, from physiological systems to patient-centred care, and acquire a broad, inclusive understanding of health and disease. In addition, it will facilitate medical schools to embed nutrition learning opportunities within the core medical training, without the need to add in a large number of new components to an already crowded programme or with additional burden for teaching staff.
The undergraduate nutrition curriculum for medical doctors is designed to support medical schools to create future doctors who will understand and recognise the role of nutrition in health. Moreover, it will equip frontline staff to feel empowered to raise nutrition-related issues with their patients as a fundamental part of enhanced care and to appropriately refer on for nutrition support with a registered associate nutritionist/registered nutritionist (ANutr/RNutr) or registered dietitian (RD) where this is likely to be beneficial.
## A nutrition curriculum for nutrition competency standards
Responsibility for the undergraduate nutrition curriculum for medical students was transferred from the Academy of Medical Royal Colleges (AoMRC) to the Association for Nutrition (AfN) in 2018. Founded in 2008, the AfN is a charity and the independent regulator that accredits university undergraduate programmes awarding undergraduate and taught postgraduate (i.e. BSc and MSc) degrees in Human Nutrition in the UK. In addition to providing quality assurance schemes for the assessment of nutrition training, AfN is responsible for assessing the professional competency of nutritionists and awarding the professional titles of registered nutritionist (RNutr) and registered associate nutritionist (ANutr)[38].
While a working group of the AoMRC had previously outlined what newly qualified doctors should understand about nutrition[39], this guidance was not embedded in competency standards. Such standards can help to determine the required knowledge and skills for safe and effective care aligned to optimise health along with patients’ priorities[40]. In addition, competency standards can provide a useful framework to support curriculum developers within medical schools to provide nutrition training that is relevant for both clinical medical practice and management of student expectations. With transfer of responsibility for the undergraduate nutrition curriculum for medical students to the AfN, the AoMRC and GMC gave their support for AfN to develop a modern curriculum that supports the achievement of GMC outcomes for medical graduates and provides the fundamental nutrition knowledge and skills needed by our future doctors[38].
## Methods
To understand how this should be best achieved, and to ensure realistic deliverability of the updated curriculum into core teaching for medical students, the AfN formed an Interprofessional Working Group on Medical Education (AfN IPG). The group brought together expert professionals and organisations, as well as those who would play a key role in delivery of, or be influenced by, the updated curriculum. The working group represented Public Health England, NHS England, nutrition and dietetic professionals, medical royal colleges, medical schools, medical students, doctors and training providers. The nutrition curriculum was developed through collaborative and open discussion between group members over a number of meetings, with an agreement reached by consensus over the required detail and structure. It was developed without external funding, with all parties freely donating their time and expertise. Any declarations of interests were required to be reported by working group members at each meeting.
Both the current requirements and multiple additional opportunities that exist within the GMC’s Outcomes for Graduates to include nutrition in their core curriculum for undergraduate medical training are outlined in Table 1. These demonstrate the key roles that nutrition plays across the central GMC themes and provide a strong rationale for the development of an integrated curriculum, whereby nutrition is taught within core and speciality training, and not as a separate stand-alone topic. Indeed, it is an integrated curriculum where, throughout students training, the relationship between nutrition, health and disease is reiterated as fundamental and essential for optimal patient-centred care.
Table 1.Identification of where the requirement or opportunity exists to include nutrition within the expected curriculum for medical students in the UK, as defined by the UK General Medical Council’s Outcomes for Graduates[25] GMC themesNutrition is a required learning outcomeNutrition not explicit but essential for good patient careNutrition could be used to enhance learning and achieve learning outcomesProfessional values and behaviours:This could be applied to five of six outcome domainsSafeguarding vulnerable patients:This includes recognition of poor nutrition and taking action or referral as appropriateDealing with complexity and uncertainty:Nutrition should be a part of considering patient’s goals and priorities for enhanced careProfessional and ethical responsibilities:Both for self-care and reflection on student/doctor’s own lifestyle, attitudes and beliefs; and how these might impact advice givenPatient safety and quality improvement:Food and nutrition projects could be used to achieve many of the learning outcomes in this domainLeadership and team working:This includes interdisciplinary working across healthcare settings that will have food and nutrition as part of their servicesProfessional skills:This could be applied to two out of four outcome domainsPrescribing medications safely:Route of administration, timing and interactions with food and dietary supplements essential for safe and effective careCommunication and interpersonal skills:Conversations about role of nutrition in health can be used to introduce these skillsProfessional knowledge:This could be applied to five out of six outcome domainsApplying biomedical scientific principles:This explicitly mentions nutrition and links it both to the science and practice of medicineApplying psychological principles:The relationship of psychological and medical conditions and impact of behaviour on treatment both are impacted by diet and nutritionClinical research and scholarship:Nutrition-focused examples can be useful as teaching material for statistics, research design and use of evidenceHealth promotion and illness prevention:This domain specifically mentions healthy weight and diet along with the role and impact of nutrition on healthApplying social science principles:Nutrition provides both good teaching examples and requires the application of social science principles to be effective in practice In September 2020, a public stakeholder consultation was held on an initial draft curriculum document. This gave a wide body of stakeholders (medical schools, royal colleges, medical and nutrition organisations, training providers, nutrition and dietetic students, medical professionals and nutrition professionals) the opportunity to provide feedback on the proposed curriculum. Targeted questions encouraged comments on the curriculum structure, content and achievability. In addition, stakeholders in medical schools were invited to comment on their local practices. Namely, details on local nutrition expertise and teaching practices, and examples of nutrition inclusion within current core teaching in respondent’s medical schools, were solicited. Stakeholders were also asked to comment on what they viewed as potential implementation barriers as well as facilitative opportunities. The working group reviewed the responses to the consultation to produce the final version of the undergraduate nutrition curriculum, alongside the identification of activities that would be beneficial to support its implementation such as the production of suitable teaching resources and capacity assessment. The iterative cycle used for the development of the curriculum and its continual evaluation is illustrated in Fig. 1.
Fig. 1.Flow chart of the development steps in creating the nutrition curriculum for undergraduate medical education. MLA, medical licensing assessment.
## The Association for Nutrition undergraduate nutrition curriculum for medical students
The AfN Undergraduate Curriculum in Nutrition for Medical Doctors[41] has been designed to be presented to medical students as an integral part of their general undergraduate training, making it clear how nutrition interrelates with the study of other systems and contributes to an inclusive understanding of health and disease. The structure of the curriculum document is outlined in Fig. 2. This illustrates how the curriculum statements with teaching points will support undergraduate medical students through the achievement of eleven graduation fundamentals to develop thirteen core nutritional competencies by the point of graduation.
Fig. 2.Key components of the nutrition curriculum for undergraduate medical education.
The curriculum statements and teaching points build knowledge and understanding in eight critical nutrition topic areas outlined in Fig. 3. Each topic is accompanied by a statement of the learning objective and detailed teaching points to support students acquisition of the key competencies. The teaching points can be achieved through a combination of facilitated learning activities, such as lectures, case-, team- or problem-based learning, supporting resources and activities and practical skills acquisition through clinical teaching, both simulated and at the ‘bedside’, thereby providing sufficient opportunity for students to meet all the learning outcomes detailed in this curriculum and achieve the key competencies for graduates in a way that complements their achievement of the GMC Outcome for Graduates[25].
Fig. 3.Diagram of the eight topic areas the curriculum and teaching points address.
## Support activities
To support the implementation of nutrition teaching, the curriculum has been mapped to facilitate the achievement of the GMC’s Outcomes for Graduates[25] at the level of key competencies, graduate fundamentals and curriculum statements. In addition, it has been identified where in the domains of the new medical licensing assessment (MLA), due to commence in 2024[41], nutritional factors can either be a causal or influencing factor, or be impacted by the clinical condition or treatment.
The AfN also hosts a resource support section on their website[42], containing case studies from medical schools that have successfully incorporated nutrition training into their core teaching. These demonstrate how programmes have approached embedding nutrition within their curriculum and provide examples other programmes can use to aid curriculum reviews. To aid identification of suitable resources to support the delivery of the curriculum’s teaching points, the AfN has introduced a quality assurance scheme for resources to be assured as evidence-based and suitable for delivery of specified teaching points. The AfN IPG will also develop a bank of suitable nutrition questions for inclusion in the MLA and will submit these to the GMC, with amendments and new questions submitted as appropriate.
## Discussion
In summary, a consensus-led, multi-stakeholder process led by the AfN has developed a modern nutrition curriculum for undergraduate medical students in the UK. The AfN IPG recommends that medical schools deliver thirteen core nutritional competencies in order for future medical doctors to master eleven graduation fundamentals in nutrition by the point of their graduation[41]. The strengths of the AfN IPG process included as follows: the involvement of a multi-stakeholder, expert group; the fundamental consideration of practical application and implementation of the curriculum by medical schools; the iterative feedback from medical schools throughout; and an expansive public consultation process. The widespread adoption of this curriculum will provide consistency for the expectations and requirements for nutrition education in a manner that fully complements the GMC’s Learning Outcomes for Graduates[25]. Therefore, the key teaching points and graduate fundamentals outlined in this new nutrition curriculum should be linked to the MLA.
## Opportunities for implementation
A potential challenge raised by medical schools will be how to incorporate, what to some may first appear to be additional material, nutrition into an already incredibly dense curriculum[24,29]. Clearly signposting to where nutritional science already exists in undergraduate medical foundational training is a clear first step that medical schools should take. For example, the core underpinning foundation of medical training, for example biochemistry and physiology, can be presented through both a medical and nutritional science lens. Fundamentally, cellular metabolism and organ physiology, both in human health and disease, are completely interdependent on the catabolism and anabolism of substrates derived originally from dietary nutrients (and therefore food). Complementing this, the considered appraisal of nutrition status during history taking and person-centred approaches to patient assessment[39,43] should be presented as requisite skills to enhance patient engagement.
The key is to explicitly highlight nutrition content where it is being taught, so medical students know it is core to their learning and can develop nutritional competencies relevant to medical practice as they progress. Rather than presenting nutrition as a stand-alone subject, it should be integrated into a holistic model of care. For example, nutrition is an important consideration in safe prescribing, to prevent drug nutrient interactions[44], as well as an important non-pharmacological treatment that can reduce polypharmacy where appropriate(45–48). Although medical schools adhere to the same rigorous consistent standards for graduate expectations, curriculum design between schools varies from more traditional models (e.g. pre-clinical science teaching in early years followed by clinical teaching later) to more integrated models that have clinical placements starting from year 1[49]. This underscores that a variety of approaches to nutrition education are required.
Indeed, the new curriculum offers an incentive for medical schools to benchmark expected nutrition education outcomes, recognised as a crucial first step to improve nutrition education implementation[40]. This provides curriculum developers the opportunity to map the nutrition that is currently taught in their programmes and further integrate nutrition where appropriate as a critical element of clinical assessment and treatment. Moreover, as highlighted in Table 1, a critical review of the GMC’s current Outcomes for Graduates[25] shows that the majority of learning domains either include nutrition explicitly or provide opportunity to enhance the learning of medical students through the inclusion of nutritional content and examples of nutritional practice in the provision of optimal healthcare. Emphasising the role of nutrition in these learning domains adds weight to the core nature of nutrition in medical practice.
Once teaching and materials have been embedded within the curricula, then formal and rigorous assessment of taught content needs to follow, and therefore be developed, as assessment can help to focus and support student learning[50]. Assessment of nutrition should be developed for the early and later years of Objective Structured Clinical Examination (OSCE) and incorporated into applied knowledge assessments like the Prescribing Safety Assessment (PSA), as well as into the MLA and postgraduate examinations. Nutrition can be integrated into complex cases to aid decision-making in situational judgement tests, which are used as a part of the selection process for employment onto foundation programmes post-graduation[51]. This will require nutrition-trained faculty to be involved in the development of these assessments, which are ultimately needed to provide authentic assessment and produce doctors who are competent to use nutrition as a therapeutic option on graduation and throughout their careers.
## Challenges
The place of nutrition in medical curricula has been reported as being peripheral[52]. Often nutrition may be included as co-curricular or extracurricular activities, such as student selected components, elective courses, research projects or external opportunities through student society-led groups such as Nutritank[53]. Currently, medical doctors report rarely including nutrition in clinical care with various cited reasons including time and confidence[29]. In part, this is likely due to a lack of nutrition education in their own training[41].
Clinical role models are a key element of professional development recognised as an important hidden curriculum in healthcare professional training[49]. Accessing appropriately qualified nutrition teachers from within the core medical teaching establishment can be problematic, necessitating a more interfaculty or even interinstitutional teaching model[49,54]. A lack of professional role models in placements and faculty trained in nutrition is a clear barrier to adequate nutrition education in medical schools and to demonstrating how nutrition is integrated into clinical practice[55]. Making better use of allied health professionals such as ANutr/RNutr, registered dietitians (RD) and nutrition-trained nurses and pharmacists in multidisciplinary teams during clinical and community training offers the opportunity to enhance both interprofessional skills and the nutrition knowledge of future medical doctors.
In some universities, there may be an opportunity to collaborate with either nutrition course faculty, ANutr/RNutr or RD, but these may be limited by availability and capacity. Although interprofessional education is becoming more prevalent in medical schools, some dietitians still report facing challenges in influencing the medical curriculum[56]. This alongside discipline differences in approaches to education may also mean nutrition and dietetic professionals would need to adapt their approach and style of delivery to match that expected on medical degrees. Structurally and culturally, medical education may historically have been distinct in its specialism approach, context and assessment, relative to other health professions. However, the drive across all healthcare education to incorporate interprofessional education provides an opportunity to explore nutrition across professional domains and challenging restrictive action of healthcare professionals being predominately taught by their own profession[57].
Many of the key aspects of the new nutrition curriculum support interprofessional education as a common thread for all health professionals as they explore patient-focused case studies through the lens of their developing professional roles. This is an approach also recognised in the recently published recommendations from the UK Obesity Care Competencies Working Group facilitated by the College of Contemporary Health[58]. These timely obesity care competencies for healthcare education in the UK [58] are complementary to the nutrition curriculum, and ideally both sets of competencies should be mapped and adopted by medical schools in tandem. Indeed, more collaboration to develop the right interprofessional education content and activities and facilitate medical teaching capacity is needed. Opportunities exist for medical schools to collaborate with local universities and organisations such as the AfN, the British Dietetic Association (BDA), the Nutrition Society, and the NNEdPro Global Centre for Nutrition and Health. In addition, there are clear opportunities to incorporate and utilise resources produced by nutrition experts from the British Association for Parenteral and Enteral Nutrition (BAPEN), the World Cancer Research Fund (WCRF) and Health Education England, among others.
Interest in nutrition will likely not be universal among medical students and faculty[59]. Although some groups, including those led by students such as Nutritank, have pushed for incorporating more nutrition training, others might not see nutrition as a priority to the immediate needs of patients on initial medical consultation. Indeed, in a European survey of medical school faculty, those responding did not feel more nutrition education was required[34]. This may reflect the traditional pharmacological and surgical approaches to medical treatments and may be more apparent in some specialities than others, for example, in acute medicine and surgery where medical priorities may represent life or death decisions. Therefore, incorporating diversity within faculties of medical educators, to include those from other health and nutrition professions, will likely facilitate the integration of nutrition into local programmes in the first instance. Once integrated, mapping learning to national graduate expectations and high-stake examinations will ensure the presence of nutrition in the curriculum of local medical schools beyond the interests of championing faculty.
## Conclusions
Nutrition is a key modifiable factor in the prevention of disease and healthy ageing. Given the current extraordinary prevalence of diet-related chronic disease in the UK, and the integral role nutrition plays in the treatment and rehabilitation of disease, it is now imperative that nutrition fundamentals be embedded in core undergraduate training for medical doctors and be assessed in the new MLA in 2024. Medical doctors do not need to become nutritionists or dietitians but should be equipped to confidently address malnutrition in all its forms. Doctors who will see thousands of patients throughout their career play a key role in helping to treat and monitor nutrition-related conditions, as well as in delivering preventative medicine. Future doctors should therefore be skilled to discuss factors such as achieving a healthy weight in an informed and sensitive manner, as well as having the knowledge to refer patients to further nutrition support when appropriate. There is a clear opportunity now for medical schools to distinguish themselves based on the integration of nutrition practice into holistic healthcare training to adequately prepare graduates with the knowledge and skills in nutrition care with the ultimate goal of improving patient care.
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|
---
title: 'The role of child diets in the association between pre-pregnancy diets and
childhood behavioural problems: a mediation analysis'
authors:
- Dereje G Gete
- Michael Waller
- Gita D Mishra
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991854
doi: 10.1017/S1368980022001410
license: CC BY 4.0
---
# The role of child diets in the association between pre-pregnancy diets and childhood behavioural problems: a mediation analysis
## Body
Childhood behavioural problems are the second leading cause of disease burden in young adolescents aged 10–14 years[1]. They have a substantial impact on adulthood productivity and function throughout the life course, including poor academic performance, occupational and psychosocial dysfunction[2].
There is a growing body of evidence that recognises the importance of maternal diet quality on offspring behaviours(3–5). Our previous study has shown that pre-pregnancy diet quality was also associated with lowering the risk of childhood behavioural problems[6]. Maternal diets may affect offspring behaviour through epigenetic changes and inflammation pathways[7,8]. Maternal diet quality has been positively linked with offspring diet quality(9–11). In turn, childhood diet has been reported as a strong predictor for behavioural disorders[12]. Several studies showed that better adherence to childhood diet quality was associated with improving their behaviours or mental health(13–15). To our knowledge, no studies, however, formally investigated the mediating role of childhood diets in the association between pre-pregnancy diets and offspring behaviours.
The Strengths and Difficulties Questionnaire (SDQ) has been widely used to assess childhood behavioural disorders. The SDQ total behavioural difficulties score comprises emotional, peer, conduct and hyperactivity subscales – which have been found to be a psychometrically sound measure of overall childhood behavioural problems in studies from around the globe(16–18). A number of studies reported a high prevalence of childhood behavioural disorders assessed by SDQ(19–21). In Australia, about 14 % of behavioural disorders were reported among children and adolescents aged 4–17 years[22].
Studies over the past decade have provided key information on the association between maternal diets and offspring behavioural problems by adjusting maternal socio-demographic, lifestyle, perinatal and childhood factors(23–25). An important unanswered issue, however, is whether the observed association is based on direct effects of maternal diet or indirect effects of childhood diet. A meta-analysis conducted by Borge et al.[26] revealed that better quality of diets in pregnancy had a modest effect on improving offspring behaviours. The included studies in the meta-analysis controlled for childhood diet as a covariate in the adjusted model, though the observed associations were largely attenuated by childhood diets and suggesting that childhood diets might have a mediating effect in the association between maternal diets and offspring behaviours. The current study is, to our knowledge, one of the first large prospective cohort studies to use a casual inference frame to formally investigate the role of childhood diets in the association between pre-pregnancy diets and offspring behavioural problems. Identifying causal pathways in the association of maternal diets and childhood behavioural disorders could provide a better scientific basis for targeted prevention strategies.
The present study aimed to quantify the mediating role of child diets in the association between pre-pregnancy diet quality and childhood behavioural problems aged 5 to 12 years using a nationally representative cohort study of Australian mothers and their children.
## Abstract
### Objective:
To quantify the mediating role of childhood diets in the relationship between maternal diets prior to pregnancy and childhood behavioural disorders.
### Design:
The Healthy Eating Index score was constructed using a semi-quantitative and validated 101-item FFQ. We assessed childhood behavioural disorders using the Strengths and Difficulties Questionnaire. Three dietary patterns were identified using principal component analysis to explore childhood dietary patterns (high fats and sugar; prudent diets; and diary). A causal inference framework for mediation analysis was used to quantify the mediating role of childhood diets in the association between pre-pregnancy diets and the risk of offspring behavioural problems.
### Setting:
This is a national representative population-based survey which covers all Australian citizens and permanent residents in Australia.
### Participants:
We included 1448 mother–child pairs from the Australian Longitudinal Study on Women’s Health and its sub-study mothers and their children’s health.
### Results:
We found a 20 % of the total effect of the poor adherence to pre-pregnancy diet quality on the risk of offspring behavioural problems was mediated through childhood high consumptions of fats and sugar. No clear mediating effect through prudent and diary childhood diets was observed.
### Conclusion:
This study suggests that childhood high fats and sugar consumption may contribute to the total effects of the pre-pregnancy diets on the risk of childhood behavioural problems.
## Study participants and design
The current study used data from the Australian Longitudinal Study on Women’s Health (ALSWH) and Mothers’ and their Children’s Health (MatCH) study. For the ALSWH study, over 14 000 women born in 1973–1978 were recruited, who were randomly selected from the National Universal Health Insurance database, including all Australian citizens and permanent residents. Fuller details of the ALSWH have been described elsewhere[27].
The MatCH is a substudy of the ALSWH young cohort, born 1973–1978, investigating childcare/school, illness/disability, diets, quality of life, social/emotional development, and growth and physical developments. In 2016, this study invited 8929 women to provide information about their children, among those, 3039 women filled out the questionnaire about their children (up to three young children) (n 5780)[28].
This study utilised data from the young Australian cohort, aged 18–23 years (born 1973–1978), who provided wide-ranging information about their offspring born between 2003 (Survey 3) and 2015 (Survey 7). Nulliparous and non-pregnant women at baseline Survey 3 and 5 were included in the study. Women who reported at least one live birth between Survey 3 and 7 were included. Only the first births born between 2003 and 2015 and were aged 5–12 years were included. We excluded women who had missing data on offspring behaviour, child diets and implausible energy intake (> 16 800 kJ/d or < 2100 kJ/d)[29]. The current study included 1448 mother–child dyads in the final analysis (Fig. 1). Data from the ALSWH were used to assess exposure (maternal dietary consumption), while the MatCH study was used to assess outcome (childhood behavioural problems) and mediator (childhood dietary consumption). The ALSWH study was approved by the Human Research Ethics Committees at the University of Newcastle and the University of Queensland, and informed consent was received from all ALSWH participants.
Fig. 1Flow chart of the final sample for the analysis of mediation by childhood diets in the association between pre-pregnancy diets and offspring behavioural problems
## Maternal dietary assessment
Women’s dietary consumption was first assessed in 2003 (Survey 3, aged 25–30 years, n 9081) and again in 2009 (Survey 5, aged 31–36, n 8200) for Epidemiologic Study version 2[30]. Women were asked to provide their habitual dietary intake of the previous 12 months using a validated and semi-quantitative 101-items FFQ. Validation of the FFQ against 7 d food diaries of sixty-three women of child-bearing age indicated moderate to strong energy-adjusted correlation coefficients for a wide range of nutrients and ranged from 0·28 for vitamin A to 0·78 for carbohydrates[31].
We computed the Healthy Eating Index (HEI-2015) score to assess pre-pregnancy diet quality, which includes thirteen components that sum to a total maximum score of 100 points. Each of the dietary components is scored on a density basis out of 1000 calories except fatty acids[32,33]. Nine components, including total fruits, whole fruits, whole grains, total vegetables, greens and beans, dairy, seafood and plant proteins, total proteins, and fatty acids, were to be consumed adequately. However, four dietary components, such as refined grains, Na, saturated fats and added sugars, were to be consumed in moderation – in which mothers with lower intake receive higher scores. We categorised the HEI-2015 score was into the tertiles approach according to its distribution among the study participants: tertile 1 (low adherence), tertile 2 (moderate adherence) and tertile 3 (high adherence) to enable practical comparisons.
## Childhood dietary assessment
This study used a validated and semi-quantitative twenty-eight-items Children’s Dietary Questionnaire (CDQ) to assess childhood dietary patterns, which measures childhood dietary consumption either in the previous week or 24 h[34]. The CDQ was developed according to the most recent national data on the dietary consumption of Australian children[35,36] and the Australian Dietary Guidelines[37,38].
Children’s dietary patterns based on twenty-five non-overlapping food groups (frequency of consumptions/day) were identified using principal component analysis (PCA) with the use of orthogonal (varimax) rotation. The number of childhood dietary patterns was chosen with the basis of eigenvalues > 1·35, the identification of a breakpoint in the scree plot and factor interpretability[39]. We used the Bartlett test of sphericity ($P \leq 0$·001) to indicate statistically correlated variables. Kaiser–Meyer–Olkin test (0·71) was used to measure sampling adequacy. Food groups with factor loadings ≥ 0·30 on a factor were considered to have a strong association with that dietary pattern and be the most explanatory in describing the factors[40].
## Offspring behaviour assessment
Women were asked to provide their offspring’s behaviours over the previous 6 months using the SDQ. Total behavioural difficulties score consists of four subscales, ranging from 0 to 40: emotional (somatic, unhappy, worried, nervous and fears), hyperactivity (distractible, restless, fidgety, reflective and attentive), peer (solitary, popular, good friend, bullied and prefers adult) and conduct problems (fights, tempers, obedient, steals and lies). Each subscale comprises five items with three-point response scales (0 for ‘not true’, 1 for ‘somewhat true’ and 2 for ‘certainly true’), with a subscale score ranging from 0 to 10[16]. Each one-point increase in the total behavioural difficulties score corresponds to an increase in the risk of developing a mental health disorder.
The data on the total behavioural difficulties score were skewed. We, therefore, dichotomised total behavioural problems score based on Goodman classifications[16], comparing children with abnormal or borderline scores with children with normal scores. In this study, the total behavioural problem is defined according to the cut-off on the total behavioural difficulties score (≥ 14 on the maternal reports).
## Assessment of confounders and covariates
Baseline maternal confounders, such as marital status, education, income, smoking, alcohol intake and physical activity, were adjusted using the self-reported data before the index birth (Survey 3 or 5). Prenatal factors, including hypertensive disorder in pregnancy and gestational diabetes mellitus, were adjusted for using the same survey as the index birth (Survey 4–7).
Women’s alcohol consumption was classified as a non-drinker, low-risk drinker (≤ 14 drinks/week), risky drinker (15–28 drinks/week) and high-risk drinker (> 28 drinks/week)[41]. However, women with high-risk drinkers were merged with a risky drinker group due to very few women reported as high-risk drinkers (n 6, 0·42 %). We categorised the smoking status as never smoker, ex-smoker and current smoker[42]. Mothers were asked to provide only activity that lasted 10 min or more using a mailed physical activity questionnaire before recording daily pedometer step counts for seven consecutive days. A physical activity score was calculated according to frequency and duration of walking and moderate and vigorous-intensity activity and categorised as sedentary/low (< 600 total metabolic equivalents (MET) min/week), moderate (600–1200 MET min/week) or high (≥ 1200 MET min/week)[43].
Mothers also were invited to provide information on their children’s sex, height, weight, history of premature birth (live birth < 37 weeks of gestation) and low birth weight (live birth weight < 2·5 kg), multiple births, and breast-feeding status in the MatCH study. Child BMI was calculated using their weight (kg)/height (m2), then categorised into underweight, normal, overweight and obese according to sex and age-specific BMI classifications for children[44].
## Statistical analyses
Maternal and childhood characteristics were described according to adherence to pre-pregnancy HEI-2015 score and offspring behavioural problems. Mean differences were examined using t test, Pearson’s chi-square and ANOVA.
Figure 2 provides a directed acyclic graph indicating potential pathways between pre-pregnancy diets, childhood diets and offspring behavioural problems. A mediation analysis was performed using the counterfactual approach to decompose the total effect of poor pre-pregnancy diet quality on offspring behavioural problems into natural direct and indirect effects through childhood diets[45,46]. The mediation analysis was conducted by fitting a logistic regression model for the binary outcome (offspring behavioural problems, yes v. no) and a linear regression model for the continuous mediator (childhood diets). We did not include exposure–mediator interaction in any model, since it was not statistically significant ($P \leq 0$·05). From these combined models, we estimated OR of natural direct effects (ORNDE) and natural indirect effects (ORNIE), and total effects (ORTE) for the binary outcomes. The proportion mediated was calculated as (ORNDE (ORNIE–1)] ÷ [ORNDE × ORNIE–1) × 100 % for the binary outcome[45,46]. The proportion mediated estimates the extent to which the effect of the pre-pregnancy diets on the offspring’s behavioural problems is mediated through childhood diets relative to the overall effect of the pre-pregnancy diets. The maternal potential confounders were selected according to the known association from previous literature and then tested the associations with the exposure and outcome, the mediator and outcome, or/and the exposure and mediator. As shown in Table 2, two separate models were fitted to observe the difference of total proportion mediated by childhood diets after adjustments for baseline and post-covariates. The first model adjusted for baseline maternal potential confounders (maternal age, education, smoking and household income), since these variables might be associated with exposure, outcome and mediator. The second model further adjusted for pregnancy complications (gestational diabetes mellitus, hypertensive disorder in pregnancy and antenatal anxiety) and child characteristics (preterm birth, low birth weight, child age and sex). These variables are important post-covariates that might influence offspring behaviours (direct effect). We retained the confounders in the adjusted model if the P-value was < 0·2 in the simple model. We further performed a sensitivity analysis to observe changes in the HEI-2015 score from preconception to during pregnancy. We ran Spearman’s correlation and paired t tests to examine the stability and changes of the HEI-2015 score at the two time points. The paramed program in STATA version 16 (StataCorp) was employed to compute natural direct, indirect and total effects. P-value ≤ 0·05 was considered statistically significant.
Fig. 2Directed acyclic graph showing potential pathways between pre-pregnancy diets, childhood diets and offspring behavioural problems
## Results
The current study included 1448 mother–child dyads using the ALSWH and MatCH study (Fig. 1). A total of 198 (13·7 %) children experienced a higher SDQ score on total behavioural problems among the 1448 children between 2003 and 2015. The mean age of women at birth was 32 (sd 2·4) years.
The mean preconception HEI-2015 score was 58·6 (sd 12·3). Women had good adherence to total protein, fruits, added sugar, and greens and beans. However, they had low adherence to Na, fatty acids, seafood and plant proteins (online Supplementary Table 1).
Three children’s dietary patterns were identified from PCA from the MatCH survey with eigenvalues > 1·35 from scree plot and factor loadings, which explained 27 % of the total variation in food intakes. The first component was labelled ‘high fats and sugar’ and had high positive factor loadings for: potato chips/crisps or savoury biscuits; lollies, muesli or fruit bars; soft drink/cordial; ice cream/ice blocks; hot chips or French fries; and chocolates and takeaway. The second component ‘prudent diets’ had high positive factor loadings for bread and grains food, meat, fish, eggs, vegetables and fruits. The third component ‘diary’ had positive factor loadings for full cream/full-fat milk and regular yogurt/custard, and negative factor loading for reduced-fat milk, yogurt and custard (online Supplementary Table 2 and 3).
It can be seen from the data in Table 1 that a greater prevalence of offspring total behavioural problems was observed among women with lower educational status and income. There was also a higher proportion of offspring behavioural problems among obese women and antenatal anxiety. Women with better adherence to diet quality were older, were well-educated and did more physical activity. There was also a higher adherence to maternal diet quality among higher-income and urban residents.
Table 1Maternal characteristics according to childhood behavioural problems aged 5–12 years and pre-pregnancy HEI-2015 score (n 1448)* Total behavioural problemsPre-pregnancy HEI-2015 scoreBaseline maternal characteristicsYes (n 198) P-value† Tertile 1 (n 483)Tertile 2 (n 483)Tertile 3 (n 482) P-value† %Mean sd %Mean sd %Mean sd %Mean sd Maternal age (years), mean (sd)31·82·40·1531·52·431·42·331·92·50·007Marital status (%)‡ 0·090·6 Married12·93235·432·5 De facto/separated/divorced11·935·53133·5 Single16·832·932·934·2Residence (%)‡ 0·260·01 Urban1331·133·635·3 Rural/remote15·238·132·629·2Education (%)‡ < 0·00010·001 Up to year 12 or equivalent22·342·630·327 Trade/apprenticeship/certificate/diploma15·239·131·129·8 University/higher degree11·229·534·635·8Smoking (%)‡ 0·180·004 Never smoked12·83234·833·2 Ex-smoker13·228·532·938·5 Current smoker17·342·728·129·2Alcohol intake (%)‡ 0·280·07 Non-drinker16·447·332·720 Rarely drinker16·735·535·528·9 Low-risk drinker13·332·332·635·1 Risky drinker7·728·842·328·8Physical activity (%)‡ 0·58< 0·0001 Sedentary/low, < 600 MET min/week14·441·832·226 Moderate, 600 to 1200 MET min/week14·530·936·732·4 High, ≥ 1200 MET min/week12·527·532·240·2Pre-pregnancy BMI (%)‡ 0·0010·59 Healthy weight, < 25 kg/m2 12·234·332·732·9 Overweight, 25–30 kg/m2 13·329·63535·4 Obese, ≥ 30 kg/m2 23·930·635·833·6Total calories intake (kJ/d), mean (sd)1555533·60·81740·9688·71512·04487·71385·0453·7< 0·0001Household income (weekly) (%)‡ 0·009< 0·0001 ≤ 999 $17·447·128·924 1000 $–1499 $11·535·83826·2 ≥ 1500 $11·328·533·538 Don’t know/don’t want to answer20·229·830·739·5During pregnancyGestational hypertension (%)‡ 0·170·58 No13·33333·533·5 Yes17·337·231·431·4Gestational diabetes (%)‡ 0·070·53 No13·333·233·233·6 Yes20·538·533·328·2Antenatal anxiety (%)‡ 0·0080·65 No13·333·233·433·3 Yes27·338·627·334·1Antenatal depression (%)‡ 0·210·76 No13·533·333·333·4 Yes21·239·430·330·3*Values are mean (sd) or percentage (%).† P-values from Pearson’s chi-square, t tests or ANOVA.‡Missing values (marital status: n 2, residence: n 12, education: n 17, smoking: n 5, alcohol intake: n 4, physical activity: n 10, pre-pregnancy BMI: n 21, income: n 69, gestational hypertension: n 4, gestational diabetes: n 5, antenatal anxiety: 5 and antenatal depression: n 4). The HEI-2015 score was categorised as tertile 1 (low adherence), tertile 2 (moderate adherence) and tertile 3 (high adherence).
Table 2Natural direct and indirect effects of the preconception diet quality on the risk of offspring behavioural problems and the proportion mediated through childhood dietary patternsOffspring behavioral problemsOR NDE A direct effect of poor preconception HEI-2015 score (lowest v. highest tertile)* 95 %CIOR NIE Mediated through child diets95 %CIOR TE Total effects of preconception HEI-2015 score95 %CIProportion mediated by child diets (%)Mediator: ‘High fats and sugar’ dietary patterns (per 1-sd increase)Model 1† 1·621·07, 2·441·101·03, 1·171·781·18, 2·6821 %Model 2‡ 1·671·10, 2·541·101·03, 1·171·831·20, 2·7820 %Mediator: ‘Prudent’ dietary pattern (per 1-sd decrease)Model 1† 1·771·18, 2·661·010·97, 1·041·781·18, 2·672·2 %Model 2‡ 1·821·20, 2·761·010·97, 1·041·831·21, 2·772·1 %Mediator: ‘Dairy’ dietary pattern (per 1-sd decrease)Model 1† 1·701·13, 2·561·050·98, 1·111·781·19, 2·6811 %Model 2‡ 1·771·16, 2·691·040·97, 1·101·831·21, 2·788 %NDE, natural direct effect; NIE, natural indirect effect; TE, total effect. Model 1 was adjusted for baseline maternal confounders. Model 2 was adjusted for model 1 and further adjusted for pregnancy complications and childhood characteristics.*OR for the effect of HEI-2015 score, lowest tertile compared with the highest tertile (reference group). A causal inference framework for mediation analysis was used to estimate OR and 95 % CI for total, natural direct and indirect effects. The natural direct and indirect effects were computed by fitting a logistic regression model for the binary outcome and a linear regression model for the continuous mediator. Total effects are equal to the product of the natural direct and indirect effects. The proportion mediated was calculated as (ORNDE (ORNIE–1)) ÷ (ORNDE × ORNIE–1) × 100 % and approximates the extent to which the effect of the exposure (preconception diets) on the outcome (childhood behavioural problems) is mediated through childhood diets relative to the overall effect of the exposure.†Adjusted for maternal age, education, smoking and income.‡Adjusted for maternal age, education, smoking, income, gestational diabetes mellitus, hypertensive disorder in pregnancy, antenatal anxiety, preterm birth, low birth weight, child age, sex and number of siblings.
A significantly higher proportion of total behavioural problems was found among low birth weight, premature and overweight/obese children. Boys were more likely to experience behavioural problems. Behavioural problems were also more likely to present in children with greater consumption of fats and sugar-sweetened beverages, and lower consumption of fruit and vegetable (online Supplementary Table 4).
Table 2 presents the mediating role of childhood diets in the association between pre-pregnancy diet quality and childhood behavioural problems. Overall, poor maternal diet quality before pregnancy had significant natural direct and total causal effects on offspring behavioural problems. The natural direct effects were stronger than natural indirect effects. The total causal effect of the preconception diet quality that was mediated was 2 % and 8 % through childhood consumptions of prudent diets and dairy, respectively.
Interestingly, childhood dietary pattern characterised by high consumptions of fats and sugar had a significant natural indirect (mediated) effect in the relationship between poor maternal diet quality and childhood behavioural problems. The proportion of the total effect of lower adherence to the preconception diet quality on the risk of childhood behavioural problems (OR = 1·83, 95 % CI: 1·20, 2·78) that was mediated through childhood ‘high fats and sugar’ consumptions (per 1-sd increase) was 20 % after adjustment for baseline maternal confounders, prenatal and childhood factors. For each model, a consistent mediated effect of childhood dietary pattern of ‘high fats and sugar’ was observed in the relationship between preconception diet quality and childhood behavioural problems at $P \leq 0$·006. However, no significant mediated effects were found for the childhood ‘prudent diets and dairy’ patterns.
From the data in Supplementary Table 5, the HEI-2015 score was quite stable from preconception to during pregnancy at $P \leq 0$·0001. Although a slight mean increment by 1·0 point in the HEI-2015 score was observed from preconception to during pregnancy, there were no statistically significant mean changes observed at the two time points ($$P \leq 0$$·06).
## Discussion
The current study set out with the aim of quantifying the mediating role of childhood diets in the association between maternal diet quality prior to pregnancy and childhood behavioural disorders. We found that a significant proportion of the total effect of pre-pregnancy diet quality on the risk of offspring behavioural disorders was mediated through childhood high consumptions of fats and sugar. Childhood diet comprising high consumptions of fats and sugar explained 20 % of the total effect of the poor adherence to preconception diet quality on the risk of offspring behavioural problems after adjustment for baseline maternal confounders, prenatal and childhood factors. No clear mediated effects were found through the childhood diet patterns labelled ‘prudent diet’ and ‘diary’.
To our knowledge, this is the first study to use a causal inference framework to examine the extent to which childhood diets contribute to the association between pre-pregnancy diets and the risk of childhood behavioural problems. Our previous study has shown the direct effects of preconception diet quality on the risk of childhood behavioural disorders[6]. However, the observed effect estimate was attenuated by childhood diets and suggesting that childhood diets might have a mediating role in the association between pre-pregnancy diets and offspring behaviours. No studies formally distinguished childhood diets as a distinct mediator or covariate in the association between preconception diets and offspring behaviours. Several reports showed an association between children’s dietary patterns and behavioural disorders, particularly attention-deficit hyperactivity disorder[12,47,48]. In the current study, children’s dietary pattern, especially high intake of fats and sugar, was also strongly associated with increased risk of behavioural problems. This may be explained by the fact that high fats and sugar consumptions have a substantial contribution to developing risk of obesity[49], which have been linked with negative neuroplasticity changes, including hippocampal dysfunction[50], oxidative stress and inflammation[51], and subsequently affect mental health[52]. In contrast, Kohlboeck et al. demonstrated that grater adherence to childhood diet quality was associated with a lower risk of behavioural disorders[13]. In a prospective cohort study conducted on Australian adolescents, Jacka et al also observed a positive association between the higher score on healthful diets and better mental health[14].
In the present study, there was a strong association between preconception diet quality and children’s dietary patterns. This finding was also reported in a Danish National Birth Cohort study, Ahrendt et al. showed a positive association between maternal diet quality and their offspring’s diet quality[53]. Accordingly, a potential association between maternal dietary patterns and children’s dietary intake could exist, eventually affecting later disease risk in offspring. Maternal dietary patterns before pregnancy are more likely to continue such habits during pregnancy and postnatally[54], which will to some extent be reflected in childhood dietary habits, subsequently affecting offspring behaviours. Overall, childhood diet has been strongly linked with maternal diet and later risk of behavioural disorders. Childhood diet, therefore, may have an important role in the association between maternal diets and offspring behaviours.
Although childhood diets, particularly ‘high fats and sugar’ intake, had a substantial contribution to the total effect of the pre-pregnancy diets on the risk of childhood behavioural disorders as a single mediating variable, there might be other possible causal pathways that most of the effects are mediated through. The risk of childhood behavioural problems has been influenced by adverse pregnancy or birth outcomes, including gestational diabetes, hypertension and low birth weight. These variables might also contribute as mediators as well as intermediate confounders (mediator-outcome confounders affected by exposure) in the association between maternal diets and offspring behavioural problems. Further studies should be undertaken to examine other possible pathways by controlling intermediate confounders and their importance in explaining these associations.
The major strengths of this study are the nationally representative sample, population-based prospective cohort study, and comprehensive information on maternal and childhood factors. Another advantage is that our study formally examined a mediation analysis using a counterfactual approach allowing us to decompose the total effect into natural direct and indirect effects. A validated FFQ was used to assess women’s dietary intake, specifically designed for use in the Australian population. However, the current study was limited using self-report data on maternal dietary intake and their offspring’s behavioural problems, which might have recall and information bias. There might be residual and intermediate confounders that the study was unable to control for, though we adjusted for a wide range of maternal and childhood factors.
In conclusion, a childhood diet comprising high consumption of fat and sugar might have an important contribution to the total effect of the pre-pregnancy diets on the risk of childhood behavioural problems. This study, therefore, suggests that better maternal diet quality before pregnancy might improve offspring behaviours substantially through optimising the quality of diets in childhood. Our findings also highlight the important role of childhood diets in the association between maternal diets and enhancing the offspring behaviours. This study, therefore, supports that maternal and childhood diet quality may be important modifiable factors to improve childhood behaviours and quality of life.
## Conflict of interest:
The authors declare no competing interests.
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|
---
title: Can food parenting practices explain the association between parental education
and children’s food intake? The Feel4Diabetes-study
authors:
- Paloma Flores-Barrantes
- Christina Mavrogianni
- Iris Iglesia
- Lubna Mahmood
- Ruben Willems
- Greet Cardon
- Flore De Vylder
- Stavros Liatis
- Konstantinos Makrilakis
- Remberto Martinez
- Peter Schwarz
- Imre Rurik
- Emese Antal
- Violeta Iotova
- Kaloyan Tsochev
- Nevena Chakarova
- Jemina Kivelä
- Katja Wikström
- Yannis Manios
- Luis A Moreno
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991856
doi: 10.1017/S1368980022000891
license: CC BY 4.0
---
# Can food parenting practices explain the association between parental education and children’s food intake? The Feel4Diabetes-study
## Body
Childhood obesity is one of the most serious global public health problems in the twenty-first century[1], and socio-economic status (SES) is associated with this condition, since it has been inversely associated with adiposity in high-income countries and directly associated in medium to low-income countries[2,3]. Unequal access to healthy foods is one mechanism by which SES influences the diet and health of the population, given that as income drops, energy-dense and nutrient-poor foods become an important source in affordable diets[4]. In this sense, among the obesity-related factors[5], dietary behaviour is one of the most relevant due to its strong impact on maintaining energy balance[6].
Significant differences have been found in the consumption of fruits according to SES status, for instance children from lower SES consume less fruit and vegetables (F&V) compared with high SES children, and these differences maintain and even grow over time[7,8]. Furthermore, evidence exists confirming that a high SES is associated with healthy eating in youth. For instance, Sandvik et al. observed that 34·0 % of children from the highest SES group reported consuming fruit every day compared with 27·6 % of children in the lower SES groups[9]. In contrast, low SES has been associated with higher consumption of nutrient-poor foods. Parental education has been identified as one of the best proxy indicators of SES[10] and has been widely used in previous studies[11,12]. In this sense, previous studies have found that children whose mothers had a low level of education tend to consume energy-dense food and drinks[13] and those whose parents had higher education levels and the highest household income were more likely to be allocated to the healthy dietary pattern, characterised by the inclusion of low-fat, vitamin-rich and wholegrain foods, among others, and less likely to be allocated to a dietary pattern characterised by high sugar consumption[14].
Parental behaviours or actions performed for child-rearing purposes in the contexts of food and feeding are defined as food parenting practices (FPP)[15]. FPP influence children’s dietary intake, as well as home food environment characteristics, such as the home availability of foods or the use of food as a reward, and they vary across SES.
Regarding associations between parental education and FPP, results from a nationally representative sample from the USA also indicated that both education and SES were positively associated with home availability of foods, such as fruits, vegetables and specifically, education was negatively associated with salty snacks and sugary drinks availability at home. On the other hand, income was positively associated with dark green vegetables, low-fat milk products and salty snacks availability at home[16]. In addition, a previous study by Campbell et al.[17] found that families with higher education levels reported a lower frequency of family meals, whereas lower fresh F&V availability at home was reported by families with lower education levels. Moreover, the accumulation of social vulnerabilities, such as lack of social network and migrant background, has also been associated with a processed dietary pattern[18].
Given that in the last years, parenting styles and specifically food parenting styles have been studied to a greater extent than FPP, it is worth mentioning that general parenting styles can be conceived as more distal, higher-order constructs, whereas parenting practices are more proximal determinants of child behaviour[19]. Regarding parenting styles and dietary intake in children, a previous systematic review in pre-school aged children reported that two out of three studies reported that authoritative parenting style was associated with higher intake of F&V and from the two studies that examined associations between parenting styles and unhealthy/non-core foods, no significant associations were observed[20]. Also, in a 3-year-longitudinal study in Australian children, 6–9-year-old boys whose mothers reported using the authoritarian style were less likely to consume F&V. In the same study, boys and girls with authoritative and permissive fathers, and girls with authoritative mothers at 4–5 years, were more likely to consume F&V 2 and 4 years later[21].
The relevance of studying these practices lies in the fact that, for example when parents use emotional feeding for a prolonged period of time, children may eventually learn to calm themselves by eating[22], which may increase their future overweight risk. A recent systematic review aimed to conclude that FPP receiving the most attention within prospective studies were generally not associated with children’s weight outcomes over time[23]. Nevertheless, several FPP, such as home food availability[24] or parental modelling of food intake[25], are associated with children’s dietary intake.
In this sense, given that previous research has showed significant associations between SES indicators, such as parental education and children’s dietary intake, it is relevant to examine through mediation analyses to what extent FPP explain this relationship. In fact, FPP such as food availability and food accessibility have been previously evaluated as potential mediators of such associations[26,27]. Also, a previous systematic review aiming to summarise existing evidence regarding the mediators of socio-economic differences in dietary behaviours among youth at the interpersonal level found that availability at home, accessibility at home, food rules and modelling were consistent mediators of this association[28]. Nevertheless, some parenting practices, such as permissiveness and allowance, had not been previously assessed as potential mediators of the associations between SES and children’s dietary intake.
However, to our concern, FPP such as the use of food as a reward have not yet been evaluated as potential mediators. Such information might help construct more effective nutrition interventions aiming to alter children’s eating behaviour and promote healthy eating, particularly in vulnerable and low-SES groups. The understanding of these practices as possible mediators of the previously mentioned associations might be useful for future interventions aiming to improve dietary intake in young children through parental behaviour modifications such as avoiding certain FPP and making efforts to use those known to have a positive impact on their children’s dietary intake. Ultimately, this can result in minimising social inequalities in diet and health[29]. Thus, this study aimed to examine the mediating role of FPP in explaining the relationship between parental education and children’s dietary intake.
## Abstract
### Objective:
This study aimed to investigate the mediating role of food parenting practices (FPP), including home availability of different types of foods and drinks, parental modelling of fruit intake, permissiveness and the use of food as a reward in the relationship between parental education and dietary intake in European children.
### Design:
Single mediation analyses were conducted to explore whether FPP explain associations between parents’ educational level and children’s dietary intake measured by a parent-reported FFQ.
### Setting:
Six European countries.
### Participants:
Parent–child dyads (n 6705, 50·7 % girls, 88·8 % mothers) from the Feel4Diabetes-study.
### Results:
Children aged 8·15 ± 0·96 years were included. Parental education was associated with children’s higher intake of water, fruits and vegetables and lower intake of sugar-rich foods and savoury snacks. All FPP explained the associations between parental education and dietary intake to a greater or lesser extent. Specifically, home availability of soft drinks explained 59·3 % of the association between parental education and sugar-rich food intake. Home availability of fruits and vegetables was the strongest mediators in the association between parental education and fruit and vegetable consumption (77·3 % and 51·5 %, respectively). Regarding savoury snacks, home availability of salty snacks and soft drinks was the strongest mediators (27·6 % and 20·8 %, respectively).
### Conclusions:
FPP mediate the associations between parental education and children’s dietary intake. This study highlights the importance of addressing FPP in future interventions targeting low-educated populations.
## Study design and setting
This cross-sectional analysis used baseline data from the ‘Families across Europe following a hEalthy Lifestyle FOR Diabetes prevention’ (Feel4Diabetes-study), a cluster-randomised study that included a school- and community-based intervention aiming to promote a healthy lifestyle and tackle obesity and obesity-related metabolic risk factors for the prevention of type 2 diabetes among families from vulnerable groups in six European countries. The recruitment of participants was performed in children of 1st, 2nd and 3rd grade (aged 6–9 years at baseline) and their parent or parents through a standardised, multistage sampling approach. More details on the recruitment strategy can be found in the study of Manios et al. [ 2018][30]. The initial study sample included 11 396 families (12 280 children) and were recruited between January and November 2016 in schools from the participating countries. These countries represented low/middle-income countries (Bulgaria and Hungary), high-income countries (Belgium and Finland) and high-income countries under austerity measures (Greece and Spain). Details of the study protocol have been previously published (https://feel4diabetes-study.eu/)[30].
## Participants
Altogether, 6705 (58·84 %) parent–child dyads (50·7 % girls and 88·8 % mothers) were included from the 11 396 families assessed at baseline. Children with complete information on parental education, children’s food intake, FPP, parental self-reported weight and height and children’s weight and height were included in the present study. In order to avoid duplicate parental information, since some families included more than one child and shared the same reporting parent, we randomly selected one child per family. After this step, 800 children were removed from the main dataset; these represented siblings of participants included in the subsequent analysis. Hence, one child from each family was included and was linked to the reported parental information. A flow diagram of the inclusion of participants is presented in Fig. 1.
Fig. 1Flow diagram of participant selection Furthermore, to avoid the effect of possible outliers, children consuming more than seven servings per day of F&V were removed from the analyses (n 255).
## Measures
One parent per child, either the mother or the father, completed a self-administered questionnaire that assessed socio-demographic characteristics, FPP and their child’s dietary intake, among other energy balance-related behaviours. Anthropometric measurements were conducted according to standardised protocols[31]. Children received the parent questionnaire in a closed envelope to take home for completion by one of the parents.
## Parental education level
Education level of both parents was reported by the parent who answered the questionnaire and was asked in a 6-point Likert-type scale question, ranging from ‘less than 6 years’ to ‘more than 16 years’ of education (< 6, 7–9, 10–12, 13–14, 15–16, and > 16). For this study, the education of the reporting parent was considered and dichotomised into ≤ 14 (low-education) and > 14 years (high-education), considering that > 14 years implies attendance of higher education (e.g. a bachelor’s program).
## Dietary intake
Children’s dietary intake was reported by parents with a FFQ, using the question: ‘How often does your child usually consume the following foods and drinks?’, which they could answer by choosing one of the following options: on a weekly (less than 1, 1–2, 3–4 or 5–6 times/week) or daily basis (1–2, 3–4, 5–6 and more than 6 times/d). Beverages assessed were water, fruit juices (freshly squeezed or prepacked without sugar), soft drinks and fruit juices containing sugar and soft drinks without sugar. Foods assessed were fruits and berries (fresh or frozen), fruits and berries (canned), vegetables, sweets and salty snacks and fast food. Intra-class coefficients (ICC) of test–retest showed good reliability of reported food items (ICC = 0·633, (0·371, 0·822)) and have previously been reported in more detail[32]. Range categories in times per week (t/w) and times per day (t/d) of the food intake items were recoded to reflect daily intake of servings (s/d) prior to data analyses (less than 1 t/$w = 0$·14 s/d, 1–2 t/$w = 0$·21 s/d, 3–4 t/$w = 0$·5 s/d, 5–6 t/$w = 0$·79 s/d, 1–2 t/$d = 1$·5 s/d, 3–4 t/$d = 3$·5 s/d, 5–6 t/$d = 5$·5 s/d, and > 6 t/$d = 6$ s/d).
In this study, four dietary outcomes were assessed: water, F&V, sugar-rich foods and salty snacks and fast food, herein referred to as savoury snacks. Water and savoury snacks were used as single food items, as they were reported. The F&V variable was calculated by summing the total number of daily servings of fruits and berries, canned fruits, 100 % fruit juice and vegetables. The total number of sugar-rich foods was calculated by summing up servings per day of soft drinks and sugary juices and sweets.
## Food parenting practices
The following FPP were included in the questionnaire:Home availability of three foods considered to be healthy: fresh fruit, fresh fruit juice and vegetables and home availability of five food items considered to be energy-dense/nutrient-poor: sugary juices, soft drinks, light soft drinks, sweets and salty snacks. Parental role modelling of fruit intake: parental consumption of fruit in front of their children. Permissiveness: allowance of sweets and salty snacks whenever the child asks for them. Use of food as a reward: defined as using sweets, salty snacks or fast food as a reward for their children.
Questions, response options and analytic coding for the analyses are shown in online supplemental Table S1. ICC showed good reliability for home availability of foods (ICC = 0·720 (0·625, 0·794)) and parental role modelling of fruit intake, permissiveness and the use of food as a reward (ICC = 0·695 (0·563, 0·793))[32]. Response options on a 5-point Likert scale ranged from ‘very often’ (‘always’ for home food availability) to ‘never’. These categories were reordered to denote increasing use of the practice, from ‘never’ to ‘very often’ (‘always’ for home food availability). To facilitate interpretation, home availability of nutrient-dense foods and parental modelling of fruit intake was classified as positive FPP, while home availability of unhealthy foods, permissiveness of sweets and salty snacks and using food as a reward were classified as negative FPP.
## Covariates: country, age, sex and BMI
Socio-demographic variables included parents’ and children’s age and sex. Children underwent anthropometric measurements that were conducted at school by trained researchers[31] using standard procedures and equipment. Body weight was measured to the nearest 100 g using a portable SECA scale (SECA 213, 214, 217, and 225, Hamburg, Germany). Height was measured to the nearest 0·1 cm using a SECA stadiometer (type SECA 217). Two readings were obtained for each measurement and the mean was used for the analysis. A third measurement was conducted if the previous measurements differed > 100 g for weight and > 1 cm for height. BMI was calculated by dividing weight in kilograms by height in meters squared (as kg/m2), and BMI Z-scores (Z-BMI) were calculated for age and sex according to Cole et al.[33]. Parental weight (kilograms) and height (meters) were self-reported, and BMI (kg/m2) was calculated.
## Data analyses
Normality of the outcome variables was checked with Shapiro–Wilk tests for continuous variables. Given that all continuous variables were not normally distributed, a Mann–Whitney U test was performed for continuous variables for between-group comparisons, while a Pearson’s Chi-square test was used to compare percentages between groups according to SES (Table 1) and sex (see online supplemental Table S2).
Table 1Study participants’ characteristics at baseline by parental education level; n 6705 ChildrenAllLow-educationHigh-education P % n % n % n Demographics670524·19162275·815083–SexGirls50·7340252·985850·025440·046Boys49·3330347·176450·02539Age (y)Mean8·158·238·12<0·001sd 0·960·990·95Weight (kg)Mean29·5730·0629·410·008sd 7·117·546·96Height (cm)Mean130·45130·13130·550·146sd 7·887·917·87BMI (kg/m2)Mean17·2017·5717·08<0·001sd 2·793·092·68 Z-BMIMean0·540·650·51<0·001sd 1·071·151·04Children’s dietary intake, servings/d Water Mean3·833·933·800·001 sd 1·731·791·72 Fruits & vegetables Mean2·912·772·95<0·001 sd 1·381·471·34 Sugar-rich foods Mean1·211·501·12<0·001 sd 1·181·640·97 Savoury snacks Mean0·340·450·29<0·001 sd 0·480·670·38Reporting parentsDemographics670524·19162275·815083–SexMothers88·8595285·3138389·94569Fathers11·275314·723910·1514<0·001CountryBelgium17·9120016·025918·5941<0·001Bulgaria19·3129517·428219·91013Finland13·89285·48816·5837Greece20·8139233·654516·7847Hungary14·798326·442910·9554Spain13·69101·21917·5891Age (years)Mean38·5437·2138·96<0·001sd 5·095·854·75BMI (kg/m2)Mean24·4024·9724·21<0·001sd 4·544·944·39SES, socio-economic status, Z-BMI, BMI Z score according to Cole et al. [ 2012]. n 6705, except for salty snacks, n 5765. Boldface indicates statistical significance between SES at $P \leq 0$·05.Chi-square test was used to test differences by SES for categorical data. Mann–Whitney U tests were performed to test differences by education in log-transformed continuous variables. Low-education was defined as <14 years of parental education and high-education was defined as > 14 years of parental education. Fruits and vegetables: fresh or frozen fruit and berries, canned fruit, fresh fruit juices and vegetables. Sugar-rich foods: Sugar-sweetened beverages (sugar juices and soft drinks) and sweets. Savoury snacks: salty snacks and fast food (e.g. one small hamburger, one small bag of chips, one slice of pizza).
As the four dietary outcome variables were not normally distributed, the data were log-transformed for the analyses (ln (x + 10). For interpretation purposes, results describing the association between SES and dietary intake of foods and beverages are also reported using the non-transformed data (normal scale).
Mediation models are used to examine the possible causal processes through which a predictor leads to an outcome[34]. To investigate whether FPP mediated the associations between parental education and children’s food intake, Baron & Kenny’s four-step approach for mediation analyses was used[35]. First, to determine whether FPP were significant mediators of the relationship between parental education and children’s dietary intake, it was necessary to show that: (i) the predictor (parental education) is associated with the mediator (FPP); and (ii) the mediator (FPP) is associated with the outcome (children’s dietary intake) while simultaneously controlling for the tested predictor (parental education). These facts explain the reason for running adjusted linear regressions to examine the associations between parental education and the potential mediators (FPP) and between children’s dietary intake and the FPP (Table 2). Then, based on the hypothesis that FPP could mediate the associations between parental education and dietary intake, the mediation models were examined using the PROCESS macro 3.1.4 software for SPSS by Andrew Hayes[36]. In PROCESS, Model 4 software was applied for simple mediations Table 2Associations between parental education, food parenting practices and dietary intake in children, n 6705Food parenting practicesParental educationWaterFruits and vegetablesSugar-rich foodsSavoury snacksAdj. R2 β P valueAdj. R2 β P valueAdj. R2 β P valueAdj. R2 β P valueAdj. R2 β P valuePositive food parenting practicesHA fruit0·0820·144<0·0010·1880·057<0·0010·1200·290<0·0010·147−0·046<0·0010·128−0·088<0·001HA fresh fruit juice0·1230·0280·0260·185−0·0100·4170·0940·240<0·0010·149−0·060<0·0010·121−0·0250·061HA vegetables0·1210·097<0·0010·1880·055<0·0010·1120·281<0·0010·146−0·0260·0300·124−0·063<0·001Parental modelling of fruit intake0·0410·0260·0440·1930·094<0·0010·1910·393<0·0010·152−0·080<0·0010·125−0·064<0·001Negative food parenting practicesHA sugar juices0·071−0·088<0·0010·191−0·079<0·0010·044−0·0390·0010·2010·243<0·0010·1410·148<0·001HA soft drinks0·212−0·122<0·0010·192−0·097<0·0010·050−0·095<0·0010·1970·257<0·0010·1520·202<0·001HA light soft drinks0·1840·0030·8130·186−0·0340·0050·0430·0150·2540·1470·0380·0020·1230·050<0·001HA sweets0·1950·0070·5680·189−0·059<0·0010·048−0·084<0·0010·2670·388<0·0010·1480·186<0·001HA salty snacks0·233−0·079<0·001−0·186−0·0430·0010·047−0·077<0·0010·1990·263<0·0010·2450·410<0·001Permissiveness0·084−0·078<0·001−0·192−0·089<0·0010·051−0·093<0·0010·2290·302<0·0010·1820·258<0·001Using food as a reward* 0·072−0·099<0·0010·189−0·063<0·0010·045−0·050<0·0010·1820·198<0·0010·1590·202<0·001Children’s food intakeWater0·1850·0410·001––––––––Fruit and vegetables0·0430·053<0·001––––––––Sugar-rich foods0·146−0·052<0·001––––––––Salty snacks and fast food0·121−0·123<0·001–––––––– β, Standardised coefficients; HA, home availability.* n 6705, except for salty snacks and fast food (savoury snacks), n 5765.Individual linear regressions were performed using the log-transformed scales of outcome variables and were adjusted for country and parental and children’s sex, age and BMI.Parental education was also included as covariate to test the associations between FPP and dietary intake. Boldface indicates statistical significance.
Mediation models were performed individually to examine the mediating role of each FPP on the association between parental education and the four outcomes. As shown in Fig. 2, the independent variable was parental education, dependent variables were children’s food intake and the potential mediator variables were the positive (e.g. home availability of fruit) and negative (e.g. permissiveness) FPP. Included covariates were country; parental age, sex and BMI; and children’s age, sex and BMI. In the model, the associations between parental education and FPP (a’ – coefficient), FPP and dietary outcomes (b’ – coefficient) and parental education and dietary outcomes (c’ – coefficient) are illustrated. The indirect effects (A * B) data, which is obtained by multiplying a’ x b’ coefficient, are generated with 95 % CI, representing P-values < 0·05 rather than generating exact P-values. Significant mediations were then calculated as percentages by dividing A * B by the total effect (c – coefficient).
Fig. 2Graphical illustration of the possible interactions between parent’s educational level, FPP and children’s food intake. Simple mediation analyses adjusted by country, parental age, BMI and sex, and children’s age, BMI and sex. Pathway A’: Association between parent’s educational level and FPP. Pathway B’: Association between FPP and children’s food intake. Pathway C’: Direct association between parent’s educational level and children’s food intake after adjustment for each mediator (FPP). Pathway C: The total effect (C) shows the association between parent’s educational level and dietary intake. A * B: Indirect effect of each FPP on the association between parent’s educational level and dietary intake. Abbreviations: FPP, food parenting practices Descriptive and mediation analyses were completed using IBM SPSS Statistics (IBM SPSS Statistics for Windows, Version 26.0. IBM Corp.).
## Participant characteristics
In total, 6705 child–parent dyads from the six participating countries from the Feel4Diabetes-study were included in this study. Table 1 shows the characteristics of the children (50·7 % girls) and the parents (88·8 % mothers) by parental education group. The mean age of the children was 8·15 ± 0·96 years. Parents’ mean age was 38·54 ± 5·09 years. Of all participating parents, 75·8 % stated that they had completed 14 years of education or more. Regarding dietary intake, children whose parents had a low education level had higher consumption of water, sugar-rich foods and savoury snacks than children from parents with high education level. Conversely, the consumption of F&V was significantly higher in the high-education level group. The description of participants’ characteristics by sex is shown in online Supplemental Table S2.
## Associations between SES, children’s dietary intake and FPP
First, the associations between parental education, FPP and children’s food intake adjusting for covariates were assessed (Table 2). Parental education was associated with higher water and F&V intake and with lower sugar-rich food and savoury snack consumption.
Parental education was associated with nine of the eleven FPP. No associations were observed between parental education and home availability of light soft drinks or sweets, and these FPP were therefore not considered for subsequent analyses.
Out of a total of fourty-four associations between FPP and children’s food intake, fourty-one were significant. Regarding FPP and children’s food intake, several direct significant associations were observed between positive FPP, like modelling of fruit intake, and water and F&V intake. Conversely, several inverse significant associations were observed between positive FPP and energy-dense/nutrient-poor foods. Nevertheless, no associations were found between the FPP of home availability of fresh fruit juice and water or savoury snack intake or the FPP of home availability of light soft drinks and F&V.
## Mediating effect of FPP on the associations between SES and children’s dietary intake
The potential mediating effect of positive and negative FPP in the association between parental education and children’s dietary intake of water, F&V, sugar-rich foods and savoury snacks was evaluated while adjusting for covariates. To facilitate interpretation of the models, a graphical illustration of the mediation pathways between parental education, FPP and dietary intake is shown in Fig. 2 and another to illustrate the proportions mediated by each FPP in Fig. 3.
Fig. 3Mediating effect of FPP on the association between parental education and dietary intake of water, fruits & vegetables, sugar-rich foods, and savoury snacks. HA, home availability Home availability of fruits and soft drinks appeared to be the most important mediator explaining the association between parental education and children’s water intake (Table 3). It is worth pointing out that the other FPP assessed were also found to be significant mediators but to a lesser extent.
Table 3Total associations (c)* direct associations (c’) and indirect effects between parental education and water intake adjusted for significant mediatorsFood parenting practicesDirect effect C’ pathIndirect effect A * B pathLog-scale† Normal – scale‡ Log-scaleNormal – scale β 95 % CI β 95 % CI β 95 % CI β 95 % CIMediation %Positive FPPHA fruit0·0530·015, 0·0910·0420·053, 0·1360·0130·007, 0·0210·0220·008, 0·03719·7HA fresh fruit juice0·0660·028, 0·1040·064–0·030, 0·1580·000–0·002, 0·0010·001–0·002, 0·004–HA vegetables0·0570·019, 0·0950·045–0·049, 0·1390·0090·004, 0·0140·0190·009, 0·03213·6Modelling of fruit intake0·0620·024, 0·1000·054–0·039, 0·1480·0040·000, 0·0080·0100·000, 0·0206·1Negative FPPHA sugar juices0·0550·017, 0·0920·039–0·055, 0·1320·0110·007, 0·0160·0250·016, 0·03616·7HA soft drinks0·0470·009, 0·0850·022–0·072, 0·1170·0190·013, 0·0260·0420·028, 0·05728·8HA salty snacks0·0600·022, 0·0980·055–0·039, 0·1490·0060·002, 0·0090·0090·001, 0·0189·1Permissiveness0·0550·017, 0·0920·041–0·052, 0·1350·0110·007, 0·0160·0230·013, 0·03416·7Use of food as reward0·0560·018, 0·0940·043–0·051, 0·1370·0100·006, 0·0150·0210·011, 0·03315·2FPP, food parenting practices; HA, home availability. n 6705.Single mediation analyses were adjusted for country and parental and children’s sex, age, and BMI.*Total effect (C pathway) of the association between parental education and water intake: β log = 0·066 (0·028, 0 104); β normal = 0·064 (-0·030, 0 158).†Results on the log-scale were used for determining statistical significance.‡Results on the normal scale (servings/d) are used for interpretation purposes only.
For the association between parental education and children’s F&V intake (Table 4), home availability of fruits, vegetables and soft drinks and parental modelling of fruit intake were found to be significant mediators, explaining 77·3 %, 51·5 %, 21·8 % and 19·7 % of this association, respectively.
Table 4Total associations (c)*, direct associations (c’) and indirect effects between parental education and fruit and vegetable intake adjusted for significant mediatorsFood parenting practicesDirect effect C’ pathIndirect effect A * B pathLog-scale† Normal – scale‡ Log-scaleNormal – scale β 95 % CI β 95 % CI β 95 % CI β 95 % CIMediation %Positive FPPHA Fruit0·014−0·016, 0·045−0·007−0·087, 0·0730·0510·041, 0·0630·1140·090, 0·13977·3HA Fresh fruit juice0·0580·027, 0·0880·0850·006, 0·1650·0080·001, 0·0150·0220·004, 0·04112·1HA Vegetables0·0320·002, 0·0620·031−0·049, 0·1110·0340·024, 0·0440·0770·054, 0·09951·5Modelling of fruit intake0·0530·025, 0·0820·0770·000, 0·1530·0130·001, 0·0250·0310·001, 0·06019·7Negative FPPHA Sugar juices0·0620·030, 0·0930·0980·016, 0·1810·0040·002, 0·0070·0090·002, 0·0176·1HA Soft drinks0·0520·020, 0·0830·073−0·010, 0·1550·0140·010, 0·0200·0350·023, 0·04721·2HA Salty snacks0·0580·027, 0·0900·0880·006, 0·1700·0080·004, 0·0110·0190·011, 0·02912·1Permissiveness0·0570·026, 0·0880·0880·006, 0·1700·0090·005, 0·0130·0190·011, 0·02913·6Use of food as reward0·0600·028, 0·0910·0950·013, 0·1770·0060·003, 0·0100·0120·004, 0·0219·1FPP, food parenting practices; HA, home availability. n 6705.Single mediation analyses were adjusted for country and parental and children’s sex, age and BMI.*Total effect (C pathway) of the association between parental education and F&V intake: β log = 0·066, 95 % CI (0·035, 0·097); β normal = 0·107 (0·025, 0 189).†Results on the log-scale were used for determining statistical significance.‡Results on the normal scale (servings/d) are used for interpretation purposes only.
Regarding the association between parental education and children’s sugar-dense food intake (Table 5), all FPP assessed were found to be significant mediators. However, the most significant mediators were found to be negative FPP, particularly home availability of soft drinks and permissiveness, for which proportions mediated were 59·3 and 45·1 %, respectively.
Table 5Total associations (c)*, direct associations (c’) and indirect effects between parental education and sugar-rich foods intake adjusted for significant mediatorsFood parenting practicesDirect effect C’ pathIndirect effect A * B pathLog-scale† Normal – scale‡ Log-scaleNormal – scale β 95 % CI β 95 % CI β 95 % CI β 95 % CIMediation %Positive FPPHA fruit−0·079−0·121, −0·038−0·226−0·293, −0·158−0·011−0·018, −0·005−0·024−0·036, −0·01312·1HA fresh fruit juice−0·088−0·129, −0·047−0·245−0·312, −0·179−0·003−0·006, −0·000−0·004−0·009, −0·0013·3HA vegetables−0·086−0·128, −0·045−0·240−0·307, −0·173−0·004−0·009, 0·000−0·010−0·019, −0·0014·4Modelling of fruit intake−0·087−0·128, −0·046−0·244−0·311, −0·178−0·004−0·007, −0·000−0·005−0·010, 0·0004·4Negative FPPHA sugar juices−0·054−0·094, −0·014−0·201−0·267, −0·136−0·037−0·048, −0·027−0·048−0·063, −0·03440·7HA soft drinks−0·037−0·077, 0·004−0·161−0·226, −0·096−0·054–0·067, –0·043−0·088−0·109, −0·06959·3HA salty snacks−0·055−0·095, −0·015−0·199−0·265, −0·134−0·036−0·047, −0·025−0·050−0·067, −0·03539·6Permissiveness−0·050−0·089, −0·011−0·192−0·256, −0·127−0·041−0·054, −0·027−0·058−0·078, −0·03845·1Use of food as reward−0·057−0·098, −0·016−0·194−0·259, −0·128−0·034−0·045, −0·024−0·056−0·075, −0·03937·4FPP, food parenting practices; HA, home availability. n 6705.Single mediation analyses were adjusted for country and parental and children’s sex, age and BMI.Sugar-rich foods included the sum of sweets (e.g. one chocolate bar or half a cup of sweets, cookies or icecream) and soft drinks (e.g. one glass of one cup of soft drinks and juices containing sugar).*Total effect (C pathway) of the association between parental education and sugar-rich foods intake: β log= −0·091, 95 % CI (−0·132, -0·050); β normal = −0·249 (−0·316, −0·183).†Results on the log-scale were used for determining statistical significance.‡Results on the normal scale (servings/d) are used for interpretation purposes only.
The association between parental education and savoury snack intake (Table 6) was explained by home availability of salty snacks (27·6 %) and home availability of soft drinks (20·8 %) as well as the other FPP assessed, which also proved to be significant mediators, but to a lesser extent.
Table 6Total associations (c)*, direct associations (c’) and indirect effects between parental education and savoury snacks adjusted for significant mediatorsFood parenting practicesDirect effect C’ pathIndirect effect A * B pathLog-scale† Normal – scale‡ Log-scaleNormal – scale β 95 % CI β 95 % CI β 95 % CI β 95 % CIMediation %Positive FPPHA fruit−0·170−0·211, −0·130−0·107−0·136, −0·079−0·021−0·029, −0·014−0·010−0·015, −0·00510·9HA fresh fruit juice−0·191−0·231, −0·151−0·117−0·145, −0·089−0·001−0·003, 0·000−0·001−0·002, 0·000–HA vegetables−0·181−0·222, −0·141−0·111−0·140, −0·083−0·010−0·016, −0·005−0·006−0·011, −0·0025·2Modelling of fruit intake−0·188−0·228, −0·148−0·116−0·144, −0·088−0·003−0·006, −0·000−0·001−0·003, 0·0001·6Negative FPPHA sugar juices−0·169−0·209, −0·129−0·105−0·133, −0·077−0·022−0·030, −0·016−0·012−0·017, −0·00811·5HA soft drinks−0·152−0·192, −0·112−0·094−0·122, −0·065−0·040−0·050, −0·030−0·024−0·031, 0·01720·8HA salty snacks−0·138−0·176, −0·101−0·091− 0·118, −0·064−0·053−0·069, −0·037−0·027-0·035, −0·01927·6Permissiveness−0·160−0·199, −0·122−0·100−0·127, −0·072−0·031−0·043, −0·021−0·18−0·024, −0·01116·1Use of food as reward−0·161−0·200, −0·122−0·098−0·126, −0·070−0·031−0·041, −0·021−0·019−0·027, −0·01216·1FPP, food parenting practices; HA, home availability. n 5765.Single mediation analyses were adjusted for country and parental and children’s sex, age and BMI.Savoury snacks include salty snacks and fast-food items like one small hamburger, one bag of chips or one slice of pizza.*Total effect (C pathway) of the association between SES and savoury snacks intake: β log= −0·192, 95 % CI (−0·232, −0·152); β normal = −0·117 (−0·146, −0·089).†Results on the log-scale were used for determining statistical significance.‡£ Results on the normal scale (servings/d) are used for interpretation purposes only.
## Discussion
The current study shows that inequalities in food intake according to parental education were partly mediated by the addressed FPP in European children from the Feel4Diabetes-study. In fact, almost all of them appeared to explain the associations to a greater or lesser extent. Given that parental education is difficult to modify in the short term, FPP appear to be interesting factors for potential modification.
Intake of assessed food items was significantly affected by parental education for all items except water (see online supplemental Table S3), being directly associated with water, F&V and inversely associated with sugar-rich foods and savoury snacks; nevertheless, water intake was used in later analyses because of the observed associations in the adjusted regression models. An explanation for this may be that the assessment method for water intake was not ideal, since repeated 24-h recalls or specific tools are preferable to FFQ for water intake[37]. Parents may have found it difficult to estimate an average daily consumption since water intake is usually distributed throughout the day and might be difficult to quantify properly. Also, water intake might not strictly depend on parental education, since all the families from our study have drinking water access, and water was therefore available in every household.
No associations were observed between parental education and home availability of light soft drinks or sweets; therefore, these FPP were not considered for subsequent mediation analyses. Nevertheless, results from the adjusted linear regressions showed that home availability of light soft drinks was inversely associated with water intake, indicating that the presence of these beverages might reduce the amount of water intake. These associations are in line with the findings of Galastri et al.[38], which aimed to assess the association between ultra-processed food consumption and total water intake in a national representative sample from the USA, indicated that the consumption of artificially sweetened beverages was associated with a reduction in water consumption. On the other hand, home availability of light soft drinks was directly associated with consumption of sugar-rich foods and savoury snacks, indicating that even though light soft drinks might not be a substantial source of calories, their availability at home may have an association with a pattern of high consumption of energy-dense foods such as savoury snacks.
In our study, permissiveness and home availability of both sugary juices and soft drinks were inversely associated with water intake. In a previous study in 6- to 8-year-old European children that aimed to evaluate the associations between parenting practices towards fruit juices and soft drinks and water consumption of children, children’s water intake was found to be favourably influenced by less parental allowance, low home availability and high parental self-efficacy in managing intake[39]. On this basis, we assessed the mediation effect of FPP on the association between parental education and water intake and found that home availability of soft drinks was the strongest mediator. This finding indicates that the association between parental education and water consumption is significantly explained by the presence of soft drinks at home, which may replace water intake in children. Interestingly, fresh fruit juice was not a significant mediator, indicating that the consumption of water is not affected or replaced by that of fresh fruit juice. As shown in our study, the physical presence of sugar-sweetened beverages at home is inversely associated with water intake; however, as shown in a randomised controlled trial aiming to decrease sugar-sweetened beverages intake in Dutch adolescents[40], the decrease in sugar-sweetened beverages consumption over time does not necessarily lead to an increase in water consumption. This indicates that water intake promotion may be necessary to achieve the corresponding recommendations.
In a previous study in 11-year-old Dutch children, that evaluated the potential mediating effect of home environment characteristics in the association between maternal educational level and children’s healthy eating behaviour, results indicated that home availability of fruit significantly mediated this association. In fact, home availability appeared to be a significant mediator when evaluated separately and in combination with other factors, such as parental fruit intake and fruit consumption rules[41]. It is worth mentioning that parental intake of fruit refers to their diet and differs from parental modelling of fruit intake, which is often used to refer to more intentional efforts made by parents to actively demonstrate healthy eating for the child[15].
In our study, important significant mediators on the association between parental education and sugar-rich food intake were primarily those considered as negative FPP, specifically, home availability of soft drinks and permissiveness. As observed by Robert et al.[42], parents with the highest mean scores for permissive parenting frequently used rewards, defined as the use of both tangible items and food to reward children for eating and behaviours. It was also found in the current study that the use of food as a reward was significantly correlated with permissiveness (data not shown), indicating that these practices may be used in combination. Moreover, the link between parental education and the use of food as a reward could be explained by the fact that food may be recognised as an easy and affordable reward that will be accepted by the child.
A previous study in 3- to 11-year-old Australian children found that covert control feeding strategies, defined as the way in which parents promote the consumption of healthy food by managing the child’s environment by providing primarily healthy foods, were significantly associated with lower unhealthy snack intake but not with healthy snack food intake by children[43], indicating a positive effect of healthy home food availability in terms of lower unhealthy snack intake over time.
Other FPP, such as food accessibility, have been explored for their potential role as mediators in the association between SES and dietary intake. For instance, a previous study in Norwegian adolescents showed that food accessibility and perceived rules were significant mediators of the associations between parental education and soft drink consumption[27], indicating that other FPP besides the ones we explored may also play an important role.
An option for replacing the use of food as a reward and its negative effects on diet, and consequently on health, would be the use of social rewards, which are inexpensive or free and can be even more powerful than material rewards. Examples of social rewards include those characterised by affection, such as hugs and smiles, and those including attention and activities, such as playing the child’s favourite game together, reading a story or encouraging them to help with home tasks like preparing dinner[44]. Permissiveness regarding food intake might be accompanied by permissiveness in other aspects of the child’s activities; in this sense, it could be interesting to evaluate if permissiveness is also applied to other aspects of life, such as physical activity and sedentary behaviours like the use of screens.
Humans learn by imitation and reference[45]; therefore, it is also possible that parents, regardless of their educational level, were raised surrounded by the same FPP they use. This means that they might have inherited lifestyle habits and family rules and they use these with their own children. At this point, it is important to break the cycle, so they recognise their behaviours and can make efforts to improve.
A major strength of our study is the large and pan-European sample and the standardisation of measurements, which was followed across all centres. Also, to our knowledge, this is the first study examining the mediating role of permissiveness and the use of food as a reward explaining differences in European children’s dietary intake by SES. However, our study has several limitations. Firstly, a FFQ was used to assess regular dietary intake, which may have introduced self-report bias, but this weakness is very hard to overcome when studying food intake[46]. In addition, parents reporting both food intake and FPP might overestimate the association between the variables. However, as some of the children were only first graders, it was not possible to get self-reported food intake data from the children. In this study, education was used as the main determinant of dietary intake; nevertheless, it could be important to include other SES variables besides education, such as occupation[47], employment status or income or even composite indices[48], considering that each of them should be chosen according to their strengths, limitations and depending on the sample‘s characteristics. We selected the education of the reporting parent as the exposure variable since we considered that it was relevant to consider how education reflects on parents’ behaviour and whether it determines if they are more permissive or tend to use food as a reward with their children. Nevertheless, for future studies, it might be important to consider the educational level and FPP of both parents and, for example, to evaluate whether they are consistent with each other or contrary in some respects.
A recent study that aimed to evaluate the associations between parents’ work status and the dietary consumption patterns of Australian pre-school children[49] found that depending on the work status and educational level attained by mothers or fathers, children presented significant differences in terms of F&V, high-fat foods and high-sugar foods consumption. This indicates that not only education but also work status has an important role in determining the dietary intake of children at this age.
Even though there was an initial sample loss of 41·2 %, the proportion of low-educated and high-educated parents was very similar in the included sample and in the excluded sample, which indicates that no selection bias might have occur.
Parents should be aware that there are modifiable practices that they can use in the home food environment, such as home availability, and they can try to enhance these to improve their children’s diet. There is a need for family-focused research that identifies social aspects of the home environment that potentially impact on dietary intake of children[50]. It remains important to encourage parents to understand the importance of avoiding negative FPP.
In conclusion, this study highlighted the role of FPP in explaining the associations between parental education and children’s intake of water, fruits and vegetables, sugar-rich foods and savoury snacks. Encouraging parents, especially those with a low level of education, to increase the use of positive FPP, such as modelling of fruit intake, and to avoid the use of negative FPP, such as home availability of soft drinks, might help to tackle health and dietary inequalities by improving children’s intake of these food groups. Health professionals should understand not only the challenges but also the opportunities and possibilities that parents can have if they improve the FPP they use. These findings may broaden the understanding of potential pathways by which various factors might influence children’s dietary intake, helping researchers to better design nutrition-focused interventions.
## Conflicts of interest:
The authors declare that they have no conflict of interest.
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|
---
title: 'Social inequalities shape diet composition among urban Colombians: the Colombian
Nutritional Profiles cross-sectional study'
authors:
- Pedro J Quiroga-Padilla
- Paula V Gaete
- Luz D Nieves-Barreto
- Angélica Montaño
- Eddy C Betancourt
- Carlos O Mendivil
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991857
doi: 10.1017/S1368980021004778
license: CC BY 4.0
---
# Social inequalities shape diet composition among urban Colombians: the Colombian Nutritional Profiles cross-sectional study
## Body
Social inequalities are key determinants of health and disease across different populations[1,2]. Health indicators like life expectancy or infant mortality have a stepwise association with socio-economic position (SEP), with health improving incrementally as social position rises[2]. Evidence from a multinational study of older individuals undertaken in five Latin American countries, India and China, showed that multiple indicators of SEP including education, occupational attainment, assets and pension receipt were inversely associated with total mortality over a 5-year period[3].
Diet may constitute a crucial link between social inequalities and disease. Obesity[4], diabetes, IHD, stroke and cancer are known to be associated with both SEP and diet quality[5,6]. Additionally, and despite methodological differences, a large number of studies show a significant influence of social status over energy, macro- and micronutrient intake[7]. A systematic review of cross-sectional studies from low- and middle-income countries found that higher SEP was associated with a larger intake of energy, protein and all fat subtypes, and with lower intakes of carbohydrates and fibre[8].
Although several Latin American countries(9–11) conduct nationally representative nutritional surveys, the association between socio-economic variables and dietary composition has been explored mostly in Brazil and Mexico. The first Brazilian National Dietary Survey (2008–2009) included 13 569 households and found that energy intake was positively and independently associated with income and years of education for both sexes[10]. In the last National Health and Nutrition Survey of Mexico (ENSANUT 2012), which included 10 886 households[9], higher SEP was associated with increased energy intake but reduced carbohydrate consumption for both sexes. A prior small, single-city study in Bucaramanga, Colombia, examined dietary intake using a 7-d food diary and encountered a positive correlation of SEP with protein and total fat intake and a negative correlation with carbohydrate intake[12].
Colombia and other middle-income Latin American countries are currently undergoing an epidemiological and nutritional transition, in which non-communicable diseases represent an ever-increasing share of the disease and mortality burden, while acute and infectious diseases have not been completely eradicated[13]. Despite its developing country status, non-communicable causes are by far the most significant source of morbidity and mortality in Colombia. This is well illustrated by the fact that CVD, cerebrovascular disease, diabetes and cancer accounted for 68 % of the total deaths in 2015[14]. Disorders of energy balance and metabolism have a deep influence on the risk of these conditions, both directly and through their effect on mediating risk factors like BMI, blood pressure, blood lipids and diabetes. The prevalence of overweight and obesity from the five main cities of Colombia in 2018 was 57·5 % in adults (36·2 % overweight, 21·3 % obesity)[15] and 31·8 % in children and adolescents (23·0 % overweight, 8·8 % obesity). These extremely worrisome figures place Colombia close to countries with a massive epidemic of overweight and obesity like Mexico and the USA. Given that dietary behaviour is a major determinant of energy balance and the risk of developing non-communicable diseases, knowledge of dietary intake and its major correlates is of essential importance for the country. Among these correlates, factors related to SEP have a preeminent relevance.
In spite of the existing evidence about the association between diet composition and indicators of social standing, this relationship and its magnitude have not been extensively studied in Latin America. In order to design and implement successful public health policies, it is imperative to assess the relative contribution of factors like income and education to the nutritional profile of different segments of the population. With this motivation, the present study aimed to dissect the influence of two relevant indicators of SEP: socio-economic stratum (SES) and educational level, on habitual dietary intake in five Colombian cities and to explore whether such influences differ between men and women.
## Abstract
### Objective:
To explore the influence of socio-economic position (SEP) on habitual dietary intake in Colombian cities.
### Design:
We conducted a cross-sectional, population-based study in five Colombian cities. Dietary intake was assessed with a 157-item semi-quantitative FFQ previously developed for the Colombian population. Nutrient analysis was performed using national and international food composition tables. SEP was assessed with two indicators: a government-defined, asset-based, household-level index called socio-economic stratum (SES) and, among adults, highest educational level attained.
### Setting:
The five main urban centers of Colombia: Bogotá, Medellin, Barranquilla, Cali and Bucaramanga.
### Participants:
Probabilistic, multi-stage sample of 1865 participants (n 1491 for analyses on education).
### Results:
For both sexes, increasing SES was associated with a lower consumption of energy (P-trend <0·001 in both sexes), carbohydrates (P-trend <0·001 in both sexes), Na (P-trend = 0·005 in males, <0·001 in females), SFA (P-trend <0·001 in both sexes) and among females, cholesterol (P-trend = 0·002). More educated men consumed significantly less energy and carbohydrates (P-trend = 0·036 and <0·001, respectively). Among men, intake of trans fats increased monotonically with educational level, being 21 % higher among college graduates relative to those with only elementary education (P-trend = 0·023). Among women, higher educational level was associated with higher MUFA intake (P-trend = 0·027).
### Conclusions:
SES and educational level are strong correlates of the usual diet of urban Colombians. Economically deprived and less educated segments of society display dietary habits that make them vulnerable to chronic diseases and should be the primary target of public health nutrition policies.
## Study area
Colombia is a Latin-American country, located at the northwestern tip of South America. In 2018, Colombia had an estimated population of 48 million, 78 % of whom lived in urban areas[16]. Colombia has a Human Development Index of 0·767, ranking eighty-three out of 189 countries[17]. Similar to other Latin American countries, *Colombia is* characterised by a marked difference in poverty levels and economic development between rural and urban locations. The prevalence of multidimensional poverty is almost 3 times larger in rural than urban areas, mostly due to differences in access to public services and literacy levels[18]. The five cities included in this study (Bogota, Medellin, Barranquilla, Cali and Bucaramanga) comprise approximately 30 % of the Colombian population and 38 % of the Colombian urban population[16].
## Sampling and data collection
COPEN (Estudio Colombiano de Perfiles Nutricionales – Colombian Study of Nutritional Profiles) was a population-based, cross-sectional, multi-stage sampling survey designed to represent five cities, one from each of Colombia’s major regions. The sampling frame was obtained from the last [2005] census of the Colombian population[19], cartography was obtained from the national geostatistical frame developed by the Colombian National Department of Statistics and data on SES came from the National Superintendence of Public Services. In the first stage of sampling, we selected cartographic sectors, within sectors we selected blocks (on average eight per cartographic sector), within blocks we selected households and within households we selected individual participants. All individuals over the age of 2 were listed and a person was randomly selected. In the case of participants under the age of 13, information was provided by the adult responsible for the participant. The sample was stratified by city, sex, age group and SES of the household.
All data were collected between June and November 2018. Information was captured using a tablet device containing digital forms with proper validation rules, developed for the study. All staff in charge of data collection was extensively trained by the study Principal Investigator. A random 10 % of participants were re-contacted by phone to double-check the accuracy of the information provided on the date of birth, sex, city of residence, marital status, job status, educational level and date of initial contact. With this design and including the design effect, the study sample yielded an overall sampling error of 2·2 % for the prevalence of overweight or obesity in the target population, which was a central objective of the COPEN study. The sampling errors for each city were respectively: Bogota 4·0 %, Medellin 5·0 %, Cali 5·0 %, Barranquilla 5·6 % and Bucaramanga 6·8 %.
## Participants
Participants were individuals between the ages of 2 and 75, residing in one of the five cities mentioned above. We excluded foreigners living in Colombia, individuals in haemodialysis or peritoneal dialysis therapy and persons with disabilities that precluded a reliable fulfilment of the study questionnaire.
## Socio-demographic and anthropometric variables
We collected information on sex, date of birth and household SES (in all participants), and marital status, individual educational level and employment status (in participants aged 18 or older), using a standardised questionnaire. SES is classified in Colombia by the Statistics Department DANE in six strata according to characteristics of the residence (with stratum 1 being the lowest and stratum 6 being the highest)[20]. Residential dwellings are classified according to their physical characteristics and environment. The methodology for this classification creates homogeneous strata taking as input information about land use, public utilities, access routes, topography, land valuation and property characteristics. Residential dwellings are classified in the predominant stratum of the sub-zone, as long as their characteristics do not differ ostensibly from the predominant conditions in the group. Otherwise, they are considered outliers and their stratum is assessed based on their particular characteristics. This information is very well established, updated and freely accessible for all the country[21]. It also has a significant correlation with household income. A single score is created, converting and weighing each variable with the Savage score method. Living places are classified in six strata, according to a cluster analysis (with stratum 1 being the lowest and stratum 6 being the highest). Given that socio-demographic, income and human development indicators are more similar for individuals living in strata 4–6 than among the other strata[21], we analysed SES in three groups, corresponding to strata 1–2 (low SES), 3 (medium SES) and 4–6 (high SES). Participants were asked to report what was the highest educational cycle they had completed: pre-school, primary school, secondary school (lasting 6 years, there is no equivalent of high school in Colombia), technical degree, college degree or post-graduate degree. For the effects of analyses, and in order to make findings more comparable with international standards, the variable educational level was operationalised in three categories as: elementary or lower, secondary or technical degree, and college or higher. Only participants aged 18 or older were asked about their educational level, as many underage individuals may still be completing their education. Hence, all analyses involving educational level include only adult participants and have a different sample size. Height was measured using a portable stadiometer supported on a firm surface. Weight was measured employing a solar digital scale with 100 g sensitivity and 200 kg capacity. For the characterisation of the study sample, we analysed BMI in participants under 18 years of age as the Z-score of the sex-specific BMI-for-age curves.
## Food frequency questionnaire
Dietary intake was assessed using a semi-quantitative FFQ with a 157-item food list, plus frequency of intake and number of standard portions consumed (with reference portion size written next to this field). This FFQ had been previously developed and piloted in the Colombian population[22]. The Colombian National Nutritional Situation Survey (Encuesta Nacional de Situación Nutricional) in its 2005 version performed a 24-h dietary recall (this was omitted from later versions of Encuesta Nacional de Situación Nutricional). This dietary recall was applied in a randomly selected day of the week to 39 413 non-pregnant male and female participants aged 2–64 years (data were provided by the responsible adult in the case of persons aged less than 13). A second 24-h dietary recall was performed in a random subsample of 3534 participants, in a second, non-consecutive day. Employing the compiled results from this dietary recall, foods were ranked from most to least frequently consumed[23]. From the 372 foods listed, 142 were consumed by at least 30 % of the studied population. To this list, three typical regional foods from each of the five regions studied were added, resulting in 157 food items in the FFQ. Portion sizes were calculated according to the coding of weights and measurements in Encuesta Nacional de Situación Nutricional 2005; the unit of measure most frequently reported for each food was used as reference portion size[22]. The average frequency of intake for each food item over the last year was registered as one of nine categories: never, 1–3 times/month, once a week, 2–4 times a week, 5–6 times a week, once a day, 2–3 times a day, 4–6 times a day or more than 6 times a day. A trained staff member administered the FFQ and registered all the information.
Estimation of daily nutrient intake was done as previously described[24]. First, a weighing factor was used to convert each frequency of intake to number of portions consumed in a day. Then a factor was used to convert the number of portions a day to 100-g units. Subsequently, an edible fraction factor was applied. Composition data were obtained from the Colombian Institute of Family Welfare (Instituto Colombiano de Bienestar Familiar) reference tables[25]. For foods not in Instituto Colombiano de Bienestar Familiar tables, composition was extracted from the Central America and Panama Nutrition Institute (Instituto de Nutrición de Centro América y Panamá) tables[26] or the US Department of Agriculture food composition database (FoodData Central)[27]. For foods not represented in any of these sources, information from the manufacturer was employed. For analyses purposes, we expressed total energy consumption in kJ/kg per d (kcal/kg per d). The consumption of carbohydrates, protein, lipids, SFA, PUFA and MUFA was expressed in g/kg per d, in order to compare diet composition removing the effect of body size. We also analysed differences in the percentage daily kilojoules (kcal) coming from each macronutrient across categories of SES and educational level. Nutrients providing no or negligible energy were analysed in weight units, namely mg/d for cholesterol, g/d for trans fats, g/d for fibre and mg/d for Na.
## Data analysis
All estimations were projected to the target study population using city, sex, age group and SES-specific expansion factors according to the study multi-stage sampling design. The mean daily kilojoules (kcal), macronutrients and lipid subtypes (per kg body weight), fibre, cholesterol and Na were compared across categories of categorical predictors using a one-way linear model (ANOVA). We focused on these nutrients because of their proven association with the risk of chronic diseases. When global ANOVA was significant, post-hoc pairwise comparisons were done against a reference category (the lowest) using Dunnett’s method. All analyses were two-tailed and carried out at a 5 % significance level. All analyses were performed in SPSS for Windows, v.21.
## Results
The study sample included 1865 participants (914 male and 951 female), most of whom were residents of Bogota (32·2 %), followed by Cali (21·1 %), Medellin (19·8 %), Barranquilla (15·7 %) and Bucaramanga (11·2 %) (Table 1). Mean BMI was 28·0 kg/m2 in female and 25·7 kg/m2 in male adult participants. Only a quarter of participants lived in high SES, and most (44·7 %) lived in low SES households. The most prevalent educational level was secondary or technical, and only 20·3 % had a college or higher degree. The age distribution of the sample resembled the Colombian population pyramid[16].
Table 1Characteristics of the study participantsMaleFemaleTotal n % n % n % n 91449·0 %95151·0 %1865100 %Age group, years 2–1114315·6 %838·7 %22612·1 % 12–17889·6 %606·3 %1487·9 % 18–3928731·4 %32534·2 %61232·8 % 40–5920422·3 %26327·6 %46725·0 % 60–7519221·0 %22023·1 %41222·1 %Socio-economic stratum Low41145·0 %42344·5 %83444·7 % Medium27329·9 %28129·5 %55429·7 % High23025·2 %24726·0 %47725·6 %City Barranquilla14515·9 %14815·6 %29315·7 % Bogota29532·3 %30532·1 %60032·2 % Bucaramanga10311·3 %10611·1 %20911·2 % Cali19221·0 %20221·2 %39421·1 % Medellin17919·6 %19020·0 %36919·8 %Educational level* Elementary or lower13720·1 %17721·9 %31421·1 % Secondary or technical degree40258·9 %47258·3 %87458·6 % College or higher14321·0 %16019·8 %30320·3 %Weight among adults (kg)74·515·467·313·370·614·8Weight-for-age Z-score among minors Mean−0·180·02−0·10 SD1·460·911·25Height among adults (cm) Mean169·7155·7162·1 SD7·28·610·6Height-for-age Z-score among minors Mean−0·46−0·38−0·43 SD1·151·091·13BMI among adults (kg/m2) Mean25·828·027·0 SD4·68·97·3 Z-score of BMI-for-age among minors Mean0·390·580·46 SD1·402·51·88*Educational level only for participants aged 18 or older (n 1491).Data are expressed as n (%) or mean (SD).
Female sex was associated with a lower intake of energy, all macronutrients (even on a per kg basis) and Na ($P \leq 0$·001 for all comparisons). In both sexes, there was a significant decreasing trend in the total intake of energy with increasing levels of SES. For males, the values went from 337 kJ/kg per d (80·5 kcal/kg per d) in low SES to 261 kJ/kg per d (62·3 kcal/kg per d) in high SES (P-trend < 0·001), while for females they went from 301 kJ/kg per d (72 kcal/kg per d) in low SES to 233 kJ/kg per d (55·8 kcal/kg per d) in high SES (P-trend < 0·001). The largest difference, however, was observed between participants in medium SES and those in high SES (Table 2). A similar result was observed for carbohydrates, which went from 10·0 g/kg per d in low SES to 7·6 g/kg per d in high SES among males (P-trend < 0·001), and from 8·9 g/kg per d to 6·4 g/kg per d among females (P-trend <0·001). The intake of all fat subtypes decreased with SES in both sexes, except for MUFA among females. Table 2Estimated intake of energy, macronutrients, trans fats, Na, fibre and cholesterol, by sex and socio-economic stratumMales (n 914)Low SES (n 411)Medium SES (n 273)High SES (n 230) P-trendMean95 % CIMean95 % CI P v lowMean95 % CI P v lowEnergy (kcal/kg per d)80·573·4, 87·675·868·6, 830·00862·358·3, 66·3<0·001<0·001Energy (kJ/kg per day)337307, 366317287, 3470·008261244, 277<0·001<0·001Protein (g/kg per d)2·812·6, 3·12·652·4, 2·90·0132·292·1, 2·4<0·0010·03Carbohydrates (g/kg per d)10·09·1, 10·99·58·6, 10·40·0157·67·0, 8·2<0·001<0·001Lipids (g/kg per d)3·162·8, 3·52·942·6, 3·20·0062·412·2, 2·6<0·001<0·001 SFA (g/kg per d)1·050·93, 1·170·970·87, 1·070·0090·810·75, 0·87<0·001<0·001 MUFA (g/kg per d)1·241·11, 1·371·191·07, 1·310·0511·050·98, 1·12<0·001<0·001 PUFA (g/kg per d)0·750·67, 0·830·660·58, 0·740·0060·530·49, 0·57<0·001<0·001Cholesterol (mg/d)773723, 823723666, 780–663605, 721–0·16Trans fats (g/d)2·172·0, 2·32·262·1, 2·5–2·492·3, 2·7–0·19Fibre (g/d)37·035·5, 38·536·534·8, 38·2–36·334·3, 38·3–0·79Na (mg/d)58985647, 614953915129, 56530·00453154987, 56430·0250·005Females (n 951)Low SES (n 423)Medium SES (n 281)High SES (n 247)Energy (kcal/kg per d)72·066·8, 77·273·867·0, 80·60·9655·852·5, 59·1<0·001<0·001Energy (kJ/kg per d)301279, 323309280, 3370·96233220, 247<0·001<0·001Protein (g/kg per d)2·512·3, 2·72·552·3, 2·80·992·021·9, 2·1<0·0010·001Carbohydrates (g/kg per d)8·98·2, 9·69·48·5, 10·30·766·46·0, 6·8<0·001<0·001Lipids (g/kg per d)2·862·6, 3·12·852·6, 3·10·972·432·3, 2·60·0010·001 SFA (g/kg per d)0·930·85, 1·010·910·82, 1·000·810·840·78, 0·900·0030·002 MUFA (g/kg per d)1·131·04, 1·221·151·04, 1·26–1·040·98, 1·10–0·14 PUFA (g/kg per d)0·690·63, 0·750·700·63, 0·770·950·520·48, 0·56<0·0010·001Cholesterol (mg/d)642597, 687641590, 6920·37560522, 5980·0030·002Trans fats (g/d)2·01·8, 2·22·021·8, 2·2–2·22·0, 2·4–0·63Fibre (g/d)33·131·6, 34·632·430·8, 34·0–33·832·1, 35·5–0·42Na (mg/d)51324913, 535150584797, 53190·8447864546, 5026<0·001<0·001The P-value for the univariate comparison against the lowest educational level (Dunnett’s test) is shown only when global ANOVA was significant.
Meanwhile, dietary patterns by educational level did not display such a clear tendency. Energy intake decreased with increasing education among men, going from 219 kJ/kg per d (52·4 kcal/kg per d) among those with elementary education to 194 kJ/kg per d (46·3 kcal/kg per d) among college graduates (P-trend = 0·036) (Table 3). More educated men also consumed less carbohydrates (6·71 g/kg per d in the lowest category v 5·25 g/kg per d in the highest, P-trend < 0·001). Women with secondary or college education displayed a higher intake of MUFA (0·77 g/kg per d in the lowest category v 0·89 g/kg per d in the highest, P-trend = 0·027).
Table 3Estimated intake of energy, macronutrients, trans fats, Na, fibre and cholesterol, by sex and educational level. These analyses included only participants aged 18 or olderMen (n 682)Elementary or lower (n 137)Secondary or technical degree (n 402)Professional or higher (n 143) P-trendMean95 % CIMean95 % CI P v elementaryMean95 % CI P v elementaryEnergy (kcal/kg per d)52·448·0, 56·854·251·6, 56·70·6746·342·7, 49·80·070·036Energy (kJ/kg per d)219201, 238227216, 2370·67194179, 2080·070·036Protein (g/kg per d)1·811·6, 2·01·931·8, 2·0–1·741·6, 1·9–0·49Carbohydrates (g/kg per d)6·716·1, 7·36·646·3, 7·00·965·254·8, 5·7<0·001<0·001Lipids (g/kg per d)1·921·7, 2·12·092·0, 2·2–1·911·7, 2·1–0·95 SFA (g/kg per d)0·600·54, 0·670·680·64, 0·72–0·610·55, 0·67–0·94 MUFA (g/kg per d)0·760·68, 0·840·870·82, 0·910·040·850·77, 0·930·170·11 PUFA (g/kg per d)0·450·39, 0·500·490·46, 0·52–0·420·37, 0·47–0·46Cholesterol (mg/d)659581, 737718668, 768–679607, 750–0·75Trans fats (g/d)1·861·6, 2·12·152·0, 2·30·0822·262·0, 2·50·040·023Fibre (g/d)36·033·3, 38·736·234·7, 37·7–33·831·3, 36·3–0·22Na (mg/d)50614631, 549152705028, 5512–45304192, 4867–0·056Women (n 809)Elementary or lower (n 177)Secondary or technical degree (n 472)Professional or higher (n 160)Energy (kcal/kg per d)47·843·6, 52·153·050·7, 55·30·03447·644·3, 50·80·990·98Energy (kJ/kg per d)200182, 218222212, 2310·034199185, 2120·990·98Protein (g/kg per d)1·621·5, 1·81·841·8, 1·90·011·741·6, 1·90·370·19Carbohydrates (g/kg per d)5·875·3, 6·46·306·0, 6·6–5·425·0, 5·8–0·24Lipids (g/kg per d)1·951·8, 2·12·222·1, 2·30·0172·051·9, 2·20·630·38 SFA (g/kg per d)0·610·54, 0·680·690·66, 0·730·040·630·57, 0·680·910·66 MUFA (g/kg per d)0·770·69, 0·860·900·85, 0·950·0110·890·82, 0·970·0580·027 PUFA (g/kg per d)0·470·41, 0·530·530·50, 0·57–0·450·40, 0·5–0·63Cholesterol (mg/d)542476, 608605567, 643–524476, 572–0·76Trans fats (g/d)1·831·6, 2·12·142·0, 2·30·0332·031·8, 2·30·360·20Fibre (g/d)30·628·4, 32·832·831·5, 34–30·228·3, 32·1–0·90Na (mg/d)42173920, 451548164616, 50150·00241023821, 43840·820·74The P-value for the univariate comparison against the lowest educational level (Dunnett’s test) is shown only when global ANOVA was significant.
As expected, the intake of all macronutrients per kg body weight decreased markedly across age groups: 73 % for carbohydrates, 69 % for total lipids and 70 % for protein comparing age group 60–75 to age group 2–11. The macronutrient composition of the diet changed only slightly with SES, with significantly less energy coming from carbohydrates (45·9 v 48·6 %, P-trend <0·001) and more from lipids (37·4 v 36·0 %, P-trend = 0·031) and protein (15·2 v 14·2 %, P-trend <0·001) in the highest compared with lowest SES (Fig. 1, Panel A). The trend towards less carbohydrate intake was somewhat more pronounced, and also significant, across educational levels: energy from carbohydrates went from 49·5 % in the lowest to 45·4 % in the highest category (P-trend <0·001, Fig. 1, Panel C). The proportion of MUFA increased with higher SES, at the expense of SFA and PUFA (P-trend < 0·001, Fig. 2, Panel A). The trend towards more energy coming from MUFA and less from PUFA was also significant as educational level increased (P-trend < 0·001, Fig. 2, Panel B).
Fig. 1Estimated intake of macronutrients by socio-economic stratum (Panel A) and educational level (Panel B) (n 1491 for Panel B). Each coloured area represents the proportion of total energy intake from the corresponding macronutrient. † $P \leq 0$·001 v lowest category, ‡ $P \leq 0$·01 v lowest category., carbohydrates;, lipids;, protein Fig. 2Estimated contribution of different dietary fat types to total energy intake by socio-economic stratum (Panel A) and educational level (Panel B) (n 1491 for Panel B). SFA, saturated fats; MUFA, monounsaturated fats; PUFA, polyunsaturated fats. † $P \leq 0$·001 v lowest category, ‡ $P \leq 0$·01 v lowest category., PUFA;, MUFA;, SFA The intake of trans fats was particularly high in adolescents relative to other age groups (Fig. 3, Panel A). The intake of trans fats increased with SES only among men. This linear trend did not reach statistical significance (P-trend = 0·19), but the pairwise comparison between extreme SES categories did ($$P \leq 0$$·04). Trans fat intake was also higher among more educated men, going from 1·86 g/kg per d in the lowest to 2·26 g/kg per d in the highest education category (P-trend = 0·023). ( Fig. 3, Panel B). Na intake was negatively correlated with SES, going from 5262 mg/d in low SES to 4627 mg/d in high SES among males (P-trend = 0·005), and from 4282 mg/d to 4028 mg/d among females (P-trend < 0·001); the same significant trend was observed for Na intake across educational levels in both sexes (Fig. 3, Panels C and D). The absolute difference in Na intake between males and females became smaller with higher education.
Fig. 3Estimated dietary intake of trans fats by age group (Panel A) and socio-economic stratum (Panel B), and dietary intake of Na by socio-economic stratum (Panel C), and educational level (Panel D) (n 1491 for Panel D). In all panels, significant P-values for the comparison v the reference category are indicated by an asterisk. This test involves all participants (male and female) in each category. In Panel A, the reference category is age 12–17, in Panels B–D, the reference category is the lowest., male;, female
## Discussion
In this population-based study of the main Colombian cities, we assessed the influence of SES and educational level on the nutritional attributes of the diet. Our results show that relative to body size, the diet of individuals in a low SES is characterised by a higher intake of Na, carbohydrates and total energy. Education also had an impact on diet composition, so that more educated men consumed slightly less carbohydrates and more detrimental (trans) fats, while more educated women consumed more of the beneficial MUFA. Such information is a valuable input for health policy makers not just from Colombia but from other countries with similar demographics and economic development.
The last two decades have witnessed an unprecedented increase in prosperity in Colombia, a change that should manifest itself in the educational attainment of the population, if social mobility is to be achieved and social inequality mitigated. Despite recent increases in participation rates, the educational system remains heterogeneous in quality, and there is a narrow bottleneck in access to tertiary education[28]. According to OECD data, only 25 % of the poorest Colombians went to university in 2016, while 61 % of the richest Colombians did[29]. Data collected between 1998 and 2007 showed that Colombian adults with only primary education had more than twice the mortality rates of those with post-secondary education. Further, there is a trend towards reduced mortality over time in the whole population, but the declines are larger for higher-educated men and women[30].
We used two measures of SEP, one at the household level (SES) and other at the individual level (educational level). According to the taxonomy proposed by Howe et al.[31], SES may be considered an asset-based measure, as the classification of household is based on the availability of utilities like electricity, sewage and running water, access routes, topography, land valuation and property characteristics. These elements may be considered a better indicator of social position in low- and middle-income countries, in which income or consumption data may be volatile and unreliable[31]. On the other hand, education provided us with a different aspect of SEP, one concerned with the achievement of general literacy, which tends to be a very good correlate of health literacy. The fact that we registered the highest educational milestone achieved helped prevent the confounding effect of individuals who take longer to complete educational cycles[31]. The association of different indicators of SEP with dietary behaviour may vary greatly across populations, especially when comparing low- and middle-income countries with more affluent societies. For example, a very large study in France found that a lower income was associated with larger intakes of meat and poultry[32], both sources of dietary protein, while in our results a higher consumption of dietary protein was observed for females with higher SES.
Even though education and income tend to be correlated, in our study of urban residents SES was associated with more aspects of dietary intake than educational level, particularly energy, carbohydrates and Na intake. Meanwhile, educational level was a more important correlate of MUFA and trans fat intake. This has not necessarily been the case in nation-wide studies from other countries like Brazil[33] or some European nations[34,35], albeit methodological differences preclude direct comparisons. Indeed, compared with income and occupation, education has a greater effect on health-related behaviours like smoking and physical activity[5], an effect that has been dubbed ‘education gradient’[36]. In a study in five Latin American countries, the factors wealth, ability to act and cognition explained between 50 and 70 % of this education gradient in dietary behaviour[37]. Evidence from China suggests that, in certain social contexts, even the educational level of the spouse may have a relevant influence on dietary behaviours, especially among women[38]. In a study undertaken in five Italian cities, being in the highest tertile of educational level was associated with several relevant qualitative dietary behaviours, among them a lower intake of complex carbohydrates and sugary drinks and a higher intake of dairy products, fish, fruits and vegetables[39]. Interestingly, a recent cross-sectional study in Portugal found a higher educational level to be the main correlate of good adherence to a Mediterranean dietary pattern[40]. In the Polish National Multi-Centre Health Examination Survey, a composite ordinal score that combined educational level and household income was correlated positively with a better overall diet quality[41]. These associations may translate into later impact on harder outcomes. In a long-term follow-up of a population study in Norway, a large difference in total mortality rates was found between the lowest and highest educational level. Health-related behaviours, of which dietary habits constituted a central part, explained between 38 and 45 % of this difference[42]. A longitudinal analysis of nationally representative data from the Australian National Nutrition and Physical Activity Survey between 1995 and 2013 showed that individuals in a better SEP (assessed by educational level, household income and area-level disadvantage) tended to improve aspects of their dietary behaviour over the study follow-up, including intakes of energy, total fat, saturated fat and fruits[43]. These results suggest that in some countries the impact of social inequalities on dietary intakes may be getting more pronounced over time. The impact of education on self-care attitudes, habits and eventually health may be enhanced in Latin America, as access to higher/tertiary education is quite limited[44,45].
Educational level seemed to have a greater impact on the quality of dietary fats. Although sex differences in dietary intake are well known[46], differences in the education–diet association between sexes have been less studied[8,10,34,47]. Given that higher education reduces gender inequality in self-rated health and mortality[48], our results support education as a nutrition public policy[49] and as an instrument for social progress[50].
A lower income has been associated with greater intake of energy-dense foods, especially those rich in carbohydrates[51,52]. A study in US children found that drinking sugary beverages was negatively associated with both SES and work stability of the parents[53]. A huge body of evidence supports the negative influence of a high carbohydrate intake on cardiovascular and metabolic health[54,55], labelling it as one of the main sources of the current obesity epidemic[56,57]. Excess carbohydrate ingestion may be related to easy access to pastries and other sugary foods and drinks, plus limited access to protein-rich foods and sugar substitutes[58].
An interesting result was that a better SEP was associated with both a greater intake of MUFA (present in a Mediterranean dietary pattern)[59] among women and a greater consumption of trans fatty acids (commonly present in ultra-processed and fast foods)[60,61]. Even though this pattern seems contradictory, several other studies have found the same association[62,63]. The larger MUFA intake in more educated individuals in Latin American countries could be explained by the influence of numerous public health campaigns about the negative effects of SFA in cardiovascular and neurological health, combined with the higher cost of MUFA-rich oils[62,64]. Concerning trans fats, the influence of US culture and habits about practical and cheap foods may be greater in higher income groups[63].
As in our investigation, many other studies have reported higher consumption of trans fats among younger persons[62,63,65]. The intake of trans fats has been increasing rapidly during the last years[62,65], despite their well-known harmful effects on CVD and mortality[66]. Different factors may explain this phenomenon in underage individuals: first, there is a positive correlation between ingestion of trans fats or sugar-sweetened beverages and time spent watching television[67]. Second, there has been a rise in fast food advertising, and third, such publicity appears mostly in time slots and channels addressing children, adolescents and young adults(68–70). Thus, the regulation of advertising to young audiences in terms of quantity and content is a priority target for public health policy.
The conflicting results from different countries concerning the association between SES and dietary intake can reflect the ‘nutrition transition’ concept. Our results illustrate that in cities from middle-income countries like Colombia, low SES groups tend to have a higher energy intake because of increased access to energy-dense foods. Meanwhile, high SES groups tend to avoid such foods because of: (i) greater exposure to information about the increasing rates of obesity and its consequences[71,72]; (ii) ability to afford non-energy-dense foods like fruits and vegetables, which may be expensive in an urban environment[73]; (iii) an interest in projecting a socially desirable image of a healthy lifestyle and self-care and[74] and (iv) the influence of body image models from the mass media that portray leanness as a sign of success and self-fulfilment[75]. In this sense, findings from China (1989–1997) are similar to ours[8]. By contrast, countries with different results(8–10,76) could be experiencing an earlier phase of the nutrition transition, in which income is the main determinant of access to nourishment, so low SES groups are exposed to food scarcity. Also, our findings may not reflect the situation in rural areas of Colombia or of other countries at a similar stage of the nutrition transition.
The main strengths of our study include the probabilistic, population-based sample, the representation of geographically and culturally different regions of the country and the use of a FFQ that was adapted to Colombian foods and preparations. The collection of information in a supervised manner by qualified staff and the use of several quality checks helped improve the reliability of study data. The main limitations of the study are those inherent to its cross-sectional design, including the impossibility to explore the long-term health effects of the observed differences in dietary behaviour by SEP. Additionally, our results do not represent dietary habits in rural areas of Colombia, and we did not take into account foods consumed by less than 30 % of the Colombian population. We employed two different indicators of SEP, one of them focused on assets (SES) and the other on education. Our results do suggest that even though these indicators may be correlated, their association with at least some aspects of dietary intake is different. SES has the particularity of being defined by household and not by individual, so within-household variations in income or assets among respondents were not captured by this measure. Nonetheless, most of the variation in income and assets in Colombia (and presumably in other countries) is explained by between-household differences. In a population-based survey in Alberta, Canada, household income was a much better predictor of future survival and health status than respondent income[77]. Self-reported educational level may be subject to misreporting, usually of a higher level, a problem that could weaken associations between the reported variable and dietary intake outcomes. However, ascertaining the maximal educational level of all participants would not be logistically feasible, as there is not a central registry of all degrees granted by Colombian institutions, so this was the best approximation possible under realistic circumstances.
In summary, our results show the strong influence that SEP has on usual diet in the main Colombian cities. Individuals living in lower SES consume significantly more total energy, more carbohydrates and more Na, characteristics extensively documented to correlate with greater risks for obesity, diabetes, CVD and total mortality. Also, this dietary pattern is very likely to be fuelling the current rise of overweight and obesity among the economically disadvantaged in Colombia[15]. A higher educational level was associated with greater intake of cardioprotective MUFA among women but also of the very harmful trans fats among men. Thus, health policies for the lower SES segments in our local context should focus more strongly on strategies aimed at the energetic adequacy of the diet, while those aimed at more educated segments should have a stronger emphasis on the quality of dietary fats.
## Conflict of interest:
Even though this study was funded by Team Foods Colombia, it was executed independently by the study authors. The sponsoring company had no influence on data analysis, on the contents of the manuscript or on the decision to publish.
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|
---
title: 'Effect of 8-week intake of the n-3 fatty acid-rich perilla oil on the gut
function and as a fuel source for female athletes: a randomised trial'
authors:
- Aki Kawamura
- Ken Nemoto
- Masaaki Sugita
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC9991858
doi: 10.1017/S0007114522001805
license: CC BY 4.0
---
# Effect of 8-week intake of the n-3 fatty acid-rich perilla oil on the gut function and as a fuel source for female athletes: a randomised trial
## Body
Physical health is strongly related to the type and number of gut bacteria in hosts. The production of SCFA such as Butyrate by health-promoting bacteria fosters immunomodulatory effects and health[1]. These bacteria are also relevant to athletes’ health and exercise performance(1–3) because the gut microbiome is closely linked to organ functions, such as the brain(4–6) and muscle[7,8]. Metabolites from gut bacteria, such as SCFA, modulate signalling[9]; activate metabolic pathways(10–12) and insulin sensitivity[13], particularly in skeletal muscle metabolism; and may affect the control of body weight and exercise performance[2,12]. SCFA are produced by gut microbiota, such as Bacteroidetes and Lachnospiraceae [14]. It has been suggested that Bacteroidetes are increased from exercise and gut bacteria, and exercise adaptations may play a role(15–17). Among SCFA, Butyrate has been shown to be a key modulator of energy metabolism and mitochondrial function by activating PGC-1α gene expression in skeletal muscles and brown adipose tissue[18]. The study has also demonstrated that dietary Butyrate supplementation improves insulin sensitivity and increases energy expenditure by enhancing mitochondrial function in animals[18]. Moreover, increasing bacterial diversity is important for improving adaptability to external stimuli, such as environment and exercise. Furthermore, the diversity of gut bacteria in athletes is higher than that of the general public, which suggests that gut bacteria are adapted to stimulation by exercise and training[19]. Taken together, the diversity of gut microbiota and exercise-induced bacteria, such as Butyrate-producing bacteria, would be beneficial for athletes who engage in high-intensity training.
The gut is highly adaptable to external factors, such as lifestyle and environmental stimuli. The composition and diversity of gut bacteria are affected by the genetic elements (age, sex and birth route[20,21]) and external factors (diet[22], exercise[23] and antibiotics[24]). In particular, diet strongly influences the gut microbiome, and this change is caused by long-term dietary patterns[22] and short-term interventions of several weeks[25,26]. For many athletes, carbohydrates are the main energy source to maintain performance and recover glycogen stores[27,28]. Recent studies have shown that increased intake of carbohydrates in the form of dietary fibre is associated with an increase in the diversity of gut bacteria[1,29]. Fats are also important substrates for energy metabolism. Although previous studies have reported that a high-fat diet reduces the diversity of gut bacteria and increases the Firmicutes ratio[30,31], the effects of a small amount of fat remain unclear. Therefore, an effective low-dose fat intake strategy is warranted to support fuelling in athletes, especially during high-intensity training periods.
There is inconsistent evidence about the effect of fat intake on the gut microbiome and functions in human and animals. In terms of types of fats, fish oil and unsaturated fatty acid intake increased probiotics, such as Bifidobacterium and Lactobacillus [32,33]. Saturated fat acid does not increase these bacteria[33]. In addition, a high saturated fat diet reduces bacterial numbers and increases the excretion of SCFA[34]. A recent review concluded that the n-3 fatty acid favours the Butyrate-producing bacterial genera, whereas a saturated fat-rich diet can attenuate the gut microbiota of these commensal bacteria[35]. Moreover, the effect of fat intake on gut microbiota depends on the type of fatty acid. n-3 fatty acids provide multiple health benefits, such as lowering blood pressure[36] and preventing diseases (37–39), including inflammatory bowel disease[40]. It also has several benefits on exercise, including post-exercise muscle recovery (41–43), training-induced muscle strength[44], reduced muscle loss and inflammation [45,46], endurance ability[47] and brain health[48]. In addition, n-3 fatty acids play an important role in physiological adaptation to produce metabolites through cell receptors [49,50].
Perilla oil is rich in α-linolenic acid, a type of n-3 fatty acid, which also contains small amounts of linoleic acid of n-6 fatty acid and oleic acid of n-9 fatty acid. These fatty acids have different properties, but through the intake of perilla oil, a combination of the benefits of these acids can be obtained. Perilla oil contains a large amount of n-3 fatty acids not found in other seed oils such as olive oil and maize oil, which are mainly composed of n-6 and n-9 fatty acid and have extremely low amounts of n-3 fatty acids. Furthermore, perilla oil is a traditional Japanese food that can be consumed daily – a notable strength as a research food in this study.
Since the gut environment is correlated with organ function, daily n-3 intake may enhance the function of other organs through the improvement of the gut environment. Athletes are required to adapt to muscle and other organ functions at a high level. Therefore, strategies for improving the gut environment to efficiently metabolise nutrients are required. Although previous studies have investigated the effects of n-3 fatty acids on the gut environment in animals, healthy humans and patients[35], we hypothesised that n-3 fatty acid supplementation could also improve the gut function of athletes. We aim to find an effective use of perilla oil that fuels energy and improves gut function in athletes and evaluate different dose-dependent effects.
## Abstract
Previous studies have examined the effects of n-3 fatty acid intake in supplement form or fish oil capsules, but there are few studies based on other foods. Perilla oil is a traditional Japanese seed oil rich in n-3 fatty acids. This randomised trial aimed to determine the appropriate n-3 fatty acid dose through consumption of perilla oil, which improves gut function and microbiota in trained athletes, and the amount of fat fuel required to provide energy to athletes involved in high-intensity training to improve athletic performance. Thirty-six female athletes training six times per week were randomly assigned to three groups according to perilla oil intake: 9 g/d (high oil intake (HOI)), 3 g/d (low oil intake (LOI)) and placebo-supplementation (PLA) groups. The HOI and LOI groups had perilla oil-containing jelly and the PLA group had placebo jelly for 8 weeks. Gut microbiota, constipation score and urinary biochemical index were measured pre- and post-intervention. The spoilage bacteria, Proteobacteria, significantly decreased ($$P \leq 0$$·036, $d = 0$·53), whereas Butyrate-producing bacteria, Lachnospiraceae, significantly increased ($$P \leq 0$$·007, $d = 1$·2) in the HOI group. Urinary indoxyl sulphate significantly decreased in the HOI group only ($$P \leq 0$$·010, $d = 0$·82). Changes in the constipation score were significantly lower in the HOI group ($$P \leq 0$$·020) and even lower in the LOI group ($$P \leq 0$$·073) than in the PLA group; there were significant differences between groups ($$P \leq 0$$·035). Therefore, perilla oil intake may improve gut function and microbiota in athletes, with higher doses resulting in further improvement.
## Participants
Thirty-six female athletes belonging to a university volleyball club (age: 20·2 (s e 1·3) years, height: 167·8 (s e 7·8) cm, body weight: 63·4 (s e 6·6) kg) were recruited. All participants trained six times a week, an average of 4·5 h per day. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving participants were approved by the ethics committee of Nippon Sport Science University (No. 018-H193) and the University Hospital Medical Information Network Clinical Trials Registry in Japan (No. UMIN000044882). All participants signed a written consent form after being informed on the purpose of the study, methods, possible health hazards, risks, privacy protection, data management and publication. None of the participants was not using supplements and medicines, and history of chronic disease and smoking. The recruitment, data collection and follow-up were conducted from October 2019 to December 2020. All data were collected pre-, during and post-intervention at the Nippon Sport Science University.
Body composition was measured using bioelectrical impedance analysis (InBody730, InBody Co., Ltd.), and the physical characteristics of participants are shown in Table 1. All participants live in university dormitories and daily meals are provided by a dietitian. Since the participants competed in the same sports club and trained six times a week in same training menus, there was no difference in the training load between participants and phases during the intervention period.
Table 1.Physical characteristics of participants in pre- and post-intervention(Mean values with their standard errors)Physical characteristicsHOILOIPLAPrePostChangePrePostChangePrePostChangeMean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Body weight (kg)64·11·663·51·6-0·60·365·12·665·62·50·50·261·41·461·21·4- 0·10·3BMI (kg/m2)22·70·322·50·3-0·20·122·70·422·90·40·20·121·70·421·60·40·00·1Body fat (%)22·41·021·71·0-0·70·421·10·721·20·80·10·521·41·621·01·6-0·40·4Skeletal muscle mass (kg)27·70·827·70·80·00·229·31·329·41·30·20·226·20·826·40·80·20·1 α-Diversity3·960·114·190·140·230·134·460·114·180·14- 0·290·144·220·124·390·150·170·13 n 12. HOI: 9 g/d perilla oil intake group, LOI: 3 g/d perilla oil intake group, PLA: placebo-intervention, Pre: pre-intervention, Post: post-intervention.
## Experiment design
Forty-eight participants were eligible for this randomised trial, of whom twelve were excluded from the trial: three of them did not meet the criteria, one declined to participate and eight were not allowed to undergo intervention owing to physical reasons. Finally, thirty-six participants were involved in the trial and equally divided into three groups by a third party according to their perilla oil intake as follows: high oil intake (HOI) (9 g/d), low oil intake (LOI) (3 g/d) and placebo-supplementation (PLA) groups (Fig. 1). The participants were blinded to their groupings, which were concealed by sequential numbers. The HOI group received 3 g of perilla oil-containing jelly three times per day (9 g/d of perilla oil), while the LOI group received 3 g of perilla oil-containing jelly once per day (3 g/d of perilla oil). The PLA group ingested a jelly with the same shape and taste as perilla oil-containing jelly once per day (0 g/d of perilla oil) during the intervention period. Subsequently, we compared the effects of high doses with those of the generally recommended dose of n-3 fatty acids for athletes [51,52] and placebo intake. Participants were instructed to maintain their diet, training or lifestyle during the intervention period. We referred to a previous double-blinded, randomised, controlled study examining the effects of nutrient intake on athletes’ gut microbiota to determine the number of participants[53].
Fig. 1.CONSORT flow diagram for the randomised controlled trials.
We investigated body weight, body composition, gut microbiota and urinary biochemical index (indoxyl sulphate, 8-hydroxydeoxyguanosine:8-OHdG). In addition, data on constipation score, subjective condition questionnaire about fatigue, sleep quality, appetite, psychological stress and training load using the visual analogue scale method and sleep hours were obtained pre-intervention and every 2 weeks thereafter (Fig. 2).
Fig. 2.Experiment design.
## Faecal microbiota
Bacterial DNA from faecal samples was collected in a solution containing 4 M guanidine thiocyanate for analysis. Faecal samples were pre-treated with zirconia beads, followed by DNA extraction and purification using an automated DNA extraction and purification system (Maxwell®, Promega Corporation). 16S rRNA bacterial primers containing the V1 and V2 regions – targets for determining the species[54,55] – were prepared. The following cycling conditions were employed: 10 s at 98°C, 10 s at 55°C and 5 s at 72°C for 20 cycles. Then, the samples were subjected to next-generation sequencing, and approximately 300 bases, including the V1–V2 variable regions, were analysed as described previously[54]. The number of effective reads per sample was approximately 30 000–100 000. Then, the α-diversity (Shannon index) was determined and bacteria were identified. Diversity analysis was performed using an Excel add-on (Ekuseru-Toukei 2015, Social Survey Research Information Co., Ltd.), and the outcomes were presented using Shannon indexes. All phylum-, family- and genus-level changes were analysed. Detection and analysis of faecal bacteria were outsourced (SheepMedical Co., Ltd.).
The α-diversity pre- and post-intervention changed from 3·96 (se 0·11) to 4·19 (se 0·14) in the HOI group ($$P \leq 0$$·147), 4·46 (se 0·11) to 4·18 (se 0·14) in the LOI group ($$P \leq 0$$·107) and 4·22 (se 0·12) to 4·39 (se 0·15) in the PLA group ($$P \leq 0$$·955). No differences were observed between the groups. Regarding bacterial changes at the phylum level, the spoilage bacteria, Proteobacteria, significantly decreased post-intervention (1·2 (se 0·2)) compared with those pre-intervention (2·0 (se 0·5)) in the HOI group ($$P \leq 0$$·036, $d = 0$·53). There was no change between pre- and post-intervention in the LOI and PLA groups. The change tended to be different among the three groups ($$P \leq 0$$·099). Firmicutes were significantly decreased post-intervention (47·2 (se 4·0)) compared with those pre-intervention (56·5 (se 4·5)) in the LOI group ($$P \leq 0$$·002, $d = 0$·59). In contrast, Bacteroidetes were significantly increased post-intervention (43·2 (se 3·7)) compared with those pre-intervention (31·6 (se 3·8)) in the LOI group ($$P \leq 0$$·004, $d = 0$·89). The changes in these bacteria were significantly different between the groups ($$P \leq 0$$·016). Additionally, the Firmicutes/Bacteroidetes (F:B) ratio was significantly decreased post-intervention (1·3 (se 0·2)) compared with that pre-intervention (2·3 (se 0·5)) in the LOI group ($$P \leq 0$$·021, $d = 0$·68). The changes were significantly different between the groups ($$P \leq 0$$·025). For the bacterial changes at the family level, Butyrate-producing bacteria, Lachnospiraceae, were significantly increased post-intervention (19·0 (se 1·6)) compared with those pre-intervention (13·8 (se 1·3)) in the HOI group ($$P \leq 0$$·007, $d = 1$·2). In contrast, they were significantly decreased post-intervention (18·2(se 2·0)) compared with those pre-intervention (25·5 (se 3·4)) in the LOI group ($$P \leq 0$$·004, $d = 0$·62) and did not change in the PLA group. The change was significantly different among the three groups ($$P \leq 0$$·001) (Table 4, Fig. 3).
Table 4.The relative abundance of faecal bacteria pre- and post-intervention at the phylum and family level(Mean values and standard deviations)LevelBacteriaHOILOIPLA P † PrePostChange P * PrePostChange P * PrePostChange P * Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd Phylum Proteobacteria 2·00·51·20·2–0·80·30·0361·70·31·80·30·10·30·7941·80·42·50·70·70·80·4150·099 Firmicutes 42·03·347·83·25·83·90·18256·54·547·24·0–9·23·30·00255·14·261·33·66·24·40·2050·025 Bacteroidetes 49·93·946·43·8–3·54·70·49231·63·843·23·711·63·10·00437·94·431·53·8–6·54·90·2330·016 Firmicutes/Bacteroidetes 1·00·21·30·30·30·30·3542·30·51·30·2–1·10·40·0211·40·22·70·81·10·80·2030·025 Other 6·11·74·61·1–1·51·70·42010·22·77·82·2–2·52·50·3685·11·34·71·1–0·41·00·6780·973Family Bifidobacteriaceae 5·51·63·50·8–2·01·80·2999·52·76·92·3–2·72·50·3314·71·34·41·1–0·31·00·7970·758 Bacteroidaceae 38·34·336·94·1–1·54·20·74722·64·030·15·07·51·80·05234·34·628·13·5–6·24·90·2480·129 Porphyromonadaceae 3·40·73·30·8–0·10·60·8582·70·62·80·50·10·50·8462·60·91·60·4–1·00·70·1740·330 Prevotellaceae 7·33·75·04·0–2·32·70·4415·02·29·44·54·32·50·1220·70·31·30·70·60·60·3560·122 Streptococcaceae 4·21·91·90·6–2·31·90·2662·91·02·00·5–0·90·90·3761·80·32·40·70·60·70·4290·913 Clostridiaceae 1·30·22·40·51·10·50·0742·40·81·40·2–1·00·70·2082·00·43·80·91·81·00·1000·032 Eubacteriaceae 2·30·73·11·00·80·60·2743·30·73·01·0–0·20·90·8443·70·74·71·11·00·40·0600·159 Lachnospiraceae 13·81·319·01·65·21·50·00725·53·418·22·0–7·32·00·00428·73·829·02·50·32·80·9270·001 Ruminococcaceae 14·82·214·22·3–0·61·90·76914·42·915·23·60·72·30·76111·82·914·52·02·62·70·3670·420 Other 9·11·410·71·61·61·60·33811·61·311·01·4–0·61·10·6209·61·810·21·10·61·70·7370·558Genus Faecalibacterium 11·12·27·92·1–3·21·20·0278·21·811·53·33·42·00·1367·41·111·01·73·61·90·0980·011 Bacteroides 38·34·336·94·1–1·54·20·74722·64·030·15·07·43·30·05234·34·628·13·5–6·24·90·2480·134 Eubacterium 3·71·04·41·10·70·60·3107·81·85·01·4–2·81·10·0338·52·69·21·8–0·72·10·7590·024 n 12. HOI: 9 g/d perilla oil intake group, LOI: 3 g/d perilla oil intake group, PLA: placebo-intervention, Pre: pre-intervention, Post: post-intervention.*Differences pre- and post-intervention within the groups.†Differences in changes between the groups.
Fig. 3.Changes in faecal microbiota pre- and post-intervention at the phylum and family level. ( a), (c), (e) Comparison of the faecal microbiota within and between the groups. ( b), (d), (f) Changes of the faecal microbiota between the groups. Data are presented as the mean values with their standard error, minimum and maximum, n 12. P-value; differences pre- and post-intervention within the group or difference of the changes between groups, HOI: 9 g/d perilla oil intake group, LOI: 3 g/d perilla oil intake group, PLA: placebo-intervention, Pre: pre-intervention, Post: post-intervention.
## Constipation score
A subjective questionnaire by Agachan et al. [ 56] was used to assess constipation status during the intervention period (pre-intervention and every 2 weeks thereafter). The survey included frequency of bowel movements, difficulty of defecation, abdominal pain, time required for the laboratory, need for assistance, number of failures and history of constipation. The total scores ranged from 0 to 30, with 0 indicating no constipation and 30 indicating severe constipation. The change in score was calculated from the pre-value minus the lowest value during the intervention.
The constipation score was significantly decreased during the intervention period in the HOI and LOI groups; however, there was no change in the PLA group. The change was significantly lower in the HOI group ($$P \leq 0$$·020) and tended to be lower in the LOI group ($$P \leq 0$$·073) than that in the PLA group (Fig. 4). Changes were significantly different between the groups ($$P \leq 0$$·035).
Fig. 4.Comparison of the constipation score between the groups. ( a) Constipation score during intervention period. ( b) Changes of the constipation score between the groups. Data are presented as the mean values with their standard error, minimum and maximum, n 12. P-value; differences pre- and during/post-intervention within the group, differences compared with PLA or differences between groups, HOI: 9 g/d perilla oil intake group, LOI: 3 g/d perilla oil intake group, PLA: placebo-intervention, Pre: pre-intervention, Post: post-intervention.
## Urinary biochemical index
Indoxyl sulphate is a gut-derived uremic toxin mainly produced from tryptophan-containing foods, such as egg white, meat, milk, cheese and soya product[57]. 8-OHdG is a biomarker of oxidative DNA damage[58]. These indicators were outsourced for analysis (Healthcare Systems Co., Ltd.).
Indoxyl sulphate (µg/mg Cr), which is an indicator of the deterioration of the intestinal environment, was significantly decreased post-intervention (26·8 (se 3·4)) compared with that pre-intervention (36·2 (se 3·3)) in the HOI group ($$P \leq 0$$·010, $d = 0$·82). The change tended to be different among the three groups ($$P \leq 0$$·054). 8-OHdG, a biomarker for oxidative damage of DNA, did not change between pre- and post-intervention in all groups (Fig. 5).
Fig. 5.Changes in urinary biochemical index pre- and post-intervention. ( a) Comparison of indoxyl sulphate within and between the groups. ( b) Comparison of 8-OHdG within and between the groups. Data are presented as the mean values with their standard error, n 12. P-value; differences pre- and post-intervention within the group or difference of the changes between groups.
## Perilla oil supplementation and nutrient intake
Perilla oil is a traditional Japanese seed oil that contains high levels of n-3 fatty acids. Perilla oil-containing jelly was provided to the HOI and LOI group participants, and placebo jelly was provided to the PLA group participants. Both jellies were lemon-flavoured, obscuring the natural nutty taste of perilla oil. Participants in the HOI group ingested one jelly after breakfast, lunch and dinner, whereas those in the LOI and PLA group ingested one jelly after lunch. Nutritional components per jelly and fatty acid composition in perilla oil are shown in Tables 2 and 3.
Table 2.Nutritional components of perilla and placebo jelly per one portionEnergy and nutrientsPerilla oil jellyPlacebo jelly(20 g)(20 g) Energy (kJ) (kcal)9 [38]3 [12] Protein (g)0·00·0 Fat (g)3·00·0 SFA (g)0·00·0 Unsaturated fatty acids (g)3·00·0 Carbohydrate (g)2·83·3 Salt (g)0·080·10 Table 3.Fatty acid composition in perilla oilFatty acidRatio (%) n-3 α-Linolenic acid62·6 n-6Linoleic acid15·4 n-9Oleic acid13·2Others7·9 A dietary assessment was conducted using a dietary record maintained for 3 consecutive days to calculate participants’ nutrient intake pre-intervention. All participants could eat freely during the intervention, and their food intakes were recorded using a food diary and camera to click food pictures. Then, a dietitian reviewed their diet and estimated participants’ energy, macronutrient intake, dietary fibre, vitamins and minerals using a software (NEW HEALTHY ver. Tokyo Shoseki Co., Ltd.).
Harms or unintended effects by perilla oil supplementation were not reported.
The daily increase in energy intake through consumption of the jelly was 114, 38 and 12 kcal in the HOI, LOI and PLA groups, respectively. The intake of unsaturated fatty acids, n-3 fatty acid and n-6 fatty acid acids by groups did not differ significantly before the intervention. Similarly, the intake of total energy, fat, carbohydrate, fibre, vitamins and minerals by the groups did not differ significantly before the intervention (Table 5). Notably, none of the participants changed their eating habits during the intervention period.
Table 5.Daily intake of nutrients pre-intervention(Mean values with their standard errors)HOILOIPLAEnergy and nutrientsMean se Mean se Mean se Energy (kJ/d) (kcal/d) 638 [2671]12 [51]660 [2762]15 [62]662 [2771]5 [21]Protein (g/d)111·88·1106·55·8108·63·9Fat (g/d)108·09·8117·92·6115·92·4SFA (g/d)27·14·131·61·030·91·3Unsaturated fatty acids (g/d)25·30·627·71·727·51·3 n-3 PUFA (g/d)4·00·63·50·33·50·2 n-6 PUFA (g/d)21·20·424·21·524·01·2Carbohydrate (g/d)297·818·4302·67·7307·15·2Total dietary fibre (g/d)18·22·317·71·817·22·2Water-soluble dietary fibre (g/d)4·60·84·40·44·20·6Insoluble dietary fibre (g/d)13·21·413·11·312·81·5K (mg/d)3489·7250·23339·3191·23320·0182·7Ca (mg/d)733·318·4581·045·6583·357·8Mg (mg/d)442·727·1422·725·9436·739·8Fe (mg/d)13·71·112·30·512·30·5Zn (mg/d)13·92·614·51·314·31·4Vitamin A (µg/d)503996765861882Vitamin D (µg/d)3·91·35·11·05·10·9Vitamin E (mg/d)90·7100·390·2Vitamin K (µg/d)592·3120·9660·098·7640·0105·6Vitamin B1 (mg/d)1·40·21·60·31·50·3Vitamin B2 (mg/d)1·90·21·90·01·90·0Vitamin B6 (mg/d)1·60·11·60·01·50·0Folic acid (µg/d)417·351·0455·737·3436·745·6Vitamin C (mg/d)12315·611613·111216·4Salt (g/d)14·51·014·40·913·21·2 n 12. HOI: 9 g/d perilla oil intake group, LOI: 3 g/d perilla oil intake group, PLA: Placebo-intervention group, Pre: pre-intervention, Post: post-intervention.
## Subjective condition
The degree of subjective conditions on fatigue, sleep quality, appetite, psychological stress and training load was measured using the visual analogue scale method. The participants were asked to indicate the degree of subjective on a 100-mm horizontal line. The left side (0 mm) indicated ‘having bad condition’, whereas the right side (100 mm) showed ‘having good condition’.
There was no significant change in the subjective condition every 2 weeks in all groups (Table 6).
Table 6.Subjective condition during the intervention period(Mean values with their standard errors)Subjective conditionHOILOIPLA0 week2 weeks4 weeks6 weeks8 weeks0 week2 weeks4 weeks6 weeks8 weeks0 week2 weeks4 weeks6 weeks8 weeksMean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Mean se Fatigue51·96·851·56·946·75·951·67·353·17·256·46·749·13·545·13·948·06·259·76·347·55·645·34·946·85·450·22·856·45·5Sleep quality61·17·764·38·064·27·273·56·572·88·060·49·762·45·958·56·774·13·967·55·958·66·565·06·865·95·578·34·665·85·3Appetite61·36·961·35·473·74·961·86·365·06·360·36·862·35·864·26·262·86·067·45·976·84·979·05·280·25·282·15·984·24·8Psychological stress61·85·360·85·472·05·370·86·163·86·157·85·860·64·765·16·471·34·568·94·662·03·061·65·361·36·270·23·468·27·0Training load61·13·847·76·050·85·747·57·250·37·859·49·348·93·955·45·455·75·460·17·552·53·640·03·845·34·849·24·355·65·2 n 12. HOI: 9 g/d perilla oil intake group, LOI: 3 g/d perilla oil intake group, PLA: placebo-intervention.
## Statistical analysis
All data are reported as mean values with their standard error. Differences within groups and between groups were determined using the paired t test and unpaired t test or ANCOVA, respectively. When significant differences were determined using ANCOVA, post hoc analyses were conducted using the Bonferroni test. For parameters with skewed distribution, the Kruskal–Wallis test was performed for comparison of the three groups. Cohen’s d was calculated to measure the effect size. Statistical analysis was performed using SPSS ver.25 (IBM Japan Inc.), and p values < 0·05 were considered statistically significant.
## Results
The participants were excellent adherence to the intervention with no dropouts.
## Body composition
There were no significant differences in body weight (kg), BMI (kg/m2), body fat (%) and skeletal muscle mass (kg) between pre- and post-intervention in all groups (Table 1). Habitual sleep hours (h:min) at pre- and post-intervention were 6:46 (se 0:12) and 6:30 (se 0:11) in the HOI group, 6:39 (se 0:23) and 6:50 (se 0:23) in the LOI group and 6:58 (se 0:22) and 6:50 (se 0:13) in the PLA group, respectively, with no difference between the groups.
## Discussion
Our study revealed that 8-week 9 g/d n-3 fatty acid intake increased the abundance of Butyrate-producing bacteria and relieved constipation in trained female athletes. We speculated that the intake of n-3 fatty acids increased gut SCFA by increasing Butyrate-producing bacteria[14] although SCFA could not be measured in this study. In contrast, 3 g/d of perilla oil decreased Lachnospiraceae. This change was considered the result of the degradation of the occupancy rate of butyric acid-producing bacteria by the significant increase in Bacteroidaceae at the family level and Bacteroidetes at the phylum level. Previous studies have reported that n-3 fatty acids cause different changes in the gut microbiota profile according to dose[59]. Our study suggested that 9 g/d perilla oil intake was sufficient to increase Butyrate-producing bacteria on trained female athletes, but 3 g/d perilla oil intake was not. Ingestion of 3 g of perilla oil increased the abundance of Bacteroidetes and decreased the F:B ratio, which may lead to the production of SCFA. In addition, Lachnospiraceae were lower at baseline in the HOI group, and Bacteroidetes in the LOI group were lower than that in other groups at baseline, which may have led to the increase of these bacteria due to perilla oil intake. The changes in these bacteria may be related to the gut microbiota of the host at baseline. Taken together, the ingestion of perilla oil is superior in increasing SCFA-producing bacteria.
Butyrate and SCFA ameliorate inflammatory bowel disease; however, the mechanism of action remains unelucidated[60]. A previous study has shown that functional constipation is associated with altered concentrations of butyric acid in mice[61]. Other studies have shown that SCFA stimulate the mucous membrane of the large intestine to promote intestinal peristalsis [62,63]. Therefore, growing Butyrate may improve gastrointestinal disorders, such as constipation. Our study revealed that constipation scores relieved 2 weeks after the intervention in the HOI group and 4 weeks after in the LOI group of female athletes. Therefore, increased intake of perilla oil relieved constipation in a short period. The relationship between relieve constipation and gut microbiota remains unelucidated, and our study suggests that Butyrate-producing bacteria may contribute to functional gut improvement. In addition, ingestion of 9 g/d perilla oil suppressed the growth of Proteobacteria, which are related to gut microbiota disturbance. In addition, the uremic toxin indoxyl sulphate was suppressed in the HOI group, which may have led to the improvement of gut function[64]. Although Proteobacteria and urinary indoxyl sulphate levels were not different between the groups at baseline, they did not change in the LOI group. Therefore, the dose of perilla oil that suppresses indicators related to gut microbiota disturbance should be investigated in the future. Therefore, perilla oil may improve gut microbiota in athletes, and ingestion of 9 g/d perilla oil that is higher than the recommended dose of n-3 fatty acid[52] further improves the gut function.
Regarding functional gut disorders, several cross-sectional studies have shown that females are more likely to report constipation than males[65,66] and nearly half of female athletes who are involved in strenuous exercise have gastrointestinal disorders[67]. Gut disorders may impair the absorption of nutrients and cause functional disorders, which leads to performance degradation in athletes. Therefore, increasing Butyrate-producing bacteria may benefit female athletes. It has been reported that stimulation and stress caused by excessive exercise may lead to degradation of the diversity of gut bacteria[68]. Since the gut environment of athletes is exposed to exercise-induced excessive stress, the gastrointestinal function of athletes tends to deteriorate. Nevertheless, athletes have a higher diversity of gut bacteria than the general population to adapt to external stimuli[19]. This study suggests that perilla oil intake would support to suppress gut stress and constipation in high-intensity trained athletes. However, there was no change in the diversity of gut bacteria, 8-OHdG and subjective conditions during the 8-week intervention in this study. In the future, long-term intervention may change these indicators by improving the gut environment.
Finally, despite the additional energy of 114 kcal/d in the HOI group and 38 kcal/d in the LOI group, body weight and body fat did not change. Therefore, daily intake of n-3 perilla oil may support athletes’ fuel intake without unexpected weight gain. A simple method to ensure the intake would be to include three teaspoons of perilla oil (approximately 9 g of oil) to daily diet such as salad, or bread and pasta. Gut microbiota differs depending on race and may be different to Japanese and people from other countries[69]. Therefore, perilla oil might be effective in improving gut function and microbiota, at least in Japanese athletes. In addition, perilla oil intake for several weeks or more is desirable to improve constipation. Since the effect on gut microbiota differs depending on the dose of perilla oil, further studies are needed to investigate the appropriate intake. Moreover, it has been shown that ingestion of n-3 fatty acid improved muscle function[44,47], in addition to producing metabolites[35]. Therefore, daily intake of perilla oil may help improve athletic performance.
Our study showed that the n-3 fatty acid-rich perilla oil increased butyric acid-producing bacteria and improved gut function. However, this study has several limitations. First, the HOI group received the intervention three times daily to reach the targeted dose, while the other two groups received it once daily. These three groups did not follow the same intervention protocols. However, the study design was unified except for the variation of intake timing. Second, daily surveys throughout the intervention period could not be conducted because it would tremendously inconvenience participants. Although we conducted a dietary survey for 3 d to confirm that there was no difference in nutrient intake between the groups at baseline, a dietary survey was needed throughout the intervention period to completely eliminate any influence of participants’ daily diet. However, a registered dietitian managed the dormitory diet throughout the intervention period; there was no change in the nutrient intake of participants. Since participants resided together in the dormitory, there was no change in diet, lifestyle and training during the intervention period. Third, we could not directly measure the changes in SCFA levels. Although Butyrate-producing bacteria promote the production of SCFA, the effect of perilla oil intake on the change in SCFA should be clarified in future studies.
In conclusion, this study showed that a daily intake of 9 g/d perilla oil enhanced the abundance of Butyrate-producing bacteria Lachnospiraceae and suppressed that of Proteobacteria and urinary indoxyl sulphate levels. This effect was not observed in 3 g/d perilla oil intake group. While, there were improvements in the gut function in both groups. The finer dose of perilla oil that stimulates the production of metabolites from gut bacteria and suppresses gut-disturbance indicators should be investigated in the future. Daily n-3 fatty acid intake through consumption of perilla oil would be beneficial for enhancing gut microbiota growth and function as well as a fuel source for trained female athletes.
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|
---
title: Cross-sectional examination of ultra-processed food consumption and adverse
mental health symptoms
authors:
- Eric M Hecht
- Anna Rabil
- Euridice Martinez Steele
- Gary A Abrams
- Deanna Ware
- David C Landy
- Charles H Hennekens
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991859
doi: 10.1017/S1368980022001586
license: CC BY 4.0
---
# Cross-sectional examination of ultra-processed food consumption and adverse mental health symptoms
## Body
Mental illnesses including depression and anxiety are leading causes of morbidity, disability and mortality[1,2]. Dietary patterns may influence mental health. For example, poor dietary patterns which lack essential nutrients, have a high glycaemic index and are high in added sugars may lead to adverse mental health symptoms(3–6). In addition, in animal models, poor diets dysregulate brain insulin which affects mood, decreases neuronal levels of serotonin and dopamine and increases neuroinflammation as measured by inflammatory cytokines(7–10). Poor diets and the consumption of non-nutrient additives in animal models can also adversely affect the intestinal microbiome which, in turn, can lead to systemic and neuroinflammation[11].
The NOVA food classification is a widely used system recently adopted by the Food and Agricultural Organization of the United Nations[12]. NOVA considers the nature, extent and purpose of food processing in order to categorise foods and beverages into four groups: unprocessed or minimally processed foods, processed culinary ingredients, processed foods and ultra-processed foods (UPF)[13,14].
UPF are defined as industrial formulations of processed food substances (oils, fats, sugars, starch, protein isolates) that contain little or no whole food and typically include flavourings, colourings, emulsifiers and other cosmetic additives[15]. UPF are convenient, low cost, quick to prepare or ready-to-eat preparations of food that result from extensive ‘physical, biological, and chemical processes’ that create food products that are deficient in original and natural food[16]. The most commonly consumed UPF include many sugar-sweetened beverages, reconstituted meat products, packaged snacks, chips, breakfast cereals, cookies, cake, chips, and breads and numerous other packaged foods. The ultra-processing of food depletes its nutritional value and also increases the number of calories, as UPF tend to be high in added sugar, saturated fat and salt, while low in protein, fibre, vitamins, minerals and phytochemicals[17,18]. Over 70 % of packaged foods in the USA are classified as UPF and represent approximately 60 % of all consumed calories[19,20].
While there is some evidence regarding UPF consumption and depression(21–23), data are sparse regarding other adverse mental health symptoms including anxiety and mentally unhealthy days. In this Research Article, we explored a nationally representative sample of the US population, whether individuals who consume high amounts of UPF report significantly more adverse mental health symptoms including depression, anxiety and mentally unhealthy days.
## Abstract
### Objective:
To explore whether individuals who consume higher amounts of ultra-processed food (UPF) have more adverse mental health symptoms.
### Design:
Using a cross-sectional design, we measured the consumption of UPF as a percentage of total energy intake in kilo-calories using the NOVA food classification system. We explored whether individuals who consume higher amounts of UPF were more likely to report mild depression, more mentally unhealthy days and more anxious days per month using multivariable analyses adjusting for potential confounding variables.
### Setting:
Representative sample from the United States National Health and Nutrition Examination Survey between 2007 and 2012.
### Participants:
10 359 adults aged 18+ without a history of cocaine, methamphetamine or heroin use.
### Results:
After adjusting for covariates, individuals with the highest level of UPF consumption were significantly more likely to report at least mild depression (OR: 1·81; 95 % CI1·09, 3·02), more mentally unhealthy (risk ratio (RR): 1·22; 95 % CI 1·18, 1·25) and more anxious days per month (RR: 1·19; 95 % CI 1·16, 1·23). They were also significantly less likely to report zero mentally unhealthy (OR: 0·60; 95 % CI 0·41, 0·88) or anxious days (OR: 0·65; 95 % CI 0·47, 0·90).
### Conclusions:
Individuals reporting higher intakes of UPF were significantly more likely to report mild depression, more mentally unhealthy and more anxious days and less likely to report zero mentally unhealthy or anxious days. These data add important information to a growing body of evidence concerning the potential adverse effects of UPF consumption on mental health.
## Data source and participants
The National Health and Nutrition Examination Survey (NHANES) is a series of cross-sectional evaluations of a representative sample of the non-institutionalised population of the USA. NHANES is comprised of four major components, including questions regarding demographics and health, health examination, laboratory testing and a 24-h dietary recall. Further details about NHANES have been described elsewhere[24,25]. Using a cross-sectional design, we combined three cycles from NHANES between 2007 and 2012. We included individuals with dietary data and information on mild depression, mentally unhealthy days, anxious days and covariates. We excluded individuals who self-reported the current or past use of cocaine, methamphetamine or heroin because of a lack validation studies using the 9-question Patient Health Questionnaire (PHQ) evaluation to detect mild depression and other mental health symptoms in individuals who use recreational drugs (n 2129). The final sample consisted of 10 359 US adults aged 18 years and older.
## Exposure of ultra-processed food
We applied the NOVA classification to all of the recorded United States Department of Agriculture’s Food and Nutrient Database for Dietary Studies (USDA FNDDS) 8-digit Food Codes to the NHANES data. The details of the procedures to classify FNDDS Food Codes according to the NOVA system have been previously described[26]. USDA’s FNDDS 2007–2012 were used to code dietary intake data and calculate Food Code energy intakes[27]. For homemade recipes, we calculated the underlying ingredient (SR Code) energy values using variables from both FNDDS 2007–2012 and USDA National Nutrient Database for Standard Reference, Legacy Release[26]. Using the average of two NHANES 24-h dietary recalls when available (and 1 d otherwise), we quantified each individual’s consumption of UPF in kilo-calories and calculated the percentage energy intake per day, in kilo-calories consumed as UPF. The proportion of respondents with one and two 24-h dietary recall was 10·6 and 89·4 %, respectively. Subjects were categorised according to their UPF consumption into five evenly divided categories. These categories allowed for a sufficiently large reference group (0–19 %) that could act as a proxy to a non-exposed group. The sample sizes for each group based upon % UPF consumption were: 0–19 %, n 305; 20–39 %, n 1860; 40–59 %, n 4023; 60–79 %, n 3286; and ≥80 %, n 885.
## Outcome: adverse mental health symptoms
We measured three mental health symptoms: [1] mild depression; [2] number of mental unhealthy days and [3] number of anxious days. Symptoms of depression were ascertained from the PHQ-9. The PHQ-9 is a validated and reliable measure for depression. Respondents with a PHQ-9 score of five points or greater were categorised as having symptoms of mild depression[28]. The number of mentally unhealthy days was obtained from the response to the question: ‘During the past 30 d, how many days was your mental health not good?’ ( range: 1–30). This question is a validated measure of mental health and is highly correlated with mental health symptoms[29]. The number of anxious days was obtained from the response to the question: ‘During the past 30 days, how many days did you feel worried, tense, or anxious?’ ( range: 1–30). This question is also a validated measure of chronic anxiety[30].
## Covariates
The following available socio-demographic covariates were included in the analysis: [1] gender (man/woman); [2] age (18–$\frac{29}{30}$–$\frac{39}{40}$–$\frac{49}{50}$–$\frac{59}{60}$–69 years old); [3] race/ethnicity (Mexican/Other Hispanic/non-Hispanic White/non-Hispanic Black/Other Race) and [4] poverty status calculated as a ratio of the monthly family income specific to family size (less than or equal to poverty level/greater than poverty level). The health-related covariates included smoking (never/former/current), exercise (no physical activity: reported no moderate or vigorous activity; less than recommend physical activity: <150 min of moderate or < 75 min of vigorous activity/week and recommended physical activity: ≥150 min of moderate or > 75 min of vigorous activity/week) as well as BMI categorised as underweight (<18·5 kg/m2), healthy weight (18·5–24·9 kg/m2), overweight (25–29·9 kg/m2) and obese (30 kg/m2 and above)[31,32].
## Data analysis
Descriptive statistics were generated for each adverse mental health symptom, mild depression, number of mentally unhealthy days and number of anxious days, as well as available covariates using frequency/percentages or medians/interquartile range, where appropriate. For mild depression, we used logistic regression to model the probability of a PHQ-9 score of five or greater which signifies at least mild depression. We then modelled the outcomes of ‘number of mentally unhealthy days’ and ‘number of anxious days’ using zero-inflated Poisson regression[33]. The zero-inflated Poisson regression model has two components, count and logit. The count component model generates risk ratios (RR) of reporting more mentally unhealthy or anxious days over the prior 30 d. The logit component model predicts the probability of a zero count of the outcome and reports Odds Ratio (OR). UPF and the covariates were tested independently in unadjusted models and covariates with P-values ≤ 0·1 in their respective unadjusted model were included in the final adjusted model. We considered statistical significance to be based on a two-sided P value of less than 0·05. All statistical analyses were performed using SAS software (v9.4; SAS Institute, Inc.) and R (R Foundation for Statistical Computing). NHANES sampling and survey weights were used in the analysis.
## Descriptive statistics
Among the 10 359 respondents, the median age was 42·2 years, 66·2 % were non-Hispanic Whites, 52·9 % were women and 84·6 % had a family poverty ratio greater than the national level. A total of 68·3 % were overweight (32·3 %) or obese (36 %), 61·0 % had never smoked and 45·6 % reported no physical activity. The median UPF consumption as defined by energy intake percentage was 57·1 % with an interquartile range from 44·9 to 68·6 %. Mild depression was reported in 21·3 % of all respondents. The median number of mentally unhealthy and anxious days were 0 (interquartile range: 0·0–3·3) and 1·1 (interquartile range: 0·0–6·0), respectively (Table 1). Distribution of these characteristics by UPF consumption category is presented in Table 1.
Table 1Baseline characteristics of 10 359 adults aged 18+ years in US NHANES 2007 through 2012Percentage of calories consumed as ultra-processed foodsOverall n 10 3590–19 % n 30520–39 % n 186040–59 % n 402360–79 % n 3286≥80 % n 885MedianIQRMedianIQRMedianIQRMedianIQRMedianIQRMedianIQRAge48·134·4–59·145·433·1–57·343·730·3–56·140·527·9–52·934·323·2–47·142·229·1–54·$5\%$95 % CI%95 % CI%95 % CI%95 % CI%95 % CI%95 % CIGender (women)45·337·4, 53·252·749·6, 55·754·052·0, 56·052·950·5, 55·251·547·1, 55·952·951·9, 54·0Race/ethnicity Mexican4·82·4, 7·310·48·2, 12·610·57·8, 13·27·25·0, 9·54·22·2, 6·18·76·5, 10·9 Other Hispanic13·07·8, 18·110·57·1, 13·96·14·4, 7·83·72·6, 4·83·01·6, 4·35·94·2, 7·6 Non-Hispanic White45·035·0, 55·156·249·9, 62·565·761·3, 70·171·767·0, 76·373·166·1, 80·166·261·7, 70·7 Non-Hispanic Black9·76·2, 13·210·88·5, 13·011·49·4, 13·313·410·2, 16·515·310·1, 20·512·39·9, 14·6 Other race27·418·2, 36·612·29·2, 15·16·45·1, 7·74·13·2, 5·04·42·4, 6·46·95·8, 8·1BMI category Underweight <181·80·0, 3·92·51·6, 3·42·01·3, 2·82·11·4, 2·73·31·7, 4·82·21·8, 2·6 Healthy 18·5–24·938·029·9, 46·034·531·1, 37·829·226·8, 31·526·924·6, 29·329·124·4, 33·829·427·5, 31·4 Overweight 25–29·930·524·3, 36·835·632·2, 38·934·031·8, 36·330·728·6, 32·827·223·7, 30·632·330·8, 33·9 Obese > 3029·720·7, 38·627·524·6, 30·534·832·3, 37·340·338·0, 42·740·536·3, 44·73634·3, 37·8 Poverty level or lower19·413·0, 25·715·013·0, 17·113·211·4, 15·016·213·5, 18·820·114·5, 25·715·413·5, 17·4Smoking status Current smoker18·712·0, 25·514·712·5, 17·014·612·8, 16·520·718·5, 22·825·821·7, 29·917·916·2, 19·6 Former smoker19·612·4, 26·821·418·2, 24·522·920·5, 25·317·715·6, 19·716·112·3, 20·020·218·8, 21·6 Never smoker61·750·7, 72·763·960·6, 67·362·559·9, 65·061·658·4, 64·958·152·8, 63·361·959·6, 64·2Physical activity No physical activity48·539·3, 57·741·337·3, 45·344·041·0, 47·147·444·4, 50·449·043·5, 54·545·643, 48·2 Less than recommended physical activity20·613·3, 28·016·714·4, 18·918·616·9, 20·419·717·6, 21·818·115·4, 20·818·617·3, 19·8 Recommended physical activity30·922·3, 39·542·037·9, 46·237·334·3, 40·432·930·0, 35·832·928·0, 37·935·833·3, 38·4MedianIQRMedianIQRMedianIQRMedianIQRMedianIQRMedianIQRUPF consumption (% of total energy intake)15·711·6–18·033·728·8–37·151·346·1–55·467·763·6–72·985·782·4–89·857·144·9, 68·6Number of anxious days0·00·0–4·01·00·0–4·70·80·0–4·91·40·0–6·61·90·0–9·31·10·0–6·0Number of mentally unhealthy days0·00·0–1·30·00·0–2·00·00·0–2·90·00·0–4·00·00·0–5·60·00·0–3·$3\%$95 % CI%95 % CI%95 % CI%95 % CI%95 % CI%95 % CIMild depression (PHQ-9 score of ≥ 5)17·311·6, 23·017·815·4, 20·219·217·2, 21·322·921·2, 24·730·326·7, 33·921·319·9, 22·6PHQ-9, Patient Health Questionnaire; IQR, interquartile range.
The missing data on outcomes were as follows: depression (n 6; 0·06 %), mentally unhealthy days (n 16; 0·15 %) and anxious days (n 12; 0·11 %). Since less than 10 % of the data were missing for the main outcome, our analyses were conducted without further weight adjustment or imputation to account for missing data[34].
## Association between ultra-processed food consumption and adverse mental health outcomes
All models were adjusted for by age, gender, race/ethnicity, BMI, poverty level, smoking status and physical activity. Respondents with the highest v. lowest level of UPF consumption had a significantly higher probability of mild depression (OR: 1·81; 95 % CI 1·09, 3·02) (Fig. 1, Table 2) and were significantly more likely to report a higher number of mentally unhealthy days (RR: 1·22; 95 % CI 1·18, 1·25) and anxious days (RR: 1·19; 95 % CI 1·16, 1·23) (Fig. 1, Table 3). For each increasing level of UPF consumption, the RR for each of these outcome measures also significantly increased (Tables 2 and 3).
Fig. 1Adjusted percentage likelihood (increase or decrease) of mild depression (OR), number of mentally unhealthy days (RR) and number of anxious days (RR) by category of ultra-processed food consumption with <20 % as the referent level Table 2Unadjusted and adjusted analyses regarding ultra-processed food exposure, relevant covariates and the outcome of mild depressionOutcome: mild depressionUnadjustedAdjusted* OR95 % CIOR95 % CIUPF consumption 20–39 %1·040·68, 1·581·050·64, 1·71 40–59 %1·140·77, 1·691·110·73, 1·69 60–79 %1·430·95, 2·141·310·84, 2·04 ≥ 80 %2·08** 1·31, 3·291·81** 1·09, 3·02 0–19 % (reference)1·001·00Age 30–39 years old0·980·82, 1·181·020·81, 1·29 40–49 years old1·160·97, 1·391·180·95, 1·46 50–59 years old1·030·87, 1·231·070·87, 1·33 60–69 years old0·80** 0·68, 0·960·820·65, 1·03 18–29 years old (reference)1·001·00Gender Women1·71** 1·53, 1·901·75** 1·53, 2·00 Men (reference)1·001·00Race/ethnicity Mexican1·120·97, 1·291·010·87, 1·17 Other Hispanic1·54** 1·27, 1·881·41** 1·15, 1·72 Non-Hispanic Black1·29** 1·11, 1·511·100·95, 1·29 Other race0·920·72, 1·180·920·69, 1·24 Non-Hispanic White (reference)1·001·00BMI category Underweight <181·491·05, 2·211·330·89, 1·99 Overweight 25–29·90·980·82, 1·171·050·85, 1·29 Obese > 301·59** 1·36, 1·871·53** 1·30, 1·79Healthy 18·5–24·9 (reference)1·001·00 Poverty level Lower than poverty level2·30** 1·91, 2·761·91** 1·62, 2·26 Greater than poverty level (reference)1·001·00Smoking status Current smoker1·991·75, 2·271·73** 1·50, 1·99 Former smoker0·980·84, 1·141·100·93, 1·30 Never smoker1·001·00Physical activity None2·25** 1·95, 2·581·99** 1·72, 2·30 Less than recommended1·42** 1·18, 1·701·30** 1·12, 1·69 Recommended (reference)1·001·00*Adjusted for age, gender, race/ethnicity, BMI category, poverty level, smoking status and physical activity.**Indicates statistical significance (< 0·05).
Table 3Unadjusted and adjusted risk ratios regarding ultra-processed food exposure, relevant covariates and the outcomes of the number of mentally unhealthy and anxious days self-reported over the prior 30 dOutcome: more mentally unhealthy daysOutcome: more of anxious daysUnadjusted risk ratio95 % CIAdjusted risk ratio* 95 % CIUnadjusted risk ratio95 % CIAdjusted risk ratio* 95 % CIUPF consumption 20–39 %0·91** 0·88, 0·930·95** 0·92, 0·980·980·95, 1·001·020·98, 1·06 40–59 %0·97** 0·94, 0·991·04** 1·01, 1·061·010·99, 1·041·06** 1·03, 1·10 60–79 %1·06** 1·03, 1·091·11** 1·08, 1·141·13** 1·10, 1·151·15** 1·12, 1·18 ≥ 80 %1·20** 1·17, 1·231·22** 1·18, 1·251·20** 1·17, 1·231·19** 1·16, 1·23 0–19 % (reference)1·001·001·001·00Age 30–39 years old1·08** 1·07, 1·101·06** 1·05, 1·071·09** 1·08, 1·101·09** 1·08, 1·10 40–49 years old1·14** 1·13, 1·161·07** 1·06, 1·081·22** 1·21, 1·231·17** 1·16, 1·18 50–59 years old1·28** 1·27, 1·301·19** 1·17, 1·201·16** 1·15, 1·171·12** 1·11, 1·13 60–69 years old1·11** 1·1, 1·121·08** 1·06, 1·091·08** 1·07, 1·091·04** 1·02, 1·05 18–29 years old (reference)1·001·001·001·00Gender Women1·04** 1·03, 1·051·03** 1·02, 1·041·09** 1·08, 1·101·08** 1·07, 1·09 Men (reference)1·001·001·001·00Race/ethnicity Mexican0·95** 0·94, 0·960·990·98, 1·000·94** 0·93, 0·950·93** 0·92, 0·94 Other Hispanic1·05** 1·04, 1·061·05** 1·04, 1·071·05** 1·04, 1·061·02** 1·01, 1·04 Non-Hispanic Black1·11** 1·10, 1·121·06** 1·05, 1·070·98** 0·97, 0·990·94** 0·93, 0·95 Other race1·02** 1·01, 1·041·06** 1·04, 1·070·92** 0·90, 0·930·990·97, 1·01 Non-Hispanic White (reference)1·001·001·001·00BMI category Underweight <181·20** 1·18, 1·231·16** 1·14, 1·191·17** 1·15, 1·191·16** 1·14, 1·19 Overweight 25–29·91·09** 1·08, 1·101·08** 1·07, 1·101·02** 1·01, 1·031·011·00, 1·03 Obese > 301·22** 1·21, 1·231·16** 1·14, 1·171·14** 1·13, 1·151·08** 1·07, 1·09 Healthy 18·5–24·9 (reference)1·001·001·001·00Poverty level Lower than poverty level1·27** 1·26, 1·281·16** 1·15, 1·171·27** 1·26, 1·291·16** 1·15, 1·17 Greater than poverty level (reference)1·001·001·001·00Smoking status Current smoker1·41** 1·40, 1·421·35** 1·34, 1·361·43** 1·42, 1·431·34** 1·34, 1·35 Former smoker1·12** 1·11, 1·131·16** 1·14, 1·171·05** 1·04, 1·061·09** 1·08, 1·10 Never smoker1·001·001·001·00Physical activity None1·43** 1·42, 1·451·26** 1·25, 1·270·980·97, 1·001·24** 1·23, 1·25 Less than recommended0·97** 0·96, 0·980·92** 0·90, 0·931·37** 1·36, 1·380·94** 0·92, 0·95 Recommended (reference)1·001·001·001·00*Adjusted for age, gender, race/ethnicity, BMI category, poverty level, smoking status and physical activity.**Indicates statistical significance (< 0·05).
In addition, after adjusting for covariates, respondents with the highest v. lowest level of UPF consumption were significantly less likely to report zero mentally unhealthy (OR: 0·60; 95 % CI 0·41, 0·88) and zero anxious days (OR: 0·65; 95 % CI 0·47, 0·90) (Table 4).
Table 4Unadjusted and adjusted OR regarding the likelihood of self-reporting zero mentally unhealthy and anxious days over the prior 30 d as well as relevant covariates, according to the level of ultra-processed food consumptionOutcome: reporting zero mentally unhealthy daysOutcome: reporting zero of anxious daysUnadjusted OR95 % CIAdjusted OR* 95 % CIUnadjusted OR95 % CIAdjusted OR* 95 % CIUPF consumption 20–39 %0·760·52, 1·100·810·57, 1·160·64** 0·47, 0·870·770·55, 1·07 40–59 %0·61**0·44, 0·860·720·51, 1·010·66** 0·49, 0·870·820·58, 1·15 60–79 %0·53** 0·37, 0·760·68** 0·48, 0·960·56** 0·43, 0·730·71** 0·51, 0·99 ≥ 80 %0·44** 0·31, 0·630·60** 0·41, 0·880·49** 0·38, 0·650·65** 0·47, 0·90 0–19 % (reference)1·001·001·001·00Age 30–39 years old1·31** 1·14, 1·501·25** 1·07, 1·460·960·86, 1·090·940·81, 1·09 40–49 years old1·110·97, 1·261·100·92, 1·310·86** 0·76, 0·970·860·74, 1·02 50–59 years old1·57** 1·35, 1·811·47** 1·18, 1·821·050·92, 1·200·980·83, 1·17 60–69 years old2·09** 1·74, 2·512·04** 1·62, 2·571·57** 1·38, 1·781·49** 1·26, 1·77 18–29 years old (reference)1·001·001·001·00Gender Women0·55** 0·50, 0·610·52** 0·46, 0·590·58** 0·53, 0·630·57** 0·51, 0·63 Men (reference)1·001·001·001·00Race/ethnicity Mexican1·34** 1·17, 1·541·43** 1·19, 1·721·21** 1·05, 1·391·28** 1·07, 1·54 Other Hispanic0·970·83, 1·131·000·83, 1·200·900·76, 1·070·940·76, 1·15 Non-Hispanic Black1·050·94, 1·161·17** 1·05, 1·301·25** 1·12, 1·411·37** 1·21, 1·55 Other race1·27** 1·04, 1·561·27** 1·00, 1·621·49** 1·28, 1·731·52** 1·27, 1·82 Non-Hispanic White (reference)1·001·001·001·00BMI category Underweight <181·090·78, 1·521·240·82, 1·870·980·72, 1·341·010·69, 1·47 Overweight 25–29·91·31** 1·15, 1·481·150·99, 1·351·21** 1·10, 1·331·15** 1·02, 1·31 Obese > 301·12** 1·01, 1·251·020·89, 1·171·090·99, 1·211·070·97, 1·19 Healthy 18·5–24·9 (reference)1·001·001·001·00Poverty level Lower than poverty level0·70** 0·63, 0·790·83** 0·73, 0·930·84** 0·77, 0·910·87** 0·78, 0·96 Greater than poverty level (reference)1·001·001·001·00Smoking status Current smoker0·70** 0·61, 0·790·71** 0·60, 0·850·960·86, 1·060·940·83, 1·07 Former smoker1·120·97, 1·310·920·78, 1·091·23** 1·08, 1·401·150·98, 1·36 Never smoker1·001·001·001·00Physical activity None0·89** 0·80, 0·990·900·79, 1·030·88** 0·78, 0·991·050·95, 1·16 Less than recommended0·81** 0·70, 0·950·84** 0·71, 0·981·000·92, 1·090·940·82, 1·08 Recommended (reference)1·001·001·001·00*Adjusted for age, gender, race/ethnicity, BMI category, poverty level, smoking status and physical activity.**Indicates statistical significance (< 0·05).
## Discussion
In this nationally representative sample of American adults, UPF constituted 57 % of total energetic intake. Individuals who consumed the most UPF as compared with those who consumed the least amount had statistically significant increases in the adverse mental health symptoms of mild depression, ‘mentally unhealthy days’ and ‘anxious days’. They also had significantly lower rates of reporting zero ‘mentally unhealthy days’ and zero ‘anxious days’.
Our data are supported by existing evidence from basic research and other descriptive and observational studies. For example, basic research provides support for the hypothesis that food additives in UPF including emulsifiers and artificial sweeteners can lead to pathophysiological changes that have been associated with mental health symptoms including impaired glucose tolerance, increases in inflammatory mediators, oxidative stress, neuroinflammation, pathogenic changes to neuronal mitochondrial function, as well as alterations in both tryptophan metabolism, and the HPA axis, and changes in the local expression of neurotrophic growth factors[35]. Several investigations, including two large prospective cohort studies in Europe, suggest that individuals whose diets lack essential nutrients, have a high glycaemic index, and are high in added sugars also have significantly increased risks of depression and anxiety. They also found that those who consume diets, high in fish, vegetables, olive oil, beans, nuts, PUFA and low in saturated fats, such as the Mediterranean diet, have significantly lower risks of depression(4–6,9,21,22,36–43).
Several meta-analyses of observational studies are compatible with the current findings. In one meta-analysis of twenty observational studies, individuals who consumed diets that included a higher intake of fruit, vegetables, fish and whole grains had lower risks of depression[4]. In another, individuals who adhered to the Mediterranean diet had significantly lower rates of depression. In a third meta-analysis, individuals who consumed a diet lower in PUFA and n-3 fatty acids reported significantly more mild depression or social anxiety[44]. Finally, in one randomised trial, which provides the most reliable evidence for small to moderate effects, those assigned to a 3-month healthy dietary intervention reported significant decreases in moderate-to-severe depression[43].
Our data also suggest that those who consume high levels of UPF consumption also experience significantly more ‘mentally unhealthy’ and ‘anxious’ days and their corresponding decrease in ‘zero mentally unhealthy days’ and ‘zero anxious days’. In another study of elderly adults, those who consumed a poor diet quality as measured by the HEI also had significantly more mentally unhealthy days[45]. To the best of our knowledge, there are no data regarding the higher consumption of UPF and the mental health outcomes of ‘zero anxious days’ and ‘zero mentally unhealthy days’.
This original research has several unique strengths. With respect to exposure, the use of the NOVA to classify dietary data allowed determining the level of food processing according to objective and standardised criteria. With respect to outcomes, we utilised three validated measures of adverse mental health symptoms. In addition, the NHANES database is a large and representative sample of the US population. This suggests that the findings are generalisable to the entire USA as well as other Western countries with similar UPF intakes.
This study also has several limitations. In addition to the descriptive study design, other limitations include the self-report of both exposure and outcomes which could result in misclassification of one or both of these measures. Dietary data obtained by 24-h recalls may suffer from recall or social desirability bias; however, the data acquisition method employed by NHANES has been shown to produce accurate intake estimates suitable for assessing population averages(35,46–48). An additional limitation is that NHANES does not consistently collect all of the information needed to assess food processing (i.e. place of meals, product brands)[49]. Nevertheless, such misclassification is more likely to be non-differential underestimating the true effect. In addition, while we attempted to control for the potential confounding effects of the available variables, residual confounding is possible especially because lifestyle risk factors tend to cluster[50]. We also calculated the proportion of UPF in the diet by using ‘energy ratio’ rather than ‘weight ratio’ which does not properly capture ‘energy devoid’ UPF (e.g. artificially sweetened beverages) and non-nutritional factors related to food processing such as alteration of the food matrix, neo-formed contaminants or food additives. Our study findings are also limited in generalisability to milder grades of depression. Despite these limitations, we believe the most plausible interpretation of these data are to add to the growing body of evidence that individuals who consume higher amounts of UPF have significantly more adverse mental health symptoms.
In summary, these data indicate that individuals with higher intakes of UPF report significantly more mild depression, as well as more mentally unhealthy and anxious days per month, and less zero mentally unhealthy or anxious days per month. When considering these data in the context of the totality of evidence, it can be hypothesised that a diet high in UPF provides an unfavourable combination of biologically active food additives with low essential nutrient content which together have an adverse effect on mental health symptoms. While further research is needed, especially randomised clinical trials, these data add important and relevant information to a growing body of evidence concerning the adverse effects of UPF consumption on mental health symptoms. Since UPF represent the majority of calories consumed by the US population, these data may also have significant clinical and public health implications.
## Conflict of interest:
Dr. Hecht, Ms. Rabil, Dr. Martinez-Steele, Ms, Ware, Dr. Abrams and Dr. Landy report no disclosures. Professor Hennekens reports that he serves as an independent scientist in an advisory role to investigators and sponsors as Chair or Member of Data Monitoring Committees for Amgen, British Heart Foundation, Cadila, Canadian Institutes of Health Research, DalCor and Regeneron; to the Collaborative Institutional Training Initiative (CITI), legal counsel for Pfizer, the United States Food and Drug Administration and UpToDate; receives royalties for authorship or editorship of 3 textbooks and as co-inventor on patents for inflammatory markers and CVD that are held by Brigham and Women’s Hospital; has an investment management relationship with the West-Bacon Group within SunTrust Investment Services, which has discretionary investment authority; does not own any common or preferred stock in any pharmaceutical or medical device company.
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|
---
title: What explains the large disparity in child stunting in the Philippines? A decomposition
analysis
authors:
- Valerie Gilbert T Ulep
- Jhanna Uy
- Lyle Daryll Casas
journal: Public Health Nutrition
year: 2022
pmcid: PMC9991861
doi: 10.1017/S136898002100416X
license: CC BY 4.0
---
# What explains the large disparity in child stunting in the Philippines? A decomposition analysis
## Body
Stunting is a marker of chronic malnutrition that affects 144 million children and causes significant disease burden worldwide[1,2]. Stunting is an important determinant of human capital and is a predictor of economic productivity(3–5). It is linked to poorer cognitive and educational outcomes[6,7], lower wages[8,9], and poorer health in adult life[3,10].
The *Philippines is* about to become an upper middle-income country, but stunting remains of very high public health significance[11]. The average stunting rate in upper middle-income countries is only 14 %, while the Philippines hovers at 30 %, comparable to the poorest countries in the world[12]. From 2000 to 2015, the country’s income per capita has increased by 3–4 % annually from 2000 to 2015[12], but stunting prevalence had barely improved during the same period with only a 0–1 % annual decline[12,13]. In contrast, many low- and middle-income countries have experienced a large decline even in countries notoriously known to have high burden[14,15]. The paradoxical relationship of economic growth and chronic malnutrition in the Philippines was against the backdrop of slow decline in poverty incidence and persistently high-income inequality. From 2000 to 2015, the percentage of the population below the poverty threshold hovered at 21 % to 25 % and the Gini coefficient at 0·46 to 0·42[16].
The high prevalence of child stunting is reinforced by the large disparity across socio-economic status. In 2015, about 36 % of under-five children from the bottom 20 % were stunted compared to 14 % among the richest 20 %[11]. This 22-percentage point absolute difference between the poor (bottom 20 %) and non-poor (top 20 %) makes the Philippines one of the countries with the highest gap[1]. Globally, the average gap is only 11 percentage points[17]. Hence, understanding the drivers of stunting inequality could guide the appropriate design of nutrition and health interventions for the country. While studies have examined the correlates of stunting in the Philippines, the magnitude of the inequality and its drivers are not well-documented(18–20).
This study aims to determine the magnitude of socio-economic inequality in stunting among 6–23 months Filipino children and to decompose the inequality into maternal, health and nutrition and socio-demographic factors. We used a nationally representative sample from the 2015 Philippine National Nutrition Survey (NNS).
## Abstract
### Objective:
About one-third of under-five Filipino children are stunted, with significant socio-economic inequality. This study aims to quantify factors that explain the large gap in stunting between poor and non-poor Filipino children.
### Design:
Using the 2015 Philippine National Nutrition Survey, we conducted a linear probability model to examine the determinants of child stunting and then an Oaxaca-Blinder decomposition to explain the factors contributing to the gap in stunting between poor and non-poor children.
### Setting:
Philippines.
### Participants:
1881 children aged 6–23 months participated in this study.
### Results:
The overall stunting prevalence was 38·5 % with a significant gap between poor and non-poor (45·0 % v. 32·0 %). Maternal height, education and maternal nutrition status account for 26 %, 18 % and 17 % of stunting inequality, respectively. These are followed by quality of prenatal care (12 %), dietary diversity (12 %) and iron supplementation in children (5 %).
### Conclusions:
Maternal factors account for more than 50 % of the gap in child stunting in the Philippines. This signifies the critical role of maternal biological and socio-economic circumstances in improving the linear growth of children.
## Data
We used the 2015 Philippine NNS, a cross-sectional nationally representative survey covering all 17 regions and 80 provinces of the country. The NNS is the official data source on nutritional status, diet and other lifestyle-related risk factors in the Philippines. The survey employed a stratified three-stage sampling. The first stage of the sampling was the selection of the primary sampling unit which consisted of one village with at least 500 households each. From these sampling units, housing units were randomly selected from enumeration areas with 150–200 households. In the last stage, households were randomly selected. In total, 42 310 households with 17 702 children aged 0–60 months were sampled in the survey. All members of the household, including children, were then included to participate[11]. We requested the expanded microdata from the Food and Nutrition Research Institute (FNRI). The details of the survey can be obtained here: http://enutrition.fnri.dost.gov.ph/site/uploads/2015_OVERVIEW.pdf.
Our analytical sample is 1881 children aged 6–23 months with complete records for anthropometric, socio-demographic, maternal and health data. We focused on the 6–23 age group only because this is the period when the sharp divergence in stunting prevalence occurs and the gap remained steady from 24 to 60th month. The figure in Appendix A shows the acceleration of the height-for-age Z-score between Q1 (poorest) and Q5 (richest), which starts at 6 months until 24 months.
## Dependent variable
Height-for-age is the dependent variable of interest. In the 2015 NNS, the height of children under-2 years of age was measured in a recumbent position using a medical plastic infantometer and the standing height of those 2 years and above was measured using a stadiometer following the standard procedures. We estimated the height-for-age Z-score using the 2006 WHO Child Growth Standards. We categorised a child as stunted if their height-for-age Z-score is two sd below the median[21].
## Socio-economic variable
We measured socio-economic status using a wealth index predicted from principal component analysis. The index is based on the ownership of wide-range assets (e.g. television, radio, refrigerator)[22,23]. We categorised households as ‘poor’ if they belonged to the bottom 40 % in the wealth distribution otherwise non-poor. We considered the bottom 40 % of the wealth distribution as poor because it captures the portion of the population living below or just above estimated national poverty threshold. It also captures the prevalence of moderate and severe food insecurity, which is around 54 % in 2015[11].
## Independent variables
To identify the independent variables to be included in our model, we adopted a framework from UNICEF [1990], which was refined by Fikru-Rizal and van Doorslaer [2019][24] (See Fig. 1). Under this framework, determinants of stunting are divided into non-modifiable and potentially modifiable factors. Non-modifiable factors include child age and sex[25]. Mother’s height was also considered as a non-modifiable factor because some part of a child’s height is explained by genetic factors[26]. Modifiable factors were categorised as basic, underlying and immediate factors. Immediate factors, that is, dietary intake and child’s disease (orange box) are conduits by which the underlying determinants such as food insecurity, feeding practices, environment and healthcare services (green box) and the basic factors such as household and parental factors and regional or geographic factors affect the child nutritional status (red box).
Fig. 1Conceptual Framework. Source: adapted from Rizal MF and van Doorslaer [2018] Under household factors, we included the following variables: sex of the household head, maternal education, age (in years) and maternal BMI, as well as maternal blood pressure. We categorised maternal education into binary high school and non-high school graduate, and BMI using WHO cut-offs for underweight, normal, overweight and obese[27]. For the maternal blood pressure, we categorised it into systolic blood pressure above and below 140 mmHg, since the cut-off for high blood pressure based on the 2020 Clinical Practice Guidelines on hypertension in the country[28].
Under underlying factors, we included food insecurity, feeding practices, environment and healthcare factors. We measured food insecurity using a score predicted from principal component analysis of five [5] food security-related questions that were categorised into high, medium and low food insecurity. Under feeding practices, we considered three variables: minimum meal frequency (MMF) and Dietary Diversity Score (DDS). MMF, a proxy indicator for a child’s energy requirements, examines the number of times children received foods other than breast milk[29]. MMF is met if a breastfed and non-breastfed children 6–23 months of age receive solid, semi-solid or soft foods or milk feed the minimum number of 4 times per day or more[29]. DDS is an indicator on the quality of diet of the child. The DDS was defined as the number of unique food groups consumed by the child the previous day[30]. Under environmental factors, we considered water, sanitation and hygiene variables such as handwashing practices, garbage disposal and availability of safe drinking water and sealed toilet facilities. Under healthcare factors, we determined the quality score using factor analysis using variables pertaining to services conducted during prenatal care such as anthropometric measurement, blood pressure treatment/diagnosis, blood test, urinalysis, ultrasound, micronutrient supplementation, tetanus toxoid and nutrition counseling; quality scores were categorised into low, medium and high. We determined whether the mother had postnatal care after giving birth, and whether the child was born in a health facility, and whether she had received three doses of DPT vaccine, received iron supplement, vitamin A supplement and had ever been dewormed. Appendix B outlines the operational definitions of independent variables.
## Data analysis
We started with bivariate analyses by examining the distribution of dependent and independent variables by socio-economic status. We used t-test to determine the significant difference in stunting and other independent variables between poor and non-poor Filipino children. After bivariate analyses, we performed two [2] inferential statistics. First, we conducted a linear probability model (LPM) to examine the determinants of stunting. LPM is an ordinary least square regression using a binary dependent variable (that is, 1 = stunted; 0 = non-stunted). In our regression model, we controlled food insecurity, feeding practices, environment, healthcare factors, household and other non-modifiable factors outlined in Fig. 1. A coefficient in an LPM is interpreted as the change in the probability that $Y = 1$ (i.e. stunted) for a one-unit change of the independent variable of interest, holding everything else constant. We chose LPM over logistic regression model because it is computationally tractable and easier to interpret the coefficients[31].
Second, we decomposed the difference in stunting prevalence between poor and non-poor children using the Oaxaca-Blinder method, an econometric tool used in labour economics to examine differential wage gaps between groups (e.g. by sex, race, residence)[32]. Oaxaca-Blinder has been used to assess disparities in health outcomes and healthcare utilisation expenditures(33–39). The decomposition aims to quantify the contribution of selected predictors in explaining the gap in the prevalence of stunting between poor and non-poor children. The gap is decomposed into three parts: the first part is known as explained or endowment effect (E) and claimed the gap due to differences in the distribution of determinants between poor and non-poor; the second part is the unexplained or coefficient effect (C) and claimed the gap due to the differences in the effect of determinants between the groups and the third is an interaction between both—endowment effect and coefficient effect (CE)[40]. The intuition behind Oaxaca Blinder decomposition can be described by the subsequent equations.
The first step is to estimate a linear regression for both poor, p and non-poor, np children. The y, is the outcome variable, that is,i.e. stunted (2 sd below median). X’s are a vector of explanatory variables as listed in Appendix B.[1] The gap between the mean outcomes, γnp, and γp is equal to:[2] where χnp and χp are vectors of explanatory variables evaluated at the means for the non-poor and poor, respectively. Assuming exogeneity, the error terms in Equation 1 are zero. Estimates of difference in the gap in mean outcomes were obtained by substituting sample means of the Xs and estimates of the parameter on β in Equation 1:[3] The differences in Xs are weighted by the coefficients of the poor group and the differences in the coefficients are weighted by the Xs of the non-poor group; thus, partitioning the gap in outcomes between two groups:[4] Whereas, the gap in outcome was from a gap in endowments (E), a gap in coefficients (C) and a gap arising from the interaction of endowments and coefficients (CE).
We conducted all our analysis using STATA 15 software, with statistical significance determined at P ≤ 0·05. We accounted for the complex design of the survey using provided survey weights in the microdata from Food and Nutrition Research Institute (FNRI).
## Ethical standards disclosure
We used public use files of the DOST-FNRI NNS that was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the FNRI Institutional Ethics Review Committee (FNRIEC). Written informed consent was obtained from all subjects and their participation was strictly voluntary and they were allowed to withdraw their participation at any time without any consequence. The NNS data were available publicly, and the study participants were anonymous and thus did not require ethics approval.
## Results
We included 1881 children with complete records for anthropometric, socio-demographic, maternal and health data aged 6–23 months in our analysis. Overall, 38·5 % of the children were stunted. The prevalence of stunting among Filipino children belonging to poor households (45 %) was significantly higher compared to non-poor children (32 %). Using a concentration curve to display disparity, Fig. 2 presents the degree of inequalities in stunting among 6–23 months children. The area below the 45° line of equality represents a progressive concentration curve, and the area below represents a regressive concentration curve. Given that the curve is above the diagonal, the stunting ‘burden’ in the *Philippines is* concentrated more heavily in the poor.
Fig. 2Concentration curve presenting the degree of socio-economic inequality in stunting among children aged 6–23 months. Source: authors’ analysis of 2015 National Nutrition Survey Table 1 shows the results of bivariate analyses between socio-economic status and dependent and independent variables. Children from poor households have lower probability to have a mother with at least high school education, mother with postnatal care after giving birth, to be food secure, to meet MMF and diversity, to have access to safe drinking water and improved toilet, to have timely and high-quality prenatal care, to have facility-based delivery, to have complete DPT vaccine and to have iron supplementation.
Table 1Description of sample of children 6–23 months of ageVariableCategoryAllPoorNon-poor P-valueStunted% Stunted38·545·032·00·00* Sex of child% Female52·352·851·80·$76\%$ Male47·747·248·20·76Child’s ageAverage age in months17·717·717·70·98Maternal heightAverage height in cm151·5150·6152·30·25Civil statusSingle9·07·79·90·08Married89·090·887·60·08Separated/widowed2·01·52·50·08Female as household head% Female1·81·91·60·45Maternal education% Below high school38·858·123·60·00* % High school graduate and above61·241·976·40·00* Maternal age at birthAverage age in years30·130·629·80·02* High systolic blood pressure of mother% with 140 mmHg or above2·73·22·30·$21\%$ below 140 mmHg97·396·897·70·21Maternal BMI% Underweight13·615·012·50·$13\%$ Normal46·551·342·80·00* % Overweight28·025·729·60·$06\%$ Obese11·87·914·80·00* Food insecurity score% Low24·610·933·30·00* % Medium34·833·535·70·00* % High40·555·631·00·00* Minimum meal frequency (MMF)% meeting MMF94·091·695·60·00* % not meeting MMF6·08·44·40·00* Dietary diversity score (DDS)Average DDS3·22·93·30·00* Breastfeeding within the first hour of life% Yes65·968·464·00·09Handwashing before preparing the food of the child% Never4·04·33·80·$53\%$ Always89·488·490·10·$53\%$ Sometimes6·67·36·10·53Dispose garbage by dumping or throwing% Yes18·825·913·30·00* Availability of safe drinking water% Yes61·947·173·40·00* Availability of toilet (categorical)% None9·620·21·30·00* % Yes, water sealed84·670·796·00·00* % Yes, not sealed5·89·12·70·00* Post-natal care% Yes91·485·895·40·00* Timely prenatal care% Yes78·275·480·50·00* Quality of prenatal care% Low32·744·423·70·00* % Medium28·530·127·20·00* % High38·825·549·10·00* Place of delivery% Home21·233·112·00·00* % Government hospital37·832·642·80·00* % Government clinics12·717·49·10·00* % Private hospital/clinic28·117·036·80·00* Complete DPT vaccine% Yes58·954·862·10·00* Iron supplementation in children% Yes19·415·122·80·00* Vitamin A supplementation in children% Yes68·971·566·80·03* Deworming% Yes60·766·756·10·00* Geographical regionsRegion I4·63·45·30·00* Region II43·44·30·00* Region III10·24·314·10·00* Region IV-A12·45·916·50·00* Region V6·38·350·00* Region VI8·7126·60·00* Region VII7·28·36·50·00* Region VIII58·22·90·00* Region IX3·85·42·80·00* Region X4·65·83·80·00* Region XI4·86·73·60·00* Region XII4·86·83·60·00* NCR11·63·816·70·00* CAR1·61·61·60·00* ARMM5·192·60·00* Region XIII33·62·60·00* MIMAROPA2·33·51·50·00* * $P \leq 0$·05.
Table 2 shows the coefficients from the LPM, which is interpreted as the change in stunting for every one-unit change of the independent variable, ceteris paribus. Among non-modifiable and maternal factors, child’s sex, maternal height, maternal education and maternal nutrition status were significantly associated with stunting. Being a female decreases the probability of being stunted by 11 percentage points, and one [1] centimeter increase in maternal height decreases the probability of being stunted by 2 percentage points. Mothers without high school education increase their probability by 6 percentage point difference). Mothers with normal BMI are less likely to have stunted children compared to underweight (11 %) and overweight (16 %) counterparts.
Table 2OLS regression coefficients using linear probability modelVariablesCoefficient se 95 % CILower limitUpper limitFemale sex−0·11* 0·02−0·15−0·06Child’s age0·060·04−0·010·13Child age squared0·000·000·000·00Maternal Height−0·02* 0·00−0·03−0·02Civil status married (Ref: single)0·020·04−0·050·10Civil status separated/widowed0·17* 0·100·010·36Female household head0·050·10−0·140·24Maternal education below high school0·060·030·010·11Maternal age at birth0·000·000·000·00Maternal high blood pressure0·000·06−0·110·12BMI normal (Ref: Underweight)−0·11* 0·03−0·18−0·05BMI overweight−0·16* 0·04−0·24−0·09BMI obese−0·20* 0·04−0·29−0·11Food insecurity medium (Ref: low food insecurity)−0·050·03−0·110·01Food insecurity high−0·010·03−0·070·05Minimum meal frequency (MMF)−0·15* 0·05−0·24−0·05Dietary diversity score (DDS)−0·02* 0·01−0·04−0·01Breastfeeding within the first hour0·010·02−0·040·05Handwashing always (Ref: never)0·080·05−0·020·19Handwashing sometimes0·030·06−0·100·16Garbage disposal−0·010·03−0·060·05Safe drinking water−0·040·02−0·080·00Toilet facility sealed (Ref: No)−0·030·04−0·100·04Toilet facility not sealed0·090·05−0·010·20On time prenatal care−0·040·03−0·090·01Quality of prenatal care medium (Ref: low)−0·020·03−0·070·03Quality of prenatal care high−0·06* 0·03−0·12−0·01Place of delivery government hospital (Ref: home)−0·270·33−0·920·04Place of delivery government clinic0·140·39−0·060·09Place of delivery private facility−0·390·360·110·03Complete DPT vaccine−0·030·02−0·070·02Iron supplementation in children−0·06* 0·03−0·11−0·01Vitamin supplementation in children−0·020·02−0·070·03Post-natal care−0·040·04−0·120·04Deworming0·000·03−0·050·05High SES−0·010·03−0·060·04Regions: Cagayan Valley (Region II) (ref: Region I)0·020·07−0·120·16Central Luzon (Region III)−0·050·06−0·160·07Bicol Region (Region V)0·000·06−0·130·12Western Visayas (Region VI)0·030·06−0·080·15Central Visayas (Region VII)−0·020·06−0·140·10Eastern Visayas (Region VIII)0·080·07−0·060·21Zamboanga Peninsula (Region IX)−0·010·07−0·150·13Northern Mindanao (Region X)0·020·07−0·120·15Davao Region (Region XI)−0·050·07−0·190·09SOCCSKSARGEN (Region XII)−0·060·07−0·200·07National Capital Region (NCR)0·050·06−0·060·17Cordillera Administrative Region (CAR)0·040·09−0·150·22Autonomous Region in Muslim Mindanao (ARMM)0·060·07−0·080·20CARAGA (Region XIII)0·060·08−0·090·21CALABARZON (Region IV-A)0·030·06−0·080·14MIMAROPA0·000·08−0·160·17_cons3·740·432·904·58OLS, ordinary least squares, t statistics in parentheses.* $P \leq 0$·05.
In terms of child feeding practices, MMF and DDS were significantly associated with child stunting. Children meeting their MMF decreases the probability of being stunted by 15 percentage points than children not meeting MMF, and one [1] unit increase in DDS decreases the probability of being stunted by 2 %. In terms of healthcare access, only high-quality prenatal care and iron supplementation were associated with stunting. Mothers having high-quality prenatal decreases the probability of their child being stunted by 6 % than those having low-quality prenatal care. On the other hand, children receiving iron supplementation decrease the probability of being stunted by 6 % than those who are not. We did not observe a significant association between stunting and other healthcare variables such as a place of delivery, postnatal care, vitamin A supplementation in children and DPT immunisation. Neither handwashing, type of toilet nor garbage dumping were significant.
Table 3 shows the results of the Oaxaca-Blinder decomposition model. It shows that children belonging to poor households had higher prevalence of stunting (45 %) than those belonging to non-poor households (32 %), a 13-percentage point absolute gap. The mean difference in stunting rates between the groups was significant (P value < 0·05). This gap accounted mostly for the explained part (about 82 %). The unexplained and interactions components contribute to the rest, but the unexplained and interaction terms are not statistically significant. Because of the lack of significance of the two parts, we will only present the endowment effect (E) of the gap.
Table 3Summary result of Oaxaca decomposition analysis showing the mean differences in stunting ratescoefficient sd P-value% contributionMean prediction high (H)0·450·020·00Mean prediction low (L)0·320·010·00Raw differential (R) {H-L}0·130·020·00 due to endowments or explained (E)0·110·020·0082 % due to unexplained (C)0·010·030·767 % due to interaction (CE)0·020·030·5912 % Table 4 shows how differences in the distribution of each determinant contributed separately to the first part of the gap (endowment effect). In particular, maternal education, height and BMI, iron supplementation in children, quality prenatal care and DDS were the significant contributors explaining the gap in stunting among children between the poor and non-poor. Maternal height contributed the highest, at 26 %, of the gap for stunting followed by maternal education, at 18 %, maternal BMI at 17 %, quality of prenatal care at 12 %, dietary diversity at 12 % and iron supplementation for children at 5 % (See Fig. 3).
Table 4Contribution of each factor in poor and non-poor differentials in stunting (endowments or explained component)VariableCoef. P-value95 % lower limit95 % upper limit% share to the gapFemale sex0·000·560·000·00−1 %Child’s age−0·010·42−0·040·02−11 %Child age squared0·010·36−0·010·0412 %Maternal height0·03*** 0·000·020·0426 %*Civil status* married (Ref: single)0·000·670·000·001 %*Civil status* separated/widowed0·000·19−0·010·00−2 %Female household head0·000·630·000·000 %Maternal education below high school0·02*** 0·040·010·0418 %Maternal age at birth0·000·760·000·00−1 %Maternal high blood pressure0·000·470·000·001 %BMI normal (Ref: Underweight)−0·010·05−0·020·00−8 %BMI overweight and obese0·02*** 0·000·000·0317 %Food insecurity medium (Ref: low food insecurity)0·000·220·000·012 %Food insecurity high0·010·56−0·010·025 %Minimum meal frequency (MMF)0·000·180·000·014 %Dietary diversity score0·01*** 0·010·000·0212 %Breastfeeding within the first hour0·000·360·000·001 %Handwashing always (Ref: never)0·000·36−0·010·00−2 %Handwashing sometimes0·000·690·000·000 %Garbage disposal0·000·92−0·010·010 %Safe drinking water0·010·25−0·010·028 %Toilet facility sealed (Ref: No)−0·010·52−0·040·02−10 %Toilet facility not sealed0·010·24−0·010·027 %On time prenatal care0·000·200·000·013 %Quality of prenatal care medium (Ref: low)0·000·24−0·010·00−2 %Quality of prenatal care high0·01*** 0·040·000·0312 %Place of delivery government hospital (Ref: home)0·000·48−0·010·00−2 %Place of delivery government clinic0·000·69−0·010·01−1 %Complete DPT vaccine0·000·700·000·011 %Iron supplementation in children0·01*** 0·030·000·015 %Vitamin supplementation in children0·000·340·000·00−1 %Postnatal care0·010·41−0·010·025 %Deworming0·000·95−0·010·010 %*** $P \leq 0$·05.
Fig. 3Contributions of each determinant to stunting inequality. Source: Authors’ analysis of 2015 National Nutrition Survey
## Discussion
Child stunting in the *Philippines is* reinforced by the large inequality. We found a significant difference in the prevalence of poor and non-poor Filipino children during the critical period of 6–23 months (45·0 % v. 32·0 %; P value: 0·000). This gap in stunting prevalence accelerated sharply after the sixth month, which may be explained by the following factors.
In this study, we quantified wide range of socio-economic, maternal and child health factors that could explain the large gap in stunting prevalence between poor and non-poor children. Maternal education, height, and BMI, quality of prenatal care, diversity of child’s diet and iron supplementation for children were the significant contributors to the large disparity between poor and non-poor. Among the factors included in our decomposition model, maternal factors (that is, maternal nutrition and maternal education) account for more than 50 % of the gap. This suggests the role of maternal circumstance in perpetuating the large disparity in chronic malnutrition in the Philippines.
Maternal education explains about 18 % of the gap in child stunting. Social and cognitive factors could explain the role of education in the disparity of child stunting. Firstly, mothers with higher levels of education have more access to health information hence they have more knowledge of optimal child feeding and rearing practices. Secondly, education increases social capital[41]. If the child is ill or malnourished, the mother has access to information from her networks on how to treat the child. Thirdly, higher levels of education provide skills that are socially valued and give women a higher status, which raises self-confidence and eases social interaction with other high-status actors including health workers[42]. Fourthly, mothers with higher levels of education are associated with employment and income, which increases their demand for health and nutrition services(43–45).
Maternal height explains about 26 % of the gap. *While* genetics plays an important role, non-genetic factors largely explain the disparity[46]. In poor settings, maternal height is a proxy for maternal nutrition practices[47]. Mothers with shorter stature are more likely to be poor and living in a constrained environment, hence complementary feeding is typically suboptimal[48]. Short maternal height, which is more prevalent among the poor, also leads to low birth weight and eventually child stunting(49–51). Mothers with shorter stature have decreased macronutrient and energy stores, and smaller reproductive organs, which limit fetal growth in utero leading to low birthweight[48].
BMI explains about 17 % of the gap. Empirical studies consistently show the association between maternal BMI and child stunting(52–55). The pathway in which underweight status affects subsequent anthropometric failure of the child starts in utero. The intergenerational transmission of maternal underweight gives infants a higher risk of low birth weight which is a manifestation of early age undernutrition, that may progress to childhood undernutrition[55,56]. In the Philippines, about 11 % of adult women were considered underweight, with large disparity across socio-economic status. The large percentage of poor women who are underweight reflects the limited access to high-quality and adequate diet and food supplement especially during pregnancy (i.e. balanced energy protein supplementation)[11].
At the same time, maternal overnutrition is also found to be associated as a risk factor contributing to the gap in stunting. Previous nutritional history of the mother may explain this because studies show that children who also experienced chronic malnutrition (i.e. stunting) at an early age may have an impact on their physiologic and metabolic characteristics that result in having higher chances of being overweight during adulthood[57,58]. An emerging phenomenon-that should also be examined is the presence of a double burden of malnutrition, wherein undernutrition and overnutrition coexist in the same household. Nutrition transition may also be related to this, wherein there is a change in lifestyle, dietary patterns and physical activity associated with economic developments[59,60].
The quality of prenatal care explains 12 % of the gap. During prenatal care visits, mothers are given prenatal advice about child feeding and rearing practices, and they are provided with appropriate health and nutrition services such as micronutrient supplementation[61]. Poor mothers are less likely to have access to high-quality prenatal care in primary care facilities because of financial and physical barriers(62–64). Although prenatal care services are subsidised by the Philippine government, other indirect expenses come with utilizing the service such as transportation expenses and other medications and supplements, which are not provided for free[62,65,66]. In the Philippines, more than 50 % of Filipinos seeking outpatient care use out-of-pocket financing[67].
DDS explain 12 % of the gap. Dietary diversity is an important component of dietary quality. Consumption of different food items and food groups is associated with improved nutritional adequacy of the diet[68,69]. A high DDS increases the density of complimentary food, which is critical in ensuring optimal growth and development for the child[70,71]. Consistent with other studies, poverty is associated with low DDS(72–74). This reflects challenges on food insecurity in the country and poor knowledge on optimal child feeding and rearing practices[73,75]. In the Philippines, 75 % of the poor (bottom 40 %) are moderate to severely food insecure[11], and 39 % of mothers do not have correct knowledge about the duration of complementary feeding[11].
Iron supplementation contributes to around 5 % of the gap. Provision of iron supplements was found to be associated with linear growth based on some evidence(76–79). Iron deficiency may lead to anaemia and may contribute to growth retardation especially in poor families[78]. Iron deficiency anaemia is characterised by low haemoglobin concentration in the blood. This happens when the iron stores are utilised and depleted following the lack of intake of iron from food or supplements[77,80]. Poor children have limited access to iron supplements because of geographical and financial barriers in accessing primary care facilities, which is the entry point of health and nutrition interventions including micronutrient supplementation[81,82]. In the Philippines, data from 2013 shows that among children under-five, prevalence of anaemia was the highest among the poorest (16·5 %) and lowest among the richest (7·9 %)[83].
Our findings are found to be consistent with studies conducted in other countries. Studies in Bangladesh, Ethiopia, India, Iran and Tanzania all reported that socio-economic disparity in stunting in their countries is observed, having higher stunting prevalence among the poor than their non-poor counterparts(84–88). Maternal factors, such as nutrition status (height and BMI) and maternal education, were consistent drivers of the socio-economic gap in stunting across the studies, parallel to our findings. Kumar and Singh [2013] noted the limited use of maternal health care services serves as the main driver of inequality, thus recommending access to these services to reduce the gap[86].
The sharp acceleration of the gap in stunting prevalence between poor and non-poor after the sixth month is also consistent with other studies[89,90]. The nutritional requirement of the child increases after the period of exclusive breastfeeding (sixth month)[91]; hence, those children coming from poor households were not able to cope as a function of a wide range of socio-economic, maternal and child health factors mentioned above (e.g. lack of access to quality prenatal care and healthcare services, diverse diet, food insecurity).
Reducing the gap in stunting requires interventions aimed to improve socio-economic circumstances of poor women and their access to essential health and nutrition services. This involves collaboration of different sectors such as education, health, social welfare, agriculture, etc. Reducing the drop-out rate in secondary education among the poor women can be improved through provision of innovative approaches (e.g. expansion of conditional cash transfers)(92–94). Teenage pregnancy is one of the common reasons why students drop-out of school. Hence, the provision of modern contraceptives to young adolescents should be explored(95–98). Mothers with formal education will be equipped with better knowledge on achieving optimal nutrition for her and their child[99,100] and will have opportunities for better jobs and income, which will be a function of their capability to access essential health and nutrition services (e.g. prenatal care)(101–103). Access to high-quality health and nutrition services can be improved by providing social protection among poor women and their families, through the expansion of social health insurance benefits and coverage(104–106). The primary care system should be strengthened because it serves as the initial point of contact of individuals and families to the health system, giving access to vast essential health and nutrition services such as nutrition education for appropriate child feeding and rearing practices, balanced-energy protein supplementation for the mother and vitamin and mineral supplementation (e.g. iron supplements) beneficial for both the mother and the child. Currently, the Philippine Health Insurance Corporation, the country’s national health insurance, does not provide comprehensive primary care benefits.
In this study, we are able to use survey data that is nationally representative, which gives us a clear picture of the determinants of the stunting inequality in the country. Unlike other studies, which examined only a few determinants, our analysis examined a wide range of maternal and child health and socio-demographic indicators in our decomposition[85,87]. There are limitations in the design and methods of the study. Our analytical sample focused only on a small number of children (n 1881) because we did not include those children aged 24–60 months and those children without complete anthropometric, socio-demographic, maternal and health data. For the measurement of socio-economic status, we used a proxy indicator of wealth index, which may not be able to account for the size and consumption of the household, and may not be able to capture income disruption[107]. We are only able to measure the effects of relevant variables in the data, but the disparity could be influenced by other factors such as child’s exposure to illnesses, food/dietary intake and birth weight.
## Conclusion
We have identified the factors that explain the large disparity in stunting between poor and non-poor Filipino children. Maternal factors (i.e. maternal education, maternal height and maternal nutrition status) account for more than 50 % of the inequality in child stunting. This reinforces the critical role of maternal socio-economic circumstances in improving the linear growth of children in addition to expanding the service coverage and quality of essential nutrition and health interventions such as prenatal care and appropriate complementary feeding.
## Conflict of interest:
There are no conflicts of interest.
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|
---
title: 'Nutrition care is an integral part of patient-centred medical care: a European
consensus'
authors:
- Nicole Erickson
- Erin Stella Sullivan
- Marianna Kalliostra
- Alessandro Laviano
- Joost Wesseling
journal: Medical Oncology (Northwood, London, England)
year: 2023
pmcid: PMC9992033
doi: 10.1007/s12032-023-01955-5
license: CC BY 4.0
---
# Nutrition care is an integral part of patient-centred medical care: a European consensus
## Abstract
While healthcare is becoming more patient-centred, evidence-based nutrition interventions are still not accessible to all patients with cancer. As nutrition interventions directly improve clinical and socioeconomic outcomes, patient-centred care is not complete without nutrition care. While awareness of the negative impact of malnutrition on clinical outcomes, quality of life, and functional and emotional wellbeing in cancer is growing, there is relatively poor awareness amongst patients, clinicians, policymakers, and payers that nutrition interventions -particularly those begun in the early stages of the disease course- are an effective method for improving such outcomes. The European Beating Cancer Plan recognises the need for a holistic approach to cancer but lacks actionable recommendations to implement integrated nutrition cancer care at member state level. When considering nutrition care as a human right, the impact on quality of life and functional status must be prioritized, as these may be equally as important to patients, especially in advanced cancer where improvements in clinical outcomes such as survival or tumour burden may not be attainable. We formulate actions needed at the regional and the European level to ensure integrated nutrition care for all patients with cancer. The 4 main Take Home Messages are as follows: 1. The goals of Europe’s Beating Cancer Plan cannot be achieved without integrating nutrition across the cancer care continuum. 2. Malnutrition negatively impacts clinical outcomes and has socioeconomic consequences for patients and healthcare systems. 3. Championing integrating nutrition care into cancer care is therefore the duty and ethical responsibility of clinicians (Hippocratic Oath—primum non nocere) and 4. Nutrition care is a cost effective, evidence-based therapy.
## Introduction
Patient-centred care (PCC) requires a shift in focus from the traditional disease-focused, clinician-centric model of care to a system which empowers and enables patients to participate in shared decision-making and self-management [1–4]. While healthcare professionals (HCPs) generally appreciate the value of PCC, many may overestimate the level of PCC being achieved in practice. Despite strong evidence for improvements in patient-reported and clinical outcomes, nutrition is poorly integrated into multidisciplinary care in many areas of medicine, including cancer care [5–16]. Unfortunately, throughout Europe, there is a lack of consistent, coordinated integration of nutrition care throughout the cancer care continuum, and as such, PCC implementation has not been truly achieved. Nonetheless, the concept of PCC is considered so essential in cancer care that the Institute of Medicine listed PCC as the first of six interconnected essential components for the delivery of high-quality cancer care [17]. These recommendations quickly gained traction throughout major medical institutions across the world and were integrated into their guidelines, including those for accreditation by the Organisation of European Cancer Institutes (OECI) [18–20]. Recognition of the fact that PCC is not complete, nor as effective without nutrition care is that what differentiates a good physician from a great physician [21].
Suboptimal integration of nutrition into cancer care not only overlooks the value of PCC including nutrition interventions on quality of life (QoL), but also the fact that medical care itself is less effective when the patient is nutritionally depleted. While awareness of the negative impact of malnutrition on clinical outcomes in cancer is growing, there is relatively poor awareness amongst clinicians and patients alike, about the role of nutrition in improving clinical outcomes despite an ever growing body of strong clinical evidence. [ 5, 22–27]. In addition to tangible positive impacts on survival, length of stay and tolerance to treatment, the QoL of people living with, and beyond cancer, is positively impacted by targeted nutrition interventions [28]. The European Beating Cancer Plan calls for a holistic approach to cancer, from prevention and early diagnosis to treatment and quality of life of patients and survivors [29]. PCC, which includes nutrition, is particularly important when patients are faced with a devastating diagnosis and/or a chronic disease such as cancer, and where quality of life is potentially more modifiable than quantity of life. The aim of this paper is therefore to highlight the clinical evidence, the ethical considerations, the economic advantages, and the patient perspectives with respect to nutrition care as an integral part of the cancer care continuum.
## Improving nutrition status directly impacts clinical outcomes
High-quality medical care requires full integration of nutrition care into all steps of the cancer care pathway [30, 31]. Consistent evidence derived from randomized controlled trials shows that integrating nutrition care into cancer care positively impacts clinically relevant outcomes including reduction of toxicities, reduced post-operative complications, increased progression free survival and overall survival [32–34]. The impact of malnutrition on mortality in cancer has long been recognised and in 1932, an autopsy study of 500 cancer patients at Harvard Medical School found cachexia to be the primary cause of death in $22\%$ of cases, and a complicating factor in many more [35]. Given that the landscape of oncology has dramatically changed since the twentieth century, these statistics are unlikely to accurately represent the natural history of cancer-related malnutrition today, however, plausible biological mechanisms have been proposed which would suggest causality in the cachexia-death relationship [36]. Furthermore, the metabolism of many anti-cancer treatments is negatively impacted by reduced muscle mass, and the resulting side effects compromise quality of life and impair physical performance, or even shorten survival due to dose-limiting toxicities interrupting the intended treatment plan [37, 37–63]. Therefore, nutrition status is an essential consideration in ensuring optimization of the efficacy of standard medical, radiation and surgical oncology interventions.
Aspects of PCC are components of the internationally recognized quality standards for nutrition care—yet the integration of professionally delivered evidence-based nutrition care into the multidisciplinary team is lacking across the European Union and globally [5–16]. In fact, it has been more than 40 years since awareness of the importance of nutrition status in hospital settings was widely acknowledged as the ‘skeleton in the hospital closet’ [64] and a large body of supporting evidence has accumulated. Nonetheless, consistent improvement in nutrition care has not materialised [65]. Relative to astounding contemporary investment in medical oncology research, with $52\%$ of 56,000 molecules in the current drug development pipeline being for cancer [66], there is comparatively little investment in non-pharmacological or complex behavioural interventions. Moreover, when non-pharmacological studies are conducted, they are especially prone to exclusion from meta-analyses, due to high levels of heterogeneity (in methodology, outcomes, interventions and reporting) which is confounded by a lack of consensus on the appropriate methodological approach to evaluating complex interventions [67]. As such, despite a large body of individual studies demonstrating the value of early nutrition care in cancer, the overall body of evidence appears to remain inconclusive due to the relatively low number of studies which can be included at the level of a systematic review or meta-analysis. However, despite a relative lack of investment and a disproportionate focus on the legitimate difficulty in reversing refractory cachexia [68], perhaps the most important point to note is that there is consistent, robust evidence demonstrating the ability of a variety of early nutrition interventions to significantly improve clinical outcomes [28, 60, 69–71]. Clinicians, patients, and stakeholders at every level need to understand that evidence-based patient-centred care includes nutrition and as such, its omission is a disservice to people living with and beyond cancer.
## Integrating nutrition into cancer care is evidence-based
The impact of nutrition status throughout the care continuum on QoL, functional and emotional well-being, and on clinical outcomes have been underappreciated in favour of quantifiable, measurable end-points of interest to pharmaceutical regulators [72–77]. Despite strong recommendations for multimodal management of cancer-related malnutrition, the implementation of such multidisciplinary care is in its infancy [7, 78–84].
A growing body of evidence reflects that nutrition status has a significant effect on clinical outcomes and QoL. A large majority of patients experience some form of nutrition-related issues during their cancer journey [6, 7, 85, 86]. Moreover, long-term side effects following curative treatment commonly include body composition changes, nutrition impact symptoms and functional limitations which, in turn, impact QoL and are amenable to rehabilitation or supportive care [87]. Physicians and other members of the MDT such as nurses should therefore regularly prescribe nutrition care to cancer patients at all stages of care, whether it be providing first-line advice, or referring to specialists such as oncology dietitians [30, 88–91].
## Integrating nutrition into cancer care is a human right
The WHO Ministerial Conference on Nutrition and Noncommunicable Diseases in the Context of Health 2020 led to the Vienna Declaration which urges the mandating of person-centred nutrition care [92] and the International Working Group for Patients’ Right to Nutritional Care (composed of experts in clinical nutrition and representatives of international nutrition organizations) argue that nutrition care is an emerging human right that lies at the intersection of the existing human right to food and the human right to health, where patients hold the ‘right to be fed’ [93]. A right-holder implies duty-bearers, in this case, the state, policymakers, institutional managers and caregivers, are ethically responsible and must be held accountable. As the performance of these professionals is of critical importance, nutrition education is a priority [94, 95]. However, this can only be achieved by developing an institutional culture that values nutrition care and recognizes the need for a multi-stakeholder approach. Ethical debates such as when to withdraw nutrition and hydration in end-of-life settings [96–98] are in fact, only applicable if we introduce nutrition care in the first place.
According to the Europe’s Beating Cancer Plan all cancer inequalities should be reduced across the EU [29]. The inalienable right to high-quality PCC care, regardless of geographic or economic region, to avoid unnecessary deaths and suffering from cancer is supported by the European Charter of Patient Rights [99]. Malnutrition is not a fringe issue, and affects large amounts of cancer patients [100, 101]. Independent of potential sequelae and impact on medical outcomes which might also be improved with nutrition [69], malnutrition has also been documented as its own psychosocial challenge [102, 103].
## Integrating nutrition into cancer care is economically advantageous
Cancer care can affect the economic circumstances of patients and their households. QoL, costs of treatment and survival can be significant and lead to further inequalities [104, 105]. Effective cancer control programs and policies should therefore consider economic aspects for all cancer patients, survivors, and their carers [106–111]. A poor nutrition status is costly [106, 112, 113] and nutrition interventions contribute to reduced healthcare costs [112, 114–117]. On the other hand, the economic cost of malnutrition also impacts the healthcare system due to the costs associated with more complications, increased length of hospital stay (LOS), readmissions and increased morbidity [112]. In the UK, it was found that the cost of treating a malnourished patient is up to 3 times higher compared to a non-malnourished patient [112] Data from the Netherlands reveal that managing disease-related malnutrition accounted for $4.9\%$ of total healthcare expenditure [118]. US economic modelling based on international health economics data suggested that widely implementing nutrition support in gastrointestinal cancer alone could account for up to US $242 million in Medicare savings each year [119]. Importantly, these savings have been demonstrated in real-world settings, showing that significant cost savings can be achieved by implementing optimal nutrition care in cancer [114, 115, 120].
## Accessible patient-centred nutrition information empowers patients to take action and improves quality of life
Nutrition has been identified as an important and essential factor for empowering patients because it internalises their locus of control (LOC), supporting development of self-efficacy at a time where patients can experience a loss of bodily autonomy and seek self-management strategies which re-embody a sense of control. With these psychological considerations in mind, PCC demands that the right information is provided, in the right way, at the right time to the right patients. Dietitian-led nutrition care aims to achieve exactly this goal and should therefore not be overlooked [121–126].
To ensure improved outcomes and prevent recurrence, it is essential that nutrition education addresses patients’ weight management goals during and after treatment, to ensure patients do not fall prey to inappropriate, non-evidence-based nutrition advice which is widespread, easily accessible online, and frequently promoted by unqualified ‘experts’ [127–132]. Patient-centred consultations utilizing effective communication strategies are thus essential for increasing awareness about the consequences of cancer diets and encourage informed decision-making. This is especially important as patients consistently report lack of access to nutrition professionals while simultaneously reporting a lack of communication about nutrition on the part of their physicians—even when questions are directly posed [6, 133, 134].
In fact, a European survey including 907 cancer patients and survivors showed that not only is access to nutrition care lacking, but also that when patients are left alone they seek nutrition information elsewhere often finding information that is not evidence-based, possible harmful and sometimes counterproductive [85]. Even information presented on cancer centres websites does not meet the universal health literacy standards [135, 136], Patients with high unmet information needs are more likely to seek out unproven complementary and alternative medicine such as restrictive diets which may potentially negatively impact nutrition status [137, 138].
Unfortunately, psychological analyses have shown that short answers without explanations make patients feel that their needs have not been acknowledged or deemed important. This can lead to patients becoming more cemented in their original, incorrect beliefs [139–142]. On the other hand, a scientifically sound answers, and more importantly, decisive identification of alternatives encourages good decision-making by patients. Such communication strategies need to be strengthened among physicians—especially with regard to nutrition advice, as many patients will never see a dietitian, and depend on this first-line advice from the clinician with whom they have most contact [143]. Where knowledge on the part of physicians is lacking, the value of a specialised dietitian as part of the multidisciplinary team becomes even more apparent. It is therefore essential that oncologists and members of the multidisciplinary care teams understand their own scope of practice with respect to medical nutrition therapy, and appropriate referral procedures are standard practice [11, 22, 144–148]. In this context, nurses are often the most accessible member of the MDT to patients. They have an essential role in ensuring access to nutrition care, in particular for non-complex cases where they can provide invaluable first-line advice, or in complex cases, where they are responsible for implementing dietitian delivered recommendations. This example demonstrates why all members of the MDT must be able and willing to champion nutrition care, within the scope of their own profession. Moreover, it highlights why nutrition education and training cannot be limited to the nutrition specialist, if our aim is for widely accessible, properly implemented, integrated nutrition care in cancer.
Importantly, patients must be made aware of the importance of good nutrition in cancer and that intervention can positively impact their personal and clinical outcomes. Without this knowledge, patients are not able to advocate for themselves and identify red-flag signs. Additionally, their cancer team should encourage and empower patients to engage in shared decision-making and be confident to request assessment and treatment of nutrition-related concerns as they arise [149, 150].
Eating is a social process and contributes to quality of life [69, 102, 121, 151–155]. Optimal nutrition care is a human right and cannot be delivered in the absence of effective communication strategies [156, 157].
## Patient-centred care means listening to patient voices
In order to practice patient-centred care, it is essential to listen to and to understand what is important to patients. Many studies have shown that patients are not getting the nutrition care they need, and desire [6, 22, 85, 133, 158]. The European Cancer Patient Coalition (ECPC) recently presented rich qualitative data collected via direct patient interviews which indicate the central role of good nutrition care in cancer, from the patient perspective [159, 160]. Figure 1 includes a selection of quotes from the ECPC booklet showing patients’ points of view in their own words [160]. The patients’ insights highlight the fact that patients recognise the need for nutrition care, but are unfortunately, not consistently receiving the personalised nutrition counselling required to implement meaningful changes in their daily life. The Information Box depicted in Fig. 2 provides guidance to patient-centred communication with regards to nutrition and cancer. Fig. 1Patient quotes (adapted with permission from [160])Fig. 2Info box for patient-centred communication regarding nutrition and cancer
## Integrating nutrition into cancer care requires a plan
Scaled integration of these and other emerging nutrition interventions into healthcare would require significant economic investment and continued rigorous research but in the end the cost benefit is proven[17, 114]. To implement integrated nutrition care in cancer, coordination of messaging is needed. To support policy makers, service providers, and patient advocates the following first steps are recommended:Step 1 Widespread advocacy is required to raise awareness amongst clinicians and patients. It should be communicated that nutrition care in cancer has a significant impact on both clinically relevant outcomes and health-related quality of life issues which are central to PCC. This message needs to reach clinicians whose referral practices to dietetics require changes. Patients need to understand this message so they can then self-advocate when they identify that nutrition has become a problem for them. Finally, this message needs to be communicated to policymakers or healthcare management whose buy-in is needed to acquire funding and coordinate human and infrastructural resources to facilitate development and ongoing provision of comprehensive dietetic services in oncology. This call to action should refer to the key aspects of evidence, human rights and economic value outlined above. Step 2 Cancer services should urgently incorporate key performance indicators (KPIs) into their regular quality assurance systems to benchmark and audit adherence to evidence-based nutrition recommendations. These KPIs should be evidence-based or at least based on expert consensus. ESPEN, ESMO, COSA and others have many such guidelines [30, 81, 88, 89, 161] which could be used as starting points to develop a quality assurance standards for Integrated Nutrition Care in Cancer. At an absolute minimum, audits should report on malnutrition screening, oncology specific dietetic staffing and availability of nutrition assessment for all patients with high-risk diagnoses (e.g., head & neck cancers, gastrointestinal cancers, high-dose chemotherapy, radiotherapy to the head & neck or pelvis).Step 3 At the European level, several of the actions of the Beating Cancer Plan should include nutrition, notably, the plan mentions the role of diet and exercise in cancer prevention but does not focus on the specific role of nutrition within the management of cancer. In order to maximise the effect of a number of the flagship initiatives arising from the Beating Cancer Plan [29], ‘National Comprehensive Cancer Centre’ accreditation should be associated with a minimum acceptable level of nutrition and dietetic service provision, in which all cancer patients are nutritionally screened, the dietitian is a core part of the multidisciplinary team, and all members of the MDT receive basic and regular nutrition training. The ‘Knowledge Centre on Cancer’, ‘Inter-Specialty Training Programme’, ‘Cancer Diagnostic and Treatment for All’, ‘Partnership on Personalised Medicine’, ‘Better life for cancer patients’, ‘Cancer Inequalities Registry’, “EU-Network of Comprehensive Cancer Centres” and ‘Guidelines and Quality Assurance’ initiatives must explicitly include nutrition care.
## Conclusion
Across the cancer continuum, and especially for people living with incurable disease, the improvement of QoL may be more significant to patients than improvement in traditionally ‘clinically relevant’ outcomes such as tumour burden or overall survival [75]. Thus, when considering nutrition care as a human right, it is only appropriate to think about the outcomes which matter to patients themselves and the impact of malnutrition on QoL and how nutrition and nutrition-related issues affect cancer patients in general.
The pivotal role of nutrition care particularly applies to cancer care and is founded on the basis that anti-cancer treatments can be more effective in patients with a balanced nutrition status, leading to less delays in treatment, and less dose-limiting toxicities [42, 162]. However, across the cancer care continuum, and for the increasing group of people living with and beyond cancer, as well as those who live with the chronic ‘late effects’ of cancer appropriate and timely nutrition care still remains a documented unmet need [163–169].
Nutrition care in cancer is an essential component of standard cancer care and is a basic right for people living with and beyond cancer. Therefore, these initial recommendations on advocacy, evidence, quality assurance and European actions should function as an essential guide to ensuring that Europe’s Beating Cancer Plan and other health programmes and policies have nutrition care at its centre.
According to Europe’s Beating Cancer Plan, adopted in 2021, the European Commission aims to reduce all cancer inequalities across the EU [29]. Equal access of all cancer patients to high-quality care, regardless of geographic or economic region, to avoid unnecessary deaths and suffering from cancer, is further supported by the European Charter of Patient Rights [99]. However, professionals should not only provide mere access to high-quality care, including nutrition, but take responsibility to educate patients and involve them in decision-making to ensure their individual needs are met.
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|
---
title: Fur removal promotes an earlier expression of involution-related genes in mammary
gland of lactating mice
authors:
- Elżbieta Król
- Frances Turner
- Davina Derous
- Sharon E. Mitchell
- Samuel A. M. Martin
- Alex Douglas
- John R. Speakman
journal: Journal of Comparative Physiology. B, Biochemical, Systemic, and Environmental
Physiology
year: 2023
pmcid: PMC9992052
doi: 10.1007/s00360-023-01474-9
license: CC BY 4.0
---
# Fur removal promotes an earlier expression of involution-related genes in mammary gland of lactating mice
## Abstract
Peak lactation occurs when milk production is at its highest. The factors limiting peak lactation performance have been subject of intense debate. Milk production at peak lactation appears limited by the capacity of lactating females to dissipate body heat generated as a by-product of processing food and producing milk. As a result, manipulations that enhance capacity to dissipate body heat (such as fur removal) increase peak milk production. We investigated the potential correlates of shaving-induced increases in peak milk production in laboratory mice. By transcriptomic profiling of the mammary gland, we searched for the mechanisms underlying experimentally increased milk production and its consequences for mother–young conflict over weaning, manifested by advanced or delayed involution of mammary gland. We demonstrated that shaving-induced increases in milk production were paradoxically linked to reduced expression of some milk synthesis-related genes. Moreover, the mammary glands of shaved mice had a gene expression profile indicative of earlier involution relative to unshaved mice. Once provided with enhanced capacity to dissipate body heat, shaved mice were likely to rear their young to independence faster than unshaved mothers.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00360-023-01474-9.
## Introduction
Provisioning young with milk is highly demanding (Clutton-Brock et al. 1989; Speakman 2008). At the tissue level, lactation requires extensive expansion and differentiation of mammary glands, which is initiated during pregnancy (Macias and Hinck 2012; Lee and Kelleher 2016). Milk production is costly in terms of energy, nutrients, bioactive components (immune factors, growth modulators and hormones) as well as water (McClellan et al. 2008; Andreas et al. 2015). Apart from the energy exported in milk, extra energy is also needed to offset milk production inefficiency (Butte and King 2005). Having milk-dependent young may increase the chance of predation due to increased foraging efforts before lactation (capital breeders) or during lactation (income breeders), and by staying in the proximity of young and actively defending young from predators or conspecifics (Holmes 1991; Rӧdel et al. 2008; Larimer et al. 2011; Nunes 2014). Foraging for extensive periods of time may substantially increase the daily costs of locomotor activity and increase thermoregulatory costs by exposing lactating females to cooler or hotter ambient temperatures (Kurta et al. 1989; Tarnaud 2006; Rogers et al. 2021). Together, the high physiological, behavioural, and ecological costs of lactation are at the core of trade-offs between current and future reproduction and contribute to limits on lifetime reproductive success (Gittleman and Thompson 1988; Stearns 1992; Hamel et al. 2010; Festa-Bianchet et al. 2019).
The conflict between the mother and the young over maternal investment during pregnancy and lactation culminates at the time of weaning (Trivers 1974; Rehling and Trillmich 2007; Haig 2010). While weaning allows the mother to exit lactational amenorrhea and prepare for another breeding event (or to focus on concurrent pregnancy), it withdraws resources from the currently reared young, forcing them to become nutritionally independent (Mandalaywala et al. 2014; Hayssen and Orr 2017). Further complication to the process of weaning may be added by within-litter variation in suckling abilities and weight gain in polytocous mammals, likely to spread the weaning over a longer period of time (Paul and Bhadra 2017). Importantly, the cessation of suckling initiates the regression of mammary tissue to the pre-pregnant state necessary for the next breeding event, in a complex process called the post-lactational involution of mammary gland (Strange et al. 1992; Lund et al. 1996; Watson 2006; Macias and Hinck 2012; Watson and Khaled 2020). To experimentally induce and synchronise mammary involution in laboratory mice (Mus musculus), all suckling pups need to be removed from non-concurrently pregnant mothers at the same time, typically at peak of lactation (Stein et al. 2004, 2007; Clarkson et al. 2004; Blanchard et al. 2007). Involution is then triggered by the accumulation of milk in the alveolar lumens (milk stasis) and occurs in two distinct phases. The first phase is reversible, lasts ~ 48 h and is characterised by rapid programmed death of mammary secretory epithelial cells with limited alveolar collapse (Green and Streuli 2004; Baxter et al. 2007). If pups are returned to the mother within 48 h of removal, the involution can be reversed and lactation resumes. If pups are not returned, the circulating levels of prolactin decline and involution enters the second phase, which is irreversible, lasts ~ 2 weeks and is marked by extensive mammary tissue remodelling and repopulation by adipocytes (Li et al. 2017; Wang et al. 2018; Zwick et al. 2018). It has been demonstrated that mammary involution may be delayed but not prevented by concurrent pregnancy (Capuco et al. 2002). Any delay not related to concurrent pregnancy or defect in the process of mammary gland involution are likely to perturb the reproductive cycle of the female, potentially affecting her lifetime reproductive success and fitness (Akhtar et al. 2016; Hughes and Watson 2018a; Jena et al. 2019).
Peak lactation is a time during which a female mammal’s milk production is at its highest and is unresponsive to elevated demands from the young (Hammond et al. 1994, 1996; Johnson et al. 2001; Król and Speakman 2003a). The factors limiting the lactation performance have been subject of intense debate due to their implications for understanding many aspects of mammalian evolution (Speakman and Król 2010a, b), human neonatal nutrition (Victora et al. 2016; Huang et al. 2021), and productivity of dairy livestock (Clay et al. 2020). To focus on physiological rather than ecological or behavioural limits to lactation, most studies have been performed in laboratory conditions, with ad libitum food supply, no predation risk and minimal costs of locomotion and thermoregulation. The physiological nature of the limits to lactation during a single breeding event has been recently demonstrated in laboratory mice (Zhao et al. 2020a). Overall, the limit-to-lactation studies aimed to remove the cap on maternal investment at peak lactation by a wide range of manipulations, including changes in [1] total metabolic demand during lactation (adding extra pups, prolonging lactation, making lactating females simultaneously pregnant, and requiring lactating females to run for food), [2] diet quality and composition, [3] environmental conditions (exposure of lactating females to different ambient temperatures), and [4] heat exchange between lactating females and the environment at a fixed ambient temperature (for review and references see Speakman and Król 2005a, 2011; Król and Speakman 2019). Views about the constraints on lactation performance have changed over time. Initially, lactation performance was thought to be limited by the capacity of digestive tract to process the ingested food (Drent and Daan 1980; Peterson et al. 1990; Weiner 1992; Koteja 1996; Sadowska et al. 2019). This was followed by consideration that lactation performance was limited by capacity of the mammary gland to produce milk (Hammond et al. 1994, 1996; Yang et al. 2013; Zhao et al. 2013; Wen et al. 2017). Finally, the concept of a heat dissipation limit (HDL) associated with the capacity of lactating females to get rid of body heat generated as a by-product of processing food and producing milk was developed (Król and Speakman 2003a, b; Król et al. 2003, 2007, 2011; Speakman and Król 2010a, b; Sadowska et al. 2016; Deng et al. 2020; Huang et al. 2020a; Ohrnberger et al. 2020; Zhao et al. 2020b). Lactogenic heat production in laboratory mice is sufficiently high to double their daily energy expenditure at peak lactation (Król and Speakman 2019), leading to the sustainably elevated maternal body temperature (Gamo et al. 2013), a phenomenon reported in several species of lactating rodents and in large domestic animals (Speakman 2008; Hansen 2009). Further increases in heat production that are not balanced by heat loss may put lactating females at risk of developing potentially fatal hyperthermia (Speakman and Król 2010a).
An experimental manipulation instrumental for formulating the HDL hypothesis was fur removal to reduce the external insulation of lactating females and thereby elevate their capacity to dissipate body heat (Fig. 1). Shaving off dorsal fur increases the thermal conductance of lactating mice by 10–$16\%$ (Zhao and Cao 2009; Sadowska et al. 2019). In MF1 mice that were shaved before peak lactation, food intake increased by on average $12.0\%$ and assimilated energy increased by on average 30.9 kJ day−1 compared with unshaved females (Król et al. 2007). With nearly identical mean litter sizes (11.4 pups for shaved and 11.3 pups for unshaved mice), shaved mothers exported on average $15.2\%$ (22.0 kJ day−1) more energy as milk than control individuals. The elevated milk production of shaved mice enabled them to wean litters that were on average $15.4\%$ (12.2 g) heavier than young produced by unshaved mice. Since then, shaving-induced increases in milk production have been demonstrated in lactating female bank voles (Myodes glareolus) and golden hamsters (Mesocricetus auratus) but not in Swiss mice or common voles (Microtus arvalis) (Table 1). These contrasting results are consistent with the idea that different species and strains may be constrained by different mechanisms and that the nature of these constraints may depend on the ambient temperature at which the experimental manipulation was performed (Speakman and Król 2011; Huang et al. 2020b).Fig. 1Lactating MF1 mouse with dorsal fur shaved off to increase heat dissipation capacity (photo by John R. Speakman)Table 1Effects of fur removal on lactation performance (maternal food intake, milk production and mass of young) in laboratory and captive rodentsSpecies/StrainTaa (°C)ShavingbFood intakecMilk productiondMass of youngSourceLaboratory mouse (Mus musculus, MF1)21Days 6, 10, 14↑ $12.0\%$↑ $15.2\%$↑ $15.4\%$e1Laboratory mouse (Mus musculus, Swiss)23Day 7↑ $6.9\%$Na ↔ 2Laboratory mouse (Mus musculus, Swiss)23Day 7↑ $8.8\%$ ↔ ↔ 3Common vole (Microtus arvalis)30Day 2 ↔ ↔ ↑ ~ $5\%$f4Bank vole (Myodes glareolus)20Days 5, 9↑ $13.2\%$↑ $11.8\%$↑ $22.1\%$g5Yunnan red-backed vole (Eothenomys miletus)25Day 7↑ $9.0\%$Na ↔ 6Lab mouse (M. musculus, Swiss Webster, high BMR)h23Day 8 ↔ Na ↔ 7Lab mouse (M. musculus, Swiss Webster, low BMR)h23Day 8 ↔ Na↓ ~ $40\%$f7Golden hamster (Mesocricetus auratus)22Day 6↑ $9.9\%$↑ $23.4\%$↑ $23.7\%$i8Laboratory mouse (Mus musculus, MF1)21Days 6, 10, 14↑ $18.7\%$↑ $19.5\%$↑ $19.5\%$eThis studyThe difference between shaved (S) and unshaved (U) females is expressed as % of significant increase (↑), % of significant decrease (↓) or no significant change (↔) relative to unshaved animals, calculated as (S − U)/U × 100. Data not available are indicated by ‘Na’Source: 1, Król et al. 2007; 2, Zhao and Cao 2009; 3, Zhao et al. 2010; 4, Simons et al. 2011; 5, Sadowska et al. 2016; 6, Zhu et al. 2016; 7, Sadowska et al. 2019; 8, Ohrnberger et al. 2020Superscripts and symbolsaAmbient temperature during lactationbDay of lactation when shaving was performed (relative to parturition on day 0)cMeasured at peak lactation in g/daydMeasured at peak lactation in kJ/dayeLitter mass at weaningfPup growth rategLitter growth ratehMice selected for high or low basal metabolic rate (BMR)iPup mass at weaning∼ Data retrieved from figures In the current study, we shaved lactating MF1 mice to establish how shaving-induced increases in milk production are mediated at the level of mammary gland transcriptome. We were particularly interested in whether shaving mice to relax the HDL and reduce the risk of maternal hyperthermia affected the time of weaning manifested by involution of mammary gland (mother–offspring conflict). Two aspects of the increases in milk production at peak lactation were also investigated—the milk synthesis machinery at the transcriptomic level, and the mammary gland gene expression correlated with milk production. By RNA-seq profiling of the mammary gland in shaved and unshaved lactating mice, we identified differentially expressed genes (DEGs) associated with shaving and then compared them with sets of genes compiled from the mouse mammary gland literature, containing milk synthesis-related genes and involution-related genes.
## Animals and experimental protocol
We used 10 virgin female mice (*Mus musculus* L., outbred MF1) kept on a 12 h:12 h light:dark cycle (lights on 07:00·h) at 21 °C (range 20–22 °C) and a relative humidity of $59\%$ (range 54–$64\%$). Food (CRM, Pelleted Rat and Mouse Breeder and Grower Diet, Special Diets Services, BP Nutrition, Witham, Essex, UK) and water were available ad libitum. At 9–11 weeks of age, mice were acclimated to the single housing environment for 1 week, after which they were paired with MF1 males for 11 days. All females became pregnant and gave birth to young. Following previous convention, the day of birth was counted as day 0 of lactation.
Female body mass and food intake together with litter size and litter mass were recorded every other day from day 4 of lactation to the end of the experiment (day 18). On day 6 of lactation, half of the lactating females ($$n = 5$$) were shaved, while the other half ($$n = 5$$) served as an unshaved control group (details below). Milk production was evaluated within the peak of lactation (approximately days 10–18 post-partum, Johnson et al. 2001) from measurements of metabolizable energy intake (MEI) and daily energy expenditure (DEE) by doubly labelled water (DLW) technique (details below). On day 18 of lactation, all mothers were sacrificed by CO2 overdose. The right inguinal mammary gland was removed, frozen immediately in liquid N2 and stored at − 80 °C prior to RNA extraction. All procedures were authorized by the College of Life Sciences and Medicine Ethics Review Board at the University of Aberdeen and carried out under UK Home Office project licence PPL $\frac{60}{2881.}$
## Fur removal
Once the phenotype measurements on day 6 of lactation were completed, all 10 lactating females were anaesthetized with gaseous isoflurane for ~ 10 min. While under anaesthesia, 5 females were shaved dorsally (using a Wella Contura Hair Clipper, Basingstoke, Hants, UK) to remove ~ $72\%$ of fur (Król et al. 2007), as depicted in Fig. 1. Hair regrowth was prevented by repeating the shaving protocol on days 10 and 14 of lactation. The remaining mice were handled and anaesthetised similarly but not shaved.
## Metabolizable energy intake (MEI)
Measurements of MEI were performed on days 12–14 of lactation. Females and their litters were placed in cages with fresh sawdust on day 12 of lactation, and a weighed portion of food was added to the hopper. Samples of the food were taken to determine dry mass content, and the food remaining in the hopper was reweighed on day 14 of lactation. Any uneaten food and faeces were removed from the cage, dried to a constant mass, and weighed. The gross energy content of food and faeces were measured with a Parr 6200 calorimeter using an 1109A semi-micro oxygen bomb (Parr Instrument Company, Moline, IL, USA). MEI was estimated as the difference between energy consumed and defecated, assuming that urinary energy loss was $3\%$ of the digestible energy intake (for details see Król et al. 2007).
## Daily energy expenditure (DEE)
DEE was measured on days 15–17 of lactation, using the DLW technique (Speakman 1997). Lactating females were injected intraperitoneally with ~ 0.25 g of water enriched with 18O (28 atom%) and 2H (16 atom%). Initial blood samples were taken from the tail tip after 1 h of isotope equilibration to estimate initial isotope enrichments (Król and Speakman 1999); final blood samples were taken 48 h later to estimate isotope elimination rates (Speakman and Racey 1988). Blood samples were immediately heat sealed into glass capillaries and stored at room temperature prior to vacuum distillation. Water from the resulting distillate was used to produce CO2 (Speakman et al. 1990) and H2 (Speakman and Król 2005b), and the isotope ratios 18O:16O and 2H:1H were analysed using gas source isotope ratio mass spectrometry (ISOCHROM μGAS system and IsoPrime IRMS, Micromass, Manchester, UK).
We used the intercept method (Coward and Prentice 1985) to calculate the initial dilution space and the single-pool model (Eq. 7.17 in Speakman 1997) to calculate the rate of CO2 production (for details see Król et al. 2007). Energy equivalents of the rate of CO2 production were calculated using a conversion factor of 24.026 J mL−1 CO2, derived from the Weir equation (Weir 1949) for a respiratory quotient of 0.85 (Speakman 1997).
## Milk energy output (MEO)
MEO was calculated as the difference between MEI (days 12–14 post-partum) and DEE (15–17 post-partum) (Król and Speakman 2003b), with MEI being the main determinant of milk production in lactating mice (Speakman 2008). Both MEI and DEE were measured within the peak of lactation (approximately days 10–18 post-partum, Johnson et al. 2001) but on different days to avoid possible changes in behaviour and feeding patterns caused by DLW injection and blood sampling (Speakman and Król 2005b). To reduce possible effects of blood sampling on gene expression, tissue harvest was done on day 18 of lactation (~ 24 h after completion of DLW experiment).
## RNA extraction
Total RNA from the inguinal mammary gland was isolated by homogenization of ~ 100 mg of tissue in TRIzol® Reagent (Ambion by Life Technologies, Carlsbad, CA, USA), using 3 mm tungsten carbide beads and a TissueLyser II Disruption System (Qiagen GmbH, Hilden, Germany). Following isolation, the RNA was quantified by spectrophotometry (NanoDrop Technologies, Wilmington, DE, USA) and its integrity was confirmed by electrophoresis (Agilent Technologies, Santa Clara, CA, USA). All RNA samples had a RIN number > 7.8, meeting the criteria for RNA-seq.
## RNA-seq library preparation and sequencing
RNA-seq library preparation and sequencing were carried at the Beijing Genomics Institute (BGI, Shenzhen, China). The libraries for each of the 10 samples were constructed using the TruSeq Stranded mRNA Sample Preparation Kit (Illumina, San Diego, CA), according to the manufacturer’s instructions. The 50 bp paired-end sequencing was performed on the HiSeq 2000 Sequencing System (Illumina, San Diego, CA) at a sequencing depth of ~ 50 million reads per library. The raw reads were trimmed and converted from BCL to FastQ format with bcl2fastq2 Conversion Software v2.19.1 (Illumina, San Diego, CA). All raw sequences have been deposited in the ArrayExpress repository (http://www.ebi.ac.uk/arrayexpress/) under accession number E-MTAB-11654.
## Read mapping
To assess the quality of the sequencing data, reads were analysed with FastQC v0.11.8 (Andrews 2010). All reads passed the quality control checks and were mapped to the mouse reference genome (GRCm38 release 81) using HISAT2 v2.1.0. ( Kim et al. 2015) with the pre-built genome index and default settings for paired-end reads. Alignment rates were above $90\%$. Aligned reads were counted at gene locations using featureCounts v1.6.4 (Liao et al. 2014).
## Differential analysis of gene expression
Gene expression levels in the mammary glands of shaved and unshaved mice were summarized using principal component analysis (PCA), as implemented by the Bioconductor package PCAtools (Blighe et al. 2018). *Differential* gene expression analysis was performed using the Bioconductor package edgeR v3.22.5 (Robinson et al. 2010). Both analyses were executed in R v3.5.1 (R Core Team 2018).
To filter out lowly expressed genes, the analyses were performed only on the transcripts with at least 2 counts per million (CPM) in a minimum of 5 samples, amounting to 10,901 such genes in total. Filtered counts were subsequently normalized using a trimmed mean of M values (TMM) between each pair of samples. The PCA analysis was performed on the normalized CPM values that have added a prior count = 1 to avoid zeros during calculation of log-values. Scores for individual mice were calculated using the eigenvectors from the first (PC1) and second (PC2) principal components.
*The* gene expression data (normalized CPM values) were then fitted with a negative binomial generalized log-linear model (glmFit), with the contrast set up to compare shaved vs unshaved lactating mice ($$n = 5$$ females per group). Differentially expressed genes (DEGs) were identified at false discovery rate (FDR) < 0.05 and absolute Log2 FC > 0.5, yielding 752 DEGs in total.
## Overlaps with gene sets from literature
Based on a literature search, we generated 2 sets of genes associated with the transcriptomic changes in the mouse mammary gland during and post lactation, including milk synthesis-related genes, and involution-related genes. The milk synthesis-related gene set was based on the research by Rudolph et al. [ 2007], Maningat et al. [ 2009], Mohammad and Haymond [2013], Lemay et al. [ 2013], Manjarin et al. [ 2014], Qian and Zhao [2014], Kobayashi et al. [ 2016], Osorio et al. [ 2016], Han et al. [ 2019], Patel et al. [ 2019], Cayre et al. [ 2020] and Martin Carli et al. [ 2020].
The mammary gland involution-related genes were identified by 3 independent microarray experiments (Stein et al. 2004; Clarkson et al. 2004; Blanchard et al. 2007), which were then combined or re-analysed by others (Stein et al. 2007; Bambhroliya et al. 2018). In all 3 studies, the involution of mammary glands was induced by pup removal (Table 2). Because each experiment and reassembly of data had a slightly different protocol and focus of investigation, we generated 4 lists of involution-related genes, according to the outputs from Stein et al. 2004, Clarkson et al. 2004, Stein et al. 2007 and Blanchard et al. 2007.Table 2Details of the mammary gland involution experiments used to generate involution-related gene setsMouse strainManipulationTissue harvestMethodComparison/sample sizeNumber of involution-related genesSourceBalb/CPup removal (day 7 of lactation)Days 0, 1, 2, 3, 4, 20 of involutionMADays 1, 2, 3, 4, 20 vs day 0 ($$n = 3$$)1121C57/Bl/6Pup removal (day 10 of lactation)Days 0, 0.5, 1, 2, 3, 4 of involutionMADays 0.5, 1, 2, 3, 4 vs day 0 ($$n = 3$$)1302Reanalysis of studies 1 and 2Reanalysis of studies 1 and 2Reanalysis of studies 1 and 2MADays 0.5, 1, 2, 3, 4 vs day 0933CD1Pup removal (day 12 of lactation)Days 0, 1 of involutionMADay 1 vs day 0 ($$n = 5$$)1014MF1Fur removal (days 6, 10, 14 of lactation)Day 18 of lactationRNA-seqShaved vs unshaved ($$n = 5$$)–This studyThe expression of these genes was studied by microarray analysis and refer to the involution of mammary gland induced by pup removal. Details of the shaving experiment (this study) are shown for comparisonSource: 1, Stein et al. 2004; 2, Clarkson et al. 2004; 3, Stein et al. 2007; 4, Blanchard et al. 2007MA microarray gene expression analysis, RNA-seq RNA-seq gene expression analysis Overlaps between DEGs induced by shaving and gene sets from the literature were evaluated using a one-sided Fisher’s exact test. The test was performed on the gene set numbers arranged in 2 × 2 contingency tables, using a function fisher.test in R v3.5.1 (R Core Team 2018), with the parameter ‘alternative’ set to ‘greater’. The p values generated by the Fisher’s exact test were subjected to Bonferroni correction for multiple comparisons.
## Functional analysis of gene expression
DEGs in the mammary glands of shaved vs unshaved mice were analysed using Ingenuity Pathway Analysis (IPA, QIAGEN Redwood City, www.qiagen.com/ingenuity). We submitted the whole RNA-seq output ($$n = 10$$,901 genes, along with their Log2 FC and FDR values) to IPA and used this dataset as a reference set for functional analysis of DEGs ($$n = 752$$, at FDR < 0.05 and absolute Log2 FC > 0.5). We used the default analysis settings, apart from species (we selected mice and excluded humans and rats). The focus of the functional analysis of DEGs were [1] canonical pathways, [2] upstream regulators, and [3] downstream effects associated with these genes. The significance of the IPA outputs was based on the Benjamini-Hochberg (B-H) multiple testing correction p value, with the overall activation/inhibition states predicted by the IPA z-score algorithm.
## Correlation analysis of gene expression
Counts from mammary gland samples were normalized using the trimmed mean of M values (TMM) method with edgeR’s calcNormFactors function. To filter out lowly expressed genes, the correlation analysis was performed only on the genes with at least 5 CPM in at least 2 mice from the shaved group (9279 genes), 2 mice from the control group (9209 genes) and 4 mice from the pooled shaved and control groups (9164 genes). Correlation analysis between mammary gland gene expression (normalized Log2 CPM values) and milk production (kJ/day) was performed separately for 5 shaved mice, 5 unshaved mice, and both shaved and unshaved mice ($$n = 10$$). All analyses were done in R v3.5.1 (R Core Team 2018), using functions cor.test (for 5 mice) and pcor.test (for 10 mice), based on the Pearson’s product moment correlation coefficient. The function pcor.test was used to identify partial correlations between milk production, shaved status, and gene expression. By doing this, the potentially confounding effects of the shaved status were removed (blocked). Correlations between gene expression and milk production were considered significant at FDR < 0.05.
## Statistical analysis of non-transcriptomic data
All non-transcriptomic data were assessed for normality and homogeneity of variance and are presented as mean ± standard deviation ($$n = 5$$). The whole-body phenotype (body mass, food intake and metabolism) and reproductive performance of shaved vs unshaved mice were compared using Welch two-sample t tests. The differences between shaved and unshaved mice in their peak lactation food intake, MEI, DEE, MEO and litter mass were then compared with $95\%$ confidence intervals for the same parameters detected as significant in our original shaving experiment performed on a larger sample size (Król et al. 2007). Measurements repeated on the same individuals (maternal body mass, food intake and litter mass) were analysed using two-way repeated measures ANOVA, with group (shaved and unshaved mice) and day of lactation as factors, and interaction group × day. When the effect of group or interaction was significant, the Holm multiple comparison procedure was applied to determine differences between the groups within each day. Furthermore, the peak lactation performance traits (i.e., food intake, MEI, DEE, MEO and litter mass) were tested for correlation with PC1 and PC2 scores from the PCA analysis of the mammary gene expression. All tests were performed in R v3.6.3 (R Core Team 2018), using default functions (t test, anova_test and cor.test).
## Whole-body phenotype and reproductive performance
Both phenotypic and performance responses to fur removal closely resembled the patterns found in our original shaving study, performed on 20 shaved and 20 unshaved mice (Król et al. 2007). Before shaving, lactating mice that were assigned to shaved and unshaved groups ($$n = 5$$ females per group) did not differ in their mean body mass or food intake (Table 3, Supplementary Table 1). On average, shaved and unshaved mothers raised a similar number of pups (11.0 ± 1.0 and 11.4 ± 0.5, respectively), with all litter sizes remaining constant from birth to weaning. Likewise, mothers assigned to shaved and unshaved groups did not differ in their litter mass prior to shaving, averaging on day 4 of lactation 34.8 ± 2.3 and 32.2 ± 2.7 g, respectively. Table 3Phenotypic characteristics and lactation performance of shaved vs unshaved mice ($$n = 5$$ females per group) before and after fur removalParameterMean ± SDDifferenceShavedUnshavedBefore fur removal (first week of lactation) Body mass (day 4, g)45.7 ± 3.045.7 ± 3.20.0 ($0.0\%$) Food intake (days 4–6, g/day)22.0 ± 3.420.7 ± 1.61.4 ($6.6\%$) Litter size (day 4)11.0 ± 1.011.4 ± 0.5 − 0.4 (− $3.5\%$) Litter mass (day 4, g)34.8 ± 2.332.2 ± 2.72.5 ($7.8\%$)After fur removal (peak of lactation) Body mass (day 12, g)49.2 ± 3.448.0 ± 2.21.3 ($2.6\%$) Food intake (days 12–14, g/day)26.0 ± 4.521.9 ± 3.94.1 ($18.7\%$) MEI (days 12–14, kJ/day)289.5 ± 42.8250.3 ± 37.239.1 ($15.6\%$) DEE (days 15–17, kJ/day)123.4 ± 16.1111.3 ± 11.312.0 ($10.8\%$) MEO (days 12–17, kJ/day)166.1 ± 41.9139.0 ± 36.327.1 ($19.5\%$) Litter size (day 16)11.0 ± 1.011.4 ± 0.5 − 0.4 (− $3.5\%$) Litter mass (day 16, g)75.7 ± 7.163.3 ± 11.412.3 ($19.5\%$)All parameters were measured during lactation, on days counted from parturition (day 0). The difference between shaved (S) and unshaved (U) mice is calculated as S − U (first value) and then expressed as % of U ((S − U)/U × 100) in the bracket. For statistical details, see Supplementary Table 1DEE daily energy expenditure, MEI metabolizable energy intake, MEO milk energy output, SD standard deviation Once shaved, lactating mice increased their peak lactation food intake and MEI (days 12–14 post-partum) along with DEE (days 15–17 post-partum) by on average 18.7, 15.6 and $10.8\%$, respectively, compared with unshaved mothers. As expected, shaved mothers produced on average more milk (by $19.5\%$) and weaned heavier litters (by $19.5\%$) than control mice. Despite the same direction and similar magnitude of change as in our original study (Król et al. 2007) (Table 1), the shaving effects in the current study did not reach significance, apart from the litter mass, a proxy for milk production (for details see Supplementary Table 1). Importantly, the differences between 5 shaved and 5 unshaved mice fell within the $95\%$ confidence intervals for the same parameters detected as significant in our previous study with $$n = 20$$ mice per group.
Two-way repeated measures ANOVA demonstrated that the effects of shaving on litter mass depended on the day of lactation (group, F1,8 = 6.0, $$p \leq 0.040$$; day, F7,56 = 120.3, $p \leq 0.001$; interaction group × day, F7,56 = 2.9, $$p \leq 0.012$$). For days 4 and 6 (before fur removal) along with day 18 of lactation, there was no significant difference between the litter mass of shaved and unshaved mice ($p \leq 0.05$). On days 8, 10, 12, 14 and 16, litters of shaved mothers were heavier than litters of unshaved mothers, by on average 7.4 g ($$p \leq 0.024$$), 11.3 g ($$p \leq 0.027$$), 12.3 g ($$p \leq 0.020$$), 12.5 g ($$p \leq 0.027$$) and 12.3 g ($$p \leq 0.043$$), respectively (Supplementary Fig. 1). The effects of shaving on maternal body mass (group, F1,8 = 0.5, $$p \leq 0.496$$; day, F9,72 = 9.1, $p \leq 0.001$; interaction group × day, F9,72 = 1.9, $$p \leq 0.064$$) and food intake (group, F1,8 = 2.6, $$p \leq 0.149$$; day, F6,48 = 6.0, $p \leq 0.001$; interaction group × day, F6,48 = 1.6, $$p \leq 0.161$$) were not significant.
## Mammary gland gene expression
PCA results for the mammary transcriptomes of shaved and unshaved lactating mice ($$n = 5$$ females per group) are summarized in Fig. 2A. The first and second principal components (PC1 and PC2) accounted for 63.0 and $12.8\%$ of the variability in the RNA-seq dataset (matrix of 10,901 transcripts × 10 samples). Both groups of mice showed substantial variability along the PC1 axis, with no clear separation of shaved mice from unshaved controls. In contrast, there was a clear separation between shaved and unshaved mice along the PC2 axis. Neither PC1 nor PC2 scores were significantly correlated with the peak lactation performance traits (Supplementary Table 2).Fig. 2Visualisation of principal component analysis (PCA) and differential gene expression analysis performed on the mammary gland transcriptomes of shaved vs unshaved lactating mice ($$n = 5$$ females per group). A Biplot of the first (PC1) and the second (PC2) principal components, with numbers 1–10 representing the animal ID, and percentage referring to the variance captured by PC1 and PC2 scores. B Volcano plot showing 752 DEGs (at FDR < 0.05 and absolute Log2 FC > 0.5) that were either upregulated (425 genes in red) or downregulated (327 genes in green) in the mammary glands of shaved mice relative to control mice. The expression of remaining 10,149 genes (in grey) was not significantly different. Gene symbols refer to the most upregulated (Cyp24a1, Angptl4 and Pdk4), downregulated (Des, AA914427 and Rspo1) and significantly altered (Ccng2 and Slc25a45) DEGs (for details see Supplementary Table 3) *Differential analysis* of gene expression performed on the mammary glands of shaved vs unshaved lactating mice identified 752 DEGs at FDR < 0.05 and absolute Log2 FC > 0.5 (Fig. 2B, Table 4, Supplementary Table 3). Among them were 721 protein-coding genes, 19 long noncoding RNA genes and 8 pseudogenes, with other gene types represented by single genes. Out of the 752 DEGs induced by shaving, 425 were upregulated and 327 were downregulated. The Log2 FC values associated with these DEGs varied from 5.5 (Cyp24a1) to − 3.8 (Des), reflecting a 46.1-fold upregulation and a 13.7-fold downregulation of gene expression, respectively. Table 4Results of the differential gene expression analysis performed on the mammary gland transcriptomes in shaved vs unshaved lactating mice ($$n = 5$$ females per group)Gene typeNumber of differentially expressed genes (DEGs)UpregulatedDownregulatedTotalProtein-coding genes408313721Long noncoding RNA genes12719Pseudogenes358Immunoglobulin gene segments101Antisense long noncoding RNA genes101Unclassified genes011Unmapped genes011All genes (total)425327752Genes were considered differentially expressed at FDR < 0.05 and absolute Log2 FC > 0.5 (for visualisation and details see Fig. 2B and Supplementary Table 3)
## Overlaps between DEGs induced by shaving and gene sets from literature
The literature search for transcriptomic changes in the mouse mammary gland identified 100 milk synthesis-related genes (Supplementary Table 4) and 345 involution-related genes (Supplementary Table 5). The milk synthesis-related genes included prolactin and insulin receptor genes, numerous transcription factors and regulators, as well as transcripts related to the synthesis of the main components of milk such as protein, fat, and lactose (Rudolph et al. 2007; Maningat et al. 2009; Mohammad and Haymond 2013; Lemay et al. 2013; Manjarin et al. 2014; Qian and Zhao 2014; Kobayashi et al. 2016; Osorio et al. 2016; Han et al. 2019; Patel et al. 2019; Cayre et al. 2020; Martin Carli et al. 2020). The involution-related gene set is a compilation of 112 (Stein et al. 2004), 130 (Clarkson et al. 2004), 93 (Stein et al. 2007) and 101 (Blanchard et al. 2007) study-specific genes (Table 2), which amounted to 345 unique involution-related genes.
Comparison of DEGs induced by shaving with the gene sets from the literature revealed 8 and 59 common genes for the milk synthesis-related and involution-related gene sets, respectively (Fig. 3, Table 5, Supplementary Tables 4 and 5). The 8 common milk synthesis-related genes were all downregulated in the mammary glands of shaved mice, but the enrichment of the DEGs with milk synthesis-related genes was not significant ($$p \leq 0.385$$) (Supplementary Table 6). In contrast, the overlap between DEGs and the involution-related gene set (59 common genes) was highly significant ($$p \leq 5.00$$E − 11), indicating substantial enrichment of DEGs with involution-related transcripts. The majority of the common involution-related genes (52 of 59) were upregulated in the mammary glands of shaved mice, which is consistent with the changes of these genes during involution induced by pup removal, with the magnitude of change (Log2 FC values) ranging from 0.5 to 5.5 (Supplementary Table 5). The remaining 7 of 59 common involution-related genes were downregulated in the mammary glands of shaved mice (Log2 FC values from − 0.6 to − 2.2).Fig. 3Venn diagram showing the number of common (at intersections) and unique (outside intersections) genes between DEGs in the mammary gland of shaved lactating mice (this study, $$n = 752$$) and gene sets from literature with milk synthesis-related genes ($$n = 100$$) and involution-related genes ($$n = 345$$) (for details see Supplementary Tables 4 and 5)Table 5Overlaps between gene sets generated in this study and from literatureGene set (this study)Milk synthesis-related genes ($$n = 100$$)Involution-related genes ($$n = 345$$)Shaving-induced DEGs ($$n = 752$$)Shaving-induced DEGs ($$n = 752$$)859*1–Milk-correlated genes in all 10 mice ($$n = 2$$)000All gene sets represent mouse mammary gland. *The* gene sets from this study include DEGs for shaved vs unshaved mice and genes correlated with milk production in both shaved and unshaved mice ($$n = 10$$) with the fur effect blocked (Supplementary Tables 3 and 10). The literature gene sets include milk synthesis-related and involution-related genes (Supplementary Tables 4 and 5). The overlap is represented by the number of common genes, with the significance evaluated by a one-sided Fisher’s exact test (Supplementary Table 6). An asterisk (*) refers to a significant overlap after applying Bonferroni correction for multiple comparisons (p value < $\frac{0.05}{2}$)1Significant at p value = 5.00E-11 The overlap between DEGs induced by shaving and the involution-related genes was also investigated at the level of study-specific gene lists (Table 2). Comparison of DEGs with the 4 involution-related gene lists revealed 33, 11, 19 and 21 common transcripts for the outputs from Stein et al. 2004, Clarkson et al. 2004, Stein et al. 2007 and Blanchard et al. 2007, respectively (Supplementary Table 5). These overlaps were significant for the gene lists from Stein et al. 2004 ($$p \leq 3.29$$E − 13), Stein et al. 2007 ($$p \leq 1.54$$E − 05) and Blanchard et al. 2007 ($$p \leq 4.13$$E − 06) but not for Clarkson et al. 2004 ($$p \leq 0.285$$) (Supplementary Table 7).
## Functional analysis of DEGs induced by shaving
IPA identified 3 canonical pathways that that were significantly altered in the mammary gland of shaved mice at B-H p value < 0.05, including p53 Signalling, Docosahexaenoic Acid (DHA) Signalling and IL-23 Signalling (Table 6). These pathways contained 18, 9 and 8 DEGs induced by shaving, which constituted 23.1, 30.0 and $32.0\%$ of all genes that make up p53 Signalling, DHA Signalling and IL-23 Signalling pathways, respectively. Because some DEGs contributed to more than one pathway, the number of unique DEGs present in all three pathways was 23 (17 upregulated and 6 downregulated). None of the pathways were significantly activated or inhibited, with IL-23 Signalling being the closest to the activation state (z-score 1.4).Table 6Details of canonical pathways altered in the mammary gland of shaved vs unshaved lactating mice ($$n = 5$$ females per group), identified by Ingenuity Pathway Analysis (IPA)Canonical pathwayB-H p valueRatioz-scoreContributing genesap53 Signalling$\frac{0.00218}{78}$ (0.231)0.3Akt1, Akt3, Apaf1, Bax, Bbc3, Ccnd1, Fas, Gadd45a, Gnl3, Hipk2, Pidd1, Pik3c2a, Pik3cb Pik3r1, Sirt1, Thbs1, Tigar, Trp53inp1Docosahexaenoic Acid (DHA) Signalling$\frac{0.0329}{30}$ (0.300)–Akt1, Akt3, Apaf1, Bax, Bik, Cycs, Pik3c2a, Pik3cb, Pik3r1IL-23 Signalling$\frac{0.0328}{25}$ (0.320)1.4Akt1, Akt3, Jak2, Pik3c2a, Pik3cb, Pik3r1, Runx1, Socs3The analysis was performed on 752 DEGs (for details see Supplementary Table 3). Pathways were considered significantly altered at Benjamini-Hochberg (B-H) multiple testing correction p value < 0.05. The ratio is calculated as the number of genes in each pathway that are present in our experimental dataset (contributing genes), divided by the total number of genes that make up that pathway and are present in the reference set. The overall activation/inhibition states of canonical pathways are predicted based on a z-score algorithm; z-score ≥ 2 predicts an increase in the pathway activity while z-score ≤ − 2 predicts a decrease in the pathway activity. No information on the pathway activity is indicated by a hyphen (–)aGene symbols (in bold) and gene names, followed by Log2 FC (in bold)Akt1, thymoma viral proto-oncogene 1, − 0.7Akt3, thymoma viral proto-oncogene 3, 0.8Apaf1, apoptotic peptidase activating factor 1, 0.6Bax, BCL2-associated X protein, 1.2Bbc3, BCL2 binding component 3, 1.3Bik, BCL2-interacting killer, 1.2Ccnd1, cyclin D1, − 1.4Cycs, cytochrome c, somatic, − 0.6Fas, Fas (TNF receptor superfamily member 6), 0.8Gadd45a, growth arrest and DNA-damage-inducible 45 alpha, 2.2Gnl3, guanine nucleotide binding protein-like 3 (nucleolar), − 0.7Hipk2, homeodomain interacting protein kinase 2, 0.6Jak2, Janus kinase 2, 0.6Pidd1, p53 induced death domain protein 1, − 0.9Pik3c2a, phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 alpha, 0.8Pik3cb, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta, 0.6Pik3r1, phosphoinositide-3-kinase regulatory subunit 1, 0.8Runx1, runt related transcription factor 1, 1.7Sirt1, sirtuin 1, 0.9Socs3, suppressor of cytokine signalling 3, 1.8Thbs1, thrombospondin 1, 1.9Tigar, Trp53 induced glycolysis regulatory phosphatase, − 0.7Trp53inp1, transformation related protein 53 inducible nuclear protein 1, 2.2 The IPA software predicted 11 upstream regulators responsible for differential gene expression in the mammary glands of shaved mice, at B-H p value < 0.05 and absolute z-score ≥ 2 (Table 7, Supplementary Table 8). Among them were 4 transcription factors (Trp53, Cdkn2a, Id3 and Zbtb33) 3 kinases (Jak1, Mapk8 and Egfr), a cytokine (Ifng), an actin-related protein (Actl6a), a peptidase (Zmpste24) and a cytoskeletal linker protein (Gas2l3). Out of the 11 upstream regulators, 7 were activated (z-score ≥ 2) and 4 were inhibited (z-score ≤ − 2). The number of DEGs regulated by each upstream regulator varied from 4 (Zbtb33) to 83 (Trp53). Because some DEGs induced by shaving appeared to be regulated by more than 1 upstream regulator, the number of unique DEGs regulated by all 11 upstream regulators was 144.Table 7Upstream regulators of gene expression changes in the mammary gland of shaved vs unshaved lactating mice ($$n = 5$$ females per group), predicted by Ingenuity Pathway Analysis (IPA)Upstream regulator (gene symbol and name)Gene typeB-H p valuez-scoreTarget genesaPredicted state of activation (z-score ≥ 2) Trp53, transformation related protein 53Transcription regulator0.01803.483 Ifng, interferon gammaCytokine0.01662.938 Cdkn2a, cyclin dependent kinase inhibitor 2ATranscription regulator0.01392.213 Jak1, Janus kinase 1Kinase0.01802.28 Mapk8, mitogen-activated protein kinase 8Kinase0.00442.213 Id3, inhibitor of DNA binding 3Transcription regulator0.04182.117 Zbtb33, zinc finger and BTB domain containing 33Transcription regulator0.04182.04Predicted state of inhibition (z-score ≤ − 2) Actl6a, actin-like 6AActin-related protein0.0044 − 2.18 Zmpste24, zinc metallopeptidase, STE24Peptidase0.0431 − 2.28 Gas2l3, growth arrest-specific 2 like 3Cytoskeletal linker protein0.0316 − 2.46 Egfr, epidermal growth factor receptorKinase0.0211 − 2.516The analysis was performed on 752 DEGs (for details see Supplementary Table 3). Upstream regulators were considered significant at Benjamini-Hochberg (B-H) multiple testing correction p value < 0.05 and absolute z-score ≥ 2. The z-score ≥ 2 predicts an activation of upstream regulators, while z-score ≤ − 2 predicts their inhibitionaNumber of differentially expressed genes (DEGs) regulated by each upstream regulator (for details see Supplementary Table 8) The consequences of transcriptomic changes in the mammary glands of shaved mice were predicted by IPA as 4 downstream effects at B-H p value < 0.05 and the number of contributing genes > 100, including Apoptosis, Cell Movement, Solid Tumour and Migration of Cells (Table 8, Supplementary Table 9). The prediction of the activation state was available only for Solid Tumour, with the z-score of − 2.9 indicating a decreased activation state. The number of DEGs contributing to each downstream effect varied from 113 (Migration of Cells) to 150 (Apoptosis), with the total number of unique DEGs associated with all 4 downstream effects being 247.Table 8Downstream effects predicted from gene expression changes in the mammary gland of shaved vs unshaved lactating mice ($$n = 5$$ females per group) by Ingenuity Pathway Analysis (IPA)Downstream effect (biological process or disease)Functional categoryB-H p valueActivation statez-scoreContributing genesaApoptosisCell death and survival0.012–0.0150Cell MovementCellular movement0.012–1.1125Solid TumourCancer0.012Decreased − 2.9122Migration of CellsCellular movement0.012–0.9113The analysis was performed on 752 DEGs (for details see Supplementary Table 3). Downstream effects were considered significant at Benjamini-Hochberg (B-H) multiple testing correction p value < 0.05 and the number of contributing genes > 100. The z-score ≥ 2 predicts increased downstream effects, while z-score ≤ − 2 predicts decreased downstream effects; the lack of prediction is indicated by a hyphen (–)aNumber of differentially expressed genes (DEGs) that are associated with each downstream effect (for details see Supplementary Table 9)
## Correlation of gene expression and milk production
Correlation analysis identified no genes in the mammary gland of shaved ($$n = 5$$) or unshaved ($$n = 5$$) mice that were correlated with milk production at FDR < 0.05 (Supplementary Table 10). When analysis was performed on both shaved and unshaved mice ($$n = 10$$) with the fur effect blocked, only two genes (Cd276 and Daglb) reached borderline significance at FDR = 0.049. Both these genes were negatively correlated with milk production at correlation coefficients of − 0.976 (Cd276) and − 0.973 (Daglb), and they were not part of milk synthesis-related, involution-related or DEG gene sets (Table 5).
## Discussion
Both humans and other animals are limited in their maximum performance by intrinsic constraints, whether it is growth, reproduction, physical activity or thermoregulation (Drent and Daan 1980; Peterson et al. 1990; Hammond and Diamond 1997; Thurber et al. 2019). Because the physiological limits to performance may also depend on environmental conditions, identifying the mechanisms constraining performance has important ramifications for understanding animal distribution and migration or human athleticism, especially under climate change (Humphries et al. 2002; El Helou et al. 2012; Haïda et al. 2013; Rogers et al. 2021). While many studies were designed to experimentally remove the cap on the performance and then measure the immediate gain in performance (reviewed in Speakman and Król 2005a, 2011; Król and Speakman 2019), the wider context and implications of such gains have not often been investigated. In our previous work, we focussed on the peak lactation performance in MF1 laboratory mice and proposed that lactating females are limited by their capacity to dissipate body heat generated as a by-product of processing food and producing milk (the HDL hypothesis) (Król and Speakman 2003a, b; Król et al. 2003; Speakman and Król 2010a). To remove the performance limit, we shaved off the dorsal fur of the lactating females to enhance their capacity to dissipate body heat and then measured the gain in performance as the shaving-induced increases in food intake, milk production and litter mass (Król et al. 2007). In the current study, we used the same model system to explore the effects of fur removal on the mammary gland, the site of milk production and secretion. By performing RNA-seq profiling of the mammary glands of shaved and unshaved lactating mice, we aimed to understand how the extra milk production is regulated at the level of gene expression.
## Phenotypic responses to shaving
The effects of fur removal on the whole-body phenotype and reproductive performance reported for MF1 mice in the current study were consistent with the results of our original shaving experiment (Król et al. 2007). Furthermore, the shaving-induced increases in food intake, milk production and litter mass in the current study were consistent with the results of shaving experiments performed in lactating bank voles (food intake, milk production and litter mass by 13.2, 11.8 and $22.1\%$, respectively) and golden hamsters (food intake, milk production and litter mass by 9.9, 23.4 and $23.7\%$, respectively) (Table 1). Overall, the phenotypic outcome of our shaving experiment was as expected from the previous work (Król et al. 2007; Sadowska et al. 2016; Ohrnberger et al. 2020).
## Transcriptomic responses to shaving
Transcriptome profiling of mammary glands has become a powerful tool for identifying genes and molecular pathways involved in tissue development and function, especially during the cycles of proliferation (pregnancy), functional differentiation (lactation), and death of alveolar epithelium (involution) that occur with each breeding event (Hennighausen and Robinson 2001; Stein et al. 2004, 2007; Clarkson et al. 2004; Blanchard et al. 2007; Cristea and Polyak 2018; Li et al. 2020). Our study demonstrates a link between fur removal and mammary gene expression in lactating mice. Consistent with the other RNA-seq experiments using a small number of biological replicates (Schurch et al. 2016), our study had sufficient power to detect DEGs with larger fold changes (absolute Log2 FC > 0.5) but not with smaller fold changes (absolute Log2 FC ≤ 0.5) (Fig. 2B). While the majority of RNA-seq analytical tools successfully control their FDR at < $5\%$ for all numbers of replicates, we specifically used edgeR recommended for a lower number of replicates, based on its superior combination of true positive and false positive performances (Robinson et al. 2010; Schurch et al. 2016). As a result, we demonstrated that shaving off dorsal fur in lactating mice significantly altered the mammary expression of 752 genes (Table 4, Supplementary Table 3). For comparison, an exposure of lactating mice to a daily 2 h heat treatment (36 °C) for 14 days was associated with the changes in the mammary expression of 409 genes ($$n = 8$$ females per group, FDR < 0.01 and absolute Log2 FC > 0.6) (Han et al. 2019). In another study, feeding lactating mice with a high-fat diet altered the mammary expression of 628 genes ($$n = 6$$ females per group, FDR < 0.1 and absolute Log2 FC > 1) (Cheng et al. 2018). Using the number of DEGs as a proxy for the magnitude of transcriptomic changes, we conclude that the mammary gland responses to shaving were of similar size to those induced by other whole-animal manipulations, including ambient temperature and diet treatments (Cheng et al. 2018; Han et al. 2019).
## Overlap with milk synthesis-related gene set
The milk synthesis machinery was represented in our study by the milk synthesis-related gene set compiled from the mouse mammary gland literature, containing 4 hormone receptors, 12 transcription factors and regulators, 25 milk protein synthesis-related, 46 milk fat synthesis-related and 13 lactose synthesis-related transcripts (Supplementary Table 4). The $19.5\%$ increase in milk production of shaved mice did not appear to be associated with any substantial changes in the milk synthesis machinery at the level of transcriptome (Table 5, Supplementary Table 6). The lack of significant overlap between mammary DEGs induced by shaving and milk synthesis-related genes may have several explanations. Firstly, there may be other groups of genes involved in the regulation of milk synthesis and its secretion into the alveolar lumen (Ramanathan et al. 2008; Wei et al. 2013). Secondly, a substantial part of such regulation may be post-transcriptional rather than transcriptional (Lemay et al. 2007; Osorio et al. 2016; Mu et al. 2021). Thirdly, milk production of shaved mice may have a different trajectory of changes during lactation than that of unshaved mice. If the latter is the case, then the separation of measurements of milk production and mammary gene expression by a few days may contribute to the data being temporarily mismatched and thus unlinked to each other. In our study, the main determinant of milk production (MEI) was measured on days 12–14 of lactation, while the mammary gene expression was evaluated on day 18 of lactation, assuming no changes in these parameters between approximately days 10–18 post-partum (Johnson et al. 2001).
Of the 100 milk synthesis-related genes, only 8 genes were differently expressed in the mammary gland of shaved mothers, including 1 transcription factor (Srebf1), 5 milk fat synthesis-related genes (Scd1, Fads1, Fads2, Gpd1 and Dhcr7) and 2 lactose synthesis-related genes (Gale and Slc35a2) (Supplementary Table 4). The transcription factor encoded by Srebf1 has been proposed to regulate the expression of genes involved in lipolysis, lipogenesis de novo, fatty acid activation, and triglyceride and cholesterol biosynthesis in the mammary gland during lactation in mice (Rudolph et al. 2010), humans (Mohammad and Haymond 2013) and cows (Ma and Corl 2012), with the confirmed regulation of 3 desaturase genes altered in our dataset (Scd1, Fads1 and Fads2) (Nakamura and Nara 2002). The other milk fat synthesis-related genes with altered expression were involved in glycerol activation (Gpd1) and cholesterol synthesis (Dhcr7), while the changes in the lactose synthesis pathway were associated with the gene regulation of UDP-galactose synthesis (Gale) and transport (Slc35a2).
All these 8 milk synthesis-related genes were paradoxically downregulated in the mammary gland of shaved mice, rather than upregulated as we would have expected in mice producing more milk because of shaving (with Log2 FC values from − 0.5 to − 1.2, Supplementary Table 4). Such a decline may indicate a gradual loss of replenishment of the secretory machinery at the mRNA level (Lemay et al. 2007), leading potentially to earlier cessation of milk production and thus earlier completion of the lactation cycle in shaved mice. Our results suggest potentially different trajectories of changes in milk production for shaved and unshaved mice.
## Overlap with involution-related gene set
Using microarray technology, transcriptional profiles of the mouse mammary gland during involution have been studied by numerous research groups (Table 2), providing a basis for the involution-related gene set with 345 transcripts in total (Supplementary Table 5). Comparison of DEGs induced by shaving and the involution-related gene set from the literature revealed a highly significant overlap of 59 protein-coding genes (Fig. 3, Table 5), with the direction of change (52 upregulated and 7 downregulated) closely resembling the expression patterns of these genes in the mice undergoing forced involution following pup removal (Stein et al. 2004, 2007; Clarkson et al. 2004; Blanchard et al. 2007).
At the onset of involution, milk stasis causes distension of the alveolar lumen, which in turn changes the shape of the mammary epithelial cells and increases the local production of leukaemia inhibitory factor (LIF) (Schere-Levy et al. 2003). In shaved mice, LIF gene expression was 6.9-fold higher than in unshaved controls. LIF acts to phosphorylate signal transducer and activator of transcription 3 (STAT3), a master regulator of mammary gland involution coordinating both programmed cell death and removal of dead cells by the immune system (Chapman et al. 1999). Once activated, STAT3 regulates the expression of genes involved in the uptake of milk lipids from the lumen back to the mammary epithelial cells for their degradation in lysosomes, which has been linked to the increased permeability of the lysosomal membranes (Sargeant et al. 2014). This then results in the leakage of cathepsin proteases into the cytosol, activating the lysosomal-mediated programmed cell death (Kreuzaler et al. 2011). Both lysosome-related genes (Scarb2 and Dnase2a) and cathepsin genes (Ctsa and Ctsl) were significantly upregulated in shaved mice. Furthermore, phosphorylation of STAT3 by LIF shifts the balance between pro- and anti-apoptotic signals in favour of programmed cell death, by activation of pro-apoptotic BCL-2 family members and downregulation of PI3K-AKT survival signalling (Hughes and Watson 2018b; Jena et al. 2019). The presence of such shift in the mammary gland of shaved mice was supported by upregulated expression of two pro-apoptotic genes from the BCL-2 family (Bax and Bik) and downregulation of the gene encoding AKT1, a serine/threonine protein kinase involved in regulation of cell survival and proliferation.
The wave of cell death that occurs during the first phase of involution is marked by the increased expression of cell death receptors and ligands (Clarkson et al. 2004; Stein et al. 2007), represented in the mammary gland of shaved mice by a number of significantly upregulated genes, including Fas, Cebpd, Igfbp5, Pik3r1 and Pik3c2a. *The* gene with the highest upregulation induced by shaving (a fold change of 46.1) was Cyp24a1, associated with the cell death programmes mediated by vitamin D (Lopes et al. 2012). The removal of dead cells from the mammary gland requires the STAT3-mediated switch of mammary epithelial cells from a secretory to a phagocytic phenotype to perform so called non-professional phagocytosis, with the involvement of professional phagocytes such as macrophages (Monks et al. 2008; Akhtar et al. 2016). The presence of phagocytic phenotype of mammary epithelial cells in shaved mice was inferred from the significantly increased expression of genes encoding C/EBPδ (a fold change of 2.2) and CD14 (a fold change of 5.4). In addition, upregulation of Ccl8, Chil1 and *Thbs1* genes in the mammary gland of shaved mice was consistent with activation of macrophages (Marion et al. 2016; Urao et al. 2016; Farmaki et al. 2020). The involvement of macrophages in the vasculature remodelling during the regression of mammary gland (Elder et al. 2020) in shaved mothers was also supported by the increased expression of angiopoietin-4 (a fold change of 11.9).
Finally, the permeability of the mammary alveolar tight junctions probably differs between shaved and unshaved mice, as indicated by changes in the gene expression of claudin-1 and claudin-4. Similarly to the involuting mammary gland following pup removal (Stein et al. 2004, 2007; Blanchard et al. 2007), the expression levels of these genes were upregulated in shaved mice. In contrast, we did not detect any shaving-induced changes in the gene expression of matrix metalloproteinases, carboxypeptidases or eosinophils/neutrophil markers, which are part of the transcriptomic signature associated with the second, irreversible phase of mammary gland involution (Clarkson et al. 2004; Stein et al. 2007). Together, our gene expression data strongly suggest that the mammary gland of shaved mothers was already in the process of regressing from secretory to non-secretory phenotype, pointing towards the first (reversible) phase of involution.
## Shaving vs pup removal experiments
Our results raise the question of why the overlap between mammary DEGs induced by shaving ($$n = 752$$) and the involution-related gene set from the literature ($$n = 345$$) was not larger than 59 genes, if the mammary gland of shaved mice was indeed involuting. The reasons for such outcome are probably not related to the differences in the statistical power of the contributing experiments as all studies were performed either on 3 mice per time point or 5 mice per group in the single-point experiments (Table 2). Instead, the natural involution accelerated in our study by fur removal and investigated on day 18 of lactation may have a slightly different transcriptomic signature than the forced involution induced by pup removal on days 7–12 of lactation (Stein et al. 2004; Clarkson et al. 2004; Blanchard et al. 2007). The differences between natural and forced involution of mammary gland have been discussed elsewhere (Silanikove 2014). Secondly, even if the transcriptomic profiles of natural and forced involution are similar, forced involution was typically synchronised across the experimental mice (by removing their litters simultaneously), which makes the rapid and dramatic changes in the mammary gene expression easier to detect (Lemay et al. 2007). In contrast, the onset of natural involution is likely to differ in time and intensity between mothers, generating the nonsynchronous expression of the involution-related genes, which in turn increases their inter-individual variability at single time point and thus decreases the chance of detection of these genes as significantly altered. Thirdly, gene expression patterns associated with mammary involution may differ between different strains of mice, as indicated by the significant overlaps between shaving-induced DEGs and the involution-related gene lists from the experiments on Balb/C mice (Stein et al. 2004) and CD1 mice (Blanchard et al. 2007), but not C57/Bl/6 mice (Clarkson et al. 2004) (Supplementary Table 7). Finally, the involution-related gene set from the literature represent the complete process of mammary regression from secretory to non-secretory phenotype (including reversible and irreversible stages of involution), while the DEGs induced by shaving contain only transcripts associated with the early (reversible) involution, which reduces the number of potential genes being in common. Insights into the potential role of shaving-induced DEGs not overlapping with the involution-related gene set ($$n = 752$$–59) were gained from the functional analysis of gene expression performed on all DEGs.
## Functional analysis of DEGs
Functional analysis of DEGs induced by fur removal identified 3 canonical pathways, 11 upstream regulators and 4 downstream effects that were significantly altered in the mammary gland of shaved mice (Tables 6, 7, 8, Supplementary Tables 8 and 9). The most striking features of our analysis were changes in the p53 tumour suppressor protein in the mammary gland of shaved mice, predicted both at the level of upstream regulators (that drive the observed changes in gene expression) and canonical pathways (that reflect the enrichment of DEGs). Specifically, p53 (encoded in mice by Trp53) was identified as the most activated upstream regulator of DEGs, while p53 Signalling was the most significantly altered pathway. The p53 protein is a master transcription factor that responds to a variety of cellular stresses and regulates key cellular processes such as DNA repair, cell-cycle progression, angiogenesis and apoptosis, with the p53-dependent pathways typically eliminating damaged cells either through apoptosis or cell-cycle arrest (reviewed in Sullivan et al. 2018). The pro-apoptotic role of p53 in mammary gland involution has been demonstrated in a series of mammary-specific knockout studies. The involution programme was delayed in mice with Trp53 null mammary gland by a few days and hyper-delayed (by a few weeks) in mice with Stat3-Trp53 doubly null gland, but successfully executed by p53 in mice with Stat3 null gland, (Jerry et al. 1998, 2002; Chapman et al. 1999; Matthews and Clarke 2005). These studies clearly demonstrate that STAT3 and p53 act together in synergistic manner to assure the regression of the mammary gland and underscore the importance of redundant apoptotic pathways in the involution programme (Allen-Petersen et al. 2010; Yallowitz et al. 2014).
Furthermore, the potential shift from a pro-survival to pro-apoptotic environment in the mammary gland of shaved mice was supported by the predicted activation of upstream regulators such as Ifng (involved in caspase-8-JAK$\frac{1}{2}$-STAT1-dependent cell death, Woznicki et al. 2021), Cdkn2a (inhibits cell proliferation through LDHA‑mediated AKT/mTOR pathway, Luan et al. 2021), Jak1 (phosphorylates STAT proteins in mammary epithelium, Sakamoto et al. 2016), Mapk8 (promotes expression of involution-related genes, Girnius et al. 2018), Id3 (inhibits cell proliferation and induces apoptosis in vitro, Chen et al. 2016) and Zbtb33 (enhances apoptosis in a p53-dependent manner, Koh et al. 2015). At the same time, the predicted inhibition of upstream regulators such Actl6a (an oncogenic driver in many human cancers, Jian et al. 2021) and Egfr (a major regulator of proliferation and differentiation in epithelial cells, Ramírez Moreno and Bulgakova 2022) points towards reduced cell survival and proliferation in the mammary gland of shaved mice.
Apart from p53 Signalling, the other pathways altered in the mammary gland of shaved mice included IL-23 Signalling and Docosahexaenoic Acid (DHA) Signalling, both potentially linked to the increased presence of immune cells in the tissue. IL-23 is a key pro-inflammatory cytokine expressed by activated monocytes, macrophages, dendritic cells and other antigen presenting cells, which signals to activate STAT proteins, predominantly STAT3 (Kortylewski et al. 2009). DHA is suggested to attenuate macrophage death and potentiate efferocytosis, with the net effect of reducing accumulation of cell corpses in the tissue (Rajasinghe et al. 2020).
Finally, Apoptosis, Cell Movement, Solid Tumour and Migration of Cells were identified by IPA as the top downstream effects predicted to be present in the mammary gland of shaved mice. These effects (especially Apoptosis, Cell Movement and Migration of Cells) may reflect processes such as programmed cell death, acute phase response as well as removal of dead cells by professional and non-professional macrophages in the involuting gland (Stein et al. 2004; Pensa et al. 2009). The predicted Solid Tumour downstream effect agrees with the upregulation and activation of tumour-promotional factors in the mammary epithelium and surrounding stroma once lactation ceases (Wallace et al. 2019; Borges et al. 2020). Together, the results of functional analysis performed on gene expression patterns associated with fur removal are consistent with the ongoing involution of the mammary gland in shaved mice.
## Mother-young conflict over weaning
Weaning from lactation is a time when the interests of the mother and the young are likely to differ, with the young expected to benefit from prolonged lactation and larger size while the mother is expected to maximise her fitness by initiating another breeding event (Trivers 1974).
The mechanisms by which prolonged lactation benefits offspring metabolism have been recently uncovered in rats (Félix-Soriano and Stanford 2022; Pena-Leon et al. 2022). In contrast, lactating female rodents typically benefit from having more litters in a short breeding season by being simultaneously pregnant, which sets the duration of lactation to the sufficient minimum rather than to the extended period of time (Roy and Wynne-Edwards 1995). These contrasting interests trigger mother–young conflict because the optimal time for weaning is likely to be later for the young than for the mother, leading to the evolution of complex behaviours such as solicitation displays in the young and the avoidance of offspring by the mother (Kӧlliker and Richner 2001; Fouts et al. 2005; Cox and Hager 2016). Typically, it is the mother who drives the onset of weaning. In house mice (Mus domesticus), this starts around day 17 post-partum when the mother starts to rest alone and remains away from the litter (Kӧnig and Markl 1987). Cross-fostering experiments (with natural litters replaced by younger or older pups) demonstrated that the time of weaning may be either fixed relative to the day of parturition (as in guinea pigs, *Cavia aperea* f. porcellus, Rehling and Trillmich 2007) or flexible in response to variation in offspring development (as in rats, Rattus norvegicus, Nicoll and Meites 1959). However, no experimental studies have investigated the effects of extra milk production on the time of weaning in the mothers with natural litters. Would the mothers with the extra milk production wean the litters earlier than normal to benefit from a shorter interbirth interval or would they rather wean the litters at normal time to benefit from the bigger young? Both the length of interbirth interval and the size of young at weaning are important life history traits that contribute to the mother’s lifetime reproductive success (Clutton-Brock et al. 1989; West and Capellini 2016).
By experimentally increasing milk production in laboratory mice, we demonstrated that on day 18 of lactation, the mammary gland of shaved mice was already involuting, providing strong evidence for shorter lactation and weaning the young earlier than normal, a strategy that could potentially lead to more frequent breeding events in these mice. Based on the chronology of transcriptomic events in the mammary gland following pup removal (Clarkson et al. 2004; Stein et al. 2007), the mammary gland of shaved mice was at the first (reversible) phase of involution that probably started within ~ 48 h prior to tissue sampling. Because the individual pups of shaved mice were on average substantially heavier than the pups of unshaved mice (75.7 g/11.0 pups = 6.9 g and 63.3 g/11.4 pups = 5.6 g, respectively, Table 3), our data are consistent with the idea that the females adjust their reproductive investment according to the size of young (a proxy for quality) by advancing or delaying the time of weaning to reach the minimum size necessary for the young to survive and breed (Kӧnig and Markl 1987; West and Capellini 2016). More work is needed to couple our transcriptomic data with the behavioural manifestations of the weaning, and to establish whether earlier involution of the mammary gland in shaved mice leads to a shorter interbirth interval.
## Study limitations
The results of our study are drawn from a relatively small number of shaved and unshaved mice, with 5 lactating females per group. In consequence, some phenotypic effects associated with fur removal did not reach significance (Supplementary Table 1). The proper interpretation of these results was possible because of the full characterisation of the shaving effects performed in our earlier study, using the four times larger sample size (Król et al. 2007). The transcriptomic results were probably also affected by $$n = 5$$, mainly by limited statistical power to detect DEGs with relatively small fold changes (Fig. 2B). Yet the pup removal studies with the sample size of 3 mice per time point (Stein et al. 2004; Clarkson et al. 2004) or 5 mice per group in the single-point experiment (Blanchard et al. 2007) were sufficient to characterise the unique transcriptomic signature of the involuting mammary gland. Similarly, our study had sufficient power to recognise that signature, despite different mechanisms behind triggering the regression of mammary gland.
Our experimental design to have a single snapshot of mammary transcriptome to cover both milk production and involution processes did not work as expected. That design was based on our earlier study indicating no changes in MEI and thus milk production between approximately days 10–18 post-partum in MF1 mice (Johnson et al. 2001). In contrast, our current study indicated potentially different trajectories of changes in milk production for shaved and unshaved mice. On day 18 of lactation, the mammary gland of shaved mice was already in the process of regressing from secretory to non-secretory phenotype. As such, changes in the mammary transcriptome on day 18 post-partum no longer represented greater milk production of shaved mice measured on days 12–14 of lactation (Table 5, Supplementary Table 6). A similar mismatch (no correlation) between milk production and mammary gene expression was also observed in unshaved mice as well all mice with the fur effect blocked (Supplementary Table 10), highlighting the need for simultaneous measurements of these parameters in future studies linking transcriptome to function.
## Conclusions
We shaved lactating MF1 mice to increase their milk production (Król et al. 2007) and investigated their mammary gene expression profiles relative to unshaved mice. The focus of the study was to search for transcriptomic clues on the mechanisms underlying the increased milk production and for consequences of the extra milk production for the mother–young conflict over weaning, manifested by advanced or delayed involution of mammary gland. We demonstrated that the mammary glands of shaved and unshaved mice were at different stages of the lactation cycle when sampled. The extensive transcriptomic analysis indicated that the mammary gland of shaved mice had a gene expression profile indicative of earlier involution relative to unshaved mice. Our interpretation of these results is that once provided with the enhanced capacity to dissipate body heat, shaved mice were likely to rear their young to independence faster than unshaved mothers, thereby potentially benefiting from shorter lactation and shorter interbirth interval to maximise their lifetime reproductive success (Clutton-Brock et al. 1989; West and Capellini 2016). Further research is needed to establish the link between earlier regression of the mammary gland and the timing of the next breeding event. We argue that the association between HDL and female fecundity is understudied and should be considered when investigating lactation performance in laboratory and natural conditions.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary Fig. 1 (DOCX 131 KB)Supplementary Table 1 (XLSX 19 KB)Supplementary Table 2 (XLSX 22 KB)Supplementary Table 3 (XLSX 148 KB)Supplementary Table 4 (XLSX 24 KB)Supplementary Table 5 (XLSX 474 KB)Supplementary Table 6 (XLSX 21 KB)Supplementary Table 7 (XLSX 10 KB)Supplementary Table 8 (XLSX 22 KB)Supplementary Table 9 (XLSX 26 KB)Supplementary Table 10 (XLSX 1589 KB)
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|
---
title: Clinical impact of manual scoring of peripheral arterial tonometry in patients
with sleep apnea
authors:
- Samuel Tschopp
- Urs Borner
- Wilhelm Wimmer
- Marco Caversaccio
- Kurt Tschopp
journal: Sleep & Breathing = Schlaf & Atmung
year: 2022
pmcid: PMC9992081
doi: 10.1007/s11325-021-02531-9
license: CC BY 4.0
---
# Clinical impact of manual scoring of peripheral arterial tonometry in patients with sleep apnea
## Abstract
### Purpose
The objective was to analyze the clinical implications of manual scoring of sleep studies using peripheral arterial tonometry (PAT) and to compare the manual and automated scoring algorithms.
### Methods
Patients with suspected sleep-disordered breathing underwent sleep studies using PAT. The recordings were analyzed using a validated automated computer-based scoring and a novel manual scoring algorithm. The two methods were compared regarding sleep stages and respiratory events.
### Results
Recordings of 130 patients were compared. The sleep stages and time were not significantly different between the scoring methods. PAT-derived apnea-hypopnea index (pAHI) was on average 8.4 events/h lower in the manually scored data (27.5±17.4/h vs.19.1±15.2/h, $p \leq 0.001$). The OSA severity classification decreased in 66 ($51\%$) of 130 recordings. A similar effect was found for the PAT-derived respiratory disturbance index with a reduction from 31.2±16.5/h to 21.7±14.4/h ($p \leq 0.001$), for automated and manual scoring, respectively. A lower pAHI for manual scoring was found in all body positions and sleep stages and was independent of gender and body mass index. The absolute difference of pAHI increased with sleep apnea severity, while the relative difference decreased. Pearson’s correlation coefficient between pAHI and oxygen desaturation index (ODI) significantly improved from 0.89 to 0.94 with manual scoring ($p \leq 0.001$).
### Conclusions
Manual scoring results in a lower pAHI while improving the correlation to ODI. With manual scoring, the OSA category decreases in a clinically relevant proportion of patients. Sleep stages and time do not change significantly with manual scoring. In the authors’ opinion, manual oversight is recommended if clinical decisions are likely to change.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11325-021-02531-9.
## Introduction
Obstructive sleep apnea (OSA) is characterized by repetitive collapse of the upper airway resulting in arousals and sleep fragmentation [1]. This leads to disturbances in many biological processes and is associated with a higher risk for hypertension, heart failure, stroke, diabetes, and other diseases [2–4]. The prevalence of OSA, defined by an apnea-hypopnea index (AHI) greater than 15/h, is estimated to be as high as $23.4\%$ in women and $49\%$ in men [5]. Due to its high prevalence and associated complications, OSA has major socioeconomic relevance. However, it is believed that $93\%$ of women and $83\%$ of men with OSA remain undiagnosed and thus untreated [6]. Therefore, it is important to offer a cost-effective and reliable diagnosis of OSA.
Currently, sleep laboratory testing using polysomnography is the gold standard for diagnosing OSA. However, home sleep apnea testing (HSAT) offers the possibility for cost-effective and accurate assessment of OSA in selected patients [7]. It is less resource intensive and allows assessment of a patient’s sleep in his habitual sleeping environment. Furthermore, multiple-night testing can be performed to reduce the night-to-night variability and the first night effect [8].
Peripheral arterial tonometry (PAT) is a novel technique for HSAT and categorized as a Type 3 device together with respiratory polygraphy according to the American Academy of Sleep Medicine [7]. The PAT device is wrist worn and includes an accelerometer to detect movement. A finger probe measures the PAT signal and oxygen saturation. A chest sensor detects body position and includes a microphone to record an audio signal. This device setup has a low technical failure rate of $5.3\%$ for at-home measurements [9].
In polysomnography, the cortical arousals from respiratory events are directly measured using electroencephalography. These cortical arousals also result in sympathetic activation and cause vasoconstriction mediated by alpha receptors. This vasoconstriction is measured in the finger to detect arousals of the autonomic nervous system. During these autonomic arousals, the PAT signal is attenuated, the heart rate increases, and the oxygen saturation decreases. Autonomic arousals are therefore a surrogate marker for cortical arousals when using electroencephalography [10].
PAT devices combine this information on arterial pulsatile arterial volume changes with heart rate variability, oxygen saturation, body position, and actigraphy to infer sleep-related breathing disturbances and sleep stages with their characteristic patterns. Since the PAT device does not measure airflow, all respiratory events are indirectly detected. To distinguish between indirectly and directly observed events, PAT events are referred to as peripheral arterial tonometry–derived apnea-hypopnea index (pAHI) and peripheral arterial tonometry–derived respiratory disturbance index (pRDI).
Previously, PAT recordings could only be analyzed using a proprietary, computer-based algorithm. This algorithm has been well validated for sleep-related breathing events and sleep stages [11, 12]. However, previously, no insight into the raw data has been possible. A novel software (zzzPAT® Itamar Medical, Caesarea, Israel) allows for manual scoring with visual oversight over the raw data of WatchPAT® recordings analogously to scoring for respiratory polygraphy or polysomnography. Zhang et al. developed an algorithm for manual oversight in an unselected patient cohort and could demonstrate that manual scoring improves the accuracy of sleep stages and respiratory events indices against polysomnography [10]. After the automated analysis is generated by the computer, the recordings are manually reviewed using visual oversight of the raw signals. First sleep stages and second respiratory events are classified by following an algorithm developed by Zhang et al. ( further described in the “Methods”) [10].
To the knowledge of the authors, the manual algorithm has never been independently analyzed. It has been developed in an unselected patient collective and its effect on recordings performed at home and of patients with OSA is unclear. This article aims to analyze the clinical impact of manual scoring on sleep study results, such as pAHI or OSA severity classification. To answer this, we compared the results of automated and manual scoring and evaluated the clinical use of manual oversight of PAT recordings.
## Methods
For this study, we retrospectively reviewed data of patients who were referred for suspected obstructive sleep apnea to our ear, nose, and throat (ENT) clinic between 2017 and 2020 for further evaluation. The cohort was an unselected patient collective, which was referred to our ENT clinic specializing in sleep medicine because of suspected OSA. OSA was suspected by the referring colleagues either based on history with typical OSA symptoms, screening questionnaires, or pathological pulse oximetry. All recordings were performed as screening and diagnostic workup before treatment. Only recordings of patients who consented to the use of their data were included and the study has been approved by the local ethics committee. Basic anthropomorphic data, such as height, weight, age, and gender, were collected. All recordings were performed at home using the WatchPAT® 200 device (Itamar Medical, Caesarea, Israel). Recordings with less than 4 h of sleep time in the automated analysis were excluded from the analysis. The PAT recordings were automatically scored using the proprietary validated computer-based scoring algorithm for WatchPAT® scoring in the zzzPAT® software (Itamar Medical, Caesarea, Israel). The automated algorithm was configured with a cut-off value of $3\%$ oxygen desaturation for respiratory events as recommended by the American Academy of Sleep Medicine [7]. The oxygen desaturation index (ODI) was calculated using the default setting of $4\%$ desaturation, which cannot be changed by the user. Manual scoring was performed according to the novel guidelines for manual scoring of PAT with the zzzPAT® software (Itamar Medical, Caesarea, Israel) by an experienced sleep technician or the authors [10]. After the automated analysis was generated by the computer, sleep stages and respiratory events were reviewed by visual overview of the raw signals. Respiratory events were deleted if no reduction in the PAT signal with a corresponding increase in heart rate was observed or if they were associated with positional change. Events were also deleted if there was a desaturation of less than $3\%$ and no snoring pattern changes were observed. Events were added if a reciprocal pattern of PAT signal reduction and heart rate increase was observed with a greater than $3\%$ desaturation. In REM sleep, desaturations of greater than $4\%$ were marked as events.
The data was analyzed for systematic differences between automated and manual scoring regarding pAHI, pRDI, and ODI over the whole night and in different sleep stages and body positions. Furthermore, the classification of OSA severity was compared between automated and manual scoring. OSA severity was classified with pAHI as no OSA <5/h, mild 5 ≤ 15/h, moderate 15 ≤ 30/h, and severe >30/h. Lastly, time and proportion of sleep in different sleep stages were compared. For positional OSA, the ratio between pAHI in supine and non-supine position was calculated (Cartwright index) [13]. The REM association, analogously, is the ratio between pAHI in REM and NREM sleep [14]. Oxygen saturation and time in different body positions were calculated, but no comparison of automated and manual scoring was performed since these parameters do not require manual editing. However, differences between automated and manual scoring may arise due to manual adjustment of sleep and wake times as well as individual sleep stages.
Since no simultaneous other recording methods, such as polysomnography, were performed, we compared the correlation of pAHI and ODI with both scoring methods. We used this correlation as a surrogate marker for improved accuracy because many publications find a linear relationship with ODI and AHI as well as ODI and RDI [15–17]. Since no simultaneous measurements were performed, this is a surrogate marker, and the focus of this article is the effect of manual scoring and not its accuracy.
Normal distributed data were analyzed using Student’s t-tests and ANOVA for multiple groups and for nonnormal distributed data Wilcoxon’s rank-sum test or Kruskal-Wallis’ test was used. Correlations were calculated using the Pearson correlation. RStudio (Boston, USA) was used for the statistical analysis. P-values below 0.05 were considered statistically significant.
## Results
PAT recordings from 130 patients were analyzed. The participants had a mean age of 53 ± 12 years and $72\%$ ($$n = 93$$) were male. The average body mass index was 27.6 ± 3.9 kg/m2 and the Epworth Sleepiness Scale was 7.9 ± 5.0. All recordings were automatically and manually scored and a comparison of both scoring methods is given in Table 1. Recording time, heart rate, and oxygen saturation do not require scoring and are therefore constant for both scoring methods. Table 1A comparison between automated and manual scoring. Recording time and parameters regarding oxygen saturation and body position are given only once since they do not require manual scoring. The oxygen desaturation index is not scored; however, differences arise due to adjustments in sleep time and sleep stages. pAHI refers to peripheral arterial tonometry–derived apnea-hypopnea index, pRDI to peripheral arterial tonometry–derived respiratory disturbance index, ODI to oxygen desaturation index, REM to rapid eye movement, and NREM to non-rapid eye movement. P-values are calculated using a two-sided Student’s t-testAutomaticManualp-valueRecording time (hours)7.9 ± 1.2Sleep time (hours6.7 ± 1.16.7 ± 1.10.87pAHI total (events/hour)27.5 ± 17.419.1 ± 15.2<0.001pAHI in REM sleep (events/hour)32.8 ± 19.321.7 ± 16.7<0.001pAHI in NREM sleep (events/hour)25.1 ± 17.717.5 ± 15.3<0.001pAHI in supine position (events/hour)38.4 ± 25.130.0 ± 23.3<0.01pAHI in non-supine position (events/hour)19.3 ± 15.811.4 ± 12.8<0.001Cartwright index (supine pAHI/non-supine pAHI)4.2 ± 10.37.6 ± 26.40.19REM association (REM pAHI/NREM pAHI)1.7 ± 1.32.3 ± 5.30.24pRDI total (events/hour)31.2 ± 16.521.7 ± 14.4<0.001pRDI in REM sleep (events/hour)36.2 ± 17.724.3 ± 15.8<0.001pRDI in NREM sleep (events/hour)29.0 ± 17.320.2± 14.7<0.001pRDI in supine position (events/hour)41.7 ± 23.733.5 ± 22.4<0.01pRDI in non-supine position (events/hour)23.6 ± 15.515.5 ± 13.5<0.001ODI total (events/hour)15.9 ± 14.515.8 ± 14.60.96ODI in REM sleep (events/hour)19.1 ± 16.519.1 ± 16.70.97ODI in NREM sleep (events/hour)14.1 ± 13.914.0 ± 14.00.93ODI in supine position (events/hour)24.8 ± 21.024.7 ± 21.10.97ODI in non-supine position (events/hour)8.8 ± 11.18.7 ± 11.10.89Mean oxygen saturation (%)94.0 ± 1.9Heart rate (beats/minute)63.8 ± 8.3Time below $90\%$ oxygen saturation (minutes)15.6 ± 46.3Supine time (% of total sleep time)45.3 ± 26.245.4 ± 26.10.96REM sleep (% of total sleep time)24.0 ± 7.123.6 ± 7.10.63Light sleep (% of total sleep time)60.1 ± 10.960.6 ± 11.00.75Deep sleep (% of total sleep time)15.8 ± 6.415.8 ± 6.30.99Sleep time (% of total sleep time)85.1 ± 6.185.3 ± 6.20.82Wake time (% of total sleep time)14.9 ± 6.114.8 ± 6.20.82 The mean pAHI of the whole night was 27.5 ± 17.4/h for automated scoring whereas it was only 19.1 ± 15.2/h ($p \leq 0.001$) for manual scoring. The mean difference of pAHI between automated and manual scoring was 8.4/h. A direct comparison shows that the manually scored recordings lie almost exclusively below the automatically scored data for the whole range of OSA severity (see Fig. 1). The differences between automated and manual scoring are graphically illustrated with a Bland-Altman plot in Fig. 2 and a boxplot in Fig. 3. The lower results for manual scoring were consistent and significant in all sleep stages and body positions (see Table 1). With increasing sleep apnea severity, the absolute difference between automated and manual scoring increased ($p \leq 0.001$), while the relative difference decreased ($p \leq 0.001$). For patients with no OSA, the difference was −1.6h (−$51\%$), for mild OSA −4.4/h (−$44\%$), for moderate OSA −8.0/h (−$38\%$), and for severe OSA −11.8/h (−$26\%$). Similarly, a lower pRDI was observed for the manually edited data. The pRDI for the total sleep time was 31.2 ± 16.5/h and 21.7 ± 14.4/h ($p \leq 0.001$), for automated and manual scoring, respectively. Fig. 1A scatter plot comparing peripheral arterial tonometry–derived apnea-hypopnea index (pAHI) for automated and manual scoring. The bold line is the line of perfect agreement between the two methods. A linear regression is given as a dashed lineFig. 2A Bland-Altman plot comparing automated and manual scoring of peripheral arterial tonometry–derived apnea-hypopnea index (pAHI). The bold line indicates the mean difference between automated and manual scoring, showing lower values for manual scoring (−8.4 events/h). The dashed lines indicate the lower and upper limit of agreement calculated as ±1.96 * the standard deviationFig. 3A comparison of automated and manual scoring. Lines connect the individual recordings illustrating a lower peripheral arterial tonometry–derived apnea-hypopnea index (pAHI) values for manual scoring Since ODI is calculated automatically and does not require scoring, differences between the scoring methods are a result of adjustments to the sleep and wake times and the individual sleep stages. Therefore, as expected only minimal, statistically not significant differences in ODI were observed with 15.9 ± 14.5/h for automated and 15.8 ± 13.5/h for manual scoring ($$p \leq 0.96$$).
The analysis of pAHI, pRDI, and ODI depending on sleep stage and body position is given in Table 1. Manually scored pAHI and pRDI were significantly lower in all sleep stages and body positions compared to automatically scored data. Surprisingly, the positional OSA, given by the Cartwright index as the ratio between pAHI in supine and non-supine position, increased. The same was observed for REM-associated OSA, given by the ratio between pAHI in REM and NREM sleep, which also increased with manual scoring. These findings indicate that pAHI in non-supine position and NREM sleep decreased more than pAHI in supine and REM sleep.
In our cohort, men had a significantly higher pAHI than women with automated scoring of 30.3±17.2/h and 19.9±15.5/h ($p \leq 0.001$) and manual scoring of 21.0±15.3/h and 13.9±13.9/h ($$p \leq 0.003$$). Manual scoring reduced pAHI in men by −9.7/h (−$34.7\%$) and women by −6.0/h (−$36.2\%$). When accounting for the significantly higher pAHI in men, gender did not significantly influence scoring results ($$p \leq 0.76$$). The body mass index category also showed no effect on scoring results ($$p \leq 0.29$$, see Supplemental Fig. 1).
Both scoring methods resulted in a similar proportion for all sleep stages with no statistically significant difference. The REM sleep proportion was 24.0±$7.1\%$ and 23.6±$7.1\%$ ($$p \leq 0.63$$) for automated and manual scoring, respectively.
The Pearson’s correlation coefficient of pAHI and ODI increased from 0.88 for automated scoring to 0.94 for manual scoring ($p \leq 0.001$, see Fig. 4). Similarly, the correlation of pRDI with ODI improved from 0.83 and 0.90, for automated and manual editing, respectively ($p \leq 0.001$).Fig. 4A correlation between peripheral arterial tonometry–derived apnea-hypopnea index (pAHI) and oxygen desaturation index (ODI) of automated (a) and manual scoring (b). The correlation significantly improves from 0.89 to 0.94 with manual oversight ($p \leq 0.001$) Manual editing lead frequently to changes in the OSA category. In only 64 recordings ($49\%$), the category remained the same, whereas it decreased in 64 cases ($49\%$) by one category and in 2 cases ($2\%$) by two categories. No case of an increased OSA category was found.
## Discussion
PAT devices are increasingly used as an HSAT to diagnose OSA. Understanding the effect of the scoring method on results is important.
The automated algorithm is well validated and reproducible as well as time- and cost-efficient [11, 12]. The computerized algorithm is also objective, which makes it ideal for clinical studies by eliminating interrater variability. However, it has been demonstrated that this algorithm tends to overestimate respiratory events in patients with less severe OSA when compared to polysomnography [18]. A large cohort study of 500 patients undergoing simultaneous PAT and polysomnography found that PAT overestimated AHI by 4/h compared to polysomnography [19]. Yuceege et al. found AHI for PAT to be significantly higher than polysomnography with a mean difference of 1.78/h [20]. Both studies show an overestimation of respiratory events using PAT, but a lower difference than we observed between automated and manual scoring.
Manual scoring introduces some degree of subjectivity and variability in the analysis of sleep studies. Zhang et al. have developed a manual algorithm to improve the accuracy of both sleep stages and respiratory events [10]. In accordance with their results, we see manual scoring as clinically feasible requiring about 10–15 min to score one recording. The algorithm has been developed in an unselected patient collective, whereas our cohort consisted of patients with suspected sleep-disordered breathing [10].
Our study shows that sleep time and sleep stages are accurately recognized with the automated scoring algorithm of PAT. In the authors’ experience, the automated algorithm for PAT is very accurate in detecting sleep stages with little or no effect added by manual scoring.
However, there are significantly fewer respiratory events in the manually scored recordings. On average, the difference of pAHI was 8.4/h, which frequently resulted in a less severe OSA category. This difference is consistent among all OSA severities, both genders, and all body mass index categories. Zhang et al. found that manual scoring improved accuracy more in women than men [10]. Manual scoring is especially important in patients with mild or moderate OSA, for whom this difference can have implications for treatment recommendation and reimbursement from healthcare insurances.
Manual editing significantly improved the correlation of pAHI and pRDI with ODI in our study, indicating an improved accuracy since the correlation between AHI and ODI has been demonstrated in the literature [15–17].
Several limitations need to be mentioned. We did not perform simultaneous recordings with another measuring method, such as polysomnography. Without a direct comparison, it is impossible to describe the accuracy of either scoring method. We used the correlation of ODI with pAHI and pRDI as a surrogate marker for accuracy. This linear relationship has been demonstrated in many publications [15–17]. A large study by Ling et al. of more than 11,000 patients demonstrated an increasing ODI/AHI with body mass index [21]. However, since our patients had a narrow distribution of body mass index, we believe that the assumption of a linear relationship is appropriate. Furthermore, Zhang et al. have already demonstrated improved accuracy of manually edited PAT recordings compared to polysomnography in the development of the manual algorithm [10].
A further limitation of our study is that our patient collective was predominantly male and middle aged with a narrow range of body mass index. The recordings were performed in an unselected collective of patients with suspected OSA and retrospectively analyzed. We cannot further characterize our patients by comorbidities or detailed anthropomorphic measurements, because during the pretreatment process only incomplete data were collected. Moreover, the WatchPAT® 200 which was used for all recordings cannot differentiate between central and obstructive respiratory events.
A strength of this study is that all measurements were performed at home in the natural sleeping environment to reflect best the normal sleeping habits of the patients. Patients were instructed to follow their normal nighttime routine and abstain from influencing factors such as sleep medication or alcohol. However, these parameters were not recorded or controlled. To our knowledge, this is the first study to analyze the effect of manual PAT scoring for patients with suspected OSA and in recordings performed at home.
## Conclusions
The automated, computer-based algorithm offers a reliable, time- and cost-effective analysis of PAT recordings and eliminates interrater variability. Manual scoring allows for visual oversight over the recording assuring its quality. It also results in significantly lower respiratory event indices but does not significantly affect sleep time and sleep stages. We, therefore, conclude that manual scoring is important for respiratory events and to a lesser degree for sleep stages. Manual scoring might have a larger impact on patients with less severe OSA since treatment recommendations are more likely to change based on the manually scored data. Moreover, manual scoring can affect reimbursement (e.g., mandibular advancement devices), which may be dependent on cut-off values for AHI as it is common in most European countries.
Until improvements to the automated algorithm are implemented and validated, the authors recommend that sleep physicians decide individually if there is a need for manual scoring depending on the clinical situation and the possible impact on decision making.
## Supplementary Information
ESM 1Supplemental Fig. 1 Difference between automated and manual scoring in peripheral arterial tonometry-derived apnea-hypopnea index (pAHI) by gender (a) and by body mass index (b). When accounting for sleep apnea severity, no statistically significant difference lies between the gender ($$p \leq 0.76$$) or body mass index categories ($$p \leq 0.29$$) (PNG 60 kb)High Resolution Image (EPS 6 kb)ESM 2(PNG 67 kb)High Resolution Image (EPS 7 kb)
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|
---
title: Wogonin induces ferroptosis in pancreatic cancer cells by inhibiting the Nrf2/GPX4
axis
authors:
- Xing Liu
- Xinhui Peng
- Shuai Cen
- Cuiting Yang
- Zhijie Ma
- Xinyuan Shi
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9992170
doi: 10.3389/fphar.2023.1129662
license: CC BY 4.0
---
# Wogonin induces ferroptosis in pancreatic cancer cells by inhibiting the Nrf2/GPX4 axis
## Abstract
Pancreatic cancer is a common gastrointestinal tract malignancy. Currently, the therapeutic strategies for pancreatic cancers include surgery, radiotherapy, and chemotherapy; however, the surgical procedure is invasive, and the overall curative outcomes are poor. Furthermore, pancreatic cancers are usually asymptomatic during early stages and have a high degree of malignancy, along with a high rate of recurrence and metastasis, thereby increasing the risk of mortality. Studies have shown that ferroptosis regulates cell proliferation and tumour growth and reduces drug resistance. Hence, ferroptosis could play a role in preventing and treating cancers. Wogonin is a flavonoid with anticancer activity against various cancers, including pancreatic cancer. It is extracted from the root of *Scutellaria baicalensis* Georgi. In this study, we show that wogonin inhibits the survival and proliferation of human pancreatic cancer cell lines and induces cell death. We performed RNA-sequencing and analysed the differentially expressed gene and potential molecular mechanism to determine if wogonin reduced cell survival via ferroptosis. Our results showed that wogonin upregulates the levels of Fe2+, lipid peroxidation and superoxide and decreases the protein expression levels of ferroptosis suppressor genes, and downregulates level of glutathione in pancreatic cancer cells. In addition, ferroptosis inhibitors rescue the ferroptosis-related events induced by wogonin, thereby confirming the role of ferroptosis. A significant increase in ferroptosis-related events was observed after treatment with both wogonin and ferroptosis inducer. These results show that wogonin could significantly reduces pancreatic cancer cell proliferation and induce ferroptosis via the Nrf2/GPX4 axis. Therefore, wogonin could be potentially used for treating patients with pancreatic cancer.
## Introduction
A significant improvement in the patient survival rate is observed due to the rapid advancement in modern medical techniques and cancer therapeutics. The diagnosis of patients at an early stage is difficult; hence, the 5-year survival rate of patients with pancreatic cancer is extremely low, and mortality is high (Ansari et al., 2016). Based on a survey conducted by the American Cancer Society in 2021, pancreatic cancer ranks seventh in cancer-related mortality (Sung et al., 2021). A study has shown that pancreatic cancer will be the second leading cause of cancer-related mortalities in the United States by 2030 (Rahib et al., 2014). Furthermore, pancreatic cancer is anticipated to surpass breast cancer as the third leading cause of cancer-related death in the European Union by 2021 (Carioli et al., 2021). A few challenges associated with pancreatic cancer treatment include a limited number of chemotherapy drugs available and drug resistance, especially resistance to gemcitabine (Qin et al., 2020). Therefore, developing and designing therapeutic strategies with good efficacy and low drug resistance for treating patients with pancreatic cancer is urgently required.
Recent studies have shown that ferroptosis is a form of cell death and differs from necrosis, apoptosis, and pyroptosis (Koren and Fuchs, 2021). The mechanism of ferroptosis regulation consists of four aspects: 1) Iron metabolism; 2) The GSH system; 3) The BH4/CoQ10 system; 4) Transcriptional regulation (Chen et al., 2022). Therefore, an imbalance in four regulatory pathways could significantly reduce the activity of GPX4 and increase the level of intracellular lipid ROS, which reduces the antioxidant activity of cells. Hence, the accumulation of additional lipid ROS could trigger ferroptosis. With the deepening of research, it has been found that ferroptosis not only effectively kills cancer cells, but also plays an important role in inhibiting tumor cell migration (Mou et al., 2019), reversing tumor drug resistance (Viswanathan et al., 2017), and improving tumor immune response (Bi et al., 2022). In addition, ferroptosis-based nanoparticle inducers are more effective and have fewer side effects than traditional chemotherapy drugs (Zhang et al., 2020). Designing combination therapies based on ferroptosis processes in combination with nanomedicine is also an attractive line of research. Natural products, such as flavonoids, quinones, alkaloids, terpenoids, saponins, polysaccharides, polyphenols, and lignans, can increase intracellular reactive oxygen species and disrupt redox homeostasis (Wu et al., 2022). However, the pharmacological mechanism of these substances in ferroptosis still needs further study.
In China, for over 1,000 years, *Scutellaria baicalensis* Georgi has been used as a traditional Chinese medicine for treating cancers, diabetes, etc. ( Zhou et al., 2021). One of the flavonoids extracted from S. baicalensis *Georgi is* wogonin (Figure 1A) (Liu et al., 2022), which possesses several properties like neuroprotection (Smith et al., 2020), anticancer, anti-inflammatory, and antiviral (Cho and Lee, 2004). Several studies have reported that wogonin exhibits significant anticancer effects in multiple diseases like breast cancer (Yang et al., 2020), colorectal cancer (Feng et al., 2018), cervical cancer (Kim et al., 2013), leukaemia (Cao et al., 2020), gastric cancer (Hong et al., 2018), liver cancer (Hong et al., 2020), lung cancer (Wang et al., 2021), glioblastoma (Lee et al., 2012), and osteosarcoma (Huynh et al., 2017). Wogonin promotes pancreatic cancer cell death by increasing ROS levels (Li et al., 2016). Nuclear factor E2-related factor 2 (Nrf2) is a key transcription factor in the regulation of antioxidant stress response. While protecting normal cells from DNA damage induced by reactive oxygen species, malignant tumor cells are also protected. Nrf2 regulates ferroptosis by targeting many genes involved in iron/metal metabolism, such as ferritin light and heavy chains (FTL/FTH1), SLC40A1, heme oxygenase-1 (HO-1), biliverdin reductase A and B (BLVRA/B), ferrochelatase (FECH), ATP-binding cassette subfamily B member 6 (ABCB6), and SLC48A1 (Shi et al., 2021). Therefore, blocking Nrf2 expression during ferroptosis targeting therapy is critical. In addition, Nrf2 expression level was high in more than $93\%$ of pancreatic adenocarcinomas (Dai et al., 2021). Nrf2 overexpression is thought to be the result of the almost universal oncogenic KRAS gene mutation and downstream activation of the MAPK pathway as well as high levels of c-Myc (Cykowiak and Krajka-Kuźniak, 2021). Notably, high nuclear expression of Nrf2 is associated with reduced survival in patients with pancreatic cancer. Targeting Nrf2 may be an effective therapeutic strategy in pancreatic cancer. However, the involvement of ferroptosis and Nrf2 in the wogonin-mediated death of pancreatic cancer cells is still unclear. Therefore, our study aims to investigate whether wogonin could induce ferroptosis and its underlying mechanism using human pancreatic cancer cell lines and xenograft mice models of pancreatic cancers.
**FIGURE 1:** *Wogonin inhibited viability and induced death in pancreatic cancer cells. (A) Chemical structure of wogonin. (B) CCK8 assay proved that treatment with wogonin for 48 h had little toxic effect on human pancreatic duct epithelial cell line (HPDE6-C7). (C) CCK8 assay showed that wogonin inhibited the viabilities of PANC-1 and AsPC-1 pancreatic cancer cells in a dosage- and time-dependent manner. (D) Colony formation assay proved that 20 μM wogonin could obviously inhibit PANC-1 and AsPC-1 pancreatic cancer cells to form colonies, which became more apparent when wogonin dosage was increased to 80 μM. (E) LDH release assay demonstrated that wogonin triggered dosage-dependently pancreatic cancer cell death. *: p < 0.05 versus control group, **: p < 0.01 versus control group. ****: p < 0.0001 versus control group.*
## Reagents
Dulbecco’s Modified Eagle’s Medium (DMEM, Biological Industries, Beit Haemek, Israel), foetal bovine serum (FBS, Biological Industries, Israel), Roswell Park Memorial Institute (RPMI) 1,640 medium (Biological Industries, Israel), Lipo6000™ Transfection Reagent (Beyotime Biotechnology, China), Phosphate-buffered saline (PBS, Beyotime Biotechnology, China), Modified Giemsa Staining Solution (Beyotime, C0131), Caspase Inhibitor Z-VAD-FMK (Beyotime Biotechnology, China), Lactate Dehydrogenase Cytotoxicity Assay Kit (Beyotime Biotechnology, China), ROS Assay Kit (Beyotime Biotechnology, China) and Superoxide Assay Kit (Beyotime Biotechnology, China), GSH assay kit (Nanjing Jiancheng Bioengineering Institute, China), Malondialdehyde assay kit (Nanjing Jiancheng Bioengineering Institute, China), and tissue iron assay kit (Nanjing Jiancheng Bioengineering Institute, China), Cell counting kit-8 (CCK-8, CK04), Liperfluo (L248), and FerroOrange (F374) were obtained from Dojindo Laboratories (Shanghai, China). Wogonin (B20489) and ferroptosis inducers like erastin (S80804), L-buthionine-sulfoximine (BSO, S51087), FIN56 (S81914), FAC (S24249), and ferroptosis inhibitors like ferrostatin-1 (Fer-1, S81461), and deferoxamine (DFO, S61301) were procured from Shanghai Yuanye Bio-Technology Co. Ltd. (Shanghai, China). Primary antibodies like β-actin (20536-1-AP), HO-1 (10701-1-AP), solute carrier family 7 member 11 (SLC7A11, 26864-1-AP), GPX4 (67763-1-Ig), and Nrf2 (16396-1-AP) were procured from Proteintech (Wuhan, China). Highly analytical chemical reagents were used in the present research.
## Cell culture
PANC-1 and AsPC-1 cells were used due to their clear genetic background and availability. Human pancreatic ductal adenocarcinoma (PAAD) cells: AsPC-1, PANC-1, and HPDE6-C7 (human normal pancreatic epithelial cells) were provided by BeNa Culture Collection (Xinyang, China). HPDE6-C7 and AsPC-1 cells were cultured and maintained in RPMI 1640 medium. DMEM was used for culturing PANC-1 cells.
## Cell viability, colony formation, and cell death assays
Cell counting kit-8 (CCK-8) was used to measure the cell viability, which was expressed as a ratio of the absorbance measured at 450 nm in treated and control cells.
For conducting colony formation assay: pancreatic cancer cells were seeded into a 6-well plate at a density of 2,000 cells/well for 24 h and treated with 20 μM and 80 μM wogonin or $0.1\%$ dimethyl sulfoxide (in culture medium) as a control for 5 days. The cells were fixed in methanol for 10 min and stained using crystal violet (200 μg/mL) for 20 min. Colonies with >50 cells were counted using a microscope.
To determine cell death, the LDH activity released from damaged cells were measured at different time points using the LDH cytotoxicity detection kit as per the manufacturer’s protocol.
## RNA sequencing after treating PANC-1 with wogonin
RNA sequencing was performed by means of Novogene RNA sequencing. The cells were treated with 80 μM wogonin and without wogonin for 24 h. Next, the total RNA was isolated using the TRIzol reagent. 2 μg total RNA/sample was used for sample preparation for RNA sequencing. Briefly, poly-T oligonucleotide-linked magnetic beads were used for purifying mRNA from the total RNA. The first complementary DNA (cDNA) strand was synthesised using random hexamers plus M-MuLV reverse transcriptase. DNA polymerase I, along with RNase H, was used for the second cDNA strand synthesis. The amplified polymerase chain reaction (PCR) product was purified using the AMPure XP system. The “DESeq2” R package (1.20.0) was used for analysing differentially expressed genes between the two conditions/groups (two biological replicates were used/condition). The “clusterProfiler” R package was used to perform Gene ontology (GO) on differentially expressed genes (DEGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment for analysing the pathways significantly enriched by DEGs (|Log2 FC| >1). *The* gene length deviation was corrected. The expression of transcripts with adjusted $p \leq 0.05$ was considered statistically significant.
## ROS and lipid peroxidation quantification assay
For ROS detection, PAAD cells were treated with 10 μM H2DCFDA at 37°C for 30 min. For lipid peroxidation, the cells were stained with 1 μM Liperfluo at 37°C for 30 min, and the fluorescence intensity were measured. Subsequently, the cells were washed with Hanks’ Balanced Salt Solution (HBSS) twice and analysed using flow cytometry.
## Superoxide assay
The superoxide levels in the cells were measured using a superoxide assay kit as indicated by the manufacturer. Briefly, 8 × 103 PAAD cells/well were cultured in 96-well plates and were treated as indicated. Subsequently, the superoxide detection reagent (200 μL/well) was added to the cells and incubated at 37°C for 3 min. The absorbance of cells was measured at 450 nm for the superoxide assay.
## Iron assay
To determine if wogonin regulates ferrous (Fe2+) ion production, we used FerroOrange (an intracellular Fe2+ ion probe) assay to measure the iron levels in cells. Briefly, 1 × 104 PAAD cells were harvested and washed with HBSS thrice. Next, the cells were incubated with 1 μM FerroOrange (Ex: 543 nm and Em: 580 nm) in serum-free medium at 37°C for 30 min in an incubator with $5\%$ CO2. The fluorescence microplate reader was used to measure the fluorescence intensity.
## GSH assay
The total glutathione levels were measured using a GSH Assay Kit. In addition, the levels were normalised by cell number based on the manufacturer’s instructions.
## Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)
Total RNA was isolated using the TRIzol reagent (Sigma-Aldrich, St. Louis, MO, United States) following the manufacturer’s protocol and reverse transcribed using a Prime Script RT-PCR Kit (ChamQ SYBR Color qPCR Master Mix, Vazyme, Nanjing, China) for cDNA synthesis. The RT-qPCR was performed using StepOnePlus™ real-time system (Applied Biosystems™, CA, United States) and SYBR mix. The cyclic conditions for PCR amplification were based on the manufacturer’s instructions.
The primer sequence is as follows: NFE2L2 forward primer (FP): 5′-TAGAGTCAGCAACGTGGAAG-3′ Reverse primer (RP): 5′-TATCGAGGCTGTGTCGACTG-3′ SLC7A11 FP: 5′-GCTGACACTCGTGCTATT-3′
RP: 5′-ATTCTGGAGGTCTTTGGT-3′ and HO-1 FP: 5′-TAGAGTCAGCAACGTGGAAG-3′ RP: 5′-TAGAGTCAGCAACGTGGAAG-3′.
## Interference and overexpression of genes
Small interfering RNA (siRNA) was transfected in cells using Lipo8000™ Transfection Reagent (Beyotime) as indicated by the manufacturer; however, the protocol was modified as required. The siRNAs were purchased from Ribobio (Guangzhou, China). NFE2L2 siRNAs sequence was 5′-GAGAAAGAATTGCCTGTAA-3′, and scrambled siRNA sequence (used as negative control) was 5′-GGCUCUAGAAAAGCCUAUGCTT-3′. A lentivirus vector was used for overexpressing NFE2L2 (NFE2L2-OE) in cells, and a negative vector (NFE2L2 -NC) was used as a control. Both vectors were synthesised by Miaolinbio (Wuhan, China). Vector transduction at a multiplicity of infection (MOI) of 1, 10, and 30 was performed, and the optimal MOI was identified as 30. For NFE2L2 overexpression, the pancreatic cancer cells were seeded and cultured in 6-well plates until the cells were $30\%$–$50\%$ confluent. The cells were transfected with lentiviral vectors at an MOI of 30, and the culture medium was replaced after 24 h. The transfection was performed in triplicates. The transfection efficiency was determined using RT-qPCR and western blotting (WB). The pancreatic cancer cells with stable NFE2L2-OE were selected by treating the cells with puromycin (2 μg/mL) for 24 h.
## In vivo pancreatic cancer mice model
Female BALB/c nude mice (5 weeks old) were procured from Hangzhou Ziyuan Laboratory Animal Technology Co., Ltd (Zhejiang, China) and given 5 days to acclimate to their surroundings. PANC-1 cells (1 × 107) in 100 μL PBS at the logarithmic growth phase were administered to mice subcutaneously in the left flank. The mice were treated with indicated treatments after nearly 10 days when the tumour size was approximately 1,000 mm3. In the control group, mice ($$n = 5$$) received intraperitoneal injections of the vehicle. In the treatment group, the mice ($$n = 5$$) were administered 50 μL of 60 mg/kg body weight of wogonin once a day for 12 days. A slide calliper size was used to measure the tumour size. The equation for calculating tumour volume is as follows: tumour volume = AB$\frac{2}{2}$, wherein A is the length, and B is the width of the tumour. The mice were sacrificed the next day after the treatment procedure was complete by cervical dislocation. The tumour tissues were harvested and snap-frozen using liquid nitrogen for subsequent analyses. All the procedures involving animals were reviewed and approved by the Animal Care and Use Committee of the Beijing University of Chinese Medicine (the ethical approval number: BUCM-2021101301-3011).
## Western blot analysis
Western blot was performed as per standard protocol. Briefly, the protein was extracted from the tumour tissue-cell lysate. 30 μg proteins were separated by SDS-PAGE, and the proteins were transferred onto polyvinylidene fluoride membranes. The following primary antibodies were used: Nrf2 at 1:1,000 dilution (ab137550, Abcam, United Kingdom), SLC7A11 at 1:1,000 dilution (ab37185, Abcam, United Kingdom), GPX4 at 1:1,000 dilution (ab125066, Abcam, United Kingdom), HO-1 and β-Actin at 1:1,000 dilution (70,081 and 4970S, respectively from Cell Signalling Tech, United States). The protein bands were detected using chemiluminescence (Millipore, MA, United States) and exposed to X-ray film (RX-U; Fujifilm, China).
## Histological examination
First, 4 mm paraffin-embedded sections of the mouse tissue were made. The sections were stained with haematoxylin and eosin for histological evaluation of the ischemia-reperfusion injury. The protocol for histological evaluation was described in the Supplementary Materials.
## Statistical analysis
GraphPad Prism 7.0 software (GraphPad, United States) was used to perform data analysis. Test like one-way analysis of variance (ANOVA) was used to perform analysis of the experimental data. All data were represented as the mean ± SD (standard deviation). $p \leq 0.05$ was considered statistically significant.
## Wogonin reduces cell viability and induces pancreatic cancer cell death
The cytotoxic and inhibitory effects of wogonin on HPDE6-C7, PANC-1 and AsPC-1 cell proliferation were determined. The cells were treated with 0, 5, 10, 20, 40, 60, 80, and 100 μM concertation of wogonin at different time points. The cell viability was measured using CCK-8 assays, and the results revealed no significant differences in HPDE6-C7 cell viability on treatment with different doses of wogonin after 48 h (Figure 1B). However, a significant decrease in the viability and proliferation of PANC-1 and AsPC-1 cells was observed on treatment with wogonin in incubation time- and concentration-dependent manner (Figure 1C). Since wogonin treatment reduces PANC-1 and AsPC-1 cell viability, the IC50 values of wogonin were calculated. The IC50 values of wogonin were 103.6 μM for PANC-1 cells and 84.71 μM for AsPC-1 cells. Therefore, 80 μM as the IC50 value of wogonin was used for treating PANC-1 and AsPC-1 cells for subsequent experiments. To determine the effect of wogonin on cell proliferation, we performed the colony-formation assay. The results revealed a significant decrease in the colony-forming ability of PANC-1 and AsPC-1 cells on treatment with wogonin (Figure 1D). Furthermore, the LDH activity release assay showed that wogonin induced PANC-1 and AsPC-1 cell death in a concentration-dependent manner at 24 h (Figure 1E). In summary, wogonin showed limited cytotoxic effects on HPDE6-C7 but could significantly reduce PANC-1 and AsPC-1 cell viability and induced cell death.
## Involvement of ferroptosis in wogonin-induced pancreatic cancer cell death
To explore the underlying mechanisms of wogonin-induced pancreatic cancer cell death, RNA-sequencing was performed on PANC-1 cells treated with 80 μM of wogonin. The results revealed that 3667 DEGs were identified, among which 1,589 DEGs were upregulated and 2078 DEGs were downregulated (Figure 2A). Furthermore, the DEGs were subjected to the GO and KEGG pathway enrichment analysis. The results revealed that the DEGs significantly enriched the ferroptosis pathway (Figures 2B, C). Therefore, these results imply that ferroptosis could be a key regulator of pancreatic cancer cell death due to wogonin.
**FIGURE 2:** *Screening of the death mode of pancreatic cancer cells induced by wogonin. (A) The genes regulated by wogonin in PANC-1 cells. (B) Enrichment analysis of GO in wogonin group compared with control group assayed by RNA-sequence in PANC-1 cells. (C) Enrichment analysis of KEGG signaling pathway in wogonin group compared with control group assayed by RNA-sequence in PANC-1 cells. (D) Effects of three inhibitors on the death of pancreatic cancer cells induced by wogonin. (E) Effects of ferroptosis inhibitors on the death of pancreatic cancer cells induced by wogonin. ****p < 0.0001 versus control group. #p < 0.05 versus wogonin-alone group; ###p < 0.001 versus wogonin-alone group.*
To further determine the specific mode of cell death induced by wogonin treatment, we treated PANC-1 and AsPC-1 cells with wogonin in the presence or absence of apoptosis, necrosis, and autophagy inhibitors. The cells were treated with Z-VAD-FMK, necroptosis inhibitors like necrostatin-1 or 3-Methyladenine (autophagy inhibitor). The results revealed that the treatment with these inhibitors could not protect against the death of pancreatic cancer cells induced by wogonin (Figure 2D), thereby indicating that wogonin used other mechanisms to induce tumour cell death. Further, PANC-1 and AsPC-1 cells were treated with ferroptosis inhibitor agents like DFO or Fer-1 and ferroptosis-inducing agents like erastin, BSO, or FIN56. The results revealed that treatment with 200 µM DFO or 1 μM Fer-1 almost completely attenuated wogonin-induced cancer cell death and 10 µM erastin, 200 μM BSO, or 150 μM FIN56 accelerate to wogonin-induced PANC-1 and AsPC-1 cell death (Figure 2E). Together, these results indicate the involvement of ferroptosis in the wogonin-induced pancreatic cancer cell death.
## Iron-regulated wogonin-induced pancreatic cancer cell death
We measured the levels of Fe2+ ions in cells to determine if wogonin could induce ferroptosis in the pancreatic cancer cells compared to the control cells. An increase in Fe2+ ion levels in both AsPC-1 and PANC-1 cells was observed on treatment with 20 μM wogonin for 12 h. Moreover, Fe2+ ions levels increased further in PANC-1 and AsPC-1 cells on treatment with 80 μM wogonin concentration or increased in the incubation time to 24 h. These results indicate that wogonin increased Fe2+ ion concentration in cells in a time as well as concentration-dependent manner (Figure 3A).
**FIGURE 3:** *Fe2+ regulated wogonin-induced pancreatic cancer cell death. (A) Intracellular Fe2+ levels in pancreatic cancer cells. (B) Fe2+ assay revealed that wogonin-induced increase of ferrous iron was inhibited by deferoxamine (DFO), but reinforced by ferric ammonium citrate (FAC). (C) RT-qPCR analysis revealed that wogonin triggered time-dependent upregulation of transferrin receptor (TFR), transferrin (TF). *p < 0.05 versus control group; **: p < 0.01 versus control group; ***: p < 0.001 versus control group; ****: p < 0.0001 versus control group; #p < 0.05 versus wogonin-alone group; ###p < 0.001 versus wogonin-alone group.*
To determine the significance of the increase in Fe2+ ion concentration, the cells were first treated with 200 μM DFO for 1 h, followed by treatment with 80 μM wogonin for 24 h. The results revealed that a DFO significantly inhibited Fe2+ levels in cells, which were high due to wogonin treatment. However, pre-treatment with 200 μM FAC for 1 h significantly enhanced the toxicity of wogonin on pancreatic cancer cells (Figure 3B). These results indicate that wogonin-induced pancreatic cancer cell death by increasing the levels of Fe2+ ions in cells. RT-qPCR was used to identify the factors regulating the increase in the levels of Fe2+ ions in AsPC-1 and PANC-1 cells upon wogonin treatment. The results showed an increase in the expression of TF (binds to iron) and TFRC (transports the TF-iron complex into cells) in AsPC-1 and PANC-1 cells on treatment with 80 μM wogonin in a time-dependent manner (Figure 3C). This indicates that wogonin induces the accumulation of Fe2+ ions in pancreatic cancer cells by increasing the influx of iron into cells.
## Wogonin induces pancreatic cancer cell death by increasing lipid peroxidation and reducing GSH levels
Lipid peroxidation is an important aspect of ferroptosis; hence, we examined the changes in lipid peroxidation in cells on treatment with wogonin. The results showed a significant increase in lipid peroxidation in cells on treatment with 20 μM wogonin for 12 h. The lipid peroxidation increased further when the concertation of wogonin was increased to 80 μM or the incubation time was increased to 24 h (Figure 4A). This indicates dose and time-dependent increase in lipid peroxidation in cells on treatment with wogonin.
**FIGURE 4:** *Wogonin induced lipid peroxidation and GSH changes in pancreatic cancer cells. (A) Wogonin induced accumulation of lipid peroxidation in pancreatic cancer cells in a dosage- and time-dependent manner. (B) The upregulation of lipid peroxidation caused by wogonin was prevented by pretreatment with Fer-1, but was promoted by BSO or FIN56. (C) Wogonin reduces the level of GSH in pancreatic cancer cells in a dosage- and time-dependent manner. *p < 0.05 versus control group; **: p < 0.01 versus control group; ***: p < 0.001 versus control group; ****: p < 0.0001 versus control group; #p < 0.05 versus wogonin-alone group; ###p < 0.001 versus wogonin-alone group.*
To investigate if lipid peroxidation is involved in wogonin-induced pancreatic cancer cell death, the cells were first treated with 1 μM Fer-1 for 1 h, followed by treatment with 80 μM wogonin for 24 h. The results revealed that a significant reduction in lipid peroxidation due to wogonin treatment was observed when the cells were pretreated with Fer-1 for 1 h. Similarly, pre-treatment with 200 μM BSO or 150 μM FIN56 for 1 h could promote the lethal effect of wogonin treatment on pancreatic cancer cells (Figure 4B). Together, our results indicate the involvement of lipid peroxidation in pancreatic cancer cell death following wogonin treatment.
GSH is a GPX4-reducing agent that catalyses lipid peroxides reduction to its corresponding alcohols, thereby protecting cells from damage due to lipid peroxidation and inhibiting ferroptosis in cells. Hence, we measured GSH levels in wogonin-treated cells. The results revealed a significant decrease in GSH levels in cells treated with 20 μM wogonin for 12 h, which was further aggravated by an increase in incubation time to 24 h or wogonin dose to 80 μM (Figure 4C). This indicates that wogonin reduces GSH levels in cells in a time- and concentration-dependent manner.
## Wogonin treatment increases the production of superoxide and ROS
Lipid peroxidation can be initiated by the Fenton reaction involving hydrogen peroxide (H2O2), considering that H2O2 can be generated from superoxide (Gutteridge, 1984). Hence, we determined the effect of wogonin on superoxide production and ROS levels in cells. Compared to the control cells, the superoxide production and the ROS accumulation in cells increased on treated with 20 μM wogonin for 12 h (Figures 5A, B), which became more obvious when the wogonin dose was increased to 80 μM or the incubation time was increased to 24 h. This indicates that wogonin induced the accumulation of superoxide and ROS in cells in a time and concentration-dependent manner.
**FIGURE 5:** *Wogonin-induced accumulation of superoxide and ROS. (A) Wogonin induced over generation of superoxide in pancreatic cancer cells in a dosage- and time-dependent manner. (B) Wogonin-induced increase of superoxide was mitigated by fer-1, but was reinforced by FIN56. (C) Wogonin induced accumulation of ROS in pancreatic cancer cells in a dosage- and time-dependent manner. (D) Wogonin-induced accumulation of ROS was alleviated in the presence of Fer-1, but was improved by BSO. *p < 0.05 versus control group; **: p < 0.01 versus control group; ***: p < 0.001 versus control group; ****: p < 0.0001 versus control group; #p < 0.05 versus wogonin-alone group; ###p < 0.001 versus wogonin-alone group.*
To investigate if superoxide and ROS were involved in pancreatic cancer cell death after wogonin treatment, the cells were treated with Fer-1 at 1 μM for 1 h, followed by 80 μM wogonin for 24 h. The results revealed that Fer-1 pre-treatment for 1 h could significantly reduce the lipid peroxidation induced by wogonin (Figure 5C). Similarly, pre-treatment with 200 μM BSO or 150 μM FIN56 for 1 h could promote the cytotoxic effect of wogonin on pancreatic cancer cells (Figure 5D). Together, this indicates that superoxide and ROS are involved in wogonin-induced pancreatic cancer cell death.
## Reduced Nrf2 expression is associated with wogonin-induced ferroptosis
Nrf2 is a primary regulator of antioxidant responses and plays a vital role in maintaining cellular redox homeostasis. GPX4 and xCT are downstream targets of Nrf2 (Dodson et al., 2019). SLC7A11, also identified as xCT, is the light chain subunit of the cysteine/glutamate reverse transporter system (Koppula et al., 2021). To determine if the Nrf2 signalling pathway is involved in wogonin-induced ferroptosis, we used NFE2L2-siRNA to knock down or overexpress the expression of Nrf2, respectively. WB results revealed that wogonin treatment could significantly reduce Nrf2 expression in cells (Figure 6A); however, Fer-1 treatment could reverse this effect (Figure 6B). When NFE2L2 is overexpressed (Figure 6C), wogonin-induced increase in peroxidation was reversed in cells overexpressing NFE2L2 (Figure 6D), this inhibitory effect was reversed on knockdown of NFE2L2 expression in cells (Supplementary Figure S1). Similar results were obtained for the levels of Fe2+ in cells, as shown in Figure 6E. Furthermore, NFE2L2 overexpression increased GSH levels in the cells, which were suppressed following wogonin treatment (Figure 6F). Therefore, these results indicate that wogonin induced ferroptosis by inhibiting NFE2L2 expression, which induced the accumulation of lipid peroxide and Fe2+ in cells and decreased GSH levels.
**FIGURE 6:** *Overexpression of Nrf2 contributed to wogonin-induced ferroptosis in pancreatic cancer cells. (A) Western blotting proved that wogonin downregulated the expression of ferroptosis relate protein, in a dosage-dependent manner. (B) Effect of ferroptosis inducer or inhibitor on wogonin regulatory protein. (C) The overexpression of NFE2L2 mRNA was detected by RT-qPCR. (D) Wogonin-induced accumulation of lipid peroxidation was prevented when Nrf2 was overexpression. (E) Overexpression of Nrf2 prevented wogonin-induced overproduction of Fe2+. (F) Wogonin-induced repression of GSH was improved when Nrf2 was overexpression. *: p < 0.05 versus control group; ****: p < 0.0001 versus the group treated with wogonin alone.*
## Wogonin treatment increased the levels of Fe2+ and lipid peroxidation in vivo
To verify the lethal effect of wogonin in pancreatic cancer in vivo, PANC-1 cells were administered into the left flank of BALB/c nude mice. A reduction in the tumours size was observed in the mice injected with 60 mg/kg of wogonin for 12 days compared to mice in the control group (Figures 7A, B). In the treatment group, the tumour growth was slower, and the tumour weight was lesser compared to the control group (Figures 7C, D). Therefore, wogonin inhibited the growth of tumours in vivo. HE staining analysis showed that wogonin had no toxic effect on mouse organs (Supplementary Figure S2). The tumours were harvested on day 12 after the treatment was complete. We analysed whether wogonin treatment could elevate Fe2+ ion, malondialdehyde and GSH levels. The results revealed a significant improvement in the levels of Fe2+ ion and malondialdehyde in the treatment group compared to the mice in the control group (Figures 7E, F). In the treatment group, a significant reduction in GSH levels was observed compared to the control group (Figure 7G). Similarly, WB results revealed a significant decrease in the expression level of proteins like Nrf2, GPX4, and SLC7A11 in the treatment group (Figure 7H). These results indicate that the wogonin-mediated inhibitory effect on pancreatic cancer cells in vivo via ferroptosis.
**FIGURE 7:** *Wogonin improved ferrous iron and lipid oxidation in vivo. (A, B) Representative images of the nude mice with xenografted pancreatic cancer showed that tumor size was significantly smaller in the mice treated with wogonin at the dosage of 60 mg/kg for consecutive 12 days than that in control group. (C, D) Statistical analysis of tumor volumes confirmed as well that wogonin inhibited tumor growth in vivo. (E) Iron assay showed ferrous iron level was significantly higher in wogonin-treated group than that in control group in vivo. (F) MDA assay proved that lipid peroxidation became more apparent in wogonin-treated group when compared with control group in vivo. (G) GSH assay showed GSH level was significantly lower in wogonin-treated group than that in control group in vivo. (H) Western blotting analysis revealed that wogonin induced marked downregulation of Nrf2, GPX4 and SLC7A11. ***p < 0.001 versus control group; ****p < 0.0001 versus control group.*
## Discussion
Pancreatic cancer is a highly malignant gastrointestinal tract tumour. The prognosis of patients with pancreatic cancer is poor, and mortality is high. Targeted and immune therapies in treating pancreatic cancer have improved the patient’s survival rate in the past decade, but the prognosis of these patients continues to remain poor (Bear et al., 2020; Hu et al., 2021). Globally, the incidence and mortality of pancreatic cancer are rising every year, which has become a major public health burden (Ushio et al., 2021).
For the last 2,000 years, S. baicalensis Georgi has been part of traditional Chinese medicine (Wang et al., 2018). It has antibacterial, anti-viral, anti-allergic, anti-inflammatory, and anti-tumour properties. Its root contains various bioactive compounds like wogonin, baicalein, baicalin, and oroxylin A (Zhao et al., 2019). Furthermore, nanoparticles and liposomes can improve the bioavailability and stability of wogonin based on its dosage form (Wang et al., 2022). Ferroptosis is an iron-dependent, non-apoptotic type of cell death. Some characteristics of ferroptosis mainly include lipid ROS accumulation and damage caused to cell membranes by oxidative stress, which ultimately leads to cell death (Dixon et al., 2012). Ferroptosis can induce or inhibit tumour cells survival via specific drugs by regulating/influencing the expression of associated genes. Studies exploring the role of ferroptosis in cancer treatment are still in the early stages, and its potential clinical applications have become increasingly important. As KRAS gene is mutated in about $85\%$–$90\%$ of pancreatic ductal adenocarcinoma (PDAC), it is the main driver of pancreatic cancer (Buscail et al., 2020). “ Ferroptosis” was first proposed to describe iron-dependent non-apoptotic cell death in RAS mutated cancer cells (Dixon et al., 2012). Theoretically, most PDAC should be sensitive to ferroptosis activators due to the mutant expression of RAS-mediated iron metabolism genes, such as TRFC (transferrin receptor), FTH1 (ferritin heavy chain 1) and FTL1 (ferritin light chain 1) (Yang and Stockwell, 2008). Induction of ferroptosis may be an attractive therapeutic approach for various types of cancer, including pancreatic ductal adenocarcinoma. Therefore, ferroptosis induction in cancer cells is gradually becoming a new therapeutic strategy for pancreatic cancer treatment. Studies have shown that wogonin mediates anti-cancer effects via different signalling pathways (Banik et al., 2022); however, its role and effect on the treatment of pancreatic cancer require additional investigation. Moreover, it is still unclear if wogonin mediates its effects via the mechanism of ferroptosis in pancreatic cancer cells. Our results show that wogonin can induce ferroptosis in pancreatic cancer cells like PANC-1 and AsPC-1. To the best of our knowledge, our study is the first to explore how wogonin regulates ferroptosis in pancreatic cancers.
Our results revealed that wogonin significantly reduced pancreatic cancer cell survival in vitro and in vivo, accompanied by an abnormal increase in the levels of Fe2+ ions, lipid peroxidation, ROS, and superoxide in cells and a decrease in GSH levels. In vitro experiments have shown that the treatment with ferroptosis inducers like FAC, FIN56 or BSO increases wogonin-induced lipid peroxidation and death of pancreatic cancer cells. Furthermore, Fer-1 or DFO treatment inhibits wogonin-induced pancreatic cancer cell death. Similarly, a study has shown that Fer-1 and DFO inhibit the accumulation of ROS induced by scutellarin, thus confirming that the generation of ROS is vital for enhancing ferroptosis (Ito et al., 2005). Our results have proved that wogonin increases the Fe2+ ion levels in cells by increasing the expression of TF and its receptors. As a free radical of oxygen molecules, superoxide spontaneously and disproportionately converts into H2O2, especially under low pH or during the catalysis of superoxide dismutase (Mintz et al., 2020). In cells, H2O2 participates in the Fenton reaction to induce ferroptosis (Bedard and Krause, 2007). Our results show that the wogonin increases superoxide levels in cells, which can be inhibited upon treatment with Fer-1, indicating that these superoxide levels come from the ferroptosis pathway.
A classical way to induce ferroptosis is to inhibit GSH synthesis and its use (Uberti et al., 2020). Our results revealed a significant reduction in GSH levels in pancreatic cancer cells treated with wogonin in a time- and concertation-dependent manner. Nrf2 is a stress-induced transcription factor, which targets genes that express proteins and enzymes responsible for preventing lipid peroxidation and removing intracellular Fe2+. In our study, NFE2L2 overexpression significantly inhibits lipid peroxidation, Fe2+ ion levels, and GSH depletion induced by wogonin in pancreatic cancer cells. Moreover, the knockdown of NFE2L2 expression using siRNA significantly increases the lipid peroxidation, Fe2+ ion levels, and GSH depletion induced by wogonin in pancreatic cancer cells.
Furthermore, Nrf2 regulates GSH levels in cells by enhancing GPX4 and SLC7A11 expression. Our results show a direct correlation between Nrf2 expression and the sensitivity of cells to ferroptosis since an increase in Nrf2 expression inhibits ferroptosis. Similarly, a study has shown that decreased Nrf2 expression increases the cancer cell sensitivity to ferroptosis inducers (Dodson et al., 2019). Furthermore, inhibiting the Nrf2 signalling pathway could reverse the drug resistance and increase the sensitivity of pancreatic cancer cells to chemotherapy (Zhou et al., 2019; Kim et al., 2020). Therefore, targeting the Nrf2 signalling pathway could be a potential strategy for pancreatic cancer treatment. Our results showed a decrease in the expression of SLC7A11, GPX4, HO-1, and Nrf2 protein on treatment with wogonin in a time and concentration-dependent manner. Furthermore, in pancreatic cancer cells treated with both FAC and wogonin, a significant decrease in Nrf2 and GPX4 protein expression was observed compared to cells treated with FAC alone. Treatment with Fer-1 can reverse Nrf2 and GPX4 expression in cells induced by wogonin. Therefore, wogonin induces lipid peroxidation by activating TF and TFRC in an iron-dependent manner but also decreases GSH levels in cells by decreases GSH levels in cells via Nrf2-GPX4 pathway, thereby inducing ferroptosis in pancreatic cancer cells (Figure 8). Tumor is a threat to human life and health, currently commonly used chemotherapy drugs to tumor patients, kill a large number of malignant cells at the same time, often bring a fatal blow to the body’s immune function. Therefore, the search for natural drugs with high efficiency and low toxicity that not only have anti-tumor activity but also can enhance immune function will eventually bring new hope to patients with malignant tumors. The above research results show that the crude drug element of tumor cell killing effect has a unique mechanism of action, and also have the function of the efficient and low toxicity, so the prevention of tumor, and tumor drug treatment with combination chemotherapy alone has a broad application prospect. However, there are still some limitations in this study. This study did not set up a positive control group of ferroptosis inducer alone, only studied the effect of wogonin combined with positive control on ferroptosis, and did not detect sham drugs after Nrf2 knockdown or overexpression in animals. These need to be follow-up for a deeper research.
**FIGURE 8:** *Schematic diagram for wogonin-induced ferroptosis in pancreatic cancer cells.*
## Conclusion
Wogonin induces pancreatic cancer cell death via ferroptosis. Wogonin can increase Fe2+ ion levels in cells by increasing TF levels. The increase in Fe2+ ion levels in cells enhances the Fenton reaction, which increases the production of ROS and increases lipid peroxidation. In addition, wogonin decreases GSH levels in cells via the Nrf2-mediated GPX4 pathway, thereby further enhancing lipid peroxidation and ROS accumulation in pancreatic cancer cells.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by the Animal Care and Use Committee of the Beijing University of Chinese Medicine.
## Author contributions
XL and XS conducted the study and wrote the manuscript. XP, SC, and CY designed the study and revised the paper. ZM and XS controlled the language editing. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1129662/full#supplementary-material
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|
---
title: 'Poor glycaemic control and ectopic fat deposition mediates the increased risk
of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes:
Insights from Bayesian-network modelling'
authors:
- T. Waddell
- A. Namburete
- P. Duckworth
- A. Fichera
- A. Telford
- H. Thomaides-Brears
- D. J. Cuthbertson
- M. Brady
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992174
doi: 10.3389/fendo.2023.1063882
license: CC BY 4.0
---
# Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling
## Abstract
### Background
An estimated $55.5\%$ and $37.3\%$ of people globally with type 2 diabetes (T2D) will have concomitant non-alcoholic fatty liver disease (NAFLD) and the more severe fibroinflammatory stage, non-alcoholic steatohepatitis (NASH). NAFLD and NASH prevalence is projected to increase exponentially over the next 20 years. Bayesian Networks (BNs) offer a powerful tool for modelling uncertainty and visualising complex systems to provide important mechanistic insight.
### Methods
We applied BN modelling and probabilistic reasoning to explore the probability of NASH in two extensively phenotyped clinical cohorts: 1) 211 participants with T2D pooled from the MODIFY study & UK Biobank (UKBB) online resource; and 2) 135 participants without T2D from the UKBB. MRI-derived measures of visceral (VAT), subcutaneous (SAT), skeletal muscle (SMI), liver fat (MRI-PDFF), liver fibroinflammatory change (liver cT1) and pancreatic fat (MRI-PDFF) were combined with plasma biomarkers for network construction. NASH was defined according to liver PDFF >$5.6\%$ and liver cT1 >800ms. Conditional probability queries were performed to estimate the probability of NASH after fixing the value of specific network variables.
### Results
In the T2D cohort we observed a stepwise increase in the probability of NASH with each obesity classification (normal weight: $13\%$, overweight: $23\%$, obese: $36\%$, severe obesity: $62\%$). In the T2D and non-T2D cohorts, elevated (vs. normal) VAT conferred a $20\%$ and $1\%$ increase in the probability of NASH, respectively, while elevated SAT caused a $7\%$ increase in NASH risk within the T2D cohort only. In those with T2D, reducing HbA1c from the ‘high’ to ‘low’ value reduced the probability of NASH by $22\%$.
### Conclusion
Using BNs and probabilistic reasoning to study the probability of NASH, we highlighted the relative contribution of obesity, ectopic fat (VAT and liver) and glycaemic status to increased NASH risk, namely in people with T2D. Such modelling can provide insights into the efficacy and magnitude of public health and pharmacological interventions to reduce the societal burden of NASH.
## Introduction
According to the World Health Organisation, the global prevalence of obesity almost tripled between 1975 and 2016 with $39\%$ of the world’s adult population (1.9 billion; $39\%$ of men, $40\%$ of women) overweight and $13\%$ (650 million; $11\%$ of men, $15\%$ of women) living with obesity in 2016 [1]. In parallel, according to the International Diabetes Federation, the global diabetes prevalence in 2019 was estimated to be $9.3\%$ (463 million people), with a projected $25\%$ increase by 2030 ($10.2\%$ prevalence; 578 million) and a projected $50\%$ increase ($10.9\%$ prevalence, 700 million) by 2045 [2].
People living with obesity and type 2 diabetes (T2D) are at a significantly greater risk of liver related complications compared to people without either condition [3]. Notably, according to a recent global meta-analysis and meta-regression, ~$55.5\%$ of people with T2D worldwide have associated non-alcoholic fatty liver disease (NAFLD), $37.3\%$ non-alcoholic steatohepatitis (NASH) and $17.3\%$ biopsy-confirmed advanced liver fibrosis [4]. Those people with NAFLD and concomitant T2D have significantly worse liver-related outcomes including higher rates of advanced fibrosis, cirrhosis and liver-related cancers compared to those with NALFD only (5–8).
20-year projections of the economic and clinical burden of NASH/NAFLD estimate that co-prevalent NASH and T2D will account for 65,000 liver transplants, 812,000 liver-related deaths and 1.37 million cardiovascular-related deaths, totalling $55.8 billion in healthcare costs [9]. Early detection of those patients with T2D at high-risk of NASH is therefore of considerable importance and would enable early access to personalised care/medicines and improved clinical and disease outcomes.
In both T2D and NASH, obesity is a significant risk factor, though the clinical utility of the body mass index (BMI) metric is limited since it describes global body mass relative to height and does not describe body fat distribution. People with T2D represent a clinical population, and relative to those without T2D, are characterised by a distinct body composition profile with significantly higher volumes of visceral adipose tissue (VAT), increased liver fat deposition and fibroinflammation and reduced skeletal muscle mass [10, 11], when measured by magnetic resonance imaging (MRI). Furthermore, elevated VAT but not subcutaneous adipose tissue (SAT), has been associated with a significant increase in circulating insulin, plasma glucose and incidence of the metabolic syndrome [12, 13], highlighting the importance of studying body fat distribution.
Multi-parametric MRI can provide quantitative tissue characterisation in multiple organs. Fat infiltration (steatosis), iron deposition and fibroinflammatory change can be measured using proton density fat fraction (PDFF), iron and fat corrected T1 mapping (cT1) with MRI. PDFF quantifies liver fat at each voxel and correlates strongly with histologic steatosis [14, 15] while liver cT1, an indicator of liver disease activity and severity, correlates strongly with histological markers of fibroinflammation and demonstrates high diagnostic accuracy for stratifying patients with NASH and those with at-risk NASH [16, 17].
In this paper, we use MRI-derived measures of body composition and liver health using PDFF and liver cT1 to identify participants with NAFLD and NASH and to overcome the intrinsic limitation of the BMI noted above by exploring regional fat distribution. We describe how applying Bayesian-networks to study the associations between MRI-derived measures of body composition and liver health, enables a comprehensive assessment of the high-risk metabolic phenotypes associated with co-prevalent T2D and NASH. We also show how BNs can be used to identify potential therapeutic targets for alleviating NASH risk.
## Data collection and preparation
We investigated two datasets: the first included 221 participants with T2D pooled from the MODIFY study (NCT04114682) [18] and the UK Biobank (UKBB) online resource under application 9914. MODIFY recruited participants with T2D from primary or secondary care settings from three sites across the UK; UKBB is a general population-based cohort study in people aged 40 to 69 years from across the UK (https://www.ukbiobank.ac.uk/). The second cohort included 135 participants without diabetes of any kind (non-T2D) drawn from the UKBB. All participants underwent an abdominal MR scan that included multi-parametric imaging of the liver and pancreas. See [10] for an in-depth description of the MR-protocol.
## Clinical details and MRI image acquisition and analysis
Body composition was examined from a 2D MR slice positioned at the third lumbar (L3) vertebra and measurements of VAT, SAT and skeletal muscle were based on manual delineations by trained analysts, see Figure 1. Measures of skeletal muscle were then indexed to the participant’s height to produce a measure of skeletal muscle index (SMI) (cm2/m2). All continuous variables were discretized based on pre-defined clinical thresholds or by splitting the data into ‘low’ and ‘high’ value groups determined at the 75th percentile value, see supplementary. For example, HbA1c within the T2D cohort was discretised by splitting the cohort into ‘low’ (HbA1c <62mmol/mol) and ‘high’ (HbA1c >=62mmol/mol) value groups. Presence of NASH was classified based on liver PDFF >$5.6\%$ and liver cT1 >800ms. Such thresholds have consistently demonstrated high diagnostic accuracy for stratifying patients with NASH, predicting liver-related outcomes, classifying between NASH and NAFLD only and are an effective alternative to liver biopsy for diagnosing NASH [17, 19].
**Figure 1:** *(Top left) Example MR images of body composition segmentation: SMI, skeletal muscle index; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue. (Top right) pancreas with example typical ROI placement (H – head, B – body, T – tail). (Middle) liver cT1 and (bottom) liver PDFF with corresponding reference values.*
Bayesian-networks (see Supplementary information for full explanation of BNs) Bayesian Networks (BNs) are directed acyclic graphs that are capable of explicitly representing and analysing complex systems and under certain assumptions, specifying causal relationships. When combined with probabilistic reasoning, BNs can be used to estimate the probability of an ‘event’ occurring in response to a fixed evidence input. For example [20], used BNs to estimate the probability of acute kidney injury after fixing certain biochemical abnormalities in patients with gastrointestinal cancer.
To investigate the probability of NASH in specific clinical characteristics, we conducted conditional probability queries that estimate the probability of an event (here, NASH), given an ‘evidence list’ containing the set values of specific network variables (for example, obesity or HbA1c status). The probability of NASH was estimated using the likelihood weighting algorithm, a Monte Carlo approximation technique utilising importance sampling. While BNs can be extended to incorporate a plethora of clinical features and sources of information, our networks have been deliberately limited to illustrate their application of the present analysis.
## Network variables
BN construction included the following variables: liver fat (PDFF %), liver cT1 (ms), pancreatic fat (PDFF %) visceral adipose tissue (cm2), subcutaneous adipose tissue (SAT) (cm2), skeletal muscle (SMI) (cm2), gender (1[male]/0[female]), BMI (kg/m2), age (yrs), HbA1c (mmol/mol), AST (IU/L), ALT (IU/L), smoking status (0[non-smoker]/1[current smoker]/2[past smoker]).
## Bayesian-network construction (See Supplementary information for full overview of network construction)
BN construction and probabilistic inference were completed using the ‘bnlearn’ package and visualised using ‘graphviz’ within the R software platform (version 3.6.1). Automated network structures, derived from a score-and-search algorithm [21], were adjusted by removing or reversing nonsensical edges and inserting edges based on domain knowledge gleaned from medical literature. Crucially, incorporation of clinical knowledge in these network structures enables the modelling of causal relationships between variables. Figures 2, 3 show the final networks from the T2D and non-T2D cohorts, respectively.
**Figure 2:** *Bayesian-network structure from the T2D cohort.* **Figure 3:** *Bayesian-network structure from the non-T2D cohort.*
## Statistical analysis
All statistical analyses used the R software platform (version 3.6.1). Descriptive statistics, showing median [inter quartile range], are reported to summarise population characteristics. Wilcoxon and X2 tests were used to make between group comparisons.
## Descriptive characteristics of participants
The baseline clinical, biochemical, and imaging characteristics are shown in Table 1. The groups were well matched for age, gender, and BMI. Despite similar liver biochemistry, participants from the T2D cohort (and thus higher HbA1c (<0.001)) had significantly elevated liver cT1 (<0.001), liver PDFF (<0.001), VAT/SAT ratio ($$p \leq 0.034$$) and greater prevalence of NASH (<0.001).
**Table 1**
| Characteristic | T2D (n = 221) | Non-T2D (n = 135) | p-value |
| --- | --- | --- | --- |
| Clinical data | Clinical data | Clinical data | Clinical data |
| Age (yrs) | 57 [52-64] | 57 [53-63] | 0.3 |
| Sex (n of male participants [%]) | 146 (66%) | 98 (73%) | 0.2 |
| BMI (kg/m2) | 30 [27-34] | 29 [26-33] | 0.3 |
| HbA1c (mmol/mol) | 50 [41-62] | 36 [33-38] | <0.001 |
| AST (IU/L) | 24 [19-29] | 27 [23-32] | <0.001 |
| ALT (IU/L) | 27 [20-36] | 26 [19-35] | 0.5 |
| MRI data | MRI data | MRI data | MRI data |
| Liver cT1 (ms) | 751 [695-827] | 709 [672-752] | <0.001 |
| Liver fat (%) | 8 [4-14] | 5 [3-11] | <0.001 |
| Pancreatic fat (%) | 5 [3-9] | 5 [3-8] | 0.6 |
| VAT (cm2) | 238 [173-307] | 215 [149-279] | 0.065 |
| SAT (cm2) | 249 [180-322] | 271 [181-366] | 0.3 |
| VAT/SAT ratio | 0.90 [0.58-1.33] | 0.79 [0.57-1.15] | 0.034 |
| SMI (cm2/m2) | 49 [42-56] | 51 [43-56] | 0.6 |
| NASH (n of participants [%]) | 69 (31%) | 13 (9%) | <0.001 |
## Estimation of NASH using probabilistic reasoning
The baseline probabilities of NASH in the T2D and non-T2D cohorts were $30\%$ and $10\%$, respectively. ‘ Intervening’ on a variable within the BN means that a specific value is assigned to it. For example, the variable ‘HbA1c’ may be assigned the values ‘0’ (‘low’ value) or ‘1’ (‘high’ value). The effect of amending level of glucose regulation is then propagated throughout the network, where the variables(s) conditionally dependent on the intervened variable(s) are updated to reflect the specified evidence.
## Impact of body mass index
We first explored the effect of obesity status on NASH risk, observing a $13\%$, $23\%$, $36\%$ and $62\%$ probability of NASH under normal weight (<25kg/m2), overweight (25-30kg/m2), obesity (30-40kg/m2), and severe obesity (>40kg/m2) settings, respectively. This equated to a $23\%$ greater probability of NASH when comparing ‘obesity’ vs. ‘normal weight’. ( Table 2; Figure 4).
## Impact of HbA1c
Increasing HbA1c status from the low (HbA1c <62 mmol/mol) to high value group (HbA1c >=62mmol/mol) increased the probability of NASH by $22\%$ ($24\%$ vs. $46\%$). ( Table 2; Figure 5).
**Figure 5:** *Probability of NASH (%) under VAT (top left), SAT (top right), SMI (bottom left) and HbA1c status (bottom right) conditions in the non-T2D (red) and T2D (blue) cohorts.*
## Impact of adipose tissue volumes (VAT and SAT)
We observed a $20\%$ and $7\%$ increase in the probability of NASH when specifying elevated vs. normal measures of VAT and SAT, respectively. ( Table 2; Figure 5).
Impact of skeletal muscle area: Reduced SMI decreased the probability of NASH by $4\%$ ($30\%$ vs $26\%$). ( Table 2; Figure 5).
## Impact of liver and pancreatic fat
Increasing liver fat from 5.6-$10\%$ to greater than $10\%$ increased the probability of NASH by $31\%$ ($25\%$ vs $56\%$). Elevated pancreatic fat increased the probability of NASH by $1\%$ ($29\%$ vs $30\%$). ( Table 2; Figure 5).
## Combination of risk factors
BNs enable a user (e.g., clinician) to specify an individual phenotype, enabling personalised ‘what if’ analysis. For example, a high-risk phenotype with obesity, elevated VAT, elevated liver fat (>$10\%$) and ‘high’ HbA1c had an $86\%$ probability of NASH. In this phenotype, reducing HbA1c to the ‘low’ value reduced the probability of NASH by $29\%$ ($67\%$).
## Cohort without T2D
We observed a $16\%$, $6\%$, $11\%$ and $30\%$ probability of NASH under normal weight, overweight, obese, and severe obesity settings. We observed a $1\%$ increase in the probability of NASH when specifying elevated vs normal measures of VAT. Increasing liver fat from 5.6-$10\%$ to >$10\%$ increased the probability of NASH by $23\%$ ($7\%$ vs. $30\%$). Increasing HbA1c from the ‘low’ <38mmol/mol) to ‘high’ (>38mmol/mol) value reduced the probability of NASH by $1\%$ ($10\%$ vs $9\%$). ( Table 2; Figures 4; 5).
Exploring the same high-risk phenotype specified within the T2D cohort, we observed a $30\%$ probability of NASH. Reducing HbA1c lowered the probability of NASH by $3\%$ ($27\%$).
## Discussion
In this paper, we applied a novel BN approach to demonstrate the mediating influence of type 2 diabetes, particularly determining the impact of poor glycaemic control and higher ectopic fat deposition, focussing on the impact of liver and visceral fat depots, on the increased risk of NASH in people with T2D and those in the general population. Firstly, we found that obesity (versus normal BMI) conferred a $23\%$ increase (within the T2D cohort) and $5\%$ decrease (in the cohort without T2D) in the probability of NASH. However, considering the intrinsic limitations of the BMI, aggregating all measures of body composition (for example, combining adipose tissue and skeletal muscle volumes) into a single measure, this metric assumes that all individuals will have a similar relative proportion of the different biological tissues.
Our work overcomes this limitation by applying BNs to study the association between MR-derived measures of body composition (using regional adipose tissue volumes, VAT and SAT and organ specific fat measurements) and NASH risk, highlighting the independent contribution of fat deposition within visceral and subcutaneous sites. Specifically, within the T2D cohort the probability increase of NASH was more than double under elevated VAT (+$20\%$) than elevated SAT (+$7\%$) conditions. Such finding is similar to that of [22] who, despite applying different statistical techniques, also observed that higher measures of VAT relative to SAT was predictive of advanced liver fibrosis in people with NAFLD.
VAT area is independently associated with NASH and correlates significantly with histology confirmed NAFLD with significant fibrosis [23]. Proposed mechanisms behind the association of elevated VAT and NASH include lipotoxicity and an overexpression of proinflammatory cytokines that promote inflammation and fibrosis within the hepatocytes [24]. Such overexpression of cytokines has been linked to fat deposition within visceral, but not subcutaneous, adipose sites [25].
In participants with T2D, our analysis revealed a $22\%$ reduction in the probability of NASH when lowering HbA1c from the ‘high’ (>62mmol/mol) to ‘low’ (<62mmol/mol) range. Furthermore, NASH risk was reduced by $29\%$ when lowering HbA1c in an example high risk phenotype. Importantly, this was achieved without specifying changes to obesity status, liver fat content or body composition. Chronically elevated blood glucose (i.e., glucotoxicity) has been linked to NASH development via its effects on increasing TCA cycle activity and synthesis of Acyl CoA that promote de novo lipogenesis and oxidative stress [26]. At the time of writing, no FDA-approved pharmacological medications are available for the treatment of NASH. To this end, our findings highlight the potential of glucose lowering therapies for mitigating NASH risk, particularly in high-risk metabolically unhealthy populations or those with overweight/obesity. Interestingly, newer T2D therapies such as GLP1-receptor agonists (liraglutide, semaglutide), novel dual and triple peptides (e.g. tirzepatide) and SGLT2-inhibitors have been shown to decrease levels of ALT and lower liver fat (measured by MRI-PDFF) in people with T2D and areas of fibrosis, ALT/AST and hepatic lipid content in murine NASH models [27, 28].
Notably, and despite similar measures of BMI and body composition (see Table 1), we found significantly greater measures of liver cT1, liver fat and prevalence of NASH within the T2D cohort. This highlights the clinical need to screen people with T2D for concomitant liver disease and NASH, where critically, we observed similar measures of ALT and significantly greater AST in the non-T2D cohort. Screening for liver disease needs to adopt a multi-modality approach that extends beyond the reliance on circulating biomarkers alone.
Limitations of this study ultimately derive from the fact that NASH is a complex and multifactorial disease that involves numerous mechanisms that are not investigated in our work to date. However, the BN methodology that we use is intrinsically extensible. Future work will seek to broaden the variables included in network construction, such as biochemical pathways associated with hepatocyte injury, for a more comprehensive assessment of NASH risk.
In conclusion, this paper applied Bayesian-networks and probabilistic reasoning to identify populations at a high risk of NASH. We emphasise the role of elevated VAT, liver fat and obesity status in driving the probability of NASH, and these effects are most significant in people with type 2 diabetes highlighting the importance of prevention and good glycaemic management as a potential therapeutic target for addressing the epidemic of NAFLD/NASH.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: Private data. UK Biobank restrictions. Requests to access these datasets should be directed to [email protected].
## Author contributions
TW analyzed the data, built the Bayesian-network, conducted the probabilistic reasoning statistics, and wrote the manuscript. PD and AN supported in revising the manuscript and offered technical assistance in study methodology. HT-B supported in revising the manuscript. DC provided clinical support in building the network, devising probabilistic reasoning questions, revising the manuscript, and producing figure plots. AT provided technical assistance on statistical methodology. AF collected and synthesized genetic data from the UK Biobank. MB assisted with drafting the manuscript, defining project scope, and revising the manuscript for publication. All authors contributed to the article and approved the submitted version.
## Conflict of interest
TW is a shareholder and an employee at Perspectum. AF is an employee at Perspectum. AT is an employee at Perspectum. HT-B is a shareholder and an employee at Perspectum. MB is a shareholder and an executive at Perspectum.
DC received investigator-initiated research funding from AstraZeneca plc and Novo Nordisk A/S.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1063882/full#supplementary-material
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|
---
title: Whole genome methylation combined with RNA-seq reveals the protective effects
of Gualou-Xiebai herb pair in foam cells through DNA methylation mediated PI3K-AKT
signaling pathway
authors:
- Zijun Jia
- Jun Mei
- Yan Zhang
- Ya Wang
- Hongqin Wang
- Anlu Wang
- Fengqin Xu
- Qingbing Zhou
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9992180
doi: 10.3389/fimmu.2023.1054014
license: CC BY 4.0
---
# Whole genome methylation combined with RNA-seq reveals the protective effects of Gualou-Xiebai herb pair in foam cells through DNA methylation mediated PI3K-AKT signaling pathway
## Abstract
DNA methylation, including aberrant hypomethylation and hypermethylation, plays a significant role in atherosclerosis (AS); therefore, targeting the unbalanced methylation in AS is a potential treatment strategy. Gualou-xiebai herb pair (GXHP), a classic herb combination, have been used for the treatment of atherosclerotic-associated diseases in traditional Chinese medicine. However, the effects and underlying mechanism of GXHP on AS remain nebulous. In this study, the CCK-8 method was applied to determine the non-toxic treatment concentrations for GXHP. The formation of foam cells played a critical role in AS, so the foam cells model was established after RAW264.7 cells were treated with ox-LDL. The contents of total cholesterol (TC) and free cholesterol (FC) were determined by Gas chromatography-mass spectrometry (GC-MS). Enzyme-linked immunosorbent assay (ELISA) was used to check the expressions of inflammatory factors including IL-1β, TNF-α, and VCAM-1. Methyl-capture sequencing (MC-seq) and RNA-seq were applied to observe the changes in genome-wide DNA methylation and gene expression, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to analyze differentially methylated genes (DMGs) and differentially expressed genes (DEGs). The targeted signaling pathway was selected and verified using western blotting (WB). The results showed that the lipids and inflammatory factors in foam cells significantly increased. GXHP significantly reduced the expression of TC, FC, and inflammatory factors. MC-seq and RNA-seq showed that GXHP not only corrected the aberrant DNA hypermethylation, but also DNA hypomethylation, thus restored the aberrant DEGs in foam cells induced by ox-LDL. GXHP treatment may target the PI3K-Akt signaling pathway. GXHP reduced the protein levels of phosphorylated(p)-PI3K and p-AKT in foam cells. Our data suggest that treatment with GXHP showed protective effects against AS through the inhibition of DNA methylation mediated PI3K-AKT signaling pathway, suggesting GXHP as a novel methylation-based agent.
## Introduction
Atherosclerosis (AS) is a complex multifactorial disease [1]. The risk factors for AS include high low-density lipoprotein (LDL) levels, inflammation, obesity, disturbed sleep, and hyperglycemia [2]. Cardiovascular diseases (CVD), in which the pathological basis is AS, are the leading cause of mortality worldwide. It is expected that by 2035, 130 million adults ($45.1\%$) in the United States will suffer from CVDs. Moreover, the mortality rate of CVDs is higher in developing countries due to racial differences, limited access to medical care, and economic development [3]. Many patients with AS become incapacitated and may lose their limbs, which increases the economic burden of social public health; thus, the discovery of novel drugs for treating AS diseases is crucial [4, 5].
Recent studies have focused on the relationship between AS and DNA methylation. DNA methylation refers to the process in which “C” bases receive methyl groups from S-adenosine methionine to form 5-methyl cytosine without changing the DNA sequence. The main biological function of DNA methylation in promoter regions is to influence the mRNA expression of genes, thereby up- or downregulating the expression [6]. Increasing evidence has shown that DNA methylation plays a key role in AS, while several aberrantly methylated genes exist in AS that are associated with reverse cholesterol transport, inflammatory response, foam cell formation, endothelial cell dysfunction, and abnormal proliferation of vascular smooth muscle cells [7]. For example, Kruppel-like factor 2 (KLF2) has an important anti-inflammatory effect. The aberrant hypermethylation of KLF2 has been identified in AS, which decreases mRNA expression, increases inflammation, and indices endothelial dysfunction in vascular endothelial cells [8]. However, aberrant hypomethylation also contributes to AS development. Indeed, Yang reported that lectin-like oxidized-LDL (ox-LDL) receptor-1 (LOX-1), a unique receptor involved in the uptake of ox-LDL, is aberrantly hypomethylated in blood vessels from ApoE-/- mice [9]. Einari et al. identified 3,997 abnormal hypomethylated sites and 782 abnormal hypermethylated sites in femoral AS plaques from 22 cases compared with 9 normal cases [10]. Moreover, gene expression analysis has revealed that the hypomethylation of genes in promoter regions correlates with increased levels of mRNA expression [10]. Since DNA methylation is reversible, targeting the aberrant methylation in AS has attracted attention [11, 12].
Gualou-Xiebai herb pair (GXHP), a classic herb combination, originates from ShangHanZaBingLun, written by Zhong-jing Zhang during the Eastern Han Dynasty. Trichosanthes kirilowii Maxim (Gua lou) and Allium macrostemon (Xie bai) have been used for the treatment of AS-associated diseases in traditional Chinese medicine (TCM) [13]. Recently, Zhou et al. [ 14] found that a GXHP decoction could inhibit inflammatory cytokines in the treatment of patients with CVD. However, the treatment mechanism for GXHP remains unclear.
In this study, a multi-omics approach was applied to clarify the molecular mechanism underlying the therapeutic effects of GXHP on AS. First, a foam cell model was constructed with RAW264.7 cells treated with ox-LDL, which was used to observe the anti-AS effects of GXHP. Furthermore, we elucidated the mechanism of GXHP granules via methyl-capture sequencing (MC-seq) and RNA-seq. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to assess the differentially methylated genes (DMGs) and differentially expressed genes (DEGs) among distinct groups. Finally, western blotting was performed to elucidate the effects of GXHP on the PI3K-Akt signaling pathway in foam cells, which were selected according to the integrated MC-seq analysis combined with RNA-seq.
## Main herbs and reagents
The GXHP granules, comprising Gua lou and Xie bai, were supplied by Sanjiu Medical & Pharmaceutical (Shenzhen, China) at a ratio of 1:1. High glucose Dulbecco’s Modified Eagle Medium (DMEM) and fetal bovine serum (FBS) were supplied by Gibco (New York, USA). Cell viability was determined by a Cell Counting Kit-8 (Dojindo Molecular Technologies, Kumamoto, Japan). Human ox-LDL was obtained from Yiyuan Biotechnology (Guangzhou, China). The GXHP granules were dissolved in DMEM supplemented with $10\%$ inactivated FBS for cell treatment.
## CCK-8 cell viability assay
RAW264.7 cells were cultured in high glucose DMEM supplemented with $10\%$ inactivated fetal bovine serum at 37°C. The cells were seeded at a density of 1× 104 cells per well in 96-well plates. Serially diluted concentrations of GXHP were added to the wells and cultured with the cells for 48 h. Then, 10 µL of CCK-8 reagent was added into each well and the absorbance value of each sample was determined with a microplate reader (Biotek, Shanghai, China) at 450 nm. We repeated the experiments thrice and calculated the cell viability of each group.
## Establishment of a foam cell model and grouping
As described in the previous study [15], RAW264.7 cells were treated with ox-LDL at 80 μg/mL for 48 h. Oil Red O staining was performed to observe the lipid droplets in the cells. In this study, there were three treatment groups: control group (RAW264.7 cells only), model group (RAW264.7 cells treated with ox-LDL), and GXHP group (RAW264.7 cells treated with ox-LDL and GXHP at 1.8 g/L). The cells in the three groups were collected for MC-seq and RNA-seq.
## Measurement of lipids and inflammatory factors
The levels of total cholesterol (TC) and free cholesterol (FC) in cell samples from different groups were determined using gas chromatography-mass spectrometry (GC-MS). The cell samples were loaded onto a GC-MS-TQ8040 NX mass spectrograph (SHIMADZU, Origin, Japan) with an RTX-5MS column. The conditions included an inlet temperature of 280°C and a ramp-up procedure that began at 150°C for 1 min, increased to 300°C at 40°C/min, and maintained for 7 min. The supernatant was collected from different groups. Then the levels of vascular cell adhesion molecule-1 (VCAM-1),tumor necrosis factor (TNF)-α, and interleukin-1β (IL-1β) were determined using an enzyme-linked immunosorbent assay (ELISA) (BioTek, Winooski, VT, USA).
## Agilent methyl-capture sequencing (MC-seq)
The cells were cultured in a six-well plate with 5×105 cells per well for 48h. And the total DNA were extracted from the three groups using a DNeasy Blood Tissue Kit [250] All-Prep DNA Mini Kit (Qiagen, Valencia, CA, USA). Agilent MC-seq was applied to assess the methylation status in the nine cell samples (Santa Clara, CA, USA). Briefly, 1.5 μg DNA per sample was needed and the DNA samples library was constructed. The concentration and size of the constructed library were measured with the Qubit® 2.0 fluorometer and Agilent 2100 Bioanalyzer, respectively. Following bisulfite treatment and amplification by polymerase chain reaction (PCR), the DNA samples were then sequenced on Illumina NovaSeq6000 (Illumina, San Diego, CA, USA). The quality of sequence was assessed using the Fastp v0.20.0 Software (https://github.com/OpenGene/fastp). Finally, the eligible differential methylation sites (DMSs) were identified and annotated using the Dispersion Shrinkage and the ClusterProfiler package.
## RNA-seq
The total RNA content was extracted from cells using the Total RNA Extraction Kit (Qiagen, Valencia, CA, USA). The concentration and quality of extracted RNA were assessed using a NanoDrop spectrophotometer and an Agilent 2100 Bioanalyzer, respectively. Following purification using RNA Clean XP Kit (Beckman Coulter, Inc. Kraemer Boulevard Brea, CA, USA), 1.5 μg RNA per sample was subjected to RNA-seq. Briefly, the RNA library was constructed using an Illumina TruSeq RNA preparation kit (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. The RNA samples were then sequenced on the Illumina NovaSeq6000 (Illumina, San Diego, CA, USA). Finally, the eligible differentially expressed genes(DEGs) were identified and annotated with the use of the DSS (Shrinkage for Sequencing Data) tool in the Bioconductor Package.
## Bioinformatics analyses
KEGG pathway enrichment analyses were performed using the DAVID Bioinformatics Resources online data platform (https://david.ncifcrf.gov/home.jsp). The related pathways and functions were selected based on the criterion $P \leq 0.05$; we selected the top 20.
## Western blotting
Cells were divided into the following five groups for western blotting: control group (untreated RAW 264.7 cells), model group (RAW 264.7 cells treated with ox-LDL), and three treatment groups (RAW 264.7 cells treated with ox-LDL and 0.2, 0.6, and 1.8 g/L of GXHP, respectively). Cells were collected, and a protein extraction kit (Gene pool, Beijing, China) was used to extract proteins according to the manufacturer’s protocol. The protein concentration was determined using a BCA protein assay (Multi Sciences, Hangzhou, China). Protein separation using $12\%$ SDS-PAGE gel was then performed, and the protein samples were transferred to a PVDF membrane. After blocking, the PVDF membrane was incubated with specific primary antibodies (Cell Signaling Technology, Danvers, MA, USA), such as PI3 kinase p85 antibody (dilution ratio was 1:500), phospho-PI3 kinase p85 antibody (dilution ratio was 1:1,000), AKT1 antibody (dilution ratio was 1:1500), and phospho-AKT1 antibody (dilution ratio was 1:500), overnight at 4°C. This was followed by 1 hour incubation at room temperature with horseradish peroxidase conjugated goat anti-rabbit IgG (Abcam, Cambridge, MA, USA)(dilution ratio was 1:5000).Antigen–antibody binding was detected using enhanced chemiluminescence reagents (ThermoFisher Scientific, Waltham, MA, USA). Quantification of each protein was determined using Quantity One v.4.6.2.
## Statistical analysis
Differences in the levels of cholesterol, inflammatory factors, and protein expression among different groups were assessed using one-way ANOVA coupled with Tukey’s multiple comparison test. $P \leq 0.05$ was considered statistically significant. Differential methylation sites (DMSs) were identified and annotated by the dispersion shrinkage for sequencing data tool in the Bioconductor package, and the thresholds were set as $P \leq 0.05$ and a cutoff of 0.1 for DNA methylation changes. DEGs were defined by a false discovery rate (q) < 0.05 and a log2 fold change threshold of > 0.5 or < -0.5. Correlation analysis was performed using Pearson’s correlation coefficient.
## Effect of GXHP on cell viability of RAW264.7 cells
As illustrated in Figure 1A, the cell viability of RAW264.7 cells was assessed using a CCK-8 assay. GXHP had dose-dependent effects on cell viability. Figure 1B shows that treatment with GXHP at a concentration of 3.6 mg/mL could significantly reduce the viability of RAW264.7 cells. Thus, a concentration of ≤ 1.8 g/L for GXHP was selected as the treatment concentration for subsequent experiments.
**Figure 1:** *Effects of GXHP on cell viability of RAW264.7 cells. (A) Dose–response curve for GXHP. (B) Comparison of cell survival rate (%) among different groups following treatment with GXHP at different concentrations ranged from 0 to 14.4 g/L for 48 h. The error bars indicate mean ± SEM. Results were from three independent experiments. ##P < 0.01, compared with control group.*
## GXHP reduced the levels of TC, FC, and inflammatory factors in foam cells
Compared with the control group, Oil Red O staining showed that orange-red lipid droplets assembled in foam cells following treatment of RAW264.7 cells with ox-LDL at 80 μg/L (Figures 2A, B). Compared with the control group, ox-LDL treatment could significantly increase the levels of TC and FC in the model group ($P \leq 0.01$). Compared with the model group, TC and FC expression in the GXHP group decreased significantly ($P \leq 0.01$) (Figures 2C, D). The expression levels of IL-1β, TNF-α, and VCAM-1 in the model group were significantly higher than those in the control group (all $P \leq 0.01$). Compared with the model group, GXHP could downregulate the expressions of IL-1β, TNF-α, and VCAM-1 ($P \leq 0.01$) (Figures 2E –G).
**Figure 2:** *Effects of GXHP on expressions of inflammatory factors in foam cells. Cells were from control group (A) and foam cell model group (B) after oil red O staining (200×). Levels of (C) TC, (D) FC, (E) IL-1β, (F) TNF-α, and (G) VCAM-1. Each bar represents the mean ± SD. Results were from five independent experiments. **P < 0.01, compared with the control group; ##P < 0.01, compared with the model group.*
## GXHP reversed DNA methylation changes in RAW264.7 cells treated with ox-LDL
In this study, MC-seq was used to determine whether GXHP treatment could alter the status of genome-wide DNA methylation in foam cells. The heatmap intuitively displayed the differential methylation in the nine samples from the three groups (Figure 3A). Compared with the control group, volcano plots showed 10,956 DMGs in the model group corresponding to 6,336 hypomethylated and 4,620 hypermethylated genes (Figure 3B). We also identified 11,107 DMGs between the GXHP and model groups, including 5,235 hypermethylated genes and 5,872 hypomethylated genes (Figure 3C).
**Figure 3:** *Effects of GXHP on DNA methylation in foam cells. (A) Heatmap showing the top 1,000 differentially methylated genes (DMGs) among GXHP, model, and control groups. (B) Volcano plot showing the DMGs between model and control groups. (C) Volcano plot showing the hypermethylated and hypomethylated genes DMGs between GXHP and model groups. (D) Venn diagram showing the differentially hypermethylated and hypomethylated genes induced by GXHP and ox-LDL. (E) Circle heatmap showing the top 20 hypermethylated and hypomethylated genes in the model group versus the control group. (F–I) Top 20 KEGG pathways for the differentially hypermethylated and hypomethylated genes induced by ox-LDL or GXHP.*
Of the DMGs regulated by ox-LDL and GXHP, 5,898 DMGs change DNA methylation levels in opposite directions. Specifically, 2,685 ox-LDL-induced hypermethylated genes were hypomethylated by GXHP treatment. Furthermore, 3,213 DMGs hypomethylated by ox-LDL were hypermethylated by GXHP treatment (Figure 3D). The circle heatmap shows the top 20 hypermethylated and hypomethylated genes induced by GXHP treatment, which included Bmp8b, Fam131b, Igf2bp2, Lpar3, Tbc1d1, and Ccdc85a (Figure 3E). These data suggest that GXHP treatment can reverse the DNA methylation changes induced by ox-LDL in foam cells.
Additionally, KEGG pathway analysis was performed to reveal the possible biological functions related to DMSs. The 6,336 hypomethylated and 4,620 hypermethylated genes induced by ox-LDL were involved in the Rap1, Hippo, Wnt, MAPK, and PI3K-Akt signaling pathways (Figures 3F, G). The 5,235 hypermethylated and 5,872 hypomethylated genes induced by GXHP were related to the PI3K-Akt, Rap1, MAPK, and Hippo signaling pathways (Figures 3H, I).
## GXHP restored gene expression changes in RAW264.7 cells treated by ox-LDL
In this study, transcriptomic sequencing was performed to observe the DEGs among the three groups. Principle component analysis (PCA) and a heatmap showed that the three groups were clearly separated (Figures 4A, B). Compared with the control group, volcano plots showed 1,344 DEGs including 874 downregulated and 470 upregulated genes in the model group (Figure 4C). Moreover, there were 2,276 DEGs in the GXHP group compared with the model group (1,232 genes were upregulated and 1,044 were downregulated following GXHP treatment) (Figure 4D). Of the DEGs regulated by ox-LDL and GXHP, 412 DEGs affected the expression in opposite directions. Specifically, 151 DEGs that were upregulated by ox-LDL were downregulated by GXHP treatment, whereas 261 downregulated DEGs induced by ox-LDL were upregulated by GXHP treatment (Figure 4E). Moreover, the top 20 DEGs including upregulated and downregulated genes induced by GXHP treatment are shown in the circle heatmap (Figure 4F). These data suggest that GXHP treatment could restore gene expression changes induced by ox-LDL in foam cells.
**Figure 4:** *Effects of GXHP on RNA expression in foam cells. (A) PCA was performed to identify the clustering profiles of the differentially expressed genes (DEGs) among the three groups. The red clustered samples from model group (Models 1–3), the green clustered samples from control group (Controls 1–3), and the blue clustered samples from GXHP group (GXHP1–3). (B) Heatmap showing the overview of DEGs among the three groups. (C) Volcano plot showing DEGs between model and control groups. (D) Volcano plot showing DEGs between GXHP and Model groups. (E) Venn diagram showing the differentially downregulated and upregulated genes induced by ox-LDL and GXHP. (F) Circle heatmap showing the DEGs including the top 20 upregulated and downregulated genes in the model versus the control groups.*
## Correlation between DNA methylation in promoter region and gene expression in foam cells treated with GXHP
Hypermethylation in the promoter region is associated with low expression of the genes. In contrast, hypomethylation is correlated with high expression of the corresponding gene. Here, we observed the opposite relationship between DEGs and DMGs in the cells from the model and GXHP groups. Compared with the model group, 387 DEGs/DMGs were identified in the GXHP group (Figure 5A). Specifically, there were 228 hypomethylated and 159 hypermethylated genes with upregulated and downregulated expressions, respectively (Figure 5B). Correlation analysis revealed the top 20 genes, which included Klf6, Evl, Mtmr4, and Slc4a8 (Figure 5C). Moreover, KEGG enrichment analysis indicated that these genes were closely related to the PI3K-Akt, Rap1, and Hippo signaling pathways (Figure 5D).
**Figure 5:** *Integrative analysis of DNA methylation and RNA expression. (A) A scatter plot of mean methylation difference versus gene expression difference (log2FC). Each point represents a CpG-gene pair. (B) Venn diagrams summarizing the intersection for differentially hypermethylated genes and DEGs with upregulated or downregulated and differentially hypomethylated genes. (C) Correlation analysis of gene expression and DNA methylation was performed using Pearson correlation analysis. (D) KEGG pathways for the differentially hypermethylated genes with downregulated RNA expression and hypomethylated genes with upregulated RNA expression induced by GXHP.*
## GXHP inhibited the PI3K-Akt signaling pathway in foam cells
Based on the results of the correlation analysis, the PI3K-Akt signaling pathway was selected for validation. The expression of the key proteins p-PI3K, PI3K, p-AKT and AKT in foam cells was upregulated following ox-LDL treatment compared with that in the control group. However, GXHP treatment (1.8 and 0.6 g/L) decreased the ratios of p-PI3K/PI3K and p-AKT/AKT, indicating that GXHP can inhibit the PI3K-Akt signaling pathway in foam cells (Figures 6A–C).
**Figure 6:** *Effects of GXHP on protein expressions of PI3K-AKT in foam cells. (A) RAW 264.7 cells were treated with ox-LDL or GXHP (0, 0.2, 0.6, and 1.8 g/L) for 48 h, and western blotting was performed to determine the protein levels of PI3K, p-PI3K, AKT1, and p-AKT1. Gray values of (B) p-PI3K/PI3K and (C) p-AKT/AKT are shown. Data are presented as the mean ± SD of three independent experiments. **P < 0.01 compared with the control group; #P < 0.05 compared with the model group; ##P < 0.01 compared with the model group.*
## Discussion
AS is the leading cause of morbidity and mortality worldwide, in which chronic inflammation coupled with elevated LDL cholesterol levels have been found to drive its development. Evidence suggests that Chinese herbal compounds in TCM are effective against AS [13, 14]. This study reports the anti-AS effects of GXHP via DNA methylation changes in vitro. Our results suggest GXHP as a potential and effective agent for treating patients with AS.
Monocyte-derived cells absorb a proportion of ox-LDL and transform into foam cells (16–18); transformation of macrophages into lipid-loaded foam cells is an early event in the pathogenesis of AS. Furthermore, ox-LDL leads to the release of proinflammatory factors in foam cells, which accelerates the development of AS (19–21). Therefore, macrophages are attractive therapeutic targets for controlling the development of AS [22, 23]. In the present study, foam cells were induced using ox-LDL at a concentration of 80 µg/mL for 48 h. Red lipid droplets and significant inflammatory cytokine upregulation were observed, which verified the successful establishment of a foam cell model. GXHP was then designed, and its anti-AS effects were tested in foam cells. A CCK-8 assay showed that foam cells could be safely treated with GXHP at 1.8 mg/mL, which was selected for subsequent experiments. Moreover, GC-MS and ELISA revealed that GXHP not only decreased the levels of TC and FC but also the expressions of IL-1β, TNF-α, and VCAM-1 compared with the model group. Taken together, our data suggest that GXHP provided a protective effect against AS.
Aberrant hypomethylation and hypermethylation play critical roles in the development of AS. For instance, aberrantly methylated genes including interferon-γ, intercellular adhesion molecule 1, nitric oxide synthase 3, 15-lipoxygenase, platelet-derived growth factor receptor alpha, and fatty acid desaturase 2 were found in AS. These hypomethylated or hypermethylated genes were closely associated with the inflammatory response, SMC proliferation, endothelial cell remodeling, and plaque development (24–28). Wang reported that the promoter region of miR-181b was hypermethylated in peripheral monocytes from patients with AS, and the hypermethylation of miR-181b could promote AS development [29]. In the present study, we also found that many differentially hypermethylated and hypomethylated genes existed in foam cells induced by ox-LDL. KEGG enrichment analysis suggested that these abnormal DEGs were involved in many important pathways, such as the Rap1, Wnt, Hippo, and PI3K-Akt signaling pathways, which were closely associated with AS. Considering the unbalanced DNA methylation in AS, the development of drugs targeting aberrant hypermethylation and hypomethylation is vital for AS treatment in the future.
In this study, a genome-wide methylation analysis was applied to observe the change of DNA methylation in foam cells after treatment with GXHP, a TCM. GXHP treatment not only induced DNA hypermethylation, but also DNA hypomethylation. The number of aberrant hypomethylated and hypermethylated genes corrected by GXHP treatment was 3,213 ($54.48\%$) and 2,685 ($45.52\%$), respectively, which revealed that GXHP might be a novel methylation-based agent. These hypomethylated and hypermethylated genes induced by GXHP treatment were involved in the PI3K-Akt, Rap1, Hippo, and Wnt signaling pathways. Notably, most of those pathways play essential roles in the development of AS. Taken together, these results revealed that targeting aberrant hypermethylation and hypomethylation may be the main mechanism of action of GXHP in AS.
DNA methylation plays a crucial role in gene transcription. In the present study, we performed genome-wide expression analysis using RNA-seq. Consequently, GXHP treatment downregulated 151 DEGs that were upregulated by ox-LDL, and upregulate 261 DEGs that were downregulated by ox-LDL. The data demonstrated that GXHP treatment could correct the aberrant gene expression induced by ox-LDL. Hypomethylation in promoter regions could lead to over-expression of the genes, while hypermethylation suppresses expression. Our results showed that the mRNA expression of the 159 hypermethylated and 228 hypomethylated genes decreased and increased in the GXHP group compared with that in the model group, respectively. Pearson correlation analysis revealed the top DMGs and DEGs including Klf6, Evl, Mtmr4, and Slc4a8, which were considered potential and important treatment targets of GXHP. Previous studies have demonstrated that knockdown or inhibition of Klf6 expression could reduce macrophage filtration, reverse endothelial dysfunction, and lower inflammatory factor expression [30, 31].
Finally, KEGG enrichment analyses revealed that the DMGs and DEGs were involved in the PI3K-Akt, Rap1, Hippo and Wnt signaling pathways, among which PI3K-Akt signaling pathway ranked first. Previous studies showed that the PI3K-Akt signaling pathway is closely associated with inflammation in AS diseases [32, 33]. Liu et al. found that inhibiting the expression of PI3K or AKT could decrease AS lesions and plaque areas and reduce the levels of IL-1β and NOD-like receptor protein 3 in ApoE−/−mice [34]. Meng et al. [ 35] showed that morin hydrate, a naturally occurring bioflavonoid, could inhibit inflammation by inhibiting the PI3K/AKT1 signaling pathway in HUVECs treated with ox-LDL. Therefore, PI3K-Akt signaling pathway was chosen for the WB verification. WB showed that ox-LDL upregulated the protein levels of p-PI3K and p-AKT. More importantly, GXHP significantly downregulated the protein expression of p-PI3K and p-AKT in foam cells, suggesting the PI3K-Akt signaling pathway as an effective target for GXHP. However, in vivo experiments were not conducted in this study. Therefore, experiments with animal models are required to validate our findings in the future.
## Conclusions
We demonstrated that GXHP showed anti-AS effects on foam cells. MC-seq combined with RNA-seq revealed that GXHP appeared to reverse gene expression changes via regulating aberrant hypermethylation and hypomethylation, thereby decreasing the levels of proteins involved in the PI3K-Akt signaling pathway in foam cells. Thus, GXHP may be a novel methylation-based agent. Clinical trials are needed to determine whether the protective effects of GXHP translate to better response in patients with AS in the future.
## Data availability statement
The data presented in the study are deposited in the Sequence Read Archive repository (https://dataview.ncbi.nlm.nih.gov/object). The accession numbers are SRR21846884, SRR21846885, SRR21846886, SRR21846887, SRR21846888, SRR21846883, SRR21846882, SRR21846881, SRR21846889, SRR21849088, SRR21849087, SRR21849095, SRR21849089, SRR21849090, SRR21849091, SRR21849092, SRR21849093 and SRR21849094.
## Author contributions
ZJ, YZ, YW, and HW contributed to the study design, practical work, and manuscript writing. JM, AW, FX, and QZ participated in the research design and manuscript revision. All authors contributed to data analysis and drafting or revising the article, and agree to be accountable for all aspects of the work. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: (20R)-panaxadiol improves obesity by promoting white fat beigeing
authors:
- Yuqian Lv
- Xiaoyan Lv
- Jianshu Feng
- Fanghui Cheng
- Zhiyi Yu
- Fengying Guan
- Li Chen
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9992182
doi: 10.3389/fphar.2023.1071516
license: CC BY 4.0
---
# (20R)-panaxadiol improves obesity by promoting white fat beigeing
## Abstract
Introduction: *Obesity is* an important cause of a range of metabolic diseases. However, the complex mechanisms of obesity and its related diseases make some weight loss methods ineffective or have safety issues. Ginseng, a specialty of Jilin Province in China with both edible and medicinal value, contains mainly ginsenosides and other components. In order to study the anti-obesity effect of ginseng, network pharmacology was used to predict and screen the active ingredients, action targets and signaling pathways of ginseng. We found (20R)-panaxadiol (PD) is a more desirable active ingredient due to its high drug-like properties and high bioavailability. Moreover, it is closely related to cAMP pathway which is more important in metabolism regulation. The corresponding pharmacodynamic targets of PD include ADRB2 (the gene encoding the β2-adrenoceptor receptor). Our study aimed to investigate whether Panaxadiol can promote white adipocyte beigeing and increase thermogenesis through modulating the β2/cAMP pathway to exert anti-obesity effects.
Methods: In vivo, we established high-fat feeding obesity model, genotypically obese mice (ob/ob) model, and administered PD (10 mg/kg). PD treatment in ob/ob mice along with β2 receptor inhibitor ICI118551. In vitro, differentiated mature 3T3-L1 cells were given palmitate (PA) to induce hypertrophy model along with PD (20 μM).
Results: The results of this study demonstrated that PD significantly reduced body weight, improved glucose tolerance and lipid levels in high-fat-induced obese mice and ob/ob mice, and also reduced lipid droplet size in PA-treated hypertrophic adipocytes in vitro. Molecular biology assays confirmed that cAMP response element binding protein (CREB) phosphorylation was increased after PD administration, and the expression of thermogenesis-related proteins UCP1, PRDM16 and mitochondrial biosynthesis-related proteins PGC-1α, TFAM and NRF1 were increased. Molecular docking results showed a low binding energy between β2 receptors and PD, indicating an affinity between the β2 receptor and PD. In addition, the β2 receptor inhibition, reversed the anti-obesity effect of PD on the body weight, lipid droplets, the expression of thermogenesis-related proteins and CREB phosphorylation in ob/ob mice.
Discussion: These results suggest that PD may promote the expression of thermogenic proteins through phosphorylation of CREB via β2 receptor activation, and thus exert anti-obesity effects.
## 1 Introduction
Obesity is a pathological state of fat accumulation, which is mainly manifested by the massive accumulation of fat resulting in adipocyte hypertrophy and adipose tissue hyperplasia (Zhang et al., 2017). Obesity has become a public health problem in China and worldwide, and patients suffer psychologically and physiologically problems to some extent. Some studies have shown that the incidence of diabetes is much higher in obese than normal populations, and obesity is a high risk factor for the development of cardiovascular diseases including atherosclerotic and heart failure (Maggio and Pi-Sunyer, 2003; Riobó Serván, 2013). In recent years, the role of effective active ingredients extracted from natural plants, i.e., phytochemicals, has received increasing attention in the prevention and treatment of obesity and obesity-related metabolic diseases (Azhar et al., 2016; Zhao et al., 2017). Several phytochemicals are employed to reduce the process of adipogenesis, carbohydrates absorption in the small intestine, collection of hepatic triglycerides, deposition of adipose tissue, weight loss, and enhances the anti-obesity potential, activity of PPAR-α and PPAR-γ-responsive genes (Kumar et al., 2022) (Shehadeh et al., 2021) (González-Castejón and Rodriguez-Casado, 2011). Ginseng is one of the most valuable herbs in traditional Chinese medicine (Yoo et al., 2012; Kim et al., 2017; Li and ji. 2018a; So et al., 2018; Yu et al., 2018), which contains a variety of active ingredients including ginsenosides (Ratan et al., 2021). Ginseng is a plant of high value for both food and medicinal purposes. In China, ginseng has been widely trusted and used as a medicinal herb that can make people strong and healthy (Sabouri-Rad et al., 2017). Recently, the anti-obesity effects of ginseng have been increasingly mentioned. Modern pharmacological studies have repeatedly suggested the weight loss and hypoglycemic effects of ginseng (Li and Ji, 2018b). A study by (Karu et al., 2007) showed that ginseng has some anti-obesity and hypolipidemic effects on high-fat diet-induced obese mice.
However, the bioavailability of each component of ginseng varies, and its specific mechanism of action is unclear for its multi-component and multi-target characteristics. Further study of the material basis, pharmacological efficacy and mechanism of ginseng’s anti-obesity is still needed. Nowadays, the emergence of network analysis has provided new ideas to study the mechanism of action of many herbal medicines such as ginseng. In the previous study, network analysis was used to predict and screen the active ingredients, the corresponding targets and signaling pathways of ginseng. We found (20R)-panaxadiol (PD) is a more desirable active ingredient due to its high drug-like properties and high bioavailability. Moreover, it is closely related to cAMP pathway which is more important in metabolism regulation. The corresponding pharmacodynamic targets of PD include ADRB2 (the gene encoding the β 2-adrenoceptor).
The cAMP pathway has been reported to be associated with thermogenesis in adipose tissue (Liu et al., 2019; Zhang et al., 2021). Adipose tissue can be divided into white and brown adipose. The main function of classical brown fat is to produce heat, while the function of white fat is to store energy, and both are important regulators of energy homeostasis. Recent studies have pointed to the existence of a potential cell type in white adipocytes that can specifically express PRDM16 (PRdomain-containing16), PGC-1α (Peroxisome proliferators-activated receptor-γ coactivator-1) and UCP1 (Uncoupling protein 1), biomarkers for brown-specific genes, known as beige adipocytes (Harms and Seale, 2013). Beigeing, which refers to white fat exhibiting brown fat-like properties after external environmental or pharmacological stimulation, is a phenomenon in which the expression of brown-specific genes in white fat (especially in SWAT). The cAMP signaling pathway plays an important role in the study of promoting beige coloration of white fat. Current studies have demonstrated that by activating ß-adrenergic receptors and thus adenylate cyclase, catalyzing the conversion of ATP to cAMP, activates PKA, phosphorylates the cAMP response element binding protein (CREB), and promotes downstream brown-specific genes expression, which increases energy expenditure for the purpose of disease treatment (Fenzl and Kiefer, 2014). In this study, obese mice and PA-treated hypertrophic adipocytes were used to investigate whether 20R-Panaxadiol can promote white adipocyte beigeing and increase thermogenesis through modulating the β 2/cAMP/CREB pathway to exert anti-obesity effects.
## 2.1 Materials
The PD used in this study was donated by Professor Yanping Chen (College of Chemistry, Jilin University). Its purity was determined to be more than $98.9\%$ by normalization method of HPLC. Total cholesterol (TC), triglyceride (TG) and total cholesterol (TCHO) diagnostic test kits were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Blood glucose test strips were purchased from Roche (Basel, Switzerland). All other reagents were purchased from Beijing Chemical Factory (Beijing, China).
## 2.2 Animals and treatments
The animal study designed in this paper was approved by the Medical Ethics Committee of Jilin University (protocol code 2021156, approval date 20210301). A number of C57BL/6J male mice and ob/ob male mice (3 weeks) were purchased from Beijing HFK Bioscience Co., Ltd. (SCXK 2019-0008) (Beijing, China). The mice were housed in a temperature-controlled animal room with a constant 12 h light/dark cycle.
After 1 week of adaptive feeding, C57BL/6J mice were taken as control group with low fat feed [CONTROL, $$n = 6$$, Research DIETS (D12450B)], high fat group with high fat feed [$$n = 14$$, Research DIETS (D12492)]. After 8 weeks of high-fat feeding, the mice gained 20 percent more weight than the control group were taken as obesity, and the modeling success rate was 85 percent. Then obesity mice randomly were divided into two groups, high fat group (HF, $$n = 6$$), (20R)-panaxadiol treatment group (PD, $$n = 6$$). PD was administered by gavage (10 mg/kg) for 5 weeks. CONTROL and HF group were given equivalent amounts of sodium carboxymethyl cellulose.
After 2 weeks of normal feeding, the ob/ob mice gained 20 percent more weight than the control group, were randomly divided into three groups, ob/ob group (OB, $$n = 6$$), 20R-Panaxadiol group (PD, ig. 10 mg/kg, $$n = 6$$) and PD+β 2 receptor inhibitor group (ICI118551, ip. 1 mg/kg, $$n = 6$$). Body weights of mice were measured weekly and treatments were administered by gavage or intraperitoneal injection (equivalent amounts of sodium carboxymethyl cellulose and normal saline were given as controls) from 9:00 to 10:00 a.m. daily for 8 weeks.
HF mice and ob/ob mice were subjected to oral glucose tolerance test (OGTT) at week fourth and week seventh of drug administration, respectively. Mice were anesthetized with isoflurane by inhalation ($1.5\%$) and euthanized by CO2 inhalation in the next week. Blood sampling was taken from the medial canthus vein and serum was collected after centrifugation at 3,000 g (4°C) for 15 min. Then serum TC, TG, TCHO were determined according to the manufacturer’s instructions. Samples of subcutaneous adipose tissue (SWAT) and interscapular brown adipose tissue (BAT) were extracted for weighing.
## 2.3 OGTT
For the OGTT, mice were fasted for 12 h and then orally gavaged with glucose dissolved in water at 2 g/kg body weight. Ten microliters of blood was obtained from the tail tip, and the concentration of glucose was measured at 0, 30, 60, 90, and 120 min.
## 2.4 Hematoxylin and eosin (HE) and immunofluorescence (IF) stainings
The adipose tissue was quickly removed from the mice, washed with normal saline, dried and then fixed with $10\%$ formalin. After rapid removal of the adipose tissue, it was placed in $4\%$ paraformaldehyde. It was then dehydrated in an ascending series of ethanol, and equilibrated with xylene, followed by embedding in paraffin and sectioning into 5–10 µm slices. Then, the samples were dewaxed with xylene and a descending series of ethanol. Continued sections were stained with both Mayer’s hematoxylin and eosin (HE).
Dried paraffin sections were dewaxed and hydrated and then closed with $1\%$ BSA for 30 min, the closure solution was blotted dry on blotting paper. Each section was incubated with UCP1 antibody (abcam1:100) overnight at 4°C in a wet box, and washed 3 times with PBS. After aspirating the residual liquid, the sections were incubated with fluorescent secondary antibody (FITC1:100) for 1 h at room temperature in a wet box protected from light, then rinse 3 times with PBS, the excess liquid was aspirated. A small amount of anti-fluorescence quencher containing DAPI dropwise was added, the sample was covered with a coverslip, and stored in a wet box protected from light for observation under a fluorescence microscope for photographs.
## 2.5 Cell culture
3T3-L1 preadipocytes (ATTC, United States) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) and $10\%$ bovine calf serum (Gibco, United States) in an atmosphere of $5\%$ CO2 at 37°C. For differentiation studies, 3T3-L1 cells were cultured in DMEM containing $10\%$ FBS, 1 μMOL dexamethasone (Sigma, United States), 0.5 mMOL IBMX (Sigma, United States) and 5 μg/mL insulin for 2 days and then replaced with DMEM culture medium without $10\%$ FBS and 5 μg/mL insulin for 2 days. Then replaced with DMEM containing $10\%$ FBS. DMEM cell culture medium containing $10\%$ FBS was changed every 2 days until the 8–10th day.
## 2.6 Oil red O staining
Differentiated mature 3T3-L1 cells were given PA and PD for 24 h. The cells were gently rinsed with PBS before staining, followed by fixation with $4\%$ paraformaldehyde for 1 h. Then cells were washed with PBS, and each well was stained with 2 mL of freshly configured Oil Red O working solution for 1–2 h. The staining solution was discarded, and $60\%$ isopropanol was rinsed quickly once, followed by rinsing with ultrapure water three times, and observed under the microscope for photographs.
## 2.7 Immunoblot analysis
Proteins were extracted from cell lysates following the manufacturer’s protocols (Beyotime, China). Protein concentration was quantified using the BCA protein assay kit (Thermo Fisher Scientific, United States) and 30 μg protein was separated in a $12\%$ SDS polyacrylamide gel and electro transferred onto polyvinylidene difluoride (PVDF) membranes (Bio-Rad, United States). Membranes were blocked with $5\%$ (w/v) BSA for 2 h at room temperature and then incubated with primary antibodies with light shaking overnight at 4°C. Primary antibodies against UCP1, PGC-1α, TFAM, NRF1, CREB, P-CREB and GAPDH (abcam) were diluted to a ratio of 1:1,000 in TBST buffer. The membranes were washed 3 times for 5 min each with 10 mL of TBST [10 mM Tris-HCl, 150 mM NaCl and $0.1\%$ (v/v) Tween-20] and then incubated with secondary antibody at room temperature for 2 h. Secondary antibodies goat anti-rabbit or goat anti-mouse (Proteintech, United States) were diluted to a ratio of 1:5,000 in TBST buffer. The membrane was incubated in Western ECL substrate (Thermo fisher or Proteintech, United States) and exposed to Tanon imager, using ImageJ software for image analyses.
## 2.8 Statistical analysis
All data are expressed as mean ± standard error of the mean. Statistical significance of differences was analyzed by one-way ANOVA with Dunnett test using GraphPad Prism8. $p \leq 0.05$ was considered to indicate a statistically significant difference.
## 3.1 Anti-obesity effect of PD on HF mice
As shown in Figures 1A,B, after 5 weeks of PD administration, the body weight of mice in PD group was significantly lower than that of the model group, and the body fat ratio was significantly reduced. It is suggested that PD can reduce the body weight and body fat ratio of obese mice induced by high-fat diet. Serum TG, T-CHO and LDL (Figures 1C–E) were significantly reduced after 5 weeks of PD treatment, suggesting that PD can effectively improve the lipid levels in high-fat diet induced obese mice.
**FIGURE 1:** *Anti-obesity effect of PD on HF mice (A, B) The changes of body weight and fat/weight (g) during the experimental course; (C–E) Serum lipid profile including circulating TG, TCHO and LDL levels; **
p < 0.01, ****
p < 0.0001, compared with the LF group. #
p < 0.05, ##
p < 0.01, ###
p < 0.001, ####
p < 0.0001, compared with the HF group. n = 6.*
## 3.2 Effects of PD on glucose tolerance, fasting glucose and subcutaneous inguinal white fat morphology in HF mice
Obesity is often accompanied by varying degrees of impairment in glucose tolerance, so we used OGTT to test the effect of PD on glucose tolerance. As shown in the Figures 2A–C, after PD treatment, the AUC (Area Under Curve) of blood glucose in the PD group was lower than that of the model group, and its recovery level was significantly faster than that of the model group, and fasting blood glucose was significantly lower. The above results suggest that the elevated fasting glucose and impaired glucose tolerance in obese mice induced by high-fat diet were improved to some extent after the administration of PD. The results of HE staining of adipose tissue (Figures 2D, E) showed that after the administration of PD treatment, the volume and diameter of adipocytes were significantly reduced and the intercellular spaces were dense. It is suggested that PD has an ameliorative effect on the morphology of subcutaneous inguinal white adipose tissue in high-fat diet induced obese mice.
**FIGURE 2:** *Effects of PD on glucose tolerance, fasting glucose and subcutaneous inguinal white fat morphology in HF mice (A) OGTT after 4 weeks of PD treatment; (B) AUC of each group was calculated during the oral glucose tolerance test; (C) Fasting blood glucose level in each group; (D) PD alleviated morphology changes of SWAT in HF mice; (E) Cell diameter changes in each group; *
p < 0.05, **
p < 0.01, ****
p < 0.0001, compared with the LF group. ##
p < 0.01, ####
p < 0.0001 compared with the HF group. n = 5.*
## 3.3 Effect of PD on thermogenic and cAMP related pathway proteins in adipose tissue of HF mice
To investigate the anti-obesity mechanism of PD, we examined the expression of thermogenic proteins PRDM16 and UCP1, mitochondrial biosynthesis-related protein PGC-1α, TFAM and cAMP pathway related protein P-CREB in adipose tissue after (20R)-panaxadiol administration. The results are shown in the Figures 3A, B, the expression of thermogenic proteins PRDM16 and UCP1 in PD treatment mice were significantly increased in immunofluorescence (Red fluorescence represents the expression of UCP1, Figure 3C). The results were the same as the western blotting results. Mitochondrial biosynthesis-related proteins PGC-1α, TFAM expression were also significantly increased (Figures 3D, E). In order to initially investigate the causes of the changes in adipose tissue thermogenesis and mitochondrial biosynthesis, we used Western blotting to detect the phosphorylation of the cAMP pathway related protein CREB, and as shown in the Figure 3F, P-CREB content increased significantly after the administration of PD. It is suggested that PD can promote thermogenesis and mitochondrial biosynthesis, and this effect may be related to the cAMP/CREB pathway.
**FIGURE 3:** *Effect of PD on thermogenic and cAMP related pathway proteins in adipose tissue of HF mice PD increased the expression of thermogenesis-related proteins in the HF group of mice. (A, C) UCP1 protein expression; (B) PRDM16 protein expression; PD increases the expression of mitochondrial biosynthesis-related proteins PGC-1α (D) and TFAM (E); (F) P-CREB content increased significantly after the administration of PD treatment; p < 0.05, compared with the LF group. #
p < 0.05, ##
p < 0.01, ###
p < 0.001 compared with the HF group. n = 6.*
## 3.4 In vitro experiments to study the ameliorative effect of PD on hypertrophic adipocytes and its association with cAMP related pathway proteins
In vitro experiments were performed using MTT assay to screen PA-modeling concentration of 0.3 mMOL and PD administration concentration of 20 μMOL. The results are shown in the Figure 4C, the lipid droplet volume of PA-stimulated adipocytes was increased compared with the control group. After giving PA stimulation along with PD protection, the lipid droplet volume was reduced compared with the PA group. To investigate the mechanism of action of 20R-Panaxadiol in vitro experiments, we examined the expression of the thermogenic proteins PRDM16 and UCP1, the mitochondrial biosynthesis-related protein TFAM and the cAMP pathway related protein P-CREB after administration of PD to hypertrophic adipocytes. As shown in the Figures 4D–G, PRDM16, TFAM and P-CREB expression were significantly increased after PD administration. It is suggested that at the cellular level, (20R)-panaxadiol also has the same effect of promoting thermogenesis and increasing mitochondrial biosynthesis in hypertrophic white adipocytes, and this effect may be through the cAMP/CREB pathway.
**FIGURE 4:** *In vitro experiments to study the ameliorative effect of PD on hypertrophic adipocytes and its association with cAMP related pathway proteins MTT assay to screen PA-modeling concentration (A) and PD administration concentration (B); (C) Oil Red O staining; (D) PRDM16 protein expression; (E) UCP1 protein expression; PD increases the expression of mitochondrial biosynthesis-related proteins TFAM (F); (G) P-CREB content increased significantly after the administration of PD treatment; *
p < 0.05, **
p < 0.01, ***
p < 0.001, ****
p < 0.0001, compared with the CONTROL group. #
p < 0.05 compared with the PA group. n = 3.*
## 3.5 Anti-obesity effect of PD via β
2 receptor in ob/ob mice
To investigate whether the weight loss effect of 20R-Panaxadiol acts through the β 2 receptor, we administered PD on genotypically obese mice ob/ob mice, and the β 2 receptor was inhibited by β 2 receptor inhibitor (ICI118551) while PD was administered. As can be seen from Figure 5A, at week four of administration, the weight gain of the PD administered group slowed down significantly and a significant difference was observed compared to the weight of the model group. At the sixth week of administration, there was a significant difference between the body weight of the β 2 receptor inhibitor group and that of PD treatment group. There was no significant difference in the food intake of ob/ob mice in each group (Figure 5B). As shown in Figure 5C, the lee’s index increased significantly in ob/ob mice and decreased significantly after PD treatment, while it increased significantly in the ICI118551 group compared with PD group. Figure 5D shows that the body fat ratio of ob/ob mice increased significantly compared with the control group and decreased significantly after PD treatment for 8 weeks. It was suggested that PD reduced body weight, body fat ratio and lee’s index in ob/ob mice, and that β 2 receptor inhibitors reversed this alteration. After PD administration, serum TG and T-CHO were significantly reduced in ob/ob mice, and LDL was also reduced but without significant difference. Compared with the PD-treated group, mice in the ICI118551 group showed significant increases in TG and T-CHO (Figures 5E–G). It was suggested that PD treatment could effectively improve lipid levels in ob/ob mice, and this effect would be diminished after β 2 receptor inhibition.
**FIGURE 5:** *Anti-obesity effect of PD via β
2 receptor in ob/ob mice (A–D) the changes of body weight, food take, Lee’s index and fat/weight (g) during the experimental course; (E–G) Serum lipid profile including circulating TG, TCHO and LDL levels; ***
p < 0.001, ****
p < 0.0001, compared with the CONTROL group. #
p < 0.05, ##
p < 0.01, ####
p < 0.0001compared with the OB group. $
p < 0.05, $$
p < 0.01, $$$
p < 0.001, compared with the PD group. n = 6.*
## 3.6 Effects of PD on glucose tolerance and subcutaneous inguinal white fat morphology in ob/ob mice via β
2 receptors
To investigate the effect of PD on glucose tolerance in genotypically obese mice, we examined the changes in glucose tolerance in mice. As can be seen from the Figures 6A, B, after PD treatment, the AUC of blood glucose in PD treatment group was lower than that of the model group, and its rate of return to normal level was significantly higher. The blood glucose of mice in the ICI118551 group was higher than that in the PD administration group. The above results suggest that glucose tolerance appears significantly impaired in ob/ob mice and is improved to some extent in ob/ob mice after the administration of PD, which may be diminished by β 2 receptor inhibitors. HE results (Figures 6C, D) showed that after PD treatment, subcutaneous inguinal white adipocytes were significantly reduced in volume and had dense cell gaps. After administration of the β 2 receptor inhibitor, the fat volume was larger compared to the PD administered group. The brown adipose fat cells in the back of PD treatment group were also significantly reduced in size, small multi-chambered lipid droplets started to appear, and the cell interstices were dense. Dorsal fat multicompartmentality was reduced after administration of ICI118551. It was suggested that PD improved adipose histomorphology in genotypically obese mice and could be reversed to some extent by β 2 receptor inhibitors.
**FIGURE 6:** *Effects of PD on glucose tolerance and subcutaneous inguinal white fat morphology in ob/ob mice via β
2 receptors (A) OGTT after 7 weeks of PD treatment; (B) AUC of each group was calculated during the oral glucose tolerance test; PD alleviated morphology changes of (C) SWAT and (D) BAT in HF mice; ****
p < 0.0001, compared with the CONTROL group. ##
p < 0.01, compared with the ob/ob group. $
p < 0.05 compared with the PD group. n = 5.*
## 3.7 Effect of PD on adipose tissue thermogenic proteins and cAMP pathway related protein in ob/ob mice via β
2 receptors
To investigate the changes in adipose tissue thermogenesis in ob/ob mice, we used Western blotting to detect the expression of thermogenesis-related proteins PRDM16, UCP1 and mitochondrial biosynthesis-related proteins PGC-1α, TFAM, NRF1 and cAMP pathway related protein CREB in subcutaneous SWAT. The results are shown in the Figures 7A,B, the expression of heat-producing proteins PRDM16 and UCP1 significantly increased after PD treatment (Red fluorescence represents the expression of UCP1, Figure 7C). Immunofluorescence results were the same as the Western blotting results. Mitochondrial biosynthesis-related proteins PGC-1α, TFAM, NRF1 expression were also significantly increased (Figures 7D–F). Thermogenic and mitochondrial biosynthesis-related proteins were significantly decreased in adipose tissue of mice given β 2 receptor inhibitors. To initially investigate the causes of adipose tissue thermogenesis and changes in mitochondrial biosynthesis, we used Western blotting to detect the phosphorylation of the cAMP pathway related protein CREB. As shown in the Figure 7G, P-CREB levels increased significantly after PD administration and decreased significantly after β 2 receptor inhibitor administration. It is suggested that the role of (20R)-panaxadiol in promoting thermogenesis and mitochondrial biosynthesis may be through the cAMP/CREB pathway, and this pathway may be mediated by β 2 receptors.
**FIGURE 7:** *Effect of PD on adipose tissue thermogenic and cAMP related pathway proteins in ob/ob mice via β
2 receptors PD increased the expression of thermogenesis-related proteins in the OB group of mice. (A, C) UCP1 protein expression; (B) PRDM16 protein expression; PD increases the expression of mitochondrial biosynthesis-related proteins TFAM (D), PGC-1α (E) and NRF1 (F); (G) P-CREB content increased significantly after the administration of PD treatment; *
p < 0.05, **
p < 0.01, ***
p < 0.001, compared with the LF group. #
p < 0.05, ##
p < 0.01, ###
p < 0.001, compared with the OB group. $
p < 0.05 compared with the PD group. n = 6.*
## 4 Discussion
Clinical findings suggest that ginseng may improve cardiovascular function, immune function, and diabetes-related diseases (Wu et al., 2014). showed that ginsenoside Rb1 regulates appetite and achieves energy balance by modulating the expression of inflammatory factors in vivo and restoring the anorexigenic effects of leptin and leptin p-STAT3 signaling in the hypothalamus in mice fed a high-fat diet. In vitro studies have shown that ginsenosides can inhibit triglyceride synthesis, cholesterol production (Lee et al., 2015), gluconeogenesis (Quan et al., 2012) by activating AMPK and inhibiting fatty acid synthase (FAS) and 3-hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGCR). Ginseng reduced adipocyte size and fat storage in high-fat diet-induced obese mice and rats (Lee et al., 2010). In conclusion, it can be seen that ginseng has certain anti-obesity effects. However, it has multi-component, multi-target and complex pathways, so there is still a long way to go to find the exact and efficient active ingredients and to elucidate their mechanisms of action. Our study found that: 1. ( 20R)-panaxadiol has a weight loss effect on both high-fat diet-induced and genotypically obese mice. 2. ( 20R)-panaxadiol can regulate the cAMP pathway, phosphorylate CREB, promote white adipocyte beige, and play a role in promoting thermogenesis and anti-obesity. 3. The anti-obesity effect of (20R)-panaxadiol may be due to the excitement of β 2 receptor.
Activation of the cAMP pathway can phosphorylate the cAMP effector element binding protein (CREB), which in turn promotes the expression of downstream thermogenic genes such as UCP1 (Reverte-Salisa et al., 2019). Recent studies have shown that beige adipocytes present in white adipocytes can also specifically express brown-specific genes such as UCP1 induced by specific external conditions to promote thermogenesis in the organism (Wu et al., 2012). This process of conversion of white fat to beige fat is called white fat beigeing. Based on the above predictions of network analysisand the support of related literature, this project was carried out to verify the effective components and mechanism of action of ginseng against obesity by conducting in vitro and in vivo experiments. Our results suggested that PD might promote the expression of thermogenic proteins in adipocytes through the cAMP/CREB pathway and thus exert anti-obesity effects.
The well-recognized upstream target of the classical signaling pathway of cAMP is the ß receptor, and the ß receptor signaling pathway plays an important role in promoting thermogenesis and beigeing (Ohyama et al., 2016; Lim et al., 2019), of which the β 3 receptor has been studied mostly. However, it has been found that the effects of β 3 receptor agonists given in some obese populations are not significant (Redman et al., 2007; Carey AL 2013 January; 56 [1]). Moreover, it has been reported that β 3 receptor expressed in obese animals and humans with impaired functional activity. For example, a variant allele of the human β 3 receptor gene (64 Trp/Arg) has been associated with inhibition of β 3 receptor signaling (Pietri-Rouxel et al., 1997; Kimura et al., 2000) and increased body mass index (BMI), then obesity was promoted (Sipilainen et al., 1997; Mitchell et al., 1998). Recently, it has been shown that β 2 receptor activation in adipocytes also promotes the activation of the cAMP/PKA pathway and exerts some anti-obesity effects. Li et al. ( Li et al., 2012) showed a significant reduction in adiposity in pigs after administration of β 2 receptor agonists, and (Ohyama et al., 2016) showed that a subtype of capsaicin could also exert some anti-obesity effects through β 2 receptors. This also suggests that the role of β 2 receptors in the ß receptor family in our anti-obesity effect needs to be looked at. After the PPI network analysis of target protein interaction, the degree of target ADRB2 (i.e., β 2 receptor coding gene) was 20, which was much higher than the average degree of 13.4. It indicated that ADRB2 may be the main pharmacological target of ginseng. In addition, the active ingredient corresponding to the target ADRB2 is also (20R)-panaxadiol. Our study subsequently used β 2 receptor inhibitor to intervene in (20R)-panaxadiol treatment of ob/ob obese mice. It was found that inhibition of β 2 receptor reversed the effect on weight loss and upregulation cAMP thermogenesis signal pathway of 20R-Panaxadiol. Based on the above theoretical calculation results and experimental validation, it can be concluded that (20R)-panaxadiol can regulate CREB phosphorylation through activating β 2 receptor and promote the improvement of body thermogenesis and the corresponding mitochondrial biosynthesis, thus achieving the anti-obesity effect.
In summary, (20R)-panaxadiol, as the active component of ginseng, can exert anti-obesity effects by increasing non-tremor thermogenesis and increasing energy expenditure in the body. The mechanism of action may be to act on β 2 receptors and promote phosphorylation of cAMP response element binding protein CREB, which in turn upregulates the expression of downstream thermogenic proteins such as UCP1 and PRDM16. Actually, Weight loss depends on energy expenditure of multiple organs, such as the muscle, the liver, and the adipose tissue. So we think PD may improve obesity by promoting white fat beigeing in part. In subsequent studies, we will take the studies to reveal the energy expenditure in other organs such as skeletal muscle, and on the alleviation of NAFLD. A comprehensive discussion of the effects of PD on obesity related metabolic diseases would be conducted.
## Data availability statement
The datasets used and/or analysed during this study are available from the corresponding author on reasonable request.
## Ethics statement
The animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Jilin University (protocol code 2021156, approval date 20210301).
## Author contributions
LC and FG designed the experiments. YL, XL, JF, and ZY contributed to the data collection; YL and XL performed the data analysis and interpreted the results; YL and FG wrote the manuscript. FC was responsible for data analysis and language polishing. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Mucosal immunization with Ad5-based vaccines protects Syrian hamsters from
challenge with omicron and delta variants of SARS-CoV-2
authors:
- Molly R. Braun
- Clarissa I. Martinez
- Emery G. Dora
- Laura J. Showalter
- Annette R. Mercedes
- Sean N. Tucker
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9992185
doi: 10.3389/fimmu.2023.1086035
license: CC BY 4.0
---
# Mucosal immunization with Ad5-based vaccines protects Syrian hamsters from challenge with omicron and delta variants of SARS-CoV-2
## Abstract
SARS-CoV-2 variant clades continue to circumvent antibody responses elicited by vaccination or infection. Current parenteral vaccination strategies reduce illness and hospitalization, yet do not significantly protect against infection by the more recent variants. It is thought that mucosal vaccination strategies may better protect against infection by inducing immunity at the sites of infection, blocking viral transmission more effectively, and significantly inhibiting the evolution of new variants of concern (VOCs). In this study, we evaluated the immunogenicity and efficacy of a mucosally-delivered, non-replicating, adenovirus type 5-vectored vaccine that expresses the spike (S) gene of Wuhan (rAd5-S-Wuhan), delta (rAd5-S-delta), or omicron (rAd5-S-omicron) SARS-CoV-2 VOCs. Hamsters were immunized with these vaccines intranasally prior to challenge with omicron or delta variants. Additionally, one group was vaccinated by oral gavage with rAd5-S-Wuhan prior to challenge with the delta variant. Both intranasal and oral administration of rAd5-S-*Wuhan* generated cross-reactive serum IgG and mucosal IgA to all variant spike and RBD proteins tested. rAd5-S-omicron and rAd5-S-delta additionally elicited cross-reactive antibodies, though rAd5-S-omicron had significantly lower binding antibody levels except against its matched antigens. Two weeks after the final vaccination, hamsters were challenged with a SARS-CoV-2 variant; omicron or delta. Whether matched to the challenge or with rAd5-S-Wuhan, all vaccines protected hamsters from weight loss and lung pathology caused by challenge and significantly reduced viral shedding compared to placebo. Vaccination with rAd5-S-Wuhan provided significant protection, although there was an improved reduction in shedding and disease pathology in groups protected by the matched VOC vaccines. Nevertheless, Wuhan-based vaccination elicited the most cross-reactive antibody responses generally. Overall, heterologous vaccination via mucosal routes may be advantageous for second-generation vaccines.
## Introduction
The currently licensed mRNA vaccines, administered parenterally, are highly effective at preventing severe disease and hospitalizations. However, control of the COVID-19 pandemic is continuously abrogated by ever-evolving variants of concern (VOCs). The mRNA vaccines have significantly reduced efficacy in preventing mild-to-moderate disease against VOCs as seen in the case of the delta and omicron variants (1–3). Further, there are reports of significant decreases in levels of pseudovirus neutralization from convalescent and vaccinated sera when compared to the ancestral strain [4]. In addition, vaccinated individuals with breakthrough infections can still transmit virus to others [5]. New FDA guidelines recommend that future booster shots target the spike (S) protein of new variants in addition to the ancestral Wuhan strain of SARS-CoV-2. Clinical trials testing a booster dose of Moderna’s BA.1-specific mRNA vaccine showed that serum from subjects vaccinated with an omicron-specific vaccine elicited higher neutralizing titers to BA$\frac{.4}{5}$ variants compared to those boosted with the currently authorized Wuhan-based vaccine [6]. Although serum neutralizing antibodies were higher from the omicron-based vaccine, in a challenge study comparing the efficacy of these two vaccines in non-human primates (NHPs), similar levels of protection were observed after infection with the omicron variant [7]. Further, recent data of individuals who received a fourth vaccination with either Wuhan- or omicron BA$\frac{.4}{5}$-based mRNA showed that BA$\frac{.4}{5}$ vaccination produced only a modest increase in neutralizing antibodies to BA$\frac{.4}{5}$ when compared to serum from individuals vaccinated with Wuhan-based vaccine [8, 9]. It remains unclear if the inclusion of variant-specific genetic material via parenteral vaccination strategies will make a significant impact on the reduction of clinical disease or inhibit the spread the virus.
The high levels of circulating virus allow for continual rounds of viral evolution and the potential for new variants. An effective method of preventing future variants would be to block transmission. For respiratory pathogens such as SARS-CoV-2, the site of infection is the upper respiratory tract (URT), a part of the mucosal immune system. The URT is often the first line of defense against infection. It is believed that mucosal vaccinations will show improved efficacy and offer enhanced protection from SARS-CoV-2 vaccination by conferring immunity at the sites of infection [10, 11]. A major component to mucosal immunity is the presence of dimeric and multimeric secretory IgA (S-IgA), which is secreted onto mucosal surfaces. Due to its valency, S-IgA is more neutralizing than monomeric antibodies [12, 13] and has been shown to play a critical role in preventing infection of respiratory pathogens (14–16). In addition, IgA from convalescent SARS-CoV-2 serum was found to be more neutralizing that IgG [17, 18]. Further, it was found that mucosal immunity elicited by viral infection with the ancestral strain of SARS-CoV-2 provided protective immunity against omicron infection that was not observed after an intramuscular vaccination [19], highlighting the importance and potential power of second-generation, mucosally-delivered vaccines.
We have previously shown that oral and intranasal administration of rAd5-S-Wuhan in hamsters followed by breakthrough infection decreased transmission of SARS-CoV-2 to naïve hamsters [20] and protected hamsters from disease caused by the ancestral strain [21]. A human phase I clinical trial showed that oral tablet immunization with VXA-CoV2-1, an adenoviral vector like the ones discussed below, expressing the Wuhan S and N, was able to generate mucosal IgA antibody responses that persisted for over one year and had higher surrogate neutralizing activity in mucosal samples than from convalescent subjects [22]. Further, these mucosal antibodies were cross-reactive to other human coronaviruses like SARS-CoV-1, suggesting that mucosal immunity would be cross-reactive to future human coronaviruses, including emerging variants of SARS-CoV-2 [22]. It remains unknown if mucosal vaccination would protect against disease caused by VOCs. In this study, we show the mucosal vaccination of Syrian hamsters with Wuhan or variant-based vaccines generates cross-reactive serum and mucosal antibodies and protects from challenge with the omicron and delta variants of SARS-CoV-2.
## Intranasal immunization with rAd5-S-Wuhan and rAd5-S-omicron antigens elicits cross-reactive serum antibody to VOCs
To understand if mucosal vaccination with ancestral or variant-specific spike antigens could protect hamsters from disease caused by omicron and delta variants of concern (VOCs), we vaccinated hamsters with a non-replicating recombinant adenovirus type 5 (rAd5) that expresses a transgene of interest in the same gene cassette as a molecular adjuvant, a short nucleotide sequence that forms a hairpin RNA which acts as an innate immune antagonist within the same cell (Figure 1A) [23, 24]. Hamsters ($$n = 6$$/group) were immunized with 1x109 infectious units (IU) of rAd5-S-omicron or rAd5-S-delta vaccines and compared to hamsters immunized with rAd5-S-*Wuhan via* the intranasal route in preparation for viral challenge (Figures 1B, C). Additionally, one group from the cohort with rAd5-S-delta received an oral administration of rAd5-S-Wuhan prior to challenge with delta virus (Figure 1C). Serum samples were collected at D-1 prior to primary vaccination, D27 (one day prior to boost), and D41 (two weeks post boost) and IgG and IgA were measured. Additionally, nasal washes and samples from oropharyngeal swabs were collected at these timepoints for the assessment of mucosal IgA (Figure 1B). Timepoints were collected before and after administration of boosting dose so that the effect of multiple vaccine doses on binding antibody levels and on antibody avidity could be assessed. At D56, one month post boost, animals were challenged with the homologous VOCs. The challenge doses were chosen based on previous Syrian hamster titration studies performed by Bioqual, Inc (Rockville, MD) where detectible levels of viral replication and/or disease could be observed. Samples from oropharyngeal swabs were collected each day after challenge and lung tissue was collected at the end of challenge for pathology and measurement of infectious viral load by TCID50. Infection with the omicron BA.1 variant causes only mild disease in hamsters and thus the challenge was ended after six days, as opposed to the 10 days observed with the delta variant challenge, so that pathology could be more easily assessed before the animals fully recovered.
**Figure 1:** *Study design for immunogenicity and efficacy of rAd5 vaccines against SARS-CoV-2 variants of concern. (A) Illustration of the rAd5 vector used in vaccination with the transgene (spike) and molecular adjuvant. (B) Schedule of vaccination, sample collection, and viral challenge. (C) Vaccination groups and administration routes in preparation for viral challenge. Figure was made with wwwbiorender.com.*
Serum was collected at indicated days post vaccination and IgG levels were quantified using the Meso Scale Discovery (MSD) platform utilizing electrochemiluminescent detection. As no spike-specific hamster antibodies exist for use as standards, values are reported as relative light units (RLU) and compared to either placebo-vaccinated or baseline (D-1) samples, rather than normalized to a standard curve. Vaccination with both rAd5-S-Wuhan and rAd5-S-omicron elicited cross reactive IgG to Wuhan, omicron and other VOC spike proteins after both prime and boost (Figure 2A). Further, a significant increase in binding antibodies to both Wuhan and omicron spike proteins was observed after boost vaccination with rAd5-S-Wuhan, but not with rAd5-S-omicron ($$p \leq 0.0119$$ and 0.0026, respectively), which reached its maximum level of binding by D27 (Figure 2A). Next, we tested whether the serum IgG at D41 were cross-reactive to other spike and RBD proteins of VOCs in addition to omicron. rAd5-S-Wuhan elicited cross-reactive IgG to variant spike and RBD proteins. In comparison, vaccination with rAd5-S-omicron still elicited cross-reactive IgG to Wuhan alpha, beta, gamma, and delta variant antigens tested, albeit at significantly reduced levels of binding antibody compared to rAd5-S-Wuhan (Figures 2B, C) ($p \leq 0.005$ – $p \leq 0.0001$). Vaccination with rAd5-S-omicron and rAd5-S-*Wuhan* generated similar levels of binding IgG to omicron antigens (Figures 2B, C).
**Figure 2:** *Serum and mucosal antibodies from Wuhan- and omicron-based vaccination. (A) Serum IgG from hamsters vaccinated with rAd5-S-Wuhan (red), rAd5-S-omicron (blue), or placebo (black) prior to vaccination (D-1), prior to boost (D27) and two weeks after boost (D41). (B, C) Levels of serum IgG to spike (B) and RBD (C) of SARS-CoV-2 VOC at D41 post prime vaccination. (D) IgA from oral swab eluates and (E) nasal washes at indicated times. Antibody signals were normalized to total IgA and plotted as fold rise of D-1. Mean and SEM plotted. 2-way ANOVA with a Geisser-Greenhouse correction and with Tukey’s multiple comparison test. *p < 0.05, **p < 0.01, ***p < 0.005, ****p < 0.0001. ns, not significant.*
## Intranasal immunization with rAd5 expressing Wuhan and omicron antigens elicits cross-reactive oral and nasal IgA to VOCs
To determine if intranasal administration of rAd5-S vaccines elicited mucosal IgA, oral swab samples from D-1, D27, and D41 were analyzed for RBD and spike-specific IgA. Because experimental variability may be observed between swab samplings, specific IgA was first normalized to total IgA and expressed as fold rise over D-1 so that a trend could be observed. Both Wuhan and omicron vaccinations elicited increased RBD and spike-specific IgA compared to samples from pre-immune and placebo animals (Figure 2D). Similar to the serum samples, there were increases in binding following boost vaccination with rAd5-S-Wuhan, whereas boosting with rAd5-S-omicron did not increase the level of binding observed after prime vaccination (Figure 2D). Although not normalized to total IgA, raw values of spike-specific IgA mirrored these results (Figure S1A). As a second measure of mucosal IgA, nasal washes were collected at D-1, D27, and D41 of the study and evaluated for RBD and spike-specific IgA. Again, relative levels of spike specific IgA were determined by first normalizing to total IgA, then calculating the fold rise over D-1. Similar trends were seen when comparing antibody responses obtained from nasal washes of immunized animals, with all groups having increased omicron RBD and spike-specific IgA by D41 (Figure 2E, Figure S1B).
## Oral administration of rAd5-S-Wuhan elicits cross-reactive serum, oral, and nasal antibodies
In human phase I clinical trials using the same vaccine platform delivered to the ileum via enterically coated tablets, long lasting secretory IgA was observed in mucosal secretions [22]. As a proxy for oral tablet delivery in humans, the second cohort of hamsters included a group that were vaccinated via oral gavage in parallel to groups with intranasal administration in preparation for challenge with the delta variant (Figure 1C). We sought to understand if the delivery of rAd5-S-Wuhan by oral gavage could generate cross-reactive antibodies in serum, nasal, and oral samples. Additionally, we wanted to see if immune responses from oral administration compared similarly to that of intranasal administration.
Serum from vaccinated animals was analyzed for cross-reactive IgG against spike and RBD proteins. As this cohort of hamsters was vaccinated in preparation for challenge with the delta variant, Wuhan and delta antigens were assayed. Vaccination by oral gavage elicited significantly higher binding IgG to Wuhan and delta antigens over D-1 ($p \leq 0.0001$ at D27 and D41) (Figure 3A). In addition, intranasal vaccination with rAd5-S-Wuhan and rAd5-S-delta were able to generate cross-reactive IgG compared to baseline levels to both Wuhan and delta spike proteins ($p \leq 0.001$) (Figure 3A). Next, we tested whether the serum IgG at D41 from this cohort were cross-reactive to other spike and RBD proteins of VOCs in addition to delta. Intranasal administration of rAd5-S-Wuhan gave higher IgG responses to RBD and spike proteins than oral administration, although this difference was only significant in the case of Wuhan and delta spike proteins ($$p \leq 0.0472$$ and $$p \leq 0.0497$$) (Figures 3B, C). Intranasal administrations of rAd5-S-Wuhan and rAd5-S-delta elicited similar levels of serum IgG against spike and RBD, although vaccination with rAd5-S-delta performed significantly better against beta ($$p \leq 0.0423$$), delta ($$p \leq 0.0012$$), and omicron (p 0.0043) spike proteins, and beta ($$p \leq 0.0374$$), gamma ($$p \leq 0.0481$$), and delta RBD proteins ($$p \leq 0.0059$$) (Figures 3B, C). Oral administration of rAd5-S-Wuhan was not statistically compared to intranasal administration of rAd5-S-delta as both the antigen and route differed between the two groups.
**Figure 3:** *Serum and mucosal antibodies from Wuhan- and delta-based vaccination. (A) Serum IgG from hamsters vaccinated by oral gavage with rAd5-S-Wuhan (red – open shapes/dotted lines), intranasally with rAd5-S-Wuhan (red), rAd5-S-delta (blue), or placebo (black) prior to vaccination (D-1), prior to boost (D27) and two weeks after boost (D41). (B, C) Levels of serum IgG to spike (B) and RBD (C) of SARS-CoV-2 VOC at D41 post prime vaccination. (D) IgA from oral swab eluates and (E) nasal washes at indicated times. Antibody signals were normalized to total IgA and plotted as fold rise of D-1. Mean and SEM plotted. 2-way ANOVA with a Geisser-Greenhouse correction and with Tukey’s multiple comparison test. *p < 0.05, **p < 0.01, ****p < 0.0001.*
In addition to quantifying the presence of serum IgG, we also observed IgA in mucosal secretions. Oropharyngeal swabs were tested for the presence of specific IgA as described in Figure 2D. At the timepoints tested, all vaccinated groups elicited higher IgA when compared to the pre-immune and placebo samples (Figure 3D). Although not normalized to total IgA, raw values of spike-specific IgA mirrored these results (Figure S1A). Lastly, we looked at nasal wash IgA as we were particularly interested to see if oral administration of rAd5 could elicit nasal IgA. By D41, all vaccinated groups tested showed cross-reactive antibodies in the nasal secretions (Figure 3E; Figure S1D). No specific or total nasal IgA was observed from the hamsters vaccinated intranasally with rAd5-S-Wuhan at D27, likely due to a technical error. Thus, this data point was excluded from analysis (Figure 3E; Figure S1D).
## Boost vaccination increases antibody avidity
To determine if the antibodies generated by boost vaccination increased antibody specificity, we tested whether antibody avidity increased following boost vaccination by measuring the avidity index [25]. In all iterations, vaccination with the matched antigen elicited the highest affinity antibodies against the homologous variant protein (Figure 4). The avidity index of both serum IgG and IgA and oral IgA to spike and/or RBD generally increased with boost. Interestingly, the IgA avidity index to omicron spike elicited by rAd5-S-omicron vaccination started high and stayed high (Figure 4C), whereas serum IgG increased in avidity with boost (Figure 4A). This phenomenon was only observed in the serum with respect to the spike protein as there was no superiority observed by antibody avidity towards omicron RBD with antibodies generated by both vaccinations giving equivalent avidity indexes (Figure 4B) and equal levels of binding antibody to omicron RBD (Figure 2C). Avidity of IgA from oral swabs also increased with boost, but this increase in avidity index was very slight (Figure 4C). Additionally, the avidity indexes of oral IgA were higher at D27 compared to the avidity index of serum IgG at D27 (Figure 4). This observation is in line with previous findings that secretory IgA is of higher avidity due to its valency. Lastly, as a comparator to other VOC proteins, the avidity index of antibodies generated after rAd5-S-Wuhan and rAd5-S-omicron vaccination were measured against delta spike and RBD proteins. In line with what was seen before, rAd5-S-*Wuhan* generated antibodies of the higher avidity when compared with the omicron vaccination, again indicating that Wuhan-based vaccination may elicit broadly cross-reactive antibodies across many VOCs, particularly in comparison to rAd5-S-omicron.
**Figure 4:** *Antibody avidity following prime and boost vaccination. (A, B) Avidity index of serum IgA (solid lines, circles) and IgG (dashed lines/triangles) at D27 and D41 against SARS-CoV-2 spike (A) and RBD proteins (B). (C) Avidity index of IgA eluted from oral swabs at D27 and D41 against SARS-CoV-2 spike protein. Mean and SEM plotted.*
## Wuhan and omicron-based vaccines protect hamsters from disease caused by VOCs
One month after vaccination series (D56), the omicron cohort of hamsters was challenged with 4.84x104 TCID50 of the omicron BA.1 variant of SARS-CoV-2 and infection proceeded for six days. Both rAd5-S-Wuhan and rAd5-S-omicron protected hamsters from weight loss due to infection ($$p \leq 0.0186$$ and 0.0013 respectively) (Figures 5A, B). In addition, viral shedding, as measured by qRT-PCR of genomic RNA (gRNA) from oropharyngeal swab, was significantly reduced by both rAd5-S-Wuhan and rAd5-S-omicron compared to placebo ($$p \leq 0.0001$$ and $p \leq 0.0001$, respectively) (Figures 5C, D). At the end of the infection period, the lungs of hamsters were collected and TCID50 was determined. Three of the six hamsters in the placebo group had detectable levels of infectious virus in their lungs while no detectable virus was recovered from the lungs of vaccinated hamsters (Figure 5E).
**Figure 5:** *rAd5-S mediated protection of hamsters from disease caused by the omicron and delta variants. (A) Body weight changes and (B) Area under the curve (AUC) of hamsters vaccinated with rAd5-S-Wuhan (red), rAd-5-S-omicron (blue), and placebo (black) measured for six days after challenge. (C) Genomic (gRNA) detected from oral swabs of vaccinated hamsters and (D) AUC of the gRNA time course. (E) TCID50 of lung tissue at day six post infection. (F) Body weight changes and (G) AUC (One-way ANOVA) of hamsters vaccinated with oral rAd5-S-Wuhan (red – open shapes/dotted lines), rAd5-S-Wuhan (red, closed circles), rAd-5-S-delta (blue), and placebo (black) measured for 10 days after challenge. (H) Genomic (gRNA) detected from oral swabs of vaccinated hamsters and (I) AUC of the gRNA time course. (J) Subgenomic (sgRNA) and (K) AUC (one-way ANOVA) of the sgRNA time course. n=6, mean and SEM plotted. Ordinary one-way ANOVA with Dunnett’s multiple comparisons test. *p < 0.05, **p < 0.01, ***p < 0.005, ****p < 0.0001.*
The second cohort of hamsters was vaccinated with rAd5-S-Wuhan and rAd5-S-delta in preparation for challenge with the delta variant of SARS-CoV-2. Hamsters were infected with 3.48x103 TCID50/animal and monitored for 10 days post challenge for weight loss and viral load. Animals vaccinated intranasally with rAd5-S-Wuhan and rAd5-S-delta experienced no weight loss compared to the placebo control. There was a small dip in body weight for hamsters vaccinated orally with rAd5-S-Wuhan, however all animals recovered quickly and all vaccine groups were significantly protected from weight loss compared to placebo (p=<0.0001 for all groups) (Figures 5F, G). Viral loads from oropharyngeal swabs were analyzed by qRT-PCR for subgenomic (sgRNA) and gRNA. Viral loads and viral replication were significantly reduced in the rAd5-S-Wuhan oral and intranasal groups with the greatest reduction of viral RNA levels in the homologous rAd5-S-delta vaccination group ($$p \leq 0.0007$$, <0.0001, and <0.0001 respectively) (Figures 5H–K).
## Mucosal vaccination protects hamsters from lung pathology resulting from omicron and delta infection
In addition to monitoring clinical symptoms of disease above, the lungs of infected animals were observed for signs of inflammation, as measured by bronchiolo-alveolar hyperplasia, and bronchoalveolar/interstitial and vascular/perivascular inflammation. Fewer animals from all vaccinated groups, as compared to placebo, showed signs of inflammation (Figures 6A–D). rAd5-S-delta was the most efficacious with the greatest decrease in severity of lung pathology compared to placebo animals in the cohort challenged with the delta variant. Bronchiolo-alveolar hyperplasia was observed in almost all animals challenged with either omicron or delta VOCs; however, severity was decreased compared to placebo treated animals. In the omicron cohort, there were no significant differences in efficaciousness between rAd5-Wuhan and r-Ad5-omicron. Overall, there were fewer animals with lung inflammation from the cohort infected with the delta variant than that of omicron. However, this is likely due to the timing of infection as by day 10 post infection with the delta variant, most of the animals had recovered in body weight (Figures 5F, G) whereas the cohort infected with the omicron variant had not yet recovered (Figures 5A, B).
**Figure 6:** *Lung pathology from placebo and rAd5-S vaccinated hamsters. (A) Number of animals with minimal/mild edema, moderate/marked bronchiolo-alveolar hyperplasia, minimal-moderate bronchoalveolar/interstitial inflammation, or minimal-mild vascular/perivascular inflammation from omicron cohort. (B) same as A, but from delta cohort. (C, D) Representative H&E of hamster lungs from the omicron cohort (C) and delta cohort (D). Arrows indicate areas brochiolo-alveolar hyperplasia.*
## Discussion
In this study we demonstrate that mucosal delivery of recombinant adenovirus expressing the spike gene of Wuhan, delta, or omicron VOCs protected hamsters from disease caused by those variants. All vaccinated hamsters in this study developed serum IgG regardless of mucosal immunization route. Additionally, IgA was found in mucosal secretions from both the oral and intranasal cavity, again, regardless of mucosal immunization route.
It has been shown that immunization with a fourth dose of mRNA vaccine increases the serum neutralizing antibody titers in humans [4]. Similarly, an increase in binding antibodies generated after boost vaccination with rAd5-S-Wuhan increased the level of binding antibodies to omicron RBD and spike proteins. We therefore wanted to determine if the avidity of antibodies generated by vaccination increased after an additional administration of vaccine. We found that both serum and mucosal antibodies increased in avidity to all spike variants tested following a booster dose.
When examining the ability of rAd5-vectored vaccines to protect against infection, immunization with the matched variant elicited the greatest reduction in clinical symptoms such as bodyweight loss and lung pathology. Viral load was also reduced in all vaccine groups compared to placebo. Genomic RNA is believed to represent primarily virion associated RNA, while subgenomic RNA is thought to represent active SARS-CoV-2 infection [26]. In the omicron cohort, sgRNA was not observed, though it was concluded that a technical failure of primer binding prevented amplification, rather than a biological phenomenon. Although vaccination with the matched vaccine elicited the strongest protection from disease, significant levels of protection were still observed in the groups vaccinated with rAd5-S-Wuhan. Wuhan-based vaccination elicited significantly higher cross-reactive antibodies to most variant spike proteins when compared to omicron-based vaccination. This suggests that the immune response generated by mucosal vaccination of a Wuhan-based vaccine may be cross-protective to emerging VOCs. It remains unclear if boosting previously vaccinated individuals with a variant-specific vaccine would enhance variant-specific antibodies or rather boost previous Wuhan specific antibodies due to immune imprinting [27]. It is likely that vaccination strategies that induce mucosal immunity, and thereby provide a more cross-reactive immune response, may be a favorable approach to future vaccination strategies rather than continuously modifying vaccines to currently circulating SARS-CoV-2 variants.
Since its emergence in 2019, SARS-CoV-2 has caused over 6.6 million deaths and over 650 million confirmed cases [28]. The currently approved mRNA vaccines prevent severe disease, but do not protect against infection from ever evolving variants, particularly those of the omicron clade [29]. Additionally, currently licensed mRNA vaccines yield reduced serum neutralization titers to the omicron variant as compared to the ancestral Wuhan strain, though the response is moderately improved after a third dose is administered (30–32). Further, immunity induced by parenteral vaccination does not effectively prevent transmission of SARS-CoV-2, allowing opportunities for continual variant evolution. New vaccination strategies are needed to curb the constant waves of new variant outbreaks. These strategies include the development of a pan-coronavirus vaccine that expresses the epitopes of several variants [33], a vaccine that targets more conserved epitopes from SARS-CoV-2 [34], or the induction of immunity at the site of infection, the respiratory mucosa, that can be cross-reactive to many variants of SARS-CoV-2. Inducing mucosal immunity would likely be an effective method to prevent transmission and block infection, largely due to the high neutralization activity of S-IgA [12, 13]. In phase I clinical trials, we showed that vaccination via oral tablet vaccine induced mucosal SARS-CoV-2 specific IgA that possessed increased surrogate neutralizing activity [22]. In pre-clinical animal models, we showed that our mucosal Wuhan-based vaccination construct protected against disease caused by the ancestral Wuhan strain of SARS-CoV-2 [20, 21]. In a study from Harvell et al, it was observed that individuals with increased mucosal IgA had improved protection from breakthrough infection with the omicron (BA.1) variant of SARS-CoV-2 [19], illustrating the importance of inducing mucosal immunity to curb new waves of infection.
Despite showing cross-reactive IgA and IgG in our clinical and preclinical studies, it was unknown whether our rAd5-S vaccine would protect against VOCs such as omicron and delta variants. Other studies have shown that variant-specific vaccination, delivered parenterally, were effective at preventing disease in hamsters caused by delta and omicron variants, reducing the viral load in animal tissues after infection and protecting the animals from lung pathology [35, 36]. Interestingly, a study by Halfmann et al. found that hamsters that had recovered from previous infection with the 614G/614D ancestral strain were better able to reduce BA.1 replication in both the nasal turbinates and lung tissue when compared with mRNA-vaccinated hamsters [37]. This suggests that mucosal vaccination, even with a Wuhan-based vaccine, may provide increased protection against the omicron and delta VOCs.
A few limitations apply to our study. First, when used for human clinical trials (NCT04563702), this vaccine is formulated into enterically coated tablets that can be swallowed by subjects. These tablets can withstand the low pH of the stomach allowing for delivery of rAd5 to the ileum (23, 38–41). One limitation for this study is the use of intranasal vaccination in place of oral vaccinations. To mimic oral delivery, we included a group of hamsters in the delta cohort who received their vaccination via oral gavage. Oral gavage of small animals is difficult to deliver accurately [42] in comparison to intranasal delivery. However we were able to see comparable immunogenicity and protection from disease when the vaccine was delivered intranasally or by oral gavage, improving our confidence that use of intranasal delivery in our experimental models can aid our understanding of mucosal delivery of rAd5 generally [42]. Another limitation is that each experiment was only performed once for both the omicron and delta cohorts, as the high cost of BSL-3 infections and the ever-evolving variants made an exact experimental repeat ill-favored. Although these challenge experiments were only performed once each, the observation that mucosal application of this rAd5-S is able to generate both serum and mucosal immune responses has been shown in other hamster studies [20, 21], NHP studies [43, 44], and studies from human clinical trials [22]. Future experiments will be performed to further confirm our observations, and the data analyzed here will inform the experimental design of studies including those that utilize this mucosal rAd5-S vaccine to boost animals that have previously been immunized with mRNA vaccines in immunogenicity studies.
One concern in the use of adenovirus vectored vaccines is the development of anti-vector immunity. In a publication by O’Brien et al, intramuscular vaccination with an Ad5 vectored vaccine resulted in an >50 fold increase of anti-Ad5 antibodies [45]. However, in a paper describing phase I human clinical trials for oral influenza vaccination, anti-Ad5 immunity did not increase after oral vaccination [40]. Multiple groups, including ours, have shown that pre-existing immunity to Ad5 is not as problematic in mucosal-delivered Ad5 compared to what has been seen with parenteral vaccination [40, 46, 47]. While anti-vector immunity was not measured in this study, we did observe a boosting effect on the antibody responses as well as an increase in avidity after boost vaccination, suggesting that a potential anti-vector response did not abrogate the boosting effect of mucosally delivered rAd5.
An orally delivered, thermo-stable, self-administered vaccine would have drastic impacts on public health and pandemic control, particularly in areas where cold-chain resources are limited. Further, generation of mucosal IgA through vaccination may have some substantial immunological benefits. Growing evidence suggests that mucosal IgA plays a key role in the prevention of infection, even when the prior exposure doesn’t match the circulating strains [19, 48]. The nasal and oral mucosal surfaces are the first lines of defense against SARS-CoV-2 infection and local secretory IgA may do a better job of inhibiting infection than a systemic serum response. In a paper by Havervall et al, as little as 20 AU/mL of nasal IgA was effective at inhibiting viral infection in humans. Even if breakthrough infection occurs, mucosal vaccines may significantly reduce the spread to others, even to unvaccinated individuals [19]. Several mucosal vaccine strategies have failed in humans, including intranasal rAd in two separate studies [49, 50]. In contrast, we have previously shown that our clinical vaccine for SARS-CoV-2, VXA-CoV2-1, was well tolerated in humans when given as an oral tablet and generated cross-reactive mucosal antibodies and potent T-cell responses. In summary, the data presented here demonstrate that vaccination via oral and intranasal routes with both Wuhan and variant-matched vaccines, protected hamsters from disease caused by the omicron and delta variants of SARS-CoV-2. This technology is currently being evaluated in additional clinical trials and may offer a different, more complete approach to suppress pandemic SARS-CoV-2 worldwide compared to needle-based mRNA vaccination.
## Animal model, study design and challenge
Male Golden Syrian hamsters ($$n = 6$$) aged 6-8 weeks, were vaccinated according to the groupings in Figure 1B with a dose of 1x109 infectious units (IU) diluted in 1xPBS in a total volume of 100 µL for intranasal vaccination (50 µL/nostril) and 1 mL total volume for oral gavage. Vaccinations occurred four weeks apart. Serum, nasal washes, and oral swabs were collected on day -1 (D1), day 27 (D27), and day 41 (D41) as described below. At D52, the animals were anesthetized by injecting 80 mg/kg ketamine and 5 mg/kg xylazine via intramuscular route in preparation for challenge. Animals were challenged with an appropriate dose via intranasal administration using a total volume of 100 μL per animal (50 μL/nostril), administered dropwise. SARS-CoV-2 delta variant (ATCC NR-56116 LOT#: 70047614) was dosed at 3.48x103 TCID50 per hamster and SARS-CoV-2 omicron variant (ATCC NR-56486 LOT#: 70049695 was dosed at 4.8x104 TCID50 per hamster as pre-determined in titration studies performed by Bioqual, Inc. Animals were monitored daily for any abnormal clinical observation and body weights were recorded. All in-life animal handling occurred at Bioqual, Inc (Rockville, MD).
## Virus generation
Adenovirus type 5 (rAd5) vaccines were generated based on the published spike sequence of SARS-CoV-2 Wuhan (Genbank Accession No. MN908947.3), delta (GISAID Accession No. EPI-ISL-2570775), and omicron (BA.1) (GISAID Accession No. EPI_ISL_6699769) variants. These sequences were inserted into a recombinant plasmid containing rAd5 sequence that is deleted in E1 and E3 genes. The respective transgenes were cloned into the E1 region that additionally contains a downstream molecular dsRNA adjuvant in the gene cassette and is expressed together with the transgene in the target cell. rAd5 particles were generated by transfection of transgene containing DNA into Expi293F cells (ThermoFisher Scientific) to generate rAd5 virions which were purified by CsCl density centrifugation [38, 51].
## Blood sample collection
Blood collection was performed one day prior to each vaccination (D-1, D27) and two weeks post final vaccination (D41). Prior to collection, animals were anesthetized with up to $5\%$ isoflurane and blood was collected via the retro-orbital sinus vein. Samples were allowed to clot for 30 minutes – 1 hr at room temperature and centrifuged at 9300 x g for 10 minutes. Serum was stored at -80˚C.
## Nasal wash and oral swab collection
Hamster nasal wash samples were collected following anesthesia using isoflurane; a soft tipped catheter was used to flush 400 µL of 1X PBS into the nasal cavity and a collection device was placed under the opposite nostril to collect the fluid. The recovery yield was documented to be approximately 200 to 250 µL. Hamster oral swab samples for antibodies were collected using a sterile flocked swab (Copan Diagnostics FLOQSwabs 501CS01) by inserting into the mouth and swabbing both cheeks for at least 30 seconds. The swab was snap-frozen until elution in 200 µL 1X PBS, 0.25 M NaCl (Corning cat#21-031-CV, Acros Organics cat#327300025) and collected by centrifugation. Post-challenge swab samples were collected in cryovials containing 1 mL 1X PBS for qPCR viral load testing.
## Serum IgG responses against SARS-CoV-2 spike and RBD variants
Vaccine-induced serum IgG specific to SARS-CoV-2 spike and RBD variants was measured with MSD V-PLEX COVID-19 Serology kit panel 22 and 23 (MSD cat#K15563U, K15571U). Plates were coated, blocked, washed, and incubated with sample and detection antibody according to the manufacturer’s instructions. Serum samples were diluted at 1:4000 in Diluent-100 (MSD cat#R50AA). A goat anti-hamster IgG antibody (Invitrogen cat#31115) was sulfo-tagged with the MSD GOLD SULFO-TAG NHS-Ester Conjugation Pack (MSD cat#R31AA) and diluted to 1 µg/mL in Diluent-100. Plates were read on a Meso QuickPlex SQ 120 instrument (MSD) and sample values were reported as relative light units (RLUs). Data analysis was performed in GraphPad Prism (Version 9.4.1).
## Mucosal IgA responses against SARS-CoV-2 delta or omicron variant spike trimer and RBD
Vaccine-induced mucosal IgA specific to SARS-CoV-2 variant spike and RBD were measured with the Mesoscale Discovery (MSD) 4-spot U-PLEX Development Pack (MSD cat#K15229N). Spots were linked with anti-Syrian hamster IgA antibody (Brookwood Biomedical cat#sab3001a) biotinylated with the EZ-Link Sulfo-NHS-LC-Biotin kit (Thermo Fisher Scientific cat#A39257) and either biotinylated Wuhan, delta, or omicron variant SARS-CoV-2 spike trimer and RBD proteins (ACROBiosystems cat#SPD-C82E9, SPN-C82Ec, SPD-C82Ed, SPN-C82Ee, SPD-C82E4). Plates were coated, blocked, washed, and incubated with sample and detection antibody according to the manufacturer’s recommendations. Nasal samples were diluted at 1:15 and oral samples were diluted at 1:5 in Diluent-100 (MSD cat#R50AA). The anti-Syrian hamster IgA antibody was sulfo-tagged with the MSD GOLD SULFO-TAG NHS-Ester Conjugation Pack (MSD cat#R31AA) and diluted with Diluent-100 to 2 µg/mL for the nasal samples and 1 µg/mL for the oral samples. Plates were read on a Meso QuickPlex SQ 120 instrument (MSD). Samples were reported as relative light units (RLUs). Due to the variability in mucosal sampling, samples were normalized by total IgA and expressed as fold change was reported as vaccinated over unvaccinated sample. Data analysis was performed in GraphPad Prism (Version 9.4.1).
## Avidity assay
Serum and mucosal antibody avidity was determined by MSD using either MSD V-PLEX COVID-19 Serology kit panel 25 (MSD cat# K15583U-2) or U-PLEX Development Pack (MSD cat#K15229N). U-plex spots were linked with biotinylated Wuhan, delta, or omicron variant SARS-CoV-2 spike trimer and RBD proteins (ACROBiosystems cat#SPD-C82E9, SPN-C82Ec, SPN-C82Ee, SPD-C82E4). Plates were coated, blocked, washed, and incubated with sample and detection antibody according to the manufacturer’s recommendations and as described above with the exception that after incubation of sample, an additional 10-minute incubation of 3M urea in 1X PBS + $0.05\%$ Tween-20 was performed. Avidity was calculated as (RLUurea)/(RLUPBST)*100.
## TCID50 assay
Tissue sections were homogenized in 0.5 mL cold medium (DMEM + $10\%$ FBS + 0.05 mg/mL gentamicin) for and centrifuged (2000 xg, 4°C, 10 minutes) to remove debris and supernatants collected. Clear flat-bottom 96-well culture microplates (BD Falcon Cat. #: 353072) were seeded with Vero TMPRSS2 cells at 2.5x104 cells per well in growth media (DMEM + $10\%$ FBS + $1\%$ Penicillin/Streptomycin + 10 µg/mL Puromycin) and incubated at 37°C, $5\%$ CO2 until 80-$100\%$ confluent. A 10-fold dilution series of processed tissue sample was plated in growth media (DMEM + $2\%$ FBS + $1\%$ Penicillin/Streptomycin + 10 µg/mL Puromycin). Plates were incubated at 37°C, $5\%$ CO2 for 4 days. After incubation, the presence of cytopathic effects (CPE) was plated, and the TCID50 value per mL was calculated using the Read-*Muench formula* based on the tissue section weight, homogenization volume, and sample volume used in the assay assigned a calculated TCID50 titer per gram of tissue. Data analysis was performed in GraphPad Prism (Version 9.4.1).
## Quantitative RT-PCR assay for SARS-CoV-2 oral swabs
Post-challenge oral swabs were analyzed at the DUKE Human Vaccine Center IVQAC. The assay for SARS-CoV-2 quantitative Polymerase Chain Reaction (qPCR) detects total RNA using the WHO primer/probe set E_Sarbeco (Charité/Berlin). A QIAsymphony DSP, automated sample preparation platform along with a virus/pathogen DSP midi kit, were used to extract viral RNA from 800 μL of sample. A reverse primer specific to the envelope gene of SARS-CoV-2 (5′-ATA TTG CAG CAG TAC GCA CAC A-3′) was annealed and then reverse transcribed into cDNA using SuperScript™ III Reverse Transcriptase with RNase Out. The resulting cDNA was treated with RNase H (Thermo Fisher Scientific) and then added to a custom 4x TaqMan™ Gene Expression Master Mix containing primers and a fluorescently labeled hydrolysis probe specific for the envelope gene of SARS-CoV-2 (forward primer 5′-ACA GGT ACG TTA ATA GTT AAT AGC GT-3′, reverse primer 5′-ATA TTG CAG CAG TAC GCA CAC A-3′, probe 5′-6FAM/AC ACT AGC C/ZENA TCC TTA CTG CGC TTC G/IABkFQ-3′). SARS-CoV-2 RNA copies per reaction were interpolated using quantification cycle data and a serial dilution of a highly characterized custom DNA plasmid containing the SARS-CoV-2 envelope gene sequence. Mean RNA copies per milliliter were then calculated by applying the assay dilution factor with a limit of detection (LOD) approximately 62 RNA copies per mL of sample.
SARS-CoV-2 N gene subgenomic mRNA was measured by a one-step RT-qPCR. *To* generate standard curves, a SARS-CoV-2 E gene sgRNA sequence, including the 5′UTR leader sequence, transcriptional regulatory sequence, and the first 228 bp of E gene, was cloned into a pcDNA3.1 plasmid. *For* generating SARS-CoV-2 N gene sgRNA, the E gene was replaced with the first 227 bp of N gene. The respectively pcDNA3.1 plasmids were linearized, transcribed using MEGAscript T7 Transcription Kit, and purified with MEGAclear Transcription Clean-Up Kit. The purified RNA products were quantified on Nanodrop, serial diluted, and aliquoted as E sgRNA or N sgRNA standards. RNA extracted from samples or standards were then measured in Taqman custom gene expression assays using TaqMan Fast Virus 1-Step Master Mix and custom primers/probes targeting the E gene sgRNA (F primer: 5′ CGATCTCTTGTAGATCTGTTCTCE 3′; R primer: 5′ ATATTGCAGCAGTACGCACACA 3′; probe: 5′ FAM-ACACTAGCCATCCTTACTGCGCTTCG-BHQ1 3′) or the N gene sgRNA (F primer: 5′ CGATCTCTTGTAGATCTGTTCTC 3′; R primer: 5′ GGTGAACCAAGACGCAGTAT 3′; probe: 5′ FAM-TAACCAGAATGGAGAACGCAGTG GG-BHQ1 3′). Standard curves were used to calculate E sgRNA in copies per mL; the limit of detections (LOD) for N sgRNA assays were approximately 31 copies per mL of sample.
## Pathology
At necropsy, the left lung of each animal was collected and placed in $10\%$ neutral buffered formalin. Tissue sections were trimmed and processed to hematoxylin and eosin (H&E) stained slides and examined by a board-certified pathologist at Experimental Pathology laboratories, Inc. (EPL) (Sterling, VA). Findings were graded from one to five (1=minimal, 2=mild, 3=moderate, 4=marked, 5=severe).
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by BIOQUAL Institutional Animal Care and Use Committee, BIOQUAL, Inc.
## Author contributions
MB and ST conceptualized and designed all experiments. ED and LS generated the vaccine material. CM, MB, and AM performed the immunological experiments in Figures 2 – 4 and S1. MB and CM analyzed the data in Figures 2 – 4 and S1. MB and staff at Bioqual, Inc. analyzed the data in Figures 5 – 6. MB and wrote the original draft with the support of ST. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Authors MB, ED, LS, and ST are employed by Vaxart, Inc. Authors CM and AM were employeed by Vaxart, Inc. The authors declare that this study received funding from Vaxart, Inc. The funder had the following involvement in the study: The experiments were designed and analyzed by Vaxart, Inc using material developed by Vaxart, Inc.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1086035/full#supplementary-material
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|
---
title: Effects of roxadustat on anemia, iron metabolism, and lipid metabolism in patients
with non-dialysis chronic kidney disease
authors:
- Keiji Hirai
- Shohei Kaneko
- Saori Minato
- Katsunori Yanai
- Momoko Hirata
- Taisuke Kitano
- Kiyonori Ito
- Yuichiro Ueda
- Susumu Ookawara
- Yoshiyuki Morishita
journal: Frontiers in Medicine
year: 2023
pmcid: PMC9992186
doi: 10.3389/fmed.2023.1071342
license: CC BY 4.0
---
# Effects of roxadustat on anemia, iron metabolism, and lipid metabolism in patients with non-dialysis chronic kidney disease
## Abstract
### Background
We determined the effects of roxadustat on the values of anemia, iron metabolism, renal function, proteinuria, and lipid metabolism and identified the associated factors of the change in hemoglobin levels after roxadustat administration in non-dialysis chronic kidney disease (CKD) patients who were receiving an erythropoietin-stimulating agent (ESA).
### Methods
We conducted retrospective analysis of the changes in hemoglobin, serum ferritin, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride levels; transferrin saturation; the estimated glomerular filtration rate; and the urinary protein/creatinine ratio over 24 weeks after the change from an ESA to roxadustat in 50 patients with non-dialysis CKD and anemia (roxadustat group). Seventy-two patients with non-dialysis CKD and anemia who proceeded ESA therapy were used as the control (ESA) group.
### Results
We observed no significant between-group differences in clinical parameters at baseline except for the significantly lower hemoglobin concentration and lower proportion of diabetes mellitus in the roxadustat group. The hemoglobin concentration was significantly higher in the roxadustat group after 24 weeks (11.3 ± 1.2 versus 10.3 ± 1.0 g/dL; value of $p \leq 0.05$), whereas the transferrin saturation, ferritin concentration, estimated glomerular filtration rate, and urinary protein/creatinine ratio were not different between the two groups. TC (135.9 ± 40.0 versus 165.3 ± 38.4 mg/dL; value of $p \leq 0.05$), LDL-C (69.1 ± 28.3 versus 87.2 ± 31.5 mg/dL; value of $p \leq 0.05$), HDL-C (41.4 ± 13.5 versus 47.2 ± 15.3 mg/dL; value of $p \leq 0.05$), and triglyceride concentrations (101.5 ± 52.7 versus 141.6 ± 91.4 mg/dL, value of $p \leq 0.05$) were significantly lower in the roxadustat group compared with the ESA group at 24 weeks. Multiple linear regression analysis showed that the roxadustat dose at baseline (standard coefficient [β] = 0.280, value of $$p \leq 0.043$$) was correlated with the change in the hemoglobin levels during the first 4 weeks of roxadustat treatment, whereas age (β = 0.319, value of $$p \leq 0.017$$) and the roxadustat dose at 24 weeks (β = −0.347, value of $$p \leq 0.010$$) were correlated with the hemoglobin concentration after 24 weeks of roxadustat administration.
### Conclusion
Roxadustat can improve anemia and reduce serum cholesterol and triglyceride levels in non-dialysis CKD patients after the patients’ treatment was switched from an ESA without affecting renal function or proteinuria. These results indicate that roxadustat has superior effects to ESAs regarding anemia and lipid metabolism at the dose selected for the comparison in patients with non-dialysis CKD.
## Introduction
Anemia is a frequently observed coexisting disease in patients with non-dialysis chronic kidney disease (CKD), and it is associated with faster decline in renal function, worse quality of life, and greater risk of mortality (1–3). Therefore, optimal and effective treatments of anemia are desired for maintaining renal function and improving the value of life and the prognosis of patients with non-dialysis CKD.
Erythropoietin-stimulating agents (ESAs) have been widely and effectively used to sustain optimal hemoglobin levels in patients with non-dialysis CKD [4]. However, ESAs are administered subcutaneously, resulting in the patient’s pain.
Roxadustat is a novel and oral hypoxia-inducible factor (HIF) prolyl hydroxylase inhibitor which raises endogenous erythropoietin concentrations by stabilizing HIF [5], and it has been recently endorsed for the treatment of anemia in patients with non-dialysis CKD [6]. A phase 3 clinical trial revealed that roxadustat raised the hemoglobin concentration after patients with non-dialysis CKD were switched from an ESA [7]. However, there are few data regarding roxadustat’s effects on renal function and proteinuria. It is also not known which factors are associated with the change in hemoglobin levels after roxadustat treatment is initiated. Another phase 3 clinical trial reported that roxadustat reduced cholesterol and triglyceride concentrations in patients on hemodialysis [8]. However, the effect of roxadustat on lipid metabolism has not been fully investigated in patients with non-dialysis CKD. Therefore, we conducted the present study to investigate roxadustat’s effects on renal function, proteinuria, and lipid metabolism and determined the associated factors of the change in hemoglobin levels after roxadustat administration, in addition to roxadustat’s effects on anemia and iron metabolism in non-dialysis patients who were receiving ESA on a clinical practice.
## Ethical approval
Due to the retrospective nature of this study, the requirement for patients’ informed consent was not applicable. Information about the study was posted on display boards in our institution’s patient reception rooms informing patients of their right to withdraw from the study. The Ethics Committee of Saitama Medical Center, Jichi Medical University approved the study (RIN 22–004), which was performed in accord with the Declaration of Helsinki.
## Participants
The data of the patients who had been medicated in 2020–2021 at Saitama Medical Center, Jichi Medical University were collected. The study’s inclusion criteria were: (i) age more than 20 years, (ii) estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2 (i.e., CKD stage G3 to G5), and (iii) having been treated with an ESA for at least 48 weeks or with roxadustat for at least 24 weeks after being treated with an ESA for at least 24 weeks. The following exclusion criteria were applied; patients who were undergoing or who had undergone hemodialysis, peritoneal dialysis, renal transplantation, malignancy, or red blood cell transfusion, and those who had shown poor compliance with their roxadustat treatment.
## Study design
A total of 122 patients were enrolled in this retrospective comparative study. Figure 1 illustrates the study design. Each patient’s demographical and clinical data were gained from his or her medical charts. Fifty patients who had been treated with roxadustat for at least 24 weeks after being treated with an ESA for at least 24 weeks were assigned to the roxadustat group. The 72 patients who had been treated with an ESA for at least 48 weeks were assigned to the ESA group (control group). The date that was considered the baseline for each of the ESA-group patients was between December 1, 2020 to March 1, 2021, during which the patients of roxadustat-group started receiving roxadustat. Roxadustat was administered orally, and the roxadustat-group patients took it 3×/week. at bedtime. The ESAs were administered subcutaneously 1×/month or 1×/2 months on the day of a hospital visit. In both the roxadustat and ESA groups, we evaluated the changes in hemoglobin, serum ferritin, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride concentrations, transferrin saturation (TSAT), eGFR, and urinary protein/creatinine ratio from baseline to 24 weeks later. We conducted a multiple linear regression analysis to identify associated factors of the change in the hemoglobin levels during the first 4 weeks of roxadustat treatment and the hemoglobin concentration after 24 weeks of roxadustat treatment.
**Figure 1:** *Study design. eGFR, estimated glomerular filtration rate; ESA, erythropoiesis-stimulating agent.*
## Laboratory methods
The Saitama Medical Center’s clinical laboratory measured the patients’ urine and blood parameters. The erythropoietin resistance index (ERI) was determined as the mean weekly dosage of epoetin (IU)/body mass (kg)/hemoglobin (g/dL) [9]. For the conversion of ESA doses to epoetin doses, ratios of 225:1 and 200:1 were used for epoetin beta pegol and darbepoetin alfa, respectively [10].
## Statistics
The data of the continuous variables were shown as the mean plus/minus standard deviation when they were normally distributing; those of the not normally distributing continuous variables were shown as the median and interquartile range. The data of the categorical variables were presented as numbers and percentages. We compared the roxadustat and control groups’ clinical data by performing Student’s t-test (for normally distributing data) and the Mann–Whitney U-test (for not normally distributing data). Fisher’s exact test was used to compare the groups’ component ratios. A repeated-measures analysis of variance accompanied by Tukey’s test was conducted to compare the serial measurements within each of the two groups. We performed a linear regression analysis to determine the change rate in the patients’ eGFR and calculated it as the slope (monthly) for each patient before the baseline and after the baseline. We used the paired t-test to compare the eGFR change rate before and after the baseline in the roxadustat and ESA groups. In a multiple linear regression analysis, we included the parameters that were significantly correlated with the change in hemoglobin levels during the first 4 weeks of roxadustat treatment and the hemoglobin concentration after 24 weeks of roxadustat treatment, in order to identify the variables that were independently correlated with the change in hemoglobin levels during the first 4 weeks of roxadustat treatment and the hemoglobin concentration after 24 weeks of roxadustat treatment. Probability (p)-values <0.05 were accepted as significant. All of the statistical analyses were conducted using JMP ver. 11 (SAS, Cary, NC, United States).
## Patient characteristics
In total, 233 patients with non-dialysis CKD who were receiving an ESA were identified. Of these 233 patients, 87 were treated with roxadustat after being treated with an ESA, and 146 were treated with only an ESA. Twenty-one of the patients treated with roxadustat did not fulfill the criteria of inclusion. Sixteen patients met at least one of the criteria of exclusion. The roxadustat group was thus comprised of 50 patients. The reasons for switching an ESA to roxadustat were resistance to ESA treatment in 9 patients and injection pain in 41 patients. Among the ESA-alone patients, 42 did not fulfill the criteria of inclusion, and 32 patients met at least one of the criteria of exclusion; the ESA group was thus the remaining 72 patients (Figure 2). Therefore, we analyzed the data of 122 patients (72 men and 50 women, mean age = 74.0 ± 11.3 years, mean body mass index = 23.9 ± 4.4 kg/m2). The mean baseline eGFR was 16.1 ± 8.8 mL/min/1.73 m2, and their CKD stages were as follows: stage G3a, 2 ($1.6\%$) patients; stage G3b, 6 ($4.9\%$) patients; stage G4, 50 ($41.0\%$) patients; and stage G5, 64 ($52.5\%$) patients. The mean values of hemoglobin and ferritin concentrations and TSAT at baseline were 10.2 ± 1.0 g/dL, 125.6 ± 115.1 ng/mL, and 32.7 ± $12.2\%$, respectively. Sixty-seven patients had received darbepoetin alfa and 55 had received epoetin beta pegol every 1 or 2 months. The mean ESA dose was 2,852 ± 2,343 IU/wk., and the mean ERI was 4.9 ± 4.2. A history of diabetes mellitus was present in $45.1\%$, myocardial infarction in $18.9\%$, and stroke in $7.4\%$ of the participants. The percentages of participants receiving each medicine were as follows: calcium-containing (based) phosphate binder, $10.7\%$; calcium-free phosphate binder, $5.7\%$; vitamin D analog, $21.3\%$; iron supplement, $15.6\%$; zinc supplement, $12.3\%$; statin, $56.6\%$; and ezetimibe, $5.7\%$. Iron supplementation was newly started after roxadustat initiation in 10 patients in the roxadustat group, but iron supplementation was not changed in the ESA group because iron stores were sufficient (ferritin level ≥ 100 μg/l or TSAT ≥$20\%$) [4]. Table 1 summarizes the patient characteristics and medications at baseline in both groups; only the proportion of patients with diabetes mellitus and the hemoglobin concentration differed significantly between the roxadustat and ESA groups.
**Figure 2:** *Patient flow diagram. ESA, erythropoiesis-stimulating agent.* TABLE_PLACEHOLDER:Table 1
## Changes in ESA and roxadustat doses
Figure 3A depicts the changes in the doses of ESA and roxadustat in the two groups. In the ESA group, the ESA dose increased significantly from 2,882 ± 2,114 IU/wk. at baseline to 3,497 ± 2,280 IU/wk. at 16 weeks (value of $p \leq 0.05$) and 3,726 ± 2,273 IU/week. at 24 weeks (value of $p \leq 0.05$). In the roxadustat group, the roxadustat dose decreased significantly from 224 ± 60 mg/wk. at baseline to 171 ± 95 mg/wk. at 8 weeks (value of $p \leq 0.05$), 154 ± 86 mg/wk. at 16 weeks (value of $p \leq 0.05$), and 151 ± 110 mg/wk. at 24 weeks (value of $p \leq 0.05$).
**Figure 3:** *Anemia and iron metabolism. (A) Changes in the ESA (IU/wk) and roxadustat (mg/wk) doses administered during the study. (B) Changes in the hemoglobin levels in the roxadustat and ESA groups. (C) Changes in the ferritin levels in the roxadustat and ESA groups. (D) Changes in the transferrin saturation in the roxadustat and ESA groups. ESA, erythropoiesis-stimulating agent; IU, international units. §, value of p < 0.05 versus − 24 weeks, −16 weeks, and baseline; §§, value of p < 0.05 versus − 24 weeks, −16 weeks, −8 weeks, and baseline. †, value of p < 0.05 versus baseline; ††, value of p < 0.05 versus baseline and 4 weeks. *, value of p < 0.05 versus the ESA group.*
## Roxadustat’s effect on anemia
The roxadustat-treated patients’ hemoglobin concentrations increased significantly from 9.8 ± 1.0 g/dL at baseline to 11.2 ± 1.3 g/dL at 4 weeks (value of $p \leq 0.05$), 11.7 ± 1.3 g/dL at 8 weeks (value of $p \leq 0.05$), 11.5 ± 1.2 g/dL at 16 weeks (value of $p \leq 0.05$), and 11.3 ± 1.2 g/dL at 24 weeks (value of $p \leq 0.05$), whereas the ESA group’s hemoglobin concentrations did not change significantly over the study term. As shown in Figure 3B, the roxadustat group’s baseline hemoglobin concentration was lower significantly than that of the ESA group (9.8 ± 1.0 g/dL versus 10.4 ± 1.0 g/dL, value of $p \leq 0.05$), but the roxadustat group’s value was higher significantly compared with the ESA group’s at 8 weeks (11.7 ± 1.3 g/dL versus 10.3 ± 1.0 g/dL, value of $p \leq 0.05$), 16 weeks (11.5 ± 1.2 g/dL versus 10.2 ± 0.9 g/dL, value of $p \leq 0.05$), and 24 weeks (11.3 ± 1.2 g/dL versus 10.3 ± 1.0 g/dL, value of $p \leq 0.05$).
## Factors associated with the change in hemoglobin levels during first 4 weeks of roxadustat administration
According to the simple linear regression analyses, the change in the patients’ hemoglobin levels during first 4 weeks of roxadustat administration was significantly correlated with statin use and the roxadustat dose at baseline. The multiple linear regression analysis using the variables that correlated significantly with the change in hemoglobin levels during first 4 weeks of roxadustat administration in the simple linear regression analyses (Table 2) revealed that only the roxadustat dose at baseline (standard coefficient [β] = 0.280, value of $$p \leq 0.043$$) was independently correlated with the change in hemoglobin levels during the first 4 weeks of roxadustat administration.
**Table 2**
| Variables | Simple linear regression analysis | Simple linear regression analysis.1 | Multiple linear regression analysis | Multiple linear regression analysis.1 |
| --- | --- | --- | --- | --- |
| Variables | Standard coefficient | p value | Standard coefficient | p value |
| Age (years) | 0.222 | 0.12 | | |
| Male sex (yes vs. no) | 0.149 | 0.30 | | |
| Body mass index (kg/m2) | 0.060 | 0.68 | | |
| Systolic blood pressure (mmHg) | 0.011 | 0.94 | | |
| Diastolic blood pressure (mmHg) | 0.010 | 0.94 | | |
| Diabetes mellitus (yes vs. no) | −0.159 | 0.27 | | |
| Hypertension (yes vs. no) | −0.010 | 0.95 | | |
| Previous myocardial infarction (yes vs. no) | 0.031 | 0.83 | | |
| Previous stroke (yes vs. no) | 0.030 | 0.83 | | |
| Calcium-containing phosphate binder use (yes vs. no) | −0.032 | 0.83 | | |
| Calcium-free phosphate binder use (yes vs. no) | −0.156 | 0.28 | | |
| Vitamin D analog use (yes vs. no) | −0.165 | 0.25 | | |
| Iron supplement use (yes vs. no) | 0.159 | 0.27 | | |
| Zinc supplement use (yes vs. no) | 0.194 | 0.18 | | |
| Statin use (yes vs. no) | 0.290 | 0.041* | 0.249 | 0.07 |
| Ezetimibe use (yes vs. no) | 0.200 | 0.16 | | |
| Estimated glomerular filtration rate at baseline (mL/min/1.73 m2) | −0.084 | 0.56 | | |
| Urinary protein excretion at baseline (g/gCr) | 0.239 | 0.10 | | |
| Hemoglobin A1c at baseline (%) | −0.196 | 0.20 | | |
| Total cholesterol at baseline (mg/dL) | −0.053 | 0.72 | | |
| Low-density lipoprotein-cholesterol at baseline (mg/dL) | −0.037 | 0.80 | | |
| High-density lipoprotein-cholesterol at baseline (mg/dL) | −0.008 | 0.96 | | |
| Triglyceride at baseline (mg/dL) | −0.120 | 0.41 | | |
| Uric acid at baseline (mg/dL) | 0.132 | 0.36 | | |
| Albumin at baseline (g/dL) | −0.171 | 0.24 | | |
| Hemoglobin at baseline (g/dL) | −0.226 | 0.11 | | |
| Sodium at baseline (mEq/L) | −0.108 | 0.46 | | |
| Potassium at baseline (mEq/L) | 0.044 | 0.76 | | |
| Chloride at baseline (mEq/L) | 0.150 | 0.30 | | |
| Total calcium at baseline (mg/dL) | −0.162 | 0.26 | | |
| Phosphorus at baseline (mg/dL) | −0.195 | 0.17 | | |
| Magnesium at baseline (mg/dL) | −0.261 | 0.07 | | |
| Brain natriuretic peptide at baseline (pg/mL) | −0.007 | 0.97 | | |
| C-reactive protein at baseline (mg/dL) | 0.186 | 0.20 | | |
| Ferritin at baseline (ng/mL) | 0.226 | 0.13 | | |
| Transferrin saturation at baseline (%) | 0.014 | 0.93 | | |
| ESA (epoetin beta pegol vs. darbepoetin alfa) | −0.034 | 0.82 | | |
| ESA dose at baseline (IU/week) | 0.079 | 0.58 | | |
| Erythropoietin resistance index at baseline (IU/week/kg/(g/dL) | 0.105 | 0.47 | | |
| Roxadustat dose at baseline (mg/week) | 0.316 | 0.025* | 0.280 | 0.043* |
## Factors associated with the hemoglobin concentration after 24 weeks of roxadustat administration
Simple linear regression analyses revealed that the hemoglobin concentration after 24 weeks of roxadustat administration was significantly correlated with age and the roxadustat dose at 24 weeks. With these two variables, we conducted a multiple linear regression analysis (Table 3), which revealed that age (β = 0.319, value of $$p \leq 0.017$$) and the roxadustat dose at 24 weeks (β = −0.347, value of $$p \leq 0.010$$) were each independently correlated with the hemoglobin concentration after 24 weeks of roxadustat administration.
**Table 3**
| Variables | Simple linear regression analysis | Simple linear regression analysis.1 | Multiple linear regression analysis | Multiple linear regression analysis.1 |
| --- | --- | --- | --- | --- |
| Variables | Standard coefficient | p value | Standard coefficient | p value |
| Age (years) | 0.308 | 0.030* | 0.319 | 0.017* |
| Male sex (yes vs. no) | −0.112 | 0.44 | | |
| Body mass index (kg/m2) | 0.041 | 0.78 | | |
| Systolic blood pressure (mmHg) | −0.069 | 0.64 | | |
| Diastolic blood pressure (mmHg) | −0.165 | 0.26 | | |
| Diabetes mellitus (yes vs. no) | 0.060 | 0.68 | | |
| Hypertension (yes vs. no) | 0.118 | 0.42 | | |
| Previous myocardial infarction (yes vs. no) | −0.012 | 0.93 | | |
| Previous stroke (yes vs. no) | 0.011 | 0.94 | | |
| Calcium-containing phosphate binder use (yes vs. no) | −0.265 | 0.06 | | |
| Calcium-free phosphate binder use (yes vs. no) | 0.177 | 0.22 | | |
| Vitamin D analog use (yes vs. no) | 0.129 | 0.37 | | |
| Iron supplement use (yes vs. no) | 0.136 | 0.35 | | |
| Zinc supplement use (yes vs. no) | 0.010 | 0.94 | | |
| Statin use (yes vs. no) | 0.208 | 0.15 | | |
| Ezetimibe use (yes vs. no) | −0.139 | 0.33 | | |
| Estimated glomerular filtration rate at baseline (mL/min/1.73 m2) | −0.005 | 0.97 | | |
| Urinary protein excretion at baseline (g/gCr) | −0.044 | 0.76 | | |
| Hemoglobin A1c at baseline (%) | 0.204 | 0.18 | | |
| Total cholesterol at baseline (mg/dL) | −0.184 | 0.20 | | |
| Low-density lipoprotein-cholesterol at baseline (mg/dL) | −0.251 | 0.08 | | |
| High-density lipoprotein-cholesterol at baseline (mg/dL) | 0.065 | 0.66 | | |
| Triglyceride at baseline (mg/dL) | −0.088 | 0.55 | | |
| Uric acid at baseline (mg/dL) | 0.137 | 0.34 | | |
| Albumin at baseline (g/dL) | −0.025 | 0.86 | | |
| Hemoglobin at baseline (g/dL) | 0.206 | 0.15 | | |
| Sodium at baseline (mEq/L) | −0.001 | 0.99 | | |
| Potassium at baseline (mEq/L) | −0.173 | 0.23 | | |
| Chloride at baseline (mEq/L) | −0.143 | 0.32 | | |
| Total calcium at baseline (mg/dL) | 0.204 | 0.16 | | |
| Phosphorus at baseline (mg/dL) | −0.031 | 0.83 | | |
| Magnesium at baseline (mg/dL) | 0.140 | 0.34 | | |
| Brain natriuretic peptide at baseline (pg/mL) | −0.251 | 0.17 | | |
| C-reactive protein at baseline (mg/dL) | 0.121 | 0.41 | | |
| Ferritin at baseline (ng/mL) | 0.120 | 0.42 | | |
| Transferrin saturation at baseline (%) | 0.091 | 0.55 | | |
| ESA (epoetin beta pegol vs. darbepoetin alfa) | 0.068 | 0.46 | | |
| ESA dose at baseline (IU/week) | −0.052 | 0.72 | | |
| Erythropoietin resistance index at baseline (IU/week/kg/(g/dL) | 0.080 | 0.58 | | |
| Roxadustat dose at 24 weeks (mg/week) | −0.336 | 0.017* | −0.347 | 0.010* |
| Average dose of roxadustat during 24 weeks (mg/week) | −0.111 | 0.44 | | |
## Roxadustat’s effect on iron metabolism
The roxadustat group’s ferritin concentration decreased significantly from 127.7 ± 117.3 ng/mL at baseline to 67.0 ± 86.5 ng/mL at 4 weeks (value of $p \leq 0.05$), 66.6 ± 87.6 ng/mL at 8 weeks (value of $p \leq 0.05$), and 76.9 ± 89.4 ng/mL at 16 weeks (value of $p \leq 0.05$), but it was not significantly different from baseline at 24 weeks. The ferritin concentrations in the ESA group at 8, 16, and 24 weeks were not significantly different from that at baseline. The roxadustat group’s ferritin concentration was significantly lower compared with the ESA group’s at 8 weeks (66.6 ± 87.6 ng/mL versus 118.4 ± 90.8 ng/mL, value of $p \leq 0.05$) and 16 weeks (76.9 ± 89.4 ng/mL versus 118.0 ± 99.7 ng/mL, value of $p \leq 0.05$; Figure 3C).
The roxadustat group’s TSAT decreased significantly from 33.0 ± 14.8 ng/mL at baseline to 23.1 ± 16.8 ng/mL at 4 weeks (value of $p \leq 0.05$) and 25.5 ± 17.4 ng/mL at 8 weeks (value of $p \leq 0.05$), but it did not significantly differ from baseline at 16 and 24 weeks. TSAT in the ESA group at 8, 16, and 24 weeks was not significantly different from that at baseline. The roxadustat group’s TSAT was significantly lower compared with the ESA group’s at 8 weeks (25.5 ± 17.4 ng/mL versus 32.6 ± 10.2 ng/mL, value of $p \leq 0.05$) and 16 weeks (26.9 ± 14.6 ng/mL versus 32.1 ± 12.4 ng/mL, value of $p \leq 0.05$; Figure 3D).
## Roxadustat’s effects on renal function and proteinuria
The change rate of eGFR did not differ significantly between before baseline and after baseline in both groups (Figure 4A). The urinary protein/creatinine ratio did not significantly change over the study term in either group (Figure 4B).
**Figure 4:** *Renal function and proteinuria. (A) Change rates in the eGFR before and after baseline in the roxadustat and ESA groups. (B) Changes in the urinary protein-to-creatinine ratio in the roxadustat and ESA groups. eGFR, estimated glomerular filtration rate; ESA, erythropoiesis-stimulating agent; NS, not significant.*
## Roxadustat’s effect on lipid metabolism
The roxadustat group’s TC concentration decreased significantly from 170.5 ± 32.4 mg/dL at baseline to 127.1 ± 34.9 mg/dL at 4 weeks (value of $p \leq 0.05$), 135.5 ± 41.7 mg/dL at 8 weeks (value of $p \leq 0.05$), 135.8 ± 39.3 mg/dL at 16 weeks (value of $p \leq 0.05$), and 135.9 ± 40.0 mg/dL at 24 weeks (value of $p \leq 0.05$), whereas the ESA group’s values did not change significantly over the study term. The roxadustat group’s TC concentration was significantly lower compared with the ESA group’s at 8 weeks (135.5 ± 41.7 mg/dL versus 165.9 ± 40.2 mg/dL, value of $p \leq 0.05$), 16 weeks (135.8 ± 39.3 mg/dL versus 161.3 ± 37.4 mg/dL, value of $p \leq 0.05$), and 24 weeks (135.9 ± 40.0 mg/dL versus 165.3 ± 38.4 mg/dL, value of $p \leq 0.05$; Figure 5A).
**Figure 5:** *Lipid metabolism. (A) Changes in total cholesterol levels in the roxadustat and ESA groups. (B) Changes in LDL-cholesterol levels in the roxadustat and ESA groups. (C) Changes in HDL-cholesterol levels in the roxadustat and ESA groups. (D) Changes in triglyceride levels in the roxadustat and ESA groups. ESA, erythropoiesis-stimulating agent; HDL, high-density lipoprotein; LDL, low-density lipoprotein. *, value of p < 0.05 versus the ESA group; †, value of p < 0.05 versus baseline.*
The roxadustat group’s LDL-C concentration decreased significantly from 88.1 ± 22.6 mg/dL at baseline to 62.3 ± 21.8 mg/dL at 4 weeks (value of $p \leq 0.05$), 67.9 ± 28.5 mg/dL at 8 weeks (value of $p \leq 0.05$), 68.3 ± 27.0 mg/dL at 16 weeks (value of $p \leq 0.05$), and 69.1 ± 28.3 mg/dL at 24 weeks (value of $p \leq 0.05$), whereas the ESA group’s values did not change significantly over the study term. The roxadustat group’s LDL-C concentration was significantly lower compared with the ESA group’s at 8 weeks (67.9 ± 28.5 mg/dL versus 86.0 ± 30.7 mg/dL, value of $p \leq 0.05$), 16 weeks (68.3 ± 27.0 mg/dL versus 83.6 ± 28.5 mg/dL, value of $p \leq 0.05$), and 24 weeks (69.1 ± 28.3 mg/dL versus 87.2 ± 31.5 mg/dL, value of $p \leq 0.05$; Figure 5B).
The roxadustat group’s HDL-C concentration decreased significantly from 50.5 ± 17.0 mg/dL at baseline to 38.5 ± 13.0 mg/dL at 4 weeks (value of $p \leq 0.05$), 40.3 ± 14.5 mg/dL at 8 weeks (value of $p \leq 0.05$), 41.1 ± 14.0 mg/dL at 16 weeks (value of $p \leq 0.05$), and 41.4 ± 13.5 mg/dL at 24 weeks (value of $p \leq 0.05$), whereas the ESA group’s values did not change significantly over the study term. The roxadustat group’s HDL-C concentration was significantly lower compared with the ESA group’s at 8 weeks (40.3 ± 14.5 mg/dL versus 48.7 ± 16.8 mg/dL, value of $p \leq 0.05$), 16 weeks (41.1 ± 14.0 mg/dL versus 47.2 ± 16.7 mg/dL, value of $p \leq 0.05$), and 24 weeks (41.4 ± 13.5 mg/dL versus 47.2 ± 15.3 mg/dL, value of $p \leq 0.05$; Figure 5C).
The roxadustat group’s triglyceride concentration decreased significantly from 132.2 ± 78.9 mg/dL at baseline to 101.5 ± 52.7 mg/dL at 24 weeks (value of $p \leq 0.05$), whereas the ESA group’s values did not change significantly over the study term. The roxadustat group’s triglyceride concentration was significantly lower compared with the ESA group’s at 8 weeks (109.6 ± 62.6 mg/dL versus 149.6 ± 182.7 mg/dL, value of $p \leq 0.05$), 16 weeks (108.0 ± 67.1 mg/dL versus 135.1 ± 108.2 mg/dL, value of $p \leq 0.05$), and 24 weeks (101.5 ± 52.7 mg/dL versus 141.6 ± 91.4 mg/dL, value of $p \leq 0.05$; Figure 5D).
## Changes of other clinical parameters and clinical adverse effects
We detected no significant changes over the study term in the patients’ body mass, systolic and diastolic blood pressure value, albumin, hemoglobin A1c, uric acid, sodium, potassium, chloride, total calcium, phosphorus, magnesium, brain natriuretic peptide, or C-reactive protein (data not shown). Ten of the patients in the roxadustat group experienced an adverse effect: nausea, $$n = 3$$; dizziness, $$n = 2$$; deep vein thrombosis, $$n = 2$$; diarrhea, $$n = 1$$; anorexia, $$n = 1$$; hepatotoxicity, $$n = 1$.$ The roxadustat treatment of these patients was thus discontinued. None of the patients in the ESA group experienced any adverse effects during the study period.
## Discussion
The results of the present study demonstrated that roxadustat improved anemia and reduced serum cholesterol and triglyceride levels in patients who were not undergoing renal replacement therapy after changing from ESA treatment. Our analyses also revealed that the roxadustat dose at baseline was correlated with the change in hemoglobin levels during the first 4 weeks of roxadustat administration, whereas age and the roxadustat dose at 24 weeks were correlated with the hemoglobin concentration after 24 weeks of roxadustat administration. However, roxadustat did not improve iron metabolism or affect renal function or proteinuria in these patients.
Roxadustat is a HIF prolyl hydroxylase inhibitor which stabilizes HIF and stimulates the expression of erythropoiesis-related genes. It increases the erythropoietin concentration within physiologic range in the liver and kidneys, thereby increasing or maintaining the hemoglobin concentration in CKD patients with anemia [5]. A recent randomized clinical trial revealed that roxadustat sustains the hemoglobin concentration in patients with non-dialysis CKD who were switched from an ESA [7]. In the present study, roxadustat increased and stabilized the hemoglobin concentration after the patients’ treatment was switched from an ESA in a similar patient group. The required dose of ESA continued to increase in the ESA group during the study period but the dose of roxadustat did not require to be increased in the roxadustat group after patients were switched from their ESA. These findings indicate that roxadustat improves and helps stabilize hemoglobin concentrations in patients with non-dialysis CKD plus anemia who were receiving an ESA on a clinical practice. In the present study, only the initial roxadustat dose was associated with the change in hemoglobin levels during the first 4 weeks of roxadustat administration. A phase 2 clinical study reported that roxadustat increased hemoglobin concentrations in a dose-dependent manner in patients with non-dialysis CKD [11]. These findings indicate that roxadustat dose-dependently improves anemia in patients with non-dialysis CKD regardless of patient demographic and clinical characteristics. In the present study, age and the roxadustat dose at 24 weeks were associated with the hemoglobin concentration after 24 weeks of roxadustat administration. It has been demonstrated that age was correlated positively with the hemoglobin concentration in patients on hemodialysis receiving ESA [12]. In our study, age was positively correlated with the hemoglobin concentration in patients with non-dialysis CKD who were taking roxadustat. These results suggest that both ESA and roxadustat might be more effective in older patients than in younger patients. Several study reported that the immunohistochemical expression of erythropoietin receptor was higher in older patients than in younger patients [13, 14]. This might explain the findings that ESA and roxadustat were more effective in older patients than in younger patients. It has been reported that the dose of ESA was correlated negatively with the hemoglobin concentration in patients on hemodialysis [15]. In our study, the roxadustat dose was negatively correlated with the hemoglobin concentration in patients with non-dialysis CKD. These negative correlations between the doses of ESA and roxadustat and the hemoglobin concentration might be because patients with severe anemia were treated with higher doses of ESA or roxadustat. Further research is necessary to elucidate the relationship between the roxadustat dose and hemoglobin concentration in patients with non-dialysis CKD.
Roxadustat was indicated to improve patient’s iron metabolism via multi-pathways. It increases ion absorption from the gastrointestinal tract, enhances release of iron from the hepatocytes, and increases serum total binding capacity of iron [5]. A recent phase 3 clinical study reported that roxadustat reduced ferritin levels in serum and TSAT in patients with non-dialysis CKD [16]. However, in the present study, we detected no significant differences in ferritin levels and TSAT between the roxadustat and ESA groups, although ferritin levels and TSAT at 8 and 16 weeks were lower significantly in the roxadustat group than in the ESA group. Several reasons can be considered to explain such differences between our present findings and those of earlier studies. First, the roxadustat dose was reduced after treatment initiation because anemia was improved, which might have contributed to the increases of ferritin levels and TSAT in the late phase of the study term. Second, iron supplementation was started in 10 patients after roxadustat initiation because of the improvement of iron metabolism, which might have been responsible for the increases of ferritin levels and TSAT in the late phase of the study term. Further research is necessary to elucidate roxadustat’s effect on iron metabolism in patients with non-dialysis CKD who switched from an ESA on a clinical practice.
Roxadustat was demonstrated to reduce serum cholesterol levels in a dose-dependent manner regardless of baseline statin use [17]. A recent phase 3 clinical study reported that roxadustat reduced serum TC, LDL-C, HDL-C, and triglyceride concentrations in patients on hemodialysis [8]. In the present study, roxadustat reduced serum TC, LDL-C, HDL-C, and triglyceride concentrations in patients with non-dialysis CKD. These results may indicate that roxadustat reduces serum cholesterol and triglyceride levels in both dialysis-dependent and dialysis-independent patients with CKD. HIF-1 accelerates degradation of HMG-CoA reductase in the liver through activation of insulin-induced gene 2 transcription, leading to reduced cholesterol synthesis [18]. HIF-2 inhibits ATP-binding cassette transporter A1 gene expression, which attenuates the formation of HDL-C [19]. HIF-1 also stimulates lipin 1 gene expression, which contributes to triglyceride accumulation in cells [20]. Therefore, roxadustat stabilizes HIF and then may reduce TC, LDL-C, HDL-C, and triglyceride levels by these mechanisms.
Roxadustat has been reported to have several clinical side effects including digestive tract disorders, dizziness, and thrombosis [21, 22]. Among the present study’s patients treated with roxadustat, five patients had gastrointestinal symptoms, two patients reported dizziness, and two patients developed deep vein thrombosis. Phase 2 and 3 clinical trials involving non-dialysis CKD patients showed that the rates of digestive tract symptoms, dizziness, and deep vein thrombosis were 29.7, 6.2, and $1.2\%$, respectively [21, 22], which are compatible with our study findings. We also observed that roxadustat did not influence the renal function or the proteinuria of patients with non-dialysis CKD. These results indicate that roxadustat can be administered safely in patients with non-dialysis CKD.
The present study had several advantages over the previously reported study [7]. First, we evaluated roxadustat’s effects on renal function, proteinuria, and lipid metabolism. Second, we also identified the associated factors of the change in hemoglobin levels after roxadustat treatment in patients with non-dialysis CKD who were switched from an ESA. Therefore, our findings may be valuable for further research on the application of roxadustat in patients with non-dialysis CKD.
The present study was different from our previous study in two aspects [23]. First, in our previous study, we assessed roxadustat’s effects on iron metabolism, anemia, residual renal function, and peritoneal membrane function in peritoneal dialysis patients. By contrast, in the present study, roxadustat’s effects on anemia, iron metabolism, renal function, proteinuria, and lipid metabolism were investigated in non-dialysis CKD patients. Therefore, the study population was totally different between our previous and present studies. Second, in our previous study, we assessed the factors that might be associated with the roxadustat dose in peritoneal dialysis patients. By contrast, in the present study, we assessed the factors that might be associated with the change in hemoglobin levels after roxadustat administration in non-dialysis CKD patients. Therefore, the clinical outcome was fundamentally different between our previous and present studies. Therefore, our present findings could be used in future studies regarding roxadustat’s effects on anemia, iron metabolism, renal function, proteinuria, and lipid metabolism and the factors that may be associated with the improvement in anemia in patients with non-dialysis CKD.
Several study limitations should be addressed. First, this study was a retrospective and observational study; therefore, sample selection bias could not have been completely avoided. Second, the patients were recruited from a single institution, which limits the results’ external validness. Third, the number of patients ($$n = 122$$) was small, and this decreases the statistical power for detecting between-group differences. Fourth, in Japan, roxadustat prescription is not restricted by any medical insurances if there are approved indications. In this study, an ESA was switched to roxadustat based on physician’s judgment or patient’s preference. However, there were significant differences in baseline characteristics including percentage of diabetes mellitus and hemoglobin concentration between the two groups. The lack of similarity regarding patients’ characteristics between the two groups might have affected the study results. Fifth, the long-term effects of roxadustat on renal function, proteinuria, and lipid metabolism were not examined. Large, long-term, prospective, randomized studies are necessary to establish roxadustat’s effects on renal function, proteinuria, and lipid metabolism and to decide which factors are associated with the change in hemoglobin levels after roxadustat administration in patients with non-dialysis CKD.
In conclusion, roxadustat improved anemia and reduced serum cholesterol and triglyceride levels in patients who were not undergoing renal replacement therapy after switching from an ESA without affecting renal function or proteinuria. The results obtained in this study indicate that roxadustat might be superior to ESAs regarding improvement effects on anemia and lipid metabolism in patients with non-dialysis CKD.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of Saitama Medical Center, Jichi Medical University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
KH and SK conceived of the study and designed the study. SM, KY, MH, and TK collected the data. KI and YU performed the statistical analysis. KH wrote the manuscript’s first draft. SO conducted critical revisions. YM endorsed the manuscript’s final version. All authors contributed to the study and manuscript and approved the manuscript’s final version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Associations of maternal and placental extracellular vesicle miRNA with preeclampsia
authors:
- Anat Aharon
- Annie Rebibo-Sabbah
- Rawan Sayed Ahmad
- Ayelet Dangot
- Tali Hana Bar-Lev
- Benjamin Brenner
- Adi Halberthal Cohen
- Chen Ben David
- Zeev Weiner
- Ido Solt
journal: Frontiers in Cell and Developmental Biology
year: 2023
pmcid: PMC9992195
doi: 10.3389/fcell.2023.1080419
license: CC BY 4.0
---
# Associations of maternal and placental extracellular vesicle miRNA with preeclampsia
## Abstract
Introduction: Gestational vascular complications (GVCs), including gestational hypertension and preeclampsia, are leading causes of maternal morbidity and mortality. Elevated levels of extracellular vesicles (EVs), in GVC have been linked to vascular injury. This study aims to characterize placental and circulating EV miRNA in GVCs, and explores the involvement of EV-miRNA in GVC, and whether they may be used to distinguish between placental and maternal pathologies.
Methods: Blood samples were obtained from 15 non-pregnant (NP), 18 healthy-pregnant (HP), and 23 women with GVC during the third trimester. Placental sections were obtained after caesarian section. Platelet-poor-plasma (PPP) and EV pellets were characterized: EV size/concentration, protein content and miRNA expression were measured by nanoparticle tracking analysis, western blot, nano-string technology and RT-PCR. The effects of EVs on trophoblasts and EC miRNA expression were evaluated.
Results: Higher EVs concentrations were observed in HP-PPP and GVC-PPP ($p \leq 0.0001$) compared to the NP-PPP. The concentration of large EVs (>100 nm) was higher in PPP and EV pellets of HP and GVC compared to the NP group. EV pellets of pregnant women demonstrated lower expression of exosomal markers CD63/CD81 compared to NP-EVs. GVC-EVs expressed more human placental lactogen (hPL) hormone than HP-EVs, reflecting their placental origin. Screening of miRNAs in EV pellets and in PPP identified certain miRNAs that were highly expressed only in EVs pellets of the HP ($13\%$) and GVC groups ($15\%$), but not in the NP group. Differences were detected in the expression of hsa-miR-16-5p, hsa-miR-210, and hsa-miR-29b-3p. The expression of hsa-miR-16-5p and hsa-miR-210 was low in EV pellets obtained from NP, higher in HP-EVs, and significantly lower in GVC-EVs. Except for hsa-miR-29b-3p, which was upregulated in GVC, no significant differences were found in the levels of other miRNAs in placental sections. Exposure to GVC-EVs resulted in higher expression of hsa-miR-29b-3p compared to cells exposed to HP-EVs in villous trophoblasts, but not in EC.
Conclusion: Expression of hsa-miR-16-5p and hsa-miR-210 reflects maternal pathophysiological status, while hsa-miR-29b-3p reflects placental status. These findings suggest that EV-miRNA are involved in GVC, and that they may be used to distinguish between pathologies of placental and maternal origins in preeclampsia.
## 1 Introduction
Gestational vascular complications (GVC) are a group of pregnancy complications or syndromes associated with placental dysfunction, deficient utero-placental circulation, or intervillous and spiral vessel thrombosis. Due to various possible etiologies, a strict definition of GVC has not been established to date, but generally includes medical syndromes such as preeclampsia, intrauterine growth restriction, placental abruption, preterm labor, preterm pre-labor rupture of membranes, fetal demise/stillbirth, and recurrent abortions (Jung et al., 2022). GVCs are associated with significant maternal morbidity and mortality (Steegers et al., 2010; ACOG Practice Bulletin No, 2019). Clinical manifestations of GVCs may have a delayed presentation, only after a primary insult of abnormal trophoblast invasion in the uterine spiral arteries during early gestation. For example, inherited and acquired thrombophilias are possible thrombogenic etiologies of GVCs (Brosens et al., 2011), while abnormal placentation has been identified as the initial trigger of maternal endothelial dysfunction underlying preeclampsia (PE) (Gilani et al., 2016). While all possible etiologies of these obstetric syndromes have yet to be determined, they all present with a characteristic long preclinical stage, placental as well as fetal involvement, adaptive clinical manifestations, gene–environment interactions, and gene–gene interactions involving both maternal and fetal genotypes (Brosens et al., 2011; Jung et al., 2022).
Extracellular vesicles (EVs) have been considered as potential biomarkers of vascular injury, pro-thrombotic states (Aharon et al., 2009), and pro-inflammatory conditions related to PE and pregnancy-induced hypertension PIH (Germain et al., 2007; Kohli et al., 2016).
Small and large EVs (exosomes <100 nm and microvesicles >1 µm, respectively, per MISEV18 guidelines) (Thery et al., 2018), function as messengers promoting intercellular crosstalk (van Niel et al., 2018). Hypoxia-induced small EV proteins regulate proinflammatory cytokines and systemic blood pressure in pregnant rats (Dutta et al., 2020). In healthy-pregnant women, about $10\%$ of circulating EVs are released from placental trophoblasts. The concentration of placenta-derived exosomes in maternal circulation increases continuously throughout the first trimester of pregnancy (Sakran et al., 2012), and the number of circulating exosomes increases by more than two-fold with gestational age (Salomon et al., 2014), EVs are important mediators of maternal-placental crosstalk (Shomer et al., 2013). In PE, excessive shedding of syncytiotrophoblast-derived EVs may lead to endothelial dysfunction, monocyte activation, and an excessive maternal inflammatory reaction (Messerli et al., 2010), *In previous* studies on EVs in healthy-pregnant women, and women with GVC, we found that the cargo within EVs, and their effects on the functions of endothelial (EC) and trophoblast cells (proliferation, migration, apoptosis and signal transduction pathways), vary according to the pregnant woman’s physiological/pathological state (Shomer et al., 2013). Others demonstrated that maternal EVs and platelets promote preeclampsia via inflammasome activation in trophoblasts (Kohli et al., 2016). The current study was performed in line with these studies and aimed to understand the underlying mechanisms responsible for these effects.
MiRNAs are small non-coding RNA molecules that regulate gene expression (O'Brien et al., 2018) and are involved in a variety of diseases and conditions. miRNAs are critical in cell development, proliferation, and apoptosis and each miRNA controls hundreds of target genes. EVs serve as the main transport vehicles for miRNAs and are a novel mechanism of genetic exchange between cells (Liu et al., 2019).
GVCs may be associated with alterations in miRNA expression in placental tissue and maternal circulation (Hornakova et al., 2020). Specifically, in pregnant women, circulating EV-miRNAs participate in maternal-fetal communication (Sarker et al., 2014; Foley et al., 2021) and placental miRNAs are released to the maternal circulation throughout pregnancy (Mouillet et al., 2015). Several miRNAs have been identified as involved in trophoblastic invasion, proliferation, and apoptosis (Cirkovic et al., 2021). Hypoxic conditions typical of early placentation affect miRNA expression in trophoblast cells (Pineles et al., 2007) and may explain the role of miRNA in development of PE and PIH (Truong et al., 2017). While previous studies have primarily focused on circulating exosomal miRNAs (Li et al., 2020), data on miRNA packed into “large” EVs is lacking. Moreover, the maternal and placental pathophysiologies underlying EV release have not yet been determined.
In order to define upstream regulators related to GVCs, the current study explored the involvement of miRNAs in EVs and in placental tissue in women with GVCs, and explored the effects of EVs on miRNA expression in trophoblast and endothelial cells.
## 2.1 Participants and collection of samples
The study was conducted from 2014 to 2020 and approved by the institutional review board of the Rambam Healthcare Campus (Approval No. 2030) in Haifa, Israel. All participants signed informed consent. Blood samples were collected from non-pregnant healthy women (NP), and from 2 groups of pregnant women: healthy-pregnant (HP) and women with GVC, classified according to the guidelines of the American College of Obstetricians and Gynecologists (ACOG Practice Bulletin No, 2019) (Supplementary Figure S1).
## 2.2.1 EV isolation
Blood samples were obtained during the third trimester of pregnancy, collected in EDTA and citrate tubes. Differential centrifugations were performed according to the current gold standard for EV isolation (MISEV 2018). Briefly, immediately following collection, blood samples were separated by two consecutive centrifugations (1500 g, 15 min, room temperature). Platelet-poor-plasma (PPP) was stored in aliquots at −80°C. Several studies demonstrated storage at −80°C and single freeze–thaw cycles were found not to have significant effects on either EV number or size (Lorincz et al., 2014; Yuana et al., 2015). To confirm that freezing did not affect EV concentration or size, Nanoparticle tracking analysis (NTA) was performed twice on selected samples that were defrosted at two different time points. We found that the concentration of EVs in PPP samples (particles/ml) remained constant throughout the freezing period (Supplementary Figure S2). Thus, all EVs were analyzed using thawed samples. EV pellets were isolated from equal volumes of PPP (250 µl) thawed samples. Citrate plasma is considered a better source for EV protein extraction (Palviainen et al., 2020). EVs of citrate plasma were also used for cell culture stimulation. Thawed EDTA PPP was used for RNA extraction, as recommended for Nanostring technology. Exosome isolation methods usually discarded 10,000 g pellet and continue with 100,000 g pellet (Coughlan et al., 2020). Recently, we emphasized the importance of the 20,000 g pellet fraction compared to samples that contained pellets of 100,000 g after discarding the 10,000 pellet (Aharon et al., 2021). In the current study, PPPs were centrifuged by MIKRO 220R, rotor 1189-A (Hettich) at 20,000g, 1h, 4°C; max acceleration, zero declaration.
Size and concentration of EVs were measured by transmission electron microscopy (TEM) and (NTA) [21].
## 2.2.2 Transmission electron microscopy (TEM)
TEM analysis: 4 μl of EVs pellet samples isolated from NP, HP and GVC PPP were applied to formvar-carbon-coated, glow-discharged EM grids (EMS) and negatively stained with $1\%$ uranyl acetate. Digital electron micrographs were acquired using a Thermo Fisher Scientific Tecnai T12 transmission electron microscope operating at 120 kV and equipped with a bottom mounted TVIPS TemCam-XF416 4k x 4k CMOS camera.
## 2.2.3 Nanoparticle tracking analysis
NTA analysis, PPP samples, EV pellets and EVs in the supernatant, at the end of UC (EV sup.) of women from the different study groups were diluted in filtered PBS (0.02 µm) and subjected to static injection in the Nanosight instrument (NS300, Malvern Instruments Ltd, United Kingdom). For each sample, five consecutive videos were taken and further analyzed by the NTA software, with a threshold set at 5. Temperature was monitored throughout the entire recording time.
## 2.2.4 Western blot
For western blot (WB) analysis, equal amounts of EV pellet (30-50 ug, measured by Pierce BCA protein assay kit, CAT 23227) obtained from similar PPP volumes (250 ul) were combined with a 2xlysis buffer (RayBiotech) supplemented with $1\%$ proteinase inhibitor and $1\%$ phosphatase inhibitors (Sigma) containing β-mercaptoethanol (1:20, Biorad). Samples were loaded and separated on $4\%$–$20\%$ Mini-PROTEAN TGX Precast Protein Gels (Bio-Rad) and then transferred to Trans-Blot Turbo Mini 0.2 μm Nitrocellulose Transfer Packs (Bio-Rad). The membranes were stained with Ponceau S solution (P7170, sigma) to ensure that proteins transferred from the gel to the membrane (Supplementary Figure S3), were washed and immunoblotted with the appropriate antibodies against exosome markers. Mouse monoclonal anti human-CD63 (ab59479) and CD81 (ab79559) were both used in 1:1000 dilutions. Anti-rabbit, anti-human placental lactogen hormone (hPL) (ab137099; 1;25,000 dilution) and Calnexin (ab10286, 1;10,000 dilution) endoplasmic reticulum (ER) protein, not expected to be enriched in EVs, serve as negative control (all from Abcam, USA). Secondary antibodies (anti-mouse 115-035-146 and anti-rabbit 111-035-144, both in 1:5000 dilution) were purchased from Jackson ImmunoResearch (PA, USA). The blot was imaged and quantified by myECL™ Imager and analyzed by My Image Analysis Software (both from Thermo Fisher Scientific, Waltham, MA USA).
## 2.3.1 EV RNA
Was isolated from representative plasma samples (1 ml PPP) and EV pellets obtained from each study group. The miRNeasy isolation kit (Qiagen, Hilden, Germany) was used, with some modifications as previously described (Aharon et al., 2020; Levin et al., 2021). RNA concentration and quality were measured by a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA). miRNA was screened by Nanostring platform by an external service (NanoString Technologies, Seattle, WA). Raw data on expression of 800 miRNAs were analyzed with the solver software (http://www.nanostring.com/products/ nSolver) (Rodosthenous et al., 2016). The nCounter assay was performed on 100 ng RNA of each sample, including six positive controls, six negative controls, five housekeeping genes (ACTB, B2M, GAPDH, RPL19, RPLP), and Non-Mammalian Spikes in miRNA probes (ath, miR159a,cel-miR-248, cel-miR-254, osa-miR414, osa-miR442).
## 2.3.2 Placental sections
Were obtained from a representative number of women belonging to each of the pregnant groups, either during cesarean sections or immediately after delivery. For villous RNA studies, dissections of the chorionic plate were collected from three distinct locations on the placental surface as recommended (Roberts et al., 2019), submerged, and stored in RNA Later Solution (ThermoFisher Scientific). RNAs were isolated as previously described (Aharon et al., 2005), using Tri-reagent (Sigma-Aldrich Israel LTD) following a standard procedure.
## 2.4 Cell culture
To distinguish between maternal and placental involvement in miRNA expression, we exposed primary cell cultures from early stage human placenta villus trophoblast (HVT) to EV pellets that were obtained from the HP and GVC groups. The primary ECs representing the maternal side were also exposed to EVs of NP, as well as EVs of HP and women with GVC.
## 2.4.1 Human early stage trophoblasts cells
Primary cells of the second trimester were purchased (Cat number 7120, ScienCell, Carlsbad, CA). As demonstrated in our previous study, EVs exert different effects on early stage trophoblast vs. term trophoblast cell cultures. Specifically, in HP women, treatment with EVs decreased apoptosis and induced higher migration in early-stage trophoblasts compared with untreated. Conversely, exposure to EVs obtained from women with GVCs increased term trophoblast apoptosis and inhibited early-stage trophoblast migration when compared to cells exposed to HP EVs (Shomer et al., 2013). Because GVCs begin earliest at 20 weeks of gestation (Peracoli et al., 2019), we preferred to study the effects of EVs on early stage trophoblasts and therefore did not culture HVT from term placentas. HVT were grown in a recommended trophoblast medium ($50\%$, catalog number 7121, Sciencell) supplemented with Dulbecco’s Modified Eagle Medium–high glucose ($22\%$, Biological Industries, Israel), F-12 (HAM) nutrient mixture ($22\%$, Biological Industries, Israel), fetal calf serum (FCS) ($10\%$, Biological Industries, Israel), and penicillin G sodium salt (10,000 units/mL) - streptomycin sulfate (10 mg/ml)–nystatin (1,250 units/mL) solution ($1\%$, Biological Industries, Israel). The cells were grown at 37°C, $5\%$ CO2. Passages 4-8 were used for experiments. HVT cell cultures were labeled with anti-hPL to ensure quality control of their contents. As in our previous studies, greater than $90\%$ labeling with anti-hPL was considered a pure trophoblast culture (Shomer et al., 2013).
## 2.4.2 Primary human umbilical vein ECs
Were isolated as previously described (Gao et al., 2019) [28]. HUVECs were labeled with anti-CD31+ to ensure quality control of their contents. Greater than $90\%$ labeling was considered a pure EC culture (Tsimerman et al., 2011).
## 2.5.1 EVs cells interaction
EV pellets from 3 samples derived from each study group (HP and GVC) were labeled with pkh67 green fluorescent cell linker (MINI67-1 KT SIGMA) as described (Hazan-Halevy et al., 2015). Labeled EV samples were washed and concentrated with Amicon® Ultra-15 Centrifugal Filter Units—10,000 NMWL (MERK) and then by Zeba Spin Desalting Columns0.5 ml (Thermo Scientific). Pkh67 green EVs (0.3 × 10E10). HVT cells were seeded on a 24-well plate in a recommended trophoblast medium. When cells reached $100\%$ coverage, 8 × 10E4, they were washed with PBS. Pkh67 green EVs were added to the cells in serum free media for 6 h. At the end of incubation, cells were washed twice and detached with trypsin. Internalization of EVs into trophoblast cells was quantified by flow cytometry and documented by ZOE™ Fluorescent Cell Imager (Bio-Rad).
## 2.5.2 EVs effects on cellular miRNA
(HVT Cells and EC were exposed to EV pellets (obtained from 250 ul of PPP) for 6 h in medium without-serum. Cells were washed and total RNA was purified with TRI reagent according to the manufacturer’s instructions. The purity and concentration of the RNA was evaluated by ultraviolet absorption at 260 nm and 280 nm (NanoDrop). cDNA was constructed using 50 ng of total RNA. Pools of five specific miRNA primers were prepared (Applied Biosystems) and their expression on treated compared to non-treated cells was validated.
## 2.6 miRNA validation by real-time polymerase chain reaction
All the reagents for these experiments were purchased from ThermoFisher Scientific (formerly Applied Biosystems). cDNA was synthesized with Taqman MicroRNA Reverse Transcription Kit and specific Taqman microRNA Assays. Different amounts of starting material were used depending on the nature of the sample (2 µl of RNA for circulating EVs or 50 ng of RNA from placentas, cells, or cell stimulated EVs). We developed a customized multiplex assay based on manufacturer’s guidelines for simultaneous amplification of several miRNAs. RT-qPCR was performed in duplicates per sample using TaqMan miRNA assays and Taqman Fast Advance Master Mix (Applied Biosystems). miRNAs from cell-samples and placental sections were normalized to U6 small RNA (Rice et al., 2015), while plasma EV samples were normalized to miR39 as recommended by the miRNeasy Serum Plasma Handbook, (QIAGEN). https://tools.thermofisher.com/content/sfs/manuals/cms_094060.pdf).
## 2.7 Statistical analyses
Data was analyzed using GraphPad Prism 5 software (CA, United States). Results were assessed by Kruskal Wallis one-way analysis of variance (ANOVA) and the Dunn’s multiple comparison test to compare the study groups. When only two groups were compared, non-parametric two-tailed Mann-Whitney U test student’s t-tests were used. $p \leq 0.05$ was considered statistically significant.
The results were expressed as mean ± SD. Effect size analysis was performed using Cohen’s d method in order to characterize the size of the differences between the groups. Small, moderate, and large effects were defined as 0.20, 0.40, and 0.80, respectively (Nunez Lopez et al., 2017; Peng et al., 2020). The exact numbers of each experiment performed (n) appears at the bottom of each graph and in the table. Easy Fisher Exact Test Calculator was used to calculate a 2 × 2 contingency table. Only some of the samples were evaluated in each test due to low sample volume. All samples were presented for each experiment. The Spearman r correlation tests were performed to evaluate the relation between disease severity (Systolic BP) and EV miRNA expression. p-value (two-tailed) < 0.05 was considered significant.
## 3 Results
A total of 56 women were enrolled. Patient characteristics including age, gestational week at sampling, and blood pressure are summarized in Table 1 and in the Supplementary Figure S1. The participants consisted of three groups: 15 non-pregnant (NP), 18 healthy-pregnant (HP), and 23 women with GVC. The ages were similar between the study groups. The mean number of gestational weeks at sampling was significantly less in the GVC than in the HP group. The mean blood pressure was higher in the GVC than in the HP group, indicating disease severity in the former. Uricemia (mg/dl) and proteinuria excretion (mg/24 h) were measured only in pregnant women with systolic BP above 140 mm Hg.
**TABLE 1**
| Parameter | Clinical Characteristics of Pregnancies for Placentas Studied | Clinical Characteristics of Pregnancies for Placentas Studied.1 | Clinical Characteristics of Pregnancies for Placentas Studied.2 | Clinical Characteristics of Pregnancies for Placentas Studied.3 |
| --- | --- | --- | --- | --- |
| | Non-pregnant women (NP), N = 15 | Healthy-pregnant women (HP), N = 18 | Women With Gestational Vascular Complications (GVC), N = 23 | Statistical significance |
| Maternal age (years) | 33.31 ± 6.750 | 33.50 ± 5.254 | 31.95 ± 5.317 | NS |
| Gestational age (weeks) | | 36.16 ± 3.154 | 34.40 ± 3.249 | HP vs. GVC, p = 0.0264 |
| Blood pressure, systolic (mm Hg) | 114.8 ± 10.23 | 112.8 ± 12.85 | 156.4 ± 17.03 | NP vs. HP, NS |
| Blood pressure, systolic (mm Hg) | 114.8 ± 10.23 | 112.8 ± 12.85 | 156.4 ± 17.03 | NP vs. GVC, p < 0.001 |
| Blood pressure, systolic (mm Hg) | 114.8 ± 10.23 | 112.8 ± 12.85 | 156.4 ± 17.03 | HP vs. GVC, p < 0.001 |
| Blood pressure, diastolic (mm Hg) | 76.36 ± 9.137 | 64.90 ± 9.727 | 98.79 ± 9.193 | NP vs. HP, p < 0.01 |
| Blood pressure, diastolic (mm Hg) | 76.36 ± 9.137 | 64.90 ± 9.727 | 98.79 ± 9.193 | NP vs. GVC, p < 0.001 |
| Blood pressure, diastolic (mm Hg) | 76.36 ± 9.137 | 64.90 ± 9.727 | 98.79 ± 9.193 | HP vs. GVC, p < 0.001 |
| Uricemia mg/dl | | | 5.96 ± 1.344 | |
| Proteinuria - an excretion per 24 h | | | 300–1000 mg, n = 13 | |
| Proteinuria - an excretion per 24 h | | | >1000mg, n = 10 | |
| Drug | | | *Labetalol, n = 4 | |
| Drug | | | *Eltroxin, n = 1 | |
## 3.1 Characterization of EV size and concentration
TEM images indicate EV heterogeneity and revealed that the sample pellets of the three study groups contained EVs of varied sizes (Figure 1A). Using NTA method, we analyzed the concentrations and size distributions of vesicles per mL of i) PPP, ii) EV pellets and iii) EV supernatant (sup.) Figure 1B presented representative graph distributions of each of the study group samples (PPP, pellet and sup). Significantly higher EV concentrations were observed in PPP of the HP (5.64e+11 ± 3.66e+11 EVs/ml, $p \leq 0.01$) and GVC (1.17e+12 ± 9.14e+11 EVs/ml, $p \leq 0.001$) groups than in PPP of the NP group (2.6e+011 ± 2.8e+11 EVs/ml) (Figure 1C).
**FIGURE 1:** *(Continued).*
The mean EV size was significantly greater in the GVC-PPP than the NP-PPP group (112. 7 ± 28.43 nm vs. 95.16 ± 22.72nm; $$p \leq 0.0316$$, with size effects differences Cohen’s $d = 0.681$) (Figure 1B). The concentration of large EVs (>100 nm) was found to be higher in GVC-PPP samples (55.33 ± $26.44\%$) compared to their concentration in the NP-PPP (26.42 ± $15.77\%$, $p \leq 0.01$, Cohen’s $d = 1.328$) and HP-PPP (43.52 ± $24.34\%$, $$p \leq 0.1206$$ (NS), Cohen’s $d = 0.834$) groups (Figure 1C). The percentage of large EVs (>100 nm) on EV pellets of HP and GVC pellets were two times higher than in their associated supernatants (HP pellet vs. sup. Cohen’s $d = 0.698$,196; GVC pellet vs. sup. Cohen’s $d = 1.55467$). EV pellets of pregnant women showed similar expression of exosome markers compared to NP EVs (Figures 1F, G). Size effect analysis displayed moderate change in CD81 expression between HP-EVs and GVC-EVs (Cohen’s $d = 0.7385$). In addition, size effect analysis display a moderate change and higher hPL expression in EVs obtained from the GVC group than EVs obtained from the HP group (Cohen’s $d = 0.510$,164). These findings may indicate that more hPL was packaged within the EVs released from placenta trophoblast cells of this group. Calnexin was used as a negative control marker of the endoplasmic reticulum (ER appear only in cells lysate and were absent t in EVs samples (Figures 1F, G).
## 3.2.1 EV miRNA screening
Double sets of screening using NanoString technology were performed using patient EV samples. We compared miRNAs in EV pellets and miRNAs in PPP (consisting of EVs and free miRNA) of the same sample. Screening identified 104 miRNAs with a significant copy number (>100) in at least one of the samples obtained from EV pellets or in PPP.
Expression levels of PPP miRNAs and miRNAs in EV pellets both differed between groups. In NP, only $30\%$ of the miRNA was found in EV pellets; however, the HP and GVC groups had significantly higher rates of miRNA in EV pellets, reaching $50\%$ and $51\%$, respectively (Figure 2A). Moreover, among the 104 miRNAs with substantial expression, select miRNAs were highly concentrated only in either the EV pellets of the HP group ($13\%$) or the GVC group ($15\%$), but not the NP group (Figure 2B). For EVs pellet of HP and GVC, two samples of each group were screened and compared to EVs pellet of single NP sample screening data results presented in Supplementary Table S1
**FIGURE 2:** *Screening to identify differences in the expression of miRNAs between those packed in extracellular vesicles and those circulating in plasma miRNA was profiled using the NanoString platform and analyzed with nSolver Software 3. The percentage of miRNA with higher or equal content in EV pellets compared to PPP (*p ≤ 0.05 Easy Fisher Exact Test Calculator). (A) The proportion of miRNAs packed in EVs versus PPP and the list of miRNAs that were more highly expressed only in the healthy-pregnant (HP) group, only in the gestational vascular complications (GVC) group, and in both pregnancy groups (HP and GVC), respectively. The proportion of miRNAs found to be “packed” in EVs is higher in all three study groups (NP, HP, and GVC), or lower in all three groups (B).*
## 3.2.2 Circulating EV miRNA validation
Average screening results of EVs pellets obtained from two GVC samples compared to average of two HP samples revealed 20 specific miRNAs which were $50\%$ more concentrated in EV pellets of the GVC compared to EV pellets of the HP group (Figure 3A). 23 other miRNAs, for which the expression was $50\%$ lower in EV pellets obtained from the GVC samples compared to the HP samples, were found (Figure 3B). From all these miRNAs, we selected certain miRNAs (Supplementary Table S1) that have been identified in previous publications as important regulators of placental regulation and GVCs (Hornakova et al., 2020; Ali et al., 2021) to ensure that our study covered the most important miRNAs related to GVC.
**FIGURE 3:** *Ratio of extracellular vesicle (EV) miRNA from women with gestational vascular complications (GVC) compared to healthy-pregnant women (HP). EV pellet miRNA was profiled using the NanoString platform and analyzed with nSolver Software 3. The graphs present the ratios of Average EV miRNA from two samples of women with GVC compared to average EV miRNA from two samples obtained from HP women. miRNA expression was greater in EV pellets from the GVC than the HP group (A) and lower in EV pellets from the GVC compared to the HP group (B).*
Validation of EV pellet miRNA expression by RT-PCR was performed for each of the study groups. Expression levels of 21 selected miRNAs (Supplementary Table S1) were assessed.
Significantly greater expression of EV miRNA, specifically of hsa-miR-16-5p ($$p \leq 0.0057$$, Cohen’s $d = 1.047.$) and hsa-miR-210 ($$p \leq 0.0007$$, Cohen’s $d = 1.0248.$), were observed in the HP compared with the NP group. In contrast, significantly lower expression of hsa-miR-16-5p and hsa-miR-210 was observed in EVs obtained from the GVC compared with that of the HP group ($$p \leq 0.$$ 0021, Cohen’s $d = 0.842$ and $$p \leq 0.0054$$, Cohen’s $d = 0.546$, respectively) (Figures 4A, B). EV miR-29b-3p levels were found to be similar between the study cohorts (Figure 4C).
**FIGURE 4:** *miRNA expression in EV pellet and placental sections from the study groups miRNA expression in EV pellets of the non-pregnant group, and EV pellets and placental sections of the pregnant groups [healthy-pregnant (HP) and gestational vascular complications (GVC)] were validated by real-time polymerase chain reaction. Each EV miRNA sample was normalized to cel-mir-39 spike-in. Each placental miRNA sample was normalized to U6, and expressed as dct. (A–C) present expression of the following miRNA EVs: hsa-miR16 (A), hsa-miR210 (B), and hsa-mir29b-5p (C). (D–F) present expression of these miRNAs in placental sections: hsa-miR16 (4 days), hsa-miR210 (E), and hsa-mir29b-5p (F).*
## 3.2.3 Placental sections miRNAs
In most of the validated miRNAs, expression was similar in placental sections from the HP and GVC groups. The hsa-miR-29b-3p level was significantly higher in the placentas of women from the GVC compared with the HP group ($$p \leq 0.0433$$, Cohen’s $d = 0.528$) and trend of increase were found in hsa-miR-210 ($$p \leq 0.098$$, Cohen’s $d = 0.541$) (Figures 4C, D).
The GVC group was characterized by significantly higher systolic blood pressure (Table 1; Supplementary Figure S1), indicative of a pathological condition. Moderate negative correlations (spearman r = -0.4) were found between systolic blood pressure in the pregnant women (the HP and GVC groups combined) and expression levels of EV miRNA16 ($$p \leq 0.0069$$) and miRNA210 ($$p \leq 0.0041$$). No correlations were found between high blood pressure and expression of EV miR29b in pregnant women (Figures 5A–C).
**FIGURE 5:** *Correlations between systolic blood pressure (BP) in pregnant women (HP and with GVC) and EV pellet miRNA expression The graphs present Spearman test correlations analysis between systolic blood pressure in the pregnant women (the HP and GVC groups combined) and expression levels of EV miRNA16 (A), miRNA210 (B) and miR29b (C).*
## 3.3.1 Trophoblast cells interaction
A similar rate of green EVs internalization trophoblast cells was found in all samples (HP-EVs 75.20 ± $14.48\%$, GVC-EVs 76.33 ± $6.749\%$). HVT exposure (6 h) to both groups of EVs (HP and GVC) did not affect viability or morphology of HVT cells (Figures 6A, B) confirming our previous study that showed that HP EVs decreased HVT cells apoptosis while GVC-EVs did not affect their viability compared to untreated cells (Shomer et al., 2013).
**FIGURE 6:** *(Continued).*
## 3.3.2 Differences in EV pellet effects on miRNA expression in endothelial and trophoblast cells
The differences in the effects induced by EVs obtained from NP, HP, and GVC groups on miRNA expression in ECs and trophoblast cells were normalized to housekeeping gene U6 and expressed as the delta-Ct (dct). Exposure of EC cells to EVs of the study cohort did not affect cell expression of miRNAs hsa-miR-16-5p, hsa-miR-29b and hsa-miR-210 (Figures 6C–G). In contrast, Exposure of trophoblast cells to EVs obtained from women with GVC significantly increase the expression of hsa-miR-29b in trophoblast cells ($$p \leq 0.0229$$, size effect Cohen’s $d = 1.230$ and induce trend of increase in hsa-miR-16-5p ($$p \leq 0.072$$, Cohen’s $d = 1.114$,574). While the effects of HP EVs and GVC EVs were similar on hsa-miR-210 expression only GVC EVs significantly reduced its expression compared to non-treated cells ($$p \leq 0.011$$, Cohen’s d 1.617) (Figure 6H).
## 4 Discussion
This study is the first to identify specific miRNAs cargo in small and large EVs of pregnant women. However, we cannot definitively state whether miRNA decorates EV corona or is packaged within the vesicles. It also sought to define the differences in miRNA expression related to either maternal vasculature (circulating EVs, ECs) or placental sites (placental sections, trophoblast cells).
## 4.1 EV size, concentration and placental origin
Pregnant woman PPP (obtained from HP and GVC) contained higher concentrations of EVs compared to NP PPP, but only the GVC group displayed a significant increase in EV size. This correlated with higher concentrations of hPL indicative of a placental origin. Trophoblast cells are lined the placental villus, invade the decidual spiral arteries and are the only placental cells that come in direct contact with maternal circulation (Zhao et al., 2021). Therefore, we can assume that placental EVs in the circulation originated from trophoblast cells.
In the current study, we demonstrated the importance of the fractions enriched with “large EVs” obtained by 20,000 g. We also showed the qualitative differences in miRNA cargo contained in pellets of enriched samples with “large EVs,” compared with samples of PPP containing both miRNAs packaged within EVs as well as freely circulating miRNAs. The increase in particle size and concentration is an important determinant of the “cargo” they carry and can transport between cells. Analysis of the size distribution demonstrated that PPP and EV pellets contained large and small EVs in similar proportions. However, using a UC isolation method to analyze the sediment only extracted part of the EVs from the PPP samples, while some EVs (mainly small EVs) remained in the supernatant as previous described [30]. While most studies based their findings on exosomal miRNA and eliminated the fraction of large EVs, the current study focused on the advantages of using a composition of small and large EVs. We assume that miRNA in EV pellets were mainly packaged inside the vesicles, while in PPP samples, they also appear as free-form molecules. In the current study, we found more miRNAs packaged in EV pellets of pregnant women than in PPP. This suggests that certain miRNAs may be selectively packaged during pregnancy. Preliminary evidence (Turchinovich et al., 2013; Mitchell et al., 2015) suggests that the relevant mechanism is apparently a non-random process (Diehl et al., 2012; Boon and Vickers, 2013; Niu et al., 2018) related to EVs as a source of miRNAs that promote GVCs. Previously, we demonstrated that EVs from women with GVCs contain higher levels of pro-inflammatory cytokines, promote apoptosis, suppress migration of ECs and early-stage trophoblasts (HVT), and inhibit tube formation (Shomer et al., 2013).
## 4.2 The role of miRNAs in placenta versus maternal circulation
Of the numerous miRNAs that were screened and validated in this study, significant differences were found in three miRNAs: hsa-miR-16-5p, hsa-miR-210, and hsa-miR-29b-3p.
Several recently published articles have comprehensively reviewed the role of miRNAs in regulating placental and fetal development (Hornakova et al., 2020; Ali et al., 2021), suggesting that miRNAs promote dysregulation of placental development, and consequently, maternal physiology and fetal growth and development. These reviews found mir-210 to be one of most dominant. Fifteen different studies showed dysregulation of miR-210 in PE, and 3 studies showed its dysregulation in IUGR. Only one other study also reported the upregulation in placental mi-16 and miR-29b that we found in the current study. Our results support findings reported in Kyoto Encyclopedia of Genes and *Genomes analysis* (KEGG map, Supplementary Figure S4; Vlachos et al. 2015) that suggest the three miRNAs hsa-miR-16-5p, hsa-miR-210, and hsa-miR-29b-3p regulate 44 genes related to the PI3K-AKT signaling pathway. These miRNAs act as the first step in the cell regulation pathway in PE. Specifically, EV miR-16 and mir-210 were significantly higher in the HP than NP and GVC groups, correlating negatively with disease severity as indicated by elevated blood pressure. In contrast, mir-29b expression was similar across EVs but higher in placental sections and in induced HVT cells.
## 4.2.1 miRNA-210
MiRNA210 is induced in response to hypoxia and regulates more than 900 genes related to placental development and placental pathologies (Ali et al., 2021). Elevated plasma levels of hsa-miR-210 have been found to correlate with PE severity (Xu et al., 2014; Biro et al., 2017), while detection of miR-210 in the urine positively correlates with the level of proteinuria (Gan ZL et al., 2017). Furthermore, increased expression of miR-210-5p has been documented in placentae of women with PE (Awamleh et al., 2019). Based on these findings, we decided to include this miRNA in the PCR validation, despite its low expression in the study screening results. In contrast to previous studies, our results showed significantly lower EV miR-210 levels in the GVC compared to the HP group. These findings were unexpected considering the increased levels of this miRNA that were found in blood and urine of PE patients. However, we did detect higher EV-miR210 expression in placental sections of women with GVC, consistent with previous studies (Luo et al., 2016). Discrepancies may be due to differences in the timing of sampling as well as the sources of RNA used to measure levels of miRNAs, both circulating in the blood and encapsulated inside of EVs. Exposure of trophoblast cells, which are the main source for placental circulating EVs, to GVC- EV pellets or to HP-EV pellets induces similar reductions of mir-210 expression compared to untreated cells. These results imply that trophoblast cells do not reflect the internal placental processes. It may be inferred that trophoblast EVs did not reflect the same expression as placental sections and the source of the decreased miRNA EVs in GVCs is primarily related to maternal pathology.
## 4.2.2 miRNA-16
Low expression of miR-16 has been reported to correlate with fetuses that are small for gestational age (Maccani et al., 2011). Altered expression of miR16 in decidua-derived mesenchymal stem cells was related to the development of PE (Wang et al., 2012). The present study found low expression of miR-16 in GVC-EVs but did not discover any differences in placental miR-16 expression under the examined conditions. This supports the presumption that EVs with altered miR-16 expression originate from maternal cells.
## 4.2.3 miRNA-29
The family of miR-29 microRNAs has been reported as possible regulators of more than 4000 gene products, with diverse roles in regulation of cell survival (Slusarz and Pulakat, 2015). miR-29b was found to induce apoptosis and inhibit invasion and angiogenesis of trophoblast cells (Li et al., 2013). Overexpression of miR-29b decreased cell proliferation of decidua-derived mesenchymal stem cells (Xin et al., 2017). In the current study, miR-29 expression was significantly higher in placental sections of the GVC group compared to the HP group. Moreover, GVC-EVs upregulated miR-29b in early stage primary placental HVT cells in culture. Previously, we demonstrated that EVs of women with GVCs contain higher pro-inflammatory cytokine levels, promote apoptosis, and suppress migration of HVTs. EVs potentiate these effects through an extracellular signal-regulated kinase pathway altering ERK signal transduction (Pineles et al., 2007). miR29b may be the missing “link in the chain” responsible for inducing placental dysfunction in GVCs.
## 4.2.4 Summary
The relationship between the development of GVCs and changes in the expression levels of tissue-specific and circulating miRNAs has been demonstrated in several studies. miRNA packaging in vesicles membranous protect them from rapid degradation in circulation and enable their penetration to target cells. While the majority of the studies support the notion that EVs contain miRNA and actively transfer their miRNAs cargo between cells, a recent study claimed that delivery of different species of RNAs as well as proteins through the EVs is an extremely inefficient process (Albanese et al., 2021).
EV-miRNAs are involved in GVC development and reflect disease severity.
Our findings suggest that EV hsa-miR-16-5p and miR-210 of maternal origin, while hsa-miR-29b-3p is essentially of placental origin. These findings highlight the potential utility of these miRNAs to serve as biomarkers for distinguishing between the placental and maternal pathophysiologies underlying PE. Moreover, based on these results and those of our previous study, in which GVC-EVs induced apoptosis in HVT [14], we suggest that miR-29b serves as one of the main regulators of trophoblast cell viability.
The current study has some limitations. Previous studies reported increased blood uric acid levels during the third trimester of pregnancy. Uric acid levels of >5 mg/dl at term and proteinuria above 300 mg/24 h can be used as both clinical markers of GVC severity and tools to distinguish preeclampsia from PIH (Johnson et al., 2011; Le et al., 2019). In the current study, we did not divide the GVC group into mild and severe PE. However, GVC EVs samples were combined for analysis as a single group. Other limitations include obtainment of blood samples at delivery is a late stage of GVC disease, obtainment of placental samples from only about half of the pregnant women, relatively small cohorts sizes, and performance of certain tests only on specific samples. Additionally, the women classified with diagnoses of GVCs presented with heterogeneous disorders (PIH, mild or severe PE).
## 5 Conclusion
Numerous studies have linked different patterns of miRNA dysregulation to GVCs, supporting the role of placental EV cargo in disease progression. However, trophoblast EVs, the main source of placental EVs, do not always reflect placental pathophysiology and function. In this study, we emphasized the importance of the large EVs which are usually discarded, and identified specific miRNAs related to GVCs that may distinguish between pathologies of maternal (hsa-miR-16-5p and hsa-miR-210) and placental (hsa-miR-29b-3p) origins in preeclampsia.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by The study was conducted during 2014–2020 and approved by the institutional review board of the Rambam Healthcare Campus (Approval No. 2030), Haifa, Israel. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AA: Designed the study aims and methodology, analyzed the data and write the manuscript. AR-S: Performed the experiments and analyzed the data. RA: Performed the experiments. TB-L: Performed the experiments. AD: Performed the experiments. BB: Designed the study aims, edited the manuscript. AC: Collected samples. CD: Collected samples, edited the manuscript. ZW: Supervision. IS: Designed the study aims and methodology, collected samples, write the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2023.1080419/full#supplementary-material
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|
---
title: 'Canagliflozin alleviates valproic acid-induced autism in rat pups: Role of
PTEN/PDK/PPAR-γ signaling pathways'
authors:
- Mariam A. Elgamal
- Dina M. Khodeer
- Basel A. Abdel-Wahab
- Ibrahim Abdel Aziz Ibrahim
- Abdullah R. Alzahrani
- Yasser M. Moustafa
- Azza A. Ali
- Norhan M. El-Sayed
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9992196
doi: 10.3389/fphar.2023.1113966
license: CC BY 4.0
---
# Canagliflozin alleviates valproic acid-induced autism in rat pups: Role of PTEN/PDK/PPAR-γ signaling pathways
## Abstract
Autism is complex and multifactorial, and is one of the fastest growing neurodevelopmental disorders. Canagliflozin (Cana) is an antidiabetic drug that exhibits neuroprotective properties in various neurodegenerative syndromes. This study investigated the possible protective effect of Cana against the valproic acid (VPA)-induced model of autism. VPA was injected subcutaneously (SC) into rat pups at a dose of 300 mg/kg, twice daily on postnatal day-2 (PD-2) and PD-3, and once on PD-4 to induce an autism-like syndrome. Graded doses of Cana were administered (5 mg/kg, 7.5 mg/kg, and 10 mg/kg, P.O.) starting from the first day of VPA injections and continued for 21 days. At the end of the experiment, behavioral tests and histopathological alterations were assessed. In addition, the gene expression of peroxisome proliferator-activated receptor γ (PPAR γ), lactate dehydrogenase A (LDHA), pyruvate dehydrogenase kinase (PDK), cellular myeloctomatosis (c-Myc) with protein expression of glucose transporter-1 (GLUT-1), phosphatase and tensin homolog (PTEN), and level of acetylcholine (ACh) were determined. Treatment with Cana significantly counteracted histopathological changes in the cerebellum tissues of the brain induced by VPA. Cana (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) improved sociability and social preference, enhanced stereotypic behaviors, and decreased hyperlocomotion activity, in addition to its significant effect on the canonical Wnt/β-catenin pathway via the downregulation of gene expression of LDHA ($22\%$, $64\%$, and $73\%$ in cerebellum tissues with $51\%$, $60\%$, and $75\%$ in cerebrum tissues), PDK ($27\%$, $50\%$, and $67\%$ in cerebellum tissues with $34\%$, $66\%$, and $77\%$ in cerebrum tissues), c-Myc ($35\%$, $44\%$, and $72\%$ in cerebellum tissues with $19\%$, $58\%$, and $79\%$ in cerebrum tissues), protein expression of GLUT-1 ($32\%$, $48\%$, and $49\%$ in cerebellum tissues with $30\%$, $50\%$, and $54\%$ in cerebrum tissues), and elevating gene expression of PPAR-γ (2, 3, and 4 folds in cerebellum tissues with 1.5, 3, and 9 folds in cerebrum tissues), protein expression of PTEN (2, 5, and 6 folds in cerebellum tissues with 6, 6, and 10 folds in cerebrum tissues), and increasing the ACh levels (4, 5, and 7 folds) in brain tissues. The current study confirmed the ameliorating effect of Cana against neurochemical and behavioral alterations in the VPA-induced model of autism in rats.
## 1 Introduction
Autism spectrum disorder (ASD), a neurodevelopmental disorder, is broadly detected within the first three years of life (Benger et al., 2018). Autism is characterized by major core behaviors: deficits in sociability either for communication or interaction, repetitive behaviors, interests, and thoughts, with other non-core traits, including self-injury, hyperactivity features, and sensitivity to stimulation (Choi et al., 2018; Dai et al., 2018; Eissa et al., 2018). Autistic patients show many variations in the cerebellum, especially loss of Purkinje cells (el Falougy et al., 2019). Moreover, cerebellum dysfunction disrupts the prefrontal cortex’s function (Forbes and Grafman, 2010).
Non-controlled epileptic attacks during pregnancy produce high risk of injury to both the mother and fetus (Klein, 2011). So, epileptic pregnant women must continue on VPA medication (Stephen et al., 2012). VPA crosses the placenta and accumulates in the fetal circulation with higher concentration than that in the maternal blood, causing toxicity and teratogenicity (Vajda, 2012). VPA exposure during early pregnancy showed classical signs of autism with developmental and behavioral delays (Christensen et al., 2013; Kim et al., 2017).
Current mainstay treatments for ASD are only behavioral treatments against the core symptoms of ASD (Aishworiya et al., 2022). There are no pharmacological treatments that treat the core symptoms of ASD. Some medications seek only to reduce co-occurring symptoms associated with ASD: attention deficit hyperactivity disorder (ADHD), self-harming behavior, anxiety, depression, seizures, sleep problems, gastrointestinal problems, phobias, intellectual disability, and speech/language impairment (Hyman et al., 2020; Aishworiya et al., 2022).
The canonical Wnt/β-catenin pathway mainly participates in central nervous system (CNS) development, especially cognitive disorders (Kwan et al., 2016). Additionally, the canonical pathway is upregulated in ASD (Mulligan and Cheyette, 2016). Upregulation of the Wnt/β-catenin pathway stimulates aerobic glycolysis, the Warburg effect, throughout the activation of the glucose transporter (GLUT), 3-phosphoinositide-dependent kinase 1 (PDK1), and lactate dehydrogenase A (LDHA) (Vallée and Vallée, 2018).
Overstimulation of the Wnt/β-catenin pathway enhances the Wnt/β-catenin target genes’ transduction process; cellular myeloctomatosis (c-Myc) expression (Yue et al., 2010) consecutively leads to further expression of genes encoding the enzymes of aerobic glycolysis: LDHA, PDK, and GLUT (Yang et al., 2012; Vallée and Vallée, 2018). GLUT subtype is crucial for the homeostasis of glucose transport (McEwen and Reagan, 2004). Moreover, activated PDK1 leads to the conversion of pyruvate to lactate through LDHA (Vallée and Vallée, 2018) while blocking its conversion to acetyl-CoA, and finally, it leads to the destruction of acetylcholine (ACh) formation from acetyl-CoA (Roche et al., 2001).
Canagliflozin (Cana) is a sodium-glucose co-transporter type 2 (SGLT2) inhibitor used for type 2 diabetes mellitus management (Naznin et al., 2017). Cana is recognized to have a neuroprotective effect on cisplatin-induced peripheral neurotoxicity in rats (Abdelsameea and Kabil, 2018). Furthermore, Cana has a valuable impact on the scopolamine induction rat model of memory impairment (Wiciński et al., 2020). Similarly, empagliflozin, another SGLT2 inhibitor, remarkably blocked the impaired cognitive function in a type 2 diabetes model in mice (Wiciński et al., 2020). Additionally, high and low doses of empagliflozin inhibited the neurological defects of the ischemia induction model in rats (Wiciński et al., 2020).
Therefore, this study evaluated the protective role of Cana in rat pups against autism induced by VPA focusing on PTEN/PDK/PPAR-γ signaling pathways and their impact on various behaviors as possible mechanisms involved in its neuroprotection.
## 2.1 Drugs and chemicals
Sodium salt of VPA was purchased from Sigma-Aldrich (St. Louis, MO, United States). VPA was prepared by dissolving it in normal saline (100 mg/mL) (Favre et al., 2013; Morakotsriwan et al., 2016). Cana was generously granted by Soficopharm Company (Cairo, Egypt). Cana was prepared by dissolving it in distilled water immediately before use. All other chemicals used in the study were of analytical grade and obtained from Adwic Co. (Cairo, Egypt).
## 2.2 Animals
Newborn Sprague–Dawley rat pups were born on postnatal day 0 (PD-0) (Mony et al., 2016; 2018). Among inclusion criteria for pups’ selection was that the mother should give birth to 6–8 pups. Pups were housed with their mothers in stainless steel cages with free access to food and water, room temperature 24°C ± 1°C, and a 12-h light–dark cycle. All animal experiments were approved by the Ethical Committee for the Animal Research of Faculty of Pharmacy, Suez Canal University (approval no. 201911PHDA1). Behavior studies were performed during the daytime between 10.00 a.m. and 4.00 p.m.
## 2.3 Induction of autism in rat pups
Rat pups were subcutaneously (SC) injected in the dorsal neck region with VPA at a dose of 300 mg/kg twice daily on PD-2 and PD-3, and once on PD-4. The control group was SC injected with an equal amount of saline (Lee et al., 2016; Mony et al., 2016).
## 2.4 Experimental design
Pups of each mother (6–8) were randomly distributed over five experimental groups (10 pups each), and they were housed with their mother. Each experimental group was marked with different colors by spots on their back, and the marks were checked every other day. The study included rat pups of both sexes, starting with a ratio of 1:1 (5:5) in all groups.
Group 1: Pups were injected with saline ($0.9\%$NaCl) (3 mL/kg, SC) parallel to VPA injection. Group 2: Pups were SC injected with VPA (300 mg/kg) to induce autism. Groups 3, 4, and 5: Pups were exposed to VPA (300 mg/kg, SC) with oral Cana at doses (5 mg/kg/day, 7.5 mg/kg/day, and 10 mg/kg/day) (Safhi et al., 2018; Abdelrahman et al., 2019) for PD-21 and continued during behavioral tests until PD-23 in volumes (6 mL/kg, 8 mL/kg, and 10 mL/kg). The behavioral experiments started at PD-21 to PD-23. Cana was administered by gastric gavages. Cana was administered 30 min before each behavioral test (Eissa et al., 2018).
The open field (OF) test was carried out on PD-21, followed by the elevated plus-maze test (EPM) on PD-22. Social behavior tests were performed on PD-23 (Mony et al., 2016; 2018). The pups were returned to the dams after completing all behavioral experiments (PD-23).
On PD-23, due to mortality, the number of each experimental group was 4 ± 1 for each sex except for the control group (no mortality). The ending number of pups was eight in all groups, except for the control group, which was 10. Rats were injected intraperitoneally with ketamine (80 mg/kg) (Salem et al., 2022) and sacrificed by cervical dislocation. Then brains were dissected out and washed with ice-cold saline. The cerebellum and cerebrum of each rat were isolated. Specimens from dissected brain tissues (cerebellum and cerebrum) were prepared for biochemical analysis (Eissa et al., 2018). One hemisphere of the cerebellum was fixed in neutral formalin for histopathological assessments (Samimi and Amin Edalatmanesh, 2016; Shona et al., 2018).
## 2.5.1 Three-chamber test (3C)
The test apparatus is a wooden box with three chambers (40 cm × 20 cm × 22 cm), and the sided chambers are separated from the center one with two openings for exploring the chambers. The test consisted of three sessions and was performed according to the process stated by DeVito et al. [ 2009] and Eissa et al. [ 2018]. In the first session, a tested rat was habituated for 10 min during which it was placed in the central chamber and allowed to freely explore the empty apparatus. A sociability session of 5 min followed the habituation; a novel rat (the same age and with no previous contact with the tested rat) of the same strain was introduced inside the wire cage of one of sided chambers (novel rat zone). An identical empty wire cage was placed in the other sided chamber (novel object zone). Then the tested rat was placed in the central chamber and allowed to explore the sided chambers. The number of instances and time that the tested rat spent in direct contact with the novel rat (the time spent in grooming, running toward, sniffing or interacting, and crawling over the wired cage) in seconds (Sec), the time spent exploring the novel rat against the novel object (time spent in each chamber) in seconds, and the time spent close to against time spent far from the novel rat in seconds were measured. In the final social novelty session, another novel rat was introduced in the previous empty wire cage (novel rat zone), while the other chamber with the familiar rat was used in the previous sociability session (familiar rat zone). The same parameters were measured as with the previous session for 5 min (Lin et al., 2018; Lu et al., 2018). Behaviors were videotaped alternatively on two sets according to the time schedule to assess sociability and social preference. The measuring parameters were quantified by two observers to videotape blind to treatment conditions (Sailer et al., 2019).
## 2.5.2 Elevated plus-maze (EPM) test
The maze test, which consisted of crossed two opened arms (30 cm × 10 cm) and two closed arms (30 cm × 10 cm × 15 cm) at 50 cm height from the floor, was performed following the methods stated by Pellow and File [1986] and Holmes et al. [ 2002]. The rats were placed in the maze center to explore the maze for 10 min. The total number of entries and the time spent with the head and forepaws in seconds in either opened and closed arms of the maze (Eissa et al., 2018), grooming in addition to rearing frequency, and the numbers of both grooming and rearing/10 min (all time of the test) were measured. Behaviors were videotaped alternatively on two sets according to the time schedule to assess anxiety-like behaviors and exploratory behavior (Nguyen et al., 2017). The time spent and numbers of entries into each arm were quantified by two experimenters to videotape blind to treatment conditions (Sailer et al., 2019).
## 2.5.3 Open field (OF) test
The apparatus was a black Plexiglas square box (60 cm × 60 cm × 30 cm height) with a black floor. The floor of the field was divided into 36 squares with a white marker (10 cm × 10 cm each). The test was conducted in a quiet place. Locomotion (the number of squares crossed by each rat as each entrance into a square of more than half the rat’s body) (Choi et al., 2018; Mansouri et al., 2019; Messiha et al., 2020) together with latency to leave the central area (in seconds) (Blázquez et al., 2019; Messiha et al., 2020), time spent in the central area (in seconds) and stereotype behaviors, grooming frequency and rearing (standing on the hind legs) frequency, and the numbers of both grooming and rearing/5 min (all time of the test) were measured. Behaviors were videotaped alternatively according to the time schedule on two sets to assess locomotor activity, exploratory behavior, anxiety-like behavior, and stereotype behaviors (Morakotsriwan et al., 2016; Messiha et al., 2020). Parameters were quantified by two experimenters blind to treatment conditions.
## 2.6 Light microscopic examination
One hemisphere of the cerebellum was fixed in $10\%$ phosphate-buffered paraformaldehyde solution (pH = 7.4) for 18 h and then embedded in paraffin (Samimi and Amin Edalatmanesh, 2016; Shona et al., 2018). Tissues were sectioned at 5 μm thickness and left at 37°C to dry overnight. Then, sections were deparaffinized, rehydrated, and prepared for histopathological assessments. Cerebellar specimens were dehydrated in ascending grades of ethyl alcohol, cleared in xylol, embedded in paraffin wax, and sectioned at 5 μm thickness. Slides were stained with hematoxylin and eosin (H and E). Cerebellar specimens were examined, and the density of Purkinje cells in the cerebellum was scored (Shona et al., 2018). Sections were examined by a blinded investigator (Atef et al., 2019). The number of Purkinje cells was estimated in different parts of the cerebellar hemisphere (Shona et al., 2018).
## 2.7 Biochemical assessment
Other specimens from the dissected brain tissues were kept at −80°C and homogenized in ice-cold saline for biochemical analysis.
## 2.7.1 Real-time quantitative polymerase chain reaction (RT-qPCR)
To measure the gene expression of PPAR-γ, LDHA, PDK, and c-Myc in the rat cerebellum and cerebrum tissues, RNA was extracted using an RNA extraction kit (Thermo Scientific, Fermentas, #K0731) according to the manufacturer’s instructions. Using a Nanodrop NA-1000 UV/vis spectrophotometer (Thermo Fisher Scientific Inc., Wilmington, DE, United States), RNA purity and concentration were measured and then stored at −80°C. Messenger RNA (mRNA) transcript levels of PPAR-γ, LDHA, PDK, and c-Myc were quantified by real-time PCR using StepOne Plus™ Real-Time PCR thermal cycler (Applied Biosystems, Waltham, MA, United States). RT-qPCR was performed using GoTaq® 1-Step RT-qPCR System. Primers used are listed in Table 1. The thermal PCR amplification protocol was as follows: 37°C for 15 min, 10 min at 95°C, followed by 40 cycles of 95°C for 10 s, 52°C for 30 s, and 72°C for 30 s. *The* generation of specific PCR products was confirmed through dissociation curve analysis. Threshold (Ct) values for each reaction were estimated. All the Ct values of the target genes were normalized to the Ct value of β-actin, which was used as a housekeeping gene.
**TABLE 1**
| Gene | Forward sequence 5′-3′ | Reverse sequence 5′-3′ |
| --- | --- | --- |
| PPAR-γ | GCCAAGAACATCCCCAACTTC | GCAAAGATGGCCTCATGCA |
| LDHA | ATGGCAACTCTAAAGGATCAGC | CCAACCCCAACAACTGTAATCT |
| PDK | CGCCACTCTCCATGAAGCA | AACGAGGTCTTTTCACAAGCATT |
| c-Myc | CTGCTGTCCTCCGAGTCCTC | GGGGGTTGCCTCTTTTCCAC |
| β-actin | AAGTCCCTCACCCTCCCAAAAG | AAGCAATGCTGTCACCTTCCC |
## 2.7.2 Western blotting analysis
For GLUT-1 and PTEN detection, the cerebellum and cerebrum were homogenized in ice-cold RIPA lysis buffer containing protease and phosphatase inhibitors to preserve the protein integrity. Then, the lysates were centrifuged at 16,000 g for 10 min, and the supernatants were stored at −80°C. Prior to loading, protein levels were measured using the Bradford assay (Bradford, 1976). The lysate was mixed with an equal amount of 2 × Laemmli sample buffer and then boiled for 5 min to confirm protein denaturation, sonicated for half a min, and centrifuged at 10,000 g for 10 min. Next, the supernatants were loaded to $12\%$ sodium dodecyl sulfate–polyacrylamide gel electrophoresis. Proteins were moved to PVDF membranes using a Bio-Rad Trans-Blot Turbo unit (Bio-Rad Laboratories Ltd., Watford, United Kingdom). The membrane was blocked for an hour in Tris-buffered saline (TBS) containing $5\%$ (wt/vol) non-fat dry milk. Afterward, the membranes were incubated with primary antibodies against GLUT-1 (catalog # ab115730, Abcam) (Waltham, MA, United States) and PTEN (catalog # sc-377573, Santa Cruz Biotechnology) (Dallas, TX, United States) (1:1,000 dilution in TBS-T with $5\%$ non-fat milk) at 4°C overnight. Blots were three times in TBS-T and incubated with the HRP-conjugated secondary antibody (goat anti-rabbit IgG-HRP-lmg goat mab, Novus Biological, 1:5,000 dilution). The signals were visualized with chemiluminescence according to the manufacturer’s protocol (Atef et al., 2019).
## 2.7.3 ACh concentrations using ELISA kit
Cerebrum and cerebellum ACh concentrations were measured by the colorimetric method using sandwich ELISA Kits (catalog #E4452, BioVision Inc.®) (catalog # ab287811) (Milpitas, CA, United States), and expressed as µmol/mg protein according to the manufacturer’s instructions.
## 2.8 Statistical analysis
Statistical analyses were performed using GraphPad Prism 9.3.1., [ 471] (San Diego, CA, United States). Data of the current study were expressed as mean ± S.E.M. Quantitative variables were evaluated using one-way ANOVA followed by Tukey’s post hoc multiple comparisons test. Some behaviors tests, including time spent to explore novel object vs. novel rat, time spent close to novel rat vs. time spent far from novel rat, time spent to explore familiar rat vs. novel rat, time spent close to novel rat vs. time spent far from novel rat, time spent in opened arms vs. closed arms with the number of entries in opened arms vs. closed arms of EPM, were analyzed using two-way ANOVA, followed by Tukey’s post hoc multiple comparisons test after assessing the normality by the Shapiro–Wilk test or Kolmogorov–Smirov test.
## 3.1 Effect of canagliflozin on mortality percentage
Pups injected with VPA (300 mg/kg, SC) twice daily on PD-2 and PD-3, and once daily on PD-4 resulted in an increase in the percentage of mortality ($20\%$) compared to a mortality percentage equals $0\%$ in vehicle-treated rats. Treatment with canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) for 21 days—starting from the first day of VPA injection—did not significantly improve the mortality of rat pups compared to the VPA group (Table 2).
**TABLE 2**
| Groups | Mortality (percentage) % |
| --- | --- |
| Control | 0 |
| VPA | 20 a |
| VPA + Cana (5 mg/kg) | 20 |
| VPA + Cana (7.5 mg/kg) | 20 |
| VPA + Cana (10 mg/kg) | 20 |
## 3.2.1.1 Effect of canagliflozin on sociability
All groups other than the VPA-treated group presented more preference toward the novel rat than the object (empty cage) (Figure 1A). Pups injected with VPA (300 mg/kg, SC) on PD-2 and PD-3 twice daily, and on PD-4 once daily, spent more time exploring the novel object and less time exploring the novel rat than vehicle-injected rats ($p \leq 0.05$, Figure 1A). Nevertheless, oral administration of Cana (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) reduced the time spent exploring the novel object, while it increased the time spent exploring the novel rat in comparison to the induction group ($p \leq 0.05$, Figure 1A). A Cana dose of 5 mg/kg showed the greatest effect compared with the VPA-treated group ($p \leq 0.05$, Figure 1A).
**FIGURE 1:** *Effect of canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) on sociability test; time explore NO versus NR (A), time of direct contact with NR (B), number of times of direct contact with NR (C), and time spent close to NR versus time spent far from NR (D) in VPA-induced autism in rats. VPA, valproic acid; Cana, canagliflozin; NO, novel object; NR, novel rat. All values are expressed as mean ± S.E.M. Results represented in Figures 1B, C were analyzed using one-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. Results represented in Figures 1A, D were analyzed using two-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. *Compared to the corresponding control group at p < 0.05, # compared to the corresponding VPA group at p < 0.05, $ compared to the corresponding VPA + Cana (5 mg/kg) group at p < 0.05, ¶ compared to the corresponding VPA + Cana (7.5 mg/kg) group, a compared to time exploring NO within the same experimental groups (A), a compared to time close to novel rat (D) at p < 0.05, n = 8, for all groups except for the control group, n = 10.*
Injection of VPA (300 mg/kg, SC) resulted in less time spent and decreased the frequency of direct contact with the novel rat compared with the vehicle-treated group ($p \leq 0.05$, Figures 1B, C). Furthermore, Cana (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) increased the time spent in direct contact with a novel rat when compared with the VPA-injected group ($p \leq 0.05$, Figures 1B, C). Cana (5 mg and 7.5 mg)-treated groups directed the greatest effect of time spent in direct contact with the novel rat (Figure 1B), while Cana 5 mg pointed to the greatest number of direct contacts with the novel rat compared to the VPA-treated group ($p \leq 0.05$, Figure 1C).
Valproic acid injected SC at a dosage of 300 mg/kg decreased time spent close to, and increased time spent far from, the novel rat compared to the vehicle-treated group ($p \leq 0.05$, Figure 1D). However, coadministration of oral Cana at doses of 5 mg/kg, 7.5 mg/kg, and 10 mg/kg with VPA increased the time spent in close contact with, and decreased the time spent far from, a novel rat compared with the VPA-treated group ($p \leq 0.05$, Figure 1D). A Cana dose of 5 mg presented the longest time spent close to the novel rat and the shortest time spent far from the novel rat compared to the VPA-treated group ($p \leq 0.05$, Figure 1D).
## 3.2.1.2 Effect of canagliflozin on social novelty
Pups injected with VPA (300 mg/kg, SC) twice daily on PD-2 and PD-3, and once daily on PD-4, spent even time exploring both familiar and novel rats, and, conversely, spent less time exploring novel rats in comparison to the vehicle-treated group ($p \leq 0.05$, Figure 2A). Nevertheless, oral administration of only Cana (7.5 mg/kg) increased the time spent exploring novel rats and decreased the time spent exploring familiar rats compared to the VPA-treated group ($p \leq 0.05$, Figure 2A).
**FIGURE 2:** *Effect of canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) on social novelty test, time explore FR versus NR (A), time of direct contacts with NR (B), number of times of direct contact with NR (C), and time spent close to NR versus time spent far from NR (D) in VPA-induced autism in rats. VPA, valproic acid; Cana, canagliflozin; FR, familiar rat; NR, novel rat. Values are expressed as mean ± S.E.M. Results represented in Figures 2B, C were analyzed using one-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. Results represented in Figure 2A, D were analyzed using two-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. *Compared to the corresponding control group at p < 0.05, # compared to the corresponding VPA group at p < 0.05, $ compared to the corresponding VPA + Cana (5 mg/kg) group at p < 0.05, ¶ compared to the corresponding VPA + Cana (7.5 mg/kg) group, a compared to time exploring FR within the same experimental groups (A), a compared to Time Close to NR (D) at p < 0.05, n = 8, for all groups except for the control group, n = 10.*
Valproic acid injected SC at a dosage of 300 mg/kg resulted in less time spent and a reduced number of direct contact with novel rats compared with vehicle-injected rats ($p \leq 0.05$, Figures 2B, C). However, coadministration of Cana at doses of 5 and 7.5 mg/kg with VPA increased the time spent in direct contact with novel rats compared to the VPA-treated group ($p \leq 0.05$, Figures 2B, C).
Injection of VPA (300 mg/kg, SC) reduced the time spent close to novel rats compared to the vehicle-treated group ($p \leq 0.05$, Figure 2D). Furthermore, Cana (5 mg/kg and 7.5 mg/kg) amplified the time spent close to novel rats compared with the VPA-treated group ($p \leq 0.05$, Figure 2D). In the opposite way, the VPA-injected group augmented the time spent far from novel rats compared with the vehicle-injected group ($p \leq 0.05$, Figure 2D). Coadministration of Cana paralleled with VPA injection at doses of 7.5 mg/kg and 10 mg/kg decreased the time spent far from novel rats compared to the VPA-injected group ($p \leq 0.05$, Figure 2D). Oral administration of a Cana dose of 7.5 mg exhibited the greatest increase in the time spent close to novel rats and the greatest decrease in the time spent far from novel rats compared with the VPA-treated group ($p \leq 0.05$, Figure 2D).
## 3.2.2 Elevated plus-maze test (EPM)
Pups injected with VPA (300 mg/kg) raised the number of groomings and rearings compared to the vehicle-injected group ($p \leq 0.05$, Figures 3A, B), whereas oral administration of Cana (5, 7.5, and 10 mg/kg) inhibited the number of groomings compared with the VPA-treated group ($p \leq 0.05$, Figure 3A). In addition, Cana at doses of 7.5 mg and 10 mg revealed the same lowest number of groomings ($p \leq 0.05$, Figure 3A), while only Cana doses of 7.5 m/kg and 10 m/kg reduced the number of rearings in comparison with the VPA-treated group ($p \leq 0.05$, Figure 3B), with a dose of 7.5 mg/kg revealing the lowest number of rearings.
**FIGURE 3:** *Effect of canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) on elevated plus-maze (EPM) test; grooming (A), rearing (B), time spent in opened arms versus closed arms (C), and number of entries in opened arms versus closed arms (D). VPA, valproic acid; Cana, canagliflozin. Values are expressed as mean ± S.E.M. Results represented in Figures 3A, B were analyzed using one-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. Results represented in Figures 3C, D were analyzed using two-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. * Compared to the corresponding control group at p < 0.05, # compared to the corresponding VPA group at p < 0.05, $ compared to the corresponding VPA + Cana (5 mg/kg) group at p < 0.05, ¶ compared to the corresponding VPA + Cana (7.5 mg/kg) group, a compared to time spent in opened arms (C) and number of entries in opened arms (D) at p < 0.05, n = 8, for all groups except for the control group, n = 10.*
Valproic acid injection at a dose of 300 mg/kg inhibited the time spent in closed arms with no significant change in the time spent in opened arms compared to the vehicle-treated group ($p \leq 0.05$, Figure 3C). A Cana dose of 5 mg/kg amplified the time spent in opened arms in comparison to the VPA-treated group ($p \leq 0.05$, Figure 3C). Furthermore, Cana (5 mg/kg and 10 mg/kg) reduced the time spent in closed arms compared with the VPA-treated group ($p \leq 0.05$, Figure 3C). All groups intensified the latency in closed arms versus in opened arms (Figure 3C).
SC injection of VPA twice daily on PD-2 and PD-3 and once daily on PD-4 raised the number of entries in both opened arms and closed arms compared to the vehicle-treated group ($p \leq 0.05$, Figure 3D). Treatment with oral Cana (5 mg/kg and 10 mg/kg) augmented the number of entries in opened arms without a change in the number of entries within closed arms compared with the VPA-injected group ($p \leq 0.05$, Figure 3D). All groups except for the VPA + Cana 10 mg/kg group increased the number of entries in closed arms compared to opened arms.
## 3.2.3 Open field test (OF)
Valproic acid injection to pups at a dose of 300 mg/kg raised locomotor activity ($p \leq 0.05$, Figure 4A), inhibited the time spent in the central area ($p \leq 0.05$, Figure 4C), and increased the number of groomings and rearings compared to the vehicle-treated rats ($p \leq 0.05$, Figures 4D, E). Oral treatment with Cana (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) reduced locomotion compared with the VPA-injected group ($p \leq 0.05$, Figure 4A), with Cana (5 mg) displaying the highest decrease. Moreover, Cana (7.5 mg/kg and 10 mg/kg) prolonged the time to leave the central area in comparison with the VPA-treated group ($p \leq 0.05$, Figure 4B), with Cana (10 mg) pointing to the highest increase. In addition, Cana (7.5 mg and 10 mg) showed the same significant decrease in groomings compared with the VPA-treated group ($p \leq 0.05$, Figure 4D). However, Cana at a 5 mg/kg dose only increased the time spent in the central area compared to the VPA-treated group ($p \leq 0.05$, Figure 4C). All Cana doses displayed no significant difference in rearings compared to the induction group (Figure 4E).
**FIGURE 4:** *Effect of canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) on open field (OF) test; locomotion (A), latency to leave central area (B), time spent in central area (C), grooming (D), and rearing (E) in VPA-induced autism in rats. VPA, valproic acid; Cana, canagliflozin. Values are expressed as mean ± S.E.M and analyzed using one-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. * Compared to the corresponding control group at p < 0.05, # compared to the corresponding VPA group at p < 0.05, $ compared to the corresponding VPA + Cana (5 mg/kg) group at p < 0.05, ¶ compared to the corresponding VPA + Cana (7.5 mg/kg) group at p < 0.05, n = 8, for all groups except for the control group, n = 10.*
## 3.3 Effect of canagliflozin on VPA-induced histopathological changes
Histopathological evaluation of control group (group I) specimens, with H and E stain, presented the normal cerebellar cortex layers, and the molecular and the Purkinje cell with the granular layers (Figure 5A). The molecular layer appeared as a pale zone with few stellate cells. The Purkinje layer contained a large number of Purkinje cells with a single row of intact oval or flask-shaped cell bodies and a large, rounded vesicular normal central nucleus with a regular intact envelope (Figures 5A, F) as well as a huge number of myelinated axons (Figure 5A). The internal granular layer displayed small, deeply stained granular cells, while the external granular layer presented with small, closely packed cells with a deeply stained, normally rounded nucleus (Figure 5A).
**FIGURE 5:** *Effect of canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) on the histopathological picture of the cerebellum tissues of the brain in VPA-induced autism in rats. Hematoxylin and eosin stain (50 µm and 20 µm) (A–E). Number of Purkinje cells of the experimental groups (F). VPA, valproic acid; Cana, canagliflozin. Values are expressed as mean ± S.E.M and analyzed using one-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. * Compared to the corresponding control group at p < 0.05, # compared to the corresponding VPA group at p < 0.05, $ compared to the corresponding VPA + Cana (5 mg/kg) group at p < 0.05, ¶ compared to the corresponding VPA + Cana (7.5 mg/kg) group at p < 0.05, n = 8, for all groups except for the control group, n = 10.*
Valproic acid injections to pups at a dose of 300 mg/kg displayed a neurotoxic effect on the cerebellum Purkinje cells. Purkinje cells displayed lowered density, and marked depletion and degeneration. In addition, they appeared shrunken and disorganized in the uneven cell membrane which enclosed vacuolated spaces (empty haloes) (Figures 5B, F). Nuclear damage of the nuclei looked shrunken with an irregular nuclear envelope (Figure 5B). Degenerated axons were located completely vacuolated with a reduction of the myelin sheath (Figure 5B). A large number of degenerated Purkinje cells, along with degenerated swollen vacuolated axons, lacked neurofilaments and organelles (Figure 5B).
The molecular and internal granular layers exhibited massive reduction (Figure 5B). The molecular layers enclosed deeply stained, scattered basket cells (Figure 5B). The internal granular layer presented with small cells packed within congested intercellular spaces (Figure 5B). An external granular layer was noticed on the cerebellar surface in some sections that showed degenerative changes in the cerebellar cortex of deeply stained cells (Figure 5B).
The experimental groups co-treated with VPA and Cana (III, IV, and V) established recovery of the degenerated cerebellar construction that appeared in the VPA-treated group (Figures 5C–E). Cana treatment preserved the normal arrangement construction of the Purkinje cells layer with intact myelinated axons and nuclei (Figures 5C–E). Purkinje cells returned to their normal oval flask-shaped appearance and number, and were arranged in a single row (Figures 5C–E). The layer’s thickness and normal arrangement of the cerebellar cortex mimicked a histopathological picture of the control group (Figures 5C–E). The molecular layer demonstrated scattered basket cells. The internal and external granular layers appeared normal with a totally intact nucleus and cytoplasm (Figures 5C–E).
## 3.4 Effect of canagliflozin on PPAR-γ, LDHA, PDK, and c-Myc gene expression
Injection with VPA of 300 mg/kg developed a downregulation in the gene expression of PPAR-γ in the cerebellum by $90\%$ and cerebrum tissues of the brain by $95\%$ compared to the vehicle-treated group ($p \leq 0.05$, Figure 6A). Oral administration of Cana doses of 7.5 mg/kg and 10 mg/kg upregulated gene expression of PPAR-γ compared to the VPA-treated group in both the cerebellum by (2, 3, and 4 folds, respectively) and cerebrum tissues of the brain by 1.5, 3, and 9 folds, respectively ($p \leq 0.05$, Figure 6A). Furthermore, treatment with Cana at a dose of 10 mg/kg displayed the highest upregulation in gene expression to reach almost 9 folds greater than the VPA group in cerebrum tissues ($p \leq 0.05$, Figure 6A2), while both Cana groups (7.5 mg and 10 mg) showed almost the same upregulation level of PPAR-γ in the cerebellum compared with the VPA-treated group ($p \leq 0.05$, Figure 6A1).
**FIGURE 6:** *Effect of canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) on the expression of PPAR-γ (A), LDHA (B), PDK (C), and c-Myc (D) gene in the cerebellum and cerebrum tissues of VPA-induced autism in rats. VPA, valproic acid; Cana, canagliflozin. Results are expressed as mean ± S.E.M and analyzed using one-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. * Compared to the corresponding control group at p < 0.05, # compared to the corresponding VPA group at p < 0.05, $ compared to the corresponding VPA + Cana (5 mg/kg) group at p < 0.05, ¶ compared to the corresponding VPA + Cana (7.5 mg/kg) group at p < 0.05, n = 8, for all groups except for the control group, n = 10.*
On the other hand, SC injection with VPA resulted in the upregulation of LDHA gene expression in both the cerebellum and cerebrum tissues of the brain compared to the vehicle-treated group ($p \leq 0.05$, Figure 6B). The expression of LDHA increased by 4 folds in the cerebellum and 3 folds in the cerebrum tissues isolated from VPA-treated animals. Oral administration of Cana (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) downregulated the gene expression of LDHA compared to the VPA group in both the cerebellum by $22\%$, $64\%$, and $73\%$, respectively, and cerebrum tissues of the brain by $51\%$, $60\%$, and $75\%$, respectively ($p \leq 0.05$, Figure 6B). Furthermore, Cana (10 mg/kg) established the highest downregulation of LDHA gene expression among all treatment groups compared to the induction group in the cerebellum and cerebrum ($p \leq 0.05$, Figure 6B).
Injection with VPA twice a day on PD2 and PD-3, and once daily on PD-4, with a dose of 300 mg/kg caused upregulation in the gene expression of PDK by 12 folds in the cerebellum and cerebrum tissues of the brain by 9 folds compared with the vehicle group ($p \leq 0.05$, Figure 6C). Administration of Cana (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) downregulated the gene expression of PDK compared to the VPA group in both the cerebellum by $27\%$, $50\%$, and $67\%$, respectively, and cerebrum tissues of the brain by $34\%$, $66\%$, and $77\%$, respectively ($p \leq 0.05$, Figure 6B). Moreover, Cana (10 mg/kg) presented the highest downregulation in PDK gene expression by $75\%$ among all treatment groups compared to the VPA-injected group in the cerebellum and cerebrum ($p \leq 0.05$, Figure 6B).
Injection with VPA (300 mg/kg, SC) resulted in a higher upregulation of c-*Myc* gene expression in the cerebellum by 7 folds and cerebrum tissues of the brain by 8 folds compared with the vehicle-treated group ($p \leq 0.05$, Figure 6D). Cana treatment doses of 5 mg/kg, 7.5 mg/kg, and 10 mg/kg downregulated c-*Myc* gene expression compared with the VPA-treated group in both the cerebellum by 35, $44\%$, and $72\%$, respectively, and cerebrum tissues of the brain by $19\%$, $58\%$, and $79\%$, respectively ($p \leq 0.05$, Figure 6D). Furthermore, treatment with Cana (10 mg/kg) presented the highest downregulation in c-*Myc* gene expression within all treatment groups compared with the VPA-treated group in both the cerebellum ($79\%$) and cerebrum ($72\%$) ($p \leq 0.05$, Figure 6D).
## 3.5 Effect of canagliflozin on GLUT-1 and PTEN protein expression
Injection with SC VPA twice daily on PD-2 and PD-3, and once on PD-4, resulted in increase in the protein expression of GLUT-1 in the cerebellum by 4 folds and cerebrum tissues of the brain by 3 folds compared to the vehicle-treated group ($p \leq 0.05$, Figures 7A, B). However, Cana administration with doses of 5 mg/kg, 7.5 mg/kg, and 10 mg/kg reduced the protein expression of GLUT-1 compared to the VPA group in both the cerebellum by $32\%$, $48\%$, and $49\%$, respectively, and cerebrum tissues by $30\%$, $50\%$, and $54\%$, respectively, of the brain ($p \leq 0.05$, Figures 7A, B). Furthermore, oral Cana at doses of 7.5 mg/kg and 10 mg/kg displayed the same highest decline in protein expression to reach almost $50\%$ compared to the VPA group ($p \leq 0.05$, Figures 7A, B) in the cerebellum and cerebrum tissues of the brain.
**FIGURE 7:** *Effect of canagliflozin (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) on the expression of GLUT-1 (B) and PTEN (C) protein in the cerebellum and cerebrum tissues of VPA-induced autism in rats. VPA, valproic acid; Cana, canagliflozin. Results are expressed as mean ± S.E.M and analyzed using one-way ANOVA, followed by Tukey’s post hoc multiple comparisons test. * Compared to the corresponding control group at p < 0.05, # compared to the corresponding VPA group at p < 0.05, $ compared to the corresponding VPA + Cana (5 mg/kg) group at p < 0.05, ¶ compared to the corresponding VPA + Cana (7.5 mg/kg) group at p < 0.05, n = 8, for all groups except for the control group, n = 10.*
Conversely, VPA injected into pups at a dosage of 300 mg/kg SC reduced the protein expression of PTEN in the cerebellum by $86\%$ and cerebrum tissues of the brain by $91\%$ compared to the vehicle-treated group ($p \leq 0.05$, Figures 7A, C). Protein expression of PTEN increased in Cana groups (5 mg/kg, 7.5 mg/kg, and 10 mg/kg) compared to the VPA group in both the cerebellum by 2, 5, and 6 folds, respectively, and cerebrum tissues of the brain by 6, 6, and 10, respectively ($p \leq 0.05$, Figures 7A, C). Furthermore, Cana group V (10 mg/kg) presented the highest increase in protein expression compared to the VPA-induced group in the cerebrum (10 folds) ($p \leq 0.05$, Figure 7.A2, C2), while both Cana doses of 7.5 and 10 mg showed the same increase of protein expression in the cerebellum (5 and 6 folds) compared to the VPA-treated group ($p \leq 0.05$, Figures 7A1, C1).
## 3.6 Effect of canagliflozin on tissue ACh levels
Valproic acid-injected rats (300 mg/kg, SC) addressed the reduction of ACh levels by $88\%$ in the cerebellum and the cerebrum tissues compared to vehicle-injected rats ($p \leq 0.05$, Figures 8A, B). Administration of Cana at doses of 5 mg/kg, 7.5 mg/kg, and 10 mg/kg increased ACh levels by 4, 5, and 7 folds, respectively, in the cerebellum and cerebrum tissues compared to the VPA-treated group ($p \leq 0.05$, Figures 8A, B). Cana 10 mg showed the highest surge in ACh levels among all treatment groups to reach 7 folds the concentration of ACh in the VPA-treated group ($p \leq 0.05$, Figures 8A, B).
**FIGURE 8:** *Effect of canagliflozin (5, 7.5, and 10 mg/kg) on cerebellum (A) and cerebrum (B) tissue level of ACh in VPA-induced autism in rats. VPA: valproic acid, Cana: canagliflozin. Results are expressed as mean ± S.E.M and analyzed using one-way ANOVA followed by Tukey post hoc multiple comparisons test. * Compared to the corresponding Control group at P < 0.05, # Compared to the corresponding VPA group at P < 0.05, $ Compared to the corresponding VPA + Cana (5 mg/kg) group at P < 0.05, ¶ Compared to the corresponding VPA + Cana (7.5 mg/kg) group at P < 0.05, n = 8, for all groups except for control group, n = 10.*
## 4 Discussion
In this behavioral study, subcutaneous injection of VPA resulted in several changes in behavioral and biochemical analyses that mimicked the changes that occur in autistic patients (Favre et al., 2013; Larner et al., 2021). Rat pups selected in the present study were of both sexes based on the study by Rasalam et al. [ 2005], which confirmed that there were no differences in the prevalence of autism in VPA-exposed children during pregnancy and considered a 1:1 male-to-female ratio (Rasalam et al., 2005).
In agreement with previous studies, numerous structural and functional changes either in the cerebral cortex or cerebellum were detected in VPA-induced ASD in rats and mice models (Schneider and Przewłocki, 2005; Wagner et al., 2006), with some resemblance to the changes studied in autistic children’ brains. Valproic acid significantly reduced the numbers and sizes of Purkinje cells in the cerebellum (Morakotsriwan et al., 2016). Similarly, prenatal VPA exposure induced remarkable cerebellar Purkinje and granular layers with axon degeneration (Hafez et al., 2018; Shona et al., 2018). Furthermore, Purkinje cell atrophy confirmed the neurotoxic effect of repeated VPA exposure on the cerebellar structure and function (Main and Kulesza, 2017).
Early postnatal cerebellum lesions increased spontaneous motor activity in rats (Bobée et al., 2000). Furthermore, the loss of Purkinje cells in mice induced significantly increased repetitive behaviors (Martin et al., 2010). Studies in rodents confirmed a vital role of the cerebellum in motor, repetitive, and exploratory behavioral deficits or anxiety-like behaviors as observed in autism (Pierce and Courchesne, 2001). The cerebellum defect resulted in many psychotic disorders, including anxiety (Baldaçara et al., 2008). Early prefrontal cortex (PFC) damage in humans impairs social interaction (Eslinger et al., 2004). Neonatal PFC lesions decreased social play and conditioned place preference associated with social contacts and social grooming in rats (Schneider and Koch, 2005).
VPA-treated rats displayed significantly decreased social behaviors, including sociability and social novelty indices (Mony et al., 2016; Morakotsriwan et al., 2016; Eissa et al., 2018). Valproic acid exposure impaired social abilities in a three-chamber social assay test in a VPA-induced rat model of autism (Chau et al., 2017; Wu et al., 2017; Rajizadeh et al., 2021). Similarly, the VPA-injected mice displayed fewer sociability and social preference behaviors (Roullet et al., 2010; Kim et al., 2014).
Rats injected with VPA showed no preference toward novel rats, either by spending more time with familiar rats and less time sniffing the novel rats compared with the saline-injected group (Campolongo et al., 2018; Larner et al., 2021) or by spending even time exploring novel and familiar mice, compared to the control group (Larner et al., 2021), and that is consistent with our findings.
Both sociability and social novelty behaviors’ studies confirmed the sociability deficits following early postnatal VPA. Furthermore, sociability and social memory are independent social behaviors which respond differently to environmental changes without any effect on each other, which is in line with a study that indicated autistic children tend to avoid social interaction (Kanner, 1943). Furthermore, the behaviors observed in children with ASD confirmed that autistic children are more secure and social with familiar individuals and objects (Mychasiuk et al., 2012). Impaired sociability is provoked via heightened anxiety or fear, which in turn leads to environmental fear stimuli (Stein et al., 2002; Tillfors, 2004).
Valproic acid-induced anxiety behavior is an autistic non-core symptom compared to control animals (Mohammadi et al., 2020). Moreover, VPA exposure from PD-2 to PD-4 stimulated both anxiety and hyperactivity behaviors in adolescent rats (Al-Amin et al., 2015; Mony et al., 2016). Early postnatal VPA exposure decreased the time spent in the center area while increasing locomotor activity compared with control mice (Bath and Pimentel, 2017). Similarly, the VPA-injected rats spent less time in the center of the open field, which is an indication of anxiety (Mohammadi et al., 2020; Larner et al., 2021). The pathological study demonstrated high rates of anxiety in VPA-exposed pediatrics (Glauser, 2004). Valproic acid exposure increased anxiety-like behaviors in EPM, which is demonstrated by spending more time in the closed arms in rats (Larner et al., 2021; Rajizadeh et al., 2021). Furthermore, disturbed anxiety levels and hyperactivity were observed in the VPA-treated group of mice in EPM (Eissa et al., 2018).
Valproic acid increases overall motor activity in rodents (Kim et al., 2014; Larner et al., 2021). Valproic acid induced an increase in the number of crossing bars of the OF test as a reflection of hyperlocomotion in rats (Schneider et al., 2008; Mony et al., 2016). The features of hyperactivity were reported in various mouse models of autism (Peñagarikano et al., 2011; Schmeisser et al., 2012).
Valproic acid treatment increased repetitive, stereotyped behavior measured in OF in rats as an ASD core symptom (Schneider and Przewłocki, 2005; Schneider et al., 2008). Prenatal VPA treatment significantly augmented the grooming and rearing number, and duration compared with the control group (Dai et al., 2018; Mohammadi et al., 2020). Mice injected with VPA increased repetitive-stereotyped movements with more time engaged compared to the control group (Zhang et al., 2012). Similarly, VPA-injected rats induced more grooming behaviors than those in the vehicle-treated group (Gandal et al., 2010; Mehta et al., 2011).
VPA resulted in social deficits in exposed mice, as changes in the ACh level triggered abnormal social, hyperactive, repetitive, and anxiety-like behaviors (Kim et al., 2014). Upregulation of acetylcholinesterase (AChE) protein expression was detected in both human and animal studies (Friedman et al., 2006; Karvat and Kimchi, 2014). Similarly, AChE expression increased in cultures treated with VPA (Kim et al., 2014). Furthermore, decreased levels of ACh in the prefrontal cortex resulted in attention deficit and impulsive behavior in mice (McTighe et al., 2013). ACh downregulation, a neurotransmitter for neuronal development in the brain (Picciotto et al., 2012), was apparently observed in the brain of ASD patients, which further resulted in behavioral changes in autistic patients (Petersen et al., 2013).
VPA administration altered PTEN expression in the brain (Ha et al., 2017). Similarly, prenatal VPA administration to mice reduced PTEN in the hippocampus and cortex, resulting in developmental delay and neuroanatomical changes (Yang et al., 2016). PTEN is downregulated in autistic glial cells (Zhou and Parada, 2012).
A deficiency of PTEN expression in the Purkinje cells of the cerebellum caused repetitive behavior, sociability deficits, and motor-learning defects in mice. PTEN-deficient mice displayed hyperactivity with impaired social activity (Ogawa et al., 2007). PTEN ± mice, detected in Purkinje cells, impaired sociability behaviors with deficits in motor learning (Lugo et al., 2014; Kwan et al., 2016). The downstream pathway of PTEN resulted in behavioral abnormalities and played a significant role in ASD (Clipperton-Allen and Page, 2014; Lugo et al., 2014).
Nuclear β-catenin translocation interacted with TCF/LEF, which stimulated the target genes, PDK and cMyc (Lecarpentier et al., 2017). Similarly, Wnt/β-catenin pathway over-activation stimulated aerobic glycolysis via induction of PDK (Lecarpentier et al., 2017; Vallée et al., 2017; Vallée and Vallée, 2018). PDK1, a glycolysis regulator, phosphorylated the PDH complex, inhibiting cetyl-CoA formation from pyruvate in mitochondria (Lecarpentier et al., 2017). Then, cytosolic pyruvate is directed for lactate formation and then released by LDHA and MCT-1 from the cell (Zhang et al., 2014).
c-Myc also activated LDHA, which stimulated the pyruvate conversion to lactate (Dang, 2010). Furthermore, the study indicated a significant increase in LDHA expression (Khemakhem et al., 2017) in ASD patients. PKM2 bound β-catenin via c-Myc in the nucleus for further induction of glycolytic enzyme expression of GLUT, LDHA, and PDK1 (Yang et al., 2012).
Downregulation of the Wnt/β-catenin pathway stimulated PPAR γ, while PPAR γ induction reduced the expression of β-catenin (Moldes et al., 2003; Jansson et al., 2005). In fact, both the Wnt/β-catenin pathway and PPAR γ counteract each other in various diseases, such as cancers (Vallée et al., 2017).
Co-administration of Cana with VPA improved the impaired behavior of VPA-treated rats, which could partly be explained by amplified ACh levels. The previous study confirmed that donepezil, the AChE inhibitor, rescued the autistic behaviors in VPA-treated mice via upregulation of the ACh level (Kim et al., 2014). Treatment with donepezil reduced impaired sociability, hyperactivity, anxiety-like behaviors, and repetitive digging behavior in mice treated with VPA (Kim et al., 2014). In addition, pre-treating mice with donepezil relieved anxiety by inhibiting the hyperactivity observed in EPM via attenuating the spending time as well as the entry number in opened arms, as an indication of having protective effects on cognitive functions in the VPA model of autism (Eissa et al., 2018). The changes in ACh levels in the cerebral cortex contributed to abnormal social and repetitive behaviors (Kim et al., 2014). The sociability index in the three-chamber test increased with donepezil administration through the elevation of ACh levels in mice (Karvat and Kimchi, 2014; Kim et al., 2014).
PPAR γ agonist stimulated PTEN expression (Vallée et al., 2017; Vallée and Vallée, 2018). Similarly, pioglitazone, as a PPAR γ agonist, recovered most of the typical behaviors of autism by correcting social as well as communication deficits in lipopolysaccharide (LPS)-induced autistic-like behaviors in rats (Kirsten et al., 2018; 2019). Pioglitazone improved behavior changes during adulthood in rats of the endotoxin model of autism (Kirsten et al., 2018). Furthermore, daily pioglitazone treatment effectively attenuates hyperactivity, stereotypic behaviors, irritability, and lethargy measured in autistic children without significant side effects (Boris et al., 2007).
Cana reduced the translocation of β-catenin in the nucleus (Hung et al., 2019). Similarly, PPAR γ agonists inhibited β-catenin; otherwise, PPAR γ was activated via canonical Wnt/β-catenin pathway inhibition (Lecarpentier et al., 2017). Furthermore, troglitazone, a PPAR γ agonist, reduced the level of c-Myc (Akinyeke and Stewart, 2011). Along the same line, PPAR γ activation selectively decreased PDK mRNA (Abbot et al., 2005). Clinical trials that studied pioglitazone suggested that PPARs be targeted for drug therapy of ASD (Boris et al., 2007; Ghaleiha et al., 2015).
Pioglitazone improved glucose utilization in addition to lactate production in brain glial cells (Pilipović et al., 2015). When increasing the dose, Cana acted on SGLT2 in addition to other glucose transporters, mainly GLUT1 (Nomura et al., 2010; Gurney et al., 2012). Furthermore, Cana blocked glucose influx-mediated β-catenin activation (Hung et al., 2019).
## 5 Conclusion
Canagliflozin provides a neuroprotective mechanism via PTEN/PDK/PPAR-γ signaling pathways in VPA-induced autism in rats. The current study confirmed that the protective effect of Cana against the induction of autism in rats with valproic acid involved significant ameliorating effect on the canonical Wnt/β-catenin pathway. This effect was reflected in improving the major core behaviors characterized for autism, enhancing sociability and social preference, inhibiting stereotypic behaviors, and decreasing hyperlocomotion activity with significant improvement of histopathological features of the brain.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by Faculty of Pharmacy, Suez Canal University.
## Author contributions
Conceptualization: ME, DK, and NES; data formation: ME, DK, BAW, II, ARA, and NES; visualization: DK, BAW, II, ARA, YM, AAA, and NES; software: II, ARA, YM, AAA, and NES; writing—original manuscript: ME and NE; reviewing and editing: ME, DK, BAW, II, ARA, YM, AAA, and NES; funding: ME, DK, BAW, II, ARA, YM, AAA, and NES; project administration: ME, DK, BAW, II, ARA, YM, AAA, and NES; supervision: ME, DK, BAW, II, ARA, YM, AAA, and NES.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Identification and validation of immune and oxidative stress-related diagnostic
markers for diabetic nephropathy by WGCNA and machine learning
authors:
- Mingming Xu
- Hang Zhou
- Ping Hu
- Yang Pan
- Shangren Wang
- Li Liu
- Xiaoqiang Liu
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9992203
doi: 10.3389/fimmu.2023.1084531
license: CC BY 4.0
---
# Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning
## Abstract
### Background
Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed.
### Methods
Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database.
### Results
Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters.
### Conclusion
By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.
## Introduction
Diabetic nephropathy (DN), characterized by proteinuria, hypertension, and progressive reductions in kidney function, is the most common cause of end-stage renal disease in developed countries and poses a serious social and economic burden (1–3). According to studies, the number of individuals with DN is rising along with the global prevalence of diabetes, which is predicted to climb from 537 million to 783 million over the course of the next 20 years or so [4]. The present course of therapy, in contrast, emphasizes renin-angiotensin system blockage, blood pressure management, and glycemic control [5]. As a result, novel targets for DN diagnosis and therapy are desperately needed. With the advancement of bioinformatics, its research techniques have been actively used in recent years to explore targets for numerous illnesses, including DN.
A significant amount of data points to the importance of immune and oxidative stress in the etiology of diabetic nephropathy [6]. In this research, we identified diagnostic genes for DN by a bioinformatic approach combining immune infiltration and oxidative stress and validated them with an additional external dataset, as shown in Figure 1 for the specific study route.
**Figure 1:** *Flowchart for research.*
## Source of data
We screened three diabetic nephropathy datasets: GSE30528 (GPL571) contained nine cases of diabetic nephropathy and thirteen controls; GSE104948 (GPL22945) served as a validation set and contained seven cases of diabetic nephropathy and eighteen controls; and GSE131882 (GPL24676) contained three early diabetic nephropathy and three control samples for single nucleus RNA sequencing. Additionally, using a relevance score of greater than 7 as a screening criterion, we were able to extract 855 genes associated with oxidative stress from the Genecard database. Table 1 displays the pertinent details.
**Table 1**
| Dataset | Database | Platform | Sample |
| --- | --- | --- | --- |
| GSE30528 | GEO | GPL571 | 9 cases of DN and 13 controls |
| GSE104948 | GEO | GPL22945 | 7 cases of DN and 18 controls |
| Oxidative stress-related genes | Genecard | Genecard | Obtaining oxidative stress-related genes from Genecard |
| GSE131882 | GEO | GPL24676 | 3 cases of DN and 3 controls |
## Identification of DEGs
With |log2 fold change (FC)| > 0.5 and $p \leq 0.05$ as screening criteria, differentially expressed genes (DEGs) from GSE30528 were identified utilizing “Limma” R package, where log FC > 0.5, $p \leq 0.05$ was Up, log FC < -0.5, $p \leq 0.05$ was Down. The heat map and volcano map of DEG were plotted using the “Pheatmap” R package and “ggplot2” R package, respectively.
Subsequently, the obtained DEGs were intersected with 855 oxidative stress-related genes to obtain differentially expressed genes related to oxidative stress (DEOSGs).
A total of 1696 DEGs were acquired from GSE30528, and another 855 oxidative stress-related genes were mined from the Genecard database, and 111 DEOSGs were generated by taking the intersection of the two (Figures 2A–C).
**Figure 2:** *Screening for DEGs. (A) Volcano plot of DEGs in GSE30528. (B) Heatmap of DEGs in GSE30528. (C) Venn diagrams of DEOSGs. DEGs, differentially expressed genes; DEOSGs, differentially expressed genes related to oxidative stress.*
## Immune infiltration analysis and construction of weighted gene co-expression networks
CIBERSORT employs a deconvolution algorithm to estimate the composition and abundance of immune cells in a mixture of cells based on transcriptome data. In the present study, we first assessed the proportion of 22 immune cell species in normal and diabetic nephropathy samples in GSE30528 using the CIBERSORT algorithm [7].
Weighted Gene Go-expression Network Analysis (WGCNA) is performed to identify modules of highly correlated genes, summarize the interconnections between modules and associations with external sample traits, and identify candidate biomarkers or therapeutic targets. In our research, WGCNA was constructed by the R package “WGCNA” to identify the modules with the highest relevance to immune cells in diabetic nephropathy patients [8]. Specifically, we preprocessed the sample data and removed the outliers. Subsequently, the correlation matrix was constructed by the “WGCNA” software package. The optimal soft threshold was chosen to convert the correlation matrix into an adjacency matrix, and a topological overlap matrix (TOM) was created from the adjacency matrix. The TOM-based phase dissimilarity metric was utilized to categorize genes with similar expression patterns into gene modules using average linkage hierarchical clustering. The two modules with the strongest relevance to immune cells were selected as key modules for subsequent analysis.
Finally, the genes in DEOSGs and key modules were intersected, and the intersected genes were described as differentially expressed immune-related oxidative stress genes (DEIOSGs) for further study.
## Gene ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis
In this research, the “clusterProfiler” R package was implemented to conduct GO and KEGG functional enrichment analysis in R to assess gene-related biological processes (BP), molecular functions (MF), cellular components (CC), and gene-related signaling pathways.
## Screening hub genes by machine learning and PPI networks
Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis is a data mining method that sets the coefficients of less important variables to zero by applying the L1-penalty (lambda) in order to filter out the significant variables and construct the best classification model [9]. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) analysis is a supervised machine learning technique for identifying the optimal core genes by dropping the feature vectors generated by SVM [10]. Random Forest (RF) analysis is a decision tree-based machine learning method that focuses on evaluating the significance of variables by scoring the importance of each variable [11]. In combination with machine learning algorithms, the cytoHubba plugin is frequently applied for the identification of key genes. On the one hand, diagnostic genes from DEIOSG were assessed using the three machine learning algorithms separately [12]. After that, the intersection of the three machine learning algorithms was established.
On the other hand, the STRING database was exploited to establish protein-protein interaction (PPI) networks, which Cytoscape then visualized. The differential genes were then evaluated using 12 algorithms in the cytoHubba plugin, and finally the top 10 genes for each algorithm were taken as intersection and visualized through the ImageGP platform [13].
Ultimately, the genes obtained by both methods in total were identified as hub genes.
Firstly, 6 genes were extracted from DEIOSGs using the LASSO regression algorithm (Figure 5A). Secondly, the SVM-RFE algorithm identified 6 genes (Figure 5B). Then, 7 genes were selected by the RF algorithm (Figure 5C). Subsequently, the three were overlapped by the Venn diagram and finally two genes were obtained, namely CD36 and SLC1A3 (Figure 5D). Meanwhile, from the PPI network, we obtained a gene, namely ITGB2, through the cytoHubba plugin (Figures 6A, B). Finally, a total of 3 hub genes were identified by both methods, all of which were up-regulated.
**Figure 5:** *Screening hub genes by machine learning. (A) LASSO regression algorithm. (B) SVM-RFE algorithm. (C) RF algorithm. (D) Venn diagrams for three algorithms. LASSO, Least Absolute Shrinkage and Selection Operator; SVM-RFE, Support Vector Machine-Recursive Feature Elimination; RF, Random Forest.* **Figure 6:** *Screening hub genes by PPI network. (A) PPI network. (B) Venn diagrams for 12 algorithms in cytoHubba plugin. PPI, protein-protein interaction.*
## Clinical analysis
The Nephroseq v5 database (http://v5.nephroseq.org) [14] is a comprehensive information platform for evaluating the correlation between gene expression levels and clinical characteristics of kidney diseases. To explore the correlation between the expression of hub genes and clinical features, we mined the Nephroseq v5 database.
In DN patients, correlation analysis revealed a negative correlation between CD36 expression and glomerular filtration rate (GFR) (r = -0.860, $p \leq 0.001$) and a positive correlation between CD36 expression and serum creatinine ($r = 0.887$, $p \leq 0.001$) (Figures 10A, B). ITGB2 expression was negatively correlated with glomerular filtration rate (GFR) (r = -0.2031, $$p \leq 0.6002$$) but not statistically different, whereas ITGB2 expression was positively correlated with serum creatinine ($r = 0.5590$, $$p \leq 0.020$$) (Figures 10C, D).
**Figure 10:** *Correlation analysis. (A, B) Correlation analysis of CD36 with GFR and serum creatinine. (C, D) Correlation analysis of ITGB2 with GFR and serum creatinine. GFR, glomerular filtration rate.*
## GSEA analysis
We performed a single-gene GSEA analysis to investigate the possible roles of hub genes.
According to GSEA findings, the CD36 high expression group was highly enriched for primary immunodeficiency and viral protein interaction with cytokines and cytokine receptors (Figure 9A). The ITGB2 high expression group was mostly concentrated in the citrate cycle (TCA cycle) and proteasome (Figure 9B). Allograft rejection, primary immunodeficiency, and systemic lupus erythematosuswere all associated with increased SLC1A3 expression (Figure 9C).
**Figure 9:** *(A-C) GSEA analysis of hub genes.*
## Regulatory network construction and potential drug prediction
The JASPAR database [15] and the TarBase database [16] were accessed by the NetworkAnalyst (https://www.networkanalyst.ca/) [17] to predict transcription factors (TFs) and miRNAs, respectively. Subsequently, the results were visualized using Cytoscape software.
We used the Enrichr platform (https://amp.pharm.mssm.edu/Enrichr/) [18] to access the DSigDB database [19] for potential drug prediction.
Using the JASPAR database, 31 TFs were finally obtained, among which, there were 9 TFs with degree≥2, and they were FOXC1, FOXL1, YY1, PPARG, STAT3, HINFP, MAX, USF1, USF2 (Figure 11A). Possible miRNAs were predicted by the TarBase database with 10 miRNAs of degree≥2 (Figure 11B).
**Figure 11:** *Regulatory network. (A) Interaction network of TFs and genes for the hub genes. (B) Network of interactions between miRNAs and the hub genes. TF, transcription factors; miRNA, microRNA.*
Eighty-seven potential therapeutic agents were screened in the DSigDB database with a cut-off value of Adjusted p-value < 0.05 (Supplementary Table 1).
## Single nucleus RNA sequencing
A single-cell sequencing database for kidney disease called the Kidney Integrative Transcriptomics (K.I.T.) database was developed by Ben Humphrey’s lab at Washington University (http://humphreyslab.com/SingleCell/) [20]. To explore the distribution of hub genes in cell groups, we applied the database for analysis and visualization of the results. In one of them, we used single nucleus RNA sequencing data from diabetic nephropathy that was initially taken from the GSE131882 dataset.
By single nucleus RNA sequencing, we determined the distribution of CD36, ITGB2 and SLC1A3 in 12 cell groups (Figure 12A), among which CD36 was mainly distributed in endothelium and ITGB2 and SLC1A3 were highly expressed in leukocyte (Figures 12B-D).
**Figure 12:** *Single Nucleus RNA Sequencing. (A) The distribution of hub genes in 12 cell groups. (B) CD36. (C) ITGB2. (D) SLC1A3. PCT, proximal convoluted tubule; CD, collecting duct; ICA, Type A intercalated cells; ICB, Type B intercalated cells; PEC, parietal epithelial cells; PC, principal cell; DCT, distal convoluted tubule; CT, connecting tubule; LOH, loop of Henle; PODO, podocyte; ENDO, endothelium; MES, mesangial cell; LEUK, leukocyte.*
## Statistical analysis
GraphPad Prism 8.0 (GraphPad Software, CA, USA) was implemented to conduct the statistical analysis. The diagnostic value of hub genes was evaluated with ROC curve analysis. *Hub* genes were analyzed for correlation with clinical features via Pearson analysis. An unpaired t-test was performed for the assessment of hub gene differential expression. $P \leq 0.05$ was defined as statistically significant.
## Immune infiltration analysis and construction of weighted gene coexpression networks
Five immune cell types, including T cells CD4 naive, T cells gamma delta, NK cells resting, Dendritic cells resting, and mast cells resting, were demonstrated to be comparable between DN and control samples using the CIBERSORT algorithm (Figure 3A).
**Figure 3:** *Immune infiltration analysis and construction of weighted gene co-expression networks. (A) 22 immune cells in samples with normal and diabetic nephropathy in GSE30528. (B) Choosing the best soft-threshold power. (C) 11 modules revealed by the WGCNA. WGCNA, weighted gene co-expression network analysis.*
The soft-threshold power in this research was calibrated to 14 (scale-free R2 = 0.85) (Figure 3B). Last but not least, a sum of 11 modules was revealed by the WGCNA analysis (Figure 3C). In particular, the green module and the magenta module had strong positive correlations with T cell CD4 naive and gamma delta subsets, respectively. Due to their significance in association with immunological infiltrating cells, the green and magenta modules were considered for additional investigation.
## Acquisition and functional enrichment analysis of DEIOSGs
DEIOSGs are the genes that overlap DEOSGs with the magenta and green modules generated by WGCNA, and a total of 24 DEIOSGs were identified (Figure 4A).
**Figure 4:** *Acquisition and functional enrichment analysis of DEIOSGs. (A) Venn diagrams of DEIOSGs. (B) The GO outcomes are displayed with a bubble plot. (C) A bubble plot was constructed to illustrate the KEGG outcomes. (D) Results of KEGG are depicted on circle charts. DEIOSGs, differentially expressed immune-related oxidative stress genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function.*
Furthermore, we performed the functional enrichment of 24 DEIOSGs via GO and KEGG. In the BP assessment, DEIOSGs were mostly engaged in superoxide metabolic processes, neutrophil activation, and other functions. DEIOSGs have been localized to the external side of the plasma membrane, endocytic vesicle, and other structures in CC. DEIOSG changes associated with MF include amide binding, integrin binding, and superoxide-generating NAD(P)H oxidase activity (Figure 4B). According to KEGG analysis, DEIOSGs are particularly abundant in leukocyte transendothelial migration, neutrophil extracellular trap formation, lipid and atherosclerosis, diabetic cardiomyopathy, natural killer cell mediated cytotoxicity and other pathways (Figures 4C, D).
## Expression of hub genes and validation of external datasets
When compared to the normal control sample, we discovered in the GSE30528 dataset that these genes were expressed more highly in DN (Figures 7A–C). We next confirmed the expression of these genes using another dataset, and the results revealed that these genes were likewise more strongly expressed in DN than control in GSE104948, and they were all statistically significant (Figures 7D–F).
**Figure 7:** *Expression of hub genes and validation of external datasets. (A-C) Expression of hub genes in the GSE30528 dataset. (D-F) Expression of hub genes in the GSE104948 dataset. * p<0.05; ** p<0.01; *** p<0.001; **** p<0.0001.*
## ROC curve analysis
To explore the diagnostic efficacy of the 3 hub genes, we implemented a ROC curve analysis in which hub genes with an AUC value > 0.7 were used as diagnostic markers. In the GSE30528 dataset, the AUC values were 0.8215 for CD36, 0.9402 for SLC1A3, and 0.9060 for ITGB2 (Figures 8A–C).
**Figure 8:** *ROC curve analysis. (A–C) Hub genes in the GSE30528 dataset were analyzed using ROC curves. (D–F) Hub genes in the GSE104948 dataset were analyzed using ROC curves.*
In the GSE104948 dataset, the AUC values of CD36 were 1.000 ($95\%$ CI: 1.000-1.000), AUC values of SLC1A3 were 0.7937 ($95\%$ CI: 0.5244-1.000), AUC values of ITGB2 were 0.9921 ($95\%$ CI: 0.9669-1.000) (Figures 8D–F).
## Discussion
Diabetic nephropathy is triggered by a combination of several factors [21]. However, its specific mechanisms remain to be explored. Due to the heterogeneity of individuals, the present therapeutic effects for diabetic nephropathy are constrained, making the necessity for novel molecular pathways that contribute to DN therapy and diagnosis essential. The progression of DN has been determined to be significantly controlled by immune infiltration and oxidative stress [22, 23]. Meanwhile, with the progression of a diverse range of informatics technologies, machine learning algorithms and WGCNA have become more mature and are widely applied for the prediction of disease markers and therapeutic targets. In this research, we retrieved transcriptomic datasets from the GEO database and, combining machine learning, WGCNA, and PPI networks, identified a set of three immune and oxidative stress-related hub genes, namely CD36, ITGB2, and SLC1A3, and validated them with an additional dataset. We implemented ROC curve analysis to assess the diagnostic value of hub genes, and the results showed that all three hub genes had excellent diagnostic efficacy.
CD36, commonly regarded as a scavenger receptor, is located in a wide range of renal cells [24], which is consistent with our single nucleus RNA sequencing analysis. Lipid metabolism, immunological inflammation, and renal fibrosis are its key areas of involvement. According to research, a possible therapeutic target for the prevention of renal fibrosis may be CD36 [25]. Little research has been performed on the function of CD36 in immune-related oxidative stress, even though CD36 is broadly investigated in the pathogenesis of DN. In this research, we discovered that CD36 expression was elevated in the renal tissues of individuals with diabetic nephropathy and had a diagnostic accuracy value (AUC > 0.80). Cohort studies revealed that sCD36 levels in plasma and urine were raised in DN patients and correlated with DN severity, indicating that sCD36 may be a diagnostic marker for DN progression [26]. Furthermore, the mechanism of CD36 engagement in DN is mostly attributed to oxidative stress triggered by lipid deposition [27], which is consistent with the results of our functional enrichment analysis. Hou Y. et al. revealed that CD36 contributed to DN progression by triggering epithelial-mesenchymal transition (EMT) through the induction of reactive oxygen species (ROS) production [28]. Additionally, the outcomes of animal studies suggested that inhibiting CD36 might shield diabetic mice from kidney harm and oxidative stress [29].
ITGB2, a member of the integrin family, is mostly expressed in immune cells and is connected to a variety of metabolic pathways as well as immune functions such as leukocyte extravasation [30]. Similarly, ITGB2 is crucial for the growth of tumors. For instance, it is primarily in charge of the invasion and metastasis of tumor cells in gliomas, which is closely connected to the immune microenvironment [31]. The engagement of ITGB2 in DN development, however, has received relatively little research. In our research, we observed that ITGB2 with upregulated expression also has excellent diagnostic efficacy (AUC > 0.90). Based on the most recent experimental research, ITGB2 is essential for the progression of diabetes, and the ITGB2 gene deficiency may hopefully prevent the disease [32]. This paves the way for ITGB2 to become a diagnostic marker for DN. Furthermore, there is a growing consensus that EMT is essential for the development of DN [33, 34]. And ITGB2 is also closely related to the regulation of EMT [35, 36].
SLC1A3, an aspartate and glutamate transporter, is abundantly expressed in cerebral and tumor tissues and is associated with immune inflammation as well as proliferation and metastasis of tumors [37]. It has also been proposed that SLC1A3 is involved in the amino acid-related metabolism of adipocytes [38]. Furthermore, insulin has been demonstrated to regulate the expression and activity of SLC1A3 [39]. And SLC1A3 is mainly involved in diabetic retinopathy in diabetic complications [40]. In our results, SLC1A3 is expressed more strongly in DN patients than in healthy controls.
According to the results of our investigation, CD36, which was upregulated in renal tissue, was significantly linked to reduced GFR and increased serum creatinine, implying that CD36 expression may be associated with reduced renal function in patients with DN. ITGB also has a similar presentation.
As we all know, the two key mechanisms in the progression of DN are oxidative stress and immunity, and they are inexorably intertwined. Hyperglycemia is a central factor in kidney damage in DN patients [41]. On one hand, hyperglycemia induces oxidative stress by activating the renin-angiotensin-aldosterone system (RAAS), which leads to renal injury [42]. On the other hand, the stress caused by persistent hyperglycemia can lead to a high production of inflammatory molecules and the accumulation of immune complexes, a process that is closely related to immune cells such as mast cells [43]. The results of immune infiltration analysis also suggest that mast cells, NK cells and T cells are closely associated with the development of DN. In addition, the results of our functional enrichment analysis also suggest that DEIOSGs are mainly enriched in immune and oxidative stress-related pathways. Therefore, therapeutic strategies targeting immune and oxidative stress are particularly important and promising.
However, this study has several limitations. The evidence is based on publicly available data, and although we performed expression validation with another dataset, further experiments are needed to validate these 3 diagnostic markers before they can be applied to the clinic.
In conclusion, by combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.
## Author contributions
The manuscript was conceived by MX. PH and MX were responsible for software operation and analysis. PH, HZ, and YP performed the data compilation. Data analysis and interpretation were performed by HZ and SW. MX completed the manuscript. XL and LL were responsible for manuscript review and revision. The article was submitted with the authorization of all authors who also contributed to the article.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1084531/full#supplementary-material
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|
---
title: Multiplatform molecular analysis of vestibular schwannoma reveals two robust
subgroups with distinct microenvironment
authors:
- Alexander P. Landry
- Justin Z. Wang
- Suganth Suppiah
- Gelareh Zadeh
journal: Journal of Neuro-Oncology
year: 2023
pmcid: PMC9992225
doi: 10.1007/s11060-022-04221-2
license: CC BY 4.0
---
# Multiplatform molecular analysis of vestibular schwannoma reveals two robust subgroups with distinct microenvironment
## Abstract
### Background
Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle and poses a significant morbidity for patients. While many exhibit benign behaviour, others have a more aggressive nature and pattern of growth. Predicting who will fall into which category consistently remains uncertain. There is a need for a better understanding of the molecular landscape, and important subgroups therein, of this disease.
### Methods
We select all vestibular schwannomas from our tumour bank with both methylation and RNA profiling available. Unsupervised clustering methods were used to define two distinct molecular subgroups of VS which were explored using computational techniques including bulk deconvolution analysis, gene pathway enrichment analysis, and drug repurposing analysis. Methylation data from two other cohorts were used to validate our findings, given a paucity of external samples with available multi-omic data.
### Results
A total of 75 tumours were analyzed. Consensus clustering and similarity network fusion defined two subgroups (“immunogenic” and “proliferative”) with significant differences in immune, stroma, and tumour cell abundance ($p \leq 0.05$). Gene network analysis and computational drug repurposing found critical differences in targets of immune checkpoint inhibition PD-1 and CTLA-4, the MEK pathway, and the epithelial to mesenchymal transition program, suggesting a need for subgroup-specific targeted treatment/trial design in the future.
### Conclusions
We leverage computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11060-022-04221-2.
## Background
Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle (CPA) and represents 6–$8\%$ of all intracranial tumours [1]. While many are discovered incidentally, symptomatic patients typically present with progressive hearing loss, tinnitus, and in larger tumours, may develop hydrocephalus, symptomatic brainstem compression, headache, and/or cranial nerve dysfunction (e.g. trigeminal pain, and in rare cases facial weakness) [2]. The majority of tumours grow slowly at an average rate of 1–2 mm/year, with up to $75\%$ of tumors showing no radiographic growth after diagnosis [3]. However, a subset of these tumours exhibit more aggressive behaviour with rapid growth and a propensity to recur after treatment with microsurgical resection, stereotactic radiosurgery, or both [4].
Histopathologically, VS is a benign neoplasm arising from Schwann cells of the vestibular nerve. It is known to be associated with the NF2 gene and it’s product, Merlin, which is associated with several key receptors such as EGFR and several entities within the Ras and Wnt pathways [5]. Several biomarkers associated with tumour growth have been identified including elements of the Merlin pathway, inflammatory signals including NFKB1, COX genes, and macrophages [6], an immune-enriched microenvironment [7], and the SH3PDX2A-HTRA1 fusion [8], though these have yet to find their way into clinical practice. While there is an ongoing push toward defining CNS tumours by their molecular features (as evidenced by recent changes to WHO tumour classification [9]), WHO continue to define VS as a single entity despite their demonstrated potential for highly heterogeneous behaviour. Similarly, while the VEGF inhibitor bevacizumab has shown promise in reducing tumour cell proliferation and improving hearing in a subset of patients [10], and our lab has previously shown that patients with SH3PDX2A-HTRA1 fusion positive tumours may respond to inhibition of the MEK-ERK pathway [8], there remain an absence of medical treatments in their standard care. Overall, there is an outstanding need for subgroup discovery, outcome prediction, and targeted therapeutics for patients with vestibular schwannoma.
In this study, we explore the transcriptome and methylome of vestibular schwannomas. We identify subgroups driven by multiplatform molecular profiling. The results add to our knowledge of schwannomas that can inform increasingly personalized care for patients with this challenging disease.
## Data collection and pre-processing
This is a retrospective cohort study. Research and Ethics board approval was obtained [18-5820]. We selected all vestibular schwannomas from our local tumour bank treated with upfront microsurgical resection that had been sequenced for both gene expression and DNA methylation to form the discovery cohort [8]. Tumours were resected via either the retrosigmoid or translabyrinthine approach depending on tumour morphology, surgeon preference, and preoperative hearing status, and fresh tissue taken intraoperatively for processing. Tumors for the UHN Tumour Bank are sent directly from the operating room, where samples are placed in aliquots marked by location in storage tubes placed in liquid nitrogen vats in the operating room. Each tumor has multiple samples from different regions collected and where possible duplicates or more. DNA and RNA were extracted from fresh frozen tumour tissue using the DNeasy Blood and Tissue Kit (Qiagen, USA), and RNAeasy Mini Kit (Qiagen, USA). DNA was quantified using Qubit dsDNA HS Assay Kit and RNA integrity was evaluated using the Agilent 2100 Bioanalyzer (RNA; Agilent, USA) with only samples that had an RNA Integrity Number (RIN) > 7 selected for subsequent sequencing. Illumina Infinium Methylation EPIC BeadChip Array were used to obtain genome-wide DNA methylation profiles on vestibular schwannomas following bisulfite conversion using the EZ DNA Methylation Kit (Zymo, USA). mRNA libraries were generated using the NEB Ultra II directional mRNA library prep kit. Libraries were sequenced on the Illumina HiSeq to obtain approximately 70 million reads per sample. FastQ files were processed and aligned to human reference genome (GRCh38) using STAR (v2.6.0a). *Raw* gene expression counts were normalized by counts-per-million. DNA methylation data was processed using the minfi package [11], quantile normalized, and beta values were used for subsequent analysis. Additional tumours from the same cohort (combined samples from Toronto and MD Anderson, Texas) [8] which were sequenced only for methylation were used as an internal validation cohort (validation cohort 1). Notably, we found a significant batch effect between sequencing groups and this was corrected using ComBat [12], a commonly used Bayesian technique. We also use available data from the DKFZ reference cohort database [13],[14] (methylation data only) as an external validation cohort (validation cohort 2). DNA was extracted from formalin fixed paraffin embedded tumour tissue using the automated Maxwell system (Promega, Madison, WI, USA) and Illumina Infinium HumanMethylation450 used for methylation profiling [14]. Subsequent computational analysis proceeded as in the discovery and internal validation cohorts. Notably, analysis in our study was done using the open-source platform R, version 3.6.3 [15].
## Subgroup discovery with molecular clustering
To identify homogeneous molecular subgroups, we first selected the 5000 most variable genes and methylation probes to be used in subsequent clustering analysis. We used three distinct clustering methods (similarity network fusion [SNF] [16] and cluster of cluster analysis [COCA] [17] using both hierarchical and k-means clustering) to identify subgroups; final group membership was determined by consensus. Similarity network fusion (SNF) applies spectral clustering to each input data modality (mRNA and methylation) which are subsequently “fused” using a mixing function to generate a single heatmap. Optimal number of clusters is determined by minimizing the eigengap. Cluster of cluster analysis generalizes clustering methods (hierarchical, k-means, etc.) across multiple platforms such that the most stable overall cluster assignment is selected. Once subgroups were identified using all three methods, any disagreement in group assignment was solved using majority. T-SNE [18] analysis was used to visualize groupings using both methylome and transcriptome as input data. Expression of each gene/methylation probe was compared between resultant subgroups using a t-test.
*To* generate similar subgroups in the validation cohorts, we created a methylation signature to define subgroups in the validation cohort. This was done by selecting the 100 most differentially expressed probes between subgroups (ordered by q value) and using the mean value of up- and down-regulated probes as regressors in a logistic regression model. 5-fold cross validation yielded $100\%$ prediction accuracy and the regression model was therefore applied to both validation cohorts to generate subgroup labels.
## Tumour microenvironment analysis
We applied the ESTIMATE tool [19] to gene expression data from the discovery cohort to determine the approximate relative populations of stromal, immune, and neoplastic cells within the microenvironment of each tumour. Briefly, this method uses bulk deconvolution to compare input gene expression data to previously established transcriptomic “signatures” of immune and stromal cell populations. These were established through differential gene expression analysis over a large pan-cancer analysis using multiple databases resulting in a stromal signature containing 141 genes and an immune signature containing 141 genes. Tumour purity (proportion of neoplastic cells) was validated against genomic data in the development of the tool as well. These characteristics were compared between subgroups using a t-test.
To validate differences in microenvironment, we sought methylation probes which best correlated with immune, stromal, and purity scores in the discovery cohort since these scores are generated from transcriptomic input. Mean beta values of the 10 most correlated probes to each parameter was selected to be a marker of these microenvironment parameters. The relative proportion of each cell type in the validation cohort was therefore estimated as the difference in these markers (between subgroups) in the validation cohort.
In additional to the transcriptomic-based ESTIMATE, we calculated the methylation-based LUMP (leukocytes unmethylation to infer tumour purity) scores on all tumours. This method uses a previously validated methylation signature to infer tumour purity [20]. Unfortunately, to our knowledge, a complementary methylation-based method for inferring immune and stromal populations using methylation data alone doesn’t exist, necessitating the indirect approach outlined above.
## Gene pathway analysis
We used Gene Set Enrichment Analysis (GSEA) [21],[22], to identify gene networks associated with targets of interest. Expression of each constituent gene was compared between subgroup, and network analysis was carried out using Cytoscape [23]. In this latter network analysis, genes which are differentially expressed between subgroups ($p \leq 0.05$) and which co-express with other genes from the same network (Pearson $p \leq 0.05$) were included.
## Drug repurposing analysis
We applied the openly available L1000 connectivity map [24] in order to identify subgroup-specific drug candidates for VS. Briefly, this method is built upon a repository of transcriptomic perturbations associated with the effects of small molecules on cell lines. By comparing these perturbations with differential gene expression from a disease state, drugs with the potential to reverse a diseased phenotype can be identified. This has a significant benefit over other methods of drug discovery given that the queried compounds are already approved and in use for other conditions, thereby eliminating the need for lengthy testing and approval processes. The top 100 upregulated and downregulated genes between subgroups were used as input.
We applied the top 100 differentially expressed genes in each direction (up- and down-regulated) for both subgroups. Importantly, MEK/MAPK pathway was present repeatedly as a potential target for group 1 tumours with trametinib as well as experimental agents BRD-K12244279 and PD-98,059 listed as candidate agents. This network consists of 40 genes of which 7 are differentially expressed between subgroups (5 upregulated in group 1 and 2 upregulated in group 2). Given the role of MEK/MAPK in cell cycling, we also examined the REACTOME cell cycle signature which contains 693 genes, of which 107 were differentially expressed (70 upregulated in group 1 and 37 upregulated in group 2) [Supplemental Data]. Given these findings, we label group 1 as the “proliferative” subgroup. Other candidate agents for group 1 included vorinostat (a histone deacetylase inhibitor) and tivozanib (a vascular endothelial growth factor receptor inhibitor), whereas candidate agents for group 2 included valrubicin (a topoisomerase inhibitor) and canertinib (an EGFR inhibitor) [Supplemental Data]. Notably, all top 10 candidates in group 2 had different mechanisms of action, unlike in group 1 where multiple MEK inhibitors were present.
## Overview of study cohort
The discovery cohort included 16 vestibular schwannomas treated with primary microsurgery and profiled for bulk transcriptomic and DNA methylation data. Mean age (SD) was 40.3 (13.6), and $\frac{10}{16}$ patients were male. The mean follow-up time was 61.8 months (SD 38.8 months, range 12–110) of the 10 patients with available data. Of these ten patients, none had preoperative facial palsy or hydrocephalus, and mean tumour size was 26.7 mm (SD 9.4 mm). One patient had postoperative progression at 105 months treated with radiotherapy and another underwent radiotherapy 17 months postoperatively due a large residual and bothersome trigeminal symptoms. Twelve patients had NF2 point mutations, six had chromosome 22 loss, and three were NF2 intact. Our internal validation cohort included 48 tumours with methylation profiling. Mean age (SD) was 50.0 (11.6) and $\frac{20}{48}$ patients were male. 35 patients had chromosome 22 loss. A second validation cohort, using publicly available DKFZ methylation data on a repository of CNS tumours [13] included 11 vestibular schwannomas. In this cohort, the mean (SD) age was 21.6 (14.6) years and $\frac{6}{11}$ patients were male. Further clinical annotation is limited in this cohort, though $\frac{3}{7}$ patients with documented status are noted to have neurofibromatosis type 214.
## Subgroup discovery
Each clustering method generated two subgroups with eight tumours each (Fig. 1). SNF and COCA-km generated identical group assignments and were taken as consensus. Interestingly, COCA analysis generated groups which were equivalent to the clustering achieved by methylation alone, suggesting that DNA methylation drives clustering for VS.
Fig. 1Methylation and mRNA data reveal two robust molecular subtypes of vestibular schwannoma. A: Similarity network fusion. Spectral clustering of methylation data reveals two groups from the methylation data and three groups from the mRNA data with the eigengap minimization method. Combining these with similarity network fusion (SNF) yields two equal-sized groups. B: Consensus clustering with hierarchical (left) and K-means clustering (right). Methylation cluster assignments are coloured orange and mRNA cluster assignment in blue. The cluster of cluster assignment is denoted by a red box. C: t-SNE analysis of tumour methylome and transcriptome, coloured by subgroup (group 1 in black and group 2 in red)
## Subgroup comparison
Our final consensus subgroups (derived equivalently from both SNF and COCA-km) consisted of eight tumours each (discovery cohort). There was no significant difference in age, sex, or tumour size between groups ($p \leq 0.05$). In group 1, no patients had postoperative progression or further treatment whereas 2 patients with documented follow up in group 2 did. In the internal validation cohort, 33 tumours were assigned as group 1 and 15 as group 2; in the external validation cohort 10 tumours were assigned group 1 and one was assigned group 2. There are no significant differences in age or sex ($p \leq 0.05$) by subgroup in the two validation cohorts (Fig. 2 A).
Fig. 2Validation of molecular subgroups on external cohorts. A: t-SNE plots of individual tumours by methylation profile (top 5000 most variable probes). Colour depicts subgroup membership (black is group 1 and red is group 2) and size depicts relative LUMP score. B: Boxplots comparing mean LUMP score by subgroup in each cohort. * $p \leq 0.05$ We next applied the ESTIMATE tool to expression data from the discovery cohort to characterize subgroup-specific differences in tumour microenvironment (Fig. 3 A). We find that group 2 has a significantly ($p \leq 0.05$) higher proportion of immune and stromal cells, whereas group 1 is enriched in tumour cells. To compare tumour microenvironment in the validation cohorts we developed “methylation signatures” of stroma, immune, and purity scores by taking the average of the ten most tightly correlated methylation probes to each output in the discovery cohort (see methods). Comparing expression of these methylation signatures in the validation cohort revealed similar patterns (Fig. 3B). Similarly, we find that the LUMP score is higher in group 1 for all cohorts, and statistical significance ($p \leq 0.05$) is achieved for the discovery and internal validation cohorts.
Fig. 3Molecular subgroups exhibit significant differences in microenvironment. A: Boxplots comparing relative stroma, immune, and neoplastic (purity) scores between subgroups using the ESTIMATE algorithm. B: Validation of key tumour microenvironment (TME) differences by subgroup. Correlation between ESTIMATE signature and the average expression of the 10 most correlated methylation probes in the discovery cohort (stroma, immune, purity from left to right; top row). Pearson correlation values are noted. Boxplots depicting average expression of these 10 methylation probes in the validation cohort 1 (middle row) and 2 (bottom row). C: Differential expression of key/targetable immune markers between subgroups in validation cohort Finally, differential gene expression analysis revealed 3898 differentially expressed genes (t-test $p \leq 0.05$) between subgroups in the discovery cohort. This includes key, and potentially targetable, immune-related genes including current targets of immune checkpoint blockade PD1 and CTLA4, T-cell markers including CD3E, and pan-immune marker CD45 (Fig. 3 C).
## Gene co-expression analysis
Given the subgroup-specific differences in tumour microenvironment identified above, we sought to further characterize related gene networks (identified using GSEA) which may form the basis of therapeutic vulnerabilities. We examined PD1 and CTLA4 given their current role in immunotherapy as well as the epithelial to mesenchymal transition (EMT) signature, a critical stroma-associated driver of tumour progression in solid tumours [25],[26]. The PD1 signature is comprised of 23 genes, of which 12 are differentially expressed between subgroups (all upregulated in group 2). The CLTA-4 signature is comprised of 14 genes, of which 7 are differentially expressed between subgroups (1 upregulated in group 1 and 6 upregulated in group 2). Finally, the EMT signature is comprised of 200 genes, of which 71 are differentially expressed between subgroups (15 upregulated in group 1 and 56 upregulated in group 2). Differentially expressed genes within each signature were represented as networks to better understand their subgroup-specific co-expression (Fig. 4). This reinforces the immune- and stroma-enriched nature of group 2 tumours, which we will henceforth refer to as the “immunogenic” subgroup.
Fig. 4Comparative gene expression analysis. A: Differential expression analysis of PD1, CTLA4, and EMT gene networks between subgroups. Mean difference in expression is plotted. Genes significantly upregulated in group 1 ($p \leq 0.05$) are coloured green, and those significantly upregulated in group 2 are coloured red. Inset: boxplot comparison of mean expression of genes by group (*$p \leq 0.05$). B: Gene network analysis. Nodes represent genes whose expression is significantly different between subgroups. Colour corresponds to direction (red is upregulated in group 2, green upregulated in group 1) and size proportional to magnitude of difference in average expression. Edges connect nodes with significant ($p \leq 0.05$) co-expression in at least one subgroup; green edges represent connections present only in group 1, red edges represent connections only present in group 2, and black edges represent connections present in both groups. Isolated nodes are not included
## Overview of results
Using a discovery cohort of 16 tumours with both methylation and expression data as well as two validation cohorts with methylation data alone, we find two robust molecular subgroups of vestibular schwannoma. In the discovery cohort, group 1 consists of a higher proportion of neoplastic cells whereas group 2 is enriched in immune and stromal microenvironment. This pattern is consistent in two the validation cohorts. Notably, subgroup assignment is based solely on a methylation signature of 100 differentially methylated probes. No patients in group 1 experienced postoperative recurrence/progression or required subsequent treatment out of four with documented follow up, and $\frac{2}{6}$ in group 2 did. Gene co-expression analysis reveals important differences in PD1, CTLA4, EMT, MEK, and cell cycle signalling pathways which suggest important subgroup-specific therapeutic vulnerabilities. We therefore label group 1 as “proliferative” and group 2 as “immunogenic” and suggest the need for subgroup-specific therapies targeted at their unique biology.
## Tumour microenvironment in vestibular schwannoma
Tumour microenvironment has become a topical area of study in oncology and has generated considerable promise for therapeutic considerations in many cancers. Once considered a simple mass of mitotically active cells, we now understand that tumours are complex ecosystems of interacting neoplastic and non-neoplastic cells. These interactions have been shown to stimulate clonal evolution, tumour heterogeneity, immune escape, and treatment resistance, making them a promising target for drug therapy [27]. Perhaps the most poignant example of the tangible results achieved from this understanding is in the effectiveness of immune checkpoint inhibition in cancers such as melanoma [28],[29]. In the CNS, glioblastoma multiforme (GBM) has also been shown to exhibit a region-specific inflammatory microenvironment [30] though PD-1 blockade did not improve overall survival in recurrent GBM when compared to standard of care bevacizumab [31]. This is felt to be secondary, at least in part, to the highly heterogeneous nature of this tumour and ongoing investigation into immunotherapeutics in GBM is underway.
The microenvironment of vestibular schwannoma remains relatively unexplored. However, it has been shown that an increasingly inflammatory microenvironment is associated with higher rates of progression. In a recent review paper [7], increasing proportion of immune cells (identified with surface markers including CD45 and CD68), secretion of inflammatory cytokines (including IL-1, IL-6, TNF), and activation of key regulatory signaling networks such as NF-kB have been associated with tumour proliferation. Interestingly, this microenvironment may be driven in part by systemic inflammation, as one study finds an association between serum C-reactive protein levels and progression-free survival in VS [32]. Immunohistochemical analysis also revealed increased expression of several pro-inflammatory cytokines including tumour necrosis factor, IL-1, IL-6 [33], and CXCR4 [34] in vestibular schwannoma compared to a normal vestibular nerve, suggesting that perhaps the subgroups in our study represent tumours at different points along a spectrum from “normal nerve” to “aggressive tumour”. This is suggested by the fact that subgroup separation in the validation cohorts is not as well defined as in the discovery cohort. It is therefore possible that some tumours fall between the two subgroups and may benefit from multi-pronged approaches to therapy. Larger studies are needed to ascertain the true distribution of tumours between subgroups and the spectrum of cases that lies between them.
While investigation into immunotherapy for vestibular schwannoma has generated some promise, there remains a lack of output. This may be related to the fact that these heterogeneous tumours are still considered a single entity; subgroup-informed analysis of vestibular schwannoma may represent the next frontier of treatment/immunotherapy for this tumour.
## Limitations
A few limitations must be considered in this study. First, the retrospective nature of our study and small numbers may limit generalizability. A lack of available clinical annotation limited our ability to incorporate important variables such as clinical and treatment details in subgroup discovery and analysis, and therefore conclusions regarding clinical behaviour of these subgroups cannot be drawn. The limited number of patients with SH3PDX2A-HTRA1 fusion in this cohort precluded an analysis of this important mutation, and further work with larger cohorts will be required to determine its relevance in the context of these newly described subgroups. Similarly, the requirement for surgical intervention in obtaining molecular data from these tumours excludes patients treated with radiotherapy or surveillance. Given the often-delayed time to postoperative progression in vestibular schwannoma, it is possible that follow up was insufficient to capture all cases. The young age of the DKFZ cohort confounds direct comparisons, particularly with the lack of clinical and genetic annotation. Finally, lack of expression data in the validation cohort forced indirect comparisons which may have misinformed subgroup generalizability, and precluded validation of subgroup-specific transcriptomic states. Nevertheless, our goal in this work was to identify molecularly relevant subgroups of VS with available data which may represent distinct therapeutic vulnerabilities and/or clinical properties.
## Future directions
The hypotheses generated in this work will serve to inform subsequent prospective studies on larger cohorts with deep multi-omic profiling, allowing validation of our subgroups and further refining their biological landscapes to identify key markers/drivers, which could be verified with immunohistochemistry, and therapeutic vulnerabilities. This will be required to ultimately incorporate our subgroups into clinical practice. Importantly, our subgroups are currently defined based on a signature consisting of only 100 differentially methylated probes. In an era of increasing reliance on whole methylome classification models, this will allow for easy tumour subclassification for preclinical/clinical trial design and ultimately the development of subgroup-specific targeted therapies.
## Conclusions
We leverage established computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities. While further confirmatory work is required, this promises to increasingly individualize the care of patients with this disease.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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---
title: Molar-incisor hypomineralisation prevalence in a cohort of Australian children
with type 1 diabetes
authors:
- C. Lim
- E. D. Jensen
- B. F. Poirier
- S. Sethi
- G. Smart
- A. S. Peña
journal: European Archives of Paediatric Dentistry
year: 2022
pmcid: PMC9992226
doi: 10.1007/s40368-022-00765-z
license: CC BY 4.0
---
# Molar-incisor hypomineralisation prevalence in a cohort of Australian children with type 1 diabetes
## Abstract
### Purpose
Systemic diseases or drugs administered early in life may cause a disruption in amelogenesis and contribute to the qualitative defect of enamel described as molar–incisor hypomineralisation (MIH). Therefore, an increase in prevalence of MIH in children with type 1 diabetes (T1D) may be expected as this systemic disorder is commonly diagnosed in early childhood. The aim of this study was to determine the prevalence of MIH in a cohort of children with T1D and investigate diagnosis of MIH with T1D factors.
### Methods
Cross-sectional study of children with T1D recruited from paediatric diabetes clinics at the Women’s and Children’s Hospital (South Australia). A detailed medical history, comprehensive dental and MIH examination according to the European Academy of Paediatric Dentistry (EAPD) long form classification was collected for each child. All upper and lower first permanent molars and central incisors were scored.
### Results
A total number of 73 participants; 35 ($47.95\%$) males were examined including 584 teeth. The mean age of the participants was 13.25 ± 2.58 years, with a mean age of diagnosis 7.75 ± 3.58 years, and a mean HbA1c of 8.5 ± $1.6\%$. 42 out of 73 children ($54.8\%$) had enamel defects on at least one of the teeth examined. However, $19.2\%$ met the criteria for MIH. Univariate and bivariate analyses were conducted but no significant associations were noted between MIH and risk factors including diabetes control ($p \leq 0.1$).
### Conclusion
There was a high prevalence of enamel defects and MIH amongst children with T1D. More research is required to establish association between T1D and MIH.
## Introduction
Molar-incisor hypomineralisation (MIH) was first described in 2001 as a qualitative defect of enamel presenting as well-demarcated areas affecting the first permanent molars (FPM) and often the permanent incisors (Weerheijm et al. 2001). The defective enamel is characterised by a reduction in mineralisation and inorganic compounds, as well an increase in protein content, resulting in discoloured and brittle enamel (Weerheijm 2004; Farah et al. 2010). The suggestion of a disturbance of ameloblasts during the maturation stage of enamel formation is widely accepted (Weerheijm 2004; Crombie et al. 2009) and hypomineralisation is understood to be a chronological enamel defect, potentially affecting any primary or permanent tooth. The exact cause of the disruption remains unknown, with several suggestions of aetiological factors such as genetics/epigenetics, environmental factors, systemic diseases, or drug exposure during the gestational period or the first three to four years of life (Weerheijm et al. 2001; Crombie et al. 2009; Serna et al. 2016; Silva et al. 2016; Hočevar et al. 2020; Lygidakis et al. 2021).
Type 1 diabetes (T1D) is one of the most common chronic, metabolic diseases that occur in childhood. Diagnosis can occur at any age with a peak between 4 and 7 years of age and during early puberty at 10–14 years of age (Atkinson et al. 2014; Lucier and Weinstock 2022). Type 1 diabetes can cause chronic hyperglycaemia and aggravate oxidative stress, which can affect tissue structure and function of the body (Giacco and Brownlee 2010; Chałas et al. 2016). Protein metabolism in individuals with poorly controlled T1D is also known to be altered with net increased protein breakdown during periods of insulin deprivation (Hebert and Nair 2010). As the potential causative factor(s) remain unknown for MIH and it is known that children with T1D can have chronic metabolic disturbances, exploration of a potential association, particularly if the prevalence is increased in those children diagnosed with T1D at a young age, is of interest (Garot et al. 2021).
Whilst there is evidence that children with chronic health conditions can have increased prevalence of MIH (Mohamed et al. 2021), there are limited studies that explore a potential association between T1D and MIH with those available limited to rodent data. Rats with TID have altered bone and enamel development (Atar et al. 2004; Abbassy et al. 2008, 2010), but literature investigating the association of T1D and MIH in humans is lacking. Therefore, this paper aims to report the prevalence of hypomineralisation and MIH in a population of children with T1D and to investigate the diagnosis of MIH with any associations to T1D parameters.
## Methods
This cross-sectional study recruited consecutively a convenience sample of 73 participants (38 females) with previously diagnosed T1D. Children were recruited from February 2018 to March 2019 through the Women’s and Children’s Hospital (Adelaide, South Australia) as previously detailed in Jensen et al. [ 2021]. Children were eligible for inclusion if aged 8 to 18 years and diagnosed with T1D by detectable islet cells autoantibodies. Children with a diabetes diagnosis other than T1D and those whose English skills hindered the provision of informed consent were excluded from this study. For children under 16 years of age, informed written consent was obtained from parents or guardians; those above 16 years of age provided informed written consent themselves. All children gave assent for the study. This project received ethical approval from the Women’s and Children's Health Network Human Research Ethics Committee (HREC/17/WCHN/165).
## Clinical data
Details regarding participant diagnosis and treatment of T1D were obtained from medical records. This included date and age of diagnosis, and HbA1c levels within three months of the dental examination. Participant HbA1c values were measured with the DCA Vantage® Analyzer (Siemens Healthcare Diagnostics, Camberley, UK), which has a high correlation coefficient ($r = 0.98$) with DCCT standardised sample control. Other details such as gestational age, mode of delivery at birth and medical history by systems were also obtained.
## Molar incisor hypomineralisation
The European Academy of Paediatric Dentistry (EAPD) examination protocol for the diagnosis of MIH (Ghanim et al. 2017) was utilised for this study. Dental examinations were performed by a trained and calibrated practitioner (E.J), using a dental mirror, a ball-ended explorer, and dental chair lighting. A ball-ended explorer was used to examine the teeth for surface irregularities, ensuring any damage on the tooth surfaces was prevented. Participants were advised to brush their teeth prior to the examination. All fully erupted teeth were examined wet to increase the accuracy of results. The long form clinical status of each tooth was recorded. Examination results were correlated with the recommended EAPD MIH diagnosis sequence (Ghanim et al. 2015, 2017). The EAPD classification was measured on the maxillary and mandibular permanent central incisors and FPM.
In accordance with EAPD criteria for MIH diagnosis, each molar and central incisor were examined for the presence, type, and severity of MIH (Ghanim et al. 2015, 2017). The extraction of an FPM due to MIH was identified through past dental records or discussed with the parent/guardian of the child (Weerheijm et al. 2003; Ghanim et al. 2017).
Several considerations for the diagnosis of MIH were used in the examination protocol. Molar–incisor hypomineralisation was diagnosed when at least one molar tooth with MIH was present. Children with affected permanent incisors were not diagnosed with MIH unless there was hypomineralisation present on at least one permanent first molar. If more than one area affected by hypomineralisation was present on a tooth, the higher severity rating was recorded as the main finding for that tooth. A tooth with five-surface full-coverage restoration (in the absence of trauma to an incisor) was considered an atypical restoration and MIH was diagnosed. Post-eruptive enamel breakdown was also recorded when an atypical restoration was missing, and no caries was present. When the tooth meant to be examined for MIH was absent, either congenitally missing or extracted due to causes other than MIH, it was not considered an extraction due to MIH ($$n = 0$$). Teeth were considered sound when the enamel defect(s) present were diffuse and one millimetre or less in diameter (Ghanim et al. 2015, 2017).
Statistical analyses were performed using SPSS for Windows version 27 (IBM SPSS Inc., Chicago, IL, USA), and included a descriptive evaluation of the results in a bivariate analysis. The association between the presence of MIH and the descriptive variables including the age of diagnosis, type of birth delivery, comorbidities, and HbA1c levels variable was evaluated with the chi-squared association or Fisher test. The level of significance was determined by p-value with values less than 0.05 considered significant.
## Results
A total number of 73 participants; 38 females were examined including 584 teeth. Forty-one participants ($56.16\%$) were 8 to 13 years in age and 32 ($43.84\%$) were 14 to 18 years old. Amongst the 41 participants aged less than 14 years, 20 ($48.78\%$) were males; and amongst 32 participants aged more than 14 years, 15 ($46.87\%$) were males (Table 1). The mean age of female and male participants was 13.5 (± 2.5) and 12.92 (± 2.67) years, respectively. The mean age of T1D diagnosis was 7.75 (± 3.75) years for males and 7.75 (± 3.42) years for females and a total of 14 ($19.18\%$) participants had been diagnosed with T1D before 4 years of age. The HbA1c levels ranged from 5.8 to $13.3\%$ (median of $8.0\%$) amongst participants. Insulin was delivered through pumps for 29 individuals ($39.73\%$). The logistic regression models did not yield any significant results and the individual numbers in each group were quite small due to small sample size of the study. Due to insignificant small numbers the models were not presented and the results were presented in a descriptive manner. Table 1Distribution of the sample study group according to age and genderSexTotalMaleFemaleNumber of participants $$n = 35$$ (%)Number of participants $$n = 38$$ (%)Number of participants $$n = 73$$ (%)Age at examination (years) 8–1320 (57.14)21 (55.26)41 (56.16) 14–1815 (42.85)17 (44.74)32 (43.84)Age of T1D diagnosis (years) < 49 (25.71)5 (13.16)14 (19.18) ≥ 426 (74.29)33 (86.84)59 (80.82)Deliverya Vaginal25 (73.53)29 (78.38)54 (76.06) C-section9 (26.47)8 (21.62)17 (23.94)HbA1c ≤ $7\%$15 (42.86)17 (44.74)32 (43.84) > $7\%$20 (57.14)21 (55.26)41 (56.16)Mode of Insulin Delivery Pump17 (48.57)15 (39.47)32 (43.84) Injection18 (51.43)23 (60.53)41 (56.16)aTwo participant’s details unavailable, $$n = 71$$T1D type 1 diabetes All 73 participants had upper and lower central incisors and FPM examined (total of 584), a total of 146 upper molar teeth, lower molar teeth, upper incisors and lower incisors, and the findings have been tabulated in Table 2. As per the diagnosis of MIH; in children with at least one affected first permanent molar ± permanent incisors; a prevalence of MIH of $19.18\%$ was found in the study population. The prevalence of children diagnosed with an enamel defect or MIH in the study population have been tabulated in Table 3. The distribution of enamel defects on the teeth examined are detailed in Table 4.Table 2Distribution of Enamel defects or MIH as per tooth morphology in the study populationTooth (N)Teeth with enamel defects % (% consistent with MIH by definition)Upper molars [146]23.29 (10.27)Lower molars [146]25.34 (9.59)Upper incisors [146]32.89 (4.79)Lower incisors [146]9.59 (2.74)Table 3Prevalence of children diagnosed and median levels of HbA1c with an enamel defect or MIH in the study populationConditionNumber of children n (%)Total enamel defects (including MIH) Present40 (54.79) Absent33 (45.21)Diagnosis of MIH Present14 (19.18) Absent59 (80.82)MIH molar-incisor hypomineralisationTable 4Distribution of MIH on teeth examinedClassification of enamel defects in long formTooth examineda16112126363141461 = Enamel defect, non-MIH 11 = Diffuse opacities1120198125511 21 = Hypoplasia00100000 13 = Amelogenesis imperfecta00000000 14 = Hypomineralisation defect on other teeth011000002 = Demarcated opacities 21 = White/creamy32130311 22 = Yellow/brown21244003 3 = Post-eruptive enamel breakdown (PEB)00000000 4 = Atypical restoration200010015 = Atypical caries000000006 = Missing due to MIH000120027 = Cannot be scored00000000aTeeth reported as per World Dental Federation notation A total of 584 teeth were examined and diffuse opacities and hypoplasia were recorded on $32.87\%$ and $9.59\%$ of upper and lower central incisors respectively, as well as $23.29\%$ of upper FPM and $25.34\%$ of lower FPM. Upper central incisors had almost double the MIH when compared to lower central incisors ($4.79\%$ and $2.74\%$).
Glycosylated haemoglobin (HbA1c) levels were used as marker of diabetes control as it measures average glucose levels over the previous 3 months (Carlson et al. 2020). Nine out of 14 of the participants who were diagnosed with MIH had HbA1c levels greater than $7\%$. However, 64 out of the 73 participants with T1D had HbA1c levels above $7.0\%$. There was no continuous or categorical association observed ($p \leq 0.1$) between HbA1c and MIH.
## Discussion
Molar–incisor hypomineralisation was present in $19.2\%$ children and adolescents with T1D, compared to an estimated global prevalence of all healthy children at $13.5\%$ (Lopes et al. 2021). There is no comparative data available regarding the prevalence of MIH amongst Australian children using the MIH classification, but there are population studies of enamel defects in Australian children with a prevalence of $44\%$ in New South Wales (Balmer et al. 2005) and a prevalence of $22\%$ in Western Australia (Arrow 2008) according to the modified developmental defects of enamel (mDDE) index. In comparison to other parts of the world, the prevalence of MIH using the EAPD classification ranges from $6.31\%$ to $20.2\%$ in children under 14 years of age (Martínez Gómez et al. 2012; Zawaideh et al. 2012; Mittal et al. 2013; Ghanim et al. 2014; Amend et al. 2020). The MIH prevalence amongst this cohort falls within the MIH prevalence range of multiple countries (Lygidakis et al. 2021). In this study, $54.79\%$ of participants had some form of enamel defect on one or more FPM or incisors. Despite nine participants having demarcated opacities that were indicative of hypomineralisation on the incisors, only five of the participants also had an affected molar and thus were diagnosed with MIH as per the EAPD classification. Mild MIH was observed in $64\%$ of participants whilst $35\%$ had severe MIH; this is typical of other reported large-cohort studies of healthy children which have reported 40–$50\%$ moderate to severe MIH (Martínez Gómez et al. 2012; Ghanim et al. 2014). However, to date there are no studies evaluating MIH in a cohort of children with T1D and healthy children. Gender was not contributory as children with MIH were $50\%$ female. Caesarean-section delivery has been considered a risk factor in the development of enamel defects (Rafatjou et al. 2018). However, 12 out of the 14 participants with MIH were delivered vaginally.
The aetiology of MIH remains unclear, with current hypotheses including disruption to enamel formation during the maturation stage of development by environmental factors, childhood illnesses and during pre, peri, post-natal periods (Crombie et al. 2009; Alaluusua 2012; Bensi et al. 2020; Butera et al. 2021; Garot et al. 2021). The ameloblasts of hypomineralised teeth appear dysfunctional during the early maturation phase (Suga 1989) which occurs from birth to the first 2 years of life for FPM and incisors. Systemic diseases can also have an impact on the cells during the maturation stage (Suga 1989; Jälevik and Norén 2000). The current understanding of the aetiology of T1D is autoimmune destruction of the pancreatic β cells which produce insulin (Todd 2010). Type 1 diabetes is the most prevalent form of diabetes mellitus amongst children. The development of the first autoantibodies amongst individuals who are genetically at risk of diabetes peaked before the age of 2 years (Ziegler et al. 2013; Krischer et al. 2015). Most individuals with a single autoantibody do not proceed into T1D (Ziegler et al. 2013) but the development of the autoantibodies occurs during the first few years of life, which may occur concomitantly with the disturbances of cells during the maturation stage of enamel formation during or before a formal diagnosis of T1D, in which the usual diagnosis of T1D is between 4 and 7 years, or 10 and 14 years (Atkinson et al. 2014; Lucier and Weinstock 2022). The sample population included an average age of T1D diagnosis of 7.75 years of age and $19.18\%$ diagnosed before 4 years of age, limiting the sample for those potentially at higher risk of MIH. Higher HbA1c was anticipated to produce an increased prevalence of MIH but this was not observed.
This study has several strengths. This comprehensive cross-sectional study was conducted using the EAPD MIH classification. This is considered the standardised method of charting MIH (Ghanim et al. 2015) as it incorporates the EAPD criteria (Weerheijm et al. 2003) and the mDDE index (Clarkson and O'mullane 1989). There has historically been a range of different criteria and methods of recording MIH, thus the EAPD classification can be considered a universally standardised method of classification and recording allowing consistency across studies (Elfrink et al. 2015). The examiner (E.J.) was able to conduct the MIH examination in a standardised, calibrated and efficient manner in all children included in the study (Elfrink et al. 2015; Ghanim et al. 2015).
There are limitations to this study, including a total of 73 participants, below the recommended number of participants for the prevalence of MIH (Elfrink et al. 2015). However, this is the first cohort of children with T1D examined for MIH prevalence. There was no control group in this study or any comparative data for the prevalence of MIH in healthy children. Despite the limitation, this study provides additional insight into potential aetiological factors of MIH by describing the prevalence of MIH in a cohort of children with T1D. Unfortunately, only $19.18\%$ of participants were diagnosed with T1D under 4 years of age, which is when potential disruption to amelogenesis would be anticipated to be a contributory factor to the development of MIH. Hypomineralisation in other primary and permanent teeth was recorded during examination. However, many participants were in the mixed dentition and a decision was made to only report first permanent molars and permanent incisors, required to make a formal diagnosis of MIH. Longitudinal studies evaluating hypomineralisation on primary and permanent dentition for children diagnosed with T1D before four years of age may further our understanding of any potential causative associations to the development of MIH.
## Conclusion
Within the limitations of the present study in a convenience sample of children with T1D, it has been shown that children with T1D revealed a higher prevalence of MIH at $19.18\%$, when compared to estimated prevalence rates of $13.5\%$ in healthy children globally. There were no significant associations noted between MIH and risk factors including average glucose levels over the previous 3 months ($p \leq 0.1$). Future large-scale longitudinal studies are necessary, particularly with larger samples of children who were diagnosed with TID at a young age to explore whether there is any association explaining the greater prevalence of MIH in children with T1D.
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|
---
title: Comparative study of a wearable intelligent sleep monitor and polysomnography
monitor for the diagnosis of obstructive sleep apnea
authors:
- Yanxia Xu
- Qiong Ou
- Yilu Cheng
- Miaochan Lao
- Guo Pei
journal: Sleep & Breathing = Schlaf & Atmung
year: 2022
pmcid: PMC9992231
doi: 10.1007/s11325-022-02599-x
license: CC BY 4.0
---
# Comparative study of a wearable intelligent sleep monitor and polysomnography monitor for the diagnosis of obstructive sleep apnea
## Abstract
### Purpose
Due to the lack of an objective population-based screening tool for obstructive sleep apnea (OSA), a large number of patients with potential OSA have not been identified in the general population. Our study compared an objective wearable sleep monitoring device with polysomnography (PSG) to provide a reference for OSA screening in a large population.
### Methods
Using a self-control method, patients admitted to our sleep center from July 2020 to March 2021 were selected for overnight PSG and wearable intelligent sleep monitor (WISM) at the same time. The sensitivity and specificity of the device for the diagnosis of OSA were evaluated.
### Results
A total of 196 participants (mean age: 45.1 ± 12.3 years [18–80 years]; 168 men [$86\%$]) completed both PSG and WISM monitoring. Using an apnea–hypopnea index (AHI) ≥ 5 events/h as the diagnostic criterion, the sensitivity, specificity, kappa value, and area under the receiver operating characteristic curve of the WISM for OSA diagnosis were $93\%$, $77\%$, 0.6, and 0.95, respectively. Using an AHI ≥ 15 events/h as the diagnostic criterion for moderate-to-severe OSA, these values were $92\%$, $89\%$, 0.8, and 0.95, respectively. The mean difference in the AHI between PSG and the artificial intelligence oxygen desaturation index from the WISM was 6.8 events/h ($95\%$ confidence interval: − 13.1 to 26.7).
### Conclusion
Compared with the PSG, WISM exhibits good sensitivity and specificity for the diagnosis of OSA. This small, simple, and easy-to-use device is more suitable for OSA screening in a large population because of its single-step application procedure.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11325-022-02599-x.
## Introduction
Obstructive sleep apnea (OSA) is a common chronic sleep-related breathing disorder [1]. Patients with untreated OSA are at significantly increased risk of cardiovascular disease, stroke, cognitive dysfunction, and cancer [2–5], and their all-cause mortality is three times higher than that of the general population [6]. The number of patients with OSA worldwide has been reported to exceed 936 million, and this number has reached 176 million in China [7]. The Wisconsin Sleep Cohort Study investigated 4925 individuals aged 30–60 years. The study showed that individuals with mild ($98\%$ women and $90\%$ men) and moderate and severe ($93\%$ women and $82\%$ men) OSA were never diagnosed for this disease [8]. Similarly, the Sleep Heart Health Study showed that up to $91.7\%$ of patients with suspected OSA with frequent snoring and sleepiness were undiagnosed [9]. These data indicate that OSA is a disease that is poorly understood by the public, and a large number of potential patients with OSA remain undiagnosed and untreated in a timely manner. Large clinical intervention trials have shown that continuous positive airway pressure (CPAP) therapy cannot prevent secondary cardiovascular events [10]. Hence, early diagnosis may be an effective way to prevent OSA-related complications and reduce morbidity and mortality.
The main diagnostic devices for OSA include polysomnography (PSG) and the type III portable monitor (PM). PSG is the “gold standard” for diagnosing OSA and grading its severity. A type III PM, which is based on the PSG, simplifies the monitoring channels and only contains three signals for respiratory airflow, chest and abdominal movement, and blood oxygen. PM can be used to diagnose patients with moderate-to-severe OSA without comorbidities [11, 12]. These objective devices are all existing diagnostic methods for OSA, and all have high requirements for equipment as well as professional and technical personnel. These devices are also expensive and relatively inefficient, making them unsuitable for screening patients in a large sample of the population. Screening for OSA in the population mainly relies on various surveys/questionnaires [13]. However, the scales also require the participation of several professional and technical personnel and are time consuming. The results are also prone to subjective influence by the participants and researchers.
With the continuous development of monitoring technology, wearable sleep respiratory monitoring is more widely used [14]. One study used a finger pulse oximeter to diagnose patients with mild or greater and moderate to severe OSA; it showed the sensitivity and specificity of 80 and $70\%$ and 86 and $91\%$, respectively. The diagnostic performance was not satisfactory, and we found that using the pulse oxygen clip for a long time caused finger discomfort during clinical practice [15]. Manoni et al. integrated photoplethysmography, accelerometer, microcontroller, and bluetooth transmission into a device that is required to be worn on the bridge of the nose, and hence, is prone to causing discomfort. This device was tested under laboratory conditions with a small sample size, and the feasibility and accuracy of screening for OSA in the population have not been verified [16]. Behar et al. and Al-Mardini et al. developed a smart phone-based device to screen for OSA. Although the device is a low-cost screening method, it requires wearing arm bands, microphones, and pulse oximeter [17, 18]. Wearing of the devices with external wires involves multiple steps that affect sleep. Further, older and less-educated individuals unfamiliar with electronics and medical devices face difficulties using them, and hence, such devices are not suitable for screening a large population. Therefore, an objective monitoring device that is small, lightweight, highly sensitive, less time-consuming, and can be used to screen hundreds of people every day remains to be developed. The wearable intelligent sleep monitor (WISM) is a portable device that can continuously monitor human oxygen saturation, heart rate, and body movement signals and analyzes oxygen desaturation index (ODI) using its own artificial intelligence (AI) algorithm. It is a compact device, has no external wires, and involves just one step to paste on the palm. The low difficulty of application can ensure the success rate of wearing and the integrity and validity of data collection. In this study, we evaluated the sensitivity and specificity of the WISM in diagnosing OSA.
## Participants
The study was conducted in accordance with the tenets of the Declaration of Helsinki. Ethical approval for this study was obtained from the ethics committee of Guangdong Provincial People’s Hospital (No. GDREC2020221H(R1)).
The participants included patients with snoring as the main symptom who visited the sleep center at the Guangdong Provincial People’s Hospital, Guangzhou, China from July 2020 to March 2021. Participants of both sexes aged ≥ 18 years were included. The exclusion criteria were as follows: pregnancy; depression, anxiety, and other psychiatric illnesses and serious underlying diseases such as acute exacerbation of chronic obstructive pulmonary disease, acute myocardial infarction, unstable angina pectoris, congestive heart failure, and active infection.
## Collection of medical histories, symptoms, and signs
After obtaining informed consent and before conducting sleep monitoring, physicians at the sleep center privately collected basic demographic information (including sex, age, height, weight, and comorbidities) from participants in a quiet environment. The body mass index (BMI) (BMI = weight [kg] / height2 [m2]) was calculated for each participant. Participants also completed an assessment of sleep health based on OSA-related clinical symptoms.
## PSG and OSA diagnosis
Patients underwent an overnight PSG and WISM session at the sleep center simultaneously, conducted by experienced technicians, starting at 23:00 and ending at 6:00 the next day. The patients did not take a nap on the day of monitoring and did not drink tea, coffee, alcohol, or other beverages that would have interfered with their sleep. The scalp electrode was applied by an experienced technician in accordance with international standards [19]. The Alice 6 polysomnography (Philips Respironics Inc., USA) and Condi polysomnography (Grael PSG, Compumedics, Singen, Germany) apparatuses were used for OSA diagnosis. Nasal and oral airflow (nasal airflow pressure sensor, nasal and oral airflow thermal sensor), chest and abdominal movements, percutaneous oxygen saturation, snoring, body position, electroencephalogram recordings (F3, F4, C3, C4, O1, O2, M1, M2), mandibular electromyogram recordings, electrooculogram recordings, and data on other physiological indicators were obtained.
The results were interpreted manually by physicians trained in sleep medicine, in accordance with the American Academy of Sleep Medicine (AASM) standards on the Interpretation of Sleep and Related Events [11]. Apnea was defined as a ≥ $90\%$ drop in nasal and oral airflow from the baseline and a continuous event of ≥ 10 s, with or without thoracic and abdominal respiratory movements. Hypopnea was defined as a decline in nasal and oral airflow of ≥ $30\%$ from the baseline with a continuous event of ≥ 10 s, accompanied by a decrease in oxygen saturation of ≥ $3\%$ or an event with arousal. The apnea–hypopnea index (AHI) was defined as the sum of the average number of apnea and hypopnea episodes per hour during sleep. The ODI obtained from the PSG was defined as the number of times oxygen saturation decreases by ≥ $3\%$/h during sleep. According to the PSG monitoring results, OSA was diagnosed using AHI of ≥ 5 and ≥ 15 events/h as thresholds. On the morning of the second day after monitoring, the patients were also required to complete a post-sleep monitoring questionnaire that collected information on the time to fall asleep, sleep at night, wake-up time, and abnormal conditions during the monitoring process.
## WISM use and analysis
The WISM (CloudCare Healthcare Ltd., Chengdu, China) is a portable sleep-monitoring device that monitors the blood oxygen saturation signal using a photoelectric reflex sensor (Supplementary Fig. S1). The original data were stored in a specific database. Based on the physical activity signals, level of pseudo-recognition, and automatic analysis of the AI software, the effective length and blood oxygen downtimes were monitored, and a report was subsequently generated. The monitoring sites were the palmar thenar major muscles. Veins, scars, spots, and locations with thick hair were avoided.
The main monitoring indices included the ODI, average oxygen saturation (AvSaO2) and lowest oxygen saturation (LSaO2), and percentage of sleep time spent with oxygen saturation below $90\%$ (CT$90\%$). The ODI obtained using the WISM was defined as a decrease in blood oxygen saturation of ≥ $3\%$ or ≥ $4\%$. The AI algorithm automatically matched the degree of risk and selected ODI$3\%$ or ODI$4\%$. The ODI referred to the total number of drops in oxygen saturation divided by the effective monitoring time.
## Statistical analysis
Data were analyzed using SPSS 23.0 software (IBM SPSS 23.0, Armonk, NY, USA). Continuous variables are presented as means ± standard deviations (for normal distributions) or medians and interquartile ranges (for non-normal distributions). The sensitivity, specificity, positive predictive value, and negative predictive value of the WISM were calculated using the AHI and ODI thresholds of ≥ 5 and ≥ 15 events/h obtained from the PSG, and the consistency of the two methods was tested using the kappa value. The correlation between the ODI, obtained from the WISM, and the AHI and ODI, obtained from the PSG, was analyzed by the Pearson correlation. The consistency of the WISM and PSG results was determined using the Bland–Altman method. The best cutoff WISM values for OSA diagnosis were determined by examining the area under the receiver operating characteristic (ROC) curve.
## Participants’ characteristics
A total of 196 participants completed the PSG and used the WISM. Of them, 168 patients were men ($86\%$), 28 were women ($14\%$), and the average age was 45.1 ± 12.3 years (18–80 years). The average BMI was 26.3 ± 3.7 kg/m2. A total of $26\%$ of the patients had obesity (BMI of ≥ 28 kg/m2). According to the PSG results, 39 patients ($20\%$) had mild OSA, and 135 ($69\%$) had moderate-to-severe OSA (Table 1).Table 1Characteristics of the study participantsCharacteristicsn = 196Age in years45.1 ± 12.3Sex (n, %) Men168 [86] Women28 [14] Height (m)1.7 ± 0.1 Weight (kg)76.4 ± 14.0 BMI (kg/m2)26.3 ± 3.7Sleep stages, % N113.0 ± 11.2 N257.3 ± 14.3 N314.4 ± 11.2 REM15.3 ± 7.8Severity of OSA (%) Non-OSA22 [11] Mild OSA39 [0] Moderate-to-severe OSA135 [69]BMI body mass index, OSA obstructive sleep apnea, REM rapid eye movement
## Sensitivity and specificity of WISM in diagnosing OSA
Table 2 shows the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and accuracy of the WISM in diagnosing OSA at the AHI ≥ 5, ≥ 15 and ODI ≥ 5, ≥ 15 thresholds when compared with the PSG. The sensitivity was high when the AHI was ≥ 5 events/h (sensitivity = $93\%$, specificity = $77\%$), and both sensitivity and specificity were high when the AHI was ≥ 15 events/h (sensitivity = $92\%$, specificity = $89\%$). The sensitivity, specificity, and accuracy values for the WISM among patients with obesity who had OSA (BMI of ≥ 28 kg/m2) were $98\%$, $100\%$, and $98\%$, respectively, as shown in Table 2.Table 2Performance metrics of the wearable intelligent sleep monitor at apnea–hypopnea index and oxygen desaturation index thresholds of ≥ 5 and ≥ 15 events/hScreening thresholdParticipants above the thresholdSensitivity (%)Specificity (%)PPV (%)NPV (%)p-LHRn-LHRAccuracy (%)κAUCAHI ≥ 5 events/h174937797594.10.1910.60.95AHI ≥ 15 events/h135928995838.00.1910.80.95ODI ≥ 5 events/h169936795622.80.1900.60.94ODI ≥ 15 events/h132948995888.60.1920.80.97Patients with obesity AHI ≥ 5 events/h5698100100670.0980.81 AHI ≥ 15 events/h5192100100640.1930.70.95 ODI ≥ 5 events/h54985096672.00.0950.50.99 ODI ≥ 15 events/h5094100100730.1950.81PPV positive predictive value, NPV negative predictive value, p-LHR positive likelihood ratio, n-LHR negative likelihood ratio, AHI apnea–hypopnea index, ODI oxygen desaturation index
## ROC curve for OSA diagnosis using the WISM cutoff values
When an AHI of ≥ 5 events/h was used as the criterion for diagnosing OSA, the area under the ROC curve was 0.95 ($P \leq 0.001$, $95\%$ confidence interval [CI] = 0.910–0.983), the best cutoff value was 7, the sensitivity was $86\%$, and the specificity was $91\%$. When an AHI of ≥ 15 events/h was used as the criterion for diagnosing OSA, the area under the ROC curve was 0.95 ($P \leq 0.001$, $95\%$ CI = 0.922–0.979), the best cutoff value was 12, the sensitivity was $93\%$, and the specificity was $88\%$ (Fig. 1).Fig. 1ROC curves for OSA diagnosis using the WISM. a ROC curve for OSA diagnosis using the WISM at an AHI threshold of ≥ 5 events/h. b ROC curve for OSA diagnosis using the WISM at an AHI threshold of ≥ 15 events/h. OSA obstructive sleep apnea, ROC receiver-operating characteristic, WISM wearable intelligent sleep monitor, AHI apnea–hypopnea index
## AHI and WISM ODI conformance analysis
The kappa coefficients of the ODI from the WISM at different thresholds are shown in Table 2. The ODI had a strong correlation with the AHI and ODI from the PSG (R2 = 0.843, R2 = 0.845) (Fig. 2; Supplementary Fig. S2). The Bland–Altman curves for the ODI from the WISM and AHI from the PSG are shown in Fig. 3; $93\%$ ($\frac{182}{196}$) of the scatter points lie within the $95\%$ limits of agreement (LOA). The Bland–Altman curves for the ODIs from the WISM and PSG are shown in Supplementary Figure S3, where $96\%$ ($\frac{188}{196}$) of the scatter points lie the $95\%$ LOA.Fig. 2Linear correlation analysis of ODI and AHI (R2 = 0.843, $P \leq 0.001$). ODI oxygen desaturation index, AHI apnea–hypopnea index, PSG polysomnographyFig. 3Bland–Altman consistency test results for the ODI calculated from the WISM and the AHI calculated from the PSG. The mean difference between the ODI from the WISM and AHI from the PSG was 6.8 (consistency limit: − 13.1–26.7 [$$n = 196$$)]. ODI oxygen desaturation index, AHI apnea–hypopnea index, PSG polysomnography, WISM wearable intelligent sleep monitor
## Discussion
The results of this study showed that there was a strong correlation and consistency between the ODI obtained from the WISM and the AHI and ODI obtained from the standard PSG, suggesting that as a screening device, the WISM showed good diagnostic performance. Our results support the use of a highly accurate and convenient monitoring tool to screen for OSA in a large population, allowing a large number of potential patients with OSA to be diagnosed and treated in a timely manner.
In our study, with the AHI ≥ 5 and PSG ODI ≥ 5 as the diagnostic threshold, the sensitivity was high, but the specificity was slightly lower, which may be related to the small sample size of the patients who did not have OSA ($\frac{22}{196}$, $\frac{27}{196}$). In contrast, with the AHI ≥ 15 and PSG ODI ≥ 15 as the diagnostic threshold, high sensitivity and good specificity were observed. For all of these threshold, the areas under the ROC curve (AUC) were ≥ 0.9; compared with the sensitivity, specificity, and AUC of the single-channel monitors in previous studies [20], the device has strong diagnostic value to screen for OSA with corresponding thresholds. In particular, with the AHI ≥ 15 threshold showing a high positive likelihood ratio and a low negative likelihood ratio, the best cutoff value for diagnosing moderate-to-severe OSA was 12, with a sensitivity and specificity of $93\%$ and $88\%$, respectively. This result is consistent with the ODI defined for the moderate-to-severe OSA in a large-scale interventional study by McEvoy et al. [ 10]. When comparing the ODI obtained from the WISM to that of the PSG, the sensitivity of WISM, at the PSG ODI ≥ 5 and ≥ 15, were $93\%$ and $94\%$. These were higher than the sensitivity of AHI at the corresponding thresholds. When the ODI was ≥ 15, the AUC reached 0.97, indicating that some patients’ ODI obtained from the WISM underestimated the AHI. This may be related to the definition of apnea and hypopnea; apnea may not be accompanied by a decrease in oxygen saturation, while hypopnea is a condition that shows decreased oxygen saturation or microarousal [11]. Therefore, patients with a negative diagnosis of WISM but OSA symptoms should be tested using the PSG. The specificity of WISM when PSG ODI ≥ 5 was lower as compared to when PSG AHI ≥ 5. This may be due to the small sample size of patients who did not have OSA, and can be further studied in a large population.
The WISM ODI < 7 was used as the threshold to screen for patients without OSA, and only two cases ($0.1\%$) were misclassified. Among the 45 WISM-diagnosed patients with mild OSA, one remained undiagnosed, while four had moderate and non-severe OSA according to the AASM OSA diagnostic criteria [11]; among the 132 WISM-diagnosed patients with moderate and severe OSA, one remained undiagnosed, while six showed mild OSA. This indicates low false-positive and misdiagnosis rates of the WISM, highlighting its capability to exclude patients without OSA, which can help to avoid economic loss, psychological stress, and unnecessary use of medical and healthcare resources caused. The WISM calculates the ODI using effective monitoring time instead of sleep time as the denominator, and hence, may underestimate the condition of OSA. PSG and follow-up should be performed for patients with OSA and insomnia. Patients with obesity are an important subgroup of patients with OSA, and the pathophysiology of OSA in patients with obesity differs from that in the general population [21]. We also observed that the sensitivity and accuracy of the WISM among the obese patients were high, with good consistency.
A recent study showed that the sensitivity/specificity (AUC) of using wearable wrist worn monitor to estimate apnea in patients with mild, moderate, and severe OSA were 77/$72\%$ (0.84), 62/$91\%$ (0.86), and 46/$98\%$ (0.85), respectively. The diagnostic performance of the devices was moderate, with slightly lower kappa values than that of the WISM [22]. Nigro et al. used blood oxygen saturation and airflow of the dual-channel portable monitors to evaluate suspected OSA and found that manual analysis was superior to the autoscoring set up by the device (AUC 0.923 vs. 0.87) [23]. However, this device has airflow lead, which is not simple enough and requires manual scoring to achieve the better diagnostic ability; it is not suitable for screening a large sample population. Electrocardiogram signal is closely related to respiration, and has been used to diagnose OSA [24]. The work conducted by Arikawa et al. have used more convenient mobile wearable heart rate sensors and R-R intervals (RRI) to analyze and predict OSA risk [25]. However, the diagnostic test is on a small sample of people, and the performance is not clear. The WISM directly monitors blood oxygen saturation, and the AI algorithm analyzes the ODI. It is small and sometimes easy to lose, but with good accuracy. It has no external lead and no sense of wearing, which can improve the participation rate of people with a high risk of OSA and make it possible to screen for OSA in a community or a large sample population.
This study had limitations. All of the participants were patients attending a sleep center, and did not represent the general population. Furthermore, the device does not distinguish between obstructive apnea and central apnea due to the absence of airflow and thermal leads. The device is also unable to monitor sleep time. Therefore, further research and development of such devices are needed in the future.
In conclusion, at present, many patients with potential OSA remain unidentified, and OSA screening in the general population mainly relies on responses to questionnaires. Thus, there is a need for an objective device that is small, lightweight, less time-consuming, and capable of screening hundreds of people a day. Compared with PSG, WISM exhibits good sensitivity and specificity, and the operation of the device requires only a single step. Our findings suggest that this device may aid in objective, rapid screening of large numbers of patients.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 302 KB)
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---
title: Effects of sacubitril-valsartan on central and obstructive apneas in heart
failure patients with reduced ejection fraction
authors:
- Youmeng Wang
- Roberto Fernandes Branco
- Matthew Salanitro
- Thomas Penzel
- Christoph Schöbel
journal: Sleep & Breathing = Schlaf & Atmung
year: 2022
pmcid: PMC9992232
doi: 10.1007/s11325-022-02623-0
license: CC BY 4.0
---
# Effects of sacubitril-valsartan on central and obstructive apneas in heart failure patients with reduced ejection fraction
## Abstract
### Objective
This study aimed to evaluate the effect of sacubitril-valsartan (SV) on central apneas (CA) and obstructive apneas (OA) in patients with heart failure with reduced ejection fraction (HFrEF).
### Methods
In patients with HFrEF, SV initiation was titrated to the highest tolerable dosage. Patients were evaluated with portable apnea monitoring, echocardiography, and cardiopulmonary exercise testing at baseline and 3 months later.
### Results
Of a total of 18 patients, 9 ($50\%$) had OA, 7 ($39\%$) had CA, and 2 ($11\%$) had normal breathing. SV therapy was related to a reduction in NT-pro BNP and an improvement in LV function after 3 months. Portable apnea monitoring revealed a significant decrease of the respiratory event index (REI) after treatment with SV (20 ± 23 events/h to 7 ± 7 events/h, $$p \leq 0.003$$). When subgrouping according to type of apneas, REI, and time spent below $90\%$ saturation (T90) decreased in patients with CA and OA (all $p \leq 0.05$).
### Conclusion
In this prospective study, SV treatment for 3 months in patients with CA and OA is associated with a significant decrease in REI.
## Introduction
Heart failure (HF) is now recognized as a severe health issue affecting almost 65 million people of all ages worldwide. The prevalence of HF is 1–$2\%$ in patients over 65 years old, and it appears to be increasing in developed countries [1]. Despite substantial breakthroughs in medical and surgical treatment of HF, approximately $30\%$ of patients are admitted annually for HF exacerbation [2]. Central apneas (CA) and obstructive apneas (OA) are increasingly recognized comorbidity in subjects with HF and may affect the prognosis of HF [3]. To date, there is consensus that the initial step in managing patients with CA/OA and HF should be optimizing HF treatment [4]. Indeed, past research has shown that optimizing pharmacological therapy [5, 6] and utilizing non-pharmacological ways to treat HF can improve CA/OA [7]. However, the best way to manage CA/OA in HF is still being debated, owing to the fact that the therapeutic benefit of additional respiration treatment for patients with HFrEF has been questioned following the SERVE-HF trial’s results and ongoing findings of the ADVENT-HF research, respectively. The results showed that not only was adaptive servo-ventilation (ASV) ineffective, but also a post hoc analysis found excessive cardiovascular mortality in patients who received the treatment [8].
Sacubitril-valsartan (SV) is a first-in-class angiotensin-receptor neprilysin inhibitor used to treat HFrEF (New York Heart Association [NYHA] functional class II–IV) [9]. Therapy with SV decreased cardiovascular death, overall mortality, and HF-related hospitalizations in the PARADIGM-HF study compared to treatment with enalapril [10]. In preliminary investigations, angiotensin-converting enzyme (ACE) inhibitors have been shown to ameliorate CA/OA in patients with HF [11]. Despite the fact that the combination therapy can improve apneas in patients with HF, there is little research on the effect of SV on CA/OA [12]. In this study, we investigated the effect of initiating SV on apneas and hypothesized that CA/OA would improve when using treatment with SV.
## Study population
This trial was a 3-month, single-center, open-label, prospective study from January 2019 to July 2021. Inclusion criteria were as follows: non-childbearing female and male patients age 60 + with HF (NYHA class II–IV); LVEF ≤ $40\%$; patients had to receive stable doses (at least 1 month) standard-of-care HF medication before the study; a blood test result of serum potassium ≤ 5.2 mmol/L, estimated glomerular filtration rate (eGFR) ≥ 30 ml/min/1.73 m2, and systolic blood pressure (SBP) ≥ 100 mmHg. Exclusion criteria were as follows: severe valvular disease, isolated right HF, secondary cardiomyopathy, hypertrophic obstructive cardiomyopathy, previous or upcoming heart transplantation, and unstable angina within half a year before the study; patients treated with a history of angioedema or significantly increased liver enzymes (at least three times higher than the upper threshold), or with combination drugs such as ACE inhibitors and angiotensin-receptor blockers (ARBs). To participate in this study, the subjects were required to provide written informed permission. Our study was registered with ClinicalTrials.gov, number NCT02768298, and the EU Clinical Trials Register, number CLCZ696BDE01.
## Study drug
According to the dosage approved by European Union, patients took SV twice a day and adjusted it for renal function and hemodynamic tolerance. Patients were advised to take the study drug simultaneously every day, according to the approved instructions that follow the current European HF guidelines’ best medical treatment recommendations.
## Home portable apnea monitoring
The ApneaLink device (ResMed Inc., Martinsried, Germany) was used to measure nasal flow and pulse oximetry in this study [13]. Participants were instructed to use the device in a standardized manner by study personnel who had undergone extensive training. Adults with apnea can be assessed using portable apnea monitoring devices instead of overnight polysomnography [14, 15]. Apnea was defined as a reduction in airflow of more than $90\%$ from baseline for more than 10 s. Apneas were further classified as OA if there was any evidence of respiratory effort, CA if there was no evidence of respiratory effort, and mixed apnea if features of both CA and OA were present. For the purposes of this study, hypopnea was described as a $30\%$ decrease in airflow lasting for more than than 10 s, followed by a $3\%$ reduction in oxygen saturation. The number of apnea and hypopnea events per hour of monitoring during a certain period was described as the respiratory event index (REI). The REI is used as a surrogate for the apnea–hypopnea index (AHI) because it measures time spent monitoring rather than total sleep time [16].
The changes in echocardiographic parameters from baseline were examined in patients with HF who had a baseline LVEF of less than $40\%$. A ramp technique was used following calibration on a treadmill, and a cardiopulmonary exercise test (CPET) was performed on the patients after taking their age and gender into consideration [17]. Normative clinical chemistry tests were performed which included a full blood count and the N-terminal segment of the pro-brain natriuretic peptide (NT-pro BNP). These procedures were supervised and managed by a clinically experienced cardiologist and nurse.
## Statistical analysis
Descriptive data are presented as means ± standard deviation (SD) or as numbers and percentages of each category unless otherwise indicated. Paired t-tests (for data with normal distribution) and Wilcoxon tests (for data with abnormal distribution) were used due to the reliance of both populations before and after. The level of statistical significance was established at $p \leq 0.05.$ All statistical data were performed using SPSS version 25.0 (IBM SPSS Statistics, Armonk, NY, USA).
## Results
A total of eighteen eligible patients were enrolled in the study. Table 1 summarizes the clinical, demographic, and medications data. Despite being given optimal medical treatment, most subjects had apneas at baseline. Only 2 patients ($11\%$) had normal breathing, 9 had OA ($50\%$), and 7 had CA ($39\%$). Among subjects with OA, 4 ($23\%$), 5 ($27\%$), and 0 ($0\%$) had mild (5 ≤ REI < 15), moderate (15 ≤ REI < 30), and severe (REI ≥ 30) apnea, respectively, while among subjects with CA, 4 ($22\%$), 0 ($0\%$), and 3 ($17\%$) had mild, moderate, and severe apnea, respectively. Before using the ApneaLink monitoring, the patients were requested to stop taking any medications that had a direct impact on ventilatory control. Table 1Baseline characteristics of patientsHF patients treated with SV ($$n = 18$$)Age (years)66.7 ± 10.7Gender (male/female, n)$\frac{15}{3}$BMI (kg/m2)43.8 ± 50.2NYHA class (%) Class II50 Class III50Atrial fibrillation (%)22CKD (%)39Diabetes (%)17Hypertension (%)78COPD (%)28Cardiac infarction (%)22Beta-blocker (%)89Loop diuretics (%)72ICD (%)72CRT (%)17BMI body mass index; COPD chronic obstructive pulmonary disease; CKD chronic kidney disease; CRT cardiac resynchronization therapy; ICD implantable cardioverter defibrillator; NYHA New York Heart Association Results of SV on cardiac function, CPET, and blood testing in the overall population are presented in Table 2 and Fig. 1. SV has been shown to be associated with a statistically significant decrease in NT-pro BNP. The administration of the drug was also associated with improved left ventricular (LV) systolic and diastolic function, as indicated by an increase in LV end-diastolic diameter, as well as with improvement in LV reverse remodeling, as indicated by increased LVEF. No statistically significant changes were noted in tricuspid annular plane systolic excursion (TAPSE) and systolic pulmonary artery pressure (sPAP). There were no differences in peak oxygen consumption or FEV1 (both $p \leq 0.05$) after therapy at CPET compared to baseline. Table 2Relation between SV treatment and heart remodeling, CPT, and blood examinationBaseline3 monthsp-valueLVEF (%)32 ± 743 ± 9 < 0.001aLVEDD (mm)59.9 ± 6.956.8 ± 9.60.025aLVESD (mm)50.7 ± 9.144.5 ± 100.001aLVESV (ml)110.4 ± 48.183.3 ± 39.70.001bTAPSE (mm)18.7 ± 4.219.7 ± 2.90.392bE/E'11.9 ± 4.912.6 ± 7.80.594asPAP (mmHg)25.8 ± 10.724.1 ± 10.80.623bFEV1 (L)2.6 ± 12.9 ± 0.80.209aMax VO2 (ml/min/kg)14.4 ± 2.313.8 ± 2.40.296aeGFR (ml/min/1.73 m2)68.8 ± 17.867.3 ± 16.60.576aNT-pro BNP (pg/ml)1792.1 ± 1271.3876.9 ± 984.20.001beGFR estimated glomerular filtration rate; FEV1 forced expiratory volume for one second; LVEF left ventricular ejection fraction; LVEDD left ventricular end-diastolic diameter; LVESD left ventricular end-systolic diameter; LVESV left ventricular end-systolic volume; NT-proBNP pro-B-type natriuretic peptide; RV-FAC right-ventricular fractional area change; sPAP systolic pulmonary artery pressure; TAPSE tricuspid annular plane systolic excursion; VO2 oxygen consumption; Pa represents the paired T-test; Pb represents Wilcoxon testFig. 1Changes in echocardiographic measures and blood examination after 3 months of treatment with SV. * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001.$ eGFR estimated glomerular filtration rate; LVEF left ventricular ejection fraction; NT-proBNP pro-B-type natriuretic peptide; sPAP systolic pulmonary artery pressure; TAPSE tricuspid annular plane systolic excursion SV treatment was found to be associated with a significant reduction in REI in the general population (Table 3 and Fig. 2). The effect of SV administration was significantly associated with a decrease in REI in the subgroup of subjects with OA (by $47\%$). In the subgroup of subjects with CA, SV was also associated with a decrease in REI (by $81\%$). SV had a decreasing effect on the minimal oxygen saturation and T$90\%$ (all $p \leq 0.05$).Table 3Relation between SV treatment and apneasBaseline3 monthsp-valueOverall population ($$n = 18$$) REI (e/h)20 ± 237 ± 70.003b SaO2 basal (%)93 ± 295 ± 20.053a SaO2 min (%)80 ± 480 ± 80.812a T90 (min)119 ± 12842 ± 860.001bPatients with CA ($$n = 7$$) REI (e/h)36 ± 327 ± 80.018b SaO2 basal (%)94 ± 294 ± 20.876a SaO2 min (%)79 ± 477 ± 110.598a T90 (min)131 ± 11719 ± 190.028bPatients with OA ($$n = 9$$) REI (e/h)14 ± 67 ± 70.039a SaO2 basal (%)92 ± 395 ± 20.025a SaO2 min (%)81 ± 282 ± 60.404b T90 (min)138 ± 15166 ± 1190.038bCA central apnea; OA obstructive apnea; REI respiratory event index; SaO2 oxygen saturation; T90 time spent with oxygen saturation < $90\%$; Pa represent the paired T test; Pb represent Wilcoxon testFig. 2Changes of REI and T90 after 3-month SV treatment in the overall population and in the subgroups with OA and with CA. * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001.$ CA central apnea; OA obstructive apnea; REI respiratory event index; T90 time spent with oxygen saturation < $90\%$
## Discussion
SV has been shown to benefit both CA and OA in patients with HFrEF. The administration of SV to optimal medical therapy was associated with a significant decrease in the REI.
A comparison of SV and enalapril has shown that the former was more effective at decreasing all-cause and sudden-death mortality, as well as limiting the progression of HF [18]. This study shows that SV is associated with an increase in LVEF, which in turn promotes LV and left atrial reverse remodeling and an improvement in REI [12]. As expected, SV also had a positive effect on NT-pro BNP [19, 20]. It is worth noting that some participants transitioned from CA to OA following therapy with SV, which consequently became the most common respiratory disorder. The administration of SV reduced CA, confirming the beneficial effect of the medication on CA stated previously in a previous case study [21]. In this study, successful cardiac function optimization by SV was related to a shift in the apnea phenotype from CA to OA. This finding is consistent with earlier studies, which have shown that improvements in cardiac performance lead to reduced CA, consequently unmasking previously undiagnosed OA [22–25]. Fox et al. found a 71-year-old man who suffered from HF and sleep-disordered breathing (SDB). Treatment with SV was associated with improved cardiac function, as measured by a decrease in NT-pro BNP and an increase in LVEF. This was associated with a significant decrease in the AHI. This is the first case to demonstrate improvement in HF and SDB following the start of SV treatment [26].
SV, by inhibition of neprilysin, prevents the degradation of natriuretic peptides, hence enhancing their natriuretic and vasodilatory actions and lowering pulmonary congestion, respectively [27, 28]. Additionally, the beneficial effects on cardiac reverse remodeling, which are associated with enhanced LVEF, may increase cardiac output [29, 30]. Overall, those effects may promote effective ventilation and gas exchange, and the chemoreflex, which reduces pulmonary stretch receptor stimulation while increasing the perfusion of peripheral chemoreceptors [31]. Furthermore, an increase in cardiac output may decrease circulation time, reducing the amount of time available for the chemoreflex system to detect and respond to changes in CO2 [32]. Finally, the medication has been shown to reduce the amount of rostral fluid shift that occurs when a person is in a reclined position. Although the PARADIGM-HF trial made a small but significant contribution to improving survival, it is tempting to conclude that this can be attributed to the reduced apneic burden. It is equally tempting to consider SV as an alternative first-line therapeutic strategy for apneas and, specifically, CA in HF [10]. Additionally, there are more alternative therapeutic approaches for hypoxemic burden. Olaf et al. discovered that transvenous phrenic nerve stimulation (TPNS) could significantly reduce nocturnal hypoxemic load. Hypoxemic burden is more predictive of mortality than AHI and should be a primary indicator for CSA treatments [33]. However, to address these intriguing challenges precisely, larger cohorts with definitive outcomes followed for longer periods of time would be required.
## Limitations
This study has several limitations. First, we acknowledge this is a single-center study and requires further studies to support the generalizability of the findings presented. In addition, our study was limited to the older population with HFrEF. Another possible limitation is that portable monitoring devices do not record CO2 levels, sleep stages, and sleep position. As a result, conclusions concerning these factors cannot be drawn and events cannot be classified into different sleep stages. In addition, no electroencephalograms were recorded in this study. Thus, it was impossible to determine if patients were asleep during the assessment, which could underestimate the severity of OA and CA. Importantly, the ApneaLink may overestimate the REI, as actual sleep time may be shorter than recorded time, implying a higher prevalence and severity of apnea.
## Conclusion
In summary, our findings obtained from patients with HFrEF show that SV had positive effects on both CA and OA. The effects of SV are more limited on OA than CA. SV may become a promising therapeutic option for CA in HFrEF.
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|
---
title: 'The bidirectional longitudinal association between health-related quality
of life and academic performance in adolescents: DADOS study'
authors:
- Mireia Adelantado-Renau
- Irene Monzonís-Carda
- Diego Moliner-Urdiales
- Maria Reyes Beltran-Valls
journal: Quality of Life Research
year: 2022
pmcid: PMC9992255
doi: 10.1007/s11136-022-03291-z
license: CC BY 4.0
---
# The bidirectional longitudinal association between health-related quality of life and academic performance in adolescents: DADOS study
## Abstract
### Purpose
Although previous evidence has suggested a relationship between health-related quality of life (HRQoL) and academic performance, the directionality of this association is understudied and remains to be clarified. Thus, the primary objective of this study was to explore the bidirectional association between HRQoL and academic performance in adolescents between two timepoints with a 24-month interval. A secondary aim was to analyze whether this association varies between boys and girls.
### Methods
This is a bidirectional longitudinal analysis with 257 adolescents (13.9 ± 0.3 years at baseline) from the DADOS study. HRQoL was measured using the KIDSCREEN-10 questionnaire. Academic performance was assessed through academic grades and the Spanish version of the Science Research Associates Test of Educational Ability.
### Results
Cross-lagged analyses revealed that HRQoL at baseline was not associated with academic performance 24 months later, while all the academic grades and the overall score of academic abilities at baseline were positively associated with HRQoL at follow-up in adolescents. Results of the stratified analyses by sex were largely similar. Specifically, in girls, math, language, physical education, and grade point average at baseline were positively associated with HRQoL 24 months later, while in boys, all the academic grades indicators (except physical education), numeric ability, and the overall score of academic abilities at baseline were positively associated with HRQoL at follow-up.
### Conclusion
These findings suggest that academic performance in early adolescence may predict HRQoL 24 months later. Health and education professionals could benefit from collaborating to achieve both improved academic performance and HRQoL in youth.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11136-022-03291-z.
## Introduction
Well-being is as a higher-order construct that integrates health (e.g., mental or physical functions), health-related (e.g., work or habits), and non-health-related domains (e.g., autonomy or integrity), which depends on complex interactions between individual and contextual factors [1]. Indeed, prior research has shown that individuals with high well-being are usually successful across multiple domains of life, including work and social relationships, and show better mental and physical health, as well as longer longevity [2]. Well-being encompasses some related but distinguishable constructs such as health-related quality of life (HRQoL), which could be defined as a broad multidimensional concept comprising the individual's perceptions of how life is going in terms of health and health-related domains [1]. HRQoL is considered a relevant health indicator, since it allows to capture the burden of disease from the perspective of an individual, and has been proposed as a predictor of mortality and morbidity [3]. From a public health perspective, assessing HRQoL during adolescence is of paramount importance, since quality of life in this period stablishes the basis for quality of life and health status in adulthood [4]. Thus, identifying factors related to adolescents’ HRQoL is of relevance to advance in the understanding of this multidimensional construct.
It has been recently suggested that HRQoL could be positively related with academic performance [5], which refers to educational goals that students have to reach in a particular period of time [6]. Importantly, academic performance may strongly shape a person’s life chances in terms of work and health. In fact, higher academic performance during adolescence has been associated with better earnings [7] and health status later in life [8]. While the relationship of adolescents’ health with HRQoL [9, 10] and academic performance has been widely investigated [6], how the psychological construct of HRQoL and academic performance are associated is understudied and remains to be clarified.
Regarding the overall construct of well-being and its association with academic performance, a recent systematic review conducted by Amholt et al. [ 11] concluded that findings from previous studies were inconsistent, since both positive and null associations were reported. Moreover, the studies included in this review, mostly with a cross-sectional design, focused on analyzing the unidirectional relationship between subjective well-being and students’ academic performance [11]. Interestingly, few previous research investigated the plausibility of a bidirectional association between these two constructs in adolescents, which suggested that the direction of this association is still unknown since divergent results were reported [12–14]. For instance, Wu et al. [ 12] investigated the bidirectional association between the main components of subjective well-being (i.e., life satisfaction, and positive and negative affect) and a standardized index of academic performance, showing that only life satisfaction and positive affect at baseline were positively correlated with adolescents’ academic performance 14 months later. Conversely, Steinmayr et al. [ 13] reported that adolescents’ grade point average (GPA) at baseline was positively associated with changes in life satisfaction at 1-year follow-up, while there was no association in the other direction. Additionally, Ng et al. [ 14], who examined the association between life satisfaction and GPA in two waves with a 5-month interval in a sample of adolescents, suggested a positive reciprocal causal relationship. Regarding the specific construct of HRQoL, there is only one study that has examined its bidirectional association with academic performance in adolescents, which reported controversial results and gender differences [15]. Specifically, Bortes et al. [ 15] found a positive association between HRQoL at baseline and GPA 24 months later, and a negative association between GPA at baseline and HRQoL at follow-up in adolescent girls, while no associations were found among boys.
The scarce literature examining the bidirectional longitudinal association between HRQoL and academic performance evidenced two major limitations. First, most previous studies included only a global indicator of academic performance, without examining the association of each single academic performance indicator with HRQoL. Moreover, in this context, prior evidence did not include academic abilities, which reflect competences and content acquired in specific areas of knowledge. Second, most of these studies present a relatively short-term longitudinal design. Therefore, given the paucity of knowledge and the lack of conclusive findings, more studies are necessary to address these gaps in the literature and to clarify the directionality of the association between HRQoL and academic performance in adolescents. Thus, the present study intended to explore the bidirectional association between HRQoL and academic performance in adolescents between two timepoints with a 24-month interval. Since the influence of sex in this association remains unclear, a secondary aim was to analyze whether this association varies between boys and girls.
## Study design and sample selection
This study is part of the DADOS (Deporte, ADOlescencia y Salud) research project, a 3-year longitudinal study aimed to examine the influence of lifestyle behaviors on health and academic performance during adolescence. The results presented in this study belong to baseline (obtained between February and May of 2015) and follow-up data (obtained between February and May of 2017). A convenience sampling technique was used to recruit participants. For that purpose, advertising leaflets about the research project were sent to secondary schools and sport clubs located in the province of Castellon (Spain), which included main information about the aim and the study protocol. The inclusion criteria were to be enrolled in second grade of secondary school, and not to be diagnosed of any physical (i.e., locomotor system injury) or mental (i.e., intellectual disability) impairment. Volunteers who met the inclusion criteria were included in the study. A total of 257 adolescents (121 girls) aged 13.9 ± 0.3 years at baseline with valid data for HRQoL and academic performance at baseline were included in the analyses.
Adolescents and their parents or guardians were informed of the nature and characteristics of the study, and all of them provided a written informed consent. The DADOS study protocol was designed in accordance with the ethical guidelines of the Declaration of Helsinki 1964 (last revision of Fortaleza, Brazil, 2013) and approved by the Research Ethics Committee of the University Jaume I of Castellon (Spain).
## Health-related quality of life
HRQoL was assessed with the KIDSCREEN-10 questionnaire, a valid and reliably scale to analyze HRQoL among youth population [16]. The reliability and validity of the questionnaire have been examined previously in adolescents showing good reliability (Cronbach’s α = 0.82) and criterion validity ($r = 0.91$) [16]. Similar reliability results have also been obtained in the current study (Cronbach’s α = 0.77). This questionnaire consists of 10 items rated in a 5-point Likert scale (i.e., 1 = “not at all”, 2 = “slightly”, 3 = “moderately”, 4 = “very”, and 5 = “extremely”). For each item, responses were coded so that higher values indicate better HRQoL. Then, the sum of the items was calculated, and it was transformed based on the RASCH-Person parameters estimates [17]. A higher score in the questionnaire indicates better HRQoL.
## Academic performance
Academic performance was assessed through the final academic grades from the first (13 years) and the third (15 years) grade of secondary school, provided by each school’s secretary office. They are based on a ten-point scale (0 indicates the lowest achievement and 10 indicates the highest achievement) and can be classified in the following categories: unsatisfactory (0 to 4.9), satisfactory (5 to 5.9), good (6 to 6.9), very good (7 to 8.9), and excellent (9 to 10). GPA score and individual grades for the following subjects were included in the analyses: natural sciences, social sciences, math, language, and physical education. GPA score was defined as the average of the scores achieved by students in all subjects.
The Spanish version of the Science Research Associates Test of Educational Ability (TEA) was used to measure academic abilities [18]. This test provides general measures of three areas of intelligence and skills of learning: verbal (i.e., command of language), numeric (i.e., speed and precision in performing operations with numbers and quantitative concepts), and reasoning (i.e., the skill to find logical order in sets of numbers, figures, or letters). Scores for the three areas were obtained by adding positive answers. Overall score was calculated by adding the three areas scores (i.e., verbal, numeric, and reasoning). Based on the age range of our sample, level three of the TEA questionnaire designed for ages 14 to 18 years was used in both assessments (reliability: verbal α = 0.74, numeric α = 0.87, reasoning α = 0.77, and overall score α = 0.89).
## Covariates
Sex, pubertal stage, waist circumference, socioeconomic status, and parents’ education level were included as covariates in the statistical analyses due to their relationship with the study variables [6, 19, 20].
Pubertal stage. Pubertal stage was self-reported according to the five stages described by Tanner and Whitehouse [21]. It is based on external primary and secondary sexual characteristics, which are described by the participants using standard pictures according to Tanner instructions.
Waist circumference. Waist circumference was measured twice to the nearest 1 mm with a non-elastic tape applied horizontally midway between the lowest rib margin and the iliac crest, at the end of gentle expiration with the adolescent in a standing position. The average measure was used for the analyses.
Socioeconomic status. The Family Affluence Scale (FAS) developed by Currie et al. [ 22] was used as a proxy of socioeconomic status (ranging from 0 to 8), which is based on material conditions in the family such as car ownership, bedroom occupancy, computer ownership, and home internet access.
Parents’ education level. Parents or legal guardians reported their education level which was categorized into two groups using the highest education level obtained by the mother or the father: (i) below university education, and (ii) university education.
## Statistical analysis
Descriptive characteristics are presented as mean and standard deviations or percentages. All variables were checked for normality using both graphical (normal probability plots) and statistical (Kolmogorov- Smirnov test) procedures.
The bidirectional association between HRQoL and academic performance indicators was examined using a cross-lagged modeling approach, through the Lavaan package in R [23]. A depiction of the general cross-lagged panel model is presented in Fig. 1. In these path analyses, all associations were adjusted for each other: that is to say, analyses are adjusted for the mutual prospective associations that represent the bidirectional associations between HRQoL and academic performance indicators (the cross-lagged pathways βCL-1 and βCL-2), the cross-sectional paths (βCS-Baseline), and the underlying associations of HRQoL over time (autoregressive path, βAR-Health-related quality of life) and academic performance indicators over time (autoregressive path, βAR-Academic performance). The autoregressive paths describe the stability of individual differences in the measured variables from baseline to follow-up. A small (closer to zero) autoregressive coefficient indicates less stability, while a larger (closer to 1) autoregressive coefficient indicates more stability of the variable over time [24].Fig. 1Visualization of cross-lagged panel modeling approach. AR Autoregressive, CL Cross-lagged path, CS Cross-sectional path Models were assessed using several fit indexes and information criteria: the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI), which should both be close to or exceed 0.95, the Root Mean Square Error of Approximation (RMSEA), which should be close to or below 0.06, and the Standardized Root Mean square Residual (SRMR), which should be close to or below 0.08 [25]. All models were adjusted for sex, pubertal stage, waist circumference, socioeconomic status, and parents’ education level.
The Full Information Maximum Likelihood Estimator was implemented to preserve all available data ($$n = 257$$) [23]. This estimator is considered a standard approach to prevent listwise deletion of participants with missing data [23]. In these analyses, we used false discovery rate based on the Benjamini–Hochberg method to adjust for multiple comparisons. Briefly, this method uses ranked p-values to determine the cut-off, at which point the Type-I error rate is below 0.05 [26]. Lastly, several sensitivity analyses were performed. Firstly, to ensure that the results were not biased due to the estimation of missing data from the second timepoint, analyses were conducted only including the participants with complete HRQoL and academic performance data at both timepoints ($$n = 184$$). Secondly, stratified analyses by sex were conducted for the cross-lagged paths in which associations were statistically significant. All the statistical analyses of this study were performed using SPSS v. 27 (IBM Corp., Armonk, NY) and R version 3.6.0 using the Lavaan package (The R foundation for Statistical Computing, Vienna, Austria), and the level of significance was set at $p \leq 0.05.$
## Results
The characteristics of the adolescents at baseline and at 24-month follow-up are shown in Table 1. Participants’ HRQoL score was 50.1 at baseline and 48.7 at follow-up. Academic grades ranged from 6.9 to 8.1 at baseline and from 6.3 to 8.2 at follow-up. Regarding academic abilities, the overall score was 49.0 at baseline and 59.8 at follow-up. Table 1Descriptive characteristics of participants at baseline and at 24-month follow-upBaselineFollow-upn257184Age (years)13.9 ± 0.315.8 ± 0.3Pubertal stage (II–V) (%)$\frac{8}{35}$/$\frac{47}{100}$/$\frac{11}{52}$/37Waist Circumference (cm)67.4 ± 5.971.4 ± 6.3Socioeconomic status (0–8)4.2 ± 1.4–Parents with university-level education (%)48.6–Health-related quality of life50.1 ± 8.148.7 ± 6.4Academic performance Academic grades (0–10) Natural Sciences7.1 ± 1.66.6 ± 1.5 Social Sciences7.1 ± 1.66.9 ± 1.8 Math6.9 ± 1.66.3 ± 1.8 Language7.0 ± 1.56.3 ± 1.6 Physical Education8.1 ± 1.18.2 ± 1.2 Grade point average7.2 ± 1.36.7 ± 1.3 Academic abilities Verbal ability (0–50)18.8 ± 5.322.4 ± 5.7 Numeric ability (0–30)13.5 ± 4.816.5 ± 5.2 Reasoning ability (0–30)16.6 ± 5.720.9 ± 4.9 Overall score (0–110)49.0 ± 12.659.8 ± 12.4Data are presented as mean ± standard deviation or percentages Bidirectional longitudinal associations between HRQoL and academic performance based on the cross-lagged panel models after adjustment for sex, pubertal stage, waist circumference, socioeconomic status, and parents’ education level are shown in Table 2. The headings used in the table are represented as pathways in Fig. 1. At baseline, after multiple comparisons correction, only GPA was cross-sectionally and positively associated with HRQoL ($$p \leq 0.010$$). HRQoL at baseline was not associated with any of the academic performance indicators 24 months later (all $p \leq 0.05$). Nevertheless, all the academic grades and the overall score of academic abilities at baseline were positively associated with HRQoL at follow-up (all $p \leq 0.05$).Table 2Bidirectional associations between health-related quality of life and academic performance based on the cross-lagged panel models ($$n = 257$$)HRQoL → APAP → HRQoLCross-sectionalFit measuresβCL-1pp FDRβCL-2pp FDRβCS-Baselinepp FDRCFIRMSEAHealth-related quality of lifeAcademic grades Natural Sciences0.003 (− 0.088, 0.094)0.9470.9470.171 (0.061, 0.282)0.0040.0080.130 (0.027, 0.234)0.0150.0500.9650.059 Social Sciences0.077 (− 0.043, 0.198)0.2090.5230.181 (0.061, 0.300)0.0040.0080.106 (0.000, 0.212)0.0560.0930.9860.034 Math− 0.123 (− 0.226, − 0.020)0.0190.1900.238 (0.124, 0.352) < 0.001 < 0.0010.121 (0.016, 0.226)0.0260.0650.9730.048 Language− 0.026 (− 0.131, 0.079)0.6250.7810.214 (0.108, 0.320) < 0.001 < 0.0010.132 (0.031, 0.234)0.0110.0500.9650.057 Physical Education0.106 (− 0.030, 0.242)0.1370.5230.156 (0.043, 0.270)0.0070.0120.120 (0.008, 0.232)0.0370.0741.0000.000 GPA− 0.014 (− 0.094, 0.066)0.7330.8140.205 (0.091, 0.320)0.0010.0030.167 (0.069, 0.264)0.0010.0100.9750.055Academic abilities Verbal ability− 0.049 (− 0.165, 0.067)0.4100.6830.106 (− 0.013, 0.225)0.0820.0910.113 (− 0.009, 0.235)0.0660.0941.0000.000 Numeric ability− 0.074 (− 0.181, 0.034)0.1760.5230.124 (− 0.017, 0.265)0.0800.0910.088 (− 0.019, 0.196)0.1120.1400.9660.060 Reasoning ability0.037 (− 0.068, 0.142)0.4920.7030.111 (− 0.018, 0.240)0.0960.0960.023 (− 0.102, 0.148)0.7190.7190.9990.009 Overall score− 0.038 (− 0.126, 0.051)0.4030.6830.146 (0.014, 0.278)0.0300.0430.091 (− 0.031, 0.212)0.1440.1600.9870.037Results showed as standardized coefficients and $95\%$ confidence intervals. HRQoL health-related quality of life, AP academic performance. βCL-1 the cross-lagged path 1, where HRQoL score at baseline predicts AP at follow-up; βCL-2 the cross-lagged path 2, where AP at baseline predicts HRQoL score at follow-up; βCS-Baseline the cross-sectional association between HRQoL and AP within baseline; p FDR significant levels adjusted for multiple testing. CFI comparative fit index, RMSEA root mean square error of approximation, GPA grade point average. Cross-lagged models were adjusted for sex, pubertal stage, waist circumference, socioeconomic status, and parents’ education level. Statistically significant values are shown in bold Autoregressive coefficients are presented in Table S1. The coefficient for HRQoL was small (β ranging from 0.458 to 0.487), while the autoregressive coefficient for academic performance indicators was excepting for physical education (β = 0.376) close to one (β ranging from 0.622 to 0.807).
Sensitivity analyses were conducted with the sample reduced to those with complete HRQoL and academic performance data at both timepoints ($$n = 184$$), and results were largely similar (Tables S2 and S3). In addition, stratified analyses by sex for the cross-lagged path in which academic performance at baseline predicts HRQoL at follow-up (βCL-2) are presented in Fig. 2. In girls, math, language, physical education, and GPA at baseline were positively associated with HRQoL at follow-up, while in boys, all the academic grades indicators (except physical education), numeric ability, and the overall score of academic abilities at baseline were positively associated with HRQoL 24 months later. Fig. 2Stratified analyses by sex for the cross-lagged path in which academic performance at baseline predicts health-related quality of life at follow-up. Results showed as standardized coefficients and $95\%$ confidence intervals. Analyses were adjusted for pubertal stage, waist circumference, socioeconomic status, and parents’ education level. Girls: $$n = 121$$, boys: $$n = 136$$
## Discussion
The main findings of the present study indicated that HRQoL at baseline did not predict academic performance 24 months later, while all the academic grades and the overall score of academic abilities at baseline predicted HRQoL at follow-up in adolescents. Results of the stratified analyses by sex were largely similar. Specifically, in girls, math, language, physical education, and GPA at baseline predicted HRQoL 24 months later, while in boys, all the academic grades indicators (except physical education), numeric ability, and the overall score of academic abilities at baseline predicted HRQoL at follow-up. The current study contributes to the existing literature by exploring how HRQoL and a wide range of academic performance indicators are related to each other across time.
To our knowledge there is only one previous study analyzing the bidirectional association between HRQoL and academic performance in youth [15], which hampers further comparisons with other studies. In this study, Bortes et al. [ 15] analyzed a sample of 723 Swedish adolescents (15 years at baseline) showing a reciprocal relationship between HRQoL and GPA in girls, but not in boys. Importantly, the bidirectional associations found in girls did not point in the same directions, that is to say, HRQoL at baseline was positively associated with GPA 24 months later, while GPA at baseline was negatively associated with HRQoL at follow-up. Although these results differ from those found in the present study, our findings partially concur with prior evidence analyzing the bidirectional association between the broad higher-order construct of well-being and academic performance. Particularly, Steinmayr et al. [ 13] examined the bidirectional association between subjective well-being (including cognitive and affective components) and GPA in 290 German adolescents. In line with our results, the authors concluded that GPA at baseline was positively associated with changes in life satisfaction (the cognitive component of well-being) at 1-year follow-up. Similarly, one study conducted in 807 Chinese children (9 years) revealed that academic performance positively predicted subjective well-being 18 months later [27]. Likewise, Ng et al. [ 14] suggested that adolescents’ GPA exerted a positive effect on their life satisfaction 5 months later. However, contrarily to our findings, they also indicated that adolescents’ life satisfaction, albeit to a lesser extent, exerted a positive effect on their subsequent GPA. Also in contrast to our results, Wu et al. [ 12] reported that life satisfaction together with positive affect positively predicted adolescents’ academic performance (measured by a single index) 14 months later. Collectively, more longitudinal studies with longer designs and including the specific construct of HRQoL and multiple academic performance indicators are needed to elucidate the directionality of the HRQoL-academic performance association.
The reasons underlying why academic performance may positively predict HRQoL 24 months later cannot be elucidated in the present study; however, we suggest some mechanisms that may explain this association. First, based on the self-determination theory developed by Ryan and Deci in the seventies and eighties, academic performance may improve HRQoL through fulfilling the need for competence, which is considered a basic need that is essential for personal well-being [28]. Second, we speculate that health status could play an important role in this association. For instance, better academic performance may increase self-esteem with positive effects on well-being [27]. In addition, it is likely that adolescents with better academic performance are more health literate, which could lead them to take better care of themselves and have a better perception of their overall health status, positively influencing their HRQoL. Lastly, both academic performance and HRQoL may be influenced by diverse school-related variables. In fact, academic engagement, which is widely acknowledged as an important determinant of successful academic performance, has also shown to predict well-being [29], which may partially explain the academic performance-HRQoL association. In addition, although academic performance seems to be strongly linked to cognition, it also involves non-cognitive skills such as school environment, motivation, effort, attitude, interest, family support, personality, autonomy, self-perception, social acceptance, or teaching influence [30, 31], which in turn, may influence HRQoL.
Regarding stratified analyses by sex, some differences in both school performance and psychological-related issues between adolescent girls and boys could partially explain the sex-specific results found in the present study. In the case of physical education, which positively predicted HRQoL 24 months later in girls (but not in boys), we speculate that since girls tend to be more aware of their personal appearance and are more frequently dissatisfied by their body image than boys [32], girls could be specially interested in the health-related contents addressed in this subject, which in turn may positively influence their HRQoL. Meanwhile, the divergent results found for natural sciences, social sciences, and numeric ability, which were positively associated to HRQoL at follow-up in boys, but not in girls, could be explained not only by sex individual differences in terms of abilities, but also by the biological and cultural transmission of gender stereotypes [33]. In this sense, on the one hand, a previous study has stated that girls tend to achieve higher grades in subjects including emotional involvement (e.g., subjects related to humanities and social sciences) because of teachers' expectancies [33]. On the other hand, the belief that boys should get higher grades in mathematics than girls may increase their self-efficacy and self-concept towards this ability [33], which in turn may positively influence their HRQoL. Collectively, these issues and the fact that girls’ HRQoL declines more than boys’ over time [4, 34] may partially explain the sex-differences found in this longitudinal study.
Our findings have several important implications from an educational and public health perspective. In this sense, according to the OECD [35], ‘‘academic achievement that comes at the expense of students’ well-being is not a full accomplishment” (p. 4). Therefore, based on our results, policy makers should consider factors that influence adolescents’ academic performance to improve their HRQoL. In this context, education and health professionals should not only focus on study skills and pedagogy to improve adolescents’ academic performance, but also for instance on the promotion of healthy habits. Specifically, the promotion of compliance with healthy lifestyles (e.g., sleep, physical activity or diet) which have been positively associated with academic performance [36, 37] and the avoidance of unhealthy ones (e.g., screen time) which have been negatively associated with academic performance [38] could improve both their educational outcomes and their HRQoL.
## Limitations and strengths
Limitations of the study comprise the fact that data were collected only at two time points. Future research should include longitudinal data collected at various points in time to provide further evidence of the temporal precedence between HRQoL and academic performance. Yet, this study has several strengths, including the use of longitudinal data that enables to focus on changes over time rather than on static relations, as well as the analyses of a wide range of academic performance indicators, including individual academic grades and abilities. In addition, our statistical analyses were controlled for sex, pubertal stage, socioeconomic variables, and waist circumference which are relevant given their association with HRQoL and academic performance [6, 19, 20].
## Conclusions
In conclusion, our findings reveal that adolescents’ academic performance was positively associated with HRQoL 24 months later, showing largely similar results in girls and boys. Health and education professionals could benefit from collaborating to achieve both improved academic performance and HRQoL in youth. Further larger longitudinal and interventional studies in adolescents are warranted to clarify the pathways by which academic performance is linked to HRQoL, as well as to corroborate the directionality of this association.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 19 KB)
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|
---
title: Ribonucleosides from tRNA in hyperglycemic mammalian cells and diabetic murine
cardiac models
authors:
- Taylor A. Dodson
- Stephan Nieuwoudt
- Chase N. Morse
- Valinteshley Pierre
- Chao Liu
- Samuel E. Senyo
- Erin G. Prestwich
journal: Life sciences
year: 2023
pmcid: PMC9992345
doi: 10.1016/j.lfs.2023.121462
license: CC BY 4.0
---
# Ribonucleosides from tRNA in hyperglycemic mammalian cells and diabetic murine cardiac models
## Abstract
### Aims:
Cardiomyopathy is a diabetic comorbidity with few molecular targets. To address this, we evaluated transfer RNA (tRNA) modifications in the diabetic heart because tRNA modifications have been implicated in diabetic etiologies.
### Main methods:
tRNA was isolated from aorta, apex, and atrial tissue of healthy and diabetic murine hearts and related hyperglycemic cell models. tRNA modifications and canonical ribonucleosides were quantified by liquid-chromatography tandem mass spectrometry (LC-MS/MS) using stable isotope dilution. Correlations between ribonucleosides and diabetic comorbidity pathology were assessed using statistical analyses.
### Key findings:
Total tRNA ribonucleoside levels were analyzed from cell types and healthy and diabetic murine heart tissue. Each heart structure had characteristic ribonucleoside profiles and quantities. Several ribonucleosides were observed as significantly different in hyperglycemic cells and diabetic tissues. In hyperglycemic models, ribonucleosides N4-acetylcytidine (ac4C), 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U), 5-methylcytidine (m5C), and N1-methylguanosine (m1G) were anomalous. Specific tRNA modifications known to be on murine tRNAIni(CAU) were higher in diabetic heart tissue which suggests that tRNA modifications could be regulating translation in diabetes.
### Significance:
We identified tRNA ribonucleosides and tRNA species associated with hyperglycemia and diabetic etiology.
## Introduction
The building blocks of RNA are canonical ribonucleosides: cytidine (rC), guanosine (rG), adenosine (rA), and uridine (rU). RNA from all domains of life contain ribonucleoside chemical modifications such as enzymatic methylations, acetylations, and thiolations [1,2]. Though all types of RNA are known to be modified, tRNAs are the most extensively modified type of RNA. Human diseases such as cancer or diabetes can be caused by aberrations to tRNA modifications [3–8]. For example, the impaired function of Cdkal1, an enzyme which catalyzes the methylthiolation of N6-threonylcarbamoyladenosine (t6A), due to iron-deficiency can lead to mistranslation and processing of proinsulin, causing diabetes in mice [3]. Additionally, cellular anomalies to endogenous modifications such as 5-methoxycarbonylmethyl-2-thiouri-dine (mcm5s2U) [9], 5-carbamoylmethyluridine (ncm5U) [10], 5-methylcytidine (m5C) [11], and N4-acetylcytidine (ac4C) [12], are associated with oxidative stress-induced impairment of tRNA modification enzymes, thereby potentially affecting translational control. Identification and characterization of tRNA modification regulatory roles are important in understanding disease etiology at the molecular level.
The incidence of heart failure is elevated in people with diabetes independent of obesity, hypertension, dyslipidemia, and coronary heart disease [13,14]. Many antidiabetic drugs target systemic hyperglycemia without reducing risk of heart disease [15]. Atherosclerosis, fibrosis, and maladaptive hypertrophy represent abnormal phenotypes of distinct cell types that manifest as heart disease during diabetes [16]. Thus, evaluating diabetic impact at the cellular scale is critical to understanding the diverse clinical presentations of diabetic heart disease [16].
Modified tRNA ribonucleosides from in vitro and in vivo samples were analyzed to assess hyperglycemic reprogramming of tRNA modifications. The results of this study will help elucidate the association of tRNA modifications and diabetic cardiac comorbidities.
## Maintenance of mammalian cell cultures in high or low glucose
MEK, 3T3, and H9C2 cells were grown and harvested using the same protocol. Cells were grown in 1× DMEM (ThermoFisher, Waltham, Massachusetts) with 10 % FBS (Pkd1del34/del34 MEK) or 2.5 % FBS (HyClone Laboratories Inc., Logan, Utah) (CRL-1446 H9C2 and CRL-1658 3T3), and 1 % Penicillin/Streptomycin (SV30010, Hyclone Laboratories Inc., Logan, Utah) at 37 °C, 5 % CO2. When cells were 70–80 % confluent, they were washed with 1× phosphate-buffered saline (PBS), trypsinized, and passaged with fresh 1× DMEM media into either 1× DMEM high glucose (4.5 g/L d-Glucose) or 1× DMEM low glucose (1 g/L d-Glucose). The cells were cultured for 4 days in either low or high glucose conditions. Biological replicates are defined as cells split from a frozen cell stock and passaged at least two times. Treated cells were trypsinized, washed with PBS, flash frozen in liquid nitrogen, and stored in −80 °C freezers until analysis. There are four MEK biological replicates per cell treatment, and five H9C2 and 3T3 biological replicates per cell treatment, respectively.
## Mouse models
Healthy female C57BL/6N mice were compared to an established type II diabetes model induced by streptozotocin drug in combination with a high fat diet [17,18]. Young adult (3–4 week old) healthy control mice ($$n = 4$$) were fed a low fat diet (10 % Kcal % fat) throughout the duration of the study. The diabetic group ($$n = 4$$) of the same age were fed a high fat diet (60 % Kcal % fat) for the duration of the study. Streptozotocin was administered to the 7-week-old diabetic mouse group for 5 consecutive days (40 mg/kg daily intraperitoneal injections) to induce pancreatic β-cell failure. Mice achieved a fasting blood glucose level (>250 mg/dL) by 10 days post-streptozotocin determined by glucometer measurements by tail snip. The established combination of high fat diet and streptozotocin achieves a type II diabetes phenotype of impaired glucose handling. For the duration of the study, glucose levels were confirmed with the glucose tolerance test at weeks 6, 9, and 12 after 6-hour fasts, respectively (Supplementary Fig. S1). Glucose tolerance was assayed after intraperitoneal injection of dextrose (0.25 mL max volume) into fasting mice at 2 mg/g body weight (~15–20 g) which approximates to 100 mg/mL injection. Blood samples were collected at 0, 15, 30, 60, and 120 min from the tail vein (repeated sampling after snip) and used for blood glucose measurement. All animals were euthanized by CO2 asphyxiation and organs harvested at 12 weeks (16-week-old mice) [17]. Organs were stored at −80 °C until nucleic acid isolation was performed. The mouse studies were performed in accordance with protocols approved by CWRU.
To address cardiac function, mice were anesthetized by 5 % isoflurane (Patterson veterinary, Greeley, CO, USA) and maintained at 1 % to 1.5 % isoflurane as previously published [19]. Body temperature was maintained at 37 °C and heart rate was kept at 400 to 500 beats per minute. M-mode and B-mode were recorded along the long axis using a Vevo 3100 ultrasound imaging system (VisualSonics) equipped with a MX550D transducer. Cardiac function was analyzed using Vevo lab (VisualSonics) at 12 weeks (Supplementary Fig. S2).
## Pseudomonas aeruginosa growth in 13C labeled glucose medium
P. aeruginosa PA14 was grown in M9 minimal media (2 mM MgSO4, 0.1 mM CaCl2, 0.4 % weight/volume (w/v) glucose, 47.9 mM Na2HPO4, 22 mM KH2PO4, 8.2 mM NaCl, 18.7 mM NH4Cl), with the only carbon source being d-glucose-13C6, (Sigma-Aldrich, Munich, Germany). To ensure all carbons were isotopically labeled (13C), cells were maintained in M9 minimal media containing 13C labeled glucose for at least two passages. P. aeruginosa was grown to OD600 ≥ 1.5 at 37 °C and cells were harvested by centrifugation and washed 1× with 0.9 % saline.
## Isolation of total RNA
Total nucleic acid was isolated from P. aeruginosa cells, mammalian cultures, and tissues utilizing an RNA isolation method [20] which was modified. About 100–500 mg of cells or tissue were lysed at 65 °C with 1 mL buffer (2 % w/v hexadecyltrimethylammonium bromide (Sigma-Aldrich), 100 mM Tris-HCl pH 8 (Fisher Scientific), 2 M NaCl (Fisher Chemical), 20 mM ethylenediamine tetraacetic acid, disodium salt dihydrate pH 8 (Fisher Chemical)). At the cell lysis step, 5–10 mg of poly (vinylpolypyrrolidone) (Sigma-Aldrich) and 30 μL 2-mercaptoethanol (VWR Life Science) were added per 1 mL of lysate. An equal volume of 24:1 chloroform (Fisher Chemical, Hampton, New Hampshire): isoamyl alcohol (Bio Basic) was added to the cell lysate, vortexed, and centrifuged. The 24:1 chloroform: isoamyl alcohol extraction was repeated once with the aqueous layer. The resulting aqueous layer was transferred to a new tube, and total nucleic acid was precipitated with ≥2 volumes of 100 % ethanol, then centrifuged. The nucleic acid pellet was solubilized in 500 μL Milli-Q nuclease-free H2O. For nuclease-free H2O, Milli-Q H2O was treated with 0.1 % diethylpyrocarbonate (Millipore Sigma), then autoclaved. The solubilized nucleic acid was incubated at 50 °C with 300 mg proteinase K (Sigma-Aldrich) to digest proteins, and then extracted with 25:24:1 phenol: chloroform: isoamyl alcohol pH 6.7 (Fisher Scientific). Two chloroform extractions were performed to remove residual phenol in the aqueous layer. To remove polysaccharides, an equal volume of diethyl ether was added to the aqueous layer and centrifuged [21]. The resulting aqueous layer was transferred to a new tube and total nucleic acid was precipitated with ≥2 volumes 100 % ethanol, and subsequently centrifuged. Total nucleic acid was solubilized in Milli-Q nuclease-free H2O and quantified via spectrophotometer. Total RNA from mammalian cells and tissues were isolated the same way except tissues were homogenized in the lysis buffer. Also, for mammalian cells and tissue, poly(vinylpolypyrrolidone) (Sigma-Aldrich) was not added, and the diethyl ether (Sigma-Aldrich) extraction was not performed.
## Isolation and collection of isotopically labeled RNA nucleosides
In 50 μL volumes, total nucleic acid (50–100 μg) was enzymatically digested to nucleosides adapted from a previously reported procedure [22] (2 mM MgCl2 (Fisher Scientific), 40 mM Tris (pH 8), 4 U Benzonase (Millipore Sigma), 2 U DNase I (Millipore Sigma), 0.1 U phosphodiesterase (US Biologicals), 10 U alkaline phosphatase (Sigma-Aldrich), 50 ng RNase A (Sigma-Aldrich), and 18.6 μM pentostatin (adenosine deaminase inhibitor) (Sigma-Aldrich)) and incubated overnight at 37 °C. After incubation, the digest was filtered through 10 kDa centrifuge filters (Pall Life Sciences) to remove enzymes. After filtration, the samples were dried under vacuum and resolubilized in 50 μL Milli-Q nuclease-free H2O.
RNA and DNA canonical nucleosides were separated from modified nucleosides using high-performance liquid chromatography (HPLC). The Varian Microsorb-MV C18 column 250 × 4.6 mm was utilized for HPLC separations at 30 °C. The solvents were 5 mM ammonium acetate (solvent A) and acetonitrile (solvent B) with a flow rate of 0.5 mL/min. The elution began with 100 % solvent A for 25 min, increased to 1 % B (25 min–40 min), followed by step gradients of 1 % B (40 min–65 min), 2 % B (65 min–75 min), then 3 % B (75 min–85 min). Then a gradient from 3 to 14 % B (85 min–120 min), then 14–60 % B (120 min–135 min). A final wash step at 98 % B was performed and then re-equilibration at 100 % A for 30 min. 50 μg of enzymatically digested 13C isotopically labeled nucleosides were injected onto the column. All of the eluent other than RNA and DNA canonical nucleosides were fraction collected, combined, dried, and solubilized in Milli-Q nuclease-free H2O (Supplementary Fig. S3). RNA and DNA canonical nucleosides were visible by UV, and were fraction collected separately.
## tRNA isolation from mammalian cells and tissue
tRNA was isolated from total nucleic acid by PAGE. Either a 15 % or 20 % denaturing (urea) PAGE was utilized to separate nucleic acid species. The RiboRuler Low Range RNA Ladder (ThermoFisher, Waltham, Massachusetts) or DynaMarker, Small RNA II Ladder (DiagnoCine, Hackensack, New Jersey) was used as a size reference. The gels were stained with ethidium bromide to visualize nucleic acid. Nucleic acid species below 100 nucleotides (nts) and above 50 nts was deemed tRNA, similarly to other work [23]. tRNA was excised from the gel and isolated by crush and soak method [24]. The resulting tRNA was quantified via spectrophotometer. Approximately 50 pmol (roughly 1.3 μg) of tRNA was digested to nucleosides via the enzymatic digest, filtered, dried, and resolubilized as described above.
## LC-MS/MS method
Ribonucleoside standards were purchased from Toronto Research Chemicals (Ontario, Canada), Carbosynth (San Diego, CA), Ark Pharm, Inc. (Arlington Heights, IL), or Sigma-Aldrich (St. Louis, MO) as defined in Supplementary Table S1. The tRNA samples were analyzed using a Shimadzu Nexera XR LC-20-AD HPLC equipped with a photodiode array detector (PDA) and a triple quadrupole mass spectrometer Shimadzu 8050 (Japan). A Phenomenex Luna Omega Polar C18 column was used (1.6 μm particle size, 100 Å pore size, 150 × 2.1 mm) at 21 °C and with a 0.17 mL/min flow rate. Solvents consisted of 0.1 % formic acid in Millipore Q water (solvent A) and 0.1 % formic acid in acetonitrile (solvent B). The elution began at 100 % A for 40 min, then linear gradients followed: 0 %–5 % B 40 min–80 min; 5 %–6 % B 80 min–100 min; 6 %–70 % B 100 min–120 min. The column was washed with 98 % B and reequilibrated at 100 % A for 30 min (Supplementary Fig. S4). The RNA canonical nucleosides were quantified via PDA and then diverted to waste, while all of the modified nucleosides were detected and quantified via MS with an electrospray ion (ESI) source. The ESI parameters were: DL temperature 125 °C, CID gas 270 kPa, nebulizing gas flow 3.0 L/min, drying gas flow 17.0 L/min, heating gas flow 3.0 L/min, interface temperature 300 °C, heat block temperature 400 °C, and interface voltage 4 kV. Samples were analyzed in positive ion mode in multiple reaction monitoring (MRM) mode. *Ribonucleosides* generally fragmented at the glycosidic bond, causing the neutral loss of the ribose or 2′-O-methylribose (Supplementary Table S1). Pseudouridine and methylated pseudouridine ribonucleosides fragmented in a unique pattern (Supplementary Fig. S5). RNA modifications were identified by their MRM and their retention times.
## Data acquisition and analysis
Approximately 1.2 pmol of 13C labeled ribonucleoside modifications collected fractions and 10.0 pmol of total digested tRNA sample (about 0.26 μg 12C total tRNA) were injected for LC-MS/MS analysis. Each sample containing 13C labeled standards and endogenous tRNA ribonucleosides were injected onto the column at 10 μL injection volumes. MRMs of 13C isotopically labeled ribonucleosides were as follows: m6,6A, m/z 308 > 171; t6A, m/z 428 > 291; m2A; m/z 293 > 156; Am, m/z 293 > 141; m6A, m/z 293 > 156; i6A, m/z 351 > 214; m5C, m/z 268 > 131; s2C, m/z 269 > 132; Cm, m/z 268 > 116; m4Cm, m/z 283 > 131; D, m/z 256 > 119; Ψ, m/z 254 > 218; m5U, m/z 269 > 132; s4U, m/z 270 > 133; m3U, m/z 269 > 132; Um, m/z 269 > 117; m7G, m/z 309 > 172; m2G, m/z 309 > 172; m1G, m/z 309 > 172; Gm, m/z 309 > 157; I, m/z 279 > 142. UV traces of canonical ribonucleosides and MS traces of modified ribonucleosides were extracted from Shimadzu LabSolutions Software using manual peak integration. Internal calibration (13C isotopically labeled ribonucleosides) and external calibration (calibration curves) were combined for quantification by division of MS signal of corresponding unlabeled (12C) and labeled (13C) ribonucleosides [25]. Any modified ribonucleosides without a respective 13C isotopically labeled standard were quantified by division of 12C MS signal over the total integrated sum of all 13C isotopically labeled ribonucleosides.
## Statistics
Data represents the average of tRNA modification levels from five biological replicates (n) of 3T3 and H9C2 cells and 4 biological replicates of MEK cells for both high and low glucose. There were 3 biological replicates analyzed from each tissue type for both healthy and diabetic tissue. Student’s unpaired t-tests were performed to determine statistical significance between cells cultured in low versus high glucose media, and healthy versus diabetic tissue. Outliers were removed via Grubbs’ tests when there were >3 biological replicates (n > 3). To determine significantly different tRNA ribonucleoside levels in cell lines cultured in low glucose or healthy tissue, respectively, one-way ANOVA with Tukey’s test for multiple comparisons were performed. Hierarchical clustering analyses were performed using hclust function in R version 4.1 with method set to complete. Heat map dendrograms were created using the pheatmap (version 1.0.12) R package (RRID: SCR_016418) [26]. PCA was performed using Prism (version 9.4.0) from GraphPad. Values excluded from data by Grubbs’ test were treated as averages for principal component analysis (PCA).
## Results
Hyperglycemia causes reactive oxygen species (ROS) [27], which have been shown to impact the activity and structures of proteins [28], DNA [29,30], and tRNA [9]. To determine the impact of high glucose levels to mammalian tRNA modification profiles, tRNA from in vitro mammalian cells and murine cardiac tissue were analyzed. Murine embryonic fibroblasts (3T3), rat embryonic cardiomyoblasts (H9C2), and murine embryonic kidney (MEK) cell lines were chosen because of their association with tissues affected by hyperglycemia [31]. The in vitro cells were passaged in high (4.5 g/L) or low (1 g/L) glucose conditions to mimic pathological hyperglycemic and low postprandial-glycemic levels, respectively. Cardiovascular disease is a common diabetic comorbidity [16,32–35], therefore murine aorta, ventricle (apex), and atrial heart tissues were compared across non-diabetic control mice and the induced diabetic mouse model.
An established model for type II diabetes was applied to C57BL/6N mice using a combination of high fat diet and streptozotocin drug. Glucose intolerance is first demonstrated at 6 weeks by a glucose tolerance test (Supplementary Fig. S1). Using echocardiography analysis at 9 weeks post-drug onset (week-12 of the study), these diabetic mice exhibit the earliest stages of cardiac dysfunction determined by trends of chamber dilation and reduced diastolic function (Supplementary Fig. S2). Of note, we did not observe cardiomyocyte hypertrophy by histological analysis (data not shown).
To analyze tRNA modifications, we developed and utilized methodologies outlined in Schematic 1. Total nucleic acid was isolated using a procedure adapted from other work [20], then separated via high percentage (15 % or 20 %) denaturing polyacrylamide gel electrophoresis (PAGE). tRNA was gel purified to ensure separation from other small RNAs (Supplementary Fig. S6) then enzymatically digested to nucleosides. Triple quadrupole MS/MS was utilized for quantitative targeted analysis of ribonucleosides in positive ion mode (Supplementary Fig. S4). For quantitative analysis of tRNA modified ribonucleosides, 13C isotopically labeled ribonucleoside standards were biosynthesized [25]. Canonical ribonucleosides were quantified via photodiode array (PDA) while ribonucleoside modifications were quantified by MS (Supplementary Fig. S4). This methodology was utilized for quantifying tRNA ribonucleoside levels in all cell and tissue models.
Many tRNA ribonucleoside levels were significantly different in 3T3, MEK, and H9C2 cells grown in several passages of low glucose media (Fig. 1A). These observations could reflect intrinsic differences in tRNA modifying enzyme activity. Variations to canonical and modified ribonucleoside abundance could also indicate changes in tRNA expression levels [36–38]. Here, total nucleic acid was analyzed via PAGE, and bands representing tRNA or multiple tRNA species were seemingly differentially abundant between the examined cell types (Supplementary Fig. S6). These findings indicate that the variations in tRNA modification levels between cell types could be due to tRNA expression levels.
Total tRNA ribonucleosides from cells cultured in high or low glucose media were analyzed. Principal component analysis (PCA) was performed to predict correlations between glucose levels, tRNA modification levels, and in vitro cell types (Supplementary Fig. S7). In response to ROS formation, Alkbh8, an enzyme required for the biosynthesis of 5-methyoxycarbonylmethyluridine (mcm5U) [10], is induced in embryonic fibroblasts [9]. Here, 3T3 cells passaged in high glucose, showed lower levels of mcm5U (Table 1). There was a correlation between 3T3 cells grown in low glucose media and the modification mcm5U (Supplementary Fig. S7). This contrasted with the higher levels of ac4C, rA, and N6-methyl-2′-O-methyladenosine (m6Am) in 3T3 cells grown in high glucose media (Table 1). These findings were confirmed via PCA as m6Am was inversely correlated with mcm5U. Additionally, rA and ac4C were correlated with 3T3 cells grown in high glucose media (Supplementary Fig. S7). The correlations associated with high glucose media were dependent upon cell type. For example, N6-isopentenyladenosine (i6A) and m6Am levels increased in 3T3 and MEK cells grown in high glucose media but decreased in H9C2 cells when cultured in high glucose media (Fig. 1B and Supplementary Table S2). These H9C2 cell hyperglycemic correlations could be explained by cardiomyocyte metabolism [39] or differences between mouse and rat tRNA [2]. Because fetal bovine serum (FBS) concentration differences can modulate molecular mechanisms in pluripotent stem cells [40], it is possible that differences in FBS concentration could cause the variations seen here between tRNA ribonucleosides in MEK cells and H9C2 or 3T3 cells.
We hypothesized that tRNA ribonucleoside trends in established diabetic versus non-diabetic mouse models could be glucose-dependent. Since the abundance of tRNA species (and likely tRNA modifications) are tissue specific [36], and tRNA modification variations were found in different parts of murine brains [23], we assessed whether different muscular heart structures exhibit diverse tRNA modification profiles. First, the aorta, apex, and atria of non-diabetic (healthy) mice were harvested for analysis. tRNA was analyzed using the methodologies outlined in Schematic 1. Levels of fifteen modified ribonucleosides and three canonical ribonucleosides were significantly distinct in different regions of the heart in non-diabetic mice (Fig. 1C). Since different cardiovascular structures are functionally and biologically distinct and can be differentially diseased (e.g., coronary artery disease, aortic aneurysms, pericardial disease, etc.), it is unsurprising that tRNA ribonucleoside modifications were different in heart structures. This is also an important observation for classification of disease states [4].
Total tRNA ribonucleoside levels were analyzed from healthy and diabetic murine aorta, apex, and atrial tissue (Supplementary Table S3). Several ribonucleosides were significantly different in hyperglycemic cells and diabetic tissues, including rA, N1-methylguanosine (m1G), m6Am, t6A, and 5-methylcytidine (m5C) compared to corresponding controls (Table 1). For example, levels of t6A in diabetic atrial tissue were similar to those in H9C2 cells (rat embryonic cardiomyoblasts) passaged in high glucose media. Amounts of t6A in the atria, aorta, and apex were all higher in diabetic mice versus healthy mice (Supplementary Table S3).
Relationships between ribonucleosides from murine tissues were visualized with hierarchical clustering (Fig. 2) and principal components (PCs) (Fig. 3A and B) and loadings (Fig. 3C and D) analyses. By PCA, the total variance expressed in the first three principal components were 80 % (61 %, 11 %, and 8 %, respectively). Hierarchical clustering and PCA revealed correlations between murine tissues and tRNA ribonucleoside levels. For example, levels of mcm5s2U were higher in diabetic aorta than healthy aorta (Table 1). PCA revealed that in addition to mcm5s2U, diabetic aorta was correlated with levels of N2-methyladenosine (m2A), isowyosine (imG2), N4-methyl-2′-O-methylcytidine (m4Cm), rU, pseudouridine (Ψ), and rC (Fig. 3A, C). Modifications ncm5U, mcm5U, 5-formylcytidine (f5C), 2-methylthio-N6-threonylcarbamoyladenosine (ms2t6A), t6A, N1-methyladenosine (m1A), N7-methylguanosine (m7G), m1G, N2-methylguanosine (m2G), N2,N2,-dimethylguanosine (m2,2G), inosine (I), N5-methyluridine (m5U), and N3-methyluridine (m3U) were associated with diabetes in both murine apex and atria (Fig. 3A, C). Evidence indicates that tRNA modification-associated diabetes etiology is tissue specific.
We report m6Am, cyclic 2-methylthio-N6-threonylcarbamoyladenosine (ms2ct6A), m4Cm, N4-acetyl-2′-O-methylcytidine (ac4Cm), imG2 (isobar of i6A), and N1-methylinosine (m1I) as tRNA ribonucleosides in murine heart tissue (Supplementary Table S3) which have not yet been identified in mouse tissue tRNA (Supplementary Table S4) [9,10,23,41,42]. We used MS fragmentation to confirm the identities of these ribonucleoside modifications (Supplementary Figs. S5 and S8). We did not detect 2-methylthio-N6-isopentenyladenosine (ms2i6A) [23,43] though this MRM was included in our LC-MS/MS method. Here, m1I and m6Am were identified in aortic, apex, and atrial tissue tRNA (Fig. 2 and Supplementary Table S3). The ribonucleoside modifications m1I and m6Am have been previously identified in small RNA under 200 nucleotides in murine tissue, but not specifically in tRNA [44]. The ribonucleoside modifications m4Cm and imG2 were correlated with diabetic aorta (Fig. 3A and C). These newly reported tRNA nucleoside modifications were closely associated with specific murine heart tissues.
The modified ribonucleoside imG2 was discovered here in tRNA from aorta, atria, and apex (Fig. 2 and Supplementary Table S3) but previously has only been found in archaea [2]. In archaea, the enzyme Taw22/Trm5a has bifunctionality of catalyzing the biosynthesis of m1G and imG2 [45]. The N1-guanine-methyltransferase mammalian ortholog (Trm5) has roughly 31 % similar identity to archaeal Trm5a (BLAST). It is possible that mammalian Trm5 may have a similar bifunctionality in mammalian cells (Supplementary Fig. S9). It is also possible that we detected an unidentified ribonucleoside with the same MRM and retention time as imG2.
We identified m4Cm and ac4Cm in tRNA (Fig. 2, Supplementary Fig. S8, Supplementary Tables S2 and S3), though these were previously identified as ribosomal RNA (rRNA) modifications [2]. Some detected tRNA modifications such as Am, m3U, and m6,6A, were predicted to originate from degraded rRNA [23]. It is also possible that the modifications detected here for the first time in murine tRNA could also be caused by contamination of degraded RNA.
The ribonucleoside modification ms2ct6A (MRM m/z 441 > 309) was identified in murine tissue (Supplementary Fig. S5) and was correlated with healthy atria (Fig. 3). The oxazolone isoform of cyclic N6-threonylcarbamoyladenosine (ct6A) was previously reported to be endogenous to *Escherichia coli* but was identified as the hydantoin isoform [46]. The hydantoin isoform of ms2ct6A has been confirmed in *Bacillus subtilis* and Trypanosoma brucei tRNALys(UUU) with characteristic collision induced dissociation (CID) fragment ions m/z 182, 208, 265, and 281 [47]. Here in murine tissue, we see fragment ions m/z 120, 129, 142, 187, and 276 (Supplementary Fig. S5) from a product ion scan of m/z 441 (putative isoxazoline isoform of ms2ct6A, MRM m/z 441 > 309).
Some ribonucleoside modifications which increased in diabetic heart tissue were closely clustered (Fig. 2). For example, modifications m5C, m1G, t6A, m1A, m2G, and m7G were closely clustered (Figs. 1B and 2), correlated with diabetic atrial tissue via PCA (Fig. 3), and levels were higher in diabetic atria than healthy atria (Table 1, Figs. 1B and 2). There is one murine tRNA known to contain m7G, m1G, t6A, m1A, m2G, and m5C: tRNAIni(CAU) [2]. Murine tRNAIni was previously identified as a substrate for enzymes tRNA methyltransferase 10A (Trmt10A), which catalyzes the biosynthesis of m1G (Supplementary Fig. S10), and AlkBH1, which preferentially demethylates m1A in tRNA [48].
## Discussion
This study evaluated tRNA ribonucleosides in a type II diabetes mouse model. The established type II diabetic model is characterized by pancreatic β-cell failure, impaired glucose regulation, obesity, and hyperlipidemia [18]. Cardiac tissues were analyzed at initial changes in cardiac function and glucose intolerance, consistent with the earliest stages of cardiomyopathy. Gender differences were not evaluated in this study; however, streptozotocin has been shown to similarly impact peripheral tissues like retinopathy regardless of gender [49]. The differences observed in modified and canonical ribonucleosides occur independent of observable pathology.
Deficiency of Trmt10A (and subsequent reduction of m1G), induces oxidative stress and triggers apoptosis in pancreatic β-cells [50]. Additionally, Trmt10A deficiency contributes to impaired glucose regulation [51] and the pathogenesis of early onset diabetes by negatively impacting β-cell mass [52]. Here, the opposite trend is reported as higher murine atrial m1G levels result from diabetes (Table 1). This suggests that Trmt10A activity in diabetes may be tissue specific.
Glucose deprivation induces AlkBH1 expression in HeLa cells, leading to lower tRNA levels of m1A [48]. Our work further demonstrates this correlation between glucose and m1A levels in apex and atria (Fig. 3A and C). Expression levels of tRNAIni also decreased with glucose deprivation in HeLa cells, causing translational repression [48]. This may indicate higher expression of tRNAIni and subsequent induction of translation in murine diabetic atrial tissue than in healthy atrial tissue.
Some modified ribonucleosides were below the LOD in some cell or tissue types. For example, m1A was below the LOD in MEK cells and aortic tissue (Supplementary Tables S2 and S3). We hypothesized that one possible cause of low m1A levels could be due to pH-dependent Dimroth rearrangement of m1A to m6A [53]. Controls testing our extraction process determined m1A and m6A levels did not significantly change. This suggests that m1A differences were not due to Dimroth rearrangement (Supplementary Fig. S11). Likely, differences in tRNA modification profiles between samples were caused by mechanisms that are tissue or cell specific.
Hierarchical clustering and PCA revealed other tRNA modification tissue-specific trends. The modified ribonucleosides Cm and Gm were closely clustered (Figs. 1B and 2) and were correlated with healthy atrial tissue (Fig. 3). Both Cm and Gm have been identified on five murine tRNA species (three tRNAPhe isodecoders, and two tRNAGln isoacceptors) [2]. The enzyme, Ftsj1, is responsible for catalyzing the 2′-O-methylation of cytidine and guanosine. It has been suggested that Ftsj1 activity relies on nucleoporin Nup155 [54]. However, mutations to nucleoporin Nup155 are associated with atrial fibrillation [55]. This implies that normal nucleoporin Nup155 and Ftsj1 activity are necessary for healthy atrial function. The correlations seen here between healthy murine atria and tRNA modifications Cm and Gm, further suggest that Ftsj1 is necessary for proper atrial function in mice.
The enzyme, NAT10, is responsible for the incorporation of an acetyl moiety on the fourth position of cytidine (Supplementary Fig. S10) and has been identified as a potential target for the treatment of diseases such as cancer [56], Hutchinson-Gilford Progeria Syndrome [57], and osteoporosis [41]. It was previously reported that hydrogen peroxide (H2O2) treatment of human cell lines induced NAT10 by activation of the NAT10 gene promoter, indicating DNA damage due to genotoxicity [12]. Levels of ac4C were higher in all cell lines cultured in high glucose media (Fig. 1B and Supplementary Table S2), though ac4C levels were lower in all diabetic heart tissue (Fig. 1B and Supplementary Table S3). This indicates that NAT10 expression or activity of in vitro mammalian cells and heart tissue could be regulated by different mechanisms or differentially influenced by ROS.
The pathways involved in the formation of uridine modifications, mcm5s2U and ncm5U, are interconnected (Supplementary Fig. S10). Uridine wobble modifications such as thiolations, methoxycarbonylmethylations, 5-carbomoylmethylations, or 5-methoxycarbonyl-methyl-2-thiolations [58–60] generally confer eukaryotic cell protection against oxidative stress [61,62]. The tRNA methyltransferase, Alkbh8, is necessary for the biosynthesis of mcm5s2U (Supplementary Fig. S10), however, the Alkbh8−/− phenotype corresponds with increased levels of ncm5U [10]. The Alkbh8−/− phenotype is associated with higher levels of ROS in mouse embryonic fibroblasts [9]. Thus, in mouse embryonic fibroblasts, higher levels of ncm5U may confer cell protection against ROS. Here, mcm5s2U levels were significantly higher in diabetic aorta than in healthy aorta (Table 1). Similarly, mcm5s2U was correlated with diabetic aorta via PCA (Fig. 3A and C). This suggests that uridine modifications such as mcm5s2U or ncm5U are potentially important for cellular protection against ROS. A higher level of mcm5s2U in diabetic aorta could be a mechanistic response to increased oxidative stress caused by hyperglycemia.
The eukaryotic enzymes Dnmt2, NSun2 [63], and Trm4 [64] incorporate a methyl moiety onto the fifth position of cytidine to biosynthesize m5C (Supplementary Fig. S10). In human fibroblasts, Dnmt2 knockdowns were susceptible to H2O2 and demonstrated increased protein carbonylation [65]. In other work, yeast exposed to H2O2 showed increased levels of m5C in total tRNA, which in turn enhanced translation of proteins. Among the yeast proteins affected by H2O2 exposure, stress response and translational proteins were significantly upregulated [11]. This reasonably suggests that the significantly higher levels of m5C in diabetic atrial tissue (Table 1) could be a mechanistic response to increased ROS formation. Similarly, this indicates that oxidative stress caused by hyperglycemia may influence protein modification and translation in murine atria. Addressing the causal role of tRNA modifications in chronic diabetes models over long periods will inform our understanding of metabolic regulation of ribonucleoside modifications and implications for disease progression.
## Conclusions
It is important to identify tRNA ribonucleoside modifications associated with disease pathogenesis [4]. Oxidative stress caused by hyperglycemia can affect biomolecules associated with the incorporation of enzymatic chemical modified ribonucleosides. We identified and quantified tRNA ribonucleosides from in vitro and in vivo models to analyze the consequences of hyperglycemia. It is also of future interest to identify tRNA expression levels and tRNA modifications specific to tRNA species, especially tRNAIni, to identify downstream translational effects of hyperglycemia [48]. Statistical analyses demonstrated tRNA modification profiles associated with healthy and diabetic cardiac murine tissues. We discuss enzymes likely affected by hyperglycemia, as results indicate ribonucleosides, ac4C, mcm5s2U, m5C, and m1G were aberrant in hyperglycemic models. We characterized tRNA species, tRNA ribonucleoside modifications, and potential tRNA modifying enzymes associated with diabetic etiology.
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|
---
title: Associations of body size with all-cause and cause-specific mortality in healthy
older adults
authors:
- Prudence R. Carr
- Katherine L. Webb
- Johannes T. Neumann
- Le T. P. Thao
- Lawrence J. Beilin
- Michael E. Ernst
- Bernadette Fitzgibbon
- Danijela Gasevic
- Mark R. Nelson
- Anne B. Newman
- Suzanne G. Orchard
- Alice Owen
- Christopher M. Reid
- Nigel P. Stocks
- Andrew M. Tonkin
- Robyn L. Woods
- John J. McNeil
journal: Scientific Reports
year: 2023
pmcid: PMC9992380
doi: 10.1038/s41598-023-29586-w
license: CC BY 4.0
---
# Associations of body size with all-cause and cause-specific mortality in healthy older adults
## Abstract
In the general population, body mass index (BMI) and waist circumference are recognized risk factors for several chronic diseases and all-cause mortality. However, whether these associations are the same for older adults is less clear. The association of baseline BMI and waist circumference with all-cause and cause-specific mortality was investigated in 18,209 Australian and US participants (mean age: 75.1 ± 4.5 years) from the ASPirin in Reducing Events in the Elderly (ASPREE) study, followed up for a median of 6.9 years (IQR: 5.7, 8.0). There were substantially different relationships observed in men and women. In men, the lowest risk of all-cause and cardiovascular mortality was observed with a BMI in the range 25.0–29.9 kg/m2 [HR25-29.9 vs 21–24.9 kg/m2: 0.85; $95\%$ CI, 0.73–1.00] while the highest risk was in those who were underweight [HRBMI <21 kg/m2 vs BMI 21–24.9 kg/m2: 1.82; $95\%$ CI 1.30–2.55], leading to a clear U-shaped relationship. In women, all-cause mortality was highest in those with the lowest BMI leading to a J-shaped relationship (HRBMI <21 kg/m2 vs BMI 21–24.9 kg/m2: 1.64; $95\%$ CI 1.26–2.14). Waist circumference showed a weaker relationship with all-cause mortality in both men and women. There was little evidence of a relationship between either index of body size and subsequent cancer mortality in men or women, while non-cardiovascular non-cancer mortality was higher in underweight participants. For older men, being overweight was found to be associated with a lower risk of all-cause mortality, while among both men and women, a BMI in the underweight category was associated with a higher risk. Waist circumference alone had little association with all-cause or cause-specific mortality risk.
Trial registration ASPREE https://ClinicalTrials.gov number NCT01038583.
## Introduction
A number of previous reviews on the relationship between BMI and all-cause mortality in older adults (≥ 65 years) have suggested that individuals with a BMI in the overweight range (BMI 25–29.9 kg/m2) (and sometimes, in those with obesity–BMI ≥ 30 kg/m2) had a similar or lower risk of all-cause mortality than those in the normal weight range1,2. Similarly, several reviews have shown that mortality tended to increase in adults at the lower end of the recommended BMI range (< 21 kg/m2), even after comprehensive adjustment for relevant confounders1–5.
These reports have predominately focused on BMI and its association with all-cause mortality, whilst much less information exists in relation to sex-specific or cause-specific mortality1. Moreover, in older age a reduction in lean body mass and an increase in fat mass leads to alterations in body composition that are not captured by BMI possibly making it less suited for accurately reflecting health risks associated with adiposity6,7. It has been suggested that waist circumference may be a better measure of adiposity and has been shown to have strong associations with mortality in younger populations8.
During the ASPREE clinical trial, standardised measures of weight, height and abdominal circumference were made on over 19,000 generally healthy men and women mostly aged 70 years or older. Over the subsequent 6.9 years, clinical records were reviewed (in most cases) to systematically identify the underlying cause of death. This information has provided the most comprehensive and reliable data on the relationship between different measures of obesity and cause-specific mortality yet available in older adults.
Given the increasing proportion of the population aged 60 years and above, which is projected to increase from 1 billion in 2020 to 2.1 billion by 20509, it is important to understand the health risk imposed by body size and the optimal measures that can be employed to assess the risk. Using data from the ASPREE clinical trial, we therefore, explored the association of BMI and waist circumference with all-cause and cause-specific mortality to determine their relative importance as predictors of mortality from different causes in later life.
## Study population and trial design
The ASPirin in Reducing Events in the Elderly (ASPREE) study was a large, randomized, double-blind, placebo-controlled trial investigating the effect of 100 mg aspirin on disability-free survival in apparently healthy men and women who were 70 years of age or older (or ≥ 65 years of age for African Americans and Hispanics in the US). All participants were required to be in good health, with no prior cardiovascular disease (CVD) events, dementia or major physical disability and expected to survive for at least five years. Details of the ASPREE trial and the primary results of the study have been published previously10–13. Briefly, from March 2010 to December 2014, 19,114 community-dwelling individuals across Australia ($$n = 16$$,703) and the US ($$n = 2411$$), gave written informed consent and were randomized. In 2017, ASPREE transitioned to a longitudinal, observational follow up study of the trial participants who at the time of this report had been followed for a median of 6.9 years since study entry (IQR: 5.7, 8.0).
The trial was conducted according to the Australian National Statement on Ethical Conduct in Human Research, the Australian Code for the Responsible Conduct of Research, the 2008 Declaration of Helsinki, and the International Conference on Harmonization Good Clinical Practice E6 and was approved by institutional review boards at all sites. All participants provided written informed consent. The ASPREE study is registered on ClinicalTrials.gov (Trial registration number: ASPREE ClinicalTrials.gov number NCT01038583, $\frac{24}{12}$/2009) and the International Standard Randomised Controlled Trial Number Registry (ISRCTN83772183). This study was reviewed and approved by the Monash University Human Research Ethics Committee (Project No. 24743).
## Study measurements
All ASPREE participants completed two baseline visits to finalize their eligibility for the study, and after randomization were assessed annually by trained study staff. At baseline and every annual study visit, participants underwent comprehensive evaluations of physical measures, collection of anthropometric and laboratory measurements, medical morbidities, lifestyle and socio-demographic factors, concomitant prescription medications, and other related health parameters. Full details on the ascertainment of these study measurements have been described in detail previously14,15.
## Exposure assessments
At study enrolment, anthropometric measures which included body weight, height and waist circumference, were taken by trained study staff according to a strict protocol. Body weight was measured in kilograms, with the participant in bare feet, wearing light clothing only. Standing height was measured in meters, with the participant in bare feet, looking straight ahead, with as many body points against the wall as possible, using a wall-mounted stadiometer, where available, but if not, using a right-angled ruler to record the height before measurement with a tape measure. BMI was calculated as weight divided by height squared (kg/m2).
Measurement of waist circumference with a ‘Figure Finder Tape Measure’ was made on bare skin, with the participant standing. The measurement was taken mid-way between the unclothed crest of the hip and the lowest rib, keeping the tape measure horizontal, ensuring the participant was breathing normally, with arms resting at their sides. All study sites were monitored regularly to ensure staff competency and adherence to standardized operating procedures.
## Ascertainment of outcomes
Full details on the ascertainment of death have been described in detail previously10. Briefly, in most cases, deaths were identified during the course of routine trial activity, by review of health records, or by the next of kin or close contact who notified the trial centre. In all cases, notifications of death required confirmation from two independent sources (the family, the primary care physician, or a public death notice). At the end of the trial, participants who had withdrawn or were lost to follow-up, were linked to the National Death Index in the relevant country. After a notification of death was confirmed, deaths were classified according to the underlying cause by adjudicators who were unaware of trial-group assignments. In the current analyses, deaths were classified as death from any cause and death related to specific causes including deaths from ischaemic CVD (any ischemic event [myocardial infarction, other coronary heart disease, sudden cardiac death, or ischemic stroke]), cancer (deaths that were related to primary or metastatic cancer), or other causes (deaths that were related to causes that were non-cancer or non-CVD [e.g. sepsis, chronic lung disease, dementia]).
## Statistical analyses
Cox proportional hazards regression was used to calculate sex-specific hazard ratios (HRs) and $95\%$ confidence intervals (CIs) to assess the relationship between the baseline anthropometric measures and all-cause and cause-specific mortality. For cause-specific mortality, participants were censored for other causes at time of death to obtain cause-specific hazard ratios. In the current analyses, BMI was used as a categorical variable to enhance comparability with existing studies, using specific cut points for older adults based on a recent meta-analysis which summarized the evidence on the association between BMI and all-cause mortality in adults ≥ 65 years1. These cut offs were: BMI < 21.0, 21.0–24.9 (reference group), 25.0–29.9, 30.0–34.9, and ≥ 35.0 kg/m2. Waist circumference was categorized into sex-specific quintiles (Q1 to Q5), with the second quintile serving as the reference group, to capture the impact at the extremes and because the second quintile contained (approximately) the WHO recommended cut-offs17. Each analysis was adjusted for age (Model 1), then additionally for potential confounders selected a priori including education level (< 12 years, ≥ 12 years), smoking status (current, former/never), diabetes status (yes, no), study treatment arm (aspirin, placebo), living situation (at home alone, with others), alcohol consumption (current, former/never), and the longest amount of time walking outside the home without sitting down to rest (less than 10 min, 10–15 min, 16–30 min, > 30 min), as a proxy for physical activity (Model 2). We also used restricted cubic splines with three knots at the 10th, 50th, and 90th percentiles to model the association of continuous BMI and waist circumference with mortality separately for men and women. Figures are presented for age-adjusted all-cause mortality and cause-specific mortality.
For further comparison with other studies, the WHO BMI recommended cut-offs were also used, with underweight defined as BMI < 18.5 kg/m2, normal weight as BMI 18.5–24.9 kg/m2 (reference group), overweight as BMI 25.0–29.9 kg/m2 and general obesity as BMI ≥ 30 kg/m216. Waist circumference was also assessed as a dichotomized variable using the WHO recommended cut offs for high disease risk (< 88 cm or ≥ 88 cm in women; < 102 cm or ≥ 102 cm in men) for further comparison with previous studies17. We also explored the interaction between the BMI categories and age. In sensitivity analyses, we repeated the main analyses after excluding those participants with a history of diabetes and after excluding the US and Australian minorities because the BMI mortality association may differ according to race or ethnicity18.
All statistical tests were two sided, and we considered $P \leq 0.05$ to determine statistical significance. Confidence intervals and P values were not adjusted for multiple comparisons. Statistical analyses were performed using R version 4.0.2 (R Core Team, 2020).
## Study participants
After participants with missing covariate information or those missing both body size measures were excluded, we analysed data from 18,209 participants ($95.3\%$ of the ASPREE study population) (Appendices Fig. 1). Among them, $56\%$ were women, $10\%$ had a history of diabetes, and $31\%$ used HMG CoA reductase inhibitors (“statins”) at baseline. The mean age was 75.1 (SD, 4.5) years, and the mean BMI was 27.9 (SD, 3.9) kg/m2 in men and 28.1 (SD, 5.2) kg/m2 in women. Mean waist circumference was 101.9 (SD, 10.8) cm in men and 93.1 (SD, 12.9) cm in women (Table 1). During a median follow up of 6.9 years (IQR: 5.7, 8.0)., we identified 1762 deaths (men: 966; women: 796).Table 1Baseline characteristics of the ASPREE participants included in the current analyses. OverallMenWomenN = 18,209n = 8054n = 10,155Age (years, mean (SD))75.1 (4.5)74.9 (4.4)75.2 (4.6)Country (n (%)) Australia15,991 (87.8)7288 (90.5)8703 (85.7) United States2218 (12.2)766 (9.5)1452 (14.3)Race/ethnicity (n (%)) White/Aus15,662 (86.0)7106 (88.2)8556 (84.3) White/US1033 (5.7)336 (4.2)697 (6.9) Black804 (4.4)284 (3.5)520 (5.1) Hispanic445 (2.4)191 (2.4)254 (2.5) Other265 (1.5)137 (1.7)128 (1.3) Weight in kg (mean (SD))176.9 (14.9)83.8 (13.2)71.4 (13.8) Height in m (mean (SD))21.7 (0.1)1.7 (0.1)1.6 (0.1) BMI in kg/m2 (mean (SD))328.0 (4.7)27.9 (3.9)28.1 (5.2) Waist circum. in cm (mean (SD))497.0 (12.8)101.9 (10.8)93.1 (12.9) Randomized to aspirin (n (%))9074 (49.8)4021 (49.9)5053 (49.8) Baseline statin use (n (%))5652 (31.0)2228 (27.7)3424 (33.7) Current smoker (n (%))691 (3.8)366 (4.5)325 (3.2) Current alcohol drinker (n (%))14,064 (77.2)6732 (83.6)7332 (72.2) Diabetes (n (%))*1891 (10.4)990 (12.3)901 (8.9) Systolic BP (mean (SD))139.2 (16.5)141.1 (15.8)137.7 (16.8) Diastolic BP (mean (SD))77.3 (10.0)78.1 (9.6)76.6 (10.2)Education level (n (%)) < 12 years8174 (44.9)3515 (43.6)4659 (45.9) ≥ 12 years10,035 (55.1)4539 (56.4)5496 (54.1)Living situation (n (%)) At home alone5929 (32.6)1654 (20.5)4275 (42.1) With others12,280 (67.4)6400 (79.5)5880 (57.9)Longest amount of time walking outside home without any rest (last 2 weeks) < 10 min535 (2.9)230 (2.9)305 (3.0) 10–15 min1800 (9.9)711 (8.8)1089 (10.7) 16–30 min4140 (22.7)1645 (20.4)2495 (24.6) More than 30 min11,734 (64.4)5468 (67.9)6266 (61.7)Number of participants missing baseline measurement for the following variables: 1n = 35, 2n = 28, 3n = 60, 4n = 172.SD, standard deviation; BMI, body mass index; BP, blood pressure.*Diabetes defined as a self-report, fasting glucose ≥ 126 mg/dL, or receiving pharmacologic treatment for diabetes (regardless of fasting glucose level).
A distribution of the baseline characteristics by BMI and waist circumference in men and women is shown in Appendices Tables 1, 2, 3, 4. *In* general, men and women with a higher BMI, had higher use of statins and antihypertensive medication at baseline and were more likely to have diabetes. Men and women with a higher BMI tended to consume less alcohol, and spent less time walking outside the home without any rest (Appendices Tables 1 and 2). A similar pattern was also seen for men and women with a higher waist circumference (Appendices Tables 3 and 4).Table 2Hazard ratio ($95\%$ CI) of all-cause mortality according to baseline body mass index (BMI) in men and women ($$n = 18$$,149).BMI (kg/m2)MenWomenNo. of deaths/No. of ptsIncidence rate per 1000pyModel 1* HR ($95\%$ CI)Model 2^ HR ($95\%$ CI)No. of deaths/No. of ptsIncidence rate per 1000pyModel 1* HR ($95\%$ CI)Model 2^ HR ($95\%$ CI)BMI < $\frac{21.040}{15342.681.97}$ (1.41, 2.76)1.82 (1.30, 2.55)$\frac{76}{54120.711.67}$ (1.28, 2.18)1.64 (1.26, 2.14)BMI 21.0–$\frac{24.9233}{164621.59}$RefRef$\frac{197}{246911.66}$RefRefBMI 25.0–$\frac{29.9458}{418316.500.84}$ (0.71, 0.98)0.85 (0.73, 1.00)$\frac{287}{395910.700.98}$ (0.81, 1.17)0.97 (0.81, 1.17)BMI 30.0–$\frac{34.9183}{163317.050.99}$ (0.82, 1.21)0.95 (0.78, 1.16)$\frac{150}{215310.321.05}$ (0.85, 1.30)0.98 (0.79, 1.22)BMI 35.0 + $\frac{48}{41717.811.16}$ (0.85, 1.58)0.98 (0.71, 1.35)$\frac{84}{99512.901.46}$ (1.12, 1.88)1.27 (0.98, 1.66)*Model 1: Adjusted for age.^Model 2: Adjusted for age, smoking status, aspirin treatment arm, diabetes status (yes, no), level of education (< 12 years, ≥ 12 years), living status (at home alone, with others), alcohol consumption (current alcohol consumption, former/never), and longest amount of time walking outside home without any rest. BMI, body mass index; py, person years; Ref., reference. Table 3Hazard ratio ($95\%$ CI) of all-cause mortality according to baseline waist circumference in men and women ($$n = 18$$,037).Waist circumference1MenWomenNo. of deaths/No. of ptsIncidence rate per 1000pyModel 1* HR ($95\%$ CI)Model 2^ HR ($95\%$ CI)No. of deaths/No. of ptsIncidence rate per 1000pyModel 1* HR ($95\%$ CI)Model 2^ HR ($95\%$ CI)Quintile $\frac{1225}{166820.561.32}$ (1.09, 1.59)1.30 (1.07, 1.58)$\frac{175}{208712.281.22}$ (0.98, 1.53)1.22 (0.97, 1.53)Quintile $\frac{2193}{182815.82}$RefRef$\frac{137}{20279.90}$RefRefQuintile $\frac{3192}{159418.271.17}$ (0.96, 1.43)1.15 (0.94, 1.40)$\frac{164}{212711.381.16}$ (0.93, 1.46)1.15 (0.92, 1.45)Quintile $\frac{4168}{140018.191.26}$ (1.03, 1.55)1.20 (0.98, 1.48)$\frac{138}{198510.371.12}$ (0.89, 1.42)1.07 (0.84, 1.36)Quintile $\frac{5176}{151417.871.33}$ (1.08, 1.63)1.20 (0.97, 1.47)$\frac{166}{180713.831.56}$ (1.25, 1.96)1.44 (1.14, 1.81)1Waist circumference Quintile Range (Mean) values for Men (cm): Q1: 56–93 (88.13); Q2: 94–99 (96.67), Q3:100–104 (101.97), Q4:105–110 (107.31), Q5: 111–153 (118.15); Female: Q1: 44–82 (76.15), Q2: 83–89 (86.13), Q3: 90–96 (92.95), Q4: 97–104 (100.2), Q5: 105–180 (112.77).*Model 1: Adjusted for age.^Model 2: Adjusted for age, smoking status, aspirin treatment arm, diabetes status (yes, no), level of education (< 12 years, ≥ 12 years), living status (at home alone, with others), alcohol consumption (current alcohol consumption, former/never), and longest amount of time walking outside home without any rest.py, person years; Ref., reference. Table 4Hazard ratio ($95\%$ CI) of cause-specific mortality1 according to baseline BMI and waist circumference in men and women (BMI: $$n = 18$$,149, WC: $$n = 18$$,037).BMI (kg/m2)Cancer deathCardiovascular deathOther deathNo. events/No. of ptsIncidence rate per 1000 pyHR2 ($95\%$ CI)No.events/ No. of ptsIncidence rate per 1000 pyHR2 ($95\%$ CI)No.events/No. of ptsIncidence rate per 1000 pyHR2 ($95\%$ CI)Men < $\frac{21.010}{15310.671.12}$ (0.58, 2.16)$\frac{10}{15310.672.15}$ (1.08, 4.27)$\frac{19}{15320.272.34}$ (1.41, 3.86)21.0–$\frac{24.994}{16468.71}$Ref$\frac{49}{16464.54}$Ref$\frac{86}{16467.97}$Ref25.0–$\frac{29.9228}{41838.211.02}$ (0.80, 1.3)$\frac{84}{41833.030.74}$ (0.52, 1.06)$\frac{138}{41834.970.72}$ (0.55, 0.94)30.0–$\frac{34.993}{16338.661.14}$ (0.85, 1.52)$\frac{39}{16333.630.97}$ (0.63, 1.50)$\frac{51}{16334.750.76}$ (0.54, 1.09) ≥ $\frac{35.025}{4179.281.20}$ (0.76, 1.90)$\frac{14}{4175.191.38}$ (0.74, 2.57)$\frac{9}{4173.340.53}$ (0.26, 1.06)Women < $\frac{21.026}{5417.091.43}$ (0.92, 2.24)$\frac{10}{5412.731.15}$ (0.57, 2.32)$\frac{37}{54110.081.94}$ (1.31, 2.88)21.0–$\frac{24.980}{24694.73}$Ref$\frac{36}{24692.13}$Ref$\frac{77}{24694.56}$Ref25.0–$\frac{29.9147}{39595.481.21}$ (0.92, 1.60)$\frac{54}{39592.011.01}$ (0.66, 1.54)$\frac{84}{39593.130.73}$ (0.53, 0.99)30.0–$\frac{34.971}{21534.891.11}$ (0.80, 1.53)$\frac{32}{21532.201.25}$ (0.77, 2.03)$\frac{46}{21533.170.77}$ (0.53, 1.11) ≥ $\frac{35.043}{9956.601.53}$ (1.04, 2.26)$\frac{18}{9952.761.65}$ (0.92, 2.99)$\frac{20}{9953.070.79}$ (0.47, 1.32)Waist circumferenceMenQ$\frac{187}{16687.951.04}$ (0.78, 1.4)$\frac{48}{16684.391.78}$ (1.13, 2.82)$\frac{86}{16687.861.41}$ (1.03, 1.94)Q$\frac{293}{18287.62}$Ref$\frac{30}{18282.46}$Ref$\frac{68}{18285.57}$RefQ$\frac{3104}{15949.91.29}$ (0.97, 1.71)$\frac{36}{15943.431.38}$ (0.85, 2.24)$\frac{47}{15944.470.80}$ (0.55, 1.16)Q$\frac{478}{14008.441.14}$ (0.84, 1.54)$\frac{39}{14004.221.82}$ (1.12, 2.93)$\frac{50}{14005.411.04}$ (0.72, 1.50)Q$\frac{586}{15148.731.17}$ (0.87, 1.58)$\frac{40}{15144.061.76}$ (1.09, 2.86)$\frac{50}{15145.081.01}$ (0.70, 1.47)WomenQ$\frac{168}{20874.770.96}$ (0.68, 1.34)$\frac{26}{20871.820.86}$ (0.51, 1.46)$\frac{76}{20875.331.97}$ (1.33, 2.94)Q$\frac{268}{20274.91}$Ref$\frac{29}{20272.10}$Ref$\frac{36}{20272.60}$RefQ$\frac{375}{21275.21.07}$ (0.77, 1.48)$\frac{36}{21272.501.18}$ (0.73, 1.93)$\frac{52}{21273.611.38}$ (0.90, 2.11)Q$\frac{469}{19855.191.07}$ (0.76, 1.50)$\frac{25}{19851.880.93}$ (0.54, 1.60)$\frac{43}{19853.231.28}$ (0.82, 2.00)Q$\frac{582}{18076.831.43}$ (1.03, 1.99)$\frac{30}{18072.501.28}$ (0.76, 2.16)$\frac{51}{18074.251.68}$ (1.08, 2.59)Waist Circumference Quintile Range (Mean) values for men: Q1:56–93 (88.13); Q2:94–99 (96.67), Q3:100–104 (101.97), Q4:105–110 (107.31), Q5: 111–153 (118.15); women: Q1: 44–82 (76.15), Q2: 83–89 (86.13), Q3: 90–96 (92.95), Q4: 97–104 (100.2), Q5: 105–180 (112.77).1Death related to specific causes including cancer (deaths that were related to primary or metastatic cancer), deaths from ischaemic CVD (any ischemic event [myocardial infarction, other coronary heart disease, sudden cardiac death, or ischemic stroke]), or other causes (deaths that were related to causes that were non-cancer or non-CVD [e.g. sepsis, chronic lung disease, dementia]).2HRs adjusted for age, smoking status, aspirin treatment arm, diabetes status (yes, no), level of education (< 12 years, ≥ 12 years), living status (at home alone, with others), alcohol consumption (current alcohol consumption, former/never), and longest amount of time walking outside home without any rest. BMI, body mass index; CI, confidence interval; HR, hazard ratio; py, person years; Ref., reference.
## All-cause mortality
The highest risk of all-cause mortality was seen in underweight men and women. In multivariable adjusted models, a BMI below 21 kg/m2 was associated with an $82\%$ higher risk in men and a $64\%$ higher risk in women compared with those with a BMI of 21–24.9 kg/m2 (HRmen:1.82, $95\%$ CI 1.30–2.55; HRwomen:1.64, $95\%$ CI 1.26–2.14) (Table 2).
Men with a BMI in the range 25.0–29.9 kg/m2 had the lowest risk of all-cause mortality (HRmen:0.85, $95\%$ CI 0.73–1.00), while in women there was little difference in mortality risk amongst those with a BMI in the range 21–35 kg/m2 (Table 2). Restricted cubic spline analysis confirmed these results, as did an analysis using WHO recommended BMI cut-offs (Fig. 1, Appendices Table 5).Figure 1Hazard ratio for all-cause mortality, cancer mortality, cardiovascular mortality, and other mortality as a function of body mass index (BMI) in men (A–D) and women (E–H). Restricted cubic spline with knots at 23.5, 27.5, 32.9 (men) and 22.2, 27.4, 34.9 (women). Hazard ratios are indicated by the solid lines and $95\%$ confidence intervals by shaded areas.
Waist circumference showed a weaker U-shaped relationship with all-cause mortality in both men and women with a modest increase in risk in both the lowest and highest quintile (Table 3). Restricted cubic splines and an analysis using the WHO waist circumference cut-offs again showed a similar pattern (Fig. 2, Appendices Table 6).Figure 2Hazard ratio for all-cause mortality, cancer mortality, cardiovascular mortality, and other mortality as a function of waist circumference (WC) in men (A–D) and women (E–H). Restricted cubic spline with knots at 89, 101, 116 cm (men) and 77, 92, 110 cm (women). Hazard ratios are indicated by the solid lines and $95\%$ confidence intervals by shaded areas.
## Cause-specific mortality
The associations of BMI and waist circumference with cause-specific mortality (cancer death, cardiovascular death and non-cardiovascular non-cancer causes of death) are shown in Table 4 and the restricted cubic spline analyses in Figs. 1, 2.
Cancer mortality: No association was observed between BMI or waist circumference and cancer mortality in either men or women, apart from a small increase in risk at the upper extreme of BMI (≥ 35 kg/m2) and waist circumference (Quintile 5) among women (Table 4).
Cardiovascular mortality: In men, similar to the relationship with all-cause mortality there was a strong U-shaped relationship between BMI and waist circumference with cardiovascular death. In multivariable adjusted models, a low BMI (< 21 kg/m2) was associated with a higher risk of cardiovascular death in men, compared with those with a BMI of 21–24.9 kg/m2 (HRcardiovascular death:2.15, $95\%$ CI 1.08–4.27), whilst the lowest risk was amongst those with a BMI in the range 25-30 kg/m2 (Fig. 1, Table 4). For waist circumference the lowest risk was found in those close to the upper cut-off of the WHO recommendations (102 cm) for men (Table 4, Fig. 2). In women, there was no evidence of a U-shaped relationship between BMI or waist circumference and cardiovascular mortality but mortality increased at the upper end of the distribution of both (Table 4, Fig. 1 and 2).
Non-cancer, non-cardiovascular mortality: ‘Other deaths’ demonstrated a weak U-shaped relationship, most prominently with increased risk at the lowest extremes of BMI and waist circumference (Table 4, Fig. 1 and 2).
## Sensitivity analyses
In sensitivity analyses excluding men and women with a history of diabetes ($$n = 1891$$), and excluding US and Australian minorities ($$n = 1514$$), the hazard ratios of all-cause and cause-specific mortality according to BMI and waist circumference were essentially unchanged (data not shown). We did not observe a significant interaction between BMI and age (P interaction > 0.05) (results not shown).
## Discussion
In this cohort of more than 18,000 relatively healthy older adults followed for almost 7 years, the principal finding was that the lowest all-cause and cardiovascular mortality occurred in men and women whose BMI and waist circumference was substantially higher than considered normal in current healthy weight guidelines. In men, the relationships were U-shaped with the highest mortality risks amongst those at the bottom and the top of the distribution. In women the increased all-cause and cardiovascular mortality risks were largely confined to the highest BMIs and waist circumferences. The U-shape relationship between mortality and BMI was stronger than the relationship with waist circumference. Amongst cause-specific mortality risks, body size parameters showed a weak relationship to cancer mortality and did not appear to explain the increased mortality amongst underweight individuals. Deaths from causes other than cancer or CVD were increased predominantly in those who were underweight.
These findings generally support the findings of a recent meta-analysis relating BMI to all-cause mortality in older populations1. In that review, Winter et al. described increased all-cause mortality amongst individuals aged 65 years and older whose BMI was below 23 kg/m2 and a minimum at a BMI of 28 kg/m21. Currently, for adults, the WHO considers a BMI ≥ 25 kg/m2 as indicative of overweight and ≥ 30 kg/m2 as obese without reference to age16. A previous meta-analysis of studies of waist circumference and mortality showed a stronger relationship with all-cause, cardiovascular and cancer mortality than was identified in our study19. This report of 29 prospective cohort studies involving older adults aged 65–74 years noted that an increased waist circumference was associated with an increased risk of all-cause mortality, CVD mortality and cancer mortality19. However, another meta-analysis found no significant association in a subgroup analysis of studies with participants older than 60 years (HR, 1.03; $95\%$ CI, 0.98–1.08, $$n = 14$$ studies)8. Many of the individual studies included in these meta-analyses were small, undertaken in past decades when modern preventive interventions were limited and/or used self-reported measures of body size1,19. Moreover, a substantial proportion were initiated at a time when CVD dominated as a cause of death in high income countries20. By contrast, the results from the ASPREE study involved a large contemporary population with objective anthropometric measures and causes of death principally adjudicated from medical records. The study provides the most comprehensive assessment to date of the relationship of BMI and waist circumference to all-cause and cause-specific mortality in both men and women. By studying a generally healthy population, free of past CVD or other life-threatening conditions, the relationships were unlikely to have been distorted by serious illnesses.
The principal sex differences were seen in the relationships between all-cause and cardiovascular mortality. In men, but not women, there was an increased risk of mortality amongst those at the lowest end of BMI and waist circumference. This led to a substantially greater U-shaped relationship in men than in women, particularly regarding BMI, and this relationship persisted after adjustment for multiple confounding factors. The increase in mortality from non-cancer non-cardiovascular causes amongst those with low BMI or waist circumference may reflect the impact of frailty and prefrailty in this population.
The stronger relationship between BMI and mortality compared to waist circumference and mortality was unexpected. While BMI is the most commonly used clinical and population measure of body size, it is only a rough guide to body fatness. Waist circumference better reflects visceral adiposity which has been positively and significantly associated with a higher risk of all-cause mortality in younger populations8. It has therefore been advocated as a more appropriate health measure8,21. However, given the current findings, there is strong argument that findings from middle aged persons cannot be generalised to older adults.
The relationship between body size and health is relevant to the advice given by clinicians and public health spokespeople who commonly provide a strong recommendation to overweight or obese patients to lose weight to reduce their future risk of CVD and cancer. Our findings add to the mounting evidence that the current ‘healthy weight range’ may not be suitable for older adults and strong steps to encourage weight loss in those moderately overweight or obese requires further evaluation. Nor is there a strong indication from these results for clinicians to rely on waist circumference rather than BMI when providing health advice. However, despite the findings of this study, overweight and obesity have various other negative health consequences that must also factor into the advice provided by clinicians16,22.
This study also has some potential limitations that require discussion. Firstly, the results of this study are most relevant to a largely Caucasian population drawn from communities with access to universal healthcare, as reflected by the high utilisation of statins and antihypertensive drugs. The high frequency of preventive medication might have blunted some adverse effects of overweight and obesity. Secondly, the population studied was drawn from volunteers for a clinical trial who are likely to have been more attentive to maintaining a healthy lifestyle that others in the community. The results may have limited relevance for South Asian, Chinese or Japanese adults where the population distributions of BMI and waist circumference may differ18. Thirdly, although we did conduct multiple analyses in this current study, we did not adjust for multiple comparisons and therefore cannot rule out chance findings. However, the focus of our analyses was on the overall patten of the relationship between BMI & waist circumference with mortality, rather than examining the significance at any one specific BMI or waist circumference value. Finally, due to the small group sizes, we had limited numbers of participants at the extremes of the body size measures, so some results should be interpreted with caution.
## Conclusions
In summary, this study found that in this older population, BMI has a significantly stronger relationship than waist circumference to both all-cause and cardiovascular mortality. In men, the lowest mortality was in those whose BMI was in the overweight range and the highest was amongst those who were underweight at entry to the study. As a result, there was a strong U-shaped relationship between BMI and all-cause and cardiovascular mortality in older men but less so in older women. Waist circumference showed a weaker relationship with mortality risk. Cancer mortality was unrelated to either measure of body size except at the upper extreme. This information may help inform the advice provided by primary care physicians, particularly to moderately overweight men.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-29586-w.
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---
title: A buprenorphine depot formulation provides effective sustained post-surgical
analgesia for 72 h in mouse femoral fracture models
authors:
- Angelique Wolter
- Christian H. Bucher
- Sebastian Kurmies
- Viktoria Schreiner
- Frank Konietschke
- Katharina Hohlbaum
- Robert Klopfleisch
- Max Löhning
- Christa Thöne-Reineke
- Frank Buttgereit
- Jörg Huwyler
- Paulin Jirkof
- Anna E. Rapp
- Annemarie Lang
journal: Scientific Reports
year: 2023
pmcid: PMC9992384
doi: 10.1038/s41598-023-30641-9
license: CC BY 4.0
---
# A buprenorphine depot formulation provides effective sustained post-surgical analgesia for 72 h in mouse femoral fracture models
## Abstract
Adequate pain management is essential for ethical and scientific reasons in animal experiments and should completely cover the period of expected pain without the need for frequent re-application. However, current depot formulations of Buprenorphine are only available in the USA and have limited duration of action. Recently, a new microparticulate Buprenorphine formulation (BUP-Depot) for sustained release has been developed as a potential future alternative to standard formulations available in Europe. Pharmacokinetics indicate a possible effectiveness for about 72 h. Here, we investigated whether the administration of the BUP-Depot ensures continuous and sufficient analgesia in two mouse fracture models (femoral osteotomy) and could, therefore, serve as a potent alternative to the application of *Tramadol via* the drinking water. Both protocols were examined for analgesic effectiveness, side effects on experimental readout, and effects on fracture healing outcomes in male and female C57BL/6N mice. The BUP-Depot provided effective analgesia for 72 h, comparable to the effectiveness of Tramadol in the drinking water. Fracture healing outcome was not different between analgesic regimes. The availability of a Buprenorphine depot formulation for rodents in Europe would be a beneficial addition for extended pain relief in mice, thereby increasing animal welfare.
## Introduction
Animals—especially mice—are still widely used and required in fundamental and translational research to study the complexity of biological and pathophysiological processes. Therefore, the active implementation of the 3R principle (Replace—Reduce—Refine), with a particular importance of Refinement forms the indispensable basis for a humane approach to conduct animal experiments. Thus, adequate pain assessment and medication in animals before, during and after the experimental procedure are crucial to decrease suffering and ensure data quality. Insufficiently treated pain and handling-induced stress can affect animal behavior and physiological responses, especially in an immunological context, leading to potential bias in the scientific outcomes and reduced reproducibility1–6. However, evidence-based data on individual pain management efficiencies in surgical mouse models is still rare6,7 and the reporting quality of the used analgesic protocols is often insufficient8,9.
After surgical intervention, the potent opioid *Buprenorphine is* often used for pain relief in rodents10,11. The plasma half-life of Buprenorphine in mice has been reported to be 3 h after i.v. injection12 and 3-5 h after s.c. injection13. In addition, several studies indicated that approx. 4 h after s.c. injection, plasma concentration in mice were lower than the therapeutic effective threshold in plasma (1 ng/ml)14–16. Although it has been described that Buprenorphine shows higher exposure in the brain when compared to the plasma concentration in mice 12 h after s.c. injection, pain alleviation measured by thermal sensitivity could not be achieved at that time point16. Therefore, frequent injections are required, resulting in repeated handling of the animals. However, the commonly reported application intervals of Buprenorphine of every 8–12 h can lead to pain peaks due to insufficient analgesic coverage9,17 and recent guidelines, therefore, suggest application intervals of 4–6 h18. The parenteral application of other opioids such as Morphine, Tramadol and *Fentanyl is* less suitable for pain alleviation in rodents, as their half-life is even shorter13,19,20.
To reduce handling-associated stress and to ensure continuous analgesic coverage, an alternative application route in form of administration of Buprenorphine or *Tramadol via* the drinking water has been routinely used e.g., in orthopedics-related mouse models21–24. However, as the uptake of analgesics via the drinking water is dependent on the drinking frequency and intake amount, the overall effectiveness of this treatment strategy might be highly influenced by e.g., reduced activity and water intake after anesthesia/surgery and circadian activity25. A drug formulation that extends the analgesic effect by sustained parenteral drug release can overcome such challenges and serve as a powerful tool to further refine today’s analgesic regimens in animal experiments. However, current depot/sustained-release formulations of Buprenorphine for mice and rats are either only available for dedicated research purposes or are only available in the United States of America (USA), e.g., Buprenorphine ER-LAB (ZooPharm) or Ethiqa XR (indexed by U.S. Food and Drug Administration; Fidelis Animal Health). Attempts to import these products to Europe have failed due to missing approval through the European Medical Evaluation Agency (EMEA).
Schreiner et al. successfully developed a poly-lactic-co-glycolic acid (PLGA) based microparticulate drug formulation for sustained drug release of Buprenorphine in mice16,26. In a proof-of-concept study, they observed therapeutic-relevant drug levels of the sustained-release Buprenorphine (BUP-Depot) in the plasma for 12–24 h and in the brain for more than 24 h, an antinociceptive effect in the hot plate test, and pain relief after a minor abdominal surgery in female C57BL/6J mice for at least 72 h16.
This present study, therefore, aims at exploring the analgesic capacities of the newly developed BUP-Depot and its potential to improve animal welfare in a wider range of mouse models in Europe. To test the effectiveness of the developed BUP-Depot in a preclinical setting of surgical interventions, we here compared the analgesic capacities of the BUP-Depot to the established application of *Tramadol via* the drinking water. Both pain management protocols were examined for their analgesic efficacy and adverse effects on experimental readouts in two femoral osteotomy models using rigid and flexible external fixators. To consider potential sex-dependent differences in response to the analgesic protocol, male and female mice were included. We monitored (i) general parameters of well-being e.g., body weight, food and water intake, nest building and explorative behavior, composite score, and (ii) model-specific pain parameters including walking behavior (limping score) and CatWalk analysis. In addition, fracture healing outcomes were examined at the end of the study to exclude negative influences on the regeneration process.
## Results
To investigate the analgesic efficacy of the BUP-Depot, we chose an integrative study design to (i) generate intra-individual controls for the assessments and (ii) reduce animal numbers used in this study (Fig. 1). In brief, animals underwent a first intervention consisting of isoflurane anesthesia and administration of the assigned analgesics. Assessments were performed at 12 h, 24 h, 48 h and 72 h (referred to in the following as “post-anesthesia”). 14 days after the first intervention, the same animals underwent a second intervention including isoflurane anesthesia, administration of the respective analgesics and an additional osteotomy of the left femur. The same assessments as post-anesthesia were performed at 12 h, 24 h, 48 h and 72 h (in the following referred to as “post-osteotomy”) (Fig. 1b). Of note, mice did not undergo osteotomy during the first intervention (post-anesthesia), but were already assigned to the respective fixation, leading to group descriptions of “rigid fixation” or “flexible fixation” even after anesthesia. Figure 1Group assignment and study design. Overview on (a) the group assignment, and (b) time points and measurements of the different parameters. To assure analgesic coverage during surgery, each animal received a single s.c. dose of Temgesic (1 mg/kg) at the beginning of each intervention. Depending on the assigned analgesic protocol, mice additionally received Tramadol (0.1 mg/ml) via the drinking water (provided one day before and for three consecutive days after the interventions) or a single dose of sustained-release BUP-Depot (1.2 mg/kg s.c) was administered at the end of the two interventions.
## Body weight, food and water intake are affected post-anesthesia and post-osteotomy independent of analgesic regime and fixation
To assess general indications for well-being, the body weight was monitored at 12 h, 24 h, 48 h and 72 h post-anesthesia (initial body weight at 0 h—males: 25.96 ± 2.1 g; females: 21.41 ± 1.3 g) and post-osteotomy (initial body weight at 0 h—males: 27.61 ± 1.9 g; females: 22.75 ± 1.3 g). The body weight showed a statistically significant reduction in the range of $5\%$ in all groups at 12 h and 24 h regardless of intervention, sex, fixation, and analgesic regime (Fig. 2). At 48 h post-anesthesia, body weight normalized in all groups and even exceeded the pre-intervention weight (Fig. 2a,b). After osteotomy, we found that male mice showed prolonged body weight loss over 48 h, when compared to post-anesthesia (Fig. 2a). Female mice had less body weight loss at 24 h post-osteotomy compared to post-anesthesia, but a similar recovery at 48 h (Fig. 2b). The body weight increase at 72 h compared to the initial value was higher post-anesthesia than post-osteotomy in all groups independent of sex, analgesia, and fixation. Until osteotomy/euthanasia, body weight was measured every other day with comparable weight development at any time point (Fig. S1).Figure 2Reduction of body weight as well as food and water intake can be observed at 24 h and 48 h post-anesthesia and post-osteotomy. ( a,b) Body weight was measured at 24 h, 48 h and 72 h; (c–f) and food/water intake was measured at 12 h, 24 h, 48 h and 72 h post-anesthesia and post-osteotomy. Body weight and food/water intake were normalized to the initial value (0 $h = 100$%). Of note, mice did not undergo osteotomy during the first intervention (= post-anesthesia). However, they were already assigned to their respective groups post-osteotomy. All graphs show median with interquartile range for $$n = 9$$–10 (body weight) and $$n = 4$$–5 (food/water intake). Non-parametric ANOVA-type test—main effects of time and of group are represented in the graphs; exact p-values are listed in Table S1–S3; *$p \leq 0.05$, ***$p \leq 0.001.$ To determine group differences Kruskal–Wallis test and Dunn’s post hoc test with Bonferroni correction were performed. ( a) Significant difference Tramadol flexible vs. BUP-Depot flexible; (b) significant difference Tramadol rigid vs. Tramadol flexible.
Food and water intake per cage were assessed at 24 h, 48 h and 72 h post-anesthesia and post-osteotomy. Initial values at 0 h covering the previous 24 h per cage were as follows: post-anesthesia—males 8.1 ± 0.7 g (food) and 9.2 ± 1.5 ml (water); females 7.8 ± 1.2 g (food) and 9.2 ± 1.2 ml (water); post-osteotomy—males 8.7 ± 1.0 g (food) and 9.9 ± 1.5 ml (water); females 8.4 ± 0.9 g (food) and 9.5 ± 0.9 ml (water). The lowest food and water intake (approximately $50\%$ reduction to initial values) across all groups and sexes was measured 24 h after each intervention and reached the level of the initial values at 48 h (post-anesthesia) or 72 h (post-osteotomy) (Fig. 2c–f). No significant main effects were detected between treatment groups (Tables S2, S3). To rule out any constipating adverse effects of the BUP-Depot, all groups were closely monitored for defecation during the assessments, as constipation is a known-side effect of chronic-opioid usage17,27. However, a reduction in defecation was only noticeable at 12 h after both interventions but showed no differences between the Tramadol and BUP-Depot groups. The reduction in defecation was considerably more pronounced in female than in male mice (Fig. S2).
## Nest building and explorative behaviors are not influenced by the analgesic regime
To detect model independent changes in spontaneous behavior, monitoring of nest complexity scores and explorative behaviors was performed at 24 h, 48 h and 72 h post-anesthesia and post-osteotomy. Analyses showed no differences in the nest building performance between the Tramadol and BUP-Depot groups post-anesthesia and post-osteotomy as medians ranged between scores of 4.5 to 5 (Fig. 3). Explorative behavior was present in all cages with male mice post-anesthesia while being reduced (no exploration) in some cages at 24 h post-osteotomy ($\frac{8}{20}$) independent of analgesic regime or fixation. In the female mice, 3 out of 20 cages (Tramadol flexible and BUP-Depot flexible) showed no explorative behavior at 24 h after both interventions (Fig. S3).Figure 3Nest building behavior remains largely unaffected post-anesthesia and post-osteotomy. Nest building was monitored per cage at 24 h, 48 h and 72 h post-anesthesia and post-osteotomy. All graphs show median with interquartile range for $$n = 4$$–5 based on cages (pair housing). B baseline measurement.
## Delta composite pain score indicates limited analgesic capacity of Tramadol in male mice with flexible fixation
The composite pain score combines parameters of facial expression (mouse grimace scale) and overall appearance and was assessed at 24 h, 48 h and 72 h post-anesthesia and post-osteotomy. Since we observed an influence of the anesthesia and analgesic regime alone on the composite score (Fig. S4; Table S4–S4.3), we corrected the individual scores post-osteotomy for the respective scores post-anesthesia to obtain a delta composite pain score, that depicts the isolated effect of the osteotomy without the interfering effects of anesthesia and analgesia. The delta composite pain score was highest in all groups at 12 h and 24 h after osteotomy, and declined after 48 h and 72 h, reaching lowest scores in all groups after 72 h (significant main time effect in both sexes, $p \leq 0.001$; Fig. 4a; Table S5). Female mice showed comparable score developments between treatment and fixation groups with the highest median scores (1–1.5) at 12 h. In male mice, we found a significantly higher delta composite pain score in the flexible fixation group treated with Tramadol at 24 h (median score = 2) and 48 h (median score = 1.5) (Kruskal–Wallis test of all groups at 24 h: $$p \leq 0.031$$ and at 48 h: $p \leq 0.001$; Table S5.1) when compared to the other groups (Dunn’s post hoc test for Tramadol flexible vs. BUP-Depot flexible, BUP-Depot rigid and Tramadol rigid, respectively: 24 h $$p \leq 0.077$$, 0.042, 0.032; 48 h $$p \leq 0.004$$, < 0.001, 0.018; Fig. 4a; Table S5.2–S5.3).Figure 4The delta composite pain score suggests adequate pain alleviation in most groups while limping score shows slight differences between groups post-osteotomy. ( a) Scoring was performed at 12 h, 24 h, 48 h and 72 h post-anesthesia and post-osteotomy. For the delta composite pain score, scores from each individual mouse post-anesthesia were subtracted from their respective scores post-osteotomy. ( b) The limping score was assessed at 24 h, 48 h, 72 h and 10 days post-osteotomy. All graphs show median with interquartile range for $$n = 8$$–10 (delta composite pain score) and $$n = 9$$–10 (limping score). Non-parametric ANOVA-type test—main effects of time and of group are represented in the graphs; exact p-values are listed in Table S5–S5.3 and Table S6; *$p \leq 0.05$, ***$p \leq 0.001.$ To determine group differences Kruskal–Wallis test and Dunn’s post hoc test with Bonferroni correction were performed. ( a) Significant difference Tramadol rigid vs. Tramadol flexible; (b) significant difference Tramadol flexible vs. BUP-Depot rigid; (c) significant difference Tramadol flexible vs. BUP-Depot flexible.
## Limping indicates model-related alterations in walking behavior with only slight differences between groups
Walking behavior was assessed by (i) using a metric limping score applied to individual 3 min videos and (ii) analyses of gait and locomotion using the Noldus CatWalk XT at 24 h, 48 h, 72 h, and 10 days post-osteotomy. Walking behavior was also videotaped at the respective time points post anesthesia, but limping was only considered post-osteotomy, as mice did not show any alterations in walking post-anesthesia during routine monitoring and videotaping.
As expected, limping was observed at all time points post-osteotomy until 10 days with a general improving trend (significant main time effect in males $p \leq 0.001$ and females $$p \leq 0.018$$; Fig. 4b; Table S6). *In* general, median scores ranged between 0 and 1 at 24 h and 48 h independent of sex and analgesics, while higher variations were seen at 72 h (male BUP-Depot rigid; female BUP-Depot flexible; Fig. 4b). Slight alterations in limping were still visible after 10 days in some individual mice in the female BUP-Depot groups and male Tramadol groups (all = score 1; sporadic limping; up to two animals, respectively).
## Gait and locomotion analysis indicates alterations in walking behavior and velocity with differences between groups
Analyses of gait and locomotion by the CatWalk XT exhibited a clear reduction in velocity after osteotomy in all groups (significant main time effect post-osteotomy for both sexes $p \leq 0.001$; Fig. 5a,b; Table S7) with an upward trend towards 10 days. At 24 h and 48 h post-osteotomy, males with flexible fixation and Tramadol treatment showed a significantly lower relative velocity compared to males with rigid fixation and BUP-Depot (non-parametric ANOVA-type test for group $$p \leq 0.034$$; Kruskal–Wallis test 24 h $$p \leq 0.052$$ and 48 h $$p \leq 0.038$$; Dunn’s post hoc test—$p \leq 0.05$ when compared to BUP-Depot rigid; Fig. 5a; Table S7.1–S7.3). In females, significant differences in velocity were also evident at 24 h post osteotomy, as mice with rigidly stabilized osteotomies exhibited elevated velocity compared to all other groups with female mice (non-parametric ANOVA-type test for group $$p \leq 0.035$$; Kruskal–Wallis test 24 h $$p \leq 0.009$$; Fig. 5b; Table S7.4–S7.5). With respect to the relative mean intensity (a measure for load bearing) and relative stand duration, we did not observe significant group effects independent of sex and analgesics (Fig. 5c–f), while time-dependent reductions at 24 h, 48 h and 72 h improved over time (relative mean intensity: non-parametric ANOVA-type test $p \leq 0.001$ in both sexes; relative stand duration $p \leq 0.001$ in males and $$p \leq 0.005$$ in females; Tables S8 and S9). However, the male mice with flexible fixation and Tramadol treatment showed numerically reduced values in relative mean intensity (Fig. 5c) when compared to the other males. In female mice, the relative stand duration only slightly increased over 10 days (median range 10 days: 0.68–0.75; Fig. 5f). The stride length was only markedly modulated over the first 72 h in female mice but then recovered to baseline values at 10 days (Fig. 5g,h; Table S10). Male mice with flexible fixation and Tramadol treatment also showed shortened stride length which was significant at 24 h compared to the BUP-Depot rigid group (non-parametric ANOVA-type test $$p \leq 0.024$$; Kruskal–Wallis test 24 h $$p \leq 0.044$$; Dunn’s post hoc test—$p \leq 0.05$ when compared to BUP-Depot rigid; Fig. 5g; Table S10–S10.2), and did not recover over 10 days. To evaluate the influence of the velocity on mean intensity, stand duration and stride length, we performed Spearman correlation analyses (Fig. S5a–c) which indicated that relative mean intensity (males $r = 0.602$, females $r = 0.336$; Fig. S5a) and relative stride length (males $r = 0.801$, females $r = 0.522$; Fig. S5c) correlated with the relative velocity (all $p \leq 0.001$) after osteotomy. Spearman correlation analyses showed only a very week correlation between relative stand duration and relative velocity (males r = −0.15, females $r = 0.046$) (both $p \leq 0.001$). Interpretation of those parameters must, therefore, be considered in context of the overall velocity. Figure 5Gait analyses show model-related alterations in walking behavior specifically pronounced in male mice with flexible fixation and Tramadol treatment. CatWalk analysis was conducted at 24 h, 48 h, 72 h and 10 days post-osteotomy and normalized to the initial mean baseline value focusing on (a,b) relative velocity, (c,d) relative mean intensity, (e,f) relative stand duration and (g,h) relative stride length. All graphs show median with interquartile range for $$n = 8$$–10. Non-parametric ANOVA-type test—main effects of time and of group are represented in the graphs; exact p-values are listed in Table S7–S10.2; *$p \leq 0.05$, ***$p \leq 0.001.$ To determine group differences Kruskal–Wallis test and Dunn’s post hoc test with Bonferroni correction were performed. ( a) Significant difference Tramadol flexible vs. BUP-Depot rigid; (b) significant difference Tramadol rigid vs. BUP-Depot flexible.
## Fracture healing outcome is affected by fixation stability but not by analgesic regime
To evaluate potential effects of the BUP-Depot on fracture healing outcomes, we performed ex-vivo µCT (3D), histomorphometric analysis (2D) and vessel staining at day 14 post-osteotomy (Fig. 6). We observed a numerically lower BV/TV when comparing flexible fixation to rigid, except for males treated with Tramadol. However, the observed numerical differences did not reach statistical significance (BV/TV; Fig. 6a,b; Fig. S6; Table S11). The differences in BV/TV were slightly more pronounced in the BUP-Depot groups (male: median rigid = $25.87\%$ vs. median flexible = $17.80\%$; female: median rigid = $26.12\%$ vs. median flexible = $20.58\%$) than in the Tramadol groups (male: median rigid = $21.89\%$ vs. median flexible = $21.52\%$; female: median rigid = $30.32\%$ vs. median flexible = $23.31\%$).Figure 6Analgesic regimes did not negatively affect fracture healing outcome at day 14, while the different fixations led to differences in new bone formation. ( a,b) Relative bone volume (BV/TV) (%), (c,d) relative bone fraction (%) and (e,f) relative cartilage fraction (%). ( g,h) Exemplary images of the Movat’s pentachrome staining: yellow = mineralized bone, green = cartilage, magenta = bone marrow; scale bar 500 µm. ( i,j) Immunofluorescence staining of vessel formation (Endomucin) including quantification and exemplary images; scale bar 200 µm. All graphs show median with interquartile range for $$n = 8$$–10 (a–h) and $$n = 6$$–9 (i,j). To determine group differences, Kruskal–Wallis test and Dunn’s post hoc test with Bonferroni correction were performed; exact p-values are listed in Table S11–S13.1; *$p \leq 0.05.$
Histomorphometric analysis revealed comparable differences between the rigid and flexible fixation in relative bone and cartilage fraction. As expected, bone formation was reduced while cartilage formation was elevated in the flexible groups compared to rigid fixation in both sexes and treatment groups (Fig. 6c–h; Table S12). Analysis of vessel formation (Endomucin/Emcn staining) within the callus area did not show differences in the male groups (Fig. 6i; Fig. S6; Table S13). Differences in vessel formation (Emcn) were shown between fixation in female mice (Kruskal–Wallis test $$p \leq 0.045$$; Dunn’s post hoc test $p \leq 0.05$ between all groups; Fig. 6j; Table S13.1) with flexible fixation groups showing less relative Emcn+ areas when compared to the rigid fixation, independent of analgesia. Furthermore, DAPI staining indicated lower cellularity in the female mice with flexible fixation compared to rigid, also independent of analgesic treatment (Fig. S6).
## Discussion
In the present study, we evaluated the analgesic efficacy and possible side-effects of a newly developed sustained-release Buprenorphine (BUP-Depot) in comparison to an already established protocol, Tramadol in the drinking water, in two mouse-osteotomy models with different fixation stiffnesses16,22,26,28. Due to individual differences in behavioral changes and pain perception22,29, we chose a consecutive study design to analyze the effect of anesthesia and analgesia alone and in combination with an osteotomy in the same animal. The BUP-Depot delivered reliable pain relief over 72 h post-surgical without side effects on fracture healing outcome.
General clinical parameters such as body weight, and food and water intake were noticeably reduced post-anesthesia and post-osteotomy. Reduction of food intake and, therefore, negatively influenced body weight development are known side-effects post-surgical but can also be related to anesthesia or Buprenorphine/Tramadol administration17,30–32. Reduction of body weight observed after osteotomy was quite low (in the range of $5\%$) and similar to post-anesthesia, which is indicative of an appropriate pain management and well-being in all animals. Lowest values in body weight and food intake were reached after 24 h in all groups, with an increase over 48 h and 72 h post-anesthesia and post-osteotomy. Interestingly, body weight loss at 24 h was less pronounced after osteotomy than after anesthesia. The mice were 10 weeks of age during the first intervention and 12 weeks old at osteotomy. Mice show a rapid body weight development until skeletal maturity between approximately 10–12 weeks, which also includes the formation of more body fat33,34. Since interventions entailing anesthesia also result in short-term starvation during the recovery period, it can be speculated that mice at 10 weeks lost body weight more rapidly due to limited body fat reserves when compared to more mature mice (12 weeks). *In* general, the BUP-Depot showed similar effects on the body weight development as well as food and water intake as the established Tramadol treatment. We did not find differences in water intake between groups, excluding a negative effect of the Tramadol-containing water on the overall drinking amount and ensuring a continuous uptake of medication as shown previously22,35. Although the measurement of the overall 24 h water intake does not allow to conclude on sufficient water intake during the first hours after surgery, we have previously shown that the drinking frequency is indeed reduced, but the intake of Tramadol remains sufficient over 48 h post-osteotomy22. To improve the food intake, food can be alternatively provided on the cage floor to prevent the animals from having to stand on their hind legs36. However, this was not possible in this study. The use of high-caloric dietary gels could also be a valid alternative ensuring the consumption of food and liquids and can be also used as a route for oral analgesic administration37.
Changes in nest building and the willingness to explore foreign objects can indicate alterations in well-being in laboratory mice22,38–41. In this study, nest building behavior was only scarcely influenced by anesthesia and osteotomy in all groups, independent of the analgesic regime, sex or fixation stiffness. This is in line with other studies suggesting that nest complexity scoring might not be sensitive enough to differentiate between minor pain and other potential stressors or reduced well-being after surgery39. However, these findings are contrary to our previous study where we reported a reduction in the nest building performance after osteotomy22. Technical variances (amount of nesting material) or individual differences in scoring might explain the variations in our findings and underline the necessity for more objective approaches. The explorative behavior in male mice seemed to be negatively impacted by osteotomy, especially at 24 h post-surgery, when compared to the post-anesthesia and female mice. Hohlbaum et al. also showed that female C57BL/6JRj mice exhibited shorter latency to explore than male mice 1 day after the last anesthesia in a repeated inhalation anesthesia trial, indicating a sex-specific difference that is in accordance with our findings with respect to explorative behavior42.
A composite pain score was used to combine the assessment of facial expressions (parts of the mouse grimace scale)43, and clinical appearance post-anesthesia and post-osteotomy. Based on our consecutive study design, we were able to calculate a delta for each individual mouse representing numerically the actual osteotomy effect. In line with other studies, the composite pain score was already slightly impacted by anesthesia and analgesia alone, indicating that some components of the composite pain score might not only be influenced by pain but rather also depict stress or discomfort22,44,45. The highest delta composite scores were reached 12 h and 24 h after osteotomy suggesting the pain/discomfort peak due to the surgical procedure. However, median delta scores varied around 1, indicating only limited residual pain and/or discomfort during the first 24 h, which constantly declined till 72 h post-surgical. The BUP-Depot provided comparable and sufficient alleviation of pain signs to the Tramadol treatment and our data indicate that pain relief can be achieved over 72 h after a single BUP-Depot injection.
As model-specific parameters, we assessed limping behavior and locomotion using gait analysis. After osteotomy, limping was observed over 72 h post-osteotomy in all groups, irrespective of sex, fixation, and analgesic, with no significant differences between the groups. A more detailed gait analysis using the Noldus CatWalk XT system revealed reduced velocity and altered gait patterns over 72 h and up to 10 days. While velocity, mean intensity and stride length ameliorated over 10 days in almost all groups (except male mice with flexible fixation and Tramadol analgesia), stand duration remained reduced in all groups. When analyzing CatWalk data, it needs to be considered that most gait parameters correlate to velocity and failing to address possible changes in velocity can affect the outcome of gait-related data46–48. In this study, for example, relative velocity in male mice correlated strongly with relative mean intensity and stride length, but only weakly with stand duration which explains the comparable improvements over time. However, as the stand duration seems to be independent of the velocity, a limited functionality, especially with regard to the full restoration of the musculature, might be a plausible explanation. As the type of surgery performed here requires splitting of the muscle and transection of the muscular insertion at the trochanter major, a certain degree of the observed gait alterations might be due to the not yet fully restored muscular function. In addition, limited mobility and changed gait are also common in human patients with e.g., proximal femur fractures of the femoral diaphysis and are not directly related to pain49,50. Thus, we propose that the gait alteration in terms of stand duration over 10 days was likely caused by an unfinished functional restoration rather than pain or discomfort which is also supported by the absence of any additional pain-indicative signs at 72 h. As the relative velocity was markedly reduced in male mice with the more flexible fixation and Tramadol as their analgesics, the reduced values in the relative mean intensity and the relative stride length in this group are most likely explained by the reduced velocity. This group also displayed the highest delta composite pain scores after 24 h and 48 h indicating a potential clinically relevant level of discomfort or pain in individual animals. An explanation could be that the effective Tramadol dose was not achieved by the application of 0.1 mg/g Tramadol due to the higher body weight of male mice. Evangelista et al. showed that male mice had lower serum concentrations of Tramadol than female mice when applying Tramadol (0.2 mg/ml) via the drinking water for up to 30 h, although the analgesic effective M1 metabolite was similar between male and female mice35. This is in line with a previous study performed in rats51. In contrast, other studies report lower sensitivity of female mice or rats to Tramadol52,53. With respect to the influence of the body weight on the local degree of interfragmentary movement, which might cause discomfort when too high, Röntgen et al. characterized two configurations of the external fixator, the rigid one (18.1 N/mm) and a very flexible one (0.82 N/mm), calculating the interfragmentary strain in a 0.5 mm osteotomy gap for a 25 g mouse with $2.8\%$ and $61\%$, respectively54. However, they did not find differences in body weight, ground reaction force and locomotion in female mice during 18 days, indicating sufficient analgesia even in the presence of higher local strains54. These observations highlight potential sex-specific differences in pain perception but also the response to analgesic medication55. Sex-specific adaptions of pain management regimes are, therefore, advisable in future studies. Our findings underline further, that pain management in animal experiments requires a constant reevaluation of the chosen protocol as well as the consideration of strain, sex, interindividual differences in animals, procedure, options to reduce re-injections and more6. As not only sex but also genetics influence experienced pain and response to analgesia in mice55,56, further studies using different strains are needed in the evaluation of commonly used analgesic regimens in laboratory rodents.
In terms of fracture healing outcome analyzed by ex-vivo µCT and histomorphometry, we found no differences between the analgesic regimens, indicating safe use of the newly developed BUP-Depot in the analyzed models. A more flexible fixation allows pronounced interfragmentary movements and, therefore, promotes cartilage rather than bone formation as well as the formation of a larger periosteal callus54, as seen in the histomorphometric analysis. Staining for endomucin, representing vessels, revealed no difference between fixations or analgesics in male mice. However, female mice with more flexible stabilized osteotomies showed a reduced Emcn-positive area as well as lower cellularity, regardless of their analgesic regime. Besides mechanical hindrance of revascularization due to higher strains, the higher proportion of cartilage observed in female mice with flexible stabilized osteotomies might have prevented revascularization due to the intrinsic anti-angiogenic nature of cartilage e.g., due to chondromodulin-157,58. This reduced vascularization is in accordance with earlier observations in sheep59. The different patterns of Emcn staining in males and females might also indicate a more advanced callus remodeling and, therefore, a more rapid healing progression in males than females60.
An effective sustained-release Buprenorphine in Europe would not only be a conceivable alternative to the application of Tramadol with the drinking water as investigated in this study, but also a potential alternative to repeated injections of Buprenorphine, which are still most frequently used for pain management in femur fracture models9. Assessment of the analgesic effect of *Buprenorphine is* most often based on measurements of plasma or blood serum levels. Studies specified therapeutic effective concentrations of Buprenorphine in plasma at a threshold of around 1 ng/ml or 1 ng/g in mice and rats10,61,62. However, Buprenorphine works through the μ-, κ- and δ-opioid receptors in the brain63,64 and thus, reliable pain alleviation is more reliant on specific binding concentration values in the brain than specific plasma concentrations16, as exemplary demonstrated by a correlation between analgesic effects and specific binding concentrations of Buprenorphine in the brain of rats65. Schreiner et al. found that the BUP-Depot showed effective concentration for up to 72 h in murine brains16. Moreover, they contemplated that specific binding concentrations of 5 ng/g (as observed 24 h after injection of the BUP-Depot) in the brain might be needed for reliable pain relief in mice. Binding concentrations of less than 3 ng/g at 48 h post-injection of BUP-Depot—a concentration comparable to levels observed 12 h after Temgesic injection—still resulted in high, but not significantly increased withdrawal latencies compared to a single injection with Temgesic or NaCl16. They, therefore, suggest that alleviation of strong pain through the BUP-Depot might require the administration every 24 h16. Based on our assessment of the clinical, behavioral, and model-specific parameters, we can postulate that the analgesic properties of the BUP-Depot are sufficient for 72 h post-operative analgesia in our specific mouse osteotomy model of moderate severity. Nonetheless, the potential use and the possible need for re-application of the BUP-Depot in other, more painful models still need to be critically assessed and evaluated.
Taken together, our assessment of clinical, behavioral, and model-specific parameters suggest that the analgesic properties of the BUP-Depot were sufficient for 72 h post-operative analgesia in male and female C57BL/6N mice after femoral osteotomy stabilized with external fixators. The BUP-Depot, therefore, provides an excellent alternative for extended pain relief in preclinical studies. The availability of such a sustained-release formulation of Buprenorphine in Europe would be substantially beneficial for mouse analgesia in animal experiments.
## Ethics and guidelines
All methods were carried out in accordance with relevant guidelines and regulations. In detail, the study was conducted according to the guidelines of the German Animal Welfare Act, National Animal Welfare Guidelines, and was approved by the local Berlin state authority (Landesamt für Gesundheit und Soziales—LAGeSo; permit number: G$\frac{0044}{20}$). Health monitoring in the animal facility was performed according to the FELASA guidelines (Supplementary Information).
## Animals and husbandry
A total of 40 male and 40 female C57BL/6N mice aged 8 weeks were either provided by the Experimental Medicine Research Facilities (Charité—Universitätsmedizin Berlin, Berlin, Germany) or purchased from Charles River Laboratories (Sulzfeld, Germany). Mice underwent the first intervention (anesthesia/analgesia) at 10 weeks (body weight—males: 25.96 ± 2.1 g; females: 21.41 ± 1.3 g) and osteotomy at 12 weeks (body weight—males: 27.61 ± 1.9 g; females: 22.75 ± 1.3 g). Mice were housed in a semi-barrier facility in individually ventilated cages (IVC, Eurostandard Type II, Tecniplast, Milan, Italy). Housing conditions encompassed a $\frac{12}{12}$–h light/dark cycle (light from 6:00 a.m. to 6:00 p.m.), room temperature of 22 ± 2 °C and a humidity of 55 ± $10\%$. Food (Standard mouse diet, Ssniff Spezialdiäten, Soest, Germany) and tap water were available ad libitum.
Mice were randomly divided into groups of two per cage. If mice had to be separated due to aggressive behavior, they were housed in a separated pair housing system in Green Line IVC Sealsafe PLUS Rat GR 900 cages (Tecniplast, Milan, Italy), which were divided in two equally sized compartments by a perforated transparent partition wall. Cages contained wooden chips (SAFE FS 14, Safe Bedding, Rosenberg, Germany), 20 g Envirodri (Shepherd Specialty Papers, USA), and a shredded paper towel as bedding and nesting material, a clear handling tube (Datesand Group, Bredbury, UK) and a mouse double swing (Datesand Group, Bredbury, UK). No houses were provided to allow unimpeded scoring and to reduce the risk of injury after osteotomy. After osteotomy, tunnels and double swings were removed from the cages to reduce the risk of injury. Two single swings (Datesand Group, Bredbury, UK) per cage were reinstalled 5 days after osteotomy. Animals were tunnel handled only and all experimenters performing analyses were female. Animal husbandry and care were in accordance with contemporary best practices.
## Study design and experimental timeline
Reporting of this study was carried out in compliance with the ARRIVE 2.0 guidelines, including the “Arrive Essential 10” and most of the “Arrive Recommended Set”. The study was pre-registered in the Animal Study Registry (Bf3R, Germany; https://doi.org/10.17590/asr.0000221).
The study included 8 groups with each $$n = 9$$–10 mice, comparing male and female mice, rigid and flexible external fixators, and two different pain management protocols: *Tramadol via* drinking water or sustained-release Buprenorphine (BUP-Depot; s.c. injection) (Fig. 1a). Cages and mice received individual random numbers that did not allow any inferences for the analgesic regimen or fixation group. Experimenters performing the pre- and post-surgical training and investigations were blinded.
After acclimation for 5 days, training was performed by one female experimenter, following a 2-week schedule to accustom the mice to the experimenter, tunnel handling, Noldus CatWalk XT (Noldus, Wageningen, Netherlands) and observation boxes (Ugo Basile, Gemonio, Italy). Baseline measurements were also obtained during this period (Fig. 1b). To correct for individual behavioral and clinical changes induced by anesthesia and analgesia alone, mice were first anesthetized and received their assigned analgesic protocol without any further surgical procedure (first intervention). Then, parameters were assessed at 12 h, 24 h, 48 h, and 72 h post-procedure. 14 days after the first intervention, the same animals were subjected to anesthesia, analgesia, and osteotomy (second intervention) and assessed at the above listed time points as well as at day 10 post-surgical. Mice were euthanized 14 days post-osteotomy to retrieve the osteotomized femur.
## Analgesic regimes
Each mouse received one s.c. injection of regular Buprenorphine (1 mg/kg Temgesic, RB Pharmaceuticals, Heidelberg, Germany) at the beginning of each intervention (anesthesia/analgesia alone and osteotomy). Depending on the randomly assigned group, mice either additionally received Tramadol administered in the drinking water (0.1 mg/ml, Tramal Drops, Grünenthal, Stolberg, Germany) or a s.c. injection of the BUP-Depot (1.2 mg/kg). Tramadol was administered in the drinking water one day before and three consecutive days after both interventions. The BUP-Depot was injected once at the end of both interventions. BUP-Depot (RG 502 H-Big) was prepared at the University of Basel, Switzerland, as described previously by Schreiner et al.16,26. Four different batches of BUP-Depot were imported in accordance with national regulations for controlled substances (BtM import authorization No. 4679477). Each batch was analyzed prior to shipment for drug content, reconstitution time, and drug release kinetics as described previously16,26. The BUP-Depot was stored as a lyophilizate in glass vials at 4 °C. Each vial was reconstituted with physiological saline ($0.9\%$ NaCl) immediately before administration.
## Anesthesia and osteotomy
Independent of the intervention, all mice were anesthetized with isoflurane (~ 2 to $3\%$; provided in $100\%$ oxygen; CP-Pharma, Burgdorf, Germany) before being weighed and moved onto a heating pad (37 °C). Anesthesia was maintained at ~ $2\%$ via a nose cone. Eye ointment, physiological saline (0.5 ml, $0.9\%$ NaCl), Clindamycin (45 mg/kg, Ratiopharm, Ulm, Germany) and a single s.c. injection of Buprenorphine were applied. Anesthesia was then upheld for 15 min for the first intervention (anesthesia and analgesia alone). For osteotomy, the left femur was shaved and disinfected with alcoholic iodine solution. The osteotomy was conducted as described previously22,66,67. A longitudinal skin incision was made between knee and hip, and the musculus vastus lateralis and musculus biceps femoris were bluntly separated to expose the femur. Two standardized external fixators (rigid: 18.1 N/mm; flexible: 3.2 N/mm, both RISystem, Davos, Switzerland) were used for stabilization. The external bar of the fixator was positioned parallel to the femur and all pins were positioned accordingly. Afterwards, an approximately 0.5 mm osteotomy gap was created between the second and third pin using a Gigli wire saw (0.44 mm; RISystem, Davos, Switzerland) and the gap was flushed with saline. Muscle and skin were closed with two layers of sutures (muscle: 5-0 Vicryl, skin: Ethilon 5-0, both Ethicon, Raritan, USA). For recovery, the mice were returned to their home cages under an infrared lamp and were closely monitored.
## Body weight and food/water intake
Animals were weighed before the intervention (defined as 0 h), and at 12 h, 24 h, 48 h and 72 h after both interventions and then every other day until osteotomy/euthanasia. Food/water intake were measured per cage (i.e., two mice) by weighing food and water bottles every 24 h, beginning 1 day prior to each intervention and ending 3 days after. The difference to the previous value was calculated. All measurements were normalized to the respective baseline values at time point 0 h.
## Explorative test and nest complexity score
Both scores were assessed before any other assessment or handling of the mice. To examine the motivation of the mice to explore and interact (sniffing, holding with forepaws, or carrying) with a foreign object, we added a Nestlet (Ancare, Bellmore, USA) to the home cages and observed the mice for one minute. The explorative test was scored 1 (interaction) or 0 (no interaction) per cage. An interaction of one animal of the cage was deemed as sufficient for a positive score. Nest complexity scoring was performed following Hess et al.68 in the home cage assigning scores between 0 and 5.
## Composite pain score
A composite pain score was used to combine the assessment of facial expressions and clinical appearance22,43 (Table 1). The maximal score was 9. At 12 h, 24 h, 48 h, and 72 h post-intervention, the mice were transferred into a clear observation box and individually filmed for 3 min after an acclimatization period of 1 min (Basler Video Recording Software, Ahrensburg, Germany). Video analysis was performed by one blinded observer. As anesthesia and analgesia alone also affected the composite pain score, we calculated the delta composite pain score for each mouse by subtracting the scores from each individual mouse post-anesthesia from their respective scores post-osteotomy. This allowed us to evaluate the effect of the surgical procedure on an individual base without the interference of behavioral or clinical changes induced by the anesthesia and analgesia alone44.Table 1Composite pain score. ParameterSpecificationScore (0 = not present)Orbital tighteningFaint narrowing of the orbital area up to a tightly closed eyelidRange: 0–20.5 = minimal1 = moderately1.5 = moderately severe2 = severeEar positionEars pulled back or rotated outwards and/or backwardsRange: 0–21 = moderately2 = severePostureCrouched posture, head and nose positioned towards the groundRange: 0–10.5 = held for less than 10 s1 = held for more than 10 sCoat conditionCoat appeared disheveled or unkemptRange: 0–10.5 = only certain body parts1 = all over the bodyErected fur, mice appear scruffyRange: 0–10.5 = only on one body part1 = on more than one body part, generalizedMovementApathetic, sedated, tipsy; crawlingRange: 0–1 each0.5 = minimal1 = moderately
## Walking behavior—limping score
To assess the walking behavior of each mouse, the limping score was assessed adapted from Jirkof et al.22. The mice were transferred to conventional type III cages that contained the same type of wooden chips as the home cages. After an acclimation period, a 3 min video was recorded. Walking behavior was examined at time points concurrent with the CatWalk analysis at 24 h, 48 h, and 72 h post-osteotomy as well as 10 days post-osteotomy. Video analysis was performed by two blinded observers and scores from 0 to 4 were assigned (Table 2). If walking seemed to be impaired due to a mechanical problem (e.g., displacement of the patella) one point was subtracted from the assigned score. Table 2Limping score. SpecificationLimping scoreNormal use, physiological gait0Complete ground contact and sporadic limping or alteration of the gait pattern1No complete ground contact or limping, constant alteration of the gait pattern2Partial non-use of the limb3Complete lack of use4
## CatWalk analysis
Specific gait analysis was performed using the CatWalk XT Gait Analysis system for rodents (Noldus, Wageningen, the Netherlands). Multiple runs per animal were acquired before interventions as baseline measurements and at 24 h, 48 h, 72 h post-anesthesia (data not shown in results) and post-osteotomy, as well as 10 days post-osteotomy. Post-acquisition the runs were screened, and non-compliant runs as well as interrupted runs (e.g., by sniffing, rearing) were excluded. Runs that noticeably differed from the rest of the runs of this trial were also excluded, leaving an average of 4.4 runs per animal and time point for analyses. All runs were classified automatically by the Noldus CatWalk XT software (version XT10.6) and revised for classification errors (i.e., incorrect identification of paws), which were corrected manually. From the obtained data, mean speed (cm/s) (velocity) and the following parameters were analyzed for the osteotomized left hind leg: mean intensity, stand duration (s) and stride length (cm). The two baseline measurements were used to calculate one baseline average value. Values at all time points were then normalized to the respective average baseline (time point measurement divided by average baseline value).
## Euthanasia and sample collection
Euthanasia was carried out according to contemporary best practice. At 14 days post-osteotomy, mice were euthanized by cervical dislocation in deep anesthesia. The osteotomized femora were retrieved and fixed in $4\%$ paraformaldehyde (PFA; Electron Microscopy Sciences, Hatfield, USA) at 4 °C for 6–8 h. The femora were then transferred into PBS until ex-vivo μCT was completed.
## Ex-vivo μCT
To determine bone formation three-dimensionally, femurs were scanned in a SkyScan 1172 high-resolution µCT (Bruker, Kontich, Belgium). Voxel size was set to 8 µm and the bones were scanned with a source energy of 70 kV, 142 µA, a rotation step of 0.2 degrees and an 0.5 mm aluminum filter. Scans were reconstructed using NRecon (Bruker, Kontich, Belgium), applying ring artefact reduction and beam hardening corrections. CT Analyser software (version 1.20.3.0; both Bruker, Kontich, Belgium) was used for 2D and 3D analyses. By excluding the original cortical bone within the callus, the total volume (TV, mm3), the total bone volume (BV, mm3) and the bone volume fraction (BV/TV) of the newly formed bone were analyzed in a manually defined volume of interest (VOI)21.
## Histology and immunofluorescence
Following ex-vivo μCT, bones were placed in ascending sugar solutions as cryoprotectant ($10\%$, $20\%$, $30\%$) at 4 °C for 24 h each, then cryo-embedded in SCEM medium (Sectionlab, Japan) and stored at −80 °C. Consecutive sections of 7 μm were prepared using a cryotome (Leica, Wetzlar, Germany) and cryotape (Cryofilm 2C[9], Sectionlab, Japan). Sections were fixed onto glass slides, air-dried, and stored at −80 °C until staining. Movat’s pentachrome staining comprised the following steps: sections were air dried for 15 min, fixed with $4\%$ PFA (30 min; Electron Microscopy Sciences, Hatfield, USA), pretreated with $3\%$ acetic acid for 3 min, stained 30 min in $1\%$ alcian blue pH 2.5, followed by washing in $3\%$ acetic acid under light microscopic control. Sections were rinsed in H2Odest and immersed in alkaline ethanol for 60 min, then washed in tap water followed by incubation in Weigert’s hematoxylin for 15 min. After washing in tap water for 10 min, sections were stained in crocein scarlet-acid fuchsin for 15 min, treated with $0.5\%$ acetic acid for 1 min, followed by 20 min incubation in $5\%$ phosphotungstic acid, and 1 min in $0.5\%$ acetic acid. The sections were washed three times for 2 min in $100\%$ ethanol, followed by incubation in alcoholic Saffron du Gâtinais for 60 min. The slides were dehydrated in $100\%$ ethanol, cleared shortly in xylene, covered with Vitro-Clud and a cover slip. Imaging was performed on a Leica light microscope using LAS X software (Leica Microsystems GmbH, Wetzlar, Germany) at 10× magnification. Quantitative analyses of the Movat’s pentachrome staining were evaluated using an ImageJ macro. All analyses were performed blinded to sex, fixation, and pain management protocol.
Immunofluorescence staining was performed as described previously66,67 using the following antibody: Endomucin (Emcn) (V.7C7 unconjugated, rat monoclonal, sc-65495, 1:100; Santa Cruz Biotechnology, Dallas, USA), goat anti-rat A647 (1:500; A-21247, polyclonal, Invitrogen, Thermo Fisher Scientific, Waltham, USA) and DAPI (1:1,000; Thermo Fisher Scientific, Waltham, USA). Blocking was performed with $10\%$ FCS/PBS and the staining solution contained $5\%$ FCS and $0.1\%$ Tween20 (Sigma Aldrich, St. Louis, USA). Images were acquired using a Keyence BZ9000 microscope (Keyence, Osaka, Japan). The images were processed and analyzed with ImageJ69,70. An area of interest was established and managed via the built-in ROI-Manager, while cell number and signal distribution within the area were determined using the plug-ins Cell-counter and Calculator Plus. Data was processed with the ImageJ plugin OriginPro.
## Statistical analysis
The sample size was calculated based on own preliminary data22 using a nonparametric ranking procedure to analyze longitudinal data. The required number of animals was modeled in R using the package nparLD71. Assuming a $20\%$ difference and a power of ~ $80\%$ resulted in $$n = 10$$ animals per group.
Statistical analysis was performed using RStudio and graphs were created in GraphPad Prism (V9). To test whether the data from female and male mice are homogenous and would allow for an integrated data analysis, body weight data of both sexes was first representatively compared using the F2-LD-F1 design. Since we found significant interaction regarding sex, both sexes were analyzed separately for all statistical analyses. Nonparametric analysis of longitudinal data (F1-LD-F1 design; named non-parametric ANOVA-type tests), was used to test for significant differences in the main effect of time and main effect of group, separated by sex. When main group differences p ≤ 0.05 were detected, group comparison for each time point was performed using the Kruskal–Wallis test72. To determine group differences, Dunn’s post hoc test with Bonferroni correction was performed for each time point73,74. Non-parametric ANOVA-type tests as well as exact p-values, chi-squared and df of all analyses are provided in the Supplementary Information. Excluded mice and data are detailed in the Supplementary Information.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30641-9.
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|
---
title: CircRNA DICAR as a novel endogenous regulator for diabetic cardiomyopathy and
diabetic pyroptosis of cardiomyocytes
authors:
- Qiong Yuan
- Yunwei Sun
- Fan Yang
- Dan Yan
- Meihua Shen
- Zhigang Jin
- Lin Zhan
- Guangqi Liu
- Ling Yang
- Qianyi Zhou
- Zhijun Yu
- Xiangyu Zhou
- Yang Yu
- Yong Xu
- Qingming Wu
- Jianfang Luo
- Xiamin Hu
- Chunxiang Zhang
journal: Signal Transduction and Targeted Therapy
year: 2023
pmcid: PMC9992392
doi: 10.1038/s41392-022-01306-2
license: CC BY 4.0
---
# CircRNA DICAR as a novel endogenous regulator for diabetic cardiomyopathy and diabetic pyroptosis of cardiomyocytes
## Abstract
In this study, we identified that a conserved circular RNA (circRNA) DICAR, which was downregulated in diabetic mouse hearts. DICAR had an inhibitory effect on diabetic cardiomyopathy (DCM), as the spontaneous cardiac dysfunction, cardiac cell hypertrophy, and cardiac fibrosis occurred in DICAR deficiency (DICAR+/−) mice, whereas the DCM was alleviated in DICAR-overexpressed DICARTg mice. At the cellular level, we found that overexpression of DICAR inhibited, but knockdown of DICAR enhanced the diabetic cardiomyocyte pyroptosis. At the molecular level, we identified that DICAR-VCP-Med12 degradation could be the underlying molecular mechanism in DICAR-mediated effects. The synthesized DICAR junction part (DICAR-JP) exhibited a similar effect to the entire DICAR. In addition, the expression of DICAR in circulating blood cells and plasma from diabetic patients was lower than that from health controls, which was consistent with the decreased DICAR expression in diabetic hearts. DICAR and the synthesized DICAR-JP may be drug candidates for DCM.
## Introduction
Circular RNA (circRNA) belongs to the non-coding RNA (ncRNA) group, which is ubiquitous, stable, and evolutionarily conserved among the eukaryotes.1 Structurally speaking, circRNAs are covalently closed at the 5’ to 3’ ends, which are more resistant to exonucleases than linear RNAs.2,3 In addition, a circRNA is able to form the loop structure, which is more stable structure in RNA. Indeed, it has been reported that the half-life of circRNA is about 24.56 ± 5.2 h, which is significantly longer than mRNA (≈16.4 h).4 The unique structure of circRNAs suggests that circRNAs may play more stable effects within the cells.
Diabetic cardiomyopathy (DCM) is defined as the abnormal myocardial structure and performance that is induced by diabetes, but is not caused by its underlying hypertension, coronary artery disease or valvular disease.5 DCM is characterized by adverse structural remodeling (including cardiac hypertrophy and fibrosis), early-onset diastolic dysfunction, and late-onset systolic dysfunction.6 Clearly, diabetic cardiomyopathy is a metabolic heart disease, in which hyperglycemia and related metabolic and endocrine disorders are the triggering factors for the myocardial damages through multiple mechanisms including energy metabolism imbalance described in our previous study.7 Due to the limited knowledge regarding the detailed molecular mechanisms responsible for the pathogenesis of diabetic cardiomyopathy, there are still lack of any specific molecular treatments for this frequently encountered heart disease.
Several kinds of programmed cell death are reported to be involved in the development of diabetic cardiomyopathy, among them apoptosis are well-studied.8,9 *Pyroptosis is* a newly identified form of programmed cell death, which could induce cell lysis and the release of pro-inflammatory factors (such as IL-1β and IL-18) and high-mobility group box-1 protein (HMGB1).10 Recently, Shao et al.11 defined pyroptosis as gasdermin-induced necrotic cell death and applied this term to all gasdermin family members-induced cell death through membrane permeabilization. It is established that the cardiomyocyte pyroptosis plays an important role in the development of DCM.1 Silencing NLRP3 was reported to suppress pyroptosis in H9c2 cardiomyocytes under high-glucose concentration and improve cardiac inflammation, pyroptosis, and fibrosis in Type 2 diabetes mellitus (T2DM) rats.12 In addition, caspase-1-induced pyroptosis in DCM is related to ncRNAs including microRNA-30 (miR-30).13 and long ncRNA (lncRNA) Kcnq1ot1c.14 Therefore, we believe that inhibiting of cell pyroptosis may offer a new therapeutic strategy for diabetic cardiomyopathy.10 It has been reported that circRNAs extensively participate in the development of various diseases, including cancer, cardiovascular diseases and T2DM.15,16 However, only a few studies on circRNAs are found to be associated with the DCM. In this respect, Yang et al.17 reported that hsa_circ_0076631 could regulate the caspase-1-inducd pyorptosis by targeting miR-214-3p in DCM. CircularRNA circ_0071269 knockdown is found to protect against from DCM by the microRNA-145/gasdermin A axis.18 In addition, the increased expression of circRNA_000203 in mouse diabetic hearts could enhance the expression of fibrosis-associated genes by sponging miR-26b-5p.19 A circRNA cerebellar degeneration-related protein 1 antisense (CDR1as) is found to be upregulated in DCM. CDR1as activates the Hippo signaling pathway by inhibiting of the mammalian sterile 20-like kinase 1 (MST1) ubiquitination level, which could induce the apoptosis in cardiomyopathy.20 Another circRNA circHIPK3 may downregulate PTEN to protect cardiomyocytes from high glucose-induced cell apoptosis.
In this study, we have identified that circRNA mm9_circ_008009 is downregulated in mouse hearts with DCM and in cardiomyocytes treated with the advanced glycation end products (AGEs). We named it as well as its conserved human circular RNA hsa_circ_0131202 as DICAR (the diabetes-induced circulation-associated circular RNA). DICAR could efficiently inhibit the pyroptosis via binding with valosin-containing protein (VCP) and blocking of the Med12 protein degradation. Synthetic DICAR-junction part (DICAR-JP) could rescue the cardiomyocytes pyroptosis induced by AGEs, suggesting its potential as a therapeutic candidate for diabetes–associated cardiac impairments.
## DICAR is associated with diabetic cardiomyopathy in mice
We screened the circRNA expression profiles in heart tissues from wild-types (WT) mice and diabetic db/db mice by circRNA microarray. The results revealed that 58 circRNAs with high homology between human and mouse were differentially expressed between the two groups by at least 1.5-fold ($P \leq 0.05$) (GSE199133). The representative results of circRNA microarray from two groups were shown in Fig. 1a. We next selected five circRNAs with the highest fold change values for further validation by RT-qPCR (Fig. 1b). We found that mm9_circ_008009 was the most decreased circRNA in the diabetic hearts (Fig. 1b). The mm9_circ_008009 was resistant to ribonuclease R digestion, whereas linear GAPDH mRNA was found to be easily degraded (Fig. 1c). According to the circBase description, the parent gene of mm9_circ_008009 is Tulp4. We found that mm9_circ_008009 was formed from the exon1 and intron of Tulp4 (Fig. 1d). The divergent primers were designed to amplify mm9_circ_008009, while the convergent primers were designed to amplify the corresponding linear mRNA using cDNA and genomic DNA (gDNA) in the mouse cardiomyocytes (MCMs). Both mm9_circ_008009 and hsa_circ_0131202 were amplified by divergent primers in cDNA, but not in gDNA, respectively (Fig. 1e, f), indicating that the circRNA species are circular in form. Sanger sequencing analysis of the PCR products demonstrated that mm9_circ_008009 was generated from exon 1 and intron of Tulp4 through the “back splices” mechanism (Fig. 1g). We blasted the sequences between mm9_circ_008009 and 36 human circRNAs transcripted from TULP4 based on circBase and found that the conserved identity between hsa_circ_0131202 and mm9_circ_008009 was $79.65\%$. In addition, the circRNA characteristic of hsa_circ_0131202 was identified, and the RNA was detected with divergent primers in cDNA, but not in gDNA of human cardiomyocytes (HCMs) (Fig. 1f). Sequence analyses revealed that approximately 20-bp fragment of the mm9_circ_008009 junction site (Fig. 1g) was similar with hsa_circ_0131202 (Fig. 1h). Moreover, the RNA models structure of hsa_circ_0131202 (Fig. 1j), which includes the back-splicing junction (BSJ), was similar to that of mm9_circ_008009 (Fig. 1i). We also predicted other human circRNAs from TULP4 (Supplementary Fig. S1); however, the deduced structures were not similar to those of mm9_circ_008009. Therefore, hsa_circ_013202 and mm9_circ_008009 with the same source, and similar structure and sequence may have the same or similar molecular and biological functions. The fluorescence in situ hybridization (FISH) experiment demonstrated that mm9_circ_008009 was mainly located in the cytoplasm of cardiac tissue cells (Fig. 1k), cultured MCMs and HCMs (Supplementary Fig. S2a). Advanced glycation end-products (AGEs) are believed to be involved in diverse complications of diabetes. The expression of mm9_circ_008009 and hsa_circ_0131202 in MCMs and HCMs was downregulated when treated by AGEs in a dose-dependent manner (Supplementary Fig. S2b, c). Therefore, we named mm9_circ_008009 and hsa_circ_0131202 as the diabetes-induced circulation-associated circRNAs (DICAR).Fig. 1Identified circRNA expression in diabetic cardiomyopathy mouse. a Microarray identified in the hearts of db/db mice. b Select significantly regulated circRNA detected by qPCR ($$n = 9$$). c CircRNA resistance detected by RNAse. d Mm9-circ-008009 structure. e Identification of the mm9-circ-008009 back-spliced junction sites. f Identification of the hsa-circ-0131202 back-spliced junction sites. g, h Sanger sequence of mm9-circ-008009 and hsa-circ-0131202. i, j Second structure prediction of mm9-circ-008009 and hsa-circ-0131202. k FISH detection of mm9-circ-008009 in the mouse heart tissues. Data are represented as mean ± SEM. ** $P \leq 0.01$ vs. WT group
## Cardiac dysfunction, cardiac cell hypertrophy, and cardiac fibrosis are found in DICAR+/− mice
To test the potential role of DICAR in diabetic cardiomyopathy, we created the DICAR knock-out mice. Unfortunately, we did not obtain the homozygous DICAR knockout mice (DICAR−/− mice), which might be possibly lethal to mice. In heterozygous DICAR knockout (DICAR+/−) mice, the knockdown efficiency and specificity of DICAR were verified by qRT-PCR. As illustrated in supplementary Fig. S3a, DICAR was expressed at a very low level, while Tulp4 mRNA was not affected in the DICAR+/− mice. The results revealed that the DICAR knockdown did not affect the expression of the parent gene Tulp4 (Supplementary Fig. S3a).
Interestingly, we found that the cardiac function was impaired in heterozygous DICAR-deficient (DICAR+/−) mice compared with their control mice. The results of echocardiographic assessment demonstrated that, when compared with wild type (WT) control mice at same age, the increased left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), and left ventricular end-diastolic dimension (LVEDD) were noted in DICAR+/− mice (Fig. 2a, b). At 3-month-old, the decreased ejection fraction (EF) and fractional shortening (FS) were noted in DICAR+/− mice (Fig. 2a, b). The results demonstrated that the decreased expression of DICAR impaired the cardiac function, which is similar to the cardiac dysfunction in diabetic patients.21 To test the potential role of DICAR in cardiac remodeling and fibrosis, we analyzed the area of cardiomyocytes and fibrosis in DICAR+/− mice. When compared with these in WT mice, age-matched DICAR+/− mice demonstrated a marked increase in the cardiomyocyte size determined by the wheat germ agglutinin (WGA) staining (Fig. 2c), a marked increase in collagen deposition in the myocardial interstitium determined by Masson staining (Fig. 2d), and the enhanced expression of collagen III (Fig. 2e), which reflected the cardiac remodeling.22 *These data* suggested that the deficiency of DICAR in DICAR+/− mice could induce cardiac dysfunction, hypertrophy, and fibrosis in vivo. Fig. 2Effect of DICAR on the cardiac functions and remodeling. a Echocardiography images of DICAR+/− mouse. b Summary of the heart functions, including stroke volume (SV), ejection fraction (EF), left ventricular end-diastolic dimension (LVEDD), cardiac output (CO), fractional shortening (FS). c Representative images and summary data of wheat germ agglutinin (WGA) staining. d Representative images and summary data of Masson staining of DICAR+/−. e Representative IHF images and summary data of collagen III. f Representative and summary data of WB images of pyroptosis detected in the heart tissues. Data are represented as mean ± SEM. * $P \leq 0.05$, **$P \leq 0.01$ vs. WT group, $$n = 6$$–8 Cardiac cell pyroptosis is a key cellular event in diabetic hearts. To test the effect of DICAR on pyroptosis, we detected a set of pyroptosis-related biomarkers in the heart tissues of DICAR+/− mice. In this experiment, the expression of cleaved GSDMD, ASC, NLRP3, and caspase-1 p21 proteins were used to evaluate the activation of pyroptosis. The results revealed that all these protein markers of pyroptosis were upregulated in the heart tissues from DICAR+/− mice compared to those in WT controls (Fig. 2f).
## DICARTg mice are resistant to heart damages induced by diabetes
To further investigate the effect of DICAR on the development of DCM, DICARTg mice were created and used to induce the diabetes mellitus type 2 (T2DM) in order to explore its potential protective effect on DCM. The knock-in efficiency and specificity of DICAR were verified by qRT-PCR. As shown in Supplementary Fig. S3a, DICAR was overexpressed, but its parent Tulp4 mRNA was not affected (Supplementary Fig. S3b) in the DICARTg mice. Echocardiographic assessment of cardiac function was performed on DICARTg mice. When compared with that from the non-diabetic wild type (WT) group, the cardiac function was impaired in wild-type diabetic (WT-DM) group as shown by parameters including the left ventricular internal diastolic diameter (LVIDd), left ventricular internal diameter in systole (LVIDs), FS, SV, LVESV, LVEDV, and CO. Clearly, the impaired cardiac function was significantly improved in diabetic DICARTg mice (Fig. 3a). These data suggest DICAR might be a key circRNA in cardiac protection in DCM. In addition, when compared with WT mice, WGA staining revealed that the cardiomyocyte size was increased in WT-DM mice, but the increased cell size was markedly inhibited in diabetic DICARTg mice (Fig. 3b). A similar effect of DICAR overexpression was noted in terms of the collagen deposition pattern in myocardial interstitium by Masson staining (Fig. 3c) and of the expression pattern of collagen III by immunohistochemistry (Fig. 3d). These data suggest DICAR might be a key circRNA in cardiac protection during the process of DCM.Fig. 3Gain of DICAR function ameliorates T2DM-induced heart function in DICARTg mouse. a Representative echocardiography images of DICARTg mouse. b The DICAR overexpression ameliorates heart functions, including stroke volume (SV), ejection fraction (EF), left ventricular end-diastolic dimension (LVEDD), left ventricular end-diastolic volume (LVEDV), cardiac output (CO), and fractional shortening (FS). c The DICAR overexpression decreased the cardiomyocytes area detected by wheat germ agglutinin (WGA) staining. d Collagen deposition detected by Masson staining. e Collagen III expression detected by IHC. f In the DICARTg mouse, the DICAR overexpression inhibited the pyroptosis of heart tissue induced by T2DM that detected by the GSDMD, ASC, IL-1β, and NLRP3 protein expression. Data are represented as mean ± SEM. ** $P \leq 0.01$ vs. Normal, ++$P \leq 0.01$ vs. Normal + T2DM, $$n = 8$.$ g Adenovirus (AD)-DICAR infected HL-1 cells inhibited the pyroptosis induced by AGEs (200 µg/mL). Data are represented as mean ± SEM. ** $P \leq 0.01$ vs. NEG, ++$P \leq 0.01$ vs. AD-DICAR-over, $$n = 4$$ We then determined the pyroptosis of heart tissues by the activation of GSDMD, NLRP3, caspase-1, and ASC. The results showed that these pyroptosis-related proteins were significantly inhibited in heart tissues from DICARTg mice with T2DM compared with these from the wild-type control mice with T2DM (Fig. 3e). We also overexpressed DICAR in cultured mouse cardiomyocytes through the transfection with AD-DICAR (adenovirus-expressing DICAR expression) for 48 h followed by treatment with AGEs (200 µg/mL) for 48 h in vitro. Western blotting displayed that the effects of AGEs on the activation of GSDMD, NLRP3, caspase-1, and ASC were inhibited by DICAR overexpression in these mouse cardiomyocytes (Fig. 3f).
## Interaction between VCP and DICAR regulates pyroptosis of cardiomyocytes induced by diabetes
Bioinformatics’ analysis of the second structure of DICAR revealed a specific stem loop structure in the junction domain (Fig. 1I). As we found that DICAR located in the cytoplasm of cardiac cells (Fig. 1g), we determined whether DICAR could interact and regulate proteins. Chromatin isolation by RNA purification-mass spectrometry (ChIRP-MS), a method used to detect the binding proteins to ncRNA, was used to identify the binding proteins of DICAR.The biotin-labeled probe of the DICAR junction site was purified and subjected to the circRNA pull-down assays by incubation with mouse heart tissue lysates, followed by MS detection. A total of 50 proteins were detected (Supplementary Table S2), among them the valosin-containing protein (VCP) had the highest score based on unique peptides and sequence coverage (Supplementary Table S2). VCP, also known as transitional endoplasmic reticulum (tER) ATPase or p97, is an abundant and highly conserved member of the ATPases family associated with a variety of cellular activities (AAA), which could control the critical steps in the ubiquitin-proteasome (Ub-Pr) degradation pathway.23 DICAR-binding VCP protein was further verified by parallel reaction monitoring (PRM), and the results revealed the inhibition of its expression was up to $31\%$ in db/db mouse hearts when compared with that in WT control hearts (Fig. 4a). However, the expression of total VCP protein was not different between the two groups (Fig. 4b). In addition, to measure the binding ability of VCP and DICAR, the RIP assay was performed with a VCP antibody, followed by qPCR for DICAR. As shown in Fig. 4c, the specificity of the IP was assessed by Western blotting, which revealed a unique VCP band detected in the VCP IP, but not in IgG IP. The amount of DICAR-binding with VCP was the highest in the DICARTg mouse hearts and moderate in the WT mouse hearts, and undetectable in DICAR+/− mouse hearts. Fig. 4Interaction between VCP and DICAR in the pyroptosis of cardiomyocytes induced by AGEs. a Representative images of VCP pulled down by DICAR probe and detected by CHIRP-MS. b Total VCP protein expression between WT and db/db mice. c RIP used to detect the combination between VCP and DICAR. d Model prediction of VCP combined with DICAR. e the KD of DICAR combined with VCP detected by SPR. f the effect of VCP-siRNA on anti-pyroptosis induced by AGEs. Data are represented as mean ± SEM. ** $P \leq 0.01$, ***$P \leq 0.001$ vs. con; ++$P \leq 0.01$ vs. AGEs (200 µg/ml, 48 h); ##$P \leq 0.01$ vs. AGEs +mDICAR-JP, $$n = 4$$ To further explore the DICAR-VCP binding model, we employed a complete structure-based computational framework, Rosetta–Vienna RNP-ΔΔG, for predicting the DICAR-VCP relative binding affinities, so as to bring together secondary structure-based energetic calculations of unbound RNA-free energies and a unified energy function for bound DICAR–VCP complexes. DICAR mainly bound three parts of the VCP protein (Supplementary Table S3). As shown in Fig. 4d, in the docking region, the secondary RNA structure of DICAR (19 bp) is bound with the amino acid residues of the VCP protein through a hydrogen bond, salt bridge, amino acid residue side chain network, and static electricity models (Supplementary Table S3). Moreover, to validate the binding affinities of the DICAR and VCP, we synthesized a 19-bp fragment of the DICAR junction part (DICAR-JP). We measured the kinetic rate constants between DICAR and VCP by the SPR assay. The results revealed that the binding constant (Kd) of DICAR-JP was 5.4 × 10−9 (Fig. 4e). Thus, we demonstrated that DICAR could bind to VCP via the junction fragment and that DICAR-JP was the key fragment of DICAR for VCP binding.
To explore the role of the DICAR-JP/VCP complex in the pyroptosis of cardiomyocytes induced by diabetes, synthetic mouse DICAR-JP (mDICAR-JP), human DICAR-JP (hDICAR-JP), and VCP-siRNA were studied in vitro. As illustrated in Supplementary Fig. S4a, AGEs induced the pyroptosis of HL-1 cardiomyocytes in a dose-dependent manner. Both mouse and human DICAR-JP exhibited the best anti-pyroptosis effect at the dose of 20 nM (Supplementary Fig. S4b, c). In addition, VCP siRNA inhibited the effect of AGEs on the pyroptosis of MCM in a dose-dependent manner (Supplementary Fig. S5). To explore the combined effect of DICAR-JP and VCP, we transfected DICAR-JP and VCP-siRNA together into MCMs. As shown in Fig. 4f, DICAR-JP inhibited pyroptosis induced by AGEs and VCP-siRNA enhanced the effect of DICAR-JP on pyroptosis. Thus, the DICAR-JP/VCP complex located in cytoplasm may play a key role in the pyroptosis in DCM.
## DICAR regulates VCP-mediated Med12 protein degradation via the Ub-protein system
VCP is involved in the formation of the tER and acts as a chaperon to export misfolded proteins from the ER to the cytoplasm, where the ubiquitinated proteins are degraded via the proteasome.24–26 *We thus* hypothesize that DICAR-JP may target VCP located in the ER to exert its function. To test it, the FISH assay was performed to detect whether DICAR was marked by the PE-DICAR probe located in the ER, which was shown with the ER-tracker Green (Fig. 5a). We also tested the DICAR regulation of VCP located in the ER. When compared with that in the WT group, DICAR+/− promoted the VCP expression in the ER and DICARTg inhibited VCP located in the ER (Fig. 5b). However, DICAR-overexpression could not reduce the VCP level in the ER to the level lower than that under normal physiological condition. We then tested whether DICAR could mediate the ubiquitination of protein by interacting with VCP during ubiquitinated protein degradation. The ubiquitination levels of proteins combined with VCP were measured in hearts. Interestingly, we found that the protein ubiquitination level was downregulated in DICAR+/− mice (Fig. 5c). We further confirmed that the special sequence of the DICAR junction site is a molecular chaperone of ubiquitinated protein, which could assist in the ubiquitination degradation of the proteins. AGEs could downregulate DICAR, which in turn promoted the VCP function in the development of cardiac pyroptosis in diabetes. The junction site of DICAR may act as the key regulator of cardiomyocyte pyroptosis. Fig. 5VCP mediated Med12 protein degradation via the Ub-protein system. a The FISH probe detected DICAR located in the endoplasmic reticulum. b VCP expression in ER, *$P \leq 0.05$ vs. WT, $$n = 3$.$ c Co-IP was used to detect the ubiquitination level of VCP in the heart tissues of DICAR+/− mice. d LC-MS + PRM was used to detect the protein expression in the heart tissues of DICAR+/− mice. e Myom1, Med12, Myh6, Myl2, and Cavin2 protein expression in the heart tissues of DICAR+/− mice detected by WB. f Myom1, Med12, Myh6, Myl2 and Cavin2 protein expression in the heart tissues of db/db mice detected by WB. Data are represented as mean ± SEM. ** $P \leq 0.01$ vs. WT, $$n = 8$.$ g Med12 mRNA in the heart tissues of DICAR + /− and DICARTg detected by qPCR. h Representative images of WB detected pyroptosis of the heart in the DICARTg mouse treated by T2DM. Data are represented as mean ± SEM. *** $P \leq 0.001$ vs. WT, $$n = 8$.$ i Representative images of DICAR-JP and VCP siRNA effect on the Med12 protein expression. Data are represented as mean ± SEM. ** $P \leq 0.01$ vs. BSA, ++$P \leq 0.01$ vs. AGEs (200 µg/mL, 48 h), $$n = 3$.$ j Representative images of Med12 siRNA-induced pyroptosis of HL-1. Data are represented as mean ± SEM. * $P \leq 0.05$, ***$P \leq 0.01$, ***$P \leq 0.001$, vs. NC-siRNA, $$n = 3$$ Based on the functions of VCP in protein degradation and its interaction with DICAR, the LC-MS was performed to detect the protein level of the heart tissues between WT and DICAR+/− mice (Fig. 5d). As shown in Fig. 5d, five proteins were downregulated in the ubiquitination process, including Myom1, Myh6, Med12, Myl2, and Cavin2. We then detected the expressions of these proteins in the heart tissues from DICAR+/− mice. As shown in Fig. 5e, among the 5 proteins, Med12 was the most downregulated proteins in mouse hearts with DICAR+/−. In order to find whether the level of Med12 mRNA was also regulated, we detected the mRNA of Med12 in DICAR+/− and DICARTg mouse model. As shown in Fig. 5f, Med12 mRNA was not changed. These five proteins were detected in the heart of T2DM mouse, in which Med12 was also the most downregulated proteins (Fig. 5g). The results suggested that downregulation of DICAR might relieve VCP to promote Med12 protein deregulation via the Ub-protein degradation system.
It has been reported that Med12 is involved in the differentiation of endothelial cells.27,28 We found that the Med12 expression was decreased in T2DM hearts, which was upregulated in the hearts from diabetic DICARTg mice (Fig. 5h). As the results shown in Fig. 5i, mDICAR reversed the dowregulated effect of AGEs (200 µg/mL, 24 h) on Med12 protein. However, co-transfected with DICAR-JP and VCP-siRNA did not play better effect on reversed Med12 expression compared with DICAR-JP (Fig. 5i). This was the limitation that VCP-overexpression plasmid be transfected into the cell to explore the block function on DICAR-JP. In addition, we had successfully founded Med12 expression interrupted HL-1 cell model via Med12 siRNA (Fig. S6). Furthermore, Med12 siRNA alone could also induce pyroptosis in the HL-1 cardiomyocytes (Fig. 5j).
## The levels of DICAR in PBMCs and in plasma are decreased in diabetic patients
To provide a clinical link between DICAR expression and diabetes in patients, we determined the levels of DICAR in peripheral blood mononuclear cells (PBMCs) and in circulating plasma from three age-matched human groups: normal heath control subjects, the patients with diabetes (T2DM) with and without cardiac dysfunction. The gender, age, HR, BMI, SBP, DBP, Cr, and BUN were not different among the three groups. TG, TC, and HG were higher in patients with diabetes (Table 1). DICAR level in healthy individuals was 1.90 ± 0.34 in PBMCs and 1.22 ± 0.32 in plasma, respectively, while it was decreased in diabetic patients with cardiac dysfunction (PBMCs: 0.84 ± 0.12; plasma: 0.88 ± 0.17) and in diabetic patients without cardiac dysfunction (PBMCs: 0.39 ± 0.06; plasma: 0.57 ± 0.10) (Table 1).Table 1Clinical information and DICAR expression in diabetic patientsGroupsHealthy ($$n = 12$$)T2DM ($$n = 21$$)T2DM with HDF ($$n = 9$$)Age68 ± 1065 ± 968 ± 12HR (/min)81 ± 1281 ± 882 ± 12BMI (kg/m2)23.1 ± 1.223.8 ± 1.323.9 ± 1.5SBP (mmHg)123.0 ± 11.2124.0 ± 10.9125.0 ± 10.3DBP (mmHg)70.0 ± 10.972.0 ± 10.771.0 ± 11.2Cr (μmol/L)72.83 ± 15.7866.14 ± 20.4376.32 ± 25.91BUN (mmol/L)4.75 ± 0.924.94 ± 1.515.38 ± 1.63TC (mmol/L)2.94 ± 2.264.79 ± 0.82**3.96 ± 1.17*TG (mmol/L)0.85 ± 0.771.46 ± 0.60**1.52 ± 1.14**HDL-C (mmol/L)1.6 ± 0.11.17 ± 0.33*1.1 ± 0.2*FBG (mmol/L)5.4 ± 0.68.8 ± 1.0**8.5 ± 1.5**EF (%)54.64 ± 0.9255.33 ± 0.6336.08 ± 3.22***DICAR in PBMC (a.u.)1.90 ± 0.340.84 ± 0.12***0.88 ± 0.17***DICAR in plasma (a.u.)1.22 ± 0.320.39 ± 0.06**0.57 ± 0.10**HDF heart dysfunction, HR heart rate, BMI body mass index, SBP systolic blood pressure, DBP dilated blood pressure, BUN, TC total cholesterol, TG triglyceride, HDL high density lipoprotein-C, FBG fasting blood glucose, EF ejection fraction. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs. Healthy individials, $$n = 9$$–21
## Discussion
Our study has identified a novel circRNA, which is decreased in diabetic hearts and is involved in the development of DCM. Based on its biological functions, we named it as DICAR. DICAR has an inhibitory effect on the development of DCM, as the spontaneous cardiac dysfunction, cardiac cell hypertrophy, and cardiac fibrosis occur in heterozygous DICAR deficiency (DICAR+/−) mice. In contrast, the DCM is alleviated in DICAR-overexpressed DICARTg mice. In addition, the homozygous DICAR deficiency (DICAR−/−) is lethal to mice, which suggests that DICAR is indeed a critical circRNA at least in mice.
The pyroptosis of cardiomyocytes is a recently identified key cellular event in the development of DCM.29 To uncover the potential cellular mechanisms involved in DICAR-mediated effect on DCM, the pyroptosis of cardiomyocytes has been determined via both gain-of-function and loss-of-functional approaches in DICAR gene-modulated mice in vivo and in cultured cardiomyocytes in vitro. The results have demonstrated that DICAR overexpression inhibits pyroptosis of cardiomyocytes in T2DM in vivo and in AGEs-treated cardiomyocytes in vitro, whereas DICAR knockdown has an opposite effect on cardiomyocyte pyroptosis. The effect of DICAR on cell apoptosis, another cell damage in DCM should also be determined in future studies. At the molecular level, we have found that DICAR downregulation under diabetic condition is able to release VCP and then mediates the Med12 protein degradation via the Ub-Pr system, which in turn could induce pyroptosis of cardiomyocytes during the development of DCM.
In this study, we determined the effects of DICAR deficiency (DICAR+/−) on cardiac dysfunction, cardiac cell hypertrophy and cardiac fibrosis, as well as on cardiac cell pyroptosis every month up to 5 months after birth. We found that the damages of cardiac function, cardiac cell hypertrophy and cardiac fibrosis in DICAR+/− mice reached the peak at 3 months old. No additional deterioration was found at 4 and 5 months old. For the cardiac cell pyroptosis, we found that it reached the peak at 2 months old. We thought that in the early stage when the DICAR+/− mouse was born, DICAR deficiency could induce cardiac cell pyroptosis and secrete inflammatory factors. Those cellular responses would gradually cause the damages of cardiac function and cardiac structures. However, in response to DICAR deficiency-induced cardiac cell pyroptosis, the cardiac cells may evoke some compensatory responses to against pyroptosis which made the cell pyroptosis to reach a peak at 2 months old. However, the damages of cardiac function and cardiac structures induced by cellular injury responses might reflect the delayed results, which reached a peak at 3 months old.
CircRNAs have more stable structure with less immunogenicity compared with the linear RNAs, which makes them have great potential for the new drug candidates.30 Based on the closed structure of DICAR, we have identified that its junction site forms a specific loop RNA second structure which makes its structure more stable even though DICAR is degraded. Also, the junction site sequence of DICAR is different part between DICAR and its parent line mRNA. We further identified that the 19-bp fragment of the DICAR-JP is a critical functional part of DICAR. Interestingly, we do find the synthesized DICAR-JP has a therapeutic effect on diabetes-related pyroptosis of cardiomyocytes. Therefore, DICAR-JP could be a novel small RNA which may be a good candidate for nucleic acid drugs in diabetes.
Recent studies have reported that circRNAs could regulate diseases and that circRNAs can be considered as potential markers for the diagnosis of several diseases as well as therapeutic target genes.31 For example, circRNA HRCR is proposed to possess the ability to sponge miR-223 and is beneficial for cardiac hypertrophy and heart failure.32 In addition, circ-Foxo3 is found to be able to promote cardiac senescence.33 To date, few studies have been performed to test the role of circRNAs in DCM. This study provides new information in this research field by identifying the role of the circRNA DICAR in the development of DCM. It is clear that DICAR-mediated biological functions are by DICAR itself, which is not related to its parent gene, as in both DICAR+/− and DICARTg mice, the parent gene Tulp4 expression remains unchanged. DICAR itself may be an important target for the treatment of DCM.
Pyroptosis, a new type of programmed cell death, was proven to be activated in DCM. Alleviating pyroptosis exerts a beneficial effect on DCM by interrupting the long ncRNA Kcnq1ot1 expression. NLRP3 is a key protein that mediates pyroptosis and NLRP3 knockdown could ameliorate DCM in type 2 diabetes.12,34 In the current study, we found that DICAR knockdown could induce pyroptosis as well as activate NLRP3 in mouse hearts. In DICARTg T2DM mice, pyroptosis is inhibited compared with that in WT mice with T2DM. In cultured cardiomyocytes, pyroptosis is activated by AGEs and that the AGEs-induced pyroptosis is reversed via DICAR overexpression. The DICAR can be considered as a key regulator of pyroptosis in diabetes. One weakness of our pyroptosis study was that the determination was mainly by western blot analysis of the pyroptosis-related proteins. Other methods such as live-cell imaging should be applied to further verify the pyroptosis results.
One of the mechanisms of circRNA’s function is sponging miRNAs. For example, caspase-1 is regulated by hsa_circ_0076631 by targeting miR-214-3p in DCM. More recently, circRNAs may act through other diverse mechanisms in addition to miRNA sponging, which include sequestering proteins, regulating gene transcription, and even providing a protein synthesis template.15 Our current research reveals that the DICAR could bind with VCP via the DICAR-JP at the junction site and modulates the action of VCP. DICAR expression is decreased and the effect of VCP on the degradation of the ubiquitinated protein is thus enhanced in diabetes.
VCP disruption of valosin-containing protein activity causes cardiomyopathy and reveals pleiotropic functions in cardiac homeostasis.35 In patients with acute coronary syndrome (ACS) group, the serum VCP levels were significantly higher than the normal groups, which could be used as a stable biomarker in predicting the development of ACS and its ventricular dysfunction (VD).36 Many cofactors can interact with VCP that regulate its function by recruiting VCP to different cellular pathways.37 Most of the cofactors bind the N-terminal of VCP, while several others bind to the C-terminal. All the previous research reports regarding the VCP binding are based on protein interaction.38 In the current study, we have identified, for the first time, that the secondary structure of a circRNA (DICAR) junction site acts as a functional domain motif to bind to proteins such as VCP.
Med12, a subunit of the RNA polymerase II transcription, acts as a key mediator in many biomedical processes.34 Previous studies have reported that Med12 is able to regulate several key signaling pathways involved in cell growth, development, and differentiation.39 Recently, one study has found that in mice with conditional cardiac-specific knockout of Med12, the mouse hearts display progressively dilated dysfunction, which suggests that Med12 is also a key molecule in maintaining normal cardiac functions.40 *In this* study, we have found that Med12 is downregulated in hearts from T2DM mice and from DICAR+/− mouse. In addition, Med12 is involved in the development of pyroptosis of MCMs. The results suggest that DICAR/VCP is able to regulate the Med12 degradation process and may mediate the pyroptosis of cardiomyocytes in DCM. It will be better to perform a rescue experiment to check whether Med12 overexpression could rescue the phenotype of pyroptosis and cardiac dysfunction of DICAR+/− mice to further confirm the role of Med12.
Heart tissue sample of DCM is very difficult to be obtained, especially from diabetic patients without serious heart failure and other serious complicated diseases. To provide an alternative clinical link of DICAR with diabetes, we have measured the levels of DICAR in circulating blood cells (PBMCs) and in plasma from diabetic patients and their health controls. In PBMCs and in plasma, the levels of DICAR are significantly lower in diabetic patients than those in health controls, which were consistent with the DICAR downregulation in diabetic mouse hearts. However, we have not found difference of DICAR expression in PBMCs and in plasma between patients with ($$n = 9$$) and without ($$n = 21$$) cardiac dysfunction. The results suggested that downregulation of DICAR might not be limited to cardiac cells and hearts under diabetic conditions. In addition, the circulating plasma has low level of DICAR, which indicates that the cells may release some of their DICAR into the extracellular space and circulating blood. DICAR in PBMCs and in plasma might be a novel biomarker for diabetes. We did not find difference between diabetic patients with and without cardiac dysfunction. We thought there were two possibilities. First, the case number of diabetic patients with cardiac dysfunction was small. The increasing of case number in each group will be required to confirm the discovery in diabetic patients. The second possibility was that, although the decrease in DICAR expression may be a general phenomenon in cells and plasma from diabetic patients, the organ tissue expression levels might be a better parameter to reflect the specific organ damages induced by diabetes. Thus, the expression levels of DICAR in heart tissues from diabetic patients should be measured in future studies.
In summary, our study has identified that the novel circRNA DICAR, which is down-regulated in diabetes, could protect against diabetes-induced pyroptosis of cardiomyocytes and the development of diabetic cardiomyopathy. The underlying molecular mechanisms are the binding of the DICAR-junction domain with VCP and regulate the pyroptosis by VCP-mediating Med12 degradation through the ubiquitin-proteasome (Ub-Pr) pathway. DICAR and the synthesized DICAR-JP may be good candidates for novel nucleic acid drugs in the prevention and treatment of diabetic cardiomyopathy.
## Patient samples
To explore the relationship with the DICAR expression and the cardiac dysfunction induced by diabetes, we collected the healthy individuals ($$n = 12$$), the age was between 50–70; T2DM patients without cardiac dysfunction or any other complication ($$n = 21$$) and T2DM patients with cardiac dysfunction ($$n = 9$$). All the patients enrolled in this study had no extracardiac complications and were from China Resource & WISCO General Hospital. The study was conducted according to the standards of the declaration of Helsinki. The study was approved by the Ethics Committee of China Resource & WISCO General Hospital. Written informed consent was obtained from all subjects.
## Reagents and antibodies
All the reagents and antibodies were listed in the supplementary key resources table.
## Animals
C57BL/KsJ WT (age: 12 weeks) and C57BL/KsJ db/db mice of both genders and with 23–28 (mM) blood glucose levels were purchased from the breeding colonies of GenePharmatech Company. DICAR+/− and DICARTg mice were all established at the Model Animal Research Center of Nanjing University, China (see the Results section). All animal studies were conducted with age- and gender-matched controls and they were maintained in a temperature-controlled (22–25 °C) environment under a 12-h light/dark cycle and free access to food and water at the Animal Center Wuhan University of Science and Technology.
## Adenoviral constructions and infection
To construct the adenoviruses, circRNA mm9-circ-008009 (renamed as DICAR) is a vector synthesized by Chenechem Company (Shanghai, China). DICAR was first inserted into pcDNA3.1 with the endogenous flanking sequence (1-kb upstream). The upstream flanking sequence was then partially copied and then inserted in the inverted orientation downstream. Adenovirus-DICAR-shRNA without the downstream reverse sequence served as the negative control. All these vectors were finally cloned into the Adeno-X TM Expression System (Clontech) in accordance with the manufacturer’s instructions.41
## Microarray analysis
CircRNA microarray analysis was performed using the Arraystar mouse V.2 (Kangcheng Biotechnology Company, Shanghai). Total RNA from each heart tissue sample was quantified using the NanoDrop ND-1000 system. The sample preparation and microarray hybridization were performed based on Arraystar’s standard protocols. Total RNAs were digested with RNAse R (Epicentre, Inc.) to remove linear RNAs and enrich circRNAs. Then, the enriched circRNAs were amplified and transcribed into fluorescent cRNA by using a random priming method (Arraystar Super RNA Labeling Kit; Arraystar). The labeled cRNAs were hybridized onto the Arraystar Mouse circRNA Array (8 x 15 K, Arraystar). After washing the slides, the arrays were scanned by the Agilent Scanner G2505C. Agilent Feature Extraction software (version 11.0.1.1) was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the R software package. Finally, the differentially expressed circRNAs with statistical significance between the two groups were identified through Volcano Plot filtering. The differentially expressed circRNAs between the two samples were identified through fold-change filtering. Hierarchical clustering was performed to demonstrate distinguishable circRNAs expression patterns among the samples.
## RNase R treatment
Total RNA was extracted from mouse heart tissues and classified into two groups. One group of RNA was pretreated with RNase R (Epicentre, USA), 3 U/µg RNA at 37 °C for 15 min, according to the manufacturer’s instructions. The other group served as control. Then, qRT-PCR was performed to detect the expression of mm9-circ-008009, hsa_circ_0131202 and GAPDH with/without RNase R treatment.
## Immunofluorescence assays and FISH
Immunofluorescence assays were performed as described in the previous study.42 Cy3-labeled DICAR, FITC-labeled TnnT were used as described by the manufacturer’s instructions.42 FL glibenclamide was used to mark the ER. The live cells were stained by FL Glibenclamide and fixed by the DICAR FISH probe A. The FV10i Confocal Microscope (Olympus, Japan) was used to capture the images.
## The peripheral monocyte isolation from diabetes patients with or without cardiac dysfunction
The blood samples were processed within 1 h of blood sampling. Every patient signed a specific informed consent form, and clinical data were collected from the medical reports and transferred to an anonymous database for statistical processing. Peripheral blood samples of patients and healthy donors were collected using ethylenediaminetetraacetic acid (EDTA) anticoagulated vacutainers (BD Biosciences). Before separation, blood was mixed with the same volume of PBS and then overlaid on a FicollHypaque gradient (1077 g/mL; Cedarlane, Cat CL5020). Density centrifugation was performed at room temperature at 1800 rpm for 30 min with no brake. Peripheral blood mononuclear cells (PBMCs) were collected and stored at −196 °C [(1 × 107 cells in $10\%$ DMSO + $90\%$ fetal calf serum (FCS)]. The study was conducted according to the standards of the Declaration of Helsinki. The study was approved by the Ethics Committee of China Resource & WISCO General Hospital. Written informed consent was obtained from all subjects.
## DICAR-VCP testing for binding affinity prediction
Rosetta-Vienna RNP-ΔΔ was employed in this study by combining the 3D structure modeling with RNA secondary structure-based energetic calculations in order to predict the RNA-protein relative binding affinities. Briefly, a complete calculation framework for RNA–protein binding affinities was referred to using bioinformatics that included a unified free-energy function for bound complexes and automated Rosetta modeling of mutations. The secondary structure-based energetic calculations were performed to model the unbound RNA states. Calculating the energy relationship between a series of VCP proteins and DICAR revealed that the mode of action between DICAR and VCP proteins was interpreted and ranked by energy. Finally, the DICAR with a better binding ability to VCP was selected in accordance with the energy score.
## Statistical analyses
All data are presented as means ± SEM. Statistical analysis for the comparison of two groups was performed using a two-tailed unpaired Student’s t-test. To compare more than two groups, a one-way analysis of variance (ANOVA), followed by Tukey’s post hoc test was performed. Adjusted two-sided P-values were calculated, and $P \leq 0.05$ was considered to indicate statistical significance. All statistical analyses were performed with the GraphPad Prism Version 6 (GraphPad Software Inc., SanDiego, CA, USA) and SPSS package (SPSS Inc., Chicago, IL, USA).
## Supplementary information
Supplementary materials orignial wb picutre The online version contains supplementary material available at 10.1038/s41392-022-01306-2.
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|
---
title: Smoking-induced subgingival dysbiosis precedes clinical signs of periodontal
disease
authors:
- Ryan Tamashiro
- Leah Strange
- Kristin Schnackenberg
- Janelle Santos
- Hana Gadalla
- Lisa Zhao
- Eric C. Li
- Emilie Hill
- Brett Hill
- Gurjit S. Sidhu
- Mariana Kirst
- Clay Walker
- Gary P. Wang
journal: Scientific Reports
year: 2023
pmcid: PMC9992395
doi: 10.1038/s41598-023-30203-z
license: CC BY 4.0
---
# Smoking-induced subgingival dysbiosis precedes clinical signs of periodontal disease
## Abstract
Smoking accelerates periodontal disease and alters the subgingival microbiome. However, the relationship between smoking-associated subgingival dysbiosis and progression of periodontal disease is not well understood. Here, we sampled 233 subgingival sites longitudinally from 8 smokers and 9 non-smokers over 6–12 months, analyzing 804 subgingival plaque samples using 16 rRNA sequencing. At equal probing depths, the microbial richness and diversity of the subgingival microbiome was higher in smokers compared to non-smokers, but these differences decreased as probing depths increased. The overall subgingival microbiome of smokers differed significantly from non-smokers at equal probing depths, which was characterized by colonization of novel minority microbes and a shift in abundant members of the microbiome to resemble periodontally diseased communities enriched with pathogenic bacteria. Temporal analysis showed that microbiome in shallow sites were less stable than deeper sites, but temporal stability of the microbiome was not significantly affected by smoking status or scaling and root planing. We identified 7 taxa—Olsenella sp., Streptococcus cristatus, Streptococcus pneumoniae, Streptococcus parasanguinis, Prevotella sp., Alloprevotella sp., and a Bacteroidales sp. that were significantly associated with progression of periodontal disease. Taken together, these results suggest that subgingival dysbiosis in smokers precedes clinical signs of periodontal disease, and support the hypothesis that smoking accelerates subgingival dysbiosis to facilitate periodontal disease progression.
## Introduction
Periodontitis is a polymicrobial infection of the gums and teeth-supporting bone affecting nearly 750 million people worldwide1. The etiology of periodontitis is multifactorial, but dysbiosis of the subgingival microbiome plays an important role2–5. Healthy subgingival space hosts a microbial community generally dominated by Streptococci and other commensal organisms4–7. In periodontal disease, the subgingival microbiome shifts towards a more diverse community characterized by putative periodontal pathogens and other gram-negative organisms2,4–7. Colonization by these organisms elicits an immune response and inflammation, leading to destruction of the surrounding tissue and tooth loss if left untreated3. While poor oral hygiene is a major contributor, systemic diseases and other behavioral lifestyle may also disrupt the ecosystem equilibrium, leading to subgingival dysbiosis8–10.
Cigarette smoking has wide ranging adverse health consequences. Smokers are at least $50\%$ more likely to develop periodontal disease than non-smokers, and smoking is associated with disease severity in a dose-dependent manner11,12. Smoking drastically alters the microbial ecology of the oral cavity through nutrient deprivation, impairment of the immune system, oxygen depletion, and anti-microbial effects13,14, thereby shifting the subgingival microbial composition and structure. Cigarette smoke increases the diversity of subgingival communities, reduces the abundance of beneficial bacteria, and favors colonization of pathogenic species10,15–20. Thus, smoking may accelerate the development of periodontal disease by altering the subgingival microbiome.
Subgingival microbiome associated with and without periodontal disease in smokers have been investigated in several studies10,15–20. However, the longitudinal nature of the subgingival microbiome has not been well characterized. Most published studies have been cross-sectional4–6, and thus the organisms associated with clinical progression of periodontal disease in smokers have not been clearly defined. In the present study, we analyzed 804 subgingival plaque samples from 233 unique subgingival sites from 8 smokers and 9 non-smokers at 3–4 time points over 6–12 months. This large longitudinal dataset allowed us to compare and contrast the temporal dynamics of subgingival microbiome in smokers and non-smokers, and identify microbes associated with progression of periodontal disease. To our knowledge, this is one of the most extensive survey of longitudinal subgingival microbiome in smokers to date.
## Results
Baseline characteristics of 8 smokers and 9 non-smokers are shown in Table 1. The average mean probing depth, clinical attachment loss, and plaque index were higher in smokers compared to non-smokers (3.88 vs. 3.17; 3.58 vs. 1.49; 0.926 vs. 0.468, respectively). For each subject, subgingival plaque samples from the same sites were collected 3–4 times over 6 to 12 months. A total of 804 samples were sequenced, generating 20,030,627 reads with a mean of 24,914 reads per sample (range = 1978–742,281 reads per sample). We identified 822 unique OTUs belonging to 12 phyla and 185 different genera. Samples from non-smokers were dominated by five phyla: Firmicutes ($28.4\%$), Actinobacteria ($19.5\%$), Bacteroidetes ($18.8\%$), Fusobacteria ($14.9\%$) and Proteobacteria ($14.4\%$), and the remaining five phyla each comprised less than $4\%$ of the total community. Subgingival microbiome from smokers were dominated by three phyla: Bacteroidetes ($31.3\%$), Fusobacteria ($22.6\%$), and Firmicutes ($20.4\%$). For the subsequent alpha and beta diversity analysis, samples were rarefied to 8000 reads, which excluded 28 ($3.5\%$) of 804 total samples due to shallow sequencing depths. Table 1Baseline characteristics of study participants. SubjectAgeSexStageSmoking statusNumber of time points (month sampled)Number of sitesProbing depth (mm)Clinical attachment loss (mm)Plaque indexProportion of samples with probing depth > 3 mm (%)AB30FINon-smoker4 ($\frac{0}{3}$/$\frac{6}{12}$)162.6 ± 0.50.0 ± 0.00.1 ± $0.32\%$AC35MINon-smoker3 ($\frac{0}{3}$/6)133.0 ± 0.40.1 ± 0.20.3 ± $0.75\%$AD61MIIINon-smoker4 ($\frac{0}{3}$/$\frac{7}{14}$)73.9 ± 1.24.5 ± 3.80.3 ± $0.550\%$AH34MINon-smoker4 ($\frac{0}{3}$/$\frac{6}{12}$)123.1 ± 0.50.0 ± 0.20.5 ± $0.617\%$AJ41FINon-smoker4 ($\frac{0}{3}$/$\frac{6}{12}$)163.2 ± 0.60.4 ± 0.60.9 ± $0.825\%$AL66FIINon-smoker4 ($\frac{0}{3}$/$\frac{6}{12}$)173.3 ± 0.82.1 ± 1.40.8 ± $0.930\%$AM43FIINon-smoker4 ($\frac{0}{3}$/$\frac{6}{15}$)153.2 ± 0.70.5 ± 0.50.8 ± $1.033\%$AX21MIINon-smoker3 ($\frac{0}{6}$/9)152.7 ± 0.51.9 ± 2.40.5 ± $0.80\%$CC20MIINon-smoker3 ($\frac{0}{4}$/8)153.1 ± 0.91.0 ± 1.20.6 ± $0.924\%$AA50FIIISmoker4 (0/?/?/?)143.9 ± 1.12.7 ± 2.30.6 ± $0.760\%$AR52FIIISmoker4 ($\frac{0}{3}$/$\frac{6}{14}$)164.4 ± 1.93.9 ± 2.21.2 ± $0.867\%$AS54FIIISmoker4 ($\frac{0}{3}$/$\frac{7}{13}$)94.3 ± 1.12.9 ± 1.81.1 ± $0.883\%$AT59MIIISmoker3 ($\frac{0}{4}$/8)154.9 ± 1.45.3 ± 2.20.8 ± $0.780\%$AU47MIISmoker4 ($\frac{0}{3}$/$\frac{6}{12}$)103.4 ± 1.00.7 ± 0.90.9 ± $1.045\%$CE39FISmoker3 ($\frac{0}{5}$/8)152.1 ± 0.60.7 ± 0.60.7 ± $0.80\%$VJ45FIIISmoker3 ($\frac{0}{4}$/8)133.4 ± 1.34.4 ± 2.42.1 ± $0.844\%$WH60MIIISmoker3 ($\frac{0}{4}$/7)154.3 ± 1.16.0 ± 2.01.0 ± $0.984\%$The months when samples were taken are shown in parenthesis separated by slashes. For clinical measurements, mean ± standard deviation is shown.
We first examined how probing depth, plaque index, and smoking status influenced the richness (Faith’s phylogenetic diversity) and diversity (Shannon diversity) of the baseline subgingival microbiome using linear mixed models (Fig. 1). With smokers as the reference group, plaque index ($b = 0.5682$, $$p \leq 0.006$$), smoking status (b = − 3.83, $$p \leq 0.005$$) and the interaction between smoking status and probing depth ($b = 0.970$, $p \leq 0.001$) were significant predictors of phylogenetic diversity while probing depth alone and the interaction between smoking status and plaque index were not. Plaque index ($b = 0.255$, $$p \leq 0.007$$), smoking status (b = − 0.803, $$p \leq 0.028$$), probing depth (b = − 0.152, $$p \leq 0.004$$) and the interaction between probing depth and smoking status ($b = 0.288$, $$p \leq 0.003$$) were also significant predictors of Shannon diversity while the interaction between plaque index and smoking status was not. At shallow probing depths, phylogenetic diversity of the subgingival microbiome was higher in smokers compared to non-smokers (Fig. 1). However, smokers and non-smokers were comparable at greater probing depths. Figure 1Alpha diversity of subgingival microbiome in smokers and non-smokers according to probing depths. Linear mixed models, which consider the non-independence of samples taken from the same sites and/or at the same time, of Faith’s phylogenetic diversity (Left) and Shannon diversity (Right) are shown as lines with the shaded region representing $95\%$ confidence band of the mean. Each point represents the alpha diversity of a single sample and the color indicates smoking status (red: smoker; blue: non-smoker).
The differences in alpha diversity in shallow sites prompted us to ask whether there were also differences in the overall subgingival microbial communities between smokers and non-smokers. Using weighted and unweighted UniFrac distance metrics, we observed a modest separation between smokers and non-smokers along the first principal axis at equal probing depths from 2 to 5 mm (Fig. 2 and Supplementary Fig. 1). Compared to non-smokers, the subgingival communities in shallow sites of smokers overlapped with communities in deeper sites of non-smokers, indicating dysbiosis or changes in microbial community structure in smokers at equal probing depths. Due to sparse datasets, comparisons between smokers and non-smokers could not be made for 1 mm and ≥ 6 mm sites. The separation between smokers and non-smokers was more pronounced in unweighted UniFrac compared to weighted Unifrac (Fig. 2 and Supplementary Figs. 1 and 2), suggesting a stronger effect of smoking on the minority members of the microbiome. Taken together, these results indicate that, at equal probing depths, the subgingival microbiome of smokers is altered and differs from non-smokers. Figure 2Subgingival microbiomes according probing depth and smoking status based on UniFrac distances. Principal coordinates analysis on (a) unweighted and (b) weighted UniFrac distances. Non-smokers (top) and smokers (bottom) are showed separately for clarity but are in the same frame for direct comparison. Each point is a single subgingival sample, and the color shows the probing depth of the subgingival site. The centroid for a given probing depth is shown by an outlined circle. For a given probing depth (e.g. green outlined circles), the centroids of smoker sites are shifted to the right compared to the centroids of non-smokers sites. In addition, the centroids of shallow sites of smokers (i.e. green or yellow outlined circles) overlap with the centroids of deeper sites in non-smokers (i.e. orange or red outlined circles), suggesting subgingival dysbiosis in smokers at equal probing depths.
To investigate the association between subgingival microbiome, plaque index, probing depth, smoking status, subject identity, and different subgingival sites, we used distance-based redundancy analysis (db-RDA) on both unweighted and weighted UniFrac distances (Table 2). The db-RDA analysis showed that all the variables examined were significantly associated with microbiome differences for both UniFrac metrics. Smoking, plaque index, and probing depth had strong effects on the subgingival microbiome, separating samples almost exclusively along the first db-RDA axis (Supplementary Fig. 3). Overall, the model explained more variation in the community structure than community membership (Table 2), suggesting that the combination of smoking status, probing depth, subject, and site identity had a greater influence on dominant OTUs compared to minority OTUs. Interestingly, we found that subject identity alone explains more variation in community membership than community structure. Table 2Distance-based redundancy analysis (db-RDA) on UniFrac distances of subgingival microbiome samples. PredictorsF statisticP valueAdjusted R2Community membership (unweighted UniFrac)Probing depth51.210.0010.515Plaque index68.320.001Smoking status142.970.001Subject identity26.320.001Site identity2.260.001Community structure (weighted UniFrac)Probing depth53.140.0010.591Plaque index104.250.001Smoking status109.980.001Subject identity14.310.001Site identity1.760.001db-RDA was constrained by smoking status, probing depth, and subject identity. F statistics and p values were generated through ANOVA like permutation tests using 999 permutations.
To identify specific phyla and OTUs associated with altered subgingival microbiome in smokers at the baseline visit, we used the Linear discriminant analysis Effect Size approach (LEfSe). The LEfSe analysis identified Tenericutes as the only phyla that were differentially abundant between smokers and non-smokers, which was more abundant in smokers. Other phyla, including Bacteroidetes, Fusobacterium, Firmicutes, and Actinobacteria, had larger effect sizes but were not statistically significant. A total of 28 differentially abundant OTUs with an LDA score of greater than 2 were identified (Fig. 3), including 14 OTUs overrepresented in smokers and 14 OTUs more abundant in non-smokers. The association between smoking and the identified taxa was consistent across both shallow (probing depth < 4 mm) and deep (probing depth ≥ 4 mm) sites. Figure 3Differentially abundant taxa between smokers and non-smokers. Differentially abundant OTUs were identified using linear discriminant analysis Effect Size (LEfSe). Color indicates the enrichment of the taxa associated with different smoking status. OTU operational taxonomic unit.
The longitudinal study design allowed us to examine factors associated with temporal stability of subgingival microbiome. First, the longitudinal data of each subgingival site was collapsed into a single measure by taking the weighted UniFrac distance between subgingival microbiome communities at time 0 and 6 months (within-site weighted UniFrac distance). Using this approach, a small UniFrac distance metric for a given site suggests temporal stability, and a large value indicates more variability. Linear mixed models were then used to assess the effects of baseline probing depth, treatment with scaling and root planing (SRP), smoking, and changes in probing depth (indicating stable or disease progression), on the within-site weighted UniFrac distance (or temporal stability). Subject identity was included as a random effect to account for non-independence of multiple sites sampled from the same individual. Linear mixed model analysis revealed that among the factors examined, only baseline probing depth (b = − 0.027, $$p \leq 0.017$$) was associated with stability of community structure (Fig. 4a). Treatment with SRP (0.029, $$p \leq 0.292$$), smoking status ($b = 0.034$, $$p \leq 0.277$$), and changes in probing depth (b = − 0.006, $$p \leq 0.526$$) were not (Fig. 4b). Thus, subgingival microbiome in shallow sites were more variable, whereas the microbiome in deep sites were more stable over time. Treatment with SRP, smoking status, and clinical stability did not impact the temporal stability of the subgingival microbiome. Figure 4Relationship between smoking status, treatment with scaling and root planing, initial probing depth, and clinical progression on temporal stability of the subgingival microbiome. The impact of (a) initial probing depth, and (b) smoking status, treatment with SRP, and change in probing depth over time on subgingival microbiome stability was evaluated using mixed linear models. Each point represents the collapsed data of longitudinal samples from a single subgingival site, where lower UniFrac distance (y-axis; determined by collapsing data from the same site) indicates temporal stability. ( a) Shows the predicted marginal effects (line) with the $95\%$ confidence intervals (shaded region). ( b) Boxplots are divided by treatment status (top heading), change in probe depth (bottom labels), and smoking status (color). Boxplots show the mean (center black line), 1st and 3rd quartiles. The whiskers extend up to $150\%$ of the interquartile range with points representing outliers of that range.
To identify specific taxa associated with changes in probing depths over time, we first classified subgingival sites into three groups based on changes in probing depth over six months: clinically progressing (∆probing depth ≥ 2 mm), clinically stable (− 1 mm ≤ Δprobing depth ≤ 1 mm), and clinically improving (∆probing depth ≤ − 2 mm). The vast majority of 233 sites sampled longitudinally had a baseline probing depth between 2 to 5 mm (Supplementary Fig. 4). Among the 233 sites, 104 sites ($44.6\%$) at baseline probed 4 mm or greater and was treated with SRP, and 129 sites ($55.4\%$) probed 3 mm or less and thus received no treatment. Among the 104 sites treated with SRP, 18 sites ($17.3\%$; 12 from smokers and 6 from non-smokers) improved by 2 mm or greater, 82 sites ($78.8\%$) were stable, and 4 sites ($3.8\%$) progressed by 2 mm or greater. Among the 129 untreated shallow sites, 124 sites ($96.1\%$) were stable, but 5 sites ($3.9\%$) progressed by 2 mm or greater. A total of 7 sites from smokers progressed clinically, which included 3 deep sites that had been treated with SRP and 4 shallow sites that were not treated. Only 2 sites from non-smokers progressed, which included 1 deep site treated with SRP and 1 shallow site that was untreated. Interestingly, the progressing sites in smokers shared very similar community membership and structure at baseline compared to clinically stable sites, and ones that progressed in smokers also shared similar community structure to sites that had deeper probing depths (Supplementary Fig. 5).
Next, we used LEfSe to compare sites that progressed to sites that were stable by matching progressed sites with stable sites that had similar baseline probing depths (+ /− 1 mm) within each subject. LEfSe analysis identified seven OTUs (Olsenella sp., Streptococcus cristatus, Streptococcus pneumoniae, Streptococcus parasanguinis, Prevotella sp., Alloprevotella sp., and a Bacteroidales sp.) that were enriched in sites associated with clinical progression (Fig. 5). These 7 taxa were not associated with smoking status except for the Bacteroidales sp., which was enriched in smokers (Fig. 3). No OTUs were associated with clinical stability. Figure 5OTUs associated with changes in probing depths determined using linear discriminant analysis Effect Size (LEfSe). Five different subjects were represented in eight progressing sites, with subjects CC, VJ, and WH each contributing two sites. One progressed site was excluded (WH13) due to the lack of a match with a similar (within 1 mm) baseline probing depth in that subject. Color represents enrichment of the OTU associated with changes in probing depths at subgingival sites. OTUs with an LDA score greater than 2 are shown. Sites were classified as progressing if the probing depth increased by 2 mm or more over 6 months. The probing depth for stable sites had no changes over 6 months. Baseline samples of progressing sites were matched to all clinically stable sites with similar (+ /− 1 mm) baseline probing depths within the same subject.
## Discussion
The adverse impacts of smoking on human health are well documented21. Here, we showed that subgingival dysbiosis is likely another consequence of cigarette smoking. In non-smokers, subgingival microbial communities in shallow sites were considerably less diverse than deep sites. In contrast, shallow sites in smokers had similar diversity as deep sites. Notably, subgingival microbiome in shallow sites of smokers resembled the microbiome dysbiosis in deeper sites of non-smokers. Differential abundance analysis revealed that many taxa associated with smokers have been previously implicated in periodontal disease. However, none of these taxa were associated with clinical progression of periodontal disease. Longitudinal analysis showed that subgingival microbiome in shallow sites were less stable compared to deeper sites, but the temporal variability was not affected by smoking status or scaling and root planing. Taken together, our results support the hypothesis that smoking facilitates the development of subgingival dysbiosis associated with periodontal disease.
Consistent with previous studies, we showed that species richness and diversity differ between smokers and non-smokers in shallow sites18 but not in deep sites17. The subgingival microbiomes of smokers share many similarities to the communities of periodontally diseased individuals. In most host-associated microbiomes, a reduction in microbial diversity is often associated with disease and dysbiosis22, as organisms are lost and key metabolic pathways are disrupted. Subgingival microbiome differs in that periodontal disease is associated with an increase in microbial richness and diversity4,5,7,19. As communities with increased diversity tend to withstand environmental perturbations and pathogen invasion23,24, the higher microbial diversity in smokers may withstand dental hygiene practices and commensal colonization, thereby facilitating the development of periodontal disease. Similarly, smoking and periodontal disease may have antagonistic effects on community structure where the impact of smoking decreases as subgingival sites deepen. UniFrac analysis showed that subgingivial microbial communities in shallow sites of smokers resembles the communities in deep sites of non-smokers, which was more pronounced in unweighted compared to weighted analysis. These findings suggest a disproportionate impact on minority members of the subgingival microbiome, leading to dysbiotic communities that are resilient and more stable over time.
Microbial biomarkers associated with periodontal disease have been well described. Putative periodontal pathogens include members of the red complex (Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola) and the orange complex (Fusobacterium nucleatum, Fusobacterium periodonticum, Eubacterium nodatum, Parviomonas micra, Prevotella intermedia, Prevotella nigrescens, and several Campylobacter species)2. Among them, none were enriched in non-smokers in our study, and P. gingivalis and F. nucleatum subspecies were differentially more abundant in smokers (Fig. 3). Several organisms have been associated with healthy subgingival microbiome4,6, many of which including *Veillonella parvula* were associated with non-smokers in our study. Recent work has implicated putative periodontal pathogens in systemic diseases. For instance, F. nucleatum has been associated with colorectal cancer and adverse pregnancy outcomes (reviewed in25), and P. gingivalis has been associated with different types of cancers (reviewed in26), Alzheimer’s disease27 and rheumatoid arthritis28. Thus, smoking may facilitate or support a microenvironment that favors putative periodontal pathogens, leading to far-reaching effects on the health of the host.
Previous work showed a high degree of inter-individual variations of the healthy subgingival microbiome but relatively low inter-individual variations in the diseased microbiome6. Our longitudinal analysis demonstrated that healthy subgingival microbiome was also characterized by high temporal variation, whereas diseased communities were less variable over time. We found that shallow probing depth was associated with high temporal microbiome variation. After accounting for the initial probing depths, smoking status, treatment with scaling and root planning, and changes in probing depth over time were not associated with variation in the microbiome. Interestingly, many of the sites that progressed to disease clustered with deep sites, irrespective of the probing depth at baseline. Thus, these results support the hypothesis that subgingival dysbiosis in smokers precedes clinical signs of periodontal disease, rather than occurring in concert.
Most studies that characterize differences between healthy and diseased subgingival microbiome have been cross-sectional4–7. Thus, the causal relationships between microbiome and progression of disease could not be evaluated. Our longitudinal design allowed us to identify specific taxa associated with clinical progression of periodontitis. Despite sampling 233 sites repeatedly from 17 subjects over 6–12 months, only 9 sites progressed by 2 mm or greater. At baseline, these sites varied in probing depths, and some sites progressed from health to disease whereas other sites had periodontal disease at baseline and progressed during the study. We identified seven OTUs associated with progression of periodontal disease. One fell within the genus Prevotella, whose members are often associated with periodontitis2,4,6. Conversely, S. cristatus has been shown to be overabundant in the healthy subgingiva6,29,30. S. cristatus is a primary adherence point for F. nucleatum and has been shown to suppress immune response to F. nucleatum infection31. F. nucleatum can serve as a “bridge species” that aids in the transition from a healthy, commensal-dominated community to a pathogenic one31,32. Late colonizers, many of which are pathogenic, cannot incorporate themselves into the subgingival biofilm in the absence of F. nucleatum32. Thus, a high level of S. cristatus may contribute to disease progression through recruitment and maintenance of F. nucleatum. We note that due to the small number of sites that progressed, we could not distinguish between markers associated progression from early dysbiosis to periodontitis and markers for progression of periodontal disease severity.
There has been considerable debate as to whether subgingival dysbiosis is a local (site-specific) or a global (whole-mouth) event. Earlier studies argued for local changes4,33 but later studies suggested a more global process5,7,10. Our extensive sampling approach allowed us to compare deep and shallow sites within individuals, and our results suggest that the discrepancies in the literature may reflect methodological rather than biological differences. For instance, PCoA on weighted UniFrac distances separated samples primarily by probing depth, whereas PCoA on unweighted UniFrac distances separate samples by subject identity and smoking status (Fig. 2). This suggests that periodontal disease is associated with shifts in the overall community structure rather than the presence or absence of certain specific bacteria. Thus, unweighted distances that quantify differences in community membership may be imperfect measures for detecting differences across healthy and diseased sites within an individual, and the results of Ganesan et al.10 may reflect a strong subject effect rather than the lack of a disease effect. Abusleme et al.5 found that within-subject matched sites that only differed in bleeding on probing did not differ. As a result, probing depth may be a better indicator of subgingival dysbiosis than bleeding on probing. Altogether, subgingival dysbiosis may be site-specific, resulting from local changes in the abundance rather than the presence of different bacteria as the probing depth increases.
This study has several limitations. First, smoking greatly alters the oral environment13,14, but whether the microenvironment allow pathogens to outcompete commensals or directly eliminate commensals remains unknown. Second, mechanistic understanding is inherently limited in observational human studies. Third, this study lacked a sufficient number of subgingival sites that progressed clinically, and the sites that progressed primarily came from smokers and were clinically heterogeneous at baseline. Finally, smokers in our study had slightly more advanced stages of periodontitis than non-smokers at baseline, and the lack of long-term follow up and information regarding radiographic bone loss precluded the determination of grading. Future studies will require full-mouth subgingival sampling in a larger number of periodontally healthy smokers and non-smokers with a much longer follow-up to uncover the successional pattern of dysbiosis and the organisms contributing to or initiating the pathogenic process.
Periodontal disease is a major public health concern. Cigarette smoking disrupts the oral environment and pre-disposes individuals to periodontitis through dysbiosis of the subgingival microbiome. Subgingival communities of smokers are diverse, pathogen-rich, and commensal-poor, but have a similar level of temporal variability as non-smokers. Temporal stability of the subgingival microbiome is modulated by periodontal disease severity. Most notably, subgingival dysbiosis in smokers precedes clinical signs of periodontal disease, supporting the hypothesis that smoking creates a microenvironment that promotes the development of subgingival dysbiosis contributing to periodontal disease. Thus, our study underscores the complex nature of subgingival microbiome and its interaction with environmental gradients. The approach described here should facilitate the design of a larger prospective cohort to further elucidate the transition of subgingival microbiome from health to disease.
## Subject recruitment and sample collection
For this pilot study, subjects were recruited from the Periodontology Clinic and the DMD Student Dental Clinic at University of Florida College of Dentistry, Gainesville, Florida from March 2012 to April 2013. All subjects were over 18 years of age and had a minimum of 20 natural teeth. Smoking history was obtained by self-report. Smokers were defined as individuals who smoked ≥ 10 cigarettes per day for at least 5 years, and those who have never smoked were non-smokers. Former smokers were not included in this study. Exclusion criteria included diabetes, pregnancy, lactation, systemic antibiotic use within the previous 6 months, periodontal treatment within the previous 12 months, known congenital or acquired immunodeficiency, and use of any immunosuppressive medications.
Clinical measurements (i.e. probing depth, clinical attachment loss, plaque index) were assessed at each visit. At the initial visit, at least 12 subgingival sites were randomly sampled whenever possible and the same sites were sampled again at three and six months. Nine of 17 subjects ($53\%$) had additional plaque samples collected from the same sites at 12 months (Table 1). At baseline, subgingival site that probed 4 mm or greater had scaling and root planing (SRP), but no subjects were re-treated with SRP during the study. Biofilm on the supragingival surface was removed using sterile gauze, and subgingival biofilm was sampled using sterile endodontic paper points. Each sample was transferred to a sterile tube containing storage buffer (MO BIO, Carlsbad, CA), placed immediately on ice, and stored at − 80 °C until DNA extraction. For each subject, subgingival sites (range: 7–17) were sampled at baseline and the same sites were sampled again at three and six months.
## DNA extraction, PCR amplification, and Illumina sequencing
Genomic DNA was extracted using the MO BIO PowerSoil DNA extraction kit (Carlsbad, CA) according to manufacturer’s instructions. For each sample, the V1-V3 hypervariable region of the 16S rRNA gene was amplified using composite 27F (5′ AGAGTTTGATCCTGGCTCAG 3′) and 534R (5′ ATTACCGCGGCTGCTGG 3′) primers. PCR reaction mixtures contained 4 µl of extracted DNA, 100 nM of the forward primer, 100 nM of the reverse primer, and 10 µL of SuperFi PCR master mix (Invitrogen, Carlsbad, CA, USA). 16S rRNA amplicons were analyzed on $1\%$ SYBR Safe agarose gel. Gel slices containing the amplicons were extracted and purified using Qiagen gel extraction kit (Qiagen, Valencia, CA, USA). Purified PCR products were quantified using Qubit HS DNA quantification kit (Invitrogen, Carlsbad, CA, USA) and pooled in equimolar concentration. qPCR was used to quantify the DNA concentration of the pool and prepare the library for sequencing. The use of barcodes allowed for multiplexing and bidirectional sequencing on the Illumina MiSeq platform (Illumina, San Diego, CA, USA).
## Data processing
Paired-end reads of 300 nt each covering the V1–V3 hypervariable region of the 16S rRNA gene were processed using custom scripts written in R34. The reads were filtered based on exact matches to the barcode/primer and an average quality score of 30. Samples were de-multiplexed according to the combination of their unique barcodes (4–8 nt long) on each paired end. The barcodes and primers (27F and 534R) were trimmed, and paired-end reads were joined using FLASh35, with a minimum overlap of 10 bp, to reconstruct the original contiguous amplicon. Reads were assigned reference OTUs using the Human Oral Microbiome Database (HOMD) version 15.2236 and USEARCH alignment with a $97\%$ identity and $80\%$ aligned query threshold. Reads that did not meet the filtering criteria or reference assignment were excluded from subsequent analysis.
OTU tables from multiple sequencing runs were merged, singletons were filtered out, and 11 samples with fewer than 20 total reads were excluded. Relative abundances were calculated from the unrarefied OTU table. The OTU table was then subsampled down to an even sequencing depth of 8000 reads (Supplementary Fig. 6). Alpha diversity and beta diversity metrics were estimated in QIIME2 (version 2018.8, https://qiime2.org/) using the core-metrics-phylogenetic pipeline. Alpha diversity was measured with species richness (Faith’s phylogenetic diversity) and species diversity (Shannon diversity). Community differences between samples were measured using unweighted and weighted UniFrac distances. Temporal stability of the microbial communities was estimated by the weighted UniFrac distance between the baseline and 6 month sample from the same site (within-site UniFrac distance. Sites with greater within-site distance indicate more variability over time, whereas sites with lower distances are more stable.
## Statistical analyses
Statistical analyses were performed in R v.3.4.234unless otherwise noted. Differences in alpha diversity across smoking status and probing depth were analyzed with linear mixed models using the lmer() function in lme4 v.1.1-1938, with site identity and time point nested within subject identity as random effects. Marginal effects for linear mixed models were calculated using ggpredict() in the ggeffects package v.0.6.039. Principal coordinates analysis (PCoA) of Unifrac distances were used to examine clustering of samples. Statistical significance of environmental variables were tested with distance-based *Redundancy analysis* (db-RDA) using the dbrda() function in vegan v.2.5–337 with smoking status, plaque index, probing depth, subject identity, and site identity included as predictors. Linear mixed models were then used to compare temporal stability of the subgingival community structure (within-site UniFrac distance) across smoking status, treatment status, initial probing depth, and change in probing depth over time. Subject identity was included as a random effect in this analysis to account for multiple sites in the same subject. Differentially abundance analysis was conducted on the unrarefied OTU table with LEfSe (Galaxy version 1.0)40, excluding samples with fewer than 8000 reads. First, smokers were compared to non-smokers, where probing depth (shallow vs deep sites) and subject identity were accounted for. Then, LEfSe was used to compare clinically progressing sites (∆probing depth ≥ 2) to within-subject matched stable sites (− 1 ≤ ∆probing depth ≤ 1) that were within + /− 1 mm of baseline probing depths. All differentially abundant OTUs met the minimum LDA score of 2.
## Ethics approval and consent to participate
All subjects provided written informed consent for study participation and procedures. The study was approved by the Institutional Review Board at the University of Florida under project #444-2011. All research was performed in accordance with relevant guidelines/regulations and in accordance with the Declaration of Helsinki.
## Supplementary Information
Supplementary Figures. The online version contains supplementary material available at 10.1038/s41598-023-30203-z.
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|
---
title: 'Barriers and facilitators to the adoption of physical activity policies in
elementary schools from the perspective of principals: An application of the consolidated
framework for implementation research–A cross-sectional study'
authors:
- Janine Wendt
- Daniel A. Scheller
- Marion Flechtner-Mors
- Biljana Meshkovska
- Aleksandra Luszczynska
- Nanna Lien
- Sarah Forberger
- Anna Banik
- Karolina Lobczowska
- Jürgen M. Steinacker
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9992422
doi: 10.3389/fpubh.2023.935292
license: CC BY 4.0
---
# Barriers and facilitators to the adoption of physical activity policies in elementary schools from the perspective of principals: An application of the consolidated framework for implementation research–A cross-sectional study
## Abstract
### Background
Studies have shown that policies to promote physical activity in schools can have a positive impact on children's physical activity behavior. However, a large research gap exists as to what determinants may influence the adoption of such policies. Applying the Consolidated Framework for Implementation Research (CFIR), we investigated barriers and facilitators to the adoption of physical activity policies in elementary schools in Baden-Wuerttemberg, Germany, from the perspective of school principals.
### Methods
A cross-sectional study was conducted between May and June 2021. School principals from elementary and special needs schools ($$n = 2$$,838) were invited to participate in the study. The online questionnaire used was developed based on the CFIR and included questions on school characteristics and constructs of the CFIR domains inner setting, characteristics of individuals, and process. Logistic regression analyses were performed to examine associations between policy adoption and school characteristics as well as CFIR determinants.
### Results
In total, 121 schools ($4\%$) participated in the survey, of which 49 ($40.5\%$) reported having adopted a policy to promote physical activity. Positive associations with policy adoption were found for general willingness among teaching staff [odds ratio (OR): 5.37, $95\%$ confidence interval (CI): 1.92–15.05], available resources (OR: 2.15, $95\%$ CI: 1.18–3.91), access to knowledge and information (OR: 2.11, $95\%$ CI: 1.09–4.09), and stakeholder engagement (OR: 3.47, $95\%$ CI: 1.24–9.75).
### Conclusions
This study provides a first insight into potential barriers and facilitators at the organizational level of schools that may be relevant to the adoption of physical activity policies, from the perspective of school principals. However, due to a low response rate, the results must be interpreted with caution. A strength of this study includes theoretical foundation through the use of the CFIR. The CFIR could be well-adapted to the school setting and provided valuable support for developing the questionnaire and interpreting the study results.
## Introduction
Physical activity can have a positive impact on children's health and academic achievement (1–3). According to the World Health Organization's (WHO) recommendations on physical activity, children and adolescents aged 5–17 years should do at least an average of 60 min of moderate-to-vigorous intensity of physical activity per day [4]. However, the recent Global Matrix 3.0 Physical Activity Report *Card analysis* showed that only 20–$26\%$ of children and adolescents in high- and very high-income countries meet this recommendation [5]. To counteract physical inactivity, the WHO recommends different evidence-based policy actions (including school-based concepts) to create active societies, environments, people, and systems [6].
Schools are an important setting for implementing health programs, as they can reach children across various sociodemographic backgrounds and over a relatively long period of time (7–9). Countries such as the United States and Canada have already developed and introduced school-based policies aimed at increasing children's daily physical activity levels (10–12), and the current evidence base underpins the effectiveness of such policies [13, 14]. *In* general, however, the effectiveness of a policy depends not only on the policy itself, but also on the way it is implemented in practice [15].
Implementation can be described as the process of putting to use or integrating innovations within a setting [16]. In addition to the setting itself, actors, strategies, the target group, and the characteristics of the policy, may influence this process. In turn, they all interact with an active and dynamic cultural, social, economic, and political context [17, 18]. Consequently, the implementation process can be influenced positively (facilitators) and negatively (barriers) in many ways [19].
If organizations such as schools have an intention, make an initial decision or take actions to try or employ an innovation, this is referred to as the implementation outcome “adoption” [20]. Adoption occurs at an early to mid-stage of the implementation process and is assessed from the organization's or provider's perspective [20]. Either adoption leads to implementation activities or adoption is rescinded [21].
To understand the underlying mechanisms relevant to policy actions, the use of implementation science theories, models, and frameworks can be supportive. Thus, determinant frameworks–in contrast to process and evaluation frameworks–show basically conceptual constructs that can have a potential impact on implementation outcomes [19]. One determinant framework that provides a systematic guide for assessing potential barriers and facilitators is the Consolidated Framework for Implementation Research (CFIR) [22]. The framework lists 26 key determinants, which are grouped into the following five domains: intervention characteristics, outer setting, inner setting, characteristics of individuals, and process [22].
Previous studies on programs promoting physical activity have examined the processes or underlying determinants of implementation, with some studies focusing on interventions [23, 24] and others on policies (25–28). Compared to interventions, policies are not only individual measures or actions, but provide a framework within which interventions are tendered, developed, financed or implemented [13]. Regarding school-based interventions, there is already some evidence on processes and barriers and facilitators that might influence adoption (24, 29–32). In a systematic review by Cassar et al. [ 24], studies were evaluated for determinants associated with the adoption of school-based physical activity or sedentary behavior interventions. The identified factors were categorized according to Durlak and DuPre's determinant framework [15], whereby most of the facilitators ($$n = 15$$) and barriers ($$n = 9$$) related to adoption (e.g., characteristics of the school) could be assigned to the domain “prevention delivery system” [24], which is a domain reflecting organizational capacity. However, research on this topic in relation to policies is rather scarce [11, 29, 33, 34]. Furthermore, the research team is not aware of any cross-national, Germany-wide, or south-west Germany-wide studies on barriers and facilitators to the adoption of physical activity policies in elementary schools. So far, there is also little information on what might influence the adoption of a policy from the perspective of school-level decision-makers [11].
Previous studies that have investigated possible determinants to the adoption of physical activity policies in schools have–if at all–used evaluation frameworks [e.g., RE-AIM Framework [35]] which, due to their focus on the evaluation of implementation, are suboptimal for assessing determinants that might impact implementation processes [19]. Although numerous frameworks exist for evaluating determinants for policies promoting physical activity [36], we chose the CFIR as it is one of the most widely used frameworks [37, 38], provides a detailed description of constructs [22], and has also been found to be applicable and appropriate for the school setting [39, 40].
The aim of this study, therefore, is to exploratively examine, which barriers and facilitators at the organizational level are associated with the adoption of physical activity policies in elementary schools in Baden-Wuerttemberg (south-west Germany) from the perspective of school principals by using the determinant framework CFIR.
## Study design, sample and recruitment
A cross-sectional study of elementary schools and schools for children with special needs was conducted between May and June 2021 in Baden-Wuerttemberg, south-west Germany. In Baden-Wuerttemberg, elementary schools range from first to fourth grade. Without taking early or deferred enrollment and possible repetitions of a grade into account, the age range of students in grades one to four is generally from six to ten years.
The sample of schools was taken from a database provided by the Federal Statistical Office Baden-Wuerttemberg. All public and private elementary schools ($$n = 2$$,495) as well as special needs schools ($$n = 1$$,063) were eligible for participation, with the following exceptions: Rudolf Steiner Schools ($$n = 57$$) were excluded as they practice a different pedagogical concept. Given that special needs schools usually contain grades five and higher, those with fewer than 15 students in grades one to four were considered to be secondary schools and thus excluded ($$n = 575$$). Moreover, special needs schools with a focus on students undergoing prolonged hospital treatment ($$n = 18$$) and physical and motor development ($$n = 21$$) were excluded, as the promotion of physical activity only plays a subordinate role due to students' physical conditions or is based on special concepts. Furthermore, duplicates among special needs schools ($$n = 49$$) were removed. Finally, 2,838 schools [elementary schools: $$n = 2$$,438 ($86\%$); special needs schools: $$n = 400$$ ($14\%$)] were invited to participate. The data sampling strategy is outlined in Figure 1.
**Figure 1:** *Data sampling flow diagram.*
An invitation letter was sent to each school asking all principals and deputy principals to participate in an online survey. The letter contained a brief description of the purpose of the study, all necessary information on the conditions of participation, confidentiality practices, and data protection measures, as well as the contact information of the study office. In addition, the letter included a QR code and URL to access the online questionnaire, as well as an individual study code for each school. To enhance recruitment, two postcards containing key information about study participation and the QR code were enclosed with the invitation letter. In addition, a short video was presented on the front page of the online questionnaire to motivate school principals and provide completion instructions. A reminder letter was sent on June 1, 2021 to increase participation rates. The survey period ran for a total of six and a half weeks between May and June 2021. The study was approved by the ethics committee of Ulm University (Application Number $\frac{252}{20}$) as well as the Ministry of Education, Youth and Sports of Baden-Wuerttemberg.
## Questionnaire and measures
The development of the questionnaire is based on the CFIR and existing survey instruments for evaluating physical activity policies or interventions in schools. Individual items of the School Physical Activity Policy Assessment (S-PAPA) [41], School Health Policies and Practices Study (SHPPS) [42], COMPASS school programs and policies (SPP) [43] and “Join the Healthy Boat” (German version) [44] questionnaires were included. The questionnaire was pre-tested by members of the research team, external colleagues, an elementary school principal, and a special education teacher to assess question comprehension, skip patterns, flow and completion time. The final version was transferred to the online survey software Unipark EFS (Enterprise Feedback Suite) [45] and comprised the following sections: [1] personal details, [2] school characteristics, [3] physical activity policies, [4] implementation determinants/attitudes toward policies, [5] physical education, [6] students with disabilities, [7] recess, [8] health promotion, [9] school environment, [10] active way to school, [11] resources and funds, and [12] final questions. The variables used in the present study are described in more detail in the following sections.
## Outcome variable: Policy adoption
Policies can be defined as “purposeful decisions, plans and actions made by voluntary or authoritative actors in a system designed to create system-level change to directly or indirectly achieve specific societal goals.” [ derived from PEN Consensus with adaptions from Lakerveld et al. [ 46]]. Based on this definition, policies aiming to promote physical activity at elementary schools in Baden-Wuerttemberg were identified through an internet search and subsequent consultation with the Ministry of Education, Youth and Sports of Baden-Wuerttemberg. Accordingly, there is one mandatory policy (physical education curriculum) and three voluntary policies. The following three voluntary policies, which were focused on, are: [1] National Recommendations for Physical Activity and Physical Activity Promotion, [2] Elementary school with a focus on sport and physical education, [3] Sports and activity-friendly schoolyard.
Through the question “Does your school implement one or more of the following physical activity policies?” and the possible response categories yes/no, the adoption of each individual policy was ascertained. In addition, the participants had the opportunity to name other policies that exist on district/municipal level through an open-ended question. Schools that had reported implementing at least one policy were classified as “adopters,” while schools that had reported not implementing any policies were classified as “non-adopters.” Respondents who indicated adopting a policy were asked additional questions about the duration of policy adoption, requirements and decision making-process, as well as reasons and requirements for adoption.
## Predictor variables: CFIR determinants
Due to the lack of evidence on potential determinants associated with the adoption of physical activity policies in elementary schools, the selection of CFIR determinants was based on a meta-review that applied the CFIR to examine implementation determinants of school-based physical activity, diet, and sedentary behavior policies [27]. In this regard, those CFIR constructs were included in the present study that were indicated as occurring in implementation processes in at least $80\%$ of reviews/stakeholder documents analyzed in the meta-review. Subsequently, the constructs “structural characteristics,” “implementation climate,” “readiness for implementation” (domain inner setting), and “knowledge and beliefs about the intervention” (domain characteristics of individuals) as well as “engaging” (domain process) were included.
To best reflect the CFIR construct structural characteristics, individual items of the aforementioned questionnaires (41–44) were adapted. Participants were asked about the number of students and staff, type of school, care concept, information about the schools surroundings and facilities as well as the minutes of daily recess. Furthermore, the CFIR Interview Guide Tool (https://cfirguide.org/guide/app/#/) was used to formulate questions on CFIR determinants regarding the (sub-)constructs “implementation climate” and “readiness for implementation” as well as on the constructs “knowledge and beliefs about the intervention” and “engaging stakeholders.” A total of 21 questions were asked to reflect the CFIR constructs. All predictor variables, the corresponding questions asked and their respective coding categories are described in Table 1.
**Table 1**
| Predictor variable | Survey question and response categories | Coding categories |
| --- | --- | --- |
| CFIR domain: Inner setting; Construct: Structural characteristics | CFIR domain: Inner setting; Construct: Structural characteristics | CFIR domain: Inner setting; Construct: Structural characteristics |
| Number of studentsa | How many children in grades 1 to 4 attend your school? | |
| Number of students with migration backgrounda | How many children at your school have a migration background? | |
| Numbers of employeesb | How many personnel in the following positions do you have at your school? (1) Teachers; (2) Integration assistants; (3) Social education workers; (4) All-day staff | |
| Type of schoolc | Please select the school type: 1 = Elementary school, 2 = Special needs school, 3 = Elementary school in combination with a comprehensive school, 4 = Other type of school, 5 = I don't know | • Elementary school/Elementary and comprehensive school (ref.) • Special needs school |
| Care conceptc | What is the care concept at your school? 1 = Half-day school; 2 = Open all-day school; 3 = All-day school; 4 = Another care concept; 5 = I don't know | • Half-day school (ref.) • Open all-day school/all-day school |
| Location of schoold | What is your school's zip code? | • Rural area (ref.) • Urban area |
| Size of schoolyardc | What is the size of the schoolyard at your school? 1 = up to 500 m2; 2 = 501–1,000 m2; 3 = 1,001–1,500 m2; 4 = 1,501–2,000 m2; 5 = 2,001 or more m2 | • ≤ 1,500 m2 (ref.) • ≥1,501 m2 |
| Number of sport facilitiese | Which of the following sports facilities are available at your school? (1) Sports hall; (2) Basketball court; (3) Football pitch; (4) Athletics facility; (5) Swimming pool; (6) None; (7) Other | • Up to 3 facilities (ref.) • 4 or more facilities |
| Recess minutesf | On average, how many total minutes per day does a student receive recess? (during regular instruction time (without afternoon care); recesses when students can be physically active, without breakfast and lunch recesses) | • ≤ 34 min (ref.) • ≥ 35 min |
| CFIR domain: Inner setting; Construct: Implementation climate | CFIR domain: Inner setting; Construct: Implementation climate | CFIR domain: Inner setting; Construct: Implementation climate |
| General climateg | There is a general willingness within the teaching staff to adopt or implement physical activity policies. | |
| Tension for changeg | At our school, lack of exercise or physical inactivity among students is a problem. | |
| Compatibilityg | The adoption or implementation of a physical activity policy can be well-integrated into existing workflows at our school. | |
| Relative priorityg | At our school, health promotion measures already exist (e.g., prevention programs on mental health or nutrition). The adoption or implementation of a physical activity policy tends to take a back seat compared to these. | |
| Organizational incentives and rewardsg | Those who are involved in the adoption or implementation of a policy at our school generally receive special recognition for it. | |
| Goals and feedbackg | The goals of existing or planned policies are generally clearly formulated and communicated to all persons involved (staff, parents, students). | |
| Learning climateg | At our school, a working climate exists in which principals and/or teachers can express their own fallibility and need for support. | |
| CFIR domain: Inner setting; Construct: Readiness for implementation | CFIR domain: Inner setting; Construct: Readiness for implementation | CFIR domain: Inner setting; Construct: Readiness for implementation |
| Leadership engagementg | At our school, it can be expected that the principal or the person responsible for the project will provide support when introducing or implementing policies. | |
| Available resourcesg | At our school, we have sufficient resources (time, staff, financial) to adopt or implement PA policies. | |
| Access to knowledge and informationg | At our school, we receive or it is planned to receive sufficient information and materials to adopt or implement physical activity policies. | |
| CFIR domain: Individual characteristics | CFIR domain: Individual characteristics | CFIR domain: Individual characteristics |
| Knowledge and beliefs about the interventiong | Research-based policies cannot be implemented in daily practice. | |
| CFIR domain: Implementation process | CFIR domain: Implementation process | CFIR domain: Implementation process |
| Engaging stakeholdersg | It is important to involve all possible stakeholders (e.g., teachers, school management, parents, students, researchers, politicians) in the development of policies. | |
## Data analysis
Descriptive statistics such as frequencies and percentages and medians and 25th-75th percentiles were computed to summarize categorical and continuous variables, respectively. Normality of data was tested by Kolmogorov-Smirnov test. The chi-square test (categorical variables) and the Mann-Whitney U-test (continuous variables) were used to analyze differences between policy “non-adopters” and “adopters.” Multiple logistic regression analysis, using the enter method, were performed to examine the association between the outcome variable policy adoption and CFIR determinants on structural characteristics of the school (model 1) and the (sub-) constructs implementation climate, readiness for implementation, knowledge and beliefs about the intervention and engaging stakeholders (model 2). In both models, a complete case analysis (CCA) restricted to schools with no missing values on both the outcome variable policy adoption and predictor variables were performed. Associations are reported as odds ratios (OR) and the respective ninety-five percent confidence intervals ($95\%$ CI). A two-sided p ≤ 0.05 was considered to be statistically significant. Because of the explorative nature of this study, all results from statistical tests must be interpreted as hypothesis-generating and not as confirmatory. An adjustment for multiple testing was not made. Data were analyzed using IBM SPSS Statistics version 28.0.1.0 [47].
## Results
A total of 121 schools ($4\%$ of those eligible) took part in the survey. The questionnaire was completed by 102 principals ($84\%$) and 19 deputy principals. About half of them ($56\%$) had more than 5 years of experience in their position and the majority were women ($61\%$). The distributions of school structural characteristics and the individual CFIR determinants are shown in Tables 2, 3, respectively.
Overall, 49 schools ($40.5\%$ of participating schools) reported implementing one or more physical activity policies at their school. “ Elementary school with a focus on sport and physical education” was the policy adopted most frequently ($$n = 38$$), followed by “Sports and activity-friendly schoolyard” ($$n = 19$$). In contrast, the “National Recommendations for Physical Activity and Physical Activity Promotion” were adopted by one school. Overall, there were nine schools that implemented two policies. The mean year of policy adoption for “Elementary school with a focus on sport and physical education” and “Sports and activity-friendly schoolyard” was 2011 (earliest: 2000; latest: 2019; missing $$n = 9$$) and 2007 (earliest: 1995; latest: 2020; missing = 5), respectively.
According to participants, the policies were implemented “completely” at four schools ($8\%$), “mostly” at 27 schools ($55\%$), and “more or less” at nine schools ($18\%$), whereas six participants ($12\%$) indicated that they “don't know” if the policy was implemented as intended (missing: $$n = 3$$, $6\%$). One or more of the following reasons were indicated as being crucial for the adoption: decision of school management/principal ($$n = 23$$, $47\%$), decision of teaching staff ($$n = 22$$, $45\%$), evidence-based policy ($$n = 9$$, $22\%$), low costs and high benefits ($$n = 7$$, $14\%$), recommendation of another school ($$n = 3$$, $6\%$), and recommendation of an authority ($$n = 2$$, $4\%$).
Proportionally, the following persons were involved in the decision-making process to adopt a policy: principal ($$n = 42$$, $86\%$), teachers ($$n = 40$$, $82\%$), deputy principal ($$n = 16$$, $33\%$), specialist coordinators, ($$n = 8$$, $16\%$), school social workers ($$n = 4$$, $8\%$), students' parliament ($$n = 4$$, $8\%$), and supervisors ($$n = 3$$, $6\%$).
Based on bivariate associations, the data show no differences on school structural characteristics between policy non-adopters and adopters, except for the type of school ($$p \leq 0.03$$) and size of schoolyard ($$p \leq 0.03$$) (Table 2). Group differences were also found for the CFIR determinants general climate ($p \leq 0.01$), available resources ($p \leq 0.01$) and, access to knowledge and information ($p \leq 0.01$) (Table 3).
For logistic regression analyses, the data of six schools had to be excluded in both models due to incomplete data. Model 1 revealed that there were no significant associations between school structural characteristics and the adoption of a physical activity policy.
Model 2 on the associations to CFIR determinants showed that for each higher level of agreement on the question about the general willingness within the teaching staff, the odds for being an adopter school was increased (OR: 5.37, $95\%$ CI: 1.92–15.05). On the other hand, the determinants tension for change (OR: 0.75, $95\%$ CI: 0.46–1.20), compatibility (OR: 0.77, $95\%$ CI: 0.35–1.71), relative priority (OR: 1.07, $95\%$ CI: 0.60–1.89), organizational incentives and rewards (OR: 1.32, $95\%$ CI: 0.68–2.52), goals and feedback (OR: 1.51, $95\%$ CI: 0.75–3.03), learning climate (OR: 0.70, $95\%$ CI: 0.24–2.10), leadership engagement (OR: 0.35, $95\%$ CI: 0.09–1.34), and knowledge and beliefs about the intervention (OR: 1.47, $95\%$ CI: 0.75–2.88) were not found to be associated with the adoption of a policy. However, each higher level of agreement in terms of available resources (OR: 2.15, $95\%$ CI: 1.18–3.91) as well as receiving sufficient information and materials (OR: 2.11, $95\%$ CI: 1.09–4.09) increased the odds of being an adopter school. In addition, the estimated odds of being an adopter school were increased for each higher level of agreement on the importance of stakeholder involvement in policy development (OR: 3.47, $95\%$ CI: 1.24–9.75). All findings from logistic regression analyses on both models are shown in Table 4.
**Table 4**
| Predictor variable | OR | 95% CI | P-value |
| --- | --- | --- | --- |
| Model 1 structural characteristics | Model 1 structural characteristics | Model 1 structural characteristics | Model 1 structural characteristics |
| Number of students | 1.00 | 0.99–1.01 | 0.99 |
| Number of students with migration background | 1.00 | 0.99–1.01 | 0.78 |
| Number of employees | 1.00 | 0.96–1.04 | 0.96 |
| Type of School; n (%) | | | |
| Elementary school/elementary and comprehensive school | ref. | | |
| Special needs school | 0.23 | 0.03–1.64 | 0.14 |
| Care concept; n (%) | | | |
| Half-day school | ref. | | |
| (Open-) All-day school | 1.26 | 0.50–3.15 | 0.63 |
| Location of school; n (%) | | | |
| Rural area | ref. | | |
| Urban area | 1.07 | 0.46–2.49 | 0.88 |
| Size of schoolyard; n (%) | | | |
| ≤ 1,500 m2 | ref. | | |
| ≥1,501 m2 | 1.95 | 0.85–4.44 | 0.11 |
| Sport facilities; n (%) | | | |
| Up to 3 facilities | ref. | | |
| 4 or more facilities | 1.23 | 0.53–2.85 | 0.64 |
| Recess | | | |
| ≤ 34 min | ref. | | |
| ≥35 min | 1.44 | 0.63–3.29 | 0.39 |
| Model 2 CFIR determinants | Model 2 CFIR determinants | Model 2 CFIR determinants | Model 2 CFIR determinants |
| General climatea | 5.37 | 1.92–15.05 | <0.01 |
| Tension for changea | 0.75 | 0.46–1.20 | 0.23 |
| Compatibilitya | 0.77 | 0.35–1.71 | 0.52 |
| Relative prioritya | 1.07 | 0.60–1.89 | 0.83 |
| Organizational incentives and rewardsa | 1.32 | 0.68–2.53 | 0.41 |
| Goals and feedbacka | 1.51 | 0.75–3.03 | 0.25 |
| Learning climatea | 0.70 | 0.24–2.10 | 0.53 |
| Leadership engagementa | 0.35 | 0.09–1.34 | 0.13 |
| Available resourcesa | 2.15 | 1.18–3.91 | 0.01 |
| Access to knowledge and informationa | 2.11 | 1.09–4.09 | 0.03 |
| Knowledge and beliefs about the interventiona | 1.47 | 0.75–2.88 | 0.26 |
| Engaging stakeholdersa | 3.47 | 1.24–9.75 | 0.02 |
## Discussion
This study was the first to examine implementation determinants from the inner setting, individual characteristics, and process domain of the CFIR that were associated with the adoption of a physical activity policy in elementary and special needs schools in Baden-Wuerttemberg, Germany. It is striking that the structural conditions of the schools, such as number of students and staff or schoolyard size and number of sports facilities, were not found to be predictors for the adoption of a policy, whereas the general willingness of the teaching staff, available resources, access to knowledge and information, and involvement of stakeholders were significantly associated with the adoption of a physical activity policy.
Based on information provided by participating principals, the present analysis revealed that a large proportion of the teaching staff were generally willing to adopt and implement a policy to promote physical activity. However, the analysis also indicated that the higher the agreement on the level of willingness, the higher the odds of being an adopter school. The question on general willingness asked in the present study, depicted the construct of implementation climate only in a generic way and could not be further explained by the sub-constructs such as learning climate and compatibility. Since organizational climate is a socially-constructed concept that reflects the perceptions of individuals involved in relation to organizational culture [48], it is conceivable that the general willingness within participating schools could have been more accurately described through other factors of the schools' social context (e.g., cultural factors such as values and norms). However, these constructs were not included in the present study.
In our study, only about $30\%$ of participating principals reported having sufficient financial, personnel, and time resources. Furthermore, slightly more than $30\%$ of principals indicated that they receive sufficient information and materials at their school to adopt or implement policies. Both higher levels of agreement on the availability of resources and access to information and materials were positively associated with the adoption of a policy. This finding reflects previous research documenting factors associated with the adoption of school-based physical activity/nutrition policies [29, 33] or interventions [24]. Overall, these findings underscore the need for external (financial) support such as from governments.
Another interesting finding of our study is the perceived importance of stakeholder involvement. Thus, $85\%$ of the participating principals indicated that the involvement of stakeholders such as teachers, school management, parents, students, researchers or politicians is important when developing policies. Here, a higher level of agreement was significantly associated with policy adoption. This is consistent with research indicating that stakeholder engagement is the key to successful implementation (49–51). In addition, non-participation or symbolic participation of stakeholders describes a top-down approach [50], which rather hampers successful implementation. Based on responses from the schools that were classified as adopters, our data show that at least $40\%$ of teachers were involved in the decision-making process, which may have facilitated the adoption process. The importance of stakeholder involvement for adoption observed in our study, might be supported by the results of similar research in the school setting [24, 29]. However, to better understand the importance of stakeholder engagement on the adoption process of school-based policies, future research should distinguish between individual stakeholder groups.
If we contrast our findings to those from reviews on barriers and facilitators to the processes of implementation of physical activity policies in schools, we can find some overlaps regarding the importance of available resources, access to knowledge and information, and general willingness. Using the Theoretical Domains Framework [52], Nathan et al. [ 25] and Weatherson et al. [ 26] identified factors such as “lack of time,” “lack of funds,” “lack of sports facilities,” “lack of training” and “teachers' attitudes toward physical activity (intention)” that may hamper implementation success. Although implementation actions and associated challenges may vary depending on the implementation stage [15, 53], it could be assumed that these factors are of particular importance already during the adoption phase, but also during later implementation stages. In order to make a decision to adopt a policy, time and information might be needed up front (e.g., dealing with the content of the policy, writing applications, applying for funds), and if the school has sufficient staff, this workload could be shared among several people. Furthermore, it could be assumed that the general willingness of all individuals involved supports these processes and individual actions in a positive way. The association with sports facilities observed by Nathan et al. [ 25] and Weatherson et al. [ 26], however, were not found in our study. One reason for this could be that the presence of sports facilities is not initially relevant for the adoption from the perspective of school principals. However, as shown by Lounsbery et al. [ 11], the availability of sports facilities might be of greater importance to teachers in terms of the quality of implementation.
## Strengths and limitations
By applying the CFIR [22] in developing the questionnaire, this study has a solid theoretical foundation. The CFIR can be considered “meta-theoretical” as it was developed by synthesizing constructs from various existing implementation theories [22]. It is widely used in implementation science [37, 38] and has already proven to be useful for assessing the implementation of health programs in schools [39, 40]. In order to best reflect the selected constructs in a quantitative survey, the CFIR Interview Guide Tool provided sufficient support in formulating corresponding questions. However, whether the respective items actually reflect the constructs adequately is uncertain, as we were not able to conduct validity tests due to limited funding and short study duration. To examine determinants of policy adoption at the organizational level of schools, a variety of constructs from the inner setting, individual characteristics and process domain were included in the analysis. However, possible associations with other domains and their constructs that might better describe, for example, the political and social context in which schools are embedded, were not investigated. Thus, only an incomplete picture could be drawn of the challenges that participating schools faced in adopting a physical activity policy.
Further limitations must be considered when interpreting the present study findings. The overall response rate of schools was low, which limits the generalizability of our results to other schools in Baden-Wuerttemberg. However, the ratio between participating elementary schools and special needs schools was about the same (eligible: $86\%$ and $14\%$; participated: $88\%$ and $12\%$). One explanation for the low response rate could be the restrictions imposed to combat the COVID-19 pandemic. Consequently, schools in Baden-Wuerttemberg could still not return to regular operation and principals were facing particular challenges due to organizational overload. Participation in a survey on physical activity among students may therefore not have been a priority. Although measures such as mailing postcards containing the QR-code, a video on the front page of the questionnaire, and a reminder letter were used to increase participation rates, an incentive could not be provided.
It is possible that schools that did not promote physical activity among their students were more likely not to have participated, thus non-response bias may have occurred. As a result, findings may be overestimated in terms of policy adoption. Some questions on the CFIR constructs related to the adoption or implementation of policies. Therefore, compared to schools that had not yet adopted a policy, the responses of schools that had already adopted physical activity policies may have been more related to the current situation of implementation rather than the previous circumstances at the time when the policy was adopted. For some schools, the date of implementation was several years ago. Consequently, our study is vulnerable to recall bias. In addition, the complexity of logistic regression models may limit generalizability. Furthermore, no adjustments were made for multiple testing as the research questions were addressed in an exploratory manner.
## Conclusion
The present study provides a first insight into possible barriers and facilitators at school level that might be of importance for decision-makers when adopting physical activity policies. It can be hypothesized that the decision of elementary school principals to adopt a physical activity policy would be facilitated if there is a general willingness within the teaching staff, relevant stakeholders are involved, implementers have access to information and sufficient personnel, financial, and time resources are available. Overall, our experience has been that the CFIR can provide good guidance to assess determinants associated with the adoption of physical activity policies. It could be well-adapted to the school setting and was helpful in designing the study and interpreting the results. Future studies could attempt to explain how the characteristics of the individuals involved affect the adoption of a policy and what importance external influences, such as the political context, may have.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary files, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of Ulm University (Application Number $\frac{252}{20}$) as well as the Ministry of Education, Youth and Sports of Baden-Wuerttemberg in March 2021. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
The study was designed by JW, DAS, MFM, and JMS. JW and DAS developed the questionnaire with expert advice from MFM, BM, NL, and JMS. JW conducted the data analysis and interpretation, wrote the draft of the manuscript and integrated author comments, and revisions into the final version. DAS, MFM, AL, NL, and JMS critically revised the subject-specific content of the draft manuscript. BM, SF, AB, and KL contributed substantially to the preparation of the draft manuscript. All authors read and approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Bullying victimization and suicide attempts among adolescents in 41 low- and
middle-income countries: Roles of sleep deprivation and body mass'
authors:
- Wenxin Bao
- Yi Qian
- Wenjing Fei
- Shun Tian
- Yiran Geng
- Shaishai Wang
- Chen-Wei Pan
- Chun-Hua Zhao
- Tianyang Zhang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9992427
doi: 10.3389/fpubh.2023.1064731
license: CC BY 4.0
---
# Bullying victimization and suicide attempts among adolescents in 41 low- and middle-income countries: Roles of sleep deprivation and body mass
## Abstract
### Background
Suicide is the fourth leading cause of death for adolescents, and globally, over $75\%$ of completed suicides occur in low- and middle-income countries (LMICs). Bullying has been proven to be closely related to suicide attempts. However, further understanding of the mechanisms underlying the relationship between bullying and adolescents' suicide attempts is urgently needed.
### Methods
We used data from the Global School-based Student Health Survey (GSHS) (2010–2017) from 41 LMICs or regions. This study was based on questions assessing bullying victimization, suicide attempts, sleep deprivation, and body mass. Chi-square tests were used to explore the correlations among the main variables. The mediating role of sleep deprivation and the moderating role of body mass index (BMI) were analyzed using PROCESS.
### Results
The results showed a positive association between bullying victimization and suicide attempts. Sleep deprivation partially mediated the relationship between the frequency of being bullied and suicide attempts. In addition, sleep deprivation played a full or partial mediating role in the relationship between different types of bullying and suicide attempts. BMI moderated the relationships between the frequency of being bullied and suicide attempts, between being made fun of about one's body and sleep deprivation, and between sleep deprivation and suicide attempts.
### Conclusion
Being bullied has a positive effect on suicide attempts, which is mediated by sleep deprivation and moderated by body mass. The results of this study are consistent with the stress-diathesis model of suicide, suggesting that being bullied is one of the stressors of suicide in adolescents, while sleep deprivation and body mass are susceptibility diatheses of suicide. The results are conducive to identifying adolescents at a high risk of suicide, suggesting that there is a need to pay more attention to bullied adolescents, especially their sleep quality and body mass, and design effective intervention measures to improve the current situation of adolescent suicide in LMICs.
## Background
Suicide is the fourth leading cause of death for adolescents, and it has become a major public health concern [1]. Rates of attempted suicide and death by suicide have been increasing for more than a decade [2]. The global age-standardized suicide rate was 9.0 per 100,000 population for 2019, while the majority of deaths by suicide occurred in low- and middle-income countries (LMICs), with a $77\%$ probability. It is worth noting that $88\%$ of adolescents who died by suicide were from LMICs, where nearly $90\%$ of the world's adolescents live [1]. According to research, suicide and bullying are strongly linked [3]. Bullying is a type of aggressive behavior that occurs repeatedly in interpersonal relationships where power imbalances exist, increasing the risk of physical and psychosocial problems through its influence [4]. The involvement of adolescents in bullying is becoming an important public health problem that is attracting the attention of researchers.
## Suicide attempt
Suicide can be classified as suicidal ideation, suicidal plan, and suicide attempt. In a previous study, suicidal ideation was reported by $24.66\%$, suicidal planning was reported by $15.55\%$, and suicide attempts were reported by $4.37\%$ of adolescents [5]. Suicide attempts are the most serious of the three, directly damaging health, with the most serious consequences being disability or even death. Attempted suicide refers to an intentional, self-inflicted, life-threatening act that results in physical injury but not death [6]. Suicide attempts are up to 30 times more common than suicides; however, they are important predictors of repeated attempts and completed suicides [7]. Suicide attempts are undertaken most frequently by young people [8]. Therefore, we need to pay more attention to suicide among teenagers, especially in LMICs.
In 1960, Bleuler highlighted the role of diathesis-stress interactions in the course of schizophrenia [9], and in 1980, DeCatanzaro extended the concept to the mechanism of suicide [10]. In the stress-diathesis model of suicide, stress is considered to be the stressful environment or life events (such as tension in relationships and academic pressure) that trigger suicidal behavior, while diathesis refers to the individual vulnerability or susceptibility to suicide. The stress-diathesis model holds that suicide is the result of an interaction between state-dependent (environmental) stressors and a similarly characterized diathesis or susceptibility to suicidal behavior. It was believed that diathesis was primarily a biological trait produced by genetic predisposition, that is, genetic vulnerability [11, 12]. Studies in recent years have also expanded the term to include cognitive and social factors that predispose people to suicidal behavior, such as traits of impulsivity [13]. Among the stressors that trigger suicidal behavior in adolescents, we need to pay special attention to bullying. Bullying is a deliberate and aggressive behavior commonly seen in school settings. The main social living environment of adolescents is school, and the adverse events occurring in school easily affect the mental health of adolescents. Researchers have found that being a victim of bullying is significantly associated with reports of suicidal ideation and attempts (14–17). In summary, being bullied can be considered a significant stressor in adolescent suicide in LMICs.
## The impact of bullying victimization on suicidal behavior
Bullying is an intentional and aggressive behavior that is performed repeatedly and based on an imbalance of power between the perpetrator and victim [18, 19]. Our research is based on the Global School-based Student Health Survey (GSHS) initiated by the WHO, which uses a self-administered questionnaire to obtain data concerning young people's health behavior and protective factors related to the leading causes of morbidity and mortality among children and adults worldwide. The GSHS subdivides the content of bullying into being hit, kicked, pushed, shoved, or locked indoors; being made fun of because of race, nationality, or color; being made fun of because of religion; being made fun of with sexual jokes, comments, or gestures; being left out of activities on purpose or being completely ignored; being made fun of because of how one's body or face looks; and being bullied in some other way. Previous research has shown that male subjects appear to engage in more physical forms and female subjects appear to engage in more relational forms of bullying and that physical bullying is more prevalent in younger age groups (20–22). In the GHSH's detailed division of bullying, being hit, kicked, pushed, shoved, and locked indoors are types of physical bullying, and the other types are more prone to relational bullying. In addition, former studies have found that bullying is related to prejudice and that the risk of bullying and victimization is not equal across student groups. A number of studies have indicated that students with disabilities or suffering from obesity and those belonging to ethnic or sexual minorities are at greater risk of being victimized than their peers (23–26). This suggests that the occurrence of bullying behaviors is also related to some inherent characteristics of the bullied. Bullying has negative health consequences for both bullies and victims, and it can have a negative impact on bystanders as well. Several longitudinal studies from different countries, along with systematic reviews and meta-analyses, have demonstrated the relationship between school bullying or the experience of being victimized and later health outcomes (27–30). These associations hold even after controlling for other childhood risk factors [31]. Bullying is even a major risk factor for adolescent suicide, which has been shown in some longitudinal and cross-sectional studies (32–35). Physical bullying perpetration, in particular, may put adolescents in situations where they are actually injured (i.e., victim defends themselves) or where there is a threat of injury. Thus, bullies may repeatedly experience physical pain and threatening situations. Through habituation and conditioning, exposure to these types of painful and provocative events makes people more capable of making potentially lethal suicide attempts [36]. Research by Barzilay et al. also showed that physical victimization is related to suicidal ideation and that relationship victimization is related to suicide attempts [37]. In addition, extensive research evidence indicates that bullying victimization is a factor related to sleep impairment in adolescents.
## The mediating effect of sleep deprivation
The average sleep needed for an adolescent to maintain health is 8–10 h per night [38]. The Youth Risk Behaviour Survey found that $72.7\%$ of students reported an average of <8 h of sleep on school nights [39]. As many as one-fourth of adolescents report sleeping 6 h or less per night [40]. Therefore, sleep deprivation is a significant public health concern [41]. The effects of sleep deprivation on adolescents can be particularly detrimental, impacting their ability to learn, manage mood and anxiety, develop relationships, avoid accidents, and stay physically healthy [42]. However, sleep deprivation, but not eveningness, is significantly linked to suicidality after controlling for mood symptoms [43]. A number of recent studies have identified sleep deprivation as a modifiable, independent suicide risk factor [44]. Studies have shown that improved sleep might reduce suicide risk [45]. In conclusion, sleep deprivation is one of the possible diatheses in the stress-diathesis model of suicide.
Adolescence is a period marked by changes in sleep timing and patterns, and several factors may contribute to sleep deprivation in adolescents, including biological and psychological aspects. Physiologically, the adolescent sleep-wake cycle is shifted, and melatonin secretion is delayed, leading to a delay in the circadian rhythm, resulting in a mismatch between adolescents' preferred sleep time and social needs such as going to school. From a psychological point of view, stressful life events such as academic pressure and interpersonal relationships, as well as mental health problems such as depression, will all lead to sleep loss over worry (SLOW) in adolescents. Bullying victimization is also an important factor in this. Previous research indicated that bully victimization was consistently and positively linked to SLOW, with greater odds of sleep deprivation among students with severe SLOW who were bullied for 3 days or more [46]. A similar pattern was found across all bullying roles, with more sleep disturbances for victims and bully victims [47]. It is possible that the fear associated with being bullied and rumination over bullying experiences may interfere with the onset of sleep and contribute to poor sleep quality. Evidence supports a robust association between bullying victimization and sleep problems (i.e., difficulties falling or remaining asleep). Bullying victimization is thus an independent risk factor for sleep disturbances and warrants deeper investigation, particularly in adolescence. Based on the above, we find that sleep deprivation is related to bullying and suicide. The purpose of our study is to explore whether sleep deprivation plays a mediating role in the pathway from bullying to suicide.
Table 2 illustrates the mediating effect of sleep deprivation. Sleep deprivation partially mediated the effect between the frequency of being bullied and suicide attempts. As expected, the total effect of the frequency of being bullied (β = 0.085, $t = 61.999$, $p \leq 0.001$) on suicide attempts in the absence of sleep deprivation was significant. When sleep deprivation was added to the analysis as a mediator, the effects of the frequency of being bullied on sleep deprivation (β = 0.163, $t = 62.713$, $p \leq 0.001$) were positive, and sleep deprivation (β = 0.086, $t = 57.316$, $p \leq 0.001$) still positively predicted suicide attempts. Bootstrapping indicated that sleep deprivation played a significant role in explaining the association between the frequency of being bullied and suicide attempts (indirect effect = 0.014, $95\%$ CI = 0.013–0.015). Regarding the different types of bullying, sleep deprivation had a full mediating effect between the associations of suicide attempts with being kicked, pushed, or shoved; being made fun of about one's sex; being left out of activities; and being bullied some other way. It had a partial mediating effect on the associations between being made fun of about one's race, being made fun of about one's body, and suicide attempt. The details are shown in Table 2.
**Table 2**
| Unnamed: 0 | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | Suicide attempts | Suicide attempts | Sleep deprivation | Sleep deprivation | Suicide attempts | Suicide attempts |
| Variable | β | t | β | t | β | t |
| The frequency of being bullied | 0.085 | 61.999*** | 0.163 | 62.713*** | 0.071 | 51.704*** |
| Sleep deprivation | | | | | 0.086 | 57.316*** |
| R 2 | 0.031 | 0.031 | 0.031 | 0.031 | 0.056 | 0.056 |
| F | 3843.840 | 3843.840 | 3932.895 | 3932.895 | 3616.259 | 3616.259 |
| Kicked, pushed, or shoved | 0.229 | 23.822*** | 0.367 | 20.300*** | 0.197 | 20.739*** |
| Made fun of race | 0.239 | 22.215*** | 0.508 | 25.209*** | 0.194 | 18.281*** |
| Made fun because of religion | 0.317 | 17.508*** | 0.577 | 17.015*** | 0.266 | 14.901*** |
| Made fun of about sex | 0.221 | 26.962*** | 0.548 | 35.626*** | 0.173 | 21.298*** |
| Left out of activities | 0.203 | 15.214*** | 0.54 | 21.582*** | 0.156 | 11.811*** |
| Made fun of about body | 0.204 | 25.651*** | 0.541 | 36.290*** | 0.156 | 19.862*** |
| Some other way | 0.141 | 23.331*** | 0.407 | 35.921*** | 0.105 | 17.585*** |
| Sleep deprivation | | | | | 0.087 | 58.051*** |
| R 2 | 0.023 | 0.023 | 0.037 | 0.037 | 0.050 | 0.050 |
| F | 417.521 | 417.521 | 671.44 | 671.44 | 796.665 | 796.665 |
Consistent with Hypothesis 2, we found that sleep deprivation is a crucial explanatory mechanism linking bullying victimization to suicide attempts among adolescents in 41 LMICs. Specifically, adolescents exposed to a higher frequency of being bullied were prone to have a higher risk of sleep deprivation, which in turn was related to suicide attempts. The results were also true for different types of bullying. This study supported previous studies showing a robust association between bullying victimization and sleep problems (i.e., difficulties falling or remaining asleep). The effects of sleep deprivation on children can be particularly detrimental, impacting their ability to learn, manage mood and anxiety, develop relationships, avoid accidents, and stay physically healthy [42]. A number of recent studies have identified sleep deprivation as a modifiable, independent suicide risk factor [44]. Therefore, sleep deprivation is not only ascribed to bullying victimization but also acts as an internal antecedent of suicide attempts. These findings extend beyond existing studies by revealing how bullying victimization is associated with adolescent suicide attempts and suggest that the frequency of being bullied and various types of bullying behavior may be closely related to adolescents' quality of sleep.
Aside from the overall mediation result, each of the individual links deserves mention. Regarding the first part of the mediation process (i.e., bullying victimization → sleep deprivation), our findings suggest that the frequency of being bullied is positively related to sleep deprivation. This finding is congruent with previous research that suggested that bully victimization and loneliness were consistently and positively linked to SLOW, with greater odds of sleep deprivation seen among students with severe SLOW who were bullied for 3 days or more [46]. Victims of bullying have been found to suffer from anxiety, posttraumatic stress, depression, and suicidal ideation, as well as somatic conditions such as headaches and stomachaches. At the same time, sleep has been associated with these negative psychosocial outcomes commonly seen among both bullies and victims [47]. With the occurrence of bullying, the victim develops emotional distress and psychological stress, which can lead to sleep deprivation. In the second phase of the mediation process (i.e., sleep deprivation → suicide attempt), we found that sleep deprivation was positively associated with suicide attempts. Sleep deprivation was associated with suicide attempts, which is supported by robust evidence. For example, a review by Zullo et al. showed that sleep deprivation and eveningness uniquely contributed to poor daytime functioning and mood-related outcomes, while the coexistence of these two conditions could confer a greater risk in adolescents. Beyond that, studies have shown that improved sleep might reduce suicide risk [45].
The results could also apply to different types of bullying. Similar intermediate relationships existed between different types of bullying and sleep deprivation. Previous studies have shown that a similar pattern was found across all types of bullying, with more sleep disturbances for victims and bully victims [47]. In this study, being made fun of about one's body is the bullying style with the most positive impact on sleep deprivation. The second is being left out of activities. Being kicked, pushed, or shoved has the least effect on sleep deprivation. Therefore, we can infer from this study that verbal bullying has the greatest impact on sleep deprivation, followed by social bullying, and that physical bullying has the least impact. The results are consistent with previous studies. Verbal bullying was found to be easier to implement and to be the dominant type of bullying among adolescents, reaching a prevalence of $53.2\%$ [66]. Both victim-only and bully victims involved in verbal bullying also reported having more bedtime fears than bully only youth, while bully victims involved in physical bullying reported more bedtime fears than bully only youth. Sleep deprivation was the highest among bully victims involved in verbal bullying, while sleep deprivation was higher among victim-only youth involved in social bullying [47]. These types of bullying had a positive effect on sleep deprivation, which could lead to suicide attempts.
Notably, this study demonstrated that sleep deprivation only partially mediates the relation between the frequency of being bullied and suicide attempts. This may be because personal definitions of bullying are different, leading to deviations in the frequency of being bullied. The mediating effect of sleep deprivation on the relationship between bullying type and suicide attempt was not only fully mediated but also partially mediated. Being “kicked, pushed, or shoved,” “left out of activities,” and “made fun of about one's sex” play a full mediating role, while being “made fun of about one's race” and “made fun of about one's body” played a partial mediating role. In spite of this, sleep deprivation is an important linking mechanism that deserves special attention. Notably, being a victim of school bullying was found to be significantly associated with reports of suicidal ideation and attempts [15]. In summary, bullying victimization can be considered a significant factor in adolescent suicide in LMICs. Sleep deprivation plays crucial direct and indirect roles in suicide attempts, the extent and mechanisms of which urgently require investigation.
## Body mass as a moderator
As early as 1966, increasingly well-powered and well-designed studies have demonstrated a relationship between a heavier BMI and a lower risk of suicide [48]. A systematic review and meta-analysis conducted by Perera and colleagues showed a negative correlation between BMI and completed suicide; the evidence for the association between BMI and suicide attempt was inconsistent, and when confounding factors were considered, there was no correlation between BMI and suicidal ideation [49]. However, there have also been different research results on the relationship between body mass and suicide. Dong and colleagues found that both lower and higher BMI were associated with an elevated risk for attempted suicide and that extreme obesity (BMI ≥ 40 kg/m2) was significantly associated with attempted suicide [50]. We think BMI might also be a vulnerability trait of suicide. However, there is still controversy about the relationship between body mass and suicidal behavior, which urgently needs to be explored, as was done in this study.
The relationships between bullying victimization, sleep deprivation and suicide are not always the same in all cases. Adolescents with overweight or obese have significantly greater odds than their healthy-weight peers of being victims of bullying [25]. Previous findings indicated that $64\%$ of the study participants reported weight-based victimization at school and that the risk of weight-based victimization increased with body weight [51]. However, research by Thakkar et al. in India found that higher BMI corresponded to less victimization for boys [52]. Koyanagi et al. found that compared with normal weight, being overweight and obese were associated with significantly higher odds of any form of bullying victimization only among girls. Among boys, these associations were not significant overall [53].
Studies have shown that a reduction in sleep time is related to an increase in eating [54]. Short sleep times reduce the activity of circuits related to control and inhibition, leading to poor resistance to temptation and self-control in the presence of food, that is, increasing the level of external eating, which may lead to an increase in BMI [55]. In addition, from a medical point of view, people with high BMI are susceptible to obstructive sleep apnea (OSA), which can lead to poor sleep quality [48]. In summary, we deduced a vicious cycle between obesity and lack of sleep. Obese people are prone to lack of sleep, and poor sleep can also aggravate the occurrence of obesity. Based on previous literature, we have known that sleep deprivation has a positive effect on suicide [44, 45]. Therefore, it is not difficult to infer that body mass can further increase the probability of bullying victims engaging in suicidal behavior through a positive effect on sleep deprivation. Based on the theoretical and empirical results discussed earlier, body mass may play a moderating role in the relationship between bullying victimization, sleep deprivation, and suicidal behavior.
Table 3 illustrates the moderating effect results for BMI. Adding BMI to the model led to the following results: (a) frequency of being bullied (see Figure 1): the product of the frequency of being bullied and BMI had a significant predictive effect on suicide attempts (β = 0.001, $t = 4.254$, $p \leq 0.001$). According to simple slope analysis, bullying frequency was positively associated with suicide attempts among adolescents with low BMI (simple slope = 0.066, $t = 33.800$, $p \leq 0.001$) and high BMI (simple slope = 0.077, $t = 41.125$, $p \leq 0.001$). The product of sleep deprivation and BMI had a significant predictive effect on suicide attempts (β = −0.002, $t = 6.213$, $p \leq 0.001$). In simple slope analysis, it was found that sleep deprivation was positively related to suicide attempts among adolescents with low BMI (simple slope = 0.076, $t = 35.893$, $p \leq 0.001$) and high BMI (simple slope = 0.094, $t = 45.830$, $p \leq 0.001$). These results indicate that as the frequency of being bullied and sleep deprivation increased, the suicide attempt of high-BMI adolescents increased more significantly. ( b) Types of bullying (see Figures 2, 3): the product of being made fun of about one's body and BMI had a significant predictive effect on sleep deprivation (β = −0.006, t = −2.268, $p \leq 0.05$). Further simple slope analysis showed that being made fun of about one's body was positively associated with sleep deprivation among adolescents with low BMI (simple slope = 0.564, $t = 28.952$, $p \leq 0.001$) and high BMI (simple slope = 0.523, $t = 32.850$, $p \leq 0.001$). These results indicate that as being made fun of about one's body increased, the sleep deprivation of low-BMI adolescents increased more significantly. Moreover, the product of sleep deprivation and BMI had a significant predictive effect on suicide attempts (β = 0.002, $t = 6.037$, $p \leq 0.001$) for all types of bullying. According to a simple slope analysis, a positive association was found between sleep deprivation and suicide attempts among adolescents with low BMI (simple slope = 0.079, $t = 39.964$, $p \leq 0.001$) and high BMI (simple slope = 0.093, $t = 51.880$, $p \leq 0.001$). These results indicate that as sleep deprivation increased, the suicide attempt of high-BMI adolescents increased more significantly.
Consistent with the results of hypothesis 3, the results of this research reveal that body mass moderates the relationship between bullying victimization and suicide attempts. The effects are stronger among adolescents with a high BMI than among those with a low BMI. The results of data analysis show that high BMI alone is a protective factor against suicide attempts. People who are underweight are more likely to attempt suicide, which is consistent with most previous studies [49, 67]. In addition, body mass plays a moderating role in the path of bullying victimization to suicide attempts. Among adolescents with different BMIs, the degree of influence of being bullied on suicide attempts differs. Adolescents with high BMI are more likely to attempt suicide after being bullied. The current understanding of the mechanism of this regulation is still unclear, but it may be related to the fact that adolescents with higher BMI are more likely to be bullied [25, 51]. Moreover, the product of sleep deprivation and body mass has a significant predictive effect on suicide attempts. This finding suggests that body mass also moderates the effect of sleep deprivation on suicide attempts. The data analysis showed that as the frequency of being bullied and sleep deprivation increased, the suicide attempt of high-BMI adolescents increased more significantly. Based on a series of previous studies, we found that there is a vicious cycle between obesity and lack of sleep [48, 54]. Obese people are prone to lack of sleep, and poor sleep can also exacerbate the occurrence of obesity. At the same time, sleep deprivation can also act as an intermediary factor between bullying victimization and suicide attempts, leading victims to attempt suicide. Therefore, it is not difficult to infer that BMI can further increase the probability of suicide by bullying victims through its positive effects on sleep deprivation. In addition, previous studies have found that obese people had difficulty regulating their emotions and were less aware of their interoceptive senses [68]. This means that obese people may have more difficulty dealing with the negative emotions brought about by being bullied and are more likely to have suicidal impulses.
In terms of the moderating role of body mass in the influence of different types of bullying on suicide attempts, the product of being made fun of about one's body and body mass has a significant predictive effect on sleep deprivation. When adolescents with low BMI were made fun of by their bodies, they were more likely to suffer from sleep deprivation. This finding is very important because previous studies have shown that obese people were more prone to sleep deprivation. On the one hand, this sleep deprivation may be related to sleep interruption caused by diseases such as obstructive sleep apnea, gastroesophageal reflux disease, and asthma, which are diseases that obese people easily suffer from. On the other hand, sleep deprivation may also be related to the close relationship between obesity and depression [69]. Regarding why adolescents with low BMI are more prone to sleep deprivation after being made fun of about their bodies, there is no appropriate explanation. This may be because adolescents with low BMI are thinner and more vulnerable to physical harm after being bullied, which in turn leads to an aggravation of sleep deprivation. For these types of bullying (being kicked, pushed, or shoved; being made fun of about one's race; being made fun because of religion; being made fun of about one's sex; being left out of activities; being made fun of about one's body; being bullied some other way), body mass moderates the effect of sleep deprivation on suicide attempts. As sleep deprivation increases, the suicide attempt of high-BMI adolescents increases more significantly, which is consistent with the moderating path of the frequency of being bullied [48, 54, 68]. Coupled with the moderating effect of body mass on suicide attempts, body mass moderates the complete path of bullying by being made fun of about one's body through sleep deprivation, leading to suicide attempts. As body mass is closely related to body image, its moderating role in this path is reasonable. For other types of bullying, body mass does not moderate the relationship between being bullied and sleep deprivation. The specific mechanism is still unclear and needs further study.
Based on the above, we believe that bullying victimization is one of the stress components in the stress-diathesis model of suicide in adolescents. Under the stress of being bullied, different individuals respond differently to the stressor due to their own diathesis factors, and diathesis-related biomarkers or characteristics can help indicate suicide risk. Our study found that sleep deprivation plays a mediating role in the pathway of bullying victimization to suicide attempts in adolescents, and bullying victimization leads to sleep deprivation, which further induces the emergence of suicide attempts, while body mass plays a moderating role in this model, teenagers with BMI values not within the normal range are more likely to attempt suicide after being bullied. Our study also further verified the feasibility of the stress-diathesis theory in suicidal behavior and clarified the interaction between stress and diathesis components in the stress-diathesis model of suicide.
## This study
The majority of previous research has focused on the association between bullying victimization and suicide. However, studies clarifying the interaction among bullying victimization, sleep deprivation, and suicide are scarce, and few studies have discussed the role of body mass. In addition, most of the previous studies have focused only on the impact of the frequency of being bullied on suicide, while this study examined both the frequency of being bullied and the different types of bullying. There is no clear understanding of the underlying mediating mechanisms and moderating factors that may affect this association. In particular, this study had multiple aims. First, we examined whether bullying victimization has an effect on the suicide attempt of adolescents. Second, we examined whether sleep deprivation mediates the relationship between bullying victimization and suicide attempts among adolescents. Third, we tested whether the relationship between bullying victimization and suicide attempts through sleep deprivation is regulated by body mass. In summary, our hypotheses were as follows: Hypothesis 1. Being bullied is positively associated with suicide attempts in adolescents, and this applies to different types of bullying content.
Hypothesis 2. Being bullied predicts suicide attempts in adolescents through sleep deprivation. Being bullied can cause the victim's sleep condition to deteriorate, thereby further inducing suicide attempts.
Hypothesis 3. BMI moderates the relationship between bullying victimization and suicide attempts in adolescents through sleep deprivation. Specifically, the influence of being bullied on sleep deprivation and the influence of sleep deprivation on suicide attempts of adolescents differ in different BMI groups, and this effect is more significant in people with high BMI.
## Sources of data
We analyzed the most recent Global School-based Student Health Survey (GSHS) data (2010–2017) from 41 LMICs or regions. GSHS data, methods, and main findings are available on the WHO (http://www.who.int/ncds/surveillance/gshs/en/) and the Centers for Disease Control and Prevention (CDC)'s websites. According to the World Health Organization (WHO), adolescents are individuals aged between 10 and 19 years, and the GSHS is a collaborative surveillance project designed to help countries measure and assess behavioral risk factors and protective factors in 10 key areas among young people mostly aged 12–17 years [53, 56]. To facilitate comparisons between countries, we excluded survey responses collected before 2010. Only the most recent GSHS was analyzed for countries that had completed more than one survey. Our final sample size is 121,869.
GSHS consistently used a two-stage cluster sampling strategy to obtain a nationally representative sample of middle school students in all countries. The first stage involved selecting random schools from a country based on their size in proportion to the probability distribution. During the second stage, classes from the schools were selected with systematic equal probability sampling and random start. In the sampling frame, all students from the selected school classes were included. It is possible to translate the questionnaire into any language. Students' records are identified, and data were collected by automated optical character recognition on computer-scannable answer sheets.
The GSHS administration is approved by either a Ministry of Education or a Health Research Ethics Committee in each participating country. Participants and their guardians in each country provided verbal or written consent.
## Main variables
The following aspects were the focus of our analysis: situation of being bullied, suicide attempt, sleep deprivation, and BMI. These questions are available from the questionnaire of GSHS [57].
The situation of being bullied consisted of two parts: the frequency of being bullied and the types of bullying. “ The frequency of being bullied” was assessed with the following question: “During the past 30 days, on how many days were you bullied?” The response options for the question were “0 days,” “1 or 2 days,” “3 to 5 days,” “6 to 9 days,” “10 to 19 days,” “20 to 29 days,” and “All 30 days.” “ The types of bullying” were assessed by the following question: “During the past 30 days, how were you bullied most often?” The response options for the question were “I was not bullied during the past 30 days,” “I was hit, kicked, pushed, shoved around, or locked indoors,” “I was made fun of because of my race, nationality, or color,” “I was made fun of because of my religion,” “I was made fun of with sexual jokes, comments, or gestures,” “I was left out of activities on purpose or completely ignored,” “I was made fun of because of how my body or face looks,” and “I was bullied in some other way.” “Suicide attempt” was assessed by the question “During the past 12 months, how many times did you actually attempt suicide.” The response options for the question were “0 times,” “1 time,” “2 or 3 times,” “4 or 5 times,” and “6 or more times,” In the chi-square test, we coded the answer “0 times” as “does not have the experience of suicide attempt” and the responses “1 time,” “2 or 3 times,” “4 or 5 times,” and “6 or more times” as “has the experience of suicide attempt.” “Sleep deprivation” was assessed by the following question: “During the past 12 months, how often have you been so worried about something that you could not sleep at night?” The response options for the question were “Never,” “Rarely,” “Sometimes,” “Most of the time,” and “Always.” “BMI” was calculated with data on height and weight using the formula BMI = weight (kg)/height (m) squared.
## Control variables
Age, gender, other mental health problems (loneliness), and relationships with peers (having close friends, if other students are kind and helpful or not) of the respondent were included as covariates.
## Statistical analyses
The statistical analyses were conducted in SPSS v.21.0 with the addition of the PROCESS plug-in for moderation and mediation analyses. The analysis of correlations among the main variables was carried out using chi-square tests. Then, the mediating role of sleep deprivation and the moderating role of BMI were analyzed in PROCESS, and robust standard errors and bootstrap confidence intervals were derived from 5,000 bootstrap samples. The result is statistically significant if the confidence interval excludes 0. We adjusted for covariates in the moderation and mediation analyses.
## Results
We identified 41 LMICs with the GSHS datasets, removed the cases with missing values, and finally included a sample size of 121,869. The proportion of male participants in the sample was $44.8\%$. The results indicated that $9.7\%$ of the participants reported having experienced a suicide attempt, and $40.9\%$ of the participants reported having been exposed to bullying.
## Descriptive statistics and chi-square test
The descriptive statistics for the variables and the results of the chi-square test are shown in Table 1. We found that being bullied more frequently can lead to higher rates of suicide attempts. Among the types of bullying, “make fun because of religion” was the most strongly associated with suicide attempts. In total, $25.0\%$ of people who were made fun of because of their religion attempted suicide. A total of $79.8\%$ of adolescents reported having sleep deprivation. When the degree of sleep deprivation was more severe, the rate of suicide attempts was higher. In total, $7.90\%$ of the adolescents who attempted suicide had a BMI lower than 18.5, and $9.80\%$ of them had a BMI higher than 30. Furthermore, the chi-square test of suicide attempts among groups was statistically significant.
**Table 1**
| Variables | Categories | Suicide attempts | Suicide attempts.1 | Suicide attempts.2 |
| --- | --- | --- | --- | --- |
| Variables | Categories | Yes | No | p |
| Age | 12 years old | 500 (7.30%) | 6,390 (92.70%) | <0.001 |
| | 13 years old | 1,892 (8.50%) | 20,461 (91.50%) | |
| | 14 years old | 2,855 (10.20%) | 25,205 (89.80%) | |
| | 15 years old | 2,988 (10.60%) | 25,265 (89.40%) | |
| | 16 years old | 2,501 (10.80%) | 20,715 (89.20%) | |
| | 17 years old | 1,064 (8.10%) | 12,033 (91.90%) | |
| Sex | Male | 4,301 (7.90%) | 50,250 (92.10%) | <0.001 |
| | Female | 7,499 (11.10%) | 59,819 (88.90%) | |
| BMI | <18.5 | 2,746 (7.90%) | 31,986 (92.10%) | <0.001 |
| | 18.5–24.9 | 7,701 (10.50%) | 65,820 (89.50%) | |
| | 25–30 | 931 (10.00%) | 8,381 (90.00%) | |
| | >30 | 422 (9.80%) | 3,882 (90.20%) | |
| Feeling lonely | Never | 2,373 (5.70%) | 39,317 (94.30%) | <0.001 |
| | Rarely | 2,273 (7.50%) | 27,834 (92.50%) | |
| | Sometimes | 4,027 (10.70%) | 33,558 (89.30%) | |
| | Most of the time | 1,974 (22.10%) | 6,948 (77.90%) | |
| | Always | 1,153 (32.30%) | 2,412 (67.70%) | |
| Close friends | 0 | 1,083 (19.80%) | 4,394 (80.20%) | <0.001 |
| | 1 | 1,497 (13.80%) | 9,342 (86.20%) | |
| | 2 | 1,875 (12.40%) | 13,245 (87.60%) | |
| | 3 or more | 7,345 (8.10%) | 83,088 (91.90%) | |
| Sleep deprivation | Never | 2,384 (5.00%) | 45,174 (95.00%) | <0.001 |
| | Rarely | 2,931 (8.80%) | 30,354 (91.20%) | |
| | Sometimes | 3,939 (12.50%) | 27,563 (87.50%) | |
| | Most of the time | 1,720 (24.60%) | 5,283 (75.40%) | |
| | Always | 826 (32.80%) | 1,695 (67.20%) | |
| The frequency of being bullied | 0 days | 6,133 (6.90%) | 82,784 (93.10%) | <0.001 |
| | 1 or 2 days | 2,972 (14.40%) | 17,724 (85.60%) | |
| | 3–5 days | 1,112 (19.80%) | 4,517 (80.20%) | |
| | 6–9 days | 498 (21.70%) | 1,796 (78.30%) | |
| | 10–19 days | 307 (20.90%) | 1,162 (79.10%) | |
| | 20–29 days | 177 (26.20%) | 499 (73.80%) | |
| | All 30 days | 601 (27.50%) | 1,587 (72.50%) | |
| Types of bullying | Not bullied | 6,973 (7.30%) | 88,405 (92.70%) | <0.001 |
| | Kicked, pushed, or shoved | 669 (20.60%) | 2,586 (79.40%) | |
| | Made fun of race | 533 (20.60%) | 2,055 (79.40%) | |
| | Made fun because of religion | 224 (25.00%) | 673 (75.00%) | |
| | Made fun of about sex | 898 (19.80%) | 3,635 (80.20%) | |
| | Left out of activities | 303 (18.20%) | 1,361 (81.80%) | |
| | Made fun of about body | 873 (18.00%) | 3,968 (82.00%) | |
| | Some other way | 1,327 (15.20%) | 7,386 (84.80%) | |
## Discussion
Bullying is a serious social behavior problem; bullying has negative health consequences for victims and is a stressor leading to victims' suicidal behaviors [31, 36]. Suicide is a major cause of death for adolescents and arouses widespread social concern. Considering that almost half of the world's youth (15–24 years) live in LMICs [58], research on suicide among these adolescents is essential. In this study of adolescents in 41 LMICs, we found a correlation between bullying victimization and suicide attempt; as the frequency of being bullied increases, the rate of suicide attempts by victims generally increases. Therefore, being bullied is positively associated with suicide attempts among adolescents, and this applies to different types of bullying, which is in line with Hypothesis 1. The frequency of being bullied and the type of bullying have different effects on adolescents' suicide attempts. In addition, sleep deprivation and body mass, as diathesis factors of suicide, also play a role in this process, which we will discuss concretely below.
## The impact of different types of bullying on suicide attempts
Through the analysis of the data, we found a correlation between bullying victimization and suicide attempts and observed that different types of bullying had varying degrees of impact on victims' suicide attempts. Bullying is a common adverse life event in adolescents, especially in LMIC, and a study recently conducted in southwest China showed that ~$12.41\%$ of adolescents had been victimized by bullying [56, 59, 60]. Previous studies have shown that the risk for bullying and victimization are not equal across student groups; a number of studies have indicated that students with disabilities and students with obesity, as well as those belonging to ethnic or sexual minorities, are at greater risk for being victimized than their peers (23–26). Our results indicate that adolescents who were bullied because of religion have the highest probability of suicide attempts, followed by those who were “kicked, pushed, or shoved” and “made fun of race”. This finding is similar to the research findings reported by Koyanagi et al. [ 61] and may have a possible link between identity cognition (such as religion and race). Minorities may experience greater stigma and insufficient self-identification due to identity-based bullying (IBB). A study by Galán et al. showed that there was a correlation between IBB victims' poor mental and physical health, including non-suicidal self-injury and suicidal ideation [62]. However, the specific mechanism is still unknown. The impact of physical bullying, such as being “kicked, pushed, or shoved”, on adolescents' suicide attempts may be derived from two aspects, i.e., mental and physical. The study by Thomas et al. showed that physical bullying was associated with high levels of psychological distress and reduced emotional wellbeing regardless of its frequency [63]. However, victims of physical bullying were more likely to suffer from physical pain and injury, which may affect their physical function and health. Exposure to painful and provocative events could make adolescents more likely to engage in behaviors leading to suicide [64]. The participants in this study were adolescents in LMICs, but the correlation between bullying victimization and suicide attempts was consistent in both low-income and high-income countries. A meta-analysis conducted by Holt et al. of 47 studies mainly conducted in high- and middle-income countries also showed that bullying victimization was significantly positively correlated with suicidal behavior [65]. Few previous studies have explored the impact of types of bullying on suicide attempts by victims, which is also one of the research topics of our study. According to the stress-diathesis model, even under the same stressor, different individuals will have different responses because of their own susceptibility. Our research also confirms this theory. The regression analysis showed that bullying victimization exerts a significant indirect impact on suicide attempts through sleep deprivation. Body mass also moderates the effects of bullying victimization on suicide attempts.
## Limitations and implications
There are a few limitations to our study that can be addressed through future research. First, although the model in our study is theoretically grounded and empirically supported, due to the shortcomings of cross-sectional designs, causality cannot be determined. There is a need for further longitudinal studies or intervention experiments to better examine the validity of this model. Additionally, the questionnaire used in the survey might lead to recall bias because it was self-reported. Third, the results of the survey may be biased by social desirability and different cultural factors across countries. Although the survey was anonymous, differences in sociocultural factors among countries can lead to different types of bullying, which might affect self-reporting about the frequency of being bullied and cause further potential bias. Finally, this survey did not measure adolescents' mental illness (depression, bipolar affective disorders, trauma, etc.) or previous psychological distress, which made them more vulnerable to the extent to which bullying victimization could easily trigger their suicide attempt. We suggest that further focused research is necessary to further understand the risk as children progress through adolescence, which is crucial for clinical management and disease prevention [70, 71].
Despite these limitations, our study has several strengths. First, the sample is large (126,763 young adolescents aged 12–17 years) and covers 41 LMICs in the five WHO regions; thus, it has good applicability and extension. In addition, this study has important theoretical and practical implications. Overall, this study provides valuable insight into the relationship between bullying victimization and suicide attempts, showing the mediating effects of sleep deprivation and the moderating role of body mass and offering a theoretical basis for creating a healthy school environment and promoting mental health among adolescents. In terms of practicality, our research results can provide a reference for the prevention of adolescent suicide attempts to reduce the incidence of teenage suicide, promoting the development of adolescents' physical health.
## Conclusion
Using a sample of adolescents in 41 LMICs, this study takes an important step in examining the mechanism by which bullying victimization is associated with suicide attempts. Bullying victimization has a positive effect on suicide attempts, which is mediated by sleep deprivation and moderated by body mass. This study provides insight into how bullying victimization impacts adolescents' suicide attempts. The findings can serve as theoretical bases for promoting adolescents' mental health and are conducive to identifying adolescents at a high risk of suicide, suggesting that more attention should be given to bullied adolescents, especially their sleep quality and body mass. Based on this study, we can design effective intervention measures to reduce the suicide possibility of bullied adolescents and improve the current situation of adolescent suicide in LMICs, such as improving sleep quality and maintaining a healthy body mass.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: Global School-based Student Health Survey (GSHS) data presented on the websites of the WHO (http://www.who.int/ncds/surveillance/gshs/en/) and Centers for Disease Control and Prevention (CDC).
## Ethics statement
In each participating country, the GSHS administration is approved by the Ministry of Education, a Health Research Ethics Committee, or both. Verbal or written consent is obtained from all the participants and their guardians in each country. This study does not require the approval of ethics or institutional review boards because the analyses are based on publicly available data.
## Author contributions
WB, YQ, and WF: visualization, formal analysis, writing (original draft), and writing (review and editing). ST: writing (original draft). YG and SW: writing (review and editing). C-WP: conceptualization, writing (review and editing), and validation. C-HZ: conceptualization, visualization, formal analysis, writing (original draft), writing (review and editing), and validation. TZ: conceptualization, visualization, formal analysis, writing (original draft), writing (review and editing), validation, and funding acquisition. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Trends and risk factors of global incidence, mortality, and disability of
genitourinary cancers from 1990 to 2019: Systematic analysis for the Global Burden
of Disease Study 2019'
authors:
- Yi-Qun Tian
- Jin-Cui Yang
- Jun-Jie Hu
- Rong Ding
- Da-Wei Ye
- Ji-Wen Shang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9992434
doi: 10.3389/fpubh.2023.1119374
license: CC BY 4.0
---
# Trends and risk factors of global incidence, mortality, and disability of genitourinary cancers from 1990 to 2019: Systematic analysis for the Global Burden of Disease Study 2019
## Abstract
### Background
The incidence of kidney, bladder, and prostate cancer ranked ninth, sixth, and third in male cancers respectively, meanwhile, the incidence of testicular cancer also increased gradually in the past 30 years.
### Objective
To study and present estimates of the incidence, mortality, and disability of kidney, bladder, prostate, and testicular cancer by location and age from 1990 to 2019 and reveal the mortality risk factors of them.
### Materials
The Global Burden of Diseases Study 2019 was used to obtain data for this research. The prediction of cancer mortality and incidence was based on mortality-to-incidence ratios (MIRs). The MIR data was processed by logistic regression and adjusted by Gaussian process regression. The association between the socio-demographic index and the incidence or disease burden was determined by Spearman's rank order correlation.
### Results
Globally in 2019, there were 371,700 kidney cancer cases with an age-standardized incidence rate (ASIR) of 4.6 per 100,000, 524,300 bladder cancer cases, with an ASIR of 6.5 per 100,000, 1,410,500 prostate cancer cases with an ASIR of 4.6 per 100,000 and 109,300 testicular cancer incident cases with an ASIR of 1.4 per 100,000, the ASIR of these four cancers increased by 29.1, 4, 22, and $45.5\%$ respectively. The incidence rate of the four cancers and the burden of kidney cancer were positively correlated with the socio-demographic index (SDI), regions with a higher SDI faced more of a burden attributable to these four cancers. High body-mass index has surpassed smoking to be the leading risk factor in the past thirty years for kidney cancer mortality. Smoking remained the leading risk factor for cancer-related mortality for bladder cancer and prostate cancer and the only risk factor for prostate cancer. However, the contribution of high fasting plasma glucose to bladder cancer mortality has been increasing.
### Conclusion
The incidence of bladder, kidney, prostate, and testicular cancer is ever-increasing. High-income regions face a greater burden attributable to the four cancers. In addition to smoking, metabolic risk factors may need more attention.
## 1. Introduction
Cancer poses a major public health problem and may surpass cardiovascular disease to be the leading cause of premature death caused by non-communicable diseases in most countries this century [1]. It is estimated that 19.3 million new cancer cases and almost 10.0 million cancer deaths occurred in 2020 [2]. With population growth and aging, the incidence and mortality of kidney, bladder, prostate, and testicular cancer are increasing rapidly [3]. According to the latest data from the International Agency for Research on Cancer, the incidence of kidney, bladder, and prostate cancer ranked ninth, sixth, and third, respectively, among males [2]. Prostate cancer has surpassed lung cancer as the most commonly diagnosed cancer among males in 112 countries, with an estimated 1.4 million ($7.3\%$) new cases in 2020 [2]. Revealing the spatial, temporal prevalence and disease burden, as well as the major risk factors for these cancers by age, sex, and economic conditions will provide a better understanding of the epidemiology, thus guiding policy-making and medical decisions in the prevention and management of these cancers.
Previous studies of genitourinary cancer have described the disease burden confined to a single kind of cancer or failed to reveal the differentiation of incidence, mortality, and risk factors between males and females for kidney and bladder cancer (4–7). In this study, we describe the up-to-date trends of national, regional, and global level mortality and disability-adjusted life years (DALYs) from 1990 to 2019 by age, sex, and socio-demographic index for kidney, bladder, prostate, and testicular cancer, according to the Global Burden of Diseases Study 2019 (GBD, 2019). We aim to reveal the changes over time in the incidence and burden of the four genitourinary cancers to provide references for health policy-making and medical management decisions.
## 2.1. Data source diseases definition
Input data from GBD 2019 was utilized to generate the estimation of genitourinary cancers incidence and burden. In the study, the data source for genitourinary cancers included hospital records, emergency department records, insurance claims, surveys, and vital registration systems globally. The methodology of data inputting, mortality estimation, and modeling for GBD 2019 has been comprehensively reviewed in previously published articles [8, 9]. The definition of genitourinary cancers counts on the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). In the GBD study, genitourinary cancers in the study have four dimensions: kidney cancer, bladder cancer, prostate cancer, and testicular cancer, their ICD-10 codes are stated as follows: C64-C65.9, D30.0-D30.1, and D41.0-D41.1 for kidney cancer; C67-C67.9, D09.0, D30.3, D41.4-D41.8, and D49.4 for bladder cancer; C61-C61.9, D07.5, D29.1, and D40.0 for prostate cancer; C62-C62.9, D29.2-D29.8, and D40.1-D40.8 for testicular cancer (Table 1S).
## 2.2. Socio-demographic index
The burden of genitourinary cancers was evaluated against country-level development restrained with the SDI [10, 11] which is a composite indicator of three indicators: lag-distributed income per capita, average educational attainment for people aged 15 years and older, and the total fertility rate (in people aged <25 years). The 204 countries and territories were classified into five groups: low SDI (<0·45), low-middle SDI (≥0·45 and <0·61), middle SDI (≥0·61 and <0·69), high-middle SDI (≥0·69 and <0·80), and high SDI (≥0·80) on the basis of the SDI values.
## 2.3. Risk factors
GBD risk factors are estimated based on a comparative risk assessment framework which includes six steps. First, the identification of risk-outcome pairs: only those risk-outcomes, which have convincing or plausible evidence according to the World Cancer Research *Fund criteria* [12], will be involved in GBD risk factor estimation. Second, relative risk (RR) as a function of exposure for each risk-outcome pair is estimated. Third, exposure for each risk factor is distributed by age, sex, location, and year. Fourth, the theoretical minimum risk exposure level (TMREL) is demonstrated. Fifth, the population attributable fraction (PAF) and attributable burden are measured. The PAF is modeled by the RR for each risk-outcome pair, exposure levels, and TMRE L [9]. The PAF of a particular risk factor is compounded by genitourinary cancer mortality to engender the mortality attributable to that risk factor. Finally, the PAF and attributable burden for the combination of risk factors are estimated. The methodology of these steps has been comprehensively reviewed in previously published articles [9]. Four [high body-mass index (BMI) for kidney cancer, occupational exposure to trichloroethylene for kidney cancer, high fasting plasma glucose for bladder cancer, and smoking for kidney, bladder and prostate cancer] of the 87 risk factors included in this GBD iteration have a non-zero contribution to the mortality of genitourinary cancers deaths. The percentage contribution of these four risk factors to genitourinary cancers is assessed.
## 2.4. Statistical analysis
In this study, the age-standardized incidence rate (ASIR), mortality rate (ASMR), and DALYs rate (ASDR), as well as the $95\%$ uncertain intervals ($95\%$ UI) are presented to show the epidemiology and burden of genitourinary cancers. We perform the $95\%$ UI in the estimation process with the Bayesian-based model DisMod-MR 2.1. The estimated annual percentage change (EAPC) was calculated via age-standardized rates (ASR) in each year from 1990 to 2019 to indicate the trends of ASIR, ASMRand ASYR with time. Supported by the assumption that the ASRs are linearly correlated with time, the estimation of EAPC was represented by the model y = a + bx + e. In this model, y represents log10 (ASR), while x indicates calendar year, and b stands for regression coefficient. The EAPC was calculated as EAPC = 100* (10∧b-1) based on this model. If the EAPC value and its lower bound of $95\%$ CI are above zero, ASR is considered to be in an upward trend, and vice versa. Spearman's rank order correlation was used to determine the correlation between the SDI and ASIR, ASMR and ASDR. Statistical significance was defined as the p-value < 0.05. R software (version 3.6.3) was used to perform all statistical analyses.
## 3.1. The incidence and temporal trends of four genitourinary cancers
Globally in 2019, there were 371,700 (131,700 females and 240,000 males) ($95\%$UI, 344,600 to 402,300) kidney cancer cases with an ASIR of 4.6 per 100,000, 524,300 (116,500 females and 407,008 males) ($95\%$ UI, 476,000 to 569,400) bladder cancer cases, with an ASIR of 6.5 per 100,000, 1,410,500 ($95\%$ UI, 1,227,900 to 1,825,800) prostate cancer cases with an ASIR of 4.6 per 100,000 and 109,300 ($95\%$ UI, 93,300 to 129,400) testicular cancer incident cases with an ASIR of 1.4 per 100,000 [1]. Compared to 1990, the ASIR increased for all four cancers in 2019, with the largest increase seen for testicular cancer ($45.5\%$; $95\%$ UI, 27.4 to $71.3\%$). At the regional level, Western European had the most incident cases and the largest ASIR for bladder cancer, despite a decreased incident rate. High-income North America had the most incidence cases in 2019 for both kidney and prostate cancer. The most incident cases of testicular cancer occurred in Western European, while the largest ASIR was in Southern Latin America in 2019. However, the largest increase in new cases for the four cancers was observed in East Asia (Table 1). Geographically, countries with high incidence rates of the four cancers were mainly located in high-income regions, such as North America, Europe, and Australia (Figure 1; Supplementary Figure 2S), while countries in Africa, the Middle East, and East Asia had relatively large increases of incidence rates, compared to those in developed regions (Supplementary Figure 1S).
## 3.2. The estimates of the disease burden attributable to four genitourinary cancers
Kidney, bladder, prostate, and testicular cancer were associated with 166,440; 228,730; 486,840; and 10,820 deaths, respectively (Supplementary Table 2S) worldwide in 2019. From 1990 to 2019, the ASMR has decreased for bladder cancer (−$15.71\%$; $95\%$ UI, −21.04 to −$8.59\%$), prostate cancer (−$9.46\%$; $95\%$ UI, −15.19 to −$0.05\%$), and testicular cancer (−$7.53\%$;$95\%$ UI, −15.88 to $1.35\%$) but has increased by $11.61\%$ ($95\%$ UI, 4.64 to $19.98\%$) for kidney cancer. This increase mainly came from males ($19.35\%$; $95\%$ UI, 10.46 to $30.02\%$). At the regional level, East Asia had the largest increase in ASMR ($85.13\%$; $95\%$ UI, 47.58 to $131.41\%$) between 1990 and 2019 for kidney cancer, followed by Central Europe ($83.17\%$; $95\%$ UI, 60.8 to $105.76\%$). Australia had the largest decrease in ASMR of bladder cancer (−$24.56\%$; $95\%$ UI, −31.37 to −$17.12\%$). High-income North America had the largest decrease for prostate cancer (−$24.92\%$; $95\%$ UI, −32.59 to $0.51\%$) and High-income Asia-Pacific had the largest decrease for testicular cancer (−$41.12\%$; $95\%$ UI, −46.8 to −$33.8\%$) (Supplementary Figure 4S). At the country level, Uruguay, Greenland, and Czechia were the three countries with the largest ASMR and ASDR in 2019 attributable to kidney cancer (Supplementary Figure 3S, Supplementary Table 3S). Mali led other countries in ASMR of bladder cancer (10.06 per 100,000; $95\%$ UI, 4.35 ~ 13.53 per 100,000) (Supplementary Figure 3S), Egypt was the leading country in bladder cancer attributable ASDR (201.75 per 100,000; $95\%$ UI, 132.14 ~ 294.39 per 100,000), meanwhile, Tonga had the largest ASDR (190.62 per 100,100;$91\%$ UI, 129.3~278.5 per 100,000) and ASMR (6.89 per 100,000; $95\%$ UI, 4.9~5.01 per 100,000) of testicular cancer. Countries with relatively high ASMR and ASDR associated with prostate cancer were mainly seen in Africa (Supplementary Figures 3S, 5S). The percentage change of ASMR between 1990 and 2019 showed the same geographical pattern as that of ASDR for all four cancers (Supplementary Figures 4S, 6S).
## 3.3. The association between incidences, the burden of the four genitourinary cancers, and SDI
From 1990 to 2019, the ASIR in different SDI regions gradually increased for kidney, bladder, prostate, and testicular cancer. During this period, high SDI regions had the largest ASIR each year compared to other regions. Spearman's rho correlation tests revealed a significantly positive association between ASIR and SDI for kidney cancer (rho, 0.8675; $p \leq 0.001$), bladder cancer (rho, 0.6928; $p \leq 0.001$), prostate cancer (rho, 0.551; $p \leq 0.001$), and testicular cancer (rho, 0.215; $p \leq 0.001$) (Figures 2, 3). For the disease burden, only in kidney cancer were the ASMR and ASDR positively correlated to SDI (Supplementary Figure 7S). In the past 30 years, the ASMR and ASDR of kidney cancer remained stable or slightly increased in different SDI regions, and gradual decreases were seen in most SDI regions for bladder and prostate cancer. As for testicular cancer, the ASMR and ASDR decreased gradually in the high and high-middle SDI regions but increased in the middle, low-middle, and low SDI regions (Supplementary Figure 8S).
**Figure 2:** *The correlation between global age standardized incidence rate (ASIR) and socio-demographic index (SDI) for kidney cancer (A) bladder cancer (B) prostate cancer (C) and testicular cancer (D) for both sexes in different regions.* **Figure 3:** *The temporal trends of age standardized incidence rate (ASIR) for kidney cancer (A) bladder cancer (B) prostate cancer (C) and testicular cancer (D) from 1990 to 2019, for both sexes by socio-demographic index (SDI).*
## 3.4. ASMR and ASDR for both sexes and different ages.
From 1990 to 2019, the ASMR and ASDR for bladder cancer were higher in males, and the trends decreased for both sexes in this period (Figure 4). A downward trend was also observed in males for prostate cancer. However, for kidney cancer, as the ASMR remained relatively stable and the ASDR slightly decreased in females, they both increased in males during this period. Meanwhile, an obvious downward trend in the ASMR and ASDR was observed in testicular cancer from 1990 to 2008. However, both ASMR and ASDR increased gradually over the next 11 years (Figure 4). For different ages, more deaths and DALYs were seen in males for both bladder and kidney cancer (Supplementary Figure 9S). The mortality rates and DALYs increased with age for the two cancers, accompanied by large differences between the two sexes (Figure 5). Prostate cancer mainly affected people older than 40 years of age, and the number of deaths and DALYs increased with age until 75 to 85 years of age, and then decreased (Supplementary Figure 9S). However, the death rates and DALYs always increased from the age of 40 years (Figure 5). As for testicular cancer, the number of deaths mainly increased in men aged 15 to 30 years old and older than 70 years old, and the number of DALYs increased in children aged 0 to 5 years old and men of 15 to 30 years old. However. they mainly decreased from the age of 40 years old (Figure 5).
**Figure 4:** *The temporal trends of global age standardized mortality rate (ASMR) and age standardized disability adjusted life year rate (ASDR) by sexes for kidney cancer (A, E) bladder cancer (B, F) prostate cancer (C, G) and testicular cancer (D, H) from 1990 to 2019.* **Figure 5:** *The change of global mortality rate and DALY rate per 100,000 people with age for kidney cancer (A, E) bladder cancer (B, F) prostate cancer (C, G) and testicular cancer (D, H) for both sexes in 2019.*
## 3.5. Risk factors for ASMR of the kidney, bladder, and prostate cancers
Figure 6; Supplementary Figure 11S show the risk factors for the ASMR of bladder, kidney, and prostate cancer in 2019. Smoking was a risk factor for mortality of all three cancers and was the leading risk factor for bladder cancer, and the only risk factor for prostate cancer. High fasting plasma glucose was another risk factor for bladder cancer and the trend of ASMR and ASDR caused by high fasting plasma glucose increased slightly in the past 15 years. For kidney cancer in both sexes, a high body-mass index had surpassed smoking to be the leading risk factor for ASMR and ASDR worldwide, as well as in all SDI regions, and remained the most important risk factor for ASMR and ASDR of kidney cancer in woman worldwide. However, smoking was still the top risk factor for ASMR and ASDR in male kidney cancer all over the world since 1990. Occupational exposure to trichloroethylene also accounted for a small portion of deaths in all SDI regions for kidney cancer (Figure 6; Supplementary Figures 10S, 11S). Globally, smoking-related deaths gradually decreased for the three cancers, but other risk factors related to death and DALYs increased for bladder and kidney cancer, indicating the important role of metabolic health in the management of bladder and kidney cancer.
**Figure 6:** *The temporal trends of global age standardized mortality rate (ASMR) attributable to risk factors of kidney cancer (A) bladder cancer (B) and prostate cancer (C) for both sexes from 1990 to 2019.*
## 4. Discussion
In this study, we found an increased age-standardized incidence rate for kidney, prostate, and testicular cancer in 2019, compared to 1990, indicating an increased incidence of these three cancers. In addition, the ASMR and ASDR increased for kidney cancer but decreased for bladder, prostate, and testicular cancer. As kidney, bladder, and prostate cancer are more likely to affect older people, the process of aging and the increased life expectancy might partially explain the growth in the incidences and the burden, especially in developed countries where population aging is more significant. In developing countries, the increase in the population also contributes to the increased cases of the four cancers. The total number of incidences of testicular cancer increased over the study period, probably as a result of population growth and the change in environments.
Of the four cancers evaluated, prostate cancer had more incident cases and a higher ASIR. From 1990 to 2019, the ASIR of prostate cancer in high SDI regions each year was greatly higher than that in the other SDI regions, which could be a result of the broadened prostate-specific antigen (PSA) screening for prostate cancer in developed countries that started in the 1990s [13]. However, the role of PSA screening in the detection and treatment of prostate cancer is still controversial [14]. A comprehensive meta-analysis suggested that PSA screening does not improve overall mortality, so the benefit of PSA screening and the risk of overdiagnosis and overtreatment should be balanced. Despite the increased incident cases and ASIR, the age-standardized mortality rate and DALYs both decreased for prostate cancer, as well as bladder and testicular cancer. These results revealed the advances in the management of the three cancers above, including advanced surgical strategies, novel chemotherapy agents, immune checkpoint inhibitors, and more effective hormone-based treatments [15, 16]. Unlike bladder, prostate, and testicular cancer, the mortality and DALYs burden increased in 2019, compared to 1990, for kidney cancer. Kidney cancer also caused considerable disability for younger patients in childhood compared to bladder and prostate cancers. Generally, the incidence rate of kidney cancer is high in more developed regions, such as Europe and North America, and low in less developed regions, such as Asia and South America [17]. Accordingly, this study also suggested that the ASMR and ASDR burden was also the largest in each year from 1990 to 2019 in high SDI regions. There was a significantly positive correlation between SDI and the ASDR or ASMR attributable to kidney cancer. Racial/ethnic factors and environmental exposures might have a role in the different kidney cancer incident patterns [18]. If the trends of kidney cancer incidence, and associated disability and mortality, continue to increase, additional medical and economic resources will be required, especially in low-income countries that lack adequate numbers of appropriately trained urologists and oncologists. Also, due to limited economic resources, most patients will have less access to standard and advanced treatment strategies. For bladder and prostate cancer, although the ASMR and ASDR remained stable or decreased in low and low-middle SDI regions, the curative and palliative care for cancer patients is still limited due to population growth and the ever-increasing incidence rate. The ASMR and ASDR of testicular cancer decreased gradually in high and high-middle SDI regions but they increased in the middle, low-middle, and low SDI regions. This was perhaps because of new therapeutic regimens and the implementation of evidence-based approaches that were widely adopted in higher SDI regions but not in the middle, low-middle, and low SDI regions [19].
Smoking is the main modifiable risk factor for bladder cancer and kidney cancer [20]. From 1990 to 2019, decreasing trends for smoking-related bladder, kidney, and prostate cancer mortality were observed in this study. However, smoking was still the leading risk factor for male kidney cancer death and bladder cancer death in all the years evaluated and was the only risk factor for prostate cancer identified in this study. The reduced smoking-related cancer death can be explained by the decreased smoking prevalence worldwide since 1990 [21]. Nevertheless, smoking remains the top risk factor for ASMR and ASDR in male kidney cancer all over the world since 1990, so there is still a vast base of smokers and one in four men worldwide smokes every day. Efforts to reduce smoking and their effectiveness also differ between countries and regions and may be affected by tobacco industry targeting (22–24). There is still much work to be done in reducing tobacco consumption and cancer prevention. Additional tobacco control and education on smoking cessation should be made to avoid adolescent initiation of smoking, as high rates still exist in this population in many countries [21].
Previous studies have reported obesity and a high BMI to be risk factors for kidney cancer incidences and mortality [25, 26]. Compared with people without obesity, the risk of kidney cancer increases by 1.32-fold in those with general and abdominal obesity [27]. In this study, the kidney cancer mortality rate attributable to high BMI has been increasing and persists as the leading risk factor for female kidney cancer deaths since 1999. In 2019, the contribution of a high BMI to kidney cancer death in both sexes even exceeded that of smoking. The high incidence mortality rate in high SDI regions might also be explained by the high proportion of people with obesity. As the obese population continues to grow worldwide [28], the prevention and management of kidney cancer will be more difficult. Obesity also contributed to bladder cancer incidence and related death [29, 30]. A gradual increase was observed in high fasting plasma glucose-associated bladder cancer mortality since 1990. Maintenance of metabolic health could be a reasonable strategy to reduce the incidence and burden of genitourinary cancers.
To our knowledge, this is the most updated estimate on genitourinary cancers epidemiology globally, which includes 204 countries and some that have not been assessed before. Furthermore, the risk factors attributable to kidney cancer and bladder cancer in different sexes were revealed for the first time. It is worth noticing that metabolic health plays an important role in the prevention of kidney cancer and bladder cancer, especially for females. However, this study has some limitations which are common for all GBD burden estimates [8, 9]. The major limitation is the availability and completeness of the source data. In addition, the source data were lacking in some regions and were predicted by covariates or trends from neighboring locations, leading to discrepant accuracy of estimates among different countries. Finally, the estimates in the most recent years also had large uncertainty due to lag in data availability, reflected in the broad uncertainty intervals of many estimates, which decreases the reliability of calculated incidences and burdens of specific cancers [31].
## 5. Conclusions
The past thirty years have seen an increased incidence of bladder, kidney, prostate, and testicular cancer, as well as increased disability and mortality to kidney cancer. High-income regions experience higher incidences and burdens attributable to these four cancers. The mortality rate of kidney, bladder, and prostate cancer increases with age and the majority of deaths are seen in elderly people, with smoking and obesity being the major causes. Smoking is still the leading risk factor for mortality for bladder, kidney, and prostate cancers, however, the role of obesity in genitourinary cancer mortality is becoming more and more important. Thus, additional tobacco control and education on smoking cessation, as well as healthy lifestyles, should be provided. Finally, the maintenance of metabolic health could be a reasonable strategy to reduce the incidence and burden of genitourinary cancers.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
D-WY had full access to all the data in the study, takes responsibility for the integrity of the data, and the accuracy of the data analysis. Study concept and design: RD and D-WY. Acquisition of data: J-CY and J-JH. Analysis and interpretation of data: Y-QT and J-CY. Drafting of the manuscript: Y-QT, J-JH, and J-CY. Critical revision of the manuscript for important intellectual content: J-JH, RD, and J-WS. Statistical analysis: Y-QT and J-WS. Supervision: D-WY and J-WS. Contributed equally to this work as co-first author: Y-QT, J-CY, J-JH, and RD. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1119374/full#supplementary-material
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|
---
title: Hypoxia-induced transcriptional differences in African and Asian versus European
diabetic cybrids
authors:
- Andrew H. Dolinko
- Marilyn Chwa
- Kevin Schneider
- Mithalesh K. Singh
- Shari Atilano
- Jie Wu
- M. Cristina Kenney
journal: Scientific Reports
year: 2023
pmcid: PMC9992459
doi: 10.1038/s41598-023-30518-x
license: CC BY 4.0
---
# Hypoxia-induced transcriptional differences in African and Asian versus European diabetic cybrids
## Abstract
Diabetic retinopathy (DR) is the most common diabetic microvascular complication and cause of blindness in adults under the age of 65. Our results suggest that, when comparing transcriptomes of cultures grown in hypoxic conditions versus room-air, cybrids containing mitochondria from African and Asian diabetic subjects ([Afr + Asi]/DM) have some uniquely different transcriptome profiles compared to European/diabetic (Euro/DM) cybrids (e.g., fatty acid metabolism: EnrichR rank 10 in [Afr + Asi]/DM, rank 85 in Euro/DM; Endocytosis: rank 25 in [Afr + Asi]/DM, rank 5 in Euro/DM; Ubiquitin Mediated Proteolysis: rank 34 in [Afr + Asi]/DM, rank 7 in Euro/DM). As determined by both RNA-seq and qRT-PCR results, transcription of the gene encoding oleoyl-ACP hydrolase (OLAH) was significantly increased in [Afr + Asi]/DM cybrids compared to Euro/DM cybrids in hypoxic conditions. Additionally, our results show that in hypoxic conditions, Euro/DM cybrids and [Afr + Asi]/DM cybrids show similar decreases in ROS production. All cybrids showed decreased ZO1-minus protein levels, but their phagocytic functions were not significantly altered in hypoxic conditions. In conclusion, our findings suggest that the "molecular memory" imparted by [Afr + Asi]/DM mtDNA may act through one of the molecular pathways seen in transcriptome analysis, such as fatty acid metabolism, without significantly changing essential RPE functions.
## Introduction
Over the past 40 years, diabetes mellitus (DM) has become one of the most widespread diseases globally. Within the past decade, its prevalence increased from 366 million people in 2011 to 451 million in 2017, and it is predicted to further increase to 693 million people by 20451,2. The characteristic hyperglycemia of this disease induces an inflammatory response that causes damage to blood vessels, resulting in the macrovascular (e.g., myocardial infarction) and microvascular complications, (e.g., nephropathy and peripheral neuropathy)3. Diabetic retinopathy (DR) is the most common diabetic microvascular complication and the most common cause of blindness in adults under the age of 654. In DR, high glucose promotes non-enzymatic glycation of proteins, leading to oxidative stress and inflammation. Together, these elements damage the intraretinal vasculature, which supports the neurons of the inner retinal layers, and the choriocapillaris, which nourishes the photoreceptors in the outer retina5.
In healthy individuals, a blood-retina barrier prevents vascular contents from leaking into the retina. The endothelial cells that line the intraretinal vessels tightly bind to each other through tight junctions proteins; however, in DR and related conditions, such as diabetic macular edema (DME), this barrier becomes more permeable6,7. The choriocapillaris, which provides nutrients to the outer retina, is fenestrated and consequently more prone to leaking its contents. To prevent vascular leakage from entering the outer retina, retinal pigment epithelial (RPE) cells form a barrier that governs molecular transport between the outer retina and the choroid vessels. In diabetes, the RPE acquires damage that impairs its barrier functions, and these dysfunctions are implicated in the development of DR and DME7,8.
The RPE performs multiple functions in order to maintain a homeostatic balance with photoreceptors and preserve the architecture of the outer neural retina. Normally, photoreceptors shed their outer segments full of retinaldehyde proteins, and the RPE phagocytoses these segments to convert the all-trans-retinal into 11-cis-retinal that photoreceptors can use to form new rhodopsins9,10. In diabetes, RPE cells are suspected to have lower phagocytotic activity as shown in one study that reported decreased phagocytosis of rod outer segments in RPE cells exposed to high glucose in vitro11. In order to constantly recycle photoreceptor outer segments, RPE cells are highly metabolic, generating a substantial amount of reactive oxygen species (ROS) from mitochondrial oxidative phosphorylation (OXPHOS) in the process12. Diabetes leads to a decline in the ability of retinal cells to reduce ROS as levels of antioxidants, such as glutathione, are decreased13. Additionally, the RPE forms the outer portion of the blood-retina barrier through tight junctions proteins, such as zonula occludens-1 (ZO-1) and occludin14,15. In diabetes, these proteins are depleted, contributing to a decrease in barrier function16–18.
While many studies have examined the effect of diabetes-induced changes in mitochondrial function19,20 or the effect of mtDNA damage21,22 on cellular health, none have examined the role of mitochondrial haplogroups on RPE function in diabetes. The ancestral origin of individuals can be categorized into haplogroups, which are defined by an accumulation of specific single nucleotide polymorphisms (SNPs) within their mtDNA23. Previously, we demonstrated that mitochondria from diabetic patients of African or Asian ancestry confer resistance against hyperglycemic and hypoxic stressors in RPE cybrid cells24. In this investigation, we used our cytoplasmic hybrid (cybrid) RPE cells with mitochondria from diabetic (DM) or nondiabetic (non-DM) patients of African and Asian ([Afr + Asi]) or European (Euro) haplogroups to explore how mtDNA affects the transcriptomes and functions of RPE cells. Our findings from this study suggest that cybrids containing mitochondria from African and Asian diabetic [Afr + Asi]/DM] subjects have significantly different transcriptomes than Euro/DM cybrids cultured in hypoxic conditions. In addition, our results show that RPE cybrids with Euro/DM or [Afr + Asi]/DM exhibit similar changes in phagocytic function and tight junction protein expression when cultured in hypoxia. Overall, our findings suggest that the unique "molecular memory" imparted by [Afr + Asi]/DM mtDNA may occur through one of the molecular pathways observed in transcriptome analysis without changing the essential RPE functions.
## Human subjects
Research involving human subjects was approved by the Institutional Review Board of the University of California, Irvine (#2003-3131). All enrolled patients provided written, informed consent. Clinical investigations were performed based on the ethical principles of the Declaration of Helsinki26.
## Cybrid generation
Patient blood was collected in tubes containing $3.2\%$ sodium citrate before being subjected to multiple centrifugations to isolate platelets. Platelet pellets were resuspended in Tris buffered saline (TBS). ARPE-19 cells, an RPE cell line (American Type Culture Collection (ATCC), Manassas, VA), were sequentially passaged in low-dose ethidium bromide to depleted cells of mtDNA (Rho0). Rho0 ARPE-19 cells were cultured in standard culture media ($10\%$ FBS, 100 units/mL Penicillin, 100 mcg/mL Streptomycin, 25 μg/mL Fungizone (Amphotericin B: Omega Scientific, Torzana, CA), and 50 μg/mL Gentamycin in DMEM/F12) supplemented with uridine25. Then, platelets were fused with Rho0 cells in polyethylene glycol-containing media in order to generate cybrids, as per a modified protocol by Chomyn26. Cybrids were cultured in standard culture media alone to select for RPE cells that successfully integrated mitochondria. The mtDNA incorporation was verified using a combination of polymerase chain reaction (PCR) and restriction enzyme digestion of these PCR products. In addition, mtDNA inclusion was verified through mtDNA sequencing to identify the mtDNA haplogroup for each cybrid27. The cohorts of Euro and [Afr + Asi] cybrids were generated from individuals of similar ages ($$p \leq 0.11$$), genders, and DM versus Non-DM status (Supplemental Table S1 and S2). In the present study, our cybrids were cultured in media containing 17.5 mM glucose.
## RNA-Seq analyses
RNA Extraction—Cybrids (Euro/Non-DM, $$n = 4$$; Euro/DM, $$n = 4$$; [Afr + Asi]/Non-DM, $$n = 4$$; and [Afr + Asi]/DM cybrids, $$n = 4$$) were cultured in 6-well plates at 500,000 cells/well in duplicate and incubated overnight at 37 °C with $5\%$ CO2, then in 2 mL standard media at 37 °C with $5\%$ CO2 in either room-air (~ $21\%$ O2) or in a $2\%$ O2 incubator (MCO-18 M O2/CO2 Incubator, Sanyo, Osaka, Japan) for 48 h. Duplicate cultures grown in identical conditions were combined and pelleted by centrifugation before isolating RNA using a PureLink RNA Mini Kit (Invitrogen, Waltham, MA) as per the manufacturer’s protocol. RNA was quantified on a NanoDrop 1000 spectrophotometer (Thermo Scientific, Waltham, MA).
RNA Library Preparation and RNA-Seq—RNA from each sample was analyzed for quality control through a combination of 2100 Bioanalyzer Nano RNA chip (Agilent Technologies, Santa Clara, CA) and NanoDrop ND-1000 (Thermo Scientific) absorbance ratios ($\frac{260}{280}$ nm and $\frac{260}{230}$ nm). Library construction was performed as per the TruSeq Stranded mRNA protocol (Illumina, San Diego, CA). For each sample, 500 ng of total RNA was enriched for mRNA using oligo-dT magnetic beads and subsequently chemically-fragmented for three minutes. Then, first-strand synthesis was performed, using random primers and reverse transcriptase to make single-stranded cDNA. After second-strand synthesis, the resultant double-stranded cDNA was cleaned using AMPure XP beads (Beckman Coultier, Brea, CA). Cleaned cDNA was end-repaired, and then the 3’ ends were adenylated. Illumina barcoded adapters were ligated on the ends of the cDNA and the adapter-ligated fragments were enriched by nine cycles of PCR. The resulting libraries were validated by qPCR and sized by the Agilent Bioanalyzer DNA high sensitivity chip. The concentrations for the libraries were normalized before multiplexing the libraries together and then sequenced using single end 100 cycles chemistry on a HiSeq 4000 sequencer (Illumina).
RNA-Seq Output Analysis—After acquiring the reads, the data were analyzed for quality control using FastQC. Reads were then quality and adaptor trimmed using Trimmomatic28,29. Trimmed reads were then aligned to the human hg19 reference genome using a splice aware short read sequence alignment tool, TopHat230. Gene expression was quantified by raw read counts using featureCounts and Reads-Per-Kilobase-per-Million mapped reads (RPKM) using Cufflinks31,32.
Quantified data were then placed into 8 groups based on whether the cybrid from which the RNA originated had Euro or [Afr + Asi] mtDNA, was from a DM or Non-DM patient, or was grown in room-air or hypoxia. *Differential* gene expression analysis between selected groups was performed using an R-based tool, DESeq233. ( Negative Binomial model, wald test) with a FDR adjusted p value threshold 0.1.
Differentially-expressed gene lists from comparisons of DM cybrids in room-air with their respective hypoxic samples ([Afr + Asi/DM/Room-Air] versus [Afr + Asi/DM/Hypoxia]) and ([Euro/DM/Room-Air] versus [Euro/DM/Hypoxia]) were analyzed for pathway enrichment terms using EnrichR34–36. The top 10 differentially-expressed pathways for each comparison were presented, using the Kyoto Encyclopedia of Genes and Genomes 2016 (KEGG 2016) classification where unique pathways in the top 10 lists were highlighted.
## ROS assay
ROS levels were measured in our cybrids ($$n = 4$$ for all groups) using a variation of the methods we have previously described37. Experiments were performed in sextuplicate. Briefly, cybrids were plated in 100 μL of standard culture media at a density of 10,000 cells/well in 2 separate 96-well, black well, clear-bottom plates, incubated overnight at 37 °C with $5\%$ CO2, and subsequently incubated 48 h at 37 °C with $5\%$ CO2 in either room-air or $2\%$ O2. After incubation, media were replaced with 100 μL of 10 μM dichlorodihydrofluorescein diacetate (H2DCFDA; Invitrogen) in Dulbecco’s PBS (DPBS; Gibco, ThermoFisher Scientific, Waltham, MA), and plates were incubated at 37 °C without additional CO2 for 30 min. Subsequently, H2DCFDA solution was removed and replaced with 100 μL DPBS. Fluorescence was measured using a SpectraMax Gemini XPS fluorescent plate reader (Molecular Devices, San Jose, CA) with excitation at 492 nm and emission at 520 nm. For each cybrid, values were normalized to the average fluorescence of samples cultured in room-air.
## Phagocytosis assay
Phagocytosis was measured in our cybrids ($$n = 4$$ Euro/Non-DM, $$n = 5$$ Euro/DM, $$n = 3$$ [Afr + Asi]/Non-DM, $$n = 3$$ [Afr + Asi]/DM) using a variation of the methods described by Vo et al.38 All experiments were performed in triplicate.
Culture of Cybrids With Fluorescent Beads—For each cybrid, cells were seeded at a density of 500,000 cells/well in 2 mL of standard culture media in 2 separate 6-well plates and then incubated overnight at 37 °C with $5\%$ CO2. Media were replaced with 2 mL of a solution of 1 μm, fluorescently-tagged latex microbeads (Fluoresbrite® YG Microspheres 1.00 μm; Polysciences, Inc., Warrington, PA) diluted 1:3,000 in standard culture media. For each cybrid, 1 plate was incubated 48 h at 37 °C with $5\%$ CO2 in $2\%$ O2 while the other plate was incubated for 48 h at 37 °C with $5\%$ CO2 in room-air.
After incubation, wells were briefly washed 3 times with 2 mL PBS-EDTA per wash and trypsinized. For each well, 2 mL of standard medium was added before pipetting each content into a separate 15 mL conical tube. Then, 1.5 mL of standard media was added to each well and then added to its respective 15 mL conical tube. Cell suspensions were then titrated with a 5 mL pipette to separate into single cells, strained through separate 35 μm nylon filters into separate 5 mL test tubes (Corning™ Falcon™ Test Tube with Cell Strainer Snap Cap; Corning Inc., Corning, NY), and centrifuged for 5 min at 1000 RPM. For each tube, media were carefully removed, and the pellet was resuspended in 50 μL of PBS-EDTA.
Flow Cytometry Analysis of Phagocytosis—Each sample was loaded into an ImageSteamX Mark II Imaging Flow Cytometer (Luminex Corp., Austin, TX), excited using a 488 nm laser, and imaged at 40 × magnification. 5,000 images were collected for each sample.
Imageset data were then analyzed using IDEAS software (Luminex Corp.) Briefly, images were first gated by object diameter to isolate images with single cells. Then, this subset was gated by fluorescence to determine the images containing microspheres. Finally, this smaller subset was gated to identify images where microspheres had been internalized. To do this, the software generates a “mask” of the cell area, “erodes” this area by a few pixels from the outer edge, and then determines if a fluorescent signal is located within the eroded mask. An internalization ratio was then calculated as follows: [Single Cells that Internalized Beads] / [Total Single Cells]. For each cybrid and condition, this ratio was then normalized to the ratio of its age-matched and haplogroup-matched, Non-DM cybrid cultured in room-air.
## Western blot of tight-junctions proteins
Protein Extraction from Cybrid Cultures—For each cybrid, cells were seeded at a density of 500,000 cells/well in 2 mL of standard culture media in all wells of 2 separate 6-well plates and then incubated at 37 °C with $5\%$ CO2 for 19 days. Then, for each cybrid, 1 plate was incubated 48 h at 37 °C with $5\%$ CO2 in $2\%$ O2 while the other plate was incubated under same conditions in room-air. Each well was lysed in 250 µL of a radioimmuniproecipitation assay (RIPA) buffer-based solution (RIPA Lysis Buffer (MilliporeSigma, Burlington, MA), PhosphataseArrest™ I (Gbiosciences, St. Louis, MO), and Protease Inhibitor Cocktail (MilliporeSigma). For each plate, lysates from wells in the same column were combined and placed in a 1.5 mL microcentrifuge tube. Lysates were centrifuged for 15 min at 14,000 × g at 4 °C and supernatants were collected. Clarified lysate protein concentrations were measured using the Pierce™ BCA Protein Assay kit (Thermo Scientific). Experiments were performed in triplicate.
Immunoblotting for Tight Junctions Proteins—For each sample, 75 μg of protein was denatured in protein loading buffer (lauryl dodecyl sulfate (Bolt™ LDS Sample Buffer; Life Technologies, ThermoFisher Scientific); dithiothreitol (Bolt™ Sample Reducing Agent; Life Technologies) at 95 °C for 5 min. Twenty μg of protein in loading buffer of each sample was loaded into the wells of 4–$12\%$ Bolt™ Mini Gels (Life Technologies) with protein ladder (Precision Plus Protein™ Dual Color Standards; BioRad, Hercules, CA) and subjected to electrophoresis for 1–2 h at 100 V. Proteins were then transferred from the gels onto PVDF membranes (ImmunBlot®; BioRad) using wet transfer for 1 h at 20 V. Membranes were blocked in $5\%$ milk in 1 × TBST (Tris Base (Trizma®; MilliporeSigma), $0.1\%$ Tween-20 (Fisher Scientific, ThermoFisher Scientific)) at room temperature for 1 h. The membrane was then cut horizontally in parts at 75 kDa ladder band with one part containing proteins less than 75 kDa and the other part containing proteins larger than 75 kDa. The membrane carrying less than or equal to 75 kDa was incubated overnight at 4 °C in $5\%$ milk in 1 × TBST with β-actin and Occludin antibodies, while the other part of the membrane was incubated with Z1 antibody (Supplemental Table S3).
Membranes were washed in 1 × TBST and incubated with secondary antibody (Supplemental Table S3 in $5\%$ milk in 1 × TBST for 1 h at room temperature. Membranes were washed again in 1 × TBST. Protein bands were detected using SuperSignal™ West Femto Maximum Sensitivity Substrate (Thermo Scientific) as per manufacturer’s instructions. The two parts of the same membrane were wrapped in plastic wrap and placed in gel doc imaging. We took a bright field image prior turning on the ECL camera to show that the two parts of the membrane were cut from the same membrane but stained with different antibodies. β-actin was used as a housekeeping protein control. Chemiluminescent images were captured using a ChemiDoc MP imager (BioRad) and quantified using ImageJ (National Institutes of Health, Bethesda, MD).
## Statistical analysis
Unless otherwise indicated, data were compared using Unpaired Student’s t-test or One-Way ANOVA with Bonferonni’s Post-Test (GraphPad Prism, version 5.0, GraphPad Software, CA). Results were adjusted using a Bonferonni correction where appropriate. Results with p ≤ 0.05 were considered statistically significant.
## Ethics approval
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the University of California, Irvine’s Institutional Review Board (UCI IRB#2003-3131).
## Consent to participate
Informed consent was obtained from all individual participants included in the study.
## African + Asian and European cybrids show unique differences in transcriptomes between hypoxic and room-air cultures
The molecular mechanisms underlying our previous observations were characterized by performing high-throughput RNA-seq on our cybrid population in room-air and hypoxic conditions. The transcriptomes of the DM cybrids cultured in room-air and hypoxia were compared as follows: [Afr + Asi]/DM/Room-*Air versus* [Afr + Asi]/DM/Hypoxia (Table 1) and Euro/DM/Room-*Air versus* Euro/DM/Hypoxia (Table 2). Genes that were significantly expressed between the two conditions were then analyzed using the EnrichR online software to determine the top-ranked, differentially-expressed pathways, as categorized by the KEGG 2016 enrichment terms. For both the Euro and [Afr + Asi] comparisons, differential gene expression of certain pathways was highly-ranked, such as those involving protein processing in the endoplasmic reticulum (3rd for [Afr + Asi], 2nd for Euro), metabolic pathways (2nd for [Afr + Asi], 3rd for Euro), and carbon metabolism (4th in both lists) (Tables 1 and 2). However, other pathways were more highly-ranked for the Euro/DM cybrids than the [Afr + Asi]/DM cybrids. In particular, more genes in pathways associated with endocytosis (25th for [Afr + Asi]/DM, 5th for Euro/DM) and degradation of ubiquitylated proteins (34th for [Afr + Asi]/DM, 7th for Euro/DM) were differentially-expressed in the Euro/DM cybrids comparison than that of the [Afr + Asi]/DM cybrids. In addition, more genes in pathways associated with fatty acid metabolism were differentially-expressed in the comparison of [Afr + Asi]/DM cybrid transcriptomes than in the comparison of Euro/DM transcriptomes (10th for [Afr + Asi], 85th for Euro). These findings indicate that the Euro/DM mitochondria differentially modulate the nuclear gene expression compared to the [Afr + Asi]/DM mitochondria because all of the cybrid cell lines possess identical nuclei but differ only in the mitochondria from different haplogroup populations. Table 1EnrichR *Pathway analysis* of differentially-expressed genes between Afr + Asi/DM cybrids cultured in room air versus $2\%$ oxygen. African and Asian [Afr + Asi] DM, hypoxia versus room airRankTermOverlapAdjusted p-valueZ-scoreCombined score1Ribosome_Homo sapiens_hsa$\frac{0301083}{1373.36816}$E−21− 1.7461492.231182Metabolic pathways_Homo sapiens_hsa$\frac{01100378}{12391.30460}$E−13− 1.9867668.850483Protein processing in endoplasmic reticulum_Homo sapiens_hsa$\frac{0414170}{1691.77845}$E−07− 1.7665535.549954Carbon metabolism_Homo sapiens_hsa$\frac{0120049}{1136.29999}$E−06− 1.6346226.593385Lysosome_Homo sapiens_hsa$\frac{0414250}{1234.15835}$E−05− 1.6741323.703206Valine, leucine and isoleucine degradation_Homo sapiens_hsa$\frac{0028025}{481.09485}$E−04− 1.7739023.075107TNF signaling pathway_Homo sapiens_hsa$\frac{0466843}{1105.61712}$E−04− 1.7531019.667678Neurotrophin signaling pathway_Homo sapiens_hsa$\frac{0472245}{1209.57908}$E−04− 1.6726117.451529Inflammatory mediator regulation of TRP channels_Homo sapiens_hsa$\frac{0475038}{981.15460}$E−03− 1.6635816.4348910Fatty acid metabolism_Homo sapiens_hsa$\frac{0121223}{489.70756}$E−04− 1.4531814.9896311HIF-1 signaling pathway_Homo sapiens_hsa$\frac{0406639}{1031.39184}$E−03− 1.4398313.6564212Insulin resistance_Homo sapiens_hsa$\frac{0493140}{1092.37148}$E−03− 1.4373812.7800418AGE-RAGE signaling pathway in diabetic complications_Homo sapiens_hsa$\frac{0493336}{1017.02269}$E−03− 1.4328110.7507725Endocytosis_Homo sapiens_hsa$\frac{0414475}{2591.54455}$E−02− 1.176267.4740634Ubiquitin mediated proteolysis_Homo sapiens_hsa$\frac{0412043}{1372.17697}$E−02− 0.669963.84699The top 10 pathways, along with others that are much more differently ranked between the Euro/DM and [Afr + Asi]/DM comparisons are listed. Table 2EnrichR pathway analysis of differentially-expressed genes between Euro/DM cybrids cultured in room air versus $2\%$ oxygen. European (Euro) DM, *Hypoxia versus* room airRankTermOverlapAdjusted p-valueZ-scoreCombined score1Ribosome_Homo sapiens_hsa$\frac{0301085}{1371.47075}$E−21− 1.7461493.678012Protein processing in endoplasmic reticulum_Homo sapiens_hsa$\frac{0414171}{1695.56805}$E−07− 1.7904334.712903Metabolic pathways_Homo sapiens_hsa$\frac{01100352}{12392.40768}$E−06− 1.9619234.369754Carbon metabolism_Homo sapiens_hsa$\frac{0120046}{1134.07692}$E−04− 1.6346219.777075Endocytosis_Homo sapiens_hsa$\frac{0414484}{2592.87911}$E−03− 1.8388117.868646AGE-RAGE signaling pathway in diabetic complications_Homo sapiens_hsa$\frac{0493339}{1013.61021}$E−03− 1.8333416.696107Ubiquitin mediated proteolysis_Homo sapiens_hsa$\frac{0412051}{1372.00388}$E−03− 1.5499015.938238HIF-1 signaling pathway_Homo sapiens_hsa$\frac{0406640}{1032.87911}$E−03− 1.6550615.642009Lysosome_Homo sapiens_hsa$\frac{0414246}{1232.87911}$E−03− 1.6238815.5112510Insulin resistance_Homo sapiens_hsa$\frac{0493141}{1093.93832}$E−03− 1.5964214.0793285Fatty acid metabolism_Homo sapiens_hsa$\frac{0121218}{486.91730}$E−020.12171− 0.55465The top 10 pathways, along with others that are much more differently ranked between the Euro/DM and [Afr + Asi]/DM comparisons are listed.
In addition, we performed a pairwise comparison of transcriptome data between Euro/DM cybrids and [Afr + Asi]/DM cybrids cultured in hypoxia. The analysis revealed that the transcript for oleoyl-ACP hydrolase (OLAH), an enzyme involved in medium fatty acid chain synthesis, was significantly more enriched in [Afr + Asi]/DM cybrids than Euro/DM cybrids (Table 3). qRT-PCR for OLAH in our DM/Hypoxia samples revealed that OLAH was approximately fourfold higher in [Afr + Asi] samples than Euro samples ($$p \leq 0.045$$) (Fig. 1). Moreover, the mitochondrial DNA (mtDNA) copy numbers of Non-DM and DM cybrids were not significantly different for both groups of cybrids (Euro/DM: 0.84 ± 0.19—fold of Euro/Non-DM, $$p \leq 0.38$$; Afr + Asi/DM: 0.98 ± 0.13—fold of Afr + Asi/DM, $$p \leq 0.89$$) (Supplemental Figure S1).Overall, these results suggest that genes in the pathways of endocytosis, ubiquitylated protein degradation, and fatty acid metabolism (such as OLAH) are differentially-expressed when different mtDNA haplogroup profiles are present. Table 3The OLAH transcript is more enriched in [Afr + Asi]/DM cybrids cultured in hypoxia than similarly-treated Euro/DM cybrids. DM/Hypoxia, *Euro versus* [Afr + Asi]GeneLog[2] of fold-changeAdjusted p valueOLAH − 2.4592204490.009283141Pairwise analysis of transcripts from Euro/DM/Hypoxia and [Afr + Asi]/DM/Hypoxia cultures. Results were normalized to the [Afr + Asi]/DM/Hypoxia transcripts (Log[2] of Fold Change for Non-Euro = 0).Figure 1[Afr + Asi]/DM cybrids express more OLAH transcript in hypoxia than similarly-treated Euro/DM cybrids. RNA from DM cybrids cultured 48 h in $2\%$ O2 was analyzed by qRT-PCR to measure transcript levels of the Oleoyl-ACP Hydrolase (OLAH) gene and compare expression between Euro/DM and [Afr + Asi]/DM cybrids. The data were normalized to average OLAH expression in Euro/DM cybrids (Fold-Change = 1).
## ROS levels decrease in hypoxia for European and African + Asian cybrids
To determine if mtDNA haplogroup and diabetes status affect the cellular functions of RPE cells, we performed an assay to detect ROS levels in our cybrids (Fig. 2). In hypoxic conditions, ROS levels significantly decreased for all groups as compared to cybrid cultures grown in room-air (Euro/Non-DM: 0.73 ± 0.05-fold of ROS levels in room-air cultures, $$p \leq 0.0020$$; Euro/DM: 0.77 ± 0.08-fold, $$p \leq 0.029$$; [Afr + Asi]/Non-DM: 0.62 ± 0.05-fold, $$p \leq 0.0003$$; [Afr + Asi]/DM: 0.77 ± 0.09,-fold $$p \leq 0.039$$) (Fig. 2). For both Euro and [Afr + Asi] haplogroup cohorts, the relative ROS levels were not significantly different between DM and Non-DM cybrids cultured in hypoxia (Euro/Non-DM/Hypoxia vs. Euro/DM/Hypoxia, $p \leq 0.05$; [Afr + Asi]/Non-DM/Hypoxia vs. [Afr + Asi]/DM/Hypoxia, $p \leq 0.05$). Altogether, these findings suggest that ROS levels similarly decrease in RPE cells in hypoxia, regardless of mtDNA haplogroup and diabetes history. Figure 2ROS levels decrease in hypoxia for European and [Afr + Asi], DM and Non-DM cybrids. ROS levels in cybrids cultured 48 h ± $2\%$ O2 was measured using an H2DCFDA-based assay. For each group of cybrids, data were normalized to relative fluorescence of cells cultured in room air.
## European and African + Asian cybrids retain phagocytic function in hypoxia
To determine if mtDNA haplogroup differentially influences phagocytic activity, cybrids were cultured in the presence of 1 μm fluorescent beads for 48 h in either room-air or $2\%$ O2, and bead internalization was measured using flow cytometry. In room-air, the Euro/Non-DM cybrids and Euro/DM cybrids showed similar phagocytic activity of internalized beads phagocytic activity (0.88 ± 0.13-fold, $$p \leq 0.42$$). In addition, phagocytic activity showed a decreased, though not statistically significant, trend in phagocytosis when Euro/Non-DM or Euro/DM cybrids were cultured in hypoxia (Euro/Non-DM/Hypoxia: 0.98 ± 0.04-fold, $$p \leq 0.81$$; Euro/DM/Hypoxia: 0.72 ± 0.17-fold, $$p \leq 0.20$$) (Fig. 3A). Phagocytic activities of [Afr + Asi] cybrids, both diabetic and non-diabetic, were mostly preserved in hypoxic conditions ([Afr + Asi]/Non-DM/Hypoxia: 0.92 ± 0.06-fold of internalized beads in [Afr + Asi]/Non-DM/Room-Air cultures, $$p \leq 0.22$$; [Afr + Asi]/DM/Hypoxia: 0.95 ± 0.05-fold, $$p \leq 0.33$$). The phagocytic activity in [Afr + Asi]/DM cybrids cultured in room-air was comparable to that in similarly-treated [Afr + Asi]/Non-DM cybrids (0.98 ± 0.07-fold, $$p \leq 0.84$$) (Fig. 3B). In our RNA-Seq data, transcripts of genes involved in phagocytosis, such as clathrin (CTLA), MERTK, and RAC1, showed similar changes between room-air and hypoxic cultures for all cybrids (Supplemental Table 4). Overall, these data suggest that phagocytosis is not significantly altered in hypoxia, regardless of mitochondrial diabetic status and mtDNA haplogroup. Figure 3Phagocytic function is retained in hypoxia for European and African + Asian, DM and Non-DM cybrids. Phagocytosis in our cybrids cultured 48 h ± $2\%$ O2 was measured using a fluorescent bead internalization-based assay for (A) European cybrids and (B) [Afr + Asi] cybrids. For both groups of cybrids, data were normalized to bead internalization for Non-DM cells cultured in room air. Afr + Asi = African + Asian; Euro = European; DM = Diabetic.
## European and African + Asian cybrids show decreases in ZO-1α-minus protein in hypoxia
Since the RPE forms part of the blood-retina barrier through tight junctions, we investigated whether mtDNA background and diabetes history affect levels of tight junction proteins, such as ZO-1 and occludin (Fig. 4A,D). For all groups, cybrids cultured in $2\%$ O2 showed levels of occludin that were not significantly different from those in cybrids cultured in room-air (Euro/Non-DM: 0.84 ± 0.25-fold of protein levels in room-air cultures, p ≈ 1; Euro/DM: 0.77 ± 0.16-fold, $$p \leq 0.58$$; [Afr + Asi]/Non-DM: 1.65 ± 0.42-fold, $$p \leq 0.57$$; [Afr + Asi]/DM: 0.92 ± 0.25-fold, p ≈ 1). In contrast, OCLN transcripts were lower in most hypoxia-treated cultures than in room-air cultures in the RNA-Seq data, suggesting that future production of occludin protein would be similarly decreased among all cybrids cultured in hypoxia (Supplemental Table 5). Using western blot analyses, the ZO-1 protein levels were examined (Fig. 4B,D). The cybrid cultures expressed two different isoforms, ZO-1α-plus and ZO-1α-minus, which differ by a span of 80 amino acids. While ZO-1α-plus is expressed on the surface of many epithelial cell types, the ZO-1α-minus isoform is only expressed on endothelial cells and specialized epithelial cells, including Sertoli, renal glomerular, and RPE cells39,40. For all groups, cybrids cultured in $2\%$ O2 exhibited levels of ZO-1α-plus that were not significantly different to those in cells cultured in room-air (represented by red dotted line, value = 1) (Euro/Non-DM: 1.04 ± 0.29-fold, p ≈ 1; Euro/DM: 0.98 ± 0.25-fold, p ≈ 1; [Afr + Asi]/Non-DM: 2.64 ± 1.84-fold, p ≈ 1; [Afr + Asi]/DM: 1.08 ± 0.52,-fold p ≈ 1) (Fig. 4C). On the other hand, ZO-1α-minus levels were significantly lower in hypoxia-treated cultures than those of cells cultured in room-air for Euro/Non-DM (0.27 ± 0.03-fold, $p \leq 0.0003$), Euro/DM (Euro/DM: 0.38 ± 0.07-fold, $$p \leq 0.0003$$), and [Afr + Asi]/Non-DM cybrids (0.40 ± 0.08-fold, $$p \leq 0.0048$$). ZO-1α-minus levels showed a decreased trend in [Afr + Asi]/DM cybrids, though this difference was not statistically significant (0.47 ± 0.17-fold, $$p \leq 0.11$$) (Fig. 4B,D). This finding is supported by the RNA-seq results showing that TJP1 (ZO-1 gene) transcripts were lower in all hypoxia-treated cultures than in room-air cultures, based on the RNA-Seq results (Supplemental Table 4). Overall, these data suggest that mtDNA haplogroup and diabetes status do not significantly alter levels of tight junction proteins in hypoxia. Figure 4Tight junction protein levels are similarly changed in hypoxia for European and [Afr + Asi], DM and Non-DM cybrids. Tight junction proteins in our cybrids cultured 48 h ± $2\%$ O2 was measured using a western blot assay for (A) occludin, (B) the 205 kDa ZO-1α-minus isoform, and (C) the 225 kDa ZO-1α-plus isoform. For both groups of cybrids, data were normalized to protein levels in cybrids cultured in room air (red dotted line, value = 1). Afr + Asi = African + Asian; Euro = European; DM = Diabetic.
## Discussion
In diseases such as DR and age-related macular degeneration (AMD), hypoxia induces changes within the RPE. Previous studies have shown that RPE cells exposed to hypoxia have decreased ATP, lower cytochrome oxidase activity, and increased levels of cytokines compared to those exposed to room-air41–43. In this study, our RNA-seq data showed that when we compared transcriptomes of DM cybrids cultured in hypoxia to those in room-air, certain gene pathways were more highly enriched for African + Asian [Afr + Asi] cybrids than European (Euro) cybrids, and vice-versa. In particular, we found that Euro cybrids in hypoxia versus room-air had more differentially-expressed genes associated with endocytosis and ubiquitin-mediated proteolysis than [Afr + Asi] cybrids. This suggests that the initial response of Euro cybrids to hypoxia uniquely involves modification of these pathways, and that further experiments are needed to clarify how the mtDNA modifies these pathways and which factors are specifically targeted. Similarly, we found that [Afr + Asi] cybrids in hypoxia versus room-air had more differentially-expressed genes associated with fatty acid metabolism than Euro cybrids. Since we found that [Afr + Asi]/DM cybrids showed increased resistance to hypoxic and hyperglycemic stresses, one may speculate this pathway contributes to the protection imparted by [Afr + Asi]/DM mtDNA.
Furthermore, OLAH expression was significantly elevated in [Afr + Asi]/DM cybrids compared to Euro/DM cybrids. OLAH encodes for Oleoyl-ACP Hydrolase, an enzyme involved in medium fatty-acid chain synthesis. Originally called thioesterase II, OLAH was observed to be specifically expressed in normal and tumor-derived breast epithelial tissue, with elevated serum levels in rat models of breast cancer44,45. However, expression of OLAH can be induced in other cell types in disease conditions. For example, monocytes from patients affected by rheumatoid arthritis expressed significantly higher levels of OLAH than monocytes from patients with osteoarthritis46. In addition, in a study of children infected by an influenza virus, OLAH was significantly enriched in patients who exhibited neurologic symptoms, such as seizures or loss of consciousness, as well as in patients with pneumonia compared to patients without any of these symptoms47.
This is particularly interesting as the pathology of retinal diseases, such as accumulation of lipid-rich drusen deposits in the Bruch’s membrane in AMD, is associated with altered fat metabolism of RPE cells48. In particular, the Friedlander group generated mouse models of hypoxia that lack Von-Hippel Lindau protein, which inhibits hypoxia-inducible factors (Hifs), and found these mice had thickened choroid with dilated blood vessels. However, double knockouts of Vhl and Hif2a did not show changes in choroid thickness, suggesting that the process is driven by Hif2a. Similarly, microarray analysis revealed that the hypoxia mouse model significantly downregulated levels of thioesterases, such as Acot7 and Acot8, compared to uninduced littermates, while this was not observed in the Vhl / Hif2a double knockouts49. Their study suggested that enzyme activity similar to that of OLAH is decreased in hypoxic RPE. Since we found that [Afr + Asi]/DM cybrids thrive in hypoxic conditions while this was not observed in the Euro/DM cybrids, one may speculate that the increased OLAH expression in [Afr + Asi]/DM cybrids allows them to use fatty acids for metabolism and avoid the accumulation of lipid deposits in RPE correlated with disease.
Since the RPE expend large amounts of energy to maintain the outer retinal microenvironment50, it generates high levels of ROS that must be managed. Previous work has shown that elevated ROS develop in RPE models of AMD and DR, particularly in response to hyperglycemia51. In our study, we discovered that hypoxia induced significant decreases in ROS for all cybrids tested. In addition, the decrease in ROS in hypoxic conditions was similar for both DM and non-DM cybrids, for both Euro and [Afr + Asi] cybrids. These data are consistent with our previous work showing that Euro cybrids and [Afr + Asi] cybrids have similar mitochondrial bioenergetics profiles24. One reason why hypoxic conditions induce decreased ROS in our cybrids may be a shared adaptive mechanism. Work by the Semenza group showed that mouse epithelial fibroblast cells decrease ROS when cultured in $1\%$ oxygen due to a Hif-1α-induced mitochondrial autophagy pathway52. Overall, our data suggest that mtDNA haplogroup and diabetic status do not significantly affect ROS levels in response to hypoxia.
Phagocytosis of sloughed outer segments of photoreceptors is an important function of RPE cells. Previous work has shown that macrophages from diabetic mouse models have impaired phagocytosis that was correlated with increased glycated protein levels53. Additionally, mouse RPE cells exposed to high glucose levels in vitro exhibit impaired phagocytic function18. However, our cybrids did not show significant decreases in phagocytic function in cells exposed to hypoxia compared to untreated cultures. Moreover, when the transcript levels of key phagocytosis pathway factors were examined, we found they changed similarly between hypoxic and room-air conditions for all cybrids tested. This finding is similar to results we obtained when examining phagocytic activity in cybrids with mtDNA from patients with AMD or unaffected patients. When cybrids from either group were treated with the anti-VEGF compounds bevacizumab (Avastin®), ranibizumab (Lucentis®), or aflibercept (Eylea®), phagocytic function was decreased, with similar fold-changes in function between AMD and normal cybrids38. However, our data showed that the differential expression of endocytosis genes in hypoxic cybrids was more highly ranked for Euro/DM cybrids than for [Afr + Asi]/DM cybrids. Since Euro/DM cultures demonstrated a decreased trend in phagocytosis (though not significant), we may speculate that the Euro/DM cybrids increase transcription of phagocytic factors to recover from an initial decrease in phagocytic function. Overall, these data suggest that the metabolic memory imparted by mtDNA does not significantly impact phagocytic function in the presence or absence of hypoxic stress. However, given the decreased trend in phagocytosis in Euro/DM cybrids, these experiments warrant further investigation.
Another RPE function is to form the outer portion of the blood-retina barrier to prevent leakage of fluid from the choroid vasculature into the neural retina. Experiments in diabetic animal models and human RPE cells stressed with diabetic conditions show decreased levels and disorganization of tight junctions proteins, such as occludin and ZO-1, decrease in amount and become disorganized, resulting in increased retinal permeability16,54. Consistent with these findings, we observed significant decreases in the ZO-1α-minus protein isoform for Euro/Non-DM, Euro/DM, and [Afr + Asi]/Non-DM cybrids cultured in hypoxia compared to protein levels of cells in room-air; [Afr + Asi]/DM cybrids showed a decreased trend in ZO-1α-minus protein levels. We also observed that non-DMs responded more to hypoxia in ZO-1 minus hypoxia-treated cybrids than DM cybrids. It is well known that the retinal pigment epithelium secretes more VEGF in a hypoxic environment. 49 Other research reveals that the expression of ZO-1 alpha plus and ZO-1 alpha minus is downregulated in vascular endothelial cells but upregulated in retinal pigmented epithelial cells in response to VEGF treatment. 40 These findings imply that hypoxia and VEGF treatment would have the same effect on retinal pigment epithelium cells. In our study, we used Rho0 cells from ARPE19 to create non-DM/DM cybrids. As a result, regardless of DM/non-DM status, there is an increase in ZO-1 alpha minus expression in response to hypoxia because it carries the common ARPE19 nucleus. Previous work described that while tight junctions containing ZO-1α-plus exhibit a more constant resistance, tight junctions containing ZO-1α-minus may fluctuate in their resistance and permit greater flow between cells 39. Thus, one may speculate that the decrease in ZO-1α-minus protein may indicate an attempt by the RPE to reduce permeability in response to hypoxic stress. In addition, we found TJP1 transcript levels were decreased in our RNA-seq analysis for all cybrids cultured in hypoxia compared to room-air cultures. Interestingly, we observed no significant change in either the ZO-1α-plus or occludin for all groups of cybrids cultured in hypoxia compared to cultures in room-air. Other work in RPE cells has shown that 24 h of hypoxia can reduce occludin in RPE cells55,56. One explanation for our findings is that occludin protein levels may decrease more gradually than transcript levels. While our protein data suggests that 48 h of hypoxia does not significantly change occludin protein levels in our cybrids, our RNA-seq data shows that OCLN transcript levels are decreased for all cybrids cultured in hypoxia, consistent with these previous studies. In both cases, our cybrid populations reacted similarly in response to hypoxic stress. In conclusion, our study suggests that mtDNA haplogroup and diabetic status do not significantly affect tight junction protein levels in response to hypoxia.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30518-x.
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|
---
title: Differentially expressed discriminative genes and significant meta-hub genes
based key genes identification for hepatocellular carcinoma using statistical machine
learning
authors:
- Md. Al Mehedi Hasan
- Md. Maniruzzaman
- Jungpil Shin
journal: Scientific Reports
year: 2023
pmcid: PMC9992474
doi: 10.1038/s41598-023-30851-1
license: CC BY 4.0
---
# Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning
## Abstract
Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.
## Introduction
Hepatocellular carcinoma (HCC) is the 3rd leading cause of cancer deaths globally1. Globally, more than of $80\%$ liver cancers are responsible for HCC2 and its prevalence is high in males compared to females3. It usually occurs in people aged 30–50 years3. Different factors such as hepatitis B or hepatitis C4,5, alcohol abuse, smoking, obesity, and type 2 diabetes (T2D) were significantly associated with HCC6. Among them, Hepatitis B is one of the prominent risk factors for the development of HCC, responsible for $50\%$ of cases7. Despite various treatment approaches, namely radiotherapy, chemotherapy, and target therapy have been commonly used to improve the prognosis and recurrence of HCC. Nevertheless, the survival rate of HCC patients is still low8. As a result, the risks of cancer death are still increased due to the lack of early detection and diagnosis of genes and limited treatment facilities. Therefore, it is essential to develop a system for identifying the key or core genes for early detection and better prognosis of HCC.
Recently, bioinformatics analysis has been widely utilized to determine the key prognostic genes or biomarkers as well as their associated molecular pathways for multiple cancers, including HCC8–58. Zhou et al.35 identified 15 prognostic biomarkers as well as their associated gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway using bioinformatics analysis. Chen et al.39,59,60 also identified 11 potential biomarkers that can play crucial roles in the development and progression of HCC patients. Qiang et al.40 proposed five core genes which were significantly associated with early diagnosis and poor prognosis of HBV-HCC. Wang et al.41 identified 36 hub DEGs and illustrated that 10 candidate genes out of the 36 have significant effect on the tumorigenesis and progression of HCC. Among them, eight candidate genes were inversely related to the survival rate of HCC patients. Dai et al.61 proposed a prognostic model for predicting the prognosis of HCC patients. They identified 17 genes that were potentially associated with the prognosis of HCC patients. These 17 genes were used to make a prognostic model using the Cox hazard regression model and validated its performance using the TCGA and GSE14520 datasets. They showed that six genes were involved in the prognosis of HCC patients. Most researchers simply used hub genes derived from the PPI network to identify the key or core genes. One of the major challenges in studying genetic data was the identification of relevant biomarkers or genes. Recently, machine learning (ML)-based techniques have gained more attraction to address this problem59,60,62–66. Despite the fact that several studies have been carried out for the identification and development of potential candidate genes for HCC8–58,67, it remains a challenging issue and still has some scope for more research for the identification of potential genes as well as understanding molecular mechanisms for the development, pathogenies, and progression of HCC.
In this work, we used three microarray gene expression (MGE) datasets as training sets to determine the key or core candidate genes for HCC. First, we selected individual DEGs for three datasets. Secondly, support vector machine (SVM) with radial basis function (RBF) was implemented on the identified DEGs from each of the three datasets and calculated the classification accuracy of each DEG. We selected the DEGs from each of the three datasets that provided a classification accuracy of more than $80.0\%$. At the same time, the overlapping or shared DEGs were identified from three datasets. These overlapping or shared DEGs were called differentially expressed discriminative genes (DEDGs). Thirdly, DAVID was used to perform enrichment analysis on common DEDGs. Fourthly, PPI networks were constructed using STRING and visualized using Cytoscape. Then the hub genes were identified using degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), closeness, and betweenness on the basis of cytoHubba. After that, the central hub genes were determined by overlapping or shared hub genes from the degree, MNC, MCC, centralities of closeness, and betweenness. Molecular Complex Detection (MCODE) was performed for cluster or module analysis and determined the important or significant modules as well as their associated genes. Moreover, the significant meta-hub genes were determined from meta-hub genes, which were extracted from existing studies. The key or core candidate genes were determined among the central hub genes, potential module hub genes, and significant meta-hub genes, which can be easily discriminated against in HCC patients compared to healthy controls. Furthermore, we used another two independent test datasets for the validation as well as to show the discriminative power of the key candidate genes. We also performed a survival analysis of the identified key candidate genes for HCC patients. Therefore, the overall flowchart of our proposed system to determine key candidate genes for HCC is presented in Fig. 1.Figure 1Flowchart of proposed system for the identification of key candidate genes for HCC.
## Identification of DEGs from each dataset
We implemented limma for identifying DEGs from each of the three GEO datasets (GSE36376, GSE39791, and GSE57957). Using the threshold of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|log_2 FC|{>1}$$\end{document}|log2FC|>1, and adj.p-value < 0.01, we identified 699 (up-regulated: 431 vs. down-regulated: 268), 428 (up-regulated: 88 vs. down-regulated: 340 DEGs), and 413 DEGs (up-regulated: 107; down-regulated: 306) DEGs between HCC and healthy controls from GSE36376, GSE39791, and GSE57957 datasets and their volcano plots and heatmap were presented in Fig. 2.Figure 2Volcano plot and heatmap of DEGs for each GEO dataset were generated using “ggplot2” version 3.3.6 package110 (https://cran.r-project.org/package=ggplot2) and “NMF” version 0.24.0 package111 (https://cran.r-project.org/package=NMF) in R. (a) Volcano plot and (b) heatmap of GSE36376 dataset; (c) Volcano plot and (d) heatmap of GSE39791 dataset; (c) Volcano plot and (d) heatmap of GSE57957. Dodger blue represents down-regulated, gray represents no significant genes, and fire brick represents up-regulated DEGs.
To identify the DEGs between HCC and healthy controls, each of the selected datasets was analyzed using the “limma” package108 in R-software with version 4.1.2. We computed the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|log_2 FC|$$\end{document}|log2FC| and adj. p-value of each gene from the selected dataset. “ Bioconductor annotation”109 package was used to convert microarray data probes into gene symbols. If multiple probes were matched with a gene symbol, take the gene with their associated expression values that provided the lowest or minimum adjusted p-value. The DEGs between HCC and healthy controls were identified with a cutoff of point: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$|log_2FC|>1$$\end{document}|log2FC|>1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$adj. p-value<0.01$$\end{document}adj.p-value<0.01 (false discovery rate). The volcano plot of DEGs was generated using the “ggplot2 version 3.3.6” package in R110. Moreover, a heat map of the expression of DEGs was generated with the “NMF” version 0.24.0 package in R111.
## Identification of common DEDGs using SVM
SVM with RBF kernel was applied on the identified DEGs (699 DEGs for GSE36376; 428 DEGs for GSE39791; and 413 DEGs for GSE57957) of each dataset in order to identify the DEDGs of HCC patients. Then, the classification accuracy was computed per gene for DEGs from each dataset. The calculation procedure is clearly discussed in the methodology section. The classification accuracies of all DEGs for individual datasets were ordered in descending order of magnitude, which is presented in Fig. 3. As shown in Fig. 3, we observed that a total of 502 from GSE36376, 169 from GSE39791, and 242 from GSE57957 DEGs were selected as DEDGs because their classification accuracy was more than or equal to $80.0\%$. Furthermore, 75 common DEDGs were determined among the identified DEDGS from GSE36376, GSE39791, and GSE57957 datasets, which is shown in Fig. 4.Figure 3Classification accuracy of individual genes using SVM for three GEO datasets: (a) GSE36376; (b) GSE39791, and (c) GSE57957.Figure 4Identification of common or overlapping DEDGs among DEDGs from GSE36376, GSE39791, and GSE57957 datasets.
## Enrichment analysis of common DEDGS
Enrichment analysis was conducted on 75 shared or overlapping DEDGs clearly grasp the mechanism and development of HCC. The functional characteristics of DEDGs were explored using GO and KEGG pathway analysis. The GO analysis was partitioned into three groups: biological process (BP), cellular component (CC), and morphological component. Using p-values \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(< 0.05)$$\end{document}(<0.05), we identified the significant GO and KEGG pathways, and chose the top five prominent GO terms and KEGG pathway. The top five GO terms, including BP, CC, and MF, are presented in Table 1.Table 1GO analysis of common DEDGs in terms of BP, CC, and MF. Top 5 items were selected. CategoryGO IDDescriptionsCountp-valueBPGO:0042572Retinol metabolic process7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.41 \times 10^{-8}$$\end{document}3.41×10-8GO:0071276Cellular response to cadmium ion5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.38 \times 10^{-5}$$\end{document}1.38×10-5GO:0001523Retinoid metabolic process4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.35 \times 10^{-4}$$\end{document}1.35×10-4GO:0071280Cellular response to copper ion4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.51 \times 10^{-4}$$\end{document}1.51×10-4GO:0006706Steroid catabolic process3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.88 \times 10^{-4}$$\end{document}1.88×10-4CCGO:0005576Extracellular region19\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.79 \times 10^{-4}$$\end{document}3.79×10-4GO:0070062Extracellular exosome180.00173GO:0005615Extracellular space160.0031GO:0034364High-density lipoprotein particle30.004GO:0016324Apical plasma membrane60.011MFGO:0004745Retinol dehydrogenase activity5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.81 \times 10^{-7}$$\end{document}5.81×10-7GO:0016491Oxidoreductase activity9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.46 \times 10^{-6}$$\end{document}2.46×10-6GO:0047023Androsterone dehydrogenase activity3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.56 \times 10^{-4}$$\end{document}3.56×10-4GO:0047044Androstan-3-alpha,17-beta-diol dehydrogenase activity3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4.56 \times 10^{-4}$$\end{document}4.56×10-4GO:0016229Steroid dehydrogenase activity30.001 For BP-based GO terms, the common DEDGs were strongly enriched with retinol metabolic process, cellular response to cadmium ion, retinoid metabolic process cellular response to copper ion, and steroid catabolic process. Moreover, the extracellular region, extracellular exosome, extracellular space, high-density lipoprotein particle, and apical plasma membrane were found to be top CC, which were significantly enriched with common DEDGs. As shown in Table 1, MF group GO terms, including retinol dehydrogenase activity; oxidoreductase activity; androsterone dehydrogenase activity; androstan-3-alpha,17-beta-diol dehydrogenase activity; and steroid dehydrogenase activity, were mainly enriched with common DEDGs.
The study of the KEGG pathway for common DEDGs is displayed in Table 2. As shown in Table 2, the common DEDGs were significantly associated with multiple pathways such as retinol metabolism, metabolic pathways, tryptophan metabolism, steroid hormone biosynthesis, and drug metabolism-cytochrome P450.Table 2KEGG pathway analysis of common DEDGs. Top five items were selected. Pathway IDDescriptionsCountp-valuehsa00830Retinol metabolism6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.28 \times 10^{-5}$$\end{document}3.28×10-5hsa01100Metabolic pathways21\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.24 \times 10^{-5}$$\end{document}7.24×10-5hsa00380Tryptophan metabolism40.001hsa00140Steroid hormone biosynthesis40.004hsa00982Drug metabolism-cytochrome P45040.007
## PPI network construction and central hub genes identification
STRING was utilized to build a PPI network to show the significant connections between proteins encoded by common DEDGs. Cytoscape was used to show the PPI network, which had 51 nodes and 144 edges (see Fig. 5a). Five hub gene-based identification algorithms, including the degree of connectivity, MNC, MCC, closeness, and betweenness in the Cytoscape plug-in cytoHubba, were implemented to determine the hub genes from PPI networks. Then we chose the top 30 hub genes from each algorithm. We made a Venn diagram among the five algorithms, which is shown in Fig. 5b. As shown in Fig. 5b, eight overlapping central hub genes were identified among these algorithms. These eight central hub genes were NUSAP1, TOP2A, CDC20, PRC1, UBE2C, ASPM, PNPLA7, and MT1E, which were utilized to determine the key or core genes for HCC.Figure 5PPI network and Venn diagram for common DEDGs and central hub genes. ( a) PPI network of common DEDGs with 51 nodes and 144 edges which was generated by Cytoscape 3.9.1118 (www.cytoscape.org); (b) identification of central hub genes among five methods (Degree, MNC, MCC, Closeness, and Betweenness based HGs). Here, HGs represent the hub genes.
## Hub modules and its associated genes identification
Module or cluster analysis was performed using MCODE to determine the prominent modules. Three clusters or modules were generated using MCODE and provided 3–6 MCODE scores. We chose the prominent modules that provided the MCODE scores of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 5$$\end{document}≥5 and the number of nodes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 5$$\end{document}≥5. Finally, we chose module 1 as a prominent hub module that contained 6 nodes and 30 edges with the highest MCODE scores of 6 and their PPI networks were displayed in Fig. 6. The correspondence six genes were treated as hub module genes. Figure 6PPI network of module 1 with 6 nodes and 30 edges which was generated by Cytoscape 3.9.1118 (www.cytoscape.org).
MCODE was used to determine the most closely connected modules from the PPI network120. We analyzed the modules with the following cutoff points: degree =2, cluster finding =haircut, nodes score =0.2, K-score =2, and max depth =100, respectively. We determined the potential modules that provided the MCODE with scores of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 6$$\end{document}≥6 and the number of nodes of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 6$$\end{document}≥6. Then, the hub module genes were identified using the following formula:5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \text {Hub Module Genes =}\bigcup _{$i = 1$}^{h_m}{\text {Genes from Module}}_i \end{aligned}$$\end{document}Hub Module Genes =⋃$i = 1$hmGenes from Moduleiwhere, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_m$$\end{document}hm is the number of significant modules.
## Identification of significant meta-hub genes from metadata
We reviewed 52 existing studies related to gene identification of HCC patients8–58. We listed their hub genes in order to make metadata which were presented in Table 3. To make metadata, we extracted 10 hub genes from Maddah et al.9, 5 hub genes from Yan et al.10, 20 from Zhao et al.11, 7 from Zhao et al.12, 10 from Liu et al.13, 11 from Meng et al.14, 42 from Rosli et al.15, 5 from Zhang et al.8, 5 from Li et al.16, 8 from Li et al.17, 5 from Tian et al.18, 12 from Wan et al.19, 10 from Zhu et al.20, 10 from Wang et al.21, 9 from Zhou et al.22, 10 from Zhang et al.23, 18 from Mou et al.24, 8 from Wu et al.25, 9 from Gui et al.26, 10 from Wang et al.27, 28 from Lu and Zhu28, 6 from Bhatt et al.29, 10 from Zhang et al.30, 13 from Jiang et al.31, 20 from Zhang et al.32, 12 from Wu et al.33, 5 from Nguyen et al.34, 15 from Zhou et al.35, 6 from Yu et al.36, 10 from Kakar et al.37, 10 from Ji et al.38, 11 from Chen et al.39, 10 from Qiang et al.40, 10 from Wang et al.41, 10 from Zhang et al.42, 14 from Kim et al.43, 10 from Zhang et al.44, 14 from Sha et al.45, 10 from Chen et al.46, 4 from He et al.47, 10 from Zhang et al.48, 4 from Hu et al.49, 9 from Zhang et al.50, 15 from Li et al.51, 5 from Cao et al.52, 7 from Yang et al.53, 5 from Wang et al.54, 9 from Jiang et al.55, 16 from Li et al.56, 15 from Xing et al.57, 10 from Zhu W et al.58, and 20 from Dai et al.61. Now, we took the union of extracted hub genes and got 214 hub genes as meta-hub genes. At the same time, we also computed the frequency of each meta-hub gene depending on how many studies got that gene as hub gene and selected 52 significant meta-hub genes because their frequency was more than 3. These selected 52 significant meta-hub genes were utilized for the determination of key genes. Table 3Formation of metadata by listing hub genes from existing studies. SNAuthorsNHGAssociated hub genesSNAuthorsNHGAssociated hub genes1Maddah et al.910BUB1, CDCA8, DLGAP5, ASPM, POLQ,CENPE, WDHD1, HELLS, TRIP13, DEPDC127Nguyen et al.345TOP2A, RRM2, NEK2, CDK1, CCNB12Yan et al.105CCNA2, PLK1, CDC20, UBE2C, AURKA28Zhou et al.3515DTL, CDK1, CCNB1, RACGAP1, ECT2, NEK2, BUB1B, PBK, TOP2A, ASPM, HMMR, RRM2, CDKN3, PRC1, ANLN3Qian et al.1116ADNP, CASP2, CBX1, CPSF6, DHX9, HCFC1, ILF3, RCC2, KANSL1, NAA40, NCOA6, RALGAPB, SENP1, SMARCD1, YEATS229Yu et al.366TOP2A, MAD2L1, CDC6, CHEK1, UBE2C, CCNB14Zhao et al.127CCNA2, CCNB1, CDK1, MAD2L1, TOP2A, RRM2, NDC8030Kakar et al.3710CDK1,CCNA2, CCNB1, CCNB2, BUB1, NDC80, BUB1B, NCAPG, MAD2L1, CDC205Liu et al.1310CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, CDC2031Ji et al.3810CDK1, CCNB1, CCNB2, PBK, ASPM,NDC80, AURKA, TPX2, KIF2C, CENPF6Meng et al.1411CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2,PBK, MELK, TPX2, KIF20A, AURKA32Chen et al.3911RRM2, NDC80, ECT2, CCNB1, ASPM, CDK1,PRC1, KIF20A, DTL, TOP2A, PBK7Rosli et al.1542CDK1, PPAP2B, CCNA2, SQLE, CCNB1,SULTIA3, NUSAP1, MAD2L1, LCAT, TOP2A, CETP, CCNB2, CFP, KIF11,FOS, NCAPG, CDK1, CDC20, TOP2A, TTK, C7, AURKA, C6, RRM2, NDC80, ACLY, MSH2, ESR1, CENPA, NDC80, MELK, CXCL12, PBK, DTL, NR1I2, IGF1, BUB1B, HBA1, PRC1, SPTBN2, KIF2C, CYP1A233Qiang et al.4010CDK1, CCNB2, CDC20, BUB1, BUB1B, CCNB1, NDC80, CENPF, MAD2L1, NUF28Zhang et al.810GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, CD8A34Wang et al.4110CDKN3, TOP2A, UBE2C, CDC20, PBK, ASPM, KIF20A, NCAPG, CCNB2, CYP3A49Li et al.165SPP1, COL1A2, IGF1, LGALS3, LPA35Zhang et al.4210CCNB1, AURKA, TOP2A, NEK2, CENPF, ASPM, KIF20A, NCAPG, CCNB2, CYP3A410Li et al.178BUB1, BUB1B, CCNA2, CCNB1, CDC20, CDK1, MAD2L1, CCNB236Kim et al.4314ANLN, ASPM, BUB1B, CCNB1, CDK1, CDKN3, ECT2,HMMR, NEK2, PBK, PRC1, RACGAP1, RRM2, TOP2A11Tian et al.185CDC20, TOP2A, RRM2, UBE2C, AOX137Zhang et al.4410CCNB1, CDC20, CCNB2, CDK1, SPC24, CENPW, ZWINT, PTTG1, AURKA, UBE2C12Wan et al.1912GF1, IGF2, NDC80, CDK1, CENPF, CDCA8, CCNB1, BIRC5, NCAPG, SPC25, CDCA5, CENPU38Sha et al.4514TOP2A, HMMR, DTL, CCNB1, NEK2, PBK, RACGAP1, PRC1, CDK1, RRM2, ECT2, BUB1B, ANLN, ASPM13Zhu et al.2010CDK1, TOP2A, CCNB1, CDC20, PLK1, BIRC5, CCNB2, FOS, AURKA, AURKB39Chen et al.4610TOP2A, CCNB2, PRC1, RACGAP1, AURKA, CDKN3, NUSAP1, ASPM, CDCA5, NCAPG14WANG et al.2110TOP2A, CDK1, ITGA2, PLK1, ESR1, CCNB2, AURKA, BUB1, CCNA2, BUB1B40He et al.474CDK1, PBK, RRM2, and ASPM15Zhou et al.229ASPM, AURKA, CCNB2, CDKN3, MELK, NCAPG, NUSAP1, PRC1, TOP2A41Zhang et al.4810NEK2, ANLN, TOP2A, CENPF, ASPM, CDC20, CDK1, CCNB1, ECT2, CCNB216Zhang et al.2310CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR, CDC4542Hu et al.494JUN, EGR1, MYC, CDKN1A17Mou et al.2418TOP2A, FOS, TK1, CDC20, ESR1, CCNB2, CXCL12, FOXO1, HMMR, VWF, ACSM3, COL4A1, ZIC2, RFC4, TXNRD1, GNAO1, CYP3A4, RAP2A43ZHANG et al.509ALDH2, PPTG1, CYP2C8, ADH4, ADH1B, CYP2C8, CDC20, TOP2A, CCNB218Wu et al.258CDKN3, CDK1, CCNB1, TOP2A, CCNA2, CENPE, KCCNB2, PRC1, RRM244Li et al.5115TOP2A, CDK1, CCNB1, BUB1, CENPF, CCNB2, TTK, KIF2C, HMMR, MELK, CENPE, KIF20A, KIF4A, PBK, DLGAP519Gui et al.264MT1X, BMI1, CAP2, TACSTD245Cao et al.525MCM3, CHEK1, KIF11, PBK, S100A920Wang et al.2710TOP2A, CDK1, NDC80, CCNB1, HMMR, CENPF, AURKA, CDKN3, FOXM1, PTTG146Yang et al.537PITX2, PNCK, GLIS1, SCNN1G, MMP1, ZNF488, SHISA921Lu2828NDUFC2, NDUFS7, NDUFB1, NDUFB9, NDUFA2, NDUFB7, NDUFA11, NDUFAF6, NDUFS6, NDUFB8, MRPS28, MRPS18A, MRPL14, MRPL12, MRPL54, MRPL55, MRPL52, MRPL13, MRPL27, MRPL24, NUF2, DSN1, GADD45GIP1, CHCHD1, STAG2, PPP1CC, CKAP5, ZWINT47WANG et al.545CDK1, CCNB1, CCNB2, MAD2L1, TOP2A22Bhatt et al.296MSH3, DMC1, ALPP, IL10, ZNF223, HSD17B748Jiang et al.559ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, TOP2A23Zhang et al.3010CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4,NOP58, AURKA, PCNA, FEN149Li et al.5616BIRC5, BUB1, CCNB2, CDC20, CDC25C, CDK1, CEP55, CXCL12, FOS, PRC1, KIF20A, NUSAP1, KIF2C, RACGAP, SPC24, TOP2A24Jiang et al.3113TLR1, TLR4, TLR7, TLR8, RIPK2, YWHAZ, FOS, FOSL2, HIF1A, FASLG, CCL4, CDK1A, DDIT350Xing et al.5715TOP2A, PCNA, CCNB2, AURKA, CDKN3, BUB1, RFC4, CEP55, DLGAP5, MCM2, PRC1, RACGAP1, TPX2, CDC20, MCM425Zhang et al.3220CDK1, CCNB1, CCNB2, CDC20, CCNA2, AURKA, MAD2L1, TOP2A, BUB1B, BUB1, ESR1, IGF1, FTCD, CYP3A4, SPP2, C8A, CYP2E1, TAT, F9, CYP2C951Zhu et al.5810UBE2C, CDK1, TK, NCAPG, TOP2A, AURKA, MAD2L1, TOP2A, BUB1B, BUB1, RAD51AP1, ASPM, PBK, DLGAP5, NUSAP126Wu et al.3312TTK, NCAPG, TOP2A, CCNB1, CDK1, PRC1, RRM2, UBE2C, ZWINT, CDKN3, AURKA, RACGAP152Dai et al.6120ANLN, DLGAP5, NDC80, NUSAP1, RACGAP1, PBK, ZWINT, BUB1B, TOP2A, NUF2, CCNB1, RRM2, DTL, KIF20A, CDKN3, HMMR, PRC1, CCL20, NPY1R, CXL12
## Key candidate genes identification
Eight central hub genes were identified from five methods (degree of connectivity, MNC, MCC, closeness, and betweenness), 6 hub module genes from potential hub modules, and 52 significant meta-hub genes from meta-hub genes. Six overlapping genes were identified using the Venn diagram from these three gene identification methods, which is presented in Fig. 7. These six genes (TOP2A, CDC20, ASPM, PRC1, UBE2C, and NUSAP1) were considered as key genes, which can be easily classified into the subjects as HCC and healthy. Figure 7Identification of key candidate genes of HCC from central hub genes, hub module genes, and significant meta-hub genes.
To identify the key candidate genes, we selected the central hub genes from the PPI network, hub module genes from significant modules, and significant meta-hub genes from existing studies. Therefore, we identified the key candidate genes for HCC using the following formula:8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \text {Key Candidate Genes =}\bigcap _{$i = 1$}^{k}{\text {Important Genes from Identification Methods}}_i \end{aligned}$$\end{document}Key Candidate Genes =⋂$i = 1$kImportant Genes from Identification Methodsiwhere, k is the number of significant gene identification methods (Here, $k = 3$). In this work, central hub genes, hub module genes, and significant gene selection methods will be considered “Important Gene Identification Methods”.
## Discriminative power analysis using ROC curve
Six key or core genes (TOP2A, CDC20, ASPM, PRC1, UBE2C, and NUSAP1) were validated using AUC, computed from ROC curves. We compared the performance of two independent test datasets (GSE76427 and TCGA-LIHC) with one of our train datasets (GSE57957) in order to show the precision of the selected key candidate genes. The ROC curves of six key genes as well as their heatmap for both training and independent test datasets were illustrated in Fig. 8.Figure 8Validation of the six key candidate genes using AUC and heatmap: (a), (b) GSE57957-based training dataset; (c), (d) GSE76427-based independent test dataset; and (e), (f) TCGA-LIHC based independent test dataset. Whereas, ROC curves were generated using pROC version 1.18.0 package121 and heatmap was generated using “NMF” version 0.24.0 package in R111.
The ROC curve of six key candidate genes with their AUC values for the training dataset (GSE57957) was displayed in Fig. 8a: TOP2A (AUC: 0.936, $95\%$ CI 0.871–1.000), CDC20 (AUC: 0.917, $95\%$ CI 0.838–0.996), ASPM (AUC: 0.919, $95\%$ CI 0.851–0.987), PRC1 (AUC: 0.938, $95\%$ CI 0.871–1.000), UBE2C (AUC: 0.803, $95\%$ CI 0.703–0.904), and NUSAP1 (AUC: 0.930, $95\%$ CI 0.895–1.000). As displayed in Fig. 8c, the AUC values of six key or core genes were more than almost 0.780. The AUC values of six key or core genes for the GSE76427 dataset were: TOP2A (AUC: 0.900, $95\%$ CI 0.851–0.949), CDC20 (AUC: 0.887, $95\%$ CI 0.883–0.941), ASPM (AUC: 0.893, $95\%$ CI 0.844–0.942), PRC1 (AUC: 0.931, $95\%$ CI 0.889–0.975), UBE2C (AUC: 0.792, $95\%$ CI 0.723–0.863), and NUSAP1 (AUC: 0.881, $95\%$ CI 0.831–0.933).
Similarly, the ROC curves of six key candidate genes with their AUC values for the TCGA-LIHC-independent test dataset were presented in Fig. 8e. As presented in Fig. 8e, it was observed that six key candidate genes were provided the AUC values of more than 0.900 and their individual AUC values were as follows: TOP2A (AUC: 0.961, $95\%$ CI 0.939–0.984), CDC20 (AUC: 0.968, $95\%$ CI 0.949–0.986), ASPM (AUC: 0.960, $95\%$ CI 0.938–0.983), PRC1 (AUC: 0.967, $95\%$ CI 0.948–0.987), UBE2C (AUC: 0.965, $95\%$ CI 0.946–0.985), and NUSAP1 (AUC: 0.919, $95\%$ CI 0.889–0.949). Therefore, these six key genes (TOP2A, CDC20, ASPM, PRC1, UBE2C, and NUSAP1) showed strong discriminative power to classify HCC patients from healthy controls. These validations would be supported our findings and provided them more robust.
In this work, we used two independent test datasets in order to validate the key candidate genes. One independent test dataset (GSE76427) was taken from the GEO database, and another independent dataset was taken from the TCGA database. The description of these independent test datasets is more clearly explained in Table 4. We validated the selected key candidate genes using the area under the curve (AUC), computed from the receiver operating characteristic curve (ROC). In ROC analysis, first, we selected one gene and class label, and then we adopted logistic regression with the leave-one-out CV protocol. We computed AUC values using the “pROC” R-package121. Moreover, we also compared the performances of independent test datasets with one of our training datasets (GSE57957) in order to show the precision of the selected key candidate genes.
## Survival analysis
In this work, we adopted survival analysis of six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) using univariate Cox regression in R and its results are presented in Fig. 9. As shown in Fig. 9, we observed that our identified six key candidate genes for HCC patinets such as TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C were strongly associated with the survival status of HCC patients (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {p}<0.05$$\end{document}$p \leq 0.05$). So, the over-expression levels of TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C had poor survival periods compared to lower expression levels of that key candidate genes. Figure 9Survival analysis of six key candidate genes for HCC: (a) TOP2A; (b) CDC20; (c) ASPM; (d) PRC1; (e) NUSAP1; and (f) UBE2C. The horizontal axis (x-axis) represents the time to event (in days) and the vertical axis (y-axis) represents survival probability. The HCC patients were divided into two groups: high-risk and low-risk and assigned a color. The red line designates the samples with high risk, and the green line represents the samples with low risk. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {p} < 0.05$$\end{document}$p \leq 0.05$ indicates a statistically significant difference in mortality between groups. The survival plots were generated using the “Survfit” package in R122.
In this work, we used TCGA-LIHC dataset for survival analysis in order to show prognostic status of key candidate genes. We classified HCC patients into high-risk and low-risk groups on the basis of median expression level of each key candidate gene. We performed survival analysis of our identified key candidate genes using the “Survfit” package in R language122. A p-value < 0.05 was considered statistically significant (“Supplementary information”).
## Discussion
In this work, we assessed three datasets, namely GSE36376, GSE39791, and GSE57957, to detect the DEGs for HCC patients. We determined 699, 428, and 413 DEGs using “limma” from the GSE36376, GSE39791, and GSE57957 datasets, which were illustrated in Fig. 2. Moreover, we implemented SVM to determine the DEDGs from individual datasets (see in Fig. 3) and selected overlapping or shared 75 DEDGs among the identified DEDGS from GSE36376, GSE39791, and GSE57957 datasets, which were clearly shown in Fig. 4. At the same time, enrichment analysis was executed on overlapping or shared DEDGs to clear understand their better exploration and molecular mechanism (see in Table 1). We found that the potential BP functional categories were strongly related to the development and progression of HCC patients. Retinol and retinoid metabolic processes have been linked to a variety of liver diseases, including fatty liver disease, which leads to HCC68,69. The rest of the BP categories were also enriched with common DEDGs, which also coincided with existing studies, like cellular response to cadmium ion42,57,70, cellular response to copper ion36,70, and steroid catabolic process42.
The top 5 GO terms were significantly enriched with common DEDGS, which were also consistent with previous results, such as extra cellular region35,37,38,57, extracellular exosome37,38, extracellular space37,38,57, high-density lipoprotein particle57, and apical plasma membrane53. In the case of MFs, common DEDGs were also enriched with top five GO terms. Existing studies supported these enrichment factional categories, including retinol dehydrogenase activity14, and oxidoreductase activity37,38,42. We also analyzed KEGG pathways and chose five pathways that were closely related to our overlapping DEDGs (see in Table 2). Different existing studies supported our findings, such as retinol metabolism35,37,38,40,43,70, metabolic pathways37,38, tryptophan metabolism38,42,70, steroid hormone biosynthesis42,70, and drug metabolism-cytochrome P45035,42,70.
A PPI network was built with shared DEDGs using Cystoscape (see in Fig. 5a and then eight central hub genes (NUSAP1, TOP2A, CDC20, PRC1, UBE2C, ASPM, PNPLA7, and MT1E) were identified from five hub gene selection methods, which were presented in Fig. 5b. The potential modules were identified using MCODE scores and module 1 was identified due to having the highest MCODE scores. We selected six hub module genes from module 1 as well as constructed their PPI network (see in Fig. 6). In addition, we examined 52 papers and took the hub genes from earlier studies8–58 in order to make metadata. At the same time, we listed 214 meta-hub genes by taking the union of extracted hub genes, which were presented in Table 3. We selected 52 significant meta-hub genes from the list of meta-hub genes whose frequency was greater than 3. Finally, we identified the six shared genes (TOP2A,CDC20, ASPM, PRC1, UBE2C, and NUSAP1) by intersecting central hub genes, hub module genes, and significant meta-hub genes, extracted from the earlier studies, known as key relevant or candidate genes, which were clearly depicted in Fig. 7. We validated these key relevant or candidate genes using AUC for one training and two independent test datasets (see Fig. 8). We observed that these six key relevant or candidate genes had high discriminative power for the differentiation of HCC patients.
TOP2A is a cell cycle-related gene that encoded a DNA topoisomerase which controls and alters the topologic states of DNA during transcription. TOP2A overexpression has been identified as a core or potential biomarker for ovarian cancers71, glioma72, and lung cancers73. A study showed that TOP2A overexpression in HCC patients was significantly correlated with progression and poor prognosis74,75. In the case of our study, TOP2A was also considered as a key or core gene for the progression and development of HCC. This finding was coincided with previous studies12,14,15,18,20–25,27,32–36,39,41–43,45,46,48,50,51,54–58,61.
CDC20 is a vital regulator of cell division in humans76,77. Overexpression or high expression of CDC20 has also been linked to lung cancer78, colorectal cancer79, breast cancer80,81, and other cancers. Moreover, CDC20 was strongly correlated with poor prognosis in gastric cancer82, bladder cancer83, and breast cancer84. A study revealed that CDC20 over-expression was significantly associated with HCC85. Another recent study demonstrated that there existed a strong relationship between CDC20 overexpression and the prognosis of HCC86. Our findings also showed that CDC20 was a potential key biomarker that played an crucial or essential role for the development and progression of HCC. Different existing studies also supported our findings10,13,14,17,18,20,24,30,32,37,40,41,44,48,50,55–57.
ASPM is a protein that have a major influence in the development of HCC. ASPM is located on chromosome 1 and band 1q31 and consists of 28 exons and 3477 amino-acid proteins87. Lots of studies have identified ASPM as a hub gene or key biomarker for multiple cancers88–90. Zhang et al.90 reported that ASPM can be a promising therapeutic target for liver. Moreover, ASPM overexpression was strongly correlated with bladder cancer and consiered as promising predictor91. Our findings also illustrated that ASPM was a novel key biomarker for HCC, which was supported by the existing studies9,22,35,38,39,41–43,45–48,58.
PRC1 is an essential protein that is the regulator of cytokinesis92. The higher expression level of PRC1 was found among HCC patients than healthy controls. The overexpression of PRC1 was associated with a poor prognosis for HCC patients93. Our work also indicated that PRC1 was a promising or key biomarker for the development of HCC, which coincided with previous studies15,22,25,33,35,39,42,43,45,46,56,57,61.
Similarly, we proposed UBE2C as a key or core predictor for development of HCC, which was supported by various existing studies10,18,33,36,41,44,58. Xiong et al.94 suggested UBE2C as a potential biomarker or gene for HCC. High expression of UBE2C was also found in HCC than healthy subjects95. UBE2C is not play a crucial role HCC but also in variety of cancers: lung cancer, gastric cancer96,97.
NUSAP1 is a protein associated with the nucleolar-spindle that have a vital role in spindle microtubule organization98. overexpression of NUSAP1 was found in a variety of malignancies, including HCC58,99, colon cancer100,101, prostate cancer102,103, and cervical carcinoma104. Moreover, overexpression of NUSAP1 was strongly linked with poor prognosis of prostate cancer103 and colon cancer101. Another study revealed that NUSAP1 is related to HCC105. Roy et al.105 illustrated that NUSAP1 expression might rise in HCC samples with low expression levels of miRNA 193a-5p, and that this overexpression was strongly associated with a shorter patient survival time. Our findings also illustrated that NUSAP1 was one of the key candidate genes that the highest expression levels were found in HCC subjects compared to healthy subjects. These findings were consistent with existing studies15,22,46,56,58,61.
Moreover, two independent test datasets were also used to validate these six key candidate genes using AUC. A survival analysis was also performed of these six candidate genes for HCC patients. In both cases, our identified six key candidate genes (TOP2A, CDC20, ASPM, PRC1, UBE2C, and NUSAP1) showed significant association with the development and progression of HCC. This finding will provide evidence and new insight to physicians and readers in determining the diagnosis of HCC as well as the correlated pathway of HCC.
## Data acquisition and preprocessing
In this work, three publicly available microarray gene expression datasets with GEO accession: GSE3637666, GSE39791106, and GSE57957107 with GPL10558 [Illumina HumanHT-12 V4.0 expression bead chip] were used to determine the key candidate genes. Another two independent test datasets were used to validate key candidate genes. One independent dataset was taken from the GEO database with accession number: GSE76427 with GPL10558 platform102 and another independent test dataset was taken from the Cancer Genome Atlas (TCGA) database. *Microarray* gene expression datasets were downloaded from the GEO database (www.ncbi.nlm.nih.gov/geo/) and TCGA-liver hepatocellular carcinoma (TCGA-LIHC) dataset was downloaded from the TCGA database (https://portal.gdc.cancer.gov/). The datasets underwent a log2 transformation and quintile normalization. Although these datasets were taken from the publicly available GEO repository, being human data, all methods were performed in accordance with the relevant guidelines and regulations. Table 4 presents a summary of the utilized datasets. Table 4Summary of utilized HCC datasets. DatasetsPlatformTotal samplesHCCControlGSE3637666GPL10558433240193GSE39791106GPL105581447272GSE57957107GPL10558783939GSE76427102GPL1055816711552TCGA-LIHC–42437450
## SVM-based identification of DEDGs from DEGs for each dataset
The main purpose of SVM is to identify a hyperplane in a high dimensional space112,113 that can easily discriminate HCC patients from healthy control patients using the following discriminate function:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} f(x)=\ \sum _{$i = 1$}^{n}{\alpha _iK(x_i,\ x_j)}+b \end{aligned}$$\end{document}f(x)=∑$i = 1$nαiK(xi,xj)+bwhere, b is the bias term.
In this study, we have used radial basis kernel, which is defined as follows:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} K(x_i,\ x_j)=\text {exp}(-\gamma \Vert x_i-x_j \Vert ^2) \end{aligned}$$\end{document}K(xi,xj)=exp(-γ‖xi-xj‖2) We set the different values of cost (C) and gamma \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\gamma)$$\end{document}(γ) and tuned these values using a grid search method and select the optimal value of C and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\gamma)$$\end{document}(γ) to improve classification accuracy. In this current study, we adopted SVM as a gene selection method, and its identification procedure is described as follows:Step 1Select one gene from a list of identified DEGs. Step 2Trained SVM-based model with five-fold cross-validation (CV) protocols. Step 3Calculate the classification accuracy for this selected gene. Step 4Repeat Step 2 to Step 3 for all identified DEGs. Step 5Sort the classification accuracy of all DEGs in descending order of magnitude. Step 6Choose the genes that will produce a classification accuracy of more than 80.0.
## Identification of common DEDGs
After selecting differentially expressed discrimination genes (DEDGs) using SVM, we identified the shared or overlapping or common DEDGs among three datasets using the following formula:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \text {Common DEDGs =}\bigcap _{$i = 1$}^{r}{\text {Identified DEDGs from GEO Datasets}}_i \end{aligned}$$\end{document}Common DEDGs =⋂$i = 1$rIdentified DEDGs from GEO Datasetsiwhere, r is the number of utilizing GEO dataset (here, $r = 3$).
## Enrichment analysis of common DEDGs
To better understand the mechanism and progression of HCC patients, we obtained enrichment analysis, including GO and KEEG analysis114,115 on DEDGs using DAVID version 6.8 tools116 (david.ncifcrf.gov). A p-value < 0.05 was considered for significant.
## PPI network analysis and central hub gene identification
The STRING version 11.5 software (www.string-db.org) was utilized to obtain the potential interactions among common DEDGs117. A protein-protein interaction (PPI) with a confidence score of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$> 0.70$$\end{document}>0.70 and a maximum number of interactors of 0 was preserved and loaded into Cystoscape version 3.9.1118 to build a PPI network. The degree of connectivity, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness were computed using cytoHubba119. Then, we sorted the values of degree of connectivity, MNC, MCC, centralities of closeness, and betweenness in descending order of magnitude and chose the top 30 DEDGs, known as hub genes. The central hub genes were selected by overlapping hub genes, which were computed from the degree of connectivity, MNC, MCC, centralities of closeness, and betweenness. Mathematically, it is defined as follows:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \text {Central Hub Genes=}\bigcap _{$i = 1$}^{hg}{\text {Hub Genes from Identification Methods}}_i \end{aligned}$$\end{document}Central Hub Genes=⋂$i = 1$hgHub Genes from Identification Methodsiwhere, hg is the number of hub gene identification methods (Here, hg=5).
## Significant meta-hub genes identification from metadata
We reviewed some existing studies related to HCC-based gene identification. To make metadata, we listed their identified hub genes for HCC, called “meta-hub genes,” which can be written as follows:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \text {Meta-Hub Genes} =\bigcup _{$i = 1$}^{m}{\text {Hub Genes from Previous Study}}_i \end{aligned}$$\end{document}Meta-Hub Genes=⋃$i = 1$mHub Genes from Previous Studyiwhere, m is the number of studies obtained from obtaining hub genes (here, $m = 52$).
We also counted the frequency of each meta-hub gene depending on how many studies identified that gene as a hub gene. Finally, we identified significant meta-hub genes from meta-hub genes whose frequency was greater than or equal to 3, which can be written as follows:7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \text {Significant Meta-Hub Genes} =\{g_i\}; $i = 1$,2,...,n \end{aligned}$$\end{document}Significant Meta-Hub Genes={gi};$i = 1$,2,...,nwhere, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g_i \in \text {meta-hub gene}$$\end{document}gi∈meta-hub gene and n is the number of meta-hub genes whose frequency is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 3$$\end{document}≥3
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30851-1.
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|
---
title: 'Association of general and central obesity, and their changes with risk of
knee osteoarthritis: a nationwide population-based cohort study'
authors:
- Dojoon Park
- Yong-Moon Park
- Seung-Hyun Ko
- Kang-Se Hyun
- Youn-Ho Choi
- Dong-Uk Min
- Kyungdo Han
- Hae-Seok Koh
journal: Scientific Reports
year: 2023
pmcid: PMC9992488
doi: 10.1038/s41598-023-30727-4
license: CC BY 4.0
---
# Association of general and central obesity, and their changes with risk of knee osteoarthritis: a nationwide population-based cohort study
## Abstract
In this study, we aimed to evaluate the association between general and central obesity, and their changes with risk of knee osteoarthritis (OA) using retrospective cohort data collected from the Korean National Health Insurance Service. We studied 1,139,463 people aged 50 and over who received a health examination in 2009. To evaluate the association between general and/or central obesity and knee OA risk, a Cox proportional hazard models were used. Additionally, we investigate knee OA risk according to the change in obesity status over 2 years for subjects who had undergone health examinations for 2 consecutive years. General obesity without central obesity (HR 1.281, $95\%$ CI 1.270–1.292) and central obesity without general obesity (HR 1.167, $95\%$ CI 1.150–1.184) were associated with increased knee OA risk than the comparison group. Individuals with both general with central obesity had the highest risk (HR 1.418, $95\%$ CI 1.406–1.429). This association was more pronounced in women and younger age group. Remarkably, the remission of general or central obesity over two years was associated with decreased knee OA risk (HR 0.884; $95\%$ CI 0.867–0.902; HR 0.900; $95\%$ CI 0.884–0.916, respectively). The present study found that both general and central obesity were associated with increased risk of knee OA and the risk was highest when the two types of obesity were accompanied. Changes in obesity status have been confirmed to alter the risk of knee OA.
## Introduction
Knee osteoarthritis (OA) is a common progressive multifactorial disorder1. It causes chronic pain, functional impairment, decreased quality of life, and economic burden2. The reported prevalence of OA in adults varies from 1.6 to $27.1\%$, depending on the definition of OA, study population and country. Recent systematic review and meta-analysis, including 88 studies, reported the global pooled prevalence of knee osteoarthritis over the age of 40 as $22.9\%$ ($95\%$ CI 19.8–$26.1\%$)3. It also shows sex and ethnic differences. Generally, women are affected more than men4. Additionally, although Asians are relatively thin, the knee OA prevalence in *Asia is* higher than that in Europe and North America3.
Knee OA accounts for nearly $80\%$ of the international burden of OA5. In Korea, knee OA ranks fifth in the number of outpatients over the age of 60 (third among chronic diseases), and the total treatment cost of outpatient and inpatient care reached almost 1.14 billion dollars in 20206. Obesity is an important risk factor in the development and progression of knee OA7. As the prevalence of obesity is steadily increasing worldwide8,9, the problem of increasing numbers of patients with knee OA is emerging.
The association between body mass index (BMI), a commonly used indicator of general obesity, and knee OA is also well known4. However, BMI makes it difficult to discriminate between body fat and lean mass or reveal central obesity10,11. Recently, waist circumference (WC) was reported to be a more appropriate indicator of central obesity-associated health risk than other obesity markers11,12. Asians are more likely to be centrally obese than Europeans with the same BMI13,14, and the difference in central fat mass can be significant even at the same BMI15,16. Previous studies have reported the relationship between knee OA and obesity based on only the BMI category4,17–19. Previous studies have evaluated the individual and combined effects of general and central obesity on knee OA. However, no nationwide studies have been conducted on the effect of obesity status and change on the risk of knee OA48,49. Understanding the association of obesity and changes in obesity status with risk of knee OA is important for developing efficient public health strategies.
The aim of this study was to evaluate the association between general and/or central obesity and the risk of knee OA in a nationwide population-based cohort study using the Korean National Health Insurance Service database. We further assessed how two-year changes in obesity status affected the risk of knee OA.
## Data and study population
The present study was conducted with retrospective cohort data obtained from the Korean National Health Insurance Service (KNHIS). The KNHIS is a government-run single insurance company with approximately $97\%$ of the people subscribed. The cohort sample extracted through the KNHIS database represents the actual Korean population20,21. The KNHIS database includes enrollee anthropometric characteristics, medical institution use, disease codes identified by physicians, and medication prescription claims data22–24. The national medical screening program recommends that all enrollees over the age of 40 years undergo a general health examination at least once every two years20,25,26. Routine health examination obtains demographic data thorough standardized measurements and information on drinking, smoking, physical activity, and medical history through self-report surveys.
This nationwide retrospective cohort database includes a random sample of 1,788,402 people aged over 50 years at the index year of 2009. To minimize reversed causality and confounders, subjects diagnosed with knee OA prior to the index year and those with knee OA within one year were excluded. Subjects with missing values were also excluded. Finally, a retrospective analysis was conducted on 1,139,463 subjects (Fig. 1). The study cohort was followed up from the index date to the date of knee OA diagnosis or until 31 December 2018. The mean follow-up period was 6.53 ± 2.71 years. Figure 1Flow chart of the cohort selection.
## General health behaviors and comorbidities
Information on lifestyle-related factors was collected using the questionnaires. The categories of smoking status were as follows: never, former and current smoker. Drinking status was categorized as none, moderate (1–< 30 g/day), and heavy (≥ 30 g/day). Regular exercise was defined as when [1] moderate physical activity for > 30 min, ≥ 5 times/week or [2] vigorous physical activity for > 20 min, ≥ 3 times/week27. Income status was classified into quartiles based on the annual insurance premium to the KNHIS.
Comorbidities were defined per a previously validated method28–31. Hypertension, type 2 diabetes mellitus (T2DM) and dyslipidemia were defined using a diagnostic combination identified through the ICD-10 code and claims data of related drugs or the measured value of the health examination. Other comorbidities considered in this study included heart failure (HF), chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), end-stage renal disease (ESRD), stroke, liver cirrhosis (LC), dementia, and cancer. Each definition is presented in Table S1. Blood collection was performed after fasting for at least 8 h from midnight to check the concentrations of glucose and creatine and determine the lipid profile.
## Definitions of general and central obesity
General obesity was defined as BMI ≥ 25 kg/m2, and BMI was calculated as the weight in kilograms divided by the square of the height in meters11. Following World Health Organization (WHO) recommendations for Asians, individuals were categorized as [1] underweight (BMI < 18.5 kg/m2), [2] normal (≥ 18.5 to < 23 kg/m2, reference group), [3] overweight (≥ 23 to < 25 kg/m2), [4] class 1 obese (≥ 25 to 30 kg/m2), and [5] class 2 obese (≥ 30 kg/m2)32.
WC was measured in the centerline between the rib cage and the iliac crest when the participant exhaled while standing by trained inspectors33. Regarding central obesity, subjects were grouped into 6 levels at 5 cm WC intervals as follows: [1] < 80 cm in men and < 75 cm in women, [2] 80–< 85 cm in men and 75–< 80 cm in women, [3] 85–< 90 cm in men and 80–< 85 cm in women (reference group), [4] 90–< 95 cm in men and 85–< 90 cm in women, [5] 95–< 100 cm in men and 90–< 95 cm in women, and [6] ≥ 100 cm in men and ≥ 95 cm in women. According to the definition of the Korean Society for the Study of obesity, central obesity was defined as a WC ≥ 90 cm in men and ≥ 85 cm in women33.
The change in obesity was evaluated as changes in obesity status between two years of the individuals who underwent routine health examination in both 2009 and 2011. Subjects were classified based on the presence of general or central obesity in the preceding and subsequent health examinations. This evaluation was conducted on 696,230 subjects by applying identical exclusion criteria for the study cohort (Fig. 1).
## Definition of knee OA
The primary endpoint was the newly developed knee OA. Based on a previous validation study, knee OA was defined by knee OA diagnostic code (M17) or any OA diagnostic code (M15, polyarthrosis or M19, other arthrosis) in combination with a procedure for a knee X-ray in the same claim34.
## Ethical considerations
The entire process of this study complied with the ethical norms of the Declaration of Helsinki. This study was approved by both the KNHIS and the IRB of the Catholic University of Korea (IRB No. VC22ZISI0014), and informed consent was exempted IRB of the Catholic University of Korea due to the anonymity of the data and the retrospective nature of the study.
## Statistical analysis
Continuous variables are presented as the means and standard deviations and were compared using the t test or ANOVA. Categorical variables are expressed as numbers and percentages and were compared by means of the chi-square test. Incidence rates were calculated by dividing the number of incident cases by the total number of person-years of follow-up and expressed as per 1,000 person-years. To evaluate the risk of knee OA associated with BMI and WC, we assessed the hazard ratios (HRs) and $95\%$ confidence intervals (CIs) using Cox’s proportional hazards models. The variables used for adjustment from Model 1 to Model 4 are as follows: (model 1) adjusted for age and sex, (modle2) adjusted for model 1 plus income, smoking, drinking, exercise, (model 3) adjusted for model 2 plus hypertension, T2DM, dyslipidemia, (model 4) adjusted for model 3 plus ESRD, COPD, stroke, LC, HF, dementia, CKD and cancer. To assess the increasing category of obesity associated with risk of knee OA, P for linear trend was applied with linear regression, using the ordinal number assigned to each category of obesity. Stratified analyses according to sex and age were performed on the combination of general and central obesity and interaction tests were performed using a likelihood ratio test.
A P values provided are two-sided, with the level of significance at 0.05. All statistical analysis procedures were performed with SAS version 9.4 (SAS Institute, Cary, NC, USA).
## Baseline characteristics
The descriptive baseline characteristics of study populations are summarized in Table 1. Overall, $33.5\%$ of subjects had general obesity (BMI ≥ 25 kg/m2, Table S2), and $46.9\%$ had central obesity (WC ≥ 90 cm in males, ≥ 85 cm in females, Table S3). Among the study population, 403,050 ($35.4\%$) developed knee OA during the follow-up period. The baseline mean BMI and WC of the knee OA group were 24.2 ± 2.9 kg/m2 and 82.1 ± 8.3 cm, respectively, and those of the non-OA group were 23.7 ± 2.9 kg/m2 and 81.8 ± 8.3 cm, respectively (both $P \leq 0.0001$). The knee OA group was more likely to be older and comprised more females, less smokers, less alcohol consumption, more hypertension, and more dyslipidemia than the non-OA group. Table 1Baseline characteristics of study participants according to knee OA development. Knee osteoarthritisNo ($$n = 736$$,413)Yes ($$n = 403$$,050)P-valueAge Mean58.5 ± 7.759.9 ± 7.6 < 0.0001Categories, n (%) 50–59460,080 (62.5)212,070 (52.6) < 0.0001 60–69194,718 (26.4)137,412 (34.1) 70–7970,034 (9.5)48,661 (12.1) 80–11,581 (1.6)4907 (1.2)Male, n (%)483,669 (65.7)179,481 (44.5) < 0.0001Low income < $25\%$, n (%)166,502 (22.6)95,279 (23.6) < 0.0001Type 2 DM, n (%)109,443 (14.9)55,838 (13.9) < 0.0001Hypertension, n (%)302,508 (41.1)179,064 (44.4) < 0.0001Dyslipidemia, n (%)177,167 (24.1)112,025 (27.8) < 0.0001Smoking, n (%) Non401,912 (54.6)278,925 (69.2) < 0.0001 Ex154,072 (20.9)60,821 (15.1) Current180,429 (24.5)63,304 (15.7)Alcohol, n (%) Non410,314 (55.7)264,540 (65.6) < 0.0001 Mild265,261 (36.0)112,450 (27.9) Heavy60,838 (8.3)26,060 (6.5)Regular exercise, n (%)165,294 (22.5)88,377 (21.9) < 0.0001Cancer, n (%)15,865 (2.2)8846 (2.2)0.1569ESRD, n (%)789 (0.1)318 (0.1) < 0.0001COPD, n (%)46,233 (6.3)33,449 (8.3) < 0.0001Stroke, n (%)18,511 (2.5)11,520 (2.9) < 0.0001LC, n (%)3826 (0.5)1511 (0.4) < 0.0001HF, n (%)5620 (0.8)3827 (1.0) < 0.0001Dementia, n (%)5406 (0.7)2848 (0.7)0.0981CKD, n (%)63,877 (8.7)37,821 (9.4) < 0.0001Height, cm162.7 ± 8.2159.8 ± 8.4 < 0.0001Weight, kg62.9 ± 10.262.0 ± 10.0 < 0.0001BMI, kg/m223.7 ± 2.924.2 ± 2.9 < 0.0001WC, cm81.8 ± 8.382.1 ± 8.3 < 0.0001Glucose, mg/dL102.8 ± 28.8101.3 ± 25.9 < 0.0001SBP, mmHg126.1 ± 15.8126.0 ± 15.70.043DBP, mmHg78.1 ± 10.377.7 ± 10.1 < 0.0001Total cholesterol, mg/dL199.6 ± 37.8201.4 ± 38.1 < 0.0001HDL-C, mg/dL54.8 ± 28.755.9 ± 31.5 < 0.0001LDL-C, mg/dL117.7 ± 39119.3 ± 39.3 < 0.0001Triglyceridea, mg/dL121.8 (121.64–121.95)119.87 (119.67–120.07) < 0.0001e-GFR, mL/min/1.73 m283.2 ± 35.283.2 ± 32.60.9422Continuous variables were presented using mean and standard deviation. Categorical variables are expressed in numbers and percentages. DM diabetes mellitus, ESRD end-stage renal disease, COPD chronic obstructive pulmonary disease, LC liver cirrhosis, HF hear failure, CKD chronic kidney disease, BMI body mass index, WC waist circumference, SBP systolic blood pressure, DBP diastolic blood pressure, HDL high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, e-GFR estimated glomerular filtration rate.aGeometric mean values for triglyceride.
## Risk of knee OA according to general or central obesity
We assessed the risk of knee OA according to BMI and WC categories (Table 2). After adjustments for confounders, the risk of knee OA increased significantly with increasing BMI and WC, and a dose-dependent association was identified (both P for trend < 0.0001). The HRs of knee OA for general obesity and central obesity compared to comparison group were 1.337 ($95\%$ CI 1.328–1.345) and 1.289 ($95\%$ CI 1.280–1.298), respectively. Of note, compared to that of the comparison group, the risk of knee OA was $73.4\%$ higher for groups with BMIs ≥ 30.0 kg/m2 and $30.1\%$ higher for groups with WCs ≥ 100 cm in men and ≥ 95 cm in women, respectively. Table 2Incidence rate and hazard ratio for the risk of knee OA according to BMI and WC.IR (per 1000)HR ($95\%$ CI)Model 1Model 2Model 3Model 4BMI, kg/m2–< 18.538.850.749 (0.731, 0.767)0.752 (0.734, 0.770)0.753 (0.736, 0.772)0.749 (0.732, 0.768)18.5–< 2347.591 (ref.)1 (ref.)1 (ref.)1 (ref.)23–< 2553.881.204 (1.194, 1.213)1.202 (1.192, 1.212)1.200 (1.190, 1.209)1.201 (1.191, 1.211)25–< 3061.571.424 (1.413, 1.435)1.419 (1.409, 1.430)1.415 (1.404, 1.426)1.417 (1.406, 1.428)30–81.161.747 (1.716, 1.778)1.740 (1.709, 1.771)1.734 (1.703, 1.766)1.734 (1.703, 1.766)P for trend < 0.0001 < 0.0001 < 0.0001 < 0.0001BMI, kg/m2–< 2549.891 (ref.)1 (ref.)1 (ref.)1 (ref.) 25–62.911.350 (1.342, 1.359)1.345 (1.337, 1.354)1.335 (1.327, 1.344)1.337 (1.328, 1.345)P value < 0.0001 < 0.0001 < 0.0001 < 0.0001WC, cm (male/female) − < 80/− < 7546.360.765 (0.758, 0.771)0.767 (0.760, 0.774)0.770 (0.763, 0.776)0.769 (0.762, 0.776)− < 85/− < 8050.890.902 (0.894, 0.910)0.902 (0.894, 0.910)0.903 (0.896, 0.911)0.904 (0.896, 0.912)− < 90/− < 8556.361 (ref.)1 (ref.)1 (ref.)1 (ref.)− < 95/− < 9061.571.103 (1.092, 1.114)1.102 (1.091, 1.113)1.101 (1.090, 1.112)1.101 (1.090, 1.112)− < 100/− < 9569.181.193 (1.177, 1.208)1.191 (1.175, 1.206)1.190 (1.174, 1.205)1.189 (1.173, 1.205)100–/95–79.751.304 (1.282, 1.327)1.303 (1.280, 1.326)1.302 (1.280, 1.325)1.301 (1.278, 1.324)P for trend < 0.0001 < 0.0001 < 0.0001 < 0.0001WC, cm (male/female) − < 90/− < 8550.931 (ref.)1 (ref.)1 (ref.)1 (ref.) 90–/85–65.531.306 (1.297, 1.315)1.302 (1.292, 1.311)1.289 (1.280, 1.299)1.289 (1.280, 1.298)P value < 0.0001 < 0.0001 < 0.0001 < 0.0001Model 1 was adjusted for age, sex. Model 2 was adjusted for age, sex, income, smoking, alcohol intake and regular exercises. Model 3 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension and dyslipidemia. Model 4 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension, dyslipidemia, cancer, end-stage renal disease, chronic obstructive pulmonary disease, stroke, liver cirrhosis, hear failure, dementia and chronic kidney disease. BMI body mass index, WC waist circumference, HR hazard ratio, CI confidence interval, OA osteoarthritis, IR incidence rate.
In the analysis of knee OA risk according to general and/or central obesity, the highest risk was identified in those who had both general with central obesity (HR 1.418; $95\%$ CI 1.406–1.429) (Table 3). Even having only central obesity without general obesity increased the risk (HR 1.167; $95\%$ CI 1.150–1.184) (Table 3). In the subgroup analysis, females (HR 1.513; $95\%$ CI 1.496–1.529) were more associated with knee OA risk than males (HR 1.317; $95\%$ CI 1.302–1.332) when accompanied by general and central obesity (Table 4, Fig. 2). It has been found that younger age groups have a higher HR of knee OA associated with general and/or central obesity (Table 4, Fig. 2).Table 3Incidence rate and hazard ratio for the risk of knee OA according to general obesity and/or central obesity composition. General obesityCentral obesityIR (per 1000)HR ($95\%$ CI)Model 1Model 2Model 3Model 4NoNo49.061 (ref.)1 (ref.)1 (ref.)1 (ref.)Yes61.491.173 (1.156, 1.190)1.171 (1.154, 1.188)1.168 (1.152, 1.185)1.167 (1.150, 1.184)YesNo58.691.290 (1.279, 1.301)1.286 (1.275, 1.297)1.279 (1.268, 1.290)1.281 (1.270, 1.292)Yes66.581.432 (1.421, 1.443)1.426 (1.415, 1.437)1.417 (1.406, 1.429)1.418 (1.406, 1.429)Model 1 was adjusted for age, sex. Model 2 was adjusted for age, sex, income, smoking, alcohol intake and regular exercises. Model 3 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension and dyslipidemia. Model 4 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension, dyslipidemia, cancer, end-stage renal disease, chronic obstructive pulmonary disease, stroke, liver cirrhosis, hear failure, dementia and chronic kidney disease. HR hazard ratio, CI confidence interval, OA osteoarthritis, IR incidence rate. Table 4Incidence rate and hazard ratio for the risk of knee OA of the general obesity and/or central obesity composition according to age and sex. General obesityCentral obesityIR (per 1000)HR ($95\%$ CI)Model 1Model 2Model 3Model 4Age categories 50–59P for interaction < 0.0001 NoNo41.701 (ref.)1 (ref.)1 (ref.)1 (ref.)Yes47.741.204 (1.176, 1.232)1.202 (1.174, 1.230)1.200 (1.172, 1.228)1.198 (1.170, 1.227) YesNo50.791.304 (1.290, 1.319)1.300 (1.286, 1.315)1.295 (1.280, 1.310)1.296 (1.281, 1.311)Yes55.561.484 (1.468, 1.501)1.477 (1.461, 1.494)1.470 (1.454, 1.487)1.470 (1.453, 1.486) 60–69 NoNo59.691 (ref.)1 (ref.)1 (ref.)1 (ref.)Yes71.341.175 (1.149, 1.202)1.173 (1.147, 1.200)1.172 (1.146, 1.199)1.170 (1.143, 1.196) YesNo75.201.271 (1.252, 1.291)1.268 (1.249, 1.288)1.263 (1.244, 1.282)1.265 (1.246, 1.285)Yes81.241.389 (1.371, 1.407)1.384 (1.366, 1.402)1.379 (1.361, 1.397)1.379 (1.361, 1.398) 70–79 NoNo66.631 (ref.)1 (ref.)1 (ref.)1 (ref.)Yes77.831.105 (1.070, 1.141)1.103 (1.069, 1.139)1.103 (1.068, 1.139)1.102 (1.067, 1.137) YesNo83.071.230 (1.194, 1.267)1.227 (1.191, 1.265)1.223 (1.187, 1.260)1.229 (1.193, 1.267)Yes87.721.278 (1.250, 1.307)1.275 (1.247, 1.304)1.272 (1.244, 1.301)1.275 (1.247, 1.304) 80– NoNo58.941 (ref.)1 (ref.)1 (ref.)1 (ref.)Yes67.251.061 (0.972, 1.159)1.060 (0.971, 1.157)1.060 (0.971, 1.158)1.061 (0.972, 1.159) YesNo70.021.169 (1.038, 1.317)1.165 (1.035, 1.313)1.161 (1.031, 1.308)1.161 (1.031, 1.308)Yes76.061.253 (1.16, 1.355)1.25 (1.157, 1.352)1.248 (1.154, 1.349)1.244 (1.151, 1.345)Sex category MaleP for interaction < 0.0001 NoNo36.081 (ref.)1 (ref.)1 (ref.)1 (ref.)Yes44.741.168 (1.144, 1.193)1.163 (1.138, 1.187)1.160 (1.136, 1.185)1.156 (1.132, 1.181) YesNo40.401.188 (1.172, 1.204)1.180 (1.165, 1.196)1.174 (1.159, 1.190)1.179 (1.164, 1.195)Yes47.301.335 (1.320, 1.351)1.323 (1.308, 1.338)1.315 (1.300, 1.330)1.317 (1.302, 1.332) Female NoNo67.381 (ref.)1 (ref.)1 (ref.)1 (ref.)Yes90.011.175 (1.153, 1.198)1.177 (1.154, 1.200)1.174 (1.151, 1.197)1.174 (1.151, 1.197) YesNo91.131.376 (1.360, 1.392)1.374 (1.359, 1.390)1.367 (1.351, 1.383)1.366 (1.350, 1.382)Yes108.141.523 (1.506, 1.539)1.523 (1.507, 1.540)1.514 (1.497, 1.530)1.513 (1.496, 1.529)Model 1 was adjusted for age, sex. Model 2 was adjusted for age, sex, income, smoking, alcohol intake and regular exercises. Model 3 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension and dyslipidemia. Model 4 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension, dyslipidemia, cancer, end-stage renal disease, chronic obstructive pulmonary disease, stroke, liver cirrhosis, hear failure, dementia and chronic kidney disease. HR hazard ratio, CI confidence interval, OA osteoarthritis, IR incidence rate. Figure 2Adjusted knee OA risk for general and/or central obesity composition, according to (a) age, (b) sex and (c) age and sex. aAdjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension, dyslipidemia, cancer, end-stage renal disease, chronic obstructive pulmonary disease, stroke, liver cirrhosis, hear failure, dementia and chronic kidney disease.
## Risk of knee OA according to changes in obesity status
Compared to those without general and central obesity over two years, the HRs of knee OA in those who developed obesity in the subsequent health examinations were 1.229 ($95\%$ CI 1.206–1.253) for general obesity and 1.226 ($95\%$ CI 1.207–1.245) for central obesity (Table 5). Remarkably, those whose general or central obese status was resolved still had a higher risk of knee OA (HR 1.216; $95\%$ CI 1.193–1.239; HR 1.227; $95\%$ CI 1.208–1.247, respectively). On the other hand, compared to those whose general or central obesity remained, the knee OA risks in those whose obesity status was resolved were significantly reduced by $11.6\%$ for general obesity and $10.0\%$ for central obesity (Table 6).Table 5Incidence rate and hazard ratio for the risk of knee OA according to the change in obesity status. Change in obesity (pre/post)IR (per 1000)HR ($95\%$ CI)Model 1Model 2Model 3Model 4General obesity No/no50.631 (ref.)1 (ref.)1 (ref.)1 (ref.) No/yes59.421.236 (1.213, 1.26)1.235 (1.212, 1.259)1.230 (1.207, 1.254)1.229 (1.206, 1.253) Yes/no59.571.222 (1.199, 1.246)1.220 (1.197, 1.243)1.215 (1.192, 1.238)1.216 (1.193, 1.239) Yes/yes64.461.388 (1.374, 1.401)1.383 (1.369, 1.396)1.374 (1.360, 1.388)1.375 (1.361, 1.389)Central obesity No/no51.171 (ref.)1 (ref.)1 (ref.)1 (ref.) No/yes63.551.235 (1.216, 1.254)1.233 (1.214, 1.252)1.226 (1.208, 1.246)1.226 (1.207, 1.245) Yes/no63.211.237 (1.217, 1.256)1.233 (1.214, 1.253)1.227 (1.208, 1.247)1.227 (1.208, 1.247) Yes/yes67.761.382 (1.365, 1.398)1.376 (1.359, 1.392)1.365 (1.348, 1.382)1.363 (1.347, 1.380)Model 1 was adjusted for age, sex. Model 2 was adjusted for age, sex, income, smoking, alcohol intake and regular exercises. Model 3 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension and dyslipidemia. Model 4 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension, dyslipidemia, cancer, end-stage renal disease, chronic obstructive pulmonary disease, stroke, liver cirrhosis, hear failure, dementia and chronic kidney disease.(Pre/post) means obesity status in the preceding and subsequent health examination. HR hazard ratio, CI confidence interval, OA osteoarthritis, IR incidence rate. Table 6Change in hazard ratio for the risk of knee OA due to the resolution of obesity in obese subjects. Change in obesity (pre/post)HR ($95\%$ CI)Model 1Model 2Model 3Model 4General obesity Yes/yes1 (ref.)1 (ref.)1 (ref.)1 (ref.) Yes/no0.881 (0.864, 0.898)0.882 (0.865, 0.899)0.884 (0.867, 0.902)0.884 (0.867, 0.902)Central obesity Yes/yes1 (ref.)1 (ref.)1 (ref.)1 (ref.) Yes/no0.895 (0.879, 0.912)0.897 (0.880, 0.913)0.899 (0.883, 0.916)0.900 (0.884, 0.916)Model 1 was adjusted for age, sex. Model 2 was adjusted for age, sex, income, smoking, alcohol intake and regular exercises. Model 3 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension and dyslipidemia. Model 4 was adjusted for age, sex, income, smoking, alcohol intake, regular exercises, diabetes, hypertension, dyslipidemia, cancer, end-stage renal disease, chronic obstructive pulmonary disease, stroke, liver cirrhosis, hear failure, dementia and chronic kidney disease.(Pre/post) means obesity status in the preceding and subsequent health examination. HR hazard ratio, CI confidence interval, OA osteoarthritis, IR incidence rate.
## Discussion
In this nationwide population-based retrospective cohort study of 1,139,463 participants aged 50 and older, we found that [1] Higher BMI and greater WC increase the risk of knee OA in a dose-dependent manner; [2] Those who had both general and central obesity had the highest risk of knee OA and its associations were stronger in women and younger people; and [3] Changes in obesity status altered the risk of knee OA. In particular, the risk was reduced in the obesity-resolved group compared to the obesity-maintaining group.
Although obesity has been reported to be an important risk factor for knee OA, to the best of our knowledge, this is the first national cohort study to determine the risk of knee OA in Asian people with general and central obesity. General obesity is defined on the basis of BMI, a standard anthropometric parameter, and high BMI is one of the strongest risk factors for knee OA. General obesity causes greater mechanical stress due to excessive weight on the joint surface, which leads to cartilage degeneration and OA. Indeed, with a weight gain of 1 kg, there is a six-fold increase in on both sides of the knee35. A meta-analysis of 22 cohort and patient-control studies reported that the pooled odds ratio of knee OA for overweight-to-normal BMI was 2.18 ($95\%$ CI 1.86–2.55), and that for obese-to-normal BMI was 2.63 ($95\%$ CI 2.28–3.05)36. In a prospective cohort study of 105,189 patients with newly developed knee OA, obese patients had more than twice the risk of knee arthroplasty surgery than normal-weight patients37. The risk of general obesity for knee OA identified in our study is also in line with previous studies.
Traditionally, the relationship between obesity and OA has been understood in terms of mechanical load, but OA in joints that are not affected by weight, such as the hands, has also been confirmed to be related to obesity38. It is presumed to be related to cytokines secreted by adipose tissues (i.e., adipokines) that regulate bone and cartilage homeostasis39–42. Among those adipokines, leptin has been identified as a major degradation factor of cartilage and a mediator for OA. Cartilage cells have leptin receptors, and high concentrations of leptin are associated with the development and progression of OA43. The leptin concentration was found to be higher in the synovial fluid of the osteoarthritic joint than in normal tissue44. The present study supports the hypothesis about the role of regional adipose tissue in OA by confirming the independent association between central obesity and the risk of knee OA. Therefore, it is inferred that the composition of general and central obesity increases the risk of knee OA due to the synergy of mechanical stress and the degradation-mediated reaction of adipose tissue.
Our findings showed that the association between obesity and risk of knee OA was stronger in female. Although the mechanism by which sex differences affect the development of knee OA is still unclear, various factors have been suggested as possible causes. First, the normal knee joint cartilage volume of males is significantly larger than that of females45. In a study using MRI to determine changes in knee articular cartilage defects over a 2.3-year period among 211 participants without clinical OA, females had a threefold higher risk of developing knee articular cartilage defects than males, even among healthy participants45. In addition, hormonal differences between males and females are also suggested to play an important role. In particular, postmenopausal estrogen reduction in females is significantly associated with an increased risk of developing knee OA46. The presence of estrogen receptors identified in joint cartilage suggests a relationship between female hormones and joint cartilage47.
Present study confirms that younger group with obesity are more susceptible to knee OA. Several studies have reported an association between general obesity and knee osteoarthritis50,51, and the metabolic effects of central obesity are also known to be associated with osteoarthritis52. Therefore, considering the cumulative effects of both types of obesity on knee joint, obesity at an early age may be associated with a greater risk than a later age.
Identifying the risk of obesity and groups more vulnerable to its harm is useful for focusing management strategies. This study highlights the stronger association between obesity and knee OA in women and younger populations. From a public health perspective, just as screening for osteoporosis is provided to postmenopausal women in their 50 s, more attention should be sought to prevent knee OA for obese women of a similar age.
In this study, changes in obesity status altered the risk of developing OA. The fact that resolution of general and central obesity in two years reduced the risk of developing knee OA by $11.6\%$ and $10.0\%$, respectively, is promising evidence for the importance of intensive interventions in obese populations. It is noteworthy that baseline normal subjects who developed new general obesity had a higher risk of knee OA than baseline general obese subjects who achieved remission of obesity for 2 years in the population aged 50 years and older (Table 5). These findings further emphasize the importance of obesity management and provide a high level of motivation for behavioral changes toward a healthy lifestyle.
The present study has several limitations. First, since the disease was defined based on the claims data in the NHIS database, the potential for bias due to misclassification cannot be overlooked, which might lead to the possibility of underestimation or overestimation. In addition, because our study was based on claims data, we could not confirm information on the grade or treatment of knee OA. Second, the change in obesity was evaluated only as a result of two years, and the cause of the change could not be identified. Third, a selection bias may exist between enrolled and missing participants for the analysis of changes in obesity status. Due to the large sample size, the difference in obesity and comorbidity between the two groups has statistically significant, but the magnitude of the difference was not large. Details are shown in Table S7. Fourth, drug use or eating habits that may affect weight were not considered. Fifth, medical history were obtained through a self-reported questionnaire. This suggests that there might be some bias that could affect the results after adjustments. Sixth, since our research was conducted with the data of the Korean NHIS, there may be limitations in generalizing our research results to other ethnic groups.
Nevertheless, our study has the strength of a nationwide population-based cohort study. The KNHIS database provides large-scale health data (big data), which is rare worldwide. In addition, this study enhances the reliability and universality of the findings by adjusting for a wide range of comorbidities. To the best of our knowledge, this is the first nationwide study to confirm that a two-year change in obesity status decreases the risk of knee OA.
Obesity and knee OA are strongly associated with personal and social burdens. Therefore, our findings support further investigation of the relationship between obesity and knee OA for the establishment of more effective OA prevention strategies.
In conclusion, our nationwide retrospective cohort study confirmed that higher BMI and WC were associated with an increased risk of knee OA in a dose-dependent manner. *Accompanying* general obesity and central obesity increase the risk more, and these effects are stronger in women and younger age groups. Changes in obesity status have been confirmed to alter the risk of knee OA.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2. The online version contains supplementary material available at 10.1038/s41598-023-30727-4.
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|
---
title: Single-cell hemoprotein (heme-SCP) exerts the prebiotic potential to establish
a healthy gut microbiota in small pet dogs
authors:
- Seungki Lee
- Ahyoung Choi
- Kyung-Hoon Park
- Seoyeon Lee
- Hyunjin Yoon
- Pil Kim
journal: Food Science and Biotechnology
year: 2022
pmcid: PMC9992493
doi: 10.1007/s10068-022-01195-9
license: CC BY 4.0
---
# Single-cell hemoprotein (heme-SCP) exerts the prebiotic potential to establish a healthy gut microbiota in small pet dogs
## Abstract
To investigate the effect of the single-cell hemoprotein (heme-SCP) source on animals, a dog-treat (100 g for each dog) harboring $0.2\%$ heme-SCP was manufactured and fed to seven pet dogs (< 10 kg) in a randomized manner (irrespective of owner’s feeding style, dogs’ health conditions, and staple diets), and the feces before and after the dog-treat diet were analyzed to define the structure of the microbiota. The total bacterial species of the seven dogs showed no difference (564–584), although the bacterial compositions varied significantly. The *Firmicutes phylum* increased (54.7–$73.7\%$), showing differential species composition before and after heme-SCP intake. Proteobacteria, Bacteroidetes, and Fusobacteria decreased (5.4–$3.8\%$, 32.9–$16.8\%$, and 6.3–$3.6\%$, respectively), which agreed with the previous observation of deliberate feeding. Therefore, it is conceivable that heme-SCP as a prebiotic can shape the gut microbiota regardless of the administration method.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10068-022-01195-9.
## Introduction
Humans coexist with trillions of microbes. Dysbiosis of the human microbiome is associated with numerous diseases, including inflammatory bowel disease, multiple sclerosis, diabetes (types I and II), allergies, asthma, autism, and even cancer (Turnbaugh et al., 2007; Ursell et al., 2012). With the development of next-generation sequencing (NGS) technology, the interactive relationship between human health and microbiome has become clearer (Jovel et al., 2016). The human gut microbiome consists of more than 400 bacterial species, most of which belong to only a few phyla, including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria (D’Argenio and Salvatore, 2015). Many bacterial species in the phylum Firmicutes have been reported to be closely related to human health; *Megamonas rupellensis* helps in type II diabetes treatment and glucose homeostasis (Díaz-Perdigones et al., 2022), *Faecalimonas umbilicata* contributes to human digestion by producing acetate (Sakamoto et al., 2018), and *Clostridium hiranonis* promotes secondary bile acid production by 7alpha-dehydroxylation activity (Kitahara et al., 2001). Species belonging to the *Bacteroidetes phylum* are also closely associated with human health, but their influence is disorienting amongst bacterial species or host health conditions. Bacteroides vulgatus is enriched in the human gut with intestinal diseases (e.g., Crohn’s disease) (Bloom et al., 2011; Dicksved et al., 2008), whereas it is depleted in patients suffering from coronary artery disease (Sieminska et al., 2021), and Bacteroides plebeius, frequently found in healthy individuals, is considered an indicator of healthy intestinal flora (Hernandez et al., 2022).
To unveil the multifarious roles of the microbiome, fecal microbiota are perpetually compared between healthy and diseased groups, including humans as well as animals representing a human-like microbiome. Although gut microbiome studies have been most extensively conducted in mice as an animal model, this species possesses physiological characteristics far different from humans, considering body size, metabolic rate, and life expectancy (Perlman, 2016). Emerging metagenomics data suggests that dogs represent gut microbiome closer to the human microbiome, compared with the microbiome of either mice or pigs (Coelho et al., 2018; Pallotti et al., 2022). Dogs became domesticated more than 14,000 years ago and are considered omnivorous, frequently sharing food resources with humans. This species often shows genetic disorders with similar pathophysiological and clinical features to the human counterpart (Pallotti et al., 2022). In this study, we investigated whether dietary intake of heme compounds could influence the composition and structure of the gut microbiota using a dog model.
Heme is an iron-bound biomolecule found in all organisms involved in respiratory metabolism, including animals, plants, and other microorganisms. Proteins harboring heme as a prosthetic group (hemoproteins or heme proteins) are widely distributed in nature and perform various biological functions; many globin proteins (i.e., hemoglobin, myoglobin, neuroglobin, leghemoglobin) attach or detach di-oxygen molecules, many cytochromes transport electrons in the respiratory chain or create radicals to degrade recalcitrant organics, heme-catalase and heme-peroxidase detoxify reactive oxygen species (ROS), diverse sensor hemoproteins deliver a variety of signals in or between cells. Many anaerobic bacteria, including lactic acid bacteria, are defective in heme production and utilization. Consequently, their respiratory metabolism is incomplete and less effective for energy generation (i.e., fermentation metabolism). Once the heme molecule is supplemented as a nutrient, many anaerobic bacteria produce more energy via respiration (Kim et al., 2021; Lechardeur et al., 2011). Likewise, numerous bacterial species colonizing the gut lack a complete heme biosynthesis pathway, but encode heme-requiring proteins (Gruss et al., 2012). Regarding the indispensable roles of heme in bacterial physiology, it is probable that dietary intake of heme compounds reshapes the composition and structure of the gut microbiota. We have previously observed that two female dogs with similar ages and body weights increased the abundance of the *Firmicutes phylum* and enhanced the gut microbiome diversity, when single-cell hemoprotein (equally, heme-SCP) was regularly administered for 6 days (Lee et al., 2021). In this study, to get an insight into the potential of heme-SCP as a prebiotic, ten pet dogs varying in age, body weight, sex, breed, and staple food were fed with heme-SCP and their gut microbiota structure were compared before and after the treatment.
## Manufacturing a dog-treat harboring single-cell hemoprotein (heme-SCP)
Heme-SCP (0.2 g, dried biomass of hemoprotein-rich bacterial cells; Cell Tech, Ltd. Co., Cheongju, Choongbuk, Korea) was mixed with acidified ingredients (frozen-dried pollack 20 g, sweet pumpkin 14.8 g, duck tenderloin 10 g, carrot 10 g, brown rice 5 g, salmon 5 g, sea mussel 5 g, cabbage 4 g, sweet potato 8 g, broccoli 3 g, coconut 3 g, tapioca starch 10 g, glycerin 5 g), dispersed in $10\%$ vinegar solution, boiled for 1 h, extruded (2 cm diameter), cut to 1 cm thickness, dried at 62 °C for 5 h, and packed as 100 g units in a feed manufacturing facility (Hi-tech Korea Ltd. Co., Seoul, Korea) as a heme-SCP harboring dog-treat.
## Dog rearing and feces collection
Ten companion dogs ($$n = 10$$; 5 males and 5 females; age ranging from 6-month-old to 15-year-old; various breeds and lifestyles living in Korean households) were recruited, and the heme-SCP harboring dog-treat was provided to every dog owner. The dog-treat (100 g) was fed as a snack in an undesigned manner: different owner feeding styles, different dog lifestyles and health conditions, different breeds, no changes in the main diet, and no administration time limit. Fecal samples of dogs before and after the 100 g-dog-treat administration were collected in capped-tubes (50-mL tubes) and kept frozen until DNA extraction for bacterial taxonomic profiling. The owners were interviewed after the test to survey weights, preferences for the treat, and any abnormal behaviors or side effects in the dogs.
## Microbiome analysis
Frozen fecal samples were obtained from dog owners. Metagenomic DNA was extracted with FastDNA Spin kit (MP Biomedicals, Irvine, CA, USA) and the V3–V4 region of the bacterial 16S rRNA gene was PCR amplified using the barcoded universal primers (Yoon et al., 2017) of 341F (5′-AATGATACGGCGACCACCGAGATCTACAC-XXXXXXXX-TCGTCGGCAGCGTC-AGATGTGTATAAGAGACAG-CCTACGGGNGGCWGCAG-3′; underlining sequence indicates the target region primer-3′) and 805R (5′-CAAGCAGAAGACGGCATACGAGAT-XXXXXXXX-GTCTCGTGGGCTCGG-AGATGTGTATAAGAGACAG-GACTACHVGGGTATCTAATCC-3′). The amplifications were carried out under the following conditions: initial denaturation at 95 °C for 3 min, followed by 25 cycles of denaturation at 95 °C for 30 s, primer annealing at 55 °C for 30 s, and extension at 72 °C for 30 s, with a final elongation at 72 °C for 5 min. Purification of the amplicons was conducted using CleanPCR (CleanNA, Waddinxveen, Netherlands). The quality and product size were assessed on a Bioanalyzer 2100 (Agilent, Palo Alto, CA, USA) using a DNA 7500 chip. The pooled barcoded amplicons were sequenced using a MiSeq sequencer on the Illumina platform (CJ Bioscience, Inc., Seoul, Korea) according to the manufacturer’s specification. Taxonomic profiling of the microbiome was conducted using the EzBioCloud 16S rRNA database (Yoon et al., 2017). Statistical analysis was carried out using Mann–Whitney U-test (SPSS IBM, New York, NY, USA) to compare the variation in taxonomic profiles between samples. For analysis of alpha-diversity, the richness and diversity were determined by Shannon, Jackknife, and Simpson diversity indices using the in-house programs of CJ Bioscience, Inc. Sequencing coverage was calculated using Good’s method (Li et al., 2009).
## Bacterial growth curve
Bacteroides vulgatus ATCC8482 was cultivated in a mixed medium (BHI:MRS = 1:1) broth, and the optical density at 600 nm was measured to estimate bacterial growth. The broth medium was supplemented with heme-SCP (2 mg/mL) or hemin (Sigma-Aldrich, St. Louis, MO, USA) at 250 µM to examine the effect of heme on bacterial growth. BHI and MRS were purchased from BD Inc. (Sparks, MD, USA) and MBCell Inc. (Seoul, Korea), respectively.
## Results and discussion
Table 1 summarizes the information on the dogs (P1–P10) enrolled in this study. Many of the dogs were administered the treat for 2–3 weeks. However, P6 dog going through the toddler phase was fed exceptionally longer (30 days) according to the owner’s own volition, not spoiling a regular diet with staple food. No significant variations in weight or signs of possible illness were observed in any of the dogs. All owners reported that their dogs preferred the treat over their main diets. They also recognized the beneficial changes after administration, presumably attributable to the treat, including less diarrhea, better digestion, better stool consistency, and less putrid smelling stool, which are all associated with gut health. P4 (15-year-old) with a chronic digestive disorder was able to consume solid foods such as beef chunks and dried jerky during the treat test, although it returned to a poor meat-digestion state within a week after the test.
Table 1Dogs included in this studyCodeaDog nameBreed (gender)Age (year)Treat (100 g) consumption (days)2Weight (Before)Weight (after)bDogs’ preference (5-point scale)cDNA extraction from fecal sampledOwners’ commenteP1ChocoPoodle (F)5104.54.7*****FIncreased appetiteP2DaebagShih Tzu (F)6154.84.7*****PLess diarrheaP3DoongiSpeech (M)6144.84.7*****PIncreased appetiteP4PukuPoodle (M)15610.010.3*****PBetter meat digestionP5Jin-juMaltese (F)13223.43.6*****PNo recognizable differenceP6MiniPomenarian (F)0.5302.32.3****PBetter stool consistencyP7SchnauzerYorkshire Terrier (M)695.45.7*****FBetter stool consistencyP8AaronChihuahua (F)5183.43.6****FLess putrid smelling stoolP9ArachiChihuahua (M)7203.33.3*****PLess diarrheaP10Byeol-iSchnauzer (M)13175.45.7*****PBetter meat digestionaTen dogs enrolled by the owners were provided with heme-SCP harboring dog-treat (100 g), and fecal samples before and after the dog treats were collected by the owners. Fecal samples from P1, P7, and P8, either before or after dog-treat, failed in 16S rRNA extraction because of late harvest or incorrect preservation, and the remainder from 7 dogs were analyzed in this studybBody weights were measured by the owners using their own scales before and after the treatcDog’s preference for the treat was estimated based on owners’ judgementdP/F: DNA extraction quality passed/failedeOwner’s comments on the changes in their dogs’ health conditions during the testAsterisks are not for statistical analysis. They indicate the dog preference toward the treat. Their preference was indicated using the number of asterisks from 1 to 5 as denoted in the table The fecal samples from P1, P7, and P8 dogs failed in the 16S rRNA sequencing because of DNA destruction either by delayed harvest or incorrect preservation. The microbiome profiles of the remaining 14 samples from seven dogs before and after the treat, are represented in Fig. 1 at the phylum level. The majority of the gut microbiota were from five phyla, namely: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria. However, the abundance of each phylum was markedly altered by the administration of dog-treat containing heme-SCP. For example, the gut microbiota of P5 exhibited a dramatic decrease in Bacteroidetes (31.4–$2.6\%$) but significant increase in Firmicutes (55.7–$78.0\%$) after the dog-treat consumption, even though the owner did not notice any changes in the dog’s health condition, such as ethological changes, altered bowel habit, and improvement of appetite. The dog-treat diet also led to directly opposing responses between Bacteroidetes (decreases) and Firmicutes (increases) in other entities, including P2, P3, P9, and P10. Notably, P9, suffering from frequent watery diarrhea before the test, did not have diarrhea during the intake of the dog-treat. Likewise, P3, whose microbiota was dominated by Firmicutes after the dog-treat consumption, was reported to smell less unpleasant. Intriguingly, the response of Bateroidetes and Firmicutes to the dog-treat was reversed in P4 (15-year-old) and P6 (0.5-year-old) dogs, showing increases in Bacteroidetes and decreases in Firmicutes. Considering that the rest are middle-aged, it is tempting to speculate that the potency of the dog-treat diet may vary in the infancy and old age groups, where the gut microbiome is immaturely established or vulnerable to exogenous stimuli. The profound effects of prebiotics on frailty and aging have been intensively explored in the recent studies (Jayanama and Theou, 2020; Mizukami et al., 2019). The effects of prebiotics on gut microbiota might be more noticeable in the senior dogs that suffer from health problems with aging, concomitantly experiencing microbial dysbiosis and metabolomic changes.
Fig. 1Alteration of fecal bacterial phyla of seven dogs before and after using the dog-treat. The fecal samples from seven dogs (P1 to P10 except P1, P7, and P8) before and after the dog treats were subjected to 16S rRNA sequencing, and bacterial abundance was analyzed at the phylum level. The alterations in species level are displayed in supplemental data (Table S1) Because the dogs in this study were of different breeds, ages, health conditions, and were subjected to different feeding styles and main diets, many variables could influence microbiome conditions. To clarify the effect of randomized feeding of the heme-SCP-harboring treat, the microbiome data from seven dogs were integrated before and after the dog-treat diet and were averaged (Fig. 2). The total number of bacterial species identified using the EzBioCloud database after the treat was 584, which was comparable to the number before administration (564 species). The phylogenetic diversity (a measure of biodiversity that incorporates phylogenetic differences between species) (Faith and Baker, 2007) of seven dogs was also comparable (average phylogenetic diversity of 177.7–192.1). Again, the bacterial composition (in phylum units) was distinctly changed by treat supply. The proportion of Firmicutes increased (54.7–$73.7\%$), whereas that of Bacteroides decreased (32.9–$16.8\%$) after the test.
Fig. 2Average of seven dogs’ bacterial communities before and after the dog-treat. The microbiome data from seven dogs were integrated before and after the dog-treat diet and were averaged. Taxonomic profiles were compared before and after the dog-treat diet. The representative bacterial species showing significant changes due to the dog-treat diet are depicted in parallel When the *Firmicutes phylum* was dissected at the species level, the dog-treat diet increased the abundance of Blautia spp. ( 14-fold, 1.4–$16.9\%$), *Ruminococcus gnavus* (2.3-fold, 6.5–$14.9\%$), and Faecalimonas umbilicate (9.7-fold, 2.2–$21.3\%$) and decreased the abundance of M. rupellensis (0.24-fold, 12.5–$3.1\%$) and C. hiranonis (0.42-fold, 12.7–$5.5\%$) (Fig. 2). Blautia spp. have been reported to flourish in low visceral (hidden) fat humans (Ozato et al., 2019), thrive in humans with whole grain-induced immunological improvements (Martínez et al., 2013), but perish in dogs with acute hemorrhagic diarrhea syndrome (AHDS) (Guard et al., 2015). Ruminococcus gnavus is enriched in the infant human gut and has been suggested as a host immune educator (Chua et al., 2018; Sagheddu et al., 2016). F. umbilicate, an acetate-producing bacterial species, contributes to the establishment of gut microbial flora by enriching acetate-metabolizing butyrate-producing bacteria (Duncan et al., 2004; Sakamoto et al., 2017). M. rupellensis, an aerobe that produces short-chain fatty acids in the gut, was reported to shrink in the host with reduced glucose metabolism (Martín-Núñez et al., 2019). C. hiranonis has been reported to metabolize primary bile acids to secondary bile acids (Ridlon et al., 2020), some (i.e., deoxycholic acid) of which may trigger cancer in the intestines of many animals (Pai et al., 2004; Yoshimoto et al., 2013).
The intake of dog-treat reduced the proportion of *Bacteroidetes phylum* per se, but at the level of bacterial species, its influence was differential. The dog-treat diet decreased the abundance of B. vulgatus (0.11-fold, 6.78–$0.76\%$) and B. plebeius (0.4-fold, 7.48–$3\%$), whereas it increased the abundance of *Bacteroides fragilis* (8.3-fold, 0.3–$2.4\%$) (Fig. 2). *In* general, the *Bacteroidetes phylum* is regarded as a commensal bacterium in the healthy gut, but the likelihood of pathogenicity varies depending on the bacterial species and host health conditions. For example, *Bacteroides fragilis* is the primary species causing Bacteroides infection when displaced into the bloodstream (Tajkarimi and Wexler, 2017). B. plebeius in the gut of people with Japanese descent is positively linked to the host’s complex carbohydrate (found in red seaweed) degradation and energy metabolism (Hehemann et al., 2012) but is also enriched in patients with cardiovascular disease (Liu et al., 2019). B. vulgatus has been reported to flourish in the gut of patients with intestinal disease (Crohn’s disease) (Dicksved et al., 2008), and decline in the gut of patients with coronary artery disease (Sieminska et al., 2021).
To assess whether the altered bacterial composition was attributable to the iron compounds added to the dog-treat, the growth of B. vulgatus was compared in the presence and absence of heme-SCP. Interestingly, the addition of heme-SCP increased the bacterial growth rate, which was in contrast to the metagenome profiles in vivo (Fig. 3). The positive role of heme in B. vulgatus growth was validated by the addition of hemin, a ferric iron-containing porphyrin compound. The bacterial requirement for iron differs between bacterial species. To date, the detailed mechanism of iron acquisition in B. vulgatus has not been identified. However, Sieminska et al. [ 2021] recently claimed that B. vulgatus exploited a Bvu-based hemophore system to scavenge heme compounds, thereby promoting bacterial growth and virulence in the presence of heme (Sieminska et al., 2021). The discrepancy between the in vitro and in vivo analyses might be due to the differences in environment. Bacteroides spp. grow much slower than other commensal bacterial species, especially those belonging to the Enterobacteriaceae family, including harmless symbionts and opportunistic pathogens. Enteric bacteria such as *Escherichia coli* possess a variety of iron-sequestration strategies, including siderophores and iron transporters (Sousa Geros et al., 2020). Therefore, it is likely that other bacterial species outcompete B. vulgatus for scavenging heme and other iron equivalents in the gut environment.
Fig. 3Effect of heme-SCP on the growth of B. vulgatus. B. vulgatus was cultivated in the presence of heme-SCP (2 mg/mL) or hemin (250 µM). The absorbance at 600 nm was measured every hour for 11 h and the values from three independent tests were plotted The dog-treat diet marginally decreased the abundance of the *Proteobacteria phylum* from 5.4 to $3.8\%$, and the dominant bacterial species was E. coli (decreased from 5.3 to $3.0\%$), the most common human gut microorganism known as a fast monosaccharide degrader (Fig. 2). In the context of gut health, E. coli species exhibits multifaceted roles among bacterial strains. Although many E. coli strains are commensal, some strains are pathogenic and cause diseases in cases of microbiome perturbations, and some (e.g. E. coli strain Nissle 1917) are probiotics that decelerate the occurrence of intestinal inflammation and diseases (Gronbach et al., 2010).
Altogether, the undesigned feeding of heme-SCP-harboring treat, regardless of feeding style, lifestyle, health condition, dog breed, and main diet, reshaped the structure of the gut microbiome, showing a tendency to improve gut health: more fat degradation (Blautia spp. up), more immune lesson (*Ruminococcus gnavus* up), more diversity by enriching butyrate-producing beneficial bacteria (F. umbilicate up), less chance of carbohydrate digestion (M. rupellensis down, B. plebeius down, E. coli down), and a lower chance of intestinal diseases (C. hiranonis down, B. vulgatus down). These results are in accordance with previous observations where the controlled feeding of heme-SCP enriched Firmicutes in a dog model, and the heme-SCP addition benefited the growth of Lactobacillus gasseri, a representative of Firmicutes (Lee et al., 2021).
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 80.8 kb)
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---
title: Long-term gastrointestinal outcomes of COVID-19
authors:
- Evan Xu
- Yan Xie
- Ziyad Al-Aly
journal: Nature Communications
year: 2023
pmcid: PMC9992516
doi: 10.1038/s41467-023-36223-7
license: CC BY 4.0
---
# Long-term gastrointestinal outcomes of COVID-19
## Abstract
A comprehensive evaluation of the risks and 1-year burdens of gastrointestinal disorders in the post-acute phase of COVID-19 is needed but is not yet available. Here we use the US Department of Veterans Affairs national health care databases to build a cohort of 154,068 people with COVID-19, 5,638,795 contemporary controls, and 5,859,621 historical controls to estimate the risks and 1-year burdens of a set of pre-specified incident gastrointestinal outcomes. We show that beyond the first 30 days of infection, people with COVID-19 exhibited increased risks and 1-year burdens of incident gastrointestinal disorders spanning several disease categories including motility disorders, acid related disorders (dyspepsia, gastroesophageal reflux disease, peptic ulcer disease), functional intestinal disorders, acute pancreatitis, hepatic and biliary disease. The risks were evident in people who were not hospitalized during the acute phase of COVID-19 and increased in a graded fashion across the severity spectrum of the acute phase of COVID-19 (non-hospitalized, hospitalized, and admitted to intensive care). The risks were consistent in comparisons including the COVID-19 vs the contemporary control group and COVID-19 vs the historical control group as the referent category. Altogether, our results show that people with SARS-CoV-2 infection are at increased risk of gastrointestinal disorders in the post-acute phase of COVID-19. Post-covid care should involve attention to gastrointestinal health and disease.
SARS-CoV-2 infection can lead to varied post-acute symptoms in the lungs and other organs, including the gastrointestinal system. Here the authors estimate the risks and 1-year burdens of a set of pre-specified incident gastrointestinal outcomes following SARS-CoV-2 infection in an electronic health care record-based cohort study.
## Introduction
SARS-CoV-2 infection can lead to a broad array of post-acute sequelae which can involve the pulmonary and several extrapulmonary organs including the gastrointestinal system1,2; the constellation of these post-acute conditions is referred to by the umbrella term Long Covid3. Studies investigating the gastrointestinal post-acute sequelae of SARS-CoV-2 infection are mostly limited to hospitalized individuals and all had a short duration of follow up of a few months, and a narrow selection of gastrointestinal outcomes4–12. A comprehensive evaluation of the risks and burdens of gastrointestinal disorders in the post-acute phase of COVID-19 is needed but has not yet been undertaken. Addressing this knowledge gap is important to inform post-acute COVID-19 care strategies.
In this work, we use the US Department of Veterans Affairs national health care databases to build a cohort of 154,068 people who survived the first 30 days of COVID-19, and two control groups including a contemporary control of 5,638,795 who lived during the same time but had no evidence of SARS-CoV-2 infection, and a historical cohort of 5,859,621 people from the pre-pandemic era. These cohorts were followed longitudinally to estimate the risks and 1-year burdens of a set of pre-specified incident gastrointestinal outcomes in the overall cohort and by care setting of the acute phase of SARS-CoV-2 infection (that is whether people were non-hospitalized, hospitalized, or admitted to intensive care).
## Results
The COVID-19, contemporary control, and historical control group consisted of 154,068, 5,638,795, and 5,859,621 participants, respectively. The COVID-19, contemporary control, and historical control groups had a median follow up of 408 (interquartile range: 378–500), 409 (379–505), and 409 (379–504) days, which corresponded to 185,399, 6,808,464, and 7,071,123 person-years of follow-up, respectively; altogether totaling to 14,064,985 person-years of follow-up.
Baseline demographic and health characteristics of the COVID-19, contemporary control, and historical control groups are presented in supplementary table 1. Characteristics after weighting are presented in supplementary table 2.
## Incident gastrointestinal outcomes in COVID-19 vs contemporary controls
The COVID-19 and contemporary control groups were balanced through the inverse probability weighting method; examination of standardized mean differences of demographic and health characteristics after weighting suggested good balance (supplementary fig. 1a).
We estimated the risks (hazard ratios; HR) and excess burdens of a set of prespecified gastrointestinal outcomes in people with COVID-19 vs the contemporary control group (reference category) (Figs. 1, 2 and supplementary table 3). The excess burdens were estimated per 1000 persons at 1-year based on the difference in the estimated incidence rate between the COVID-19 and contemporary control group at 1 year. Fig. 1Risks and 1-year burdens of incident post-acute COVID-19 gastrointestinal outcomes compared with the contemporary control cohort. Outcomes were ascertained 30 d after the COVID-19-positive test until the end of follow-up. COVID-19 cohort ($$n = 154$$,068) and contemporary control cohort ($$n = 5$$,638,795). Panel A describes the risks and burdens of incident diagnoses (light green) and panel B describes the risks and burdens of incident laboratory abnormalities (orange). Adjusted HRs (dots) and $95\%$ (error bars) CIs are presented, as are estimated excess burdens (bars) and $95\%$ CIs (error bars). Burdens are presented per 1000 persons at 12 months of follow up. The dashed line marks a HR of 1.00; lower limits of $95\%$ CIs with values greater than 1.00 indicate significantly increased risk. GERD, gastroesophageal reflux disorder; IBS irritable bowel syndrome, PT prothrombin time, PTT partial thromboplastin time, INR international normalized ratio, ALT alanine transaminase, AST aspartate transaminase, LDH lactate dehydrogenase, CRP c-reactive peptide, ALP alkaline phosphatase, GGT γ-glutamyl transferase. Fig. 2Risks and 1-year burdens of incident post-acute COVID-19 composite gastrointestinal outcomes compared with the contemporary control cohort. Composite outcomes consisted of incident diagnoses (GERD, PUD, acute pancreatitis, functional dyspepsia, acute gastritis, IBS, and cholangitis), signs and symptoms (constipation, abdominal pain, diarrhea, vomiting, and bloating), coagulation studies (PT, PTT, INR), liver and biliary tree function tests (albumin, ALT, total protein, AST, LDH, CRP, ALP, total bilirubin, GGT, direct bilirubin, lipase, and amylase) and any gastrointestinal outcome (incident occurrence of any gastrointestinal outcome studied). Outcomes were ascertained 30d after the COVID-19-positive test until the end of follow-up. COVID-19 cohort ($$n = 154$$,068) and contemporary control cohort ($$n = 5$$,638,795). Adjusted HRs (dots) and $95\%$ (error bars) CIs are presented, as are estimated excess burdens (bars) and $95\%$ CIs (error bars). Burdens are presented per 1000 persons at 12 months of follow up. The dashed line marks a HR of 1.00; lower limits of $95\%$ CIs with values greater than 1.00 indicate significantly increased risk. GERD gastroesophageal reflux disorder, IBS irritable bowel syndrome, PT prothrombin time, PTT partial thromboplastin time, INR international normalized ratio, ALT alanine transaminase, AST aspartate transaminase, LDH lactate dehydrogenase, CRP c-reactive peptide, ALP alkaline phosphatase, GGT γ-glutamyl transferase.
## Incident diagnoses
People who survived the first 30 days of COVID-19 exhibited increased risk of gastroesophageal reflux disease (GERD) (HR 1.35 (1.31, 1.39); burden 15.50 (13.83, 17.21) per 1000 persons at 1-year); for all hazard ratios and burdens, parenthetical ranges refer to $95\%$ confidence intervals), peptic ulcer disease (PUD) (HR 1.62 (1.46, 1.79); burden 1.57 (1.18, 2)), acute pancreatitis (HR 1.46 (1.23, 1.75); burden 0.6 (0.29, 0.97)), functional dyspepsia (HR 1.36 (1.22, 1.51); burden 0.63 (0.39, 0.9)), acute gastritis (HR 1.47 (1.25, 1.72); burden 0.47 (0.26, 0.73)), irritable bowel syndrome (HR 1.54 (1.28, 1.86); burden 0.44 (0.22, 0.69)), and cholangitis (HR 2.02 (1.55, 2.63); burden 0.22 (0.12, 0.35)). The risk and burden of a composite of any incident diagnosis were 1.37 (1.33, 1.41) and 17.37 (15.62, 19.17).
## Signs and symptoms
included constipation (HR 1.60 (1.54, 1.66); burden 11.34 (10.27, 12.46)), abdominal pain (HR 1.44 (1.4, 1.49); burden 10.53 (9.42, 11.67)), diarrhea (HR 1.58 (1.52, 1.65); burden 7.99 (7.13, 8.9)), vomiting (HR 1.52 (1.38, 1.67)); burden 1.21 (0.90, 1.56)), and bloating (HR 1.46 (1.33, 1.61); burden 0.92 (0.65, 1.21)). The risk and burden of a composite of these signs and symptoms were 1.54 (1.5, 1.58), and 24.02 (22.30, 25.78).
## Coagulation studies
included prothrombin time>13 s (HR 1.61 (1.54, 1.68); burden 7.90 (6.99, 8.84)); partial thromboplastin time>35 s (HR 1.49 (1.38, 1.61); burden 2.11 (1.63, 2.64)), and international normalized ratio>1 (HR 1.48 (1.33, 1.65); burden 1.37 (0.93, 1.85)). The risk and burden of a composite of these coagulation studies were 1.59 (1.52, 1.65), and 9.27 (8.27, 10.31).
## Liver and biliary tree function tests
included albumin<3.5 g/dL (HR 1.50 (1.46, 1.54); burden 20.9 (19.27, 22.57)); alanine transaminase>35 U/L (HR 1.25 (1.23, 1.28); burden 16.39 (14.60, 18.22)); total protein<6.0 g/dL (HR 1.50 (1.45, 1.54)); burden 14.54 (13.26, 15.85)), aspartate transaminase>35 U/L (HR 1.27 (1.23, 1.30); burden 10.69 (9.33, 12.10)), lactate dehydrogenase>100 U/L (HR 1.54 (1.48, 1.60); burden 10.22 (9.17, 11.31)), c-reactive peptide>0.8 mg/dL (HR 1.63 (1.56, 1.69); 9.43 (8.49, 10.40)), alkaline phosphatase>92 U/L (HR 1.28 (1.25, 1.33); burden 9.21 (7.94, 10.52)), total bilirubin>1.2 mg/dL (HR 1.22 (1.18, 1.26); burden 7.40 (6.11, 8.73)), γ-glutamyl transferase>30 U/L (HR 1.30 (1.24, 1.37); burden 3.25 (2.54, 3.99)), direct bilirubin>0.3 mg/dL (HR 1.30 (1.20, 1.40); burden 1.43 (0.98, 1.92)), lipase>95 U/L (HR 1.49 (1.27, 1.74); burden 0.54 (0.30, 0.82)) and amylase>130 U/L (HR 1.54 (1.05, 2.26); burden 0.07 (0.01, 0.16)). The risk and burden of a composite of these liver and biliary tree function tests were 1.30 (1.28, 1.32), and 43.49 (40.30, 46.72).
## Any gastrointestinal outcome
Compared to the contemporary control group, the risk of having any gastrointestinal outcome (defined as the occurrence of any incident prespecified gastrointestinal outcome included in this study) was increased in COVID-19 group (HR 1.36 (1.34, 1.39); burden 62.34 (57.82, 66.92)).
## Subgroup analyses
We examined the risks of composite gastrointestinal outcomes in COVID-19 group compared to contemporary control in prespecified subgroups. The results showed that the risks of composite gastrointestinal outcome were evident in all subgroups based on age, race, sex, obesity, smoking, cardiovascular disease, chronic kidney disease, diabetes, hyperlipidemia, and hypertension (Fig. 3 and supplementary table 4).Fig. 3Subgroup analyses of the risks of incident post-acute COVID-19 composite gastrointestinal outcomes compared with the contemporary control cohort. Composite outcomes consisted of incident diagnoses (GERD, PUD, acute pancreatitis, functional dyspepsia, acute gastritis, IBS, and cholangitis), signs and symptoms (constipation, abdominal pain, diarrhea, vomiting, and bloating), coagulation studies (PT, PTT, INR), liver and biliary tree function tests (albumin, ALT, total protein, AST, LDH, CRP, ALP, total bilirubin, GGT, direct bilirubin, lipase, and amylase) and any gastrointestinal outcome (incident occurrence of any gastrointestinal outcome studied). Outcomes were ascertained 30d after the COVID-19-positive test until the end of follow-up. COVID-19 cohort ($$n = 154$$,068) and contemporary control cohort ($$n = 5$$,638,795). Adjusted HRs (dots) and $95\%$ (error bars) CIs are presented. The dashed line marks a HR of 1.00; lower limits of $95\%$ CIs with values greater than 1.00 indicate significantly increased risk. GERD gastroesophageal reflux disorder, IBS irritable bowel syndrome, PT prothrombin time, PTT partial thromboplastin time, INR international normalized ratio, ALT alanine transaminase, AST aspartate transaminase, LDH lactate dehydrogenase, CRP c-reactive peptide, ALP alkaline phosphatase, GGT γ-glutamyl transferase.
## Incident gastrointestinal outcomes in COVID-19 vs contemporary controls by care setting of the acute infection
We then stratified the COVID-19 cohort into mutually exclusive groups based on the care setting of the acute infection comprised of those who were non-hospitalized ($$n = 131$$,915), hospitalized ($$n = 16$$,764), or admitted to intensive care ($$n = 5389$$) during the acute phase of COVID-19. The risks and burdens of prespecified gastrointestinal outcomes were then estimated. Demographic and health characteristic data of these three groups before and after weighting are described in supplementary tables 5 and 6, respectively. After application of inverse probability weighting, calculation of standardized mean differences of demographic and health characteristics suggested good balance (Supplementary fig. 1b).
Compared to the contemporary control group, the risks and burdens of the prespecified gastrointestinal outcomes were evident even among those who were not hospitalized during the acute phase of COVID-19 and increased in a graded fashion according to the care setting of the acute phase of the disease from non-hospitalized to hospitalized to those admitted to intensive care (Fig. 4, Fig. 5, and Supplementary table 7).Fig. 4Risks and 1-years burdens of incident post-acute COVID-19 gastrointestinal outcomes compared with the contemporary control cohort by care setting of the acute infection. Risks and burdens were assessed at 1-year in mutually exclusive groups comprising non-hospitalized individuals with COVID-19 (green), individuals hospitalized for COVID-19 (orange) and individuals admitted to intensive care for COVID-19 during the acute phase (first 30 d) of COVID-19 (blue). Outcomes were ascertained 30 d after the COVID-19-positive test until the end of follow-up. The contemporary control cohort served as the referent category. Within the COVID-19 cohort, non-hospitalized ($$n = 131$$,915), hospitalized ($$n = 16$$,764), admitted to intensive care ($$n = 5389$$) and contemporary control cohort ($$n = 5$$,606,761). Panel A describes the risks and burdens of incident diagnoses and panel B describes the risks and burdens of incident laboratory abnormalities. Adjusted HRs (dots) and $95\%$ (error bars) CIs are presented, as are estimated excess burdens (bars) and $95\%$ CIs (error bars). Burdens are presented per 1000 persons at 12 months of follow up. The dashed line marks a HR of 1.00; lower limits of $95\%$ CIs with values greater than 1.00 indicate significantly increased risk. GERD gastroesophageal reflux disorder, IBS irritable bowel syndrome, PT prothrombin time, PTT partial thromboplastin time, INR international normalized ratio, ALT alanine transaminase, AST aspartate transaminase, LDH lactate dehydrogenase, CRP c-reactive peptide, ALP alkaline phosphatase, GGT γ-glutamyl transferase. Fig. 5Risks and 1-year burdens of incident post-acute COVID-19 composite gastrointestinal outcomes compared with the contemporary control cohort by care setting of the acute infection. Risks and burdens were assessed at 1-year in mutually exclusive groups comprising non-hospitalized individuals with COVID-19 (green), individuals hospitalized for COVID-19 (orange) and individuals admitted to intensive care for COVID-19 during the acute phase (first 30 d) of COVID-19 (blue). Composite outcomes consisted of incident diagnoses (GERD, PUD, acute pancreatitis, functional dyspepsia, acute gastritis, IBS, and cholangitis), signs and symptoms (constipation, abdominal pain, diarrhea, vomiting, and bloating), coagulation studies (PT, PTT, INR), liver and biliary tree function tests (albumin, ALT, total protein, AST, LDH, CRP, ALP, total bilirubin, GGT, direct bilirubin, lipase, and amylase) and any gastrointestinal outcome (incident occurrence of any gastrointestinal outcome studied). Outcomes were ascertained 30 d after the COVID-19-positive test until the end of follow-up. The contemporary control cohort served as the referent category. Within the COVID-19 cohort, non-hospitalized ($$n = 131$$,915), hospitalized ($$n = 16$$,764), admitted to intensive care ($$n = 5389$$) and contemporary control cohort ($$n = 5$$,606,761). Adjusted HRs (dots) and $95\%$ (error bars) CIs are presented, as are estimated excess burdens (bars) and $95\%$ CIs (error bars). Burdens are presented per 1000 persons at 12 months of follow up. The dashed line marks a HR of 1.00; lower limits of $95\%$ CIs with values greater than 1.00 indicate significantly increased risk. GERD gastroesophageal reflux disorder, IBS, irritable bowel syndrome, PT prothrombin time, PTT partial thromboplastin time, INR international normalized ratio, ALT alanine transaminase, AST aspartate transaminase, LDH lactate dehydrogenase, CRP c-reactive peptide, ALP alkaline phosphatase, GGT γ-glutamyl transferase.
## Incident gastrointestinal outcomes in COVID-19 vs historical controls
We tested the robustness of our study results by evaluating the associations between COVID-19 and the prespecified gastrointestinal outcomes in analyses utilizing a historical control group (which did not experience the pandemic) as the referent group. Demographic and health characteristics before and after weighting are presented in supplementary tables 1, 2, 8, and 9. Calculation of the standard mean differences after application of inverse weighting suggested that covariates were well balanced (Supplementary fig. 1c, d). Our results indicated increased risks and burdens of the prespecified outcomes in analyses comparing the overall COVID-19 and the historical control groups (Supplementary figs. 2, 3, and supplementary table 10), in analyses of subgroups (Supplementary fig. 4 and supplementary table 11), and analyses by care setting of the acute phase of COVID-19 (Supplementary figs. 5, 6 and supplementary table 12). Both the direction and the magnitude of risks and burdens were consistent with results from analyses using the contemporary control as the referent category.
## Incident gastrointestinal outcomes in hospitalized COVID-19 vs hospitalized influenza
Additionally, we evaluated the risk of incident composite gastrointestinal outcomes in people hospitalized with COVID-19 ($$n = 22$$,153) and those hospitalized for a seasonal influenza infection ($$n = 11$$,050). Compared to seasonal influenza, COVID-19 was associated with an increased risk of abnormal coagulation studies, abnormal liver function tests, and the composite outcome any gastrointestinal outcome (supplementary table 13). While the hazard ratios for the other outcomes (coagulation studies and liver function tests) were above one, the $95\%$ confidence intervals crossed 1 suggesting insufficient precision to reject the null hypothesis.
## Sensitivity analyses
To further test the robustness of our results, we conducted several sensitivity analyses testing the outcome of having any gastrointestinal disorder in comparisons involving the COVID-19 groups and the contemporary control and – separately – the COVID-19 group and the historical control, and additionally in analyses by care setting of the acute phase of COVID-19 and both control groups. 1) We tested the results of models specified to include only predefined covariates (that is, no algorithmically selected high dimensional covariates were used to build the inverse probability weight); 2) we expanded the algorithmic high dimensional covariate selection to include 300 high dimensional covariates to build the inverse probability weight (whereas the primary approach included 100 algorithmically selected high dimensional variables); 3) we employed the doubly robust method through application of both weighting and covariate adjustment in the survival models (instead of the inverse weighting method used in the primary analyses) as an alternative approach to examine the associations between COVID-19 and the risk of the prespecified gastrointestinal outcomes. The results from all sensitivity analyses were consistent with those generated using the primary approach and are presented in supplementary tables 14 and 15.
## Positive and negative outcome controls
To assess whether our approach would reproduce known association, we tested fatigue as a positive outcome control. Our results showed that COVID-19 was associated with increased risk of fatigue in comparisons vs the contemporary and historical control groups (supplementary table 16).
We then subjected our analytic approach to testing a set of 4 negative outcome controls where prior knowledge suggests no expected associations. Consistent with a priori expectations, the results showed no significant association between COVID-19 and any of the negative outcome controls in comparisons with both the contemporary and historical control groups (supplementary table 16).
## Negative exposure control
To further test the rigor of our approach, we examined the associations between a pair of negative exposure controls and our prespecified gastrointestinal outcomes. We hypothesized that receiving the influenza vaccination on odd- vs even- numbered calendar days between March 1, 2020 and January 15, 2021 would not be associated with either increased or decreased risk of each of the prespecified outcomes utilized in this analysis. We therefore tested the associations between receiving the influenza vaccine in even- ($$n = 571$$,291) vs odd- ($$n = 605$$,453) numbered calendar days and the prespecified gastrointestinal outcomes. Data sources, cohort design, analytic approach (encompassing covariate specification and weighting method), and prespecified outcomes were identical to those used in the primary analysis. Consistent with pre-test expectations, our results indicated that receiving the influenza vaccination in odd-numbered vs even-numbered calendar days was not significantly associated with any of the prespecified outcomes (Supplementary table 17).
## Discussion
In this work involving 11,652,484 people including 154,068 people with COVID-19, 5,638,795 contemporary controls, and 5,859,621 historical controls — which altogether correspond to 14,064,985 person years of follow up, we provide evidence that beyond the first 30 days of infection, people with COVID-19 exhibited increased risks and 1-year burdens of incident gastrointestinal disorders spanning several disease categories including motility disorders, acid related disorders (dyspepsia, GERD, PUD), functional intestinal disorders, acute pancreatitis, hepatic and biliary disease. The risks were evident in subgroups based on age, race, sex, obesity, smoking, cardiovascular disease, chronic kidney disease, diabetes, hyperlipidemia and hypertension. The risks were evident in people who were not hospitalized during the acute phase of COVID-19 and increased in a graded fashion across the severity spectrum of the acute phase of COVID-19 (from non-hospitalized to hospitalized individuals, to those admitted to intensive care). The risks were consistent in comparisons including the COVID-19 vs the contemporary control group and COVID-19 vs the historical control group as the referent category. A comparative analysis suggested that those hospitalized with COVID-19 are at increased risk of several gastrointestinal outcomes compared to those hospitalized with seasonal influenza. The results were consistently robust to challenge in several sensitivity analyses; and examination of a positive outcome control, a battery of negative outcome controls, and a pair of exposure controls yielded results consistent with pre-test expectations. The constellation of findings suggests that people with SARS-CoV-2 infection are at increased risk of gastrointestinal disorders in the post-acute phase of COVID-19. The risks and burdens are not trivial – suggesting that post-acute covid care strategies should include attention to gastrointestinal disease.
Our findings suggest that gastrointestinal disease is another facet of the multifaceted Long Covid2,13,14. The risks were evident even in people whose acute disease did not necessitate hospitalization. This group represents the majority of people with COVID-19. Although the absolute burdens (expressed per 1000 persons at 1-year) may appear small, because of the large number of people with SARS-CoV-2 infection, these rates may translate into large number of affected people. This will have ramifications not only for the personal health of affected individuals, but also on health systems which will have to address the care needs of people with post-acute COVID-19 gastrointestinal disorders13,14.
Beyond the acute phase, SARS-CoV-2 infection is associated with increased risk of post-acute sequelae in several organ systems including — as we report here — the gastrointestinal system1,15–21. Evidence from other work suggests that vaccines reduce but do not completely abrogate the risk of post-acute sequelae and that reinfection (even in vaccinated individuals) contributes additional risks of health sequelae in both the acute and post-acute phase22,23. Altogether the evidence base reinforces the need for continued emphasis on primary prevention of SARS-CoV-2 infection (and prevention of reinfection) as the foundation of the public health response. Woven together with the evidence amassed thus far on the scale and breadth of organ dysfunction in Long Covid, the findings in this report call for the urgent need to develop strategies to prevent and treat the post-acute sequelae of SARS-CoV-2 infection13.
Our comparative analyses showing increased risk of gastrointestinal outcomes in people hospitalized with COVID-19 and those hospitalized with seasonal influenza —are useful to benchmark the risk against a well characterized respiratory viral infection24.
Several hypotheses have been proposed to explain the myriad manifestations of Long Covid including gastrointestinal manifesations25–27. These mechanisms include intestinal microbiome dysbiosis, persistence of the virus in immune privileged sites and subsequent chronic inflammation that may provoke organ injury, autoimmune mechanisms, and tissue injury during the acute phase which lead to clinical sequelae in the post-acute phase of the disease25,27–37. Other putative mechanisms may involve the angiotensin-converting enzyme 2 which is constitutively expressed on the brush border of the small intestinal mucosa and several other gastrointestinal cell types27. An emerging body of evidence suggests SARS-CoV-2 liver and other gastrointestinal tissue SARS-CoV-2 tropism, residual viral antigens in gastrointestinal and hepatic tissues, persistence of the virus in gastrointestinal tract reservoirs, and ongoing viral replication in the appendix in the post-acute phase of the disease and alteration of gut microbiota in people with Long Covid30,34,38–43. Studies integrating multi-dimensional immune phenotyping and machine learning suggested that compared to matched controls, people with Long Covid had increased levels of humoral responses directed against SARS-CoV-2, elevated antibody responses directed against Epstein-*Barr virus* and elevated cortisol levels but not autoantibodies to human exoproteome – altogether suggesting that persistent antigen, reactivation of latent herpesviruses, and chronic inflammation may be key mechanisms for Long Covid33. Despite remarkable progress, a better and deeper understanding of the biologic mechanisms of the post-acute sequelae of SARS-CoV-2 is needed to identify potential intervention opportunities to prevent and treat the condition.
We conceptualize Long Covid using a counterfactual approach — comparing those with SARS-CoV-2 infection versus those without — which allows us to assess all the consequences of SARS-CoV-2 infection. As such the post-acute gastrointestinal sequelae catalogued in this report may represent de novo disease, acceleration of underlying preclinical disease, or adverse treatment effects of SARS-CoV-2. However, regardless of the mechanistic pathway, these post-acute sequelae still represent consequences of the infection and may not have materialized at all or may not have materialized so soon without an infection with SARS-CoV-2.
This study has several strengths. We leveraged the breadth and depth of the national healthcare databases of the US Department of Veterans Affairs to build a large cohort of people with COVID-19. We evaluated the risk of a set of pre-specified outcomes versus two control groups - a contemporary control and a historical control group. We adjusted through inverse weighting for a large set of predefined covariates (specified based on prior knowledge) which were complemented by a set of algorithmically selected covariates from several data domains including diagnostic codes, medications, and laboratory test results. We examined risks and burdens across care settings of the acute infection (non-hospitalized, hospitalized and admitted to intensive care). The results were robust to challenge in several sensitivity analyses, and our approach withstood the scrutinous application of positive and negative outcome controls and negative exposure controls. Finally, we estimated risks based on the relative scale (hazard ratio), and also on the absolute scale (burden) which (because it also incorporates baseline risk) provides more meaningful assessment of population-level risk than risk on the ratio scale (e.g., hazard ratio).
This study has several limitations. The demographic characteristics of our cohorts (majority male) may limit generalizability of the results; however, because of the large size, and although the proportion is tilted toward males, the number of female cohort participants were 1,153,894. We leveraged the vast databases of the US Department of Veterans Affairs to conduct this study, and although we used validated outcome definitions, and took care to adjust the analyses for a large set of predefined and algorithmically selected variables, we cannot completely rule out misclassification bias and residual confounding that may bias study results. It is possible that some cohort participants may have had SARS-CoV-2 infection but were not tested for it and as a result these participants would have been included in the contemporary control group and may have biased the results in favor of the null hypothesis; however, our findings were also consistent in comparisons vs the historical control group from an era that predates the pandemic (when SARS-CoV-2 infection in humans had not been reported). As the pandemic continues, it is likely that further mutations of the virus, increased uptake of vaccine and antivirals, and waning vaccine immunity may result in changes in the epidemiology of the post-acute gastrointestinal sequelae of SARS-CoV-2 infection44.
In sum, in this study of 154,068 people who survived the acute phase of COVID-19, we show increased risk and burden of post-acute gastrointestinal sequelae spanning several disease categories including acid disorders, functional intestinal disorders, pancreatic disorders, hepatic and biliary disease. The risks were evident even among those whose acute COVID-19 did not necessitate hospitalization. Our findings suggest that post-acute COVID-19 care strategies should include attention to gastrointestinal health and disease.
## Ethics statement
This study was approved by the institutional review board (IRB) of the VA St. Louis Health Care System; because of the observational and retrospective nature of the study, the IRB granted a waiver of informed consent (protocol number 1606333). Participants were not compensated.
## Setting
Data from the US Department of Veterans Affairs’ electronic healthcare databases was utilized in this study. The Veterans Health Administration (VHA) is a branch of the US Department of Veterans affairs; VHA operates the largest nationally integrated healthcare system in the US which is comprised of 1255 healthcare facilities (including 170 VA Medical Centers and 1074 outpatient sites). Veterans who enroll in the VHA gain access to a comprehensive medical benefit package consisting of preventative and health maintenance care, outpatient and inpatient hospital care, prescriptions, mental health care, home health care, primary care, specialty care, geriatric and extended care, and medical equipment and prosthetics.
## Cohort
A flow chart describing cohort construction is provided in supplementary fig. 7.
Overall, we built a cohort of people with SARS-CoV-2 positive test who survived the first 30 days after the date of the positive test and compared them to a contemporary control group, and separately, to a historical control group where similar cohort selection criteria including surviving the first 30 days of the follow up were applied.
Veterans who used the VHA in 2019 ($$n = 6$$,244,069) with a positive COVID-19 test between March 1st, 2020 and January 15th, 2021 were enrolled into the COVID-19 cohort ($$n = 169$$,476). To ensure only post-acute COVID-19 outcomes were examined, we excluded participants who died within 30 days of receiving a positive COVID-19 test result, yielding a cohort of 154,068 participants. The date of the first COVID-19 positive test was set as the start of follow-up, denoted by T0; the end of follow-up was set to be the first occurrence of death or January 15th, 2022.
We then built 2 control groups, a contemporary control group of people who lived contemporaneously during the same enrollment period as those in COVID-19 group and, separately, a historical control group from a pre-pandemic era.
The contemporary control cohort initially consisted of veterans who used the VHA in 2019 ($$n = 6$$,244,069). Those alive by March 1st, 2020 ($$n = 5$$,963,205) and were not already in the COVID-19 cohort were further enrolled into the contemporary control cohort ($$n = 5$$,809,137). To ensure a similar distribution of follow-up between the COVID-19 and contemporary control, the start of follow-up for participants in the contemporary control was randomly assigned following the same distribution as participants receiving their first positive COVID-19 test result in the COVID-19 group. Out of the 5,660,999 participants alive at the beginning of follow-up, 5,638,795 were alive 30 days after the beginning of follow-up and were selected as the contemporary control cohort. Follow-up concluded on the first occurrence of death or January 15th, 2022.
We also built a historical control group consisting of 6,463,487 individuals who used the VHA in 2017. Out of those alive on March 1st, 2018 ($$n = 6$$,152,185), 6,009,794 participants who were not already part of the COVID-19 cohort were enrolled into the historical control. T0 was randomly assigned in the historical group using the same follow-up distribution as the COVID-19 group minus 2 years (730 d). In total, out of the 5,876,880 participants who were alive at T0, 5,859,621 were alive 30 days after T0 and were subsequently selected into the historical control group. Follow-up concluded on the first occurrence of death or January 15th, 2020.
## Data sources
Electronic health records from the VA Corporate Data Warehouse (CDW) were used in this study. Patient demographic information was obtained from the CDW Patient Domain. Outpatient and inpatient clinical information were obtained from the CDW Outpatient Encounters domain and CDW Inpatient Encounters domains, respectively. Medication prescriptions and fillings were obtained from the CDW Outpatient Pharmacy and CDW Bar Code Medication Administration domains. Laboratory test data was collected from the CDW Laboratory Results domain and the COVID-19 Shared Data Resource domain provided information relevant to COVID-19. Additionally, we used the Area Deprivation Index (ADI), defined as a summary measure of income, education, employment, and housing, as a composite variable of contextual factors present at each participants’ residential location45.
## Pre-specified outcomes
Pre-specified outcomes were selected based on our prior work on the systematic characterization of Long COVID1,17,22 and from evidence in prior literature8,11,46–50. Each gastrointestinal outcome was defined based on a corresponding international classification of diseases, 10th revision (ICD10) diagnostic codes1,16,17,19,51 or from laboratory test results. Additionally, individual outcomes were also aggregated into a related composite outcome (for example, coagulation outcome consisted of abnormally elevated PT, PTT, and INR). Furthermore, we specified a composite of any gastrointestinal outcome as the first incident occurrence of any of the predefined gastrointestinal outcomes examined in this study (including those based on diagnostic codes or laboratory tests). Incident individual and composite gastrointestinal outcomes during the post-acute phase of COVID-19 were assessed during the follow-up period between the 30 days after T0 until the end of follow-up in participants without any history of the outcome in the year prior to T0. In instances where the occurrence of outcome may result from medication use (e.g., PT, PTT, INR), the incident outcome was ascertained in participants without history of the related outcome and without exposure to the medications that may affect it in the year prior to T0.
## Covariates
We utilized a two pronged approach to covariate selection: 1) covariates were selected based on prior knowledge1,4,6,7,9,10,16–20,24,26,44,51,52, 2) in recognition that our knowledge of COVID-19 is evolving, we also employed an algorithmic approach to identify covariates in data domains consisting of diagnoses, medications and laboratory test results. Pre-defined and algorithmically selected covariates were used in modeling and were assessed in the year prior to T0.
Pre-defined covariates consisted of age, race (white, black, and other), sex, ADI, body mass index, smoking status (current, former, and never), and measures of healthcare utilization (number of outpatient encounters as well as long-term care utilization1,16,18). Additionally, several comorbidities including cancer, cardiovascular disease, chronic kidney disease, chronic lung disease, diabetes, and hypertension were used as pre-defined covariates. Laboratory values consisting of estimated glomerular filtration rate, systolic, and diastolic pressure were also used as pre-defined covariates. Continuous variables were transformed into restricted cubic spline functions to account for possible non-linear relationships.
To supplement our pre-defined covariates, we utilized algorithmically selected covariates from high dimensional data domains consisting of diagnoses, medications, and laboratory test results53. Data from patient encounter, prescription, and laboratory domains collected in the year prior to T0 were organized into 540 diagnostic groups, 543 medication types, and 62 laboratory test abnormalities. From these three domains (diagnoses, medications, and laboratory test results) we selected variables which occurred in at least 100 participants within each exposure group in acknowledgment of the fact that exceedingly rare variables (those that occurred in fewer than 100 participants in these cohorts) may not substantially influence the examined associations. Univariate relative risks between each variable and exposure was estimated and 100 variables with the highest relative risks were selected for use in statistical analyses54. The algorithmic selection process described above was used to independently select high dimensional covariates in each comparison (for example, the COVID-19 vs contemporary control and the COVID-19 vs historical control analyses to assess incident GERD).
## Statistical analysis
Baseline characteristics of the COVID-19, contemporary, and historical control groups were described, and the standardized mean differences between COVID-19 and contemporary control, and between COVID-19 and historical control were calculated.
To estimate the risk of each incident gastrointestinal outcome, we first constructed a sub-cohort of participants without a history of the outcome of interest (for example, the risk of incident GERD was estimated within a sub-cohort of participants without any history of GERD) in the year prior to cohort enrollment.
Within each sub-cohort, three logistic regressions were built to estimate the probabilities of belonging to the target population of VHA users in 2019 (equivalent to the combination of the COVID-19 group and the contemporary control group) for the COVID-19, contemporary, and historical control groups. These probabilities were estimated based on pre-defined and comparison specific algorithmically selected high-dimensional variables and ultimately used as the propensity score. The propensity score was then used to calculate the inverse probability weight (propensity score/(1-propensity score)). To account for the influence of extreme weights and the sample size difference between comparison groups, we prespecified our analytic plan to truncate weight greater than 1000. There were no weights larger than 1000 hence no truncation was conducted. Covariate balance was assessed by standardized mean differences after application of weighting.
After application of inverse probability weighting, cause-specific hazard models where death was considered as a competing risk were used to estimate hazard ratios of incident gastrointestinal outcomes between the COVID-19 and contemporary control groups and the COVID-19 and historical control groups. The survival probability at 1-year within each group was used to estimate the burdens per 1000 participants at 1 year of follow-up in the COVID-19 and control groups; the difference of the estimated burdens between the COVID-19 and control groups was used to compute the excess burdens per 1000 participants at 1 year. Additionally, we conducted analyses in subgroups comprised of age, race, sex, obesity, smoking, diabetes, cardiovascular disease, chronic kidney disease, hyperlipidemia, and hypertension.
The association between COVID-19 and the risks of post-acute gastrointestinal outcomes were further examined by stratifying the COVID-19 cohort into mutually exclusive groups determined by each participants’ care setting during the acute phase of COVID-19 (that is, whether participants were non-hospitalized, hospitalized, or admitted to the intensive care unit during the first 30 days of infection). The statistical approach outlined in the previous paragraph was used to estimate inverse probability weights for each care setting group. Cause-specific hazard models utilizing inverse probability weighting were applied, and hazard ratios, burdens, and excess burdens were calculated.
We conducted a comparative analysis of individuals hospitalized with COVID-19 vs those hospitalized with seasonal influenza. Admission to the hospital was ascertained in the first 30 days after a positive test result (for both COVID-19 and seasonal influenza). Comparisons were conducted using weighted cause-specific hazard models.
We further tested the robustness of our study design by conducting multiple sensitivity analysis. [ 1] we modified our covariate selection by increasing covariate inclusion to 300 high dimensional variables (instead of the 100 high dimensional variables used in the main analysis) when constructing the inverse probability weight; [2] we restricted covariate selection only to pre-defined variables when constructing the inverse probability weight (no algorithmically selected variables were used); and [3] we applied a doubly robust approach, where associations were estimated by applying both covariate adjustment and the inverse probability weights to survival models55.
We tested whether our approach would reproduce known associations by testing fatigue as an outcome – considered a cardinal manifestation of Long COVID – as a positive outcome control. Additionally, we used the approach outlined by Lipsitch et al.56 to specify and test a set of negative outcome controls where no prior evidence supports the existence of a causal relationship between COVID-19 exposure and the specified negative outcome controls. Lastly, we tested a pair of negative-exposure controls. We hypothesized that exposure to the influenza vaccine on odd- vs even-numbered calendar days between March 1st, 2020 and January 15th, 2021 would not be associated with increased or decreased risks of the gastrointestinal outcomes examined in our analysis. If successful, application of these negative outcome and negative exposure controls might reduce concern about the presence of spurious biases in study design, covariate selection, analytic approach, outcome ascertainment, residual confounding, and other sources of latent biases56.
Estimation of variance when applying weightings was achieved through robust sandwich variance estimators. For every analysis, evidence of statistical significance was considered when a $95\%$ confidence interval excluded unity. All analyses were conducted using SAS Enterprise Guide version 8.2 (SAS Institute), and visualization of results was accomplished using R version 4.04.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplemental information Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-36223-7.
## Source data
Source data
## Peer review information
Nature Communications thanks Viet-Thi Tran, Abhilash Perisetti and the other, anonymous, reviewer for their contribution to the peer review of this work.
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|
---
title: Long non-coding RNA rhabdomyosarcoma 2-associated transcript contributes to
neuropathic pain by recruiting HuR to stabilize DNA methyltransferase 3 alpha mRNA
expression in dorsal root ganglion neuron
authors:
- Xinying Guo
- Gaolong Zhang
- Weihua Cai
- Fa Huang
- Jingwen Qin
- Xingrong Song
journal: Frontiers in Molecular Neuroscience
year: 2023
pmcid: PMC9992530
doi: 10.3389/fnmol.2022.1027063
license: CC BY 4.0
---
# Long non-coding RNA rhabdomyosarcoma 2-associated transcript contributes to neuropathic pain by recruiting HuR to stabilize DNA methyltransferase 3 alpha mRNA expression in dorsal root ganglion neuron
## Abstract
### Introduction
Long non-coding RNAs (lncRNAs) act as key regulators in multiple human diseases. In particular, the dysfunction of lncRNAs in dorsal root ganglion (DRG) contributes to the pathogenesis of neuropathic pain (NP). Nevertheless, the role and mechanism of most lncRNAs in NP remain unclear.
### Methods
Two classic chronic NP models, including L4 spinal nerve ligation (SNL) model and chronic constriction injury (CCI) of the sciatic nerve, were performed. Mechanical allodynia and heat hyperalgesia were used to evaluate neuropathic pain. DRG microinjection was used to deliver agents into DRG. qRT-PCR, immunofluorescence, immunoprecipitation, western blotting, siRNA transfection, AAV transduction were performed to investigate the phenotypes and molecular basis.
### Results
Here, we discovered that Rmst as a lncRNA was specifically expressed in Atf3+ injured DRG neurons and significantly upregulated following peripheral nerve damage. Rmst overexpression by direct DRG injection of AAV5-Rmst causes neuropathic symptoms in the absence of nerve damage. Conversely, blocking Rmst expression in injured DRGs alleviated nerve injury-induced pain hypersensitivities and downregulated Dnmt3a expression. Furthermore, we found peripheral nerve damage induced Rmst increase could interact with RNA-binding protein HuR to stabilize the Dnmt3a mRNA.
### Conclusion
Our findings reveal a crucial role of Rmst in damaged DRG neurons under NP condition and provide a novel target for drug development against NP.
## Introduction
Chronic pain has been a perplexing problem for decades. Globally, approximately one-fifth of people suffer from chronic pain (Tompkins et al., 2017), and about thirty percent of these patients have gone through the symptoms of neuropathic pain (NP) (van Hecke et al., 2014). Patients with NP have detrimental effects on their quality of life and ability. In addition to personal suffering, chronic pain is a substantial financial burden on society, and lies billions of dollars every year (van Velzen et al., 2020). However, there are limited treatments available for NP. Dorsal root ganglia (DRG) neuron is responsible for conveying the nociception in peripheral nerve injury. Peripheral nerve damage-induced maladaptive alterations at transcriptional and translational levels of pain-associated genes in the primary afferents of DRG neurons contribute to these abnormal spontaneous activities and subclinical behavioral patterns (Li et al., 2020; Pan et al., 2021). Hence, identifying new targets and mechanisms in damaged DRGs could open a novel avenue against NP.
As potent and multifaceted roles of long non-coding RNAs (lncRNAs) in gene regulation, lncRNAs are paid much attention in many human illnesses, including NP (Wu et al., 2016, 2019). However, the function of most lncRNAs in NP is not thoroughly understood. A lncRNA called rhabdomyosarcoma 2-associated transcript (Rmst) was essential for neuronal differentiation (Ng et al., 2012) and neurogenesis (Ng et al., 2013). Although previous studies showed that Rmst was involved in neurological disorders (Hou and Cheng, 2018; Ma et al., 2021; Zhao et al., 2021; Li et al., 2022), little is known about its role in NP.
DNA methyltransferase 3 alpha gene encodes the Dnmt3a protein and is responsible for catalyzing 5-methylcytosine methylation. The aberrant regulation of Dnmt3a is implicated in multiple nervous diseases (Feng et al., 2005; Clemens and Gabel, 2020), especially in NP (Guo et al., 2019). Increased Dnmt3a have been found in injured DRG (Shao et al., 2017; Zhao et al., 2017). What’s more, the increased-Dnmt3a was able to mediate the epigenetic inaction of the voltage-dependent potassium channel subunit (Kcna2) via DNA hypermethylation of Kcna2 promoter region (Zhao et al., 2017). Depletion of Dnmt3a in the injured DRGs effectively attenuated NP by restoring Kcna2 expression (Zhao et al., 2017, p. 3). These studies suggested that Dnmt3a plays a pivotal role in regulating DNA methylation of nerve damage related gene alterations. Nevertheless, it is unclear how peripheral nerve damage triggers the activation of Dnmt3a in injured DRG.
Here, we sought to characterize a lncRNA rhabdomyosarcoma 2-associated transcript (Rmst) in injured DRG neurons was substantially increased in NP. The nerve damage triggered Rmst expression in injured DRGs contributes to regulating Dnmt3a through interaction with RNA binding protein HuR in NP. Thus, the role and mechanism of Rmst may provide novel and insightful directions for NP management in clinic.
## Bioinformatics
RNA sequencing dataset for mouse DRG after peripheral nerve injury was obtained from previous research (Wu et al., 2016). The mouse DRG was harvested 7 days after L4 spinal nerve ligation (SNL) model and sequencing was performed on the Illumina HiSeq2500 platform with 2 × 100-bp paired-end reads.
For scRNA-seq dataset for mouse DRG after peripheral nerve injury, we obtained the cell count matrix and metadata from Gene Expression Omnibus (GEO) with the series record GSE155622 (Wang K. et al., 2021). The mouse DRG was harvested for Smart-seq2 as smart-seq2 was better to captured low abundance transcripts as well as more lncRNAs (Wang X. et al., 2021). All metadata was also obtained from GSE155622.
## Animals
C57BL/6J adult mice were from SPF Biotechnology Co., Ltd (Beijing, China). A 12-h light-dark cycle environment with unlimited access to food and water was used to house mice. All processes were endorsed by the Animal Care and Use Committee at Guangzhou Medical University.
## Chronic neuropathic pain model
Two classic chronic NP models, including L4 SNL model and chronic constriction injury (CCI) of the sciatic nerve, were performed as described previously (Li et al., 2020). SNL model was established by tightly ligating L4 spinal nerve distal to DRG with 7-0 silk suture. L4 spinal nerve was exposed in the matched sham mice, however, the L4 spinal nerve was neither ligated nor transected. CCI model was established by loosely ligated the sciatic nerve at three spots with 1 mm-intervals by 7-0 silk suture. Sham animals did not receive the ligation of the sciatic nerve.
## Behavioral tests
As previous described, mechanical allodynia was quantified by measuring paw withdrawal frequency by low (0.07 g) and median (0.4 g) von Frey filaments (Stoelting Co., Wood Dale, IL, USA) (Li et al., 2020). We use two calibrated plastic filaments to stimuli the central of plantar surface of hind paws. A positive response is quick withdrawal of the paw. Mice were totally received 10 applications. The paw withdrawal frequency describes the positive withdrawal responses within 10 applications.
Heat hyperalgesia was quantified by measuring paw withdrawal latencies after heat stimulation as described (Li et al., 2020). All mice before behavior test were left in a glass surface in individual plexiglas cages. A beam of light was applied to the central of hind paw. The performance of a positive response is a swift raise of the hind paw. The Model 336 Analgesia Meter (IITC Inc., Life Science Instruments. Woodland Hills, CA, USA) was automatically records the withdrawal latency from heating source. 4–5 trials on each side were performed at intervals of 5 min.
## Dorsal root ganglion microinjection
As described previously, DRG microinjection was carried out (Li et al., 2020). After 3-cm-long skin incision, we firstly exposed the corresponding spinal nerve (L4 and/or L3). After that, we used rongeur to remove the unilateral articular processes for DRG microinjection. The glass micropipette was carefully inserted into the exposed ipsilateral L4 and/or L3 DRGs and 1 μl of either siRNA solution or viral solution was injected into L4 and/or L3 DRG. The injection was performed at a rate of 10 nl/s. After the injection was completed, the pipette was left in place for 10 min before removal to allow the fluid to distribute and the pressure within the DRG to equalize. The skin was sutured with 6-0 silk and mice were kept on a heating pad. All reagent and surgical instruments are sterilized in advance.
## Dorsal root ganglion neuronal culture
We performed DRG neuron cultures as described (Li et al., 2020). We first prepared complete neurobasal medium (CNM) including $10\%$ fetal bovine serum, and 1x antibiotics, $2\%$ B-27 supplement, and $1\%$ GluMax supplement. 3–4 weeks mice were used for collecting DRGs. The collected DRGs were incubated with collagenase solution including dispase, collagenase type I in HBSS. All reagents are from Thermofisher Scientific Company (Waltham, MA, USA).
## Quantitative real-time RT–PCR
The TRIzol Reagent (Cat. No:15596026, Invitrogen Corporation, Carlsbad, CA, USA) was used for extracting RNA from DRGs, followed by reverse transcription using PrimeScript RT Master Mix (Cat. No: RR036A, Takara Bio Inc, Shiga, Japan). Quantitative PCR were performed using TB Green Premix Ex Taq II (Tli RNaseH Plus) (Cat. No: RR820A, Takara Bio Inc, Shiga, Japan) on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Finally, relative fold changes of each gene were calculated by ΔCt method (2–ΔΔCt). Supplementary Table 1 included all primer information.
## Ribonucleic acid stability assay
Primary neurons grew in 6-well plate and were treated with actinomycin D (Cat. No:A1410, Sigma-Aldrich, Burlington, MA, USA) for testing the mRNA stability. After that, we collected neurons at 0, 2, 4, 8, and 12 h after actinomycin D treatment for RNA extraction.
## Nuclear/cytoplasmic ribonucleic acid fraction isolation
Cytoplasmic and Nuclear RNA purification Kit were purchased from Norgen Biteck Corp. (Cat. No: NGB-21000, Thorold, ON, Canada). After Nuclear and cytoplasmic RNA fraction isolation, various gene expression levels in both nuclear and cytoplasmic fractions of all samples were calculated by RT-PCR as protocol above.
## Nuclear/cytoplasmic protein isolation
Nuclear and cytoplasmic protein were separated using the NE-PER nuclear and cytoplasmic extraction reagents (Cat. No: 78833, Thermofisher Scientific Company, Waltham, MA, USA) following the manufacturer’s instructions. The collected protein was aliquoted and stored at −80°C.
## Plasmids constructs and virus production
The pAAV-CMV-mRmst-:WPRE vector (vector ID: VB220510-1032tdc) and AAV5-*Rmst virus* packaging was designed and constructed to overexpress the Rmst expression by VectorBuilder Company (Chicago, IL, USA). Briefly, the full-length cDNA of Rmst (NR_028262.1) was amplified by RT-PCR. After that, double enzyme-digested PCR products were ligated into the mammalian ncRNA expression AAV vector as a plasmid. AAV5-Gfp (vector ID: VB150925-10026) is used as negative control. SiRNA for Rmst and Dnmt3a were designed and produced by Tsingke Biotechnology Co., Ltd. (Beijing, China). All siRNA sequencing used in this work were listed at Supplementary Table 1.
## Immunohistochemistry staining
Mice were perfused with $4\%$ PFA after deep isoflurane before being analyzed by immunohistochemistry. The DRGs were collected and post-fixed in $4\%$ PFA overnight, followed by dehydrating in $30\%$ sucrose for two nights at 4°C. Finally, the DRGs were sectioned at 15–20 μm and kept them in −80°C refrigerator.
Before primary antibody incubation, the section was blocked in 1X PBS with $10\%$ donkey serum and $0.3\%$ Triton X-100. The sections were then incubated with anti-DNMT3a (Santa Cruz, Dallas, TX, USA) overnight at 4°C followed by incubating secondary antibody conjugated to Cy3 (1:500, Jackson ImmunoResearch, West Grove, PA, USA) for 2 h. Finally, the sections were mounted using Fluoroshield™ with DAPI (Cat. No: F6057, Sigma-Aldrich, Burlington, MA, USA).
## Ribonucleic acid-binding protein immunoprecipitation (RIP)
The Magna RIP Kit were purchased from EMD Millipore (Burlington, MA, USA) company for RIP assay. A anti-HuR antibody (Santa Cruz, Dallas, TX, USA) was used in RIP assay. After purification of RNA, RT-PCR was performed following the previous protocol.
## Western blotting
The collected protein was firstly separated using SDS-PAGE electrophoresis on the basis of size, followed by moving to PVDF membranes with appropriate size. The blot was then immediately placed in $5\%$ fresh non-fat milk powder for blocking for 1 h. Next, the appropriate primary and secondary antibodies were used to incubate the transferred membrane according to the recommended dilution and time in datasheet. The rabbit anti-DNMT3b (1:500), rabbit anti-DNMT3a (1:500), and rabbit anti-histone H3 (1:1,000) were purchased from Cell Signaling Technology (Danvers, MA, USA). The mouse anti-HuR (1:500) and rabbit anti-GAPDH (1:1,000) were purchased from Santa Cruz company (Dallas, TX, USA). Membranes were visualized by the Clarity Western ECL Substrate (Cat. No: 170-5060, Bio-Rad Laboratories, Inc., Hercules, CA, USA), exposed by ChemiDoc Touch (Bio-Rad Laboratories, Inc., Hercules, CA, USA) and analyzed by Image J.
## Statistical analysis
Sample size estimation was based on certain assumptions, including significance level, expected placebo mean, expected treatment mean, standard deviation, power, expected dropout. In our case, expected placebo mean, expected treatment mean, standard deviation, and expected dropout were depended on our pilot studies and previous report in the field (Li et al., 2020; Pan et al., 2021). After that, sample sizes were calculated using nQUERY software, assuming a significance level of 0.05, $90\%$ power and homogeneous variances for the 2 samples to be compared, with the means and SEM for different parameters predicted from pilot study. The data is presented as mean + SEM and analyzed by GraphPad Prism 8. The Shapiro–Wilk test was used for normal distribution test. If the data passed the normality test, the t-test or ANOVA was used in this study by GraphPad Prism 8 software according to experimental design and the detailed statistical method was listed in figure legend. The interaction of factors after ANOVA is provided in result. A P-value of less than 0.05 was considered statistical significant.
## Increased rhabdomyosarcoma 2-associated transcript in damaged dorsal root ganglions was found in neuropathic pain
As the critical role of lncRNAs during the formation of NP, we extracted the 10 most up-and downregulated lncRNAs in L4 DRGs 7 days after SNL model (Figure 1A) from the previous RNA sequencing dataset (Wu et al., 2016). Among them, we found Rmst, a previously reported brain specific lncRNA (Ng et al., 2012), was also expressed in damaged DRGs (Figure 1A). As previous research has been found that Rmst was specific to neuron (Briese et al., 2016), we are wondering the distribution of Rmst in the neuron subtype after nerve injury. Thus, we analyzed the scRNA sequencing dataset from DRGs with peripheral nerve damage (Wang K. et al., 2021). As the smart-seq2 technology was better to captured low abundance transcripts as well as more lncRNAs (Wang X. et al., 2021), we focused on the smart-seq2 analysis to explore DRG neuron subtype. In t-distributed stochastic neighbor embedding (tSNE), 6 traditional classification neuronal subtype was found (Figure 1B), including S100b+ neurofilament (NF), Mgpra3+ non-peptidergic neurons (NP), Th+ tyrosine hydroxylase (TH), Tac1+ and Sst+ peptidergic neurons (PEP), and Atf3+ injured neuron (Figures 1B,C). Interestingly, we found Rmst was specifically expressed in Atf3+ injured neuron (Figure 1C), suggesting that Rmst may be involved in NP.
**FIGURE 1:** *Rhabdomyosarcoma 2-associated transcript (Rmst) was specifically expressed in Atf3+ injured neurons. (A) The 10 most up- and downregulated lncRNAs in L4 DRGs after SNL model. The heatmap was created using Z-score values obtained from RNA-seq dataset. (B) TSNE plot of all cells from DRGs after nerve injury. Each dot was color-coded and annotated by neural subtypes. NF: neurofilament, NP: non-peptidergic neurons, TH: tyrosine hydroxylase, PEP: peptidergic neurons. (C) TSNE plots showing scRNA-seq data, colored by gene expression level, showing S100b, Th, Tac1, Mrgpra3, Sst, Atf3, and Rmst expression.*
To provide the adequate evidence, we performed animal neuropathic pain model to validate the result. We found lncRNA Rmst expression was increased early and persistently at least 28 days in damaged DRGs after SNL surgery but not sham surgery (Figure 2A) [F[5,36] = 5.942]. To be specific, Rmst expression was elevated 1.58-fold on day 3, 2.07-fold on day 7, 2.01-fold on day 14, 1.99-fold on day 21, 1.88-fold on day 28 after SNL but not in the contralateral L4 DRGs and L3 DRGs (Figures 2A–D) [Figure 2B: F[5,36] = 1.896; Figure 2C: F[5,36] = 0.7694; Figure 2D: F[5,36] = 2.980]. A similar phenomenon was observed in another NP mouse model called CCI model (Figures 2E,F) [Figure 2E: F[5,36] = 3.530]; [Figure 2F: F[5,36] = 0.8966]. The Rmst expression in damaged DRGs were elevated 1.97-fold on day 3, 2.23-fold on day 7, 2.23-fold on day 14, 2.46-fold on day 21, 2.3-fold on day 28 after CCI model (Figure 2E). Taken together, our data showed peripheral nerve damage could trigger the elevated Rmst expression in the damaged DRGs that maintained at least until 1-month post-SNL, suggesting that Rmst may take part in the nerve damage-induced NP.
**FIGURE 2:** *Rhabdomyosarcoma 2-associated transcript (Rmst) upregulation was found in damaged DRG of mice after SNL or CCI model. (A–D) Level of Rmst in ipsilateral L4 DRGs (A), contralateral L4 DRGs (B), ipsilateral L3 DRGs (C), and contralateral L3 DRGs (D) after SNL or sham surgery n = 16. (E,F) Levels of Rmst in the ipsilateral (E) and contralateral (F) side L3/4 DRGs after CCI or sham surgery n = 8. Two-way ANOVA followed by post-hoc Tuckey test. *P < 0.05 and **P < 0.01 compared with sham group at each time point.*
## Blocking rhabdomyosarcoma 2-associated transcript expression in damaged dorsal root ganglions alleviated neuropathic pain
As the apparent change of Rmst in damaged DRGs, we further ask whether blocking Rmst expression in injured DRG could alleviate pain hypersensitivity. We first confirmed that DRG microinjection of siRmst, but not Scr, 3 days before SNL could block SNL-induced increased Rmst expression (Figure 3A) [F[3,8] = 65.29]. Microinjection of siRmst also slightly reduced basal expression of Rmst expression (Figure 3A). More importantly, we found pre-microinjection of siRmst could ameliorate SNL-induced nociceptive hypersensitivities, including mechanical allodynia (Figures 3B,C) [Figure 3B: F[12,80] = 9.633; Figure 3C: F[12,80] = 10.15] and heat hyperalgesia (Figure 3D) [Figure 3D: F[12,80] = 17.99]. Neither Scr nor siRmst changed basal paw response to mechanical or heat stimuli while mice received sham surgery (Figures 3B–D). We then ask the role of Rmst during the maintenance period after SNL model. We first confirmed that DRG microinjection of siRmst in maintenance period after SNL (Figure 3E) [F[3,8] = 30.85]. As expected, siRmst delivery through DRG microinjection on day 7 post-SNL rescued pain hypersensitivity (Figures 3F–H) [Figure 3F: F[18,112] = 3.614; Figure 3G: F[18,112] = 3.312; Figure 3H: F[18,112] = 20.60]. Our data strongly support that nerve damage triggered nociceptive hypersensitivity may be attributed to elevated Rmst expression in the damaged DRG.
**FIGURE 3:** *Blocking nerve damage triggered rhabdomyosarcoma 2-associated transcript (Rmst) expression in DRG mitigated the nerve damaged-induced nociceptive hypersensitivity. (A) Level of Rmst in the L4 DRGs on day 14 after nerve injury model in presence of siRmst or Scr. n = 12 mice, One-way ANOVA followed by post-hoc Tukey test, **P < 0.01 compared with Sham + Scr group and #P < 0.05 compared with SNL + Scr group. (B–D) The PWF to von Frey filament (B,C) and the PWL to thermal stimuli (D) on the development period of NP. n = 5 mice, two-way ANOVA followed by post-hoc Tukey test, *P < 0.05 and **P < 0.01 compared with the SNL + Scr group. (E) Level of Rmst in the L4 DRGs on day 28 after nerve injury model in presence of siRmst or Scr. n = 12 mice, One-way ANOVA followed by post-hoc Tukey test, **P < 0.01 compared with Sham + Scr group and #P < 0.05 compared with SNL + Scr group. (F–H) The PWF to von Frey filament (F,G) and the PWL to thermal stimuli (H) on the maintenance period of NP. n = 5 mice, two-way ANOVA followed by post-hoc Tukey test, *P < 0.05 and **P < 0.01 compared with SNL + Scr group. Paw withdrawal frequency: PWF; Paw withdrawal latency: PWL.*
## Dorsal root ganglion long non-coding RNA Rmst overexpression produces nociceptive hypersensitivity
Next, we asked if DRG Rmst overexpression in neuron is sufficient for NP production. As the previous paper has reported Rmst was specifically expressed in the neuron (Briese et al., 2016), we utilized Adeno-associated Virus 5 (AAV5) following the previous papers (Li et al., 2020) to package Rmst full-length vector (AAV5-Rmst) and microinjected to the ipsilateral DRG to overexpress the Rmst expression. As a proof of concept, we found DRG microinjection of AAV5-Rmst in the ipsilateral side could particularly increase Rmst expression in ipsilateral side but neither contralateral side nor AAV5-Gfp (Figure 4A) [F[3,8] = 84.17]. More importantly, pain symptoms (Figures 4B–D) [Figure 4B: F[15,96] = 5.679; Figure 4C: F[15,96] = 3.432; Figure 4D: F[15,96] = 14.98] were induced third weeks after Rmst overexpression in DRG of naïve mice. It means that, even without nerve damage, DRG Rmst overexpression in neuron could result in NP-like symptoms.
**FIGURE 4:** *Overexpression of DRG rhabdomyosarcoma 2-associated transcript (Rmst) produced nociceptive hypersensitivity in naïve mice. (A)
Rmst expression in L3/4 DRGs 6 weeks after receiving AAV5-Rmst or AAV5-Gfp. n = 6 mice, two-tailed unpaired Student’s t-test, **P < 0.01 compared with AAV5-Gfp group. (B–D) The PWF to von Frey filament (B,C) and the PWL to thermal stimuli (D) on mice with DRG microinjection of AAV5-Rmst or AAV5-Gfp. n = 6, two-way ANOVA followed by post-hoc Tukey test, **P < 0.01 compared with AAV5-Gfp-Ipsi group.*
## Rhabdomyosarcoma 2-associated transcript participated in the nerve damage induced dorsal root ganglion DNA methyltransferase 3 alpha expression after spinal nerve ligation
Next, the detailed mechanism of Rmst involved in NP was investigated. As the subcellular location of lncRNA can provide significant information on its function, we utilized cytoplasmic and nucleus RNA extraction protocol to purify populations of subcellar RNA fractions. Consistent with previously report in vitro (Zhao et al., 2021), Rmst in naïve mouse DRG was distributed predominantly in the cytoplasm (Figure 5A). Cytoplasmic lncRNAs can function in the posttranscriptional gene expression through mRNA stability and translation (Rashid et al., 2016). It has been reported that Rmst could upregulate DNA methyltransferase 3 (Dnmt3) by increasing the stability for its mRNA (Peng et al., 2020) in MCF7 cells, a breast cancer related epithelial cell line. We then examined whether overexpression of Rmst could also increase the Dnmt3a and Dnmt3b mRNA in primary DRG neuron. Surprisingly, only Dnmt3a mRNA as well as protein level increased but not Dnmt3b was observed in cultured neurons co-transduced with AAV5-Rmst (Figures 5B–D). In fact, Dnmt3a in primary afferent neurons was reported to participate in NP by repressing the potassium voltage-gated channel Kv1.2 encoded by Kcna2 (Zhao et al., 2017). To elucidate whether Rmst participated in regulating Dnmt3a mRNA stability, actinomycin D was used to inhibit the RNA synthesis. We found that Rmst could stabilize Dnmt3a mRNA transcripts (Figure 5E). The half-life of Dnmt3a mRNA was around 8.5 h for Rmst overexpressed neurons, as compared to 6.5 h for Gfp control (Figure 5E) [F[4,20] = 2.384]. Next, we found microinjection of siRmst, but not Scr, could abolish the SNL- induced Dnmt3a increases (Figure 5F) [F[3,8] = 17.11]. Decreased Dnmt3a protein in damaged DRG with the siRmst microinjection after SNL was observed in the nucleus (Figures 5G,H) [F[3,8] = 45.02].
**FIGURE 5:** *Dorsal root ganglion (DRG) rhabdomyosarcoma 2-associated transcript (Rmst) participated in the nerve damage induced Dnmt3a expression by stabilizing Dnmt3a mRNA. (A) Transcript abundance of cytoplasmic and nuclear RNA fractions for Gapdh mRNA, Tuba1a mRNA, Malat1 mRNA, and Rmst mRNA from mouse DRGs. n = 3 mice. (B) Level of Dnmt3a and Dnmt3b mRNA expression in DRG neuron with AAV5-Rmst. T-test was used for statistic analysis. **P < 0.01 versus AAV5-Gfp group. (C,D) Dnmt3a and Dnmt3b protein expression in DRG neuron with AAV5-Rmst. T-test was used for statistic analysis. **P < 0.01 versus AAV5-Gfp group. (E) Primary DRG neurons with the treatment of ActD (5 μg/mL) at multiple time point as shown were treated with AAV5-Rmst or AAV5-Gfp. n = 3 biological repeats. *P < 0.05 versus AAV5-Rmst group. (F,G) Levels of Dnmt3a mRNA (F) and protein expression (G) in ipsilateral L4 DRGs of SNL mice pre-received with siRmst or Scr. n = 12, one-way ANOVA followed by post-hoc Tukey test, **P < 0.01 versus Sham + Scr group and #P < 0.05, ##P < 0.01 versus SNL + Scr group. (H) Immunostaining for the ipsilateral L4 DRGs showed Dnmt3a protein expression in mice post-SNL and pre-delivered with siRmst or Scr. Scale bar = 40 μm.*
To further confirm whether *Rmst is* responsible for stabilizing Dnmt3a mRNA in the damaged DRG, we inhibited Dnmt3a expression through delivering Dnmt3a siRNA into the DRG after AAV5-Rmst microinjection. We found blocking Rmst overexpression-induced Dnmt3a increase could not lower Rmst expression (Figure 6A) [F[2,6] = 19.28] but lead to a fall of Dnmt3a mRNA and protein (Figures 6B,C) [Figure 6B: F[2,6] = 62.27; Figure 6C: F[2,6] = 31.06]. More importantly, blocking Dnmt3a expression attenuated the Rmst induced-nociceptive hypersensitivity (Figures 6D–F) [Figure 6D: F[8,60] = 13.12; Figure 6E: F[8,60] = 3.500; Figure 6F: F[8,59] = 10.35]. Collectively, nerve injury induced Rmst upregulation participates in NP by stabilizing the Dnmt3a mRNA expression.
**FIGURE 6:** *Blocking DNA methyltransferase 3 alpha (Dnmt3a) expression mitigated the rhabdomyosarcoma 2-associated transcript (Rmst) triggered-mechanical allodynia and heat hyperalgesia. (A–C)
Rmst mRNA (A), Dnmt3a mRNA (B), and Dnmt3a protein (C) in mice after microinjection with siDnmt3a (Scr as control) and AAV5-Rmst (AAV5-Gfp as control). **P < 0.01 versus AAV5-Gfp + Scr group and #P < 0.05 versus AAV5-Rmst + Scr group. (D–F) The ipsilateral PWF to von Frey filament (D,E) and the PWL to thermal stimuli (F) after microinjection of siDnmt3a or Scr in mice pre-received with AAV5-Rmst or AAV5-Gfp. **P < 0.01 versus AAV5-Rmst + Scr group.*
## Rhabdomyosarcoma 2-associated transcript regulates the DNA methyltransferase 3 alpha mRNA stability by interaction with HuR under neuropathic pain condition
Finally, we asked the potential mechanism of Rmst induced Dnmt3a upregulation in injured DRG neuron. Given that RNA-binding proteins (RBPs) affect the targeted mRNA stability, we focused on one of well-characterized RBPs, HuR. Notably, HuR was reported as a contributor to nociceptive pain (Kunder et al., 2022) and an anti-HuR could alleviate nerve-injury induced NP (Borgonetti and Galeotti, 2021). In particular, it was HuR reported to stabilize the Dnmt3 mRNA (Peng et al., 2020). Therefore, under NP condition, we asked whether HuR contributed to Rmst-mediated increased Dnmt3a.
We first determined the alteration of HuR in DRGs after SNL or Rmst overexpression. Unexpectedly, we found neither SNL nor Rmst could regulate the HuR expression in injured DRG (Figure 7A). In fact, in many cancerous settings, HuR was increased subcellular localization within the cytoplasm to stabilize various prosurvival mRNA (Schultz et al., 2020). Therefore, we examine whether SNL induced cytoplasmic accumulation of HuR. We found SNL caused a steep increase of HuR protein in the cytoplasm of the damaged DRG while Rmst overexpression in DRG was found similar phenomenon (Figure 7B). This may suggest that under NP condition, HuR is transported from the nucleus to cytoplasm. Next, RNA immunoprecipitation (RIP) assay revealed that HuR was capable to enrich Rmst and Dnmt3a in SNL group, approximately 20-fold, and 60-fold, respectively, compared to sham group (Figure 7C). Finally, we found overexpression of Rmst in primary neuron promoted the binding between Dnmt3a mRNA and HuR (Figure 7D). Thus, Rmst appeared to be the essential regulator that promoted Dnmt3a expression through interacting with HuR in DRG neuron.
**FIGURE 7:** *Rhabdomyosarcoma 2-associated transcript (Rmst) was interaction with HuR to regulate the Dnmt3a mRNA Stability Under NP Condition. (A,B) Level of HuR expression in total protein (A) and cytoplasm protein (B) in DRG with SNL model and AAV5-Rmst (AAV5-Gfp as control) DRG microinjection. (C) Level of Rmst or Dnmt3a mRNA immunoprecipitated by anti-HuR on day 7 post-SNL surgery n = 3 biological repeats. **P < 0.01 versus sham group. (D) Level of Dnmt3a immunoprecipitated by HuR in DRG neuron with AAV5-Rmst. n = 3 biological repeats. **P < 0.01 compared with AAV5-Gfp group.*
## Discussion
This is the first study to examine the molecular and cellular function of Rmst, a lncRNA in injured DRG neuron that modulates NP. Specifically, the increased Rmst was positively regulated Dnmt3a by promoting its mRNA stability and interacting with HuR, which leads to NP. Blocking the elevation of Rmst could reverse nerve injury-induced Dnmt3a upregulation and alleviate pain hypersensitivities (Figure 8). The present study suggests that Rmst in DRGs is likely a key regulator in NP.
**FIGURE 8:** *Proposed mechanism on rhabdomyosarcoma 2-associated transcript (Rmst) in NP. Nerve injury-triggered Rmst upregulation interacts with HuR in injured DRGs, resulting in stabilizing Dnmt3a mRNA and protein. The latter one has been reported as a critical NP regulator in DRG neurons. In contrast, in normal DRG neuron, Dnmt3a mRNA would be degraded without Rmst.*
In 2013, the first lncRNA involved in NP was reported in detail (Zhao et al., 2013, p. 2). After that, the regulation of lncRNA on NP are popping up over the past few years (Wu et al., 2019). And we are the first to report lncRNA *Rmst is* involved in the NP. In fact, Rmst was first identified as non-coding RNA in 2012, and it was essential for neuronal specification in human embryonic stem cells (Ng et al., 2012). Under normal condition, RMST in both human and mouse occurs primarily in CNS (Uhde et al., 2010; Ng et al., 2012) and, more importantly, is abundant in neuron (Julian et al., 2013, p. 2; Briese et al., 2016). RMST physically interacts with SOX2 and regulates neural fate by regulating neurogenesis related genes (Ng et al., 2013, p. 2). RMST deficiency in neural stem cells resulted in glia differentiation (Ng et al., 2013), indicating that RMST is important for neural differentiation in particularly during brain development period. However, increased Rmst expression was reported in CNS diseases, including stroke (Zhao et al., 2021; Li et al., 2022) and Parkinson’s disease (Ma et al., 2021). Blocking Rmst expression could protect from neuronal apoptosis and improve neurological function (Ma et al., 2021; Zhao et al., 2021; Li et al., 2022), which suggests that excessive Rmst expression may cause CNS disease aggravations. Our present study found Rmst was also increased expressed in DRG neuron under NP condition. More importantly, blocking Rmst expression in injured DRGs could mitigate nerve injury-induced nociceptive hypersensitivity. However, why Rmst siRNA in DRGs did not alter response to mechanical and heat stimuli is unclear, which may be due to low Rmst expression in physiological condition. Together, the strong evidence indicates that the dysregulation of Rmst in neuron may contribute to NP development.
Evidence has been emerged lncRNAs mediates DNA methylation in various pathological condition (Huang et al., 2022), including schizophrenia (Ni et al., 2021, p. 006), diabetic retinopathy (He et al., 2021, p. 3), colon cancer (Merry et al., 2015) and so on. In particular, RMST was characterized as a positive regulator for DNMT3 but not DNMT1 by increasing DNMT3 mRNA stability in cancer (Peng et al., 2020). In our study, Rmst upregulation may contribute to the nerve damage-triggered Dnmt3a increase by stabilizing its mRNA in NP. Notably, when Rmst was overexpressed in DRG neuron, only DNMT3a but not DNMT3b appear to generate, and blocking DRG Rmst expression abolished SNL-induced DNMT3a upregulation, which was consistent with previous study (Peng et al., 2020, p. 3). Furthermore, Rmst did not directly modulate nociceptive hypersensitivity as in absence of Dnmt3a expression in DRG neuron the overexpression of Rmst failed to completely mimic nerve damage-induced nociceptive hypersensitivity. In fact, it has been reported DNMT3a in DRG neurons is involved in the NP (Guo et al., 2019). Knockout DNMT3a in DRG significantly attenuated nociceptive hypersensitivity (Zhao et al., 2017). What’s more, in NP, transcriptional factors, such as CREB (Yang et al., 2021) and Oct1 (Zhao et al., 2017), could bind to the promoter region of Dnmt3a to boost Dnmt3a expression. Our study demonstrated that *Rmst is* required for the stability of Dnmt3a mRNA, enhancing DNMT3a expression. It should also be noted that Rmst also regulates other NP related genes including sex-determining region Y-box2 (Sox2) (Ng et al., 2013, p. 2; Zhang et al., 2019, p. 2) and heterogeneous nuclear ribonucleoprotein D (hnRNPD) (Liu et al., 2020; Feng et al., 2021). Whether these genes are also regulated by Rmst in NP remains to be determined.
Mechanistically, nerve injury-induced elevated Rmst could recruit HuR protein, thereby stabilizing the Dnmt3a mRNA and reducing the Dnmt3a mRNA degradation. HuR, as an RNA-binding protein, is capable to stabilize AU-rich elements (AREs)-containing reporter mRNA in the cytoplasm through binding AREs sequences (Gallouzi et al., 2000). The new finding has pointed out anti-HuR delivery has been proven effective to relieve pain hypersensitivity by inhibiting spinal neuroinflammation (Borgonetti and Galeotti, 2021). Therefore, the effectiveness of analgesics of anti-HuR may also be due to the degradation of Dnmt3a mRNA. However, further experiments are needed.
In conclusion, our study indicated that blocking Rmst expression in injured DRGs mitigated NP at least in part through enhancing degradation of Dnmt3a mRNA. Thus, Rmst may become a promising target and provide insightful directions for NP treatment.
## Data availability statement
The RNA sequencing dataset for mouse DRG after peripheral nerve injury was obtained from previous research (https://doi.org/$\frac{10.1177}{1744806916629048}$). The ScRNA-seq dataset for mouse DRG after peripheral nerve injury was from Gene Expression Omnibus (GEO) with the series record GSE155622.
## Ethics statement
This animal study was reviewed and approved by Guangzhou Medical University.
## Author contributions
XG and XS conceptualized and designed the study. XG and WC contributed to write the manuscript. GZ and FH performed animal model and behavior test. XG and JQ performed molecular experiments. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer B-CJ declared a past co-authorship with one of the author XG to the handling editor.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2022.1027063/full#supplementary-material
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|
---
title: 'Relationship between the Mediterranean diet and risk of hepatic fibrosis in
patients with non-alcoholic fatty liver disease: A cross-sectional analysis of the
RaNCD cohort'
authors:
- Mahsa Miryan
- Mitra Darbandi
- Mozhgan Moradi
- Farid Najafi
- Davood Soleimani
- Yahya Pasdar
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9992532
doi: 10.3389/fnut.2023.1062008
license: CC BY 4.0
---
# Relationship between the Mediterranean diet and risk of hepatic fibrosis in patients with non-alcoholic fatty liver disease: A cross-sectional analysis of the RaNCD cohort
## Abstract
### Background
Despite evidence supporting the beneficial effects of the Mediterranean diet (MedDiet) on hepatic steatosis in subjects with non-alcoholic fatty liver disease (NAFLD), the relationship of the MedDiet with hepatic fibrosis is as yet unclear. The aim of the present study was to explore this association in Iranian adults with NAFLD.
### Methods
This cross-sectional study included 3,325 subjects with NAFLD from the Ravansar Noncommunicable Disease (RaNCD) cohort. Dietary intake data were collected by a validated food frequency questionnaire (FFQ). The MedDiet score was computed based on a nine-point scale constructed by Trichopoulou et al. Fatty liver index (FLI) and fibrosis-4 (FIB-4) index were used to predict hepatic steatosis and fibrosis in the population. Multivariate regression models were applied to determine associations.
### Results
Subjects in the highest tertile of MedDiet score had a higher platelet and a lower weight, total cholesterol (TC), LDL-c, and FLI than those in the lowest tertile (p-value < 0.05). Adherence to the MedDiet was associated with a 7.48 ($95\%$CI: 5.376 to 9.603; p-value: 0.001) × 103/μl; −0.417 ($95\%$CI: −0.819 to −0.014; p-value: 0.042) kg, −2.505 ($95\%$CI: −3.835 to −1.175; p-value: 0.001) mg/dl; and −1.93 ($95\%$CI: −2.803 to −1.061; p-value: 0.001) mg/dl change in platelet, weight, TC, and LDL-c for each SD increase in the score, respectively. A significant linear trend was observed in odds of hepatic fibrosis across the tertiles of the MedDiet score (P-trend: 0.008). This linear trend was attenuated but remained significant after the adjustment of the relevant confounders (P-trend: 0.032). Adherence to the MedDiet was independently associated with about $16\%$ lower odds of having hepatic fibrosis in patients with NAFLD for each SD increase in the score.
### Conclusion
Adherence to the MedDiet characterized by a high intake of whole grains, fruits, vegetables, legumes, nuts, and fish was associated with a lower risk of having hepatic fibrosis in patients with NAFLD. Further studies are required to elucidate the causal relationship of observed association in individuals of all ages, ethnicities, and etiologies of hepatic steatosis.
## Introduction
Nonalcoholic fatty liver disease (NAFLD) is characterized by the pathological accumulation of fat in the hepatocytes in the absence of excessive alcohol use and known secondary causes of steatosis such as hepatitis B and C. NAFLD is the most common chronic liver disease, affecting approximately 25 percent of the general population in the world [1]. This disease represents a wide spectrum of histologic abnormalities in the liver from non–alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH). NASH is the more progressive form of NAFLD to fibrosis, cirrhosis, and hepatocellular carcinoma [2]. The progression rate of fibrosis is 0.07 and 0.14 stages per year in NAFL and NASH patients, respectively [3]. NAFLD has been predicted to become the leading cause of liver transplantation in the United States by 2030 [4]. This disease also is independently associated with an increased risk of cardiovascular diseases (CVDs) [5]. So far, no specific pharmaceutical agent has been approved by FDA for the treatment of NAFLD, and lifestyle modification remains the first-line treatment.
NAFLD is a consequence of the interaction between genes and various environmental factors. Diet as a modifiable environmental factor plays an essential role in NAFLD pathogenesis and progression. In this context, considerable research interest has been devoted to associations between dietary patterns and histological and clinical features of NAFLD. The MedDiet is a healthy dietary pattern associated with favorable health outcomes, mainly in relation to CVD. A high intake of whole grains, fruits, vegetables, seafood, beans, nuts, and olive oils characterizes this dietary pattern. Many observational studies revealed that adherence to the MedDiet is negatively associated with the serum levels of liver enzymes, the onset and severity of hepatic steatosis, and the presence of NASH in patients with NAFLD (6–8). In line with these efforts, several clinical trials showed that the MedDiet can substantially improve liver enzymes, hepatic steatosis, and insulin sensitivity in patients with NAFLD (9–11).
Despite evidence supporting the beneficial effect of the MedDiet on hepatic steatosis in patients with NAFLD, the relationship between the MedDiet and hepatic fibrosis is as yet unclear. To our knowledge, there is scarce literature on the association of MedDiet with hepatic fibrosis in NAFLD patients. The ATTICA cohort, which was conducted in Greece, showed a negative relationship between the MedDiet score and fibrosis-4 score (FIB-4), a proxy measure of hepatic fibrosis [12]. Another study conducted on diabetic patients with NAFLD in Spain showed a lower adherence to MedDiet in patients with hepatic fibrosis [13]. However, these results may not be applicable to Iranian adults due to different lifestyles, genetic predisposition to the disease, and confounding factors such as physical activities and alcohol consumption. Thus, we designed a population-based study to explore the association between adherence to MedDiet and the risk of hepatic fibrosis in Iranian adults with NAFLD based on valid non-invasive methods.
## Study population and design
The present cross-sectional analysis was performed within the framework of the Ravanser Non-Communicable Disease (RaNCD) cohort. The RaNCD study is part of Prospective Epidemiological Research Studies in IRAN (PERSIAN). The RaNCD cohort was originally designed to determine the incidence of non-communicable diseases (NCDs), the main risk factors for NCDs, and the relationship between these risk factors and NCDs in the Kurdish ethnic population aged 35 to 65 years who permanently live in the Ravanser district of Kermanshah province, a region in western Iran. The RaNCD cohort recruited 10,065 participants between March 2015 and February 2017 who are still being followed up [14]. The RaNCD cohort was conducted in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants. Of the original sample, we excluded participants who reported energy intake outside the normal ranges (600–3,500 kcal.day−1 for women and 800–4,200 kcal.day−1 for men), had missing relevant data, had the fatty liver index (FLI) < 60, had a history of alcohol intake, or had the secondary causes of steatosis (eg, viral hepatitis B and C). We also excluded subjects with missing data. Our final sample comprised 3,325 subjects with NAFLD defined through FLI. The present work was approved by the steering committee of the RaNCD and the ethics committee of Kermanshah University of Medical Sciences (IR.KUMS.REC.1401.223). The manuscript was approved by the members of the steering committee of the RaNCD, who assume responsibility for the integrity of the data and the overall content of the manuscript.
## Non-invasive index of hepatic steatosis and fibrosis
The fatty liver index (FLI) is a well-known non-invasive and accurate predictor of hepatic steatosis in the general population. FLI is an algorithm of both anthropometric data and biochemical tests, including body mass index (BMI), waist circumference (WC), gamma-glutamyl transferase (GGT), and triglyceride (TG). We calculated FLI by the following formula [15]: FLindex=(e0.953×log e(TG)+0.139×BMI+0.718×log e(GGT)+0.053×WC−15.745) /(1+e0.953×log e(TG)+0.139×BMI+0.718×log e(GGT)+0.053×waist circumference−15.745)×100 FLI values are between 0 and 100, where an FLI ≥ 60 detects hepatic steatosis with an accuracy of 0.84 ($95\%$ confidence interval (CI) 0.81–0.87) [15]. This index is widely used to detect hepatic steatosis in extensive epidemiological studies. FLI is also strongly associated with the severity of hepatic steatosis in the general population [16].
The fibrosis-4 (FIB-4) index is a non-invasive scoring system for predicting hepatic fibrosis in many large-scale epidemiological studies [12, 17]. FIB-4 is an algorithm of biochemical tests and calculated using the following formula: The cut-off points of FIB for predicting hepatic fibrosis were set at 1.05 in individuals aged ≤ 49 years, 1.24 in 50–59 years, 1.88 in 60–69 years, and 1.95 in ≥ 70 years with the area under the receiver operating characteristic curve (AUROC) of 0.917, 0.849, and 0.855, respectively [18].
## Dietary data assessment and MedDiet score computation
Usual food intake was determined using the national Iranian food frequency questionnaire (FFQ) at the time of recruitment. This FFQ included questions about the frequency intake of 118 food items and appropriate standard portion sizes (e.g., a glass, cup, slice, teaspoon, tablespoon, spatula, cube, etc.) for each food item. Participants reported the average frequencies and portion sizes of consumed foods over the past year. In this study to decline the recall bias, the FFQ was taken from the participants by trained nutrition experts, and the participants were given enough time to remember the consumption of each food item. The FFQs were analyzed to obtain energy and nutrient intakes using the Nutritionist IV software (First Databank Inc., Hearst Corp., San Bruno, CA, United States) based on the U.S. Department of Agriculture food composition data. In nutritional epidemiological studies, subjects who under-reported (-3SD) or over-reported (+3SD) their energy intake based on FFQ analysis should be excluded from the study. According to a previous study, the under-reporting of energy intake in men and women is estimated at 800 and 600 kcal per day, and the over-reporting is estimated at 4200 and 3,500 kcal per day, respectively [19].
The MedDiet score was computed based on a nine-point scale constructed by Trichopoulou et al. This scale consists of nine dietary components: whole grains, fruits, vegetables, legumes, nuts, fish/seafood, monounsaturated to saturated fat ratio (MUFA/SFA) as healthy items; red and processed meats as unhealthy items; and alcohol as an item for which a moderate consumption was recommended. The food items included in each food group are shown in Table 1. Each component is assigned a value of 0 or 1 according to the sex-specific median of the studied population as the cut-off point. We measured each component (except for alcohol) in grams per 1,000 kilocalories to make intake independent of total energy intake [20]. For healthy components, individuals with an intake at or above the median receive 1 point, otherwise they receive 0 points. This scoring algorithm is reversed for the unhealthy components. For alcohol, individuals with moderate intake (males: 10–50 g/day; females: 5–25 g/day) receive 1 point. Overall, the MedDiet score ranges from 0 (low adherence) to 9 (high adherence).
**Table 1**
| Whole grains | Whole-grain bread, oat, barley, oatmeal |
| --- | --- |
| Fruits | Cantaloupe, honeydew melon, watermelon, apricot, cherries, peaches, prunes, strawberries, plums, figs, grapes, pears, apples, kiwifruit, citrus, pomegranate, banana, persimmon, date, dried fruits, raisin, fruit juices |
| Vegetables | Lettuce, cabbage, tomato, cucumber, leafy green, eggplant, celery, beet, carrot, garlic, onion, pepper, mushroom, green peas, green beans, zucchini, mixed vegetables |
| Nuts | Walnuts, peanut, other nuts, seeds |
| Legumes | Beans, chickpea, lentil, soybean, pea |
| Fish/Seafood | Fish, tuna |
| Meats | Red meat, chicken, processed meat |
## Anthropometric assessment
Weight was measured with participants wearing light clothing and without shoes using the InBody 770 (InBody Co, Seoul, Korea) machine. Height was measured without shoes in the standing position using the automatic stadiometer (BSM 370; Biospace Co, Seoul, Korea). Then, body mass index (BMI) was computed using Quetelet’s index from weight in kilograms divided by height in meters squared. BMI was categorized according to the World Health *Organization criteria* as underweight (<18.5 kg/m2), normal weight (18.5 to 24.9 kg/m2), overweight (25 to 29.9 kg/m2), and obesity (≥30 kg/m2). While the participants were in the standing position and at the end of gentle expiration, waist circumference (WC) was measured in the standing position at the minimum circumference between the iliac crest and the rib cage. Also, hip circumference (HC) was measured in the standing position at the maximum circumference of the buttocks.
## Demographic data
Age (years), sex (male, female), marital status (married, single), education level (illiterate, elementary school, middle school, high school diploma, college degrees), regular consumption of dietary supplements (yes, no), smoking status (non-smoker, current smoker, former smoker, passive smoker), sleep duration (hours), physical activity level (low: 24–36.5 METs, medium: 36.6–44.9 METs, high: ≥45 METs), medication use (no, yes), diabetes mellitus (no, yes), hypertension (no, yes), cardiovascular diseases (no, yes) were collected at baseline by self-administered questionnaires. The single marital status referred to never married, widowed, and divorced. The dietary supplement’s item included multi-vitamin minerals, multi-vitamin, calcium, calcium + vitamin D, vitamin D, folic acid, omega 3 or fish oil, iron, and zinc.
## Statistical analysis
All statistical analyses were conducted with the SPSS software. We also excluded subjects with missing data. The normal distribution of continuous data was ascertained using the Kolmogorov–Smirnov test. Data are presented as means ± standard deviations (SD) for normally distributed variables, medians [interquartile ranges] for non-normally distributed variables, and percentages (n%) for qualitative variables. We categorized the MedDiet score according to tertiles as high adherence (third tertile) and low adherence (first tertile). Differences across the tertiles of the MedDiet score were determined by the one-way analysis of variance (ANOVA) with the Bonferroni post hoc test and Kruskal-Wallis test for normally and non-normally distributed variables, respectively. The chi-square test was applied to determine the distribution of quantitative variables across the tertiles of MedDiet. Also, linear trends were assessed using the one-way ANOVA test and the Jonckheere-Terpstra test.
Binary logistic regression models (Enter Method) were used to estimate odds ratios (ORs) with $95\%$ confidence intervals ($95\%$ CI) for having hepatic fibrosis across the tertiles of the MedDiet score. Three multivariate models were run to adjust possible confounding factors which were obtained from the comparison of demographic, anthropometric, and biochemical characteristics of participants across the tertiles of the Mediterranean diet scores (Tables 2 and 3) and our previous knowledge [21]. We defined adjusted models as follows:
We also applied univariate and multivariate linear regression models to determine changes in variables for each SD increase in the MedDiet score. p-values less than 0.05 were considered to indicate statistical significance.
## Results
Subjects in the present study were recruited from the RaNCD cohort including 10,065 Kurdish participants, of whom 6,740 were excluded because of missing relevant data (n:18), implausible energy intake (n:987), FLI less than 60 (n:5587), alcohol intake (n:147), and hepatitis B (n:2). The final number of subjects included in the analyses was 3,325 (Figure 1). The mean (±SD) age was 48.34 (±8.05) years, BMI was 31.23 (±3.86) kg/m2, and FLI was 77.25 (±10.56), and 1939 subjects ($58.3\%$) were female and 381 ($11.5\%$) had hepatic fibrosis according to the FIB-4 index. Subjects with hepatic fibrosis were more likely to be male (15.8 vs. $8.4\%$; p-value: 0.001), intake the dietary supplement (16.1 vs. $11\%$; p-value: 0.007), and have lower BMI (30.43 ± 3.81 vs. 31.24 ± 4.16; p-value: 0.001) than those without hepatic fibrosis. Groups did not differ in terms of marital status, educational levels, smoking, medication use, diabetes mellitus, hypertension, cardiovascular diseases, sleep duration, physical activity levels, weight, WC, HC, and WHR (p-value > 0.05).
**Figure 1:** *The flow chart of the study.*
Table 2 shows the demographic characteristics of subjects by the tertiles of the MedDiet score. Subjects with higher adherence to the MedDiet (highest tertile) were more likely to be female and non-smokers, intake dietary supplements, and have diabetes mellitus and higher educational levels but lower physical activity than those with the lower adherence to the MedDiet (lowest tertile). Further analysis showed that the MedDiet score was significantly higher in females than males (mean difference: 0.28; $95\%$ CI: 0.40–0.16), in subjects with a college’s degree than in illiterate (mean difference: 0.65; $95\%$ CI: 0.31–0.98), in non-consumers of dietary supplements than consumers (mean difference: 0.75; $95\%$ CI: 0.95–0.55), in subjects with low physical activity than high physical activity (mean difference: 0.35; $95\%$ CI: 0.13–0.57), and in subjects with diabetes mellitus than others (mean difference: 0.16; $95\%$ CI: 0.01–0.33). There were no significant differences between the MedDiet score and other demographic data (p-value > 0.05).
A significant linear trend was observed in FLI values across the tertiles of the MedDiet score (P-trend: <0.001). Subjects in the highest tertile of the MedDiet score had a lower FLI value than those in the lowest tertile (75.42 ± 10.11 vs. 77.61 ± 10.70; p-value: <0.001). The mean (±SD) of anthropometric and biochemical characteristics of participants across the tertiles of the MedDiet score are shown in Table 3. A significant linear trend was observed in weight, platelet count, total cholesterol (TC), and LDL-c across the tertiles of the MedDiet score. Subjects in the highest tertile of the MedDiet score had a higher platelet count and a lower weight, TC, and LDL-c than those in the lowest tertile (p-value < 0.05). The linear regression model showed that adherence to the MedDiet was associated with a 7.48 ($95\%$CI: 5.376–9.603; p-value: 0.001) × 103/μl increase in platelet count and 0.417 ($95\%$CI: 0.819–0.014; p-value: 0.042) kg, 2.505 ($95\%$CI: 3.835–1.175; p-value: 0.001) mg/dl and 1.93 ($95\%$CI: 2.803–1.061; p-value: 0.001) mg/dl reduction in weight, TC, and LDL-c for each SD increase in the score, respectively.
Odds ratios ($95\%$ CIs) for having hepatic fibrosis by tertiles of the MedDiet score are shown in Table 4. A significant linear trend was observed in the odds of hepatic fibrosis across the tertiles of the MedDiet score. Subjects in the highest tertiles of the MedDiet score had lower odds of hepatic fibrosis than the lowest tertile (p-value: 0.004). This linear trend was attenuated but remained significant after the adjustment of the potential confounding factors related to demographic (model I), biochemical, anthropometric characteristics (model II), and severity of hepatic steatosis (model III). Further analysis showed that adherence to the MedDiet was associated with about $16\%$ lower odds of having hepatic fibrosis in patients with NAFLD for each SD increase in the score. Regardless of the cut-off points of FIB-4 for having hepatic fibrosis, a significant relationship was observed between the FIB-4 values and the Mediterranean diet scores (Beta Coefficient: -0.023; $95\%$CI: −0.033 to-0.012).
**Table 4**
| Models | Odds ratio (95% CI) across tertiles (T) of the Mediterranean diet score | Odds ratio (95% CI) across tertiles (T) of the Mediterranean diet score.1 | Odds ratio (95% CI) across tertiles (T) of the Mediterranean diet score.2 | P-trend | Odds ratio (95% CI) per SD |
| --- | --- | --- | --- | --- | --- |
| Models | T1 low adherence | T2 | T3 high adherence | P-trend | Odds ratio (95% CI) per SD |
| Crude model | 1.00 (ref.) | 0.917 (0.729–1.154) | 0.613 (0.438–0.857) | 0.008 | 0.817 (0.734–0.910) |
| Adjusted model I | 1.00 (ref.) | 0.953 (0.755–1.202) | 0.666 (0.474–0.937) | 0.038 | 0.843 (0.755–0.942) |
| Adjusted model II | 1.00 (ref.) | 0.935 (0.740–1.181) | 0.656 (0.466–0.923) | 0.028 | 0.835 (0.747–0.933) |
| Adjusted model III | 1.00 (ref.) | 0.929 (0.735–1.173) | 0.665 (0.472–0.937) | 0.032 | 0.841 (0.752–0.940) |
## Discussion
Using data on 3,325 adult participants from the RaNCD cohort, we found that greater adherence to the MedDiet was associated with a lower risk of hepatic fibrosis, as measured by the FIB-4 score. In addition, our results provided supportive evidence for the benefits of adherence to the MedDiet in controlling weight, hepatic steatosis, and dyslipidemia. To our knowledge, this is the first large-scale study investigating the relationship between the MedDiet and hepatic fibrosis in the Iranian population.
The MedDiet emphasizes a high intake of whole grains, fruits, vegetables, legumes, nuts, and fish and a low intake of red and processed meats and dairy products. Our results showed that adherence to the MedDiet significantly reduces the risk of hepatic fibrosis in individuals with NAFLD. Despite differences in participating population, MedDiet capture, and methods of hepatic fibrosis measure; the findings of previous research are consistent with our results. Baseline data of the ATTICA cohort including 3,042 Greek adults showed that individuals in the highest tertile of the MedDiet score had a lower age, weight, FLI, FIB-4, and proportion of diabetes mellitus but higher physical activity than those in the lowest tertile [12]. In a cross-sectional study conducted on 160 diabetic patients with NAFLD proven through biopsy, subjects with hepatic fibrosis had lower adherence to the MedDiet compared to those without fibrosis [13]. A quasi-interventional study on 44 Greek Caucasian patients with non-fibrotic NAFLD showed that increased adherence to the MedDiet significantly associated with improvements in liver imaging, NAFLD fibrosis score (NFS), and C-reactive protein [22]. Furthermore, exploratory dietary patterns almost similar to the MedDiet were associated with a lower risk of hepatic fibrosis. In this context, Soleimani et al. reported that adherence to a healthy dietary pattern that emphasizes high intakes of low-fat dairy products, vegetables, fruits, nuts, white meats (poultry and fish), and vegetable oils and low intakes of red and processed meats significantly related to lower odds of hepatic fibrosis in patients with NAFLD, whereas the adherence to a western dietary characterized by high intakes of red and processed meats, refined grains, potato, white meat, eggs, and soft drinks and low intakes of red and processed vegetables, fruits, and nuts related to the increased odds of hepatic fibrosis [21].
As expected, individuals with a higher adherence to the Mediterranean showed a better overall health profile including lower weight, TC, LDL-c, higher educational status, and better smoking status. This phenomenon could influence the relationship of the MedDiet with hepatic fibrosis. Our results when adjusted for confounding factors such as weight, liver fat count, TC, LDL-c, educational status, and smoking were attenuated but remained significant (multivariate adjusted OR per SD increment: 0.841; $95\%$ CI: 0.752–0.940). Therefore, our results seemed to be more robust than the previous studies in controlling potential confounding factors [12, 13]. Furthermore, more tendency toward healthier diets following intermediary events related to NAFLD such as dyslipidemia and diabetes would tend to weaken the relationship between the MedDiet and hepatic fibrosis [21].
Several potential mechanisms may explain the inverse association between adherence to the MedDiet and the risk of hepatic fibrosis in patients with NAFLD. Weight is considered an independent predictor for the progression of hepatic steatosis to fibrosis in NAFLD [23]. It has been proven that weight loss leads to improve hepatic fibrosis in obese patients with NASH [24, 25]. Our results are consistent with most studies that adherence to the MedDiet was significantly associated with better metabolic control, especially weight [26, 27]. The high-fiber content in the MedDiet can protect individuals against overweight and obesity development by suppressing the person’s appetite sensation and reducing the diet’s calorie density along with glycemic index [28, 29]. We observed that the association of the MedDiet with hepatic fibrosis remained significant after the adjustment of weight and other confounding factors. This observation implies that this diet might have a direct impact on liver fibrogenesis. Hepatic stellate cells (HSCs) are the main cells in hepatic fibrogenesis by synthesizing extracellular matrix proteins (ECMs), including collagen and fibronectins. In response to fibrogenic stimuli such as reactive oxygen species (ROS) and inflammatory cytokines, quiescent HSCs become active, secrete ECMs, and decrease their degradation [30]. Activated HSCs also migrate to the site of damage and transdifferentiate into proliferative, contractile, and ECMs-secreting myofibroblasts [31]. Nowadays, HSCs inhibitors have been considered a promising approach for hepatic fibrosis treatment [30, 31]. The MedDiet contains large amounts of antioxidants such as polyphenols, carotenoids, vitamin C, vitamin E, and selenium that may act synergistically to reduce the production of pro-inflammatory mediators as well as ROS by inhibiting nuclear factor-κB [32, 33]. Our recent trial showed that propolis)a honeybee product with high but diverse contents of polyphenols (significantly reduces inflammation and hepatic fibrosis in patients with NAFLD [34]. In addition, the MedDiet emphasizes a low intake of SFAs–rich sources including red meats, processed meats, and dairy products. A high intake of SFAs is associated with the up-regulation of inflammatory-related genes [35]. Altogether, the MedDiet may directly protect the liver from fibrosis which in part is due to its anti-inflammatory and antioxidative properties.
The major strengths of our study include the large sample size, population-based design, homogeneous participants, and use of different confounding factors in statistical models. Also, liner regression test regenerated the results of between-group analysis that imply the absence of miss classification across the tertiles of MDiet score in our study. However, the current study is not able to draw the causal relationship between the MedDiet and hepatic fibrosis because of the nature of the cross-sectional study design. It should also be noted that recall bias is a common error in retrospective studies (like our study), especially when the control group and the case group do not have the same behavior in remembering the exposure factors such as diet. Additionally, undesirable habits such as eating unhealthy foods tend to be under-reported, and are therefore subject to recall bias. In the current study to decline the recall bias, the FFQ was taken from the participants by trained nutrition experts, and the participants were given enough time to remember the consumption of each food item. Therefore, the longitudinal study design and clinical trial are warranted to overcome the recall bias and elucidate the causal relationship of the MedDiet and its components with the risk of hepatic fibrosis in NAFLD. Another limitation of this study is the likely presence of measurement error due to the use of FFQ. This questionnaire may not accurately measure the exact intake of foods and nutrients but it is a validated instrument for investigating diet-disease relations in epidemiological studies [36]. Furthermore, our findings are not generalizable to all adults because the RaNCD study only includes patients 35–65 years old (as a limitation) and to individuals with other causes of hepatic steatosis than NAFLD because of the different nature of their pathogenesis in hepatic fibrosis.
## Conclusion
The current investigation that adherence to the MedDiet characterized by a high intake of whole grains, fruits, vegetables, legumes, nuts, and fish was significantly associated with a lower risk of having hepatic fibrosis in patients with NAFLD. Further studies are required to elucidate mechanisms and the causal relationship of observed association in individuals of all ages, ethnicities, and etiologies of hepatic steatosis.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: *The data* presented in this study are available on request from the steering committee of the RaNCD. The data are not publicly available as this is an ongoing cohort study. Requests to access these datasets should be directed to YP; Email: [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by the ethics committee of Kermanshah University of Medical Sciences (IR.KUMS.REC.1401.223). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MMi, MD, DS, and MMo wrote the main manuscript text. FN and YP administrated project. All authors contributed to the article and approved the submitted version.
## Funding
We sincerely appreciate all participants and the Student Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran (IR.KUMS.REC.1401.223) and PERSIAN cohort Study collaborators for their financial support of the study. The Iranian Ministry of Health and Medical Education has contributed to the funding used in the PERSIAN Cohort through Grant no $\frac{700}{534.}$
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Dairy product consumption was associated with a lower likelihood of non-alcoholic
fatty liver disease: A systematic review and meta-analysis'
authors:
- Wei Dai
- Huiyuan Liu
- Tingjing Zhang
- Qing Chang
- Yuhong Zhao
- Chuanji Guo
- Yang Xia
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9992538
doi: 10.3389/fnut.2023.1119118
license: CC BY 4.0
---
# Dairy product consumption was associated with a lower likelihood of non-alcoholic fatty liver disease: A systematic review and meta-analysis
## Abstract
### Background and aims
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease. Several epidemiological studies attempted to assess the association between dairy product and the likelihood of NAFLD, but the contribution of dairy consumption to NAFLD remains controversial. We conducted a meta-analysis to investigate the association between dairy product consumption and NAFLD.
### Methods
We conducted a literature search using the PubMed, Web of Science and Scopus databases, we conducted a thorough search of the literature published before January 5, 2023. Combined odds ratios (ORs) and $95\%$ confidence intervals (CIs) of NAFLD in relation to dairy product intake were estimated using random-effects models. Subgroup analysis and meta-regression were performed according to the study design, region, sex, body mass index (BMI), type of exposure, NAFLD diagnostic criteria, and exposure assessment tools.
### Results
We initially identified 4,634 relevant studies, of which 25 complied with the inclusion criteria, including seven cross-sectional studies, six case–control studies and one cohort study. A total of 51,476 participants (14,546 patients with NAFLD) were included in the meta-analysis. There was an inverse association between dairy product consumption and NAFLD (OR = 0.97, $95\%$ CI = 0.94–0.99). Subgroup analysis demonstrated that lower likelihood of NAFLD was associated with dairy product consumption in subgroups of Asian populations, women, patients diagnosed using NAFLD-related scores, patients with a BMI of 18.5–24.9 kg/m2, dairy intake assessed using a food frequency questionnaire, milk consumption, and yogurt consumption. No noteworthy connection was observed in the other subgroups.
### Conclusion
Our meta-analysis findings revealed that dairy product consumption is inversely associated with NAFLD. Consumption of dairy products could help prevent the development of non-alcoholic fatty liver disease.
## Introduction
In the world today, non-alcoholic fatty liver disease (NAFLD) is rapidly becoming the most prevalent form of chronic liver disease [1], affecting approximately 25 and $10\%$ of the global adult and pediatric populations, respectively [2, 3]. NAFLD is defined by steatosis of at least $5\%$ of hepatocytes, confirmed primarily by histology or high-resolution imaging, while excluding known hepatotoxic factors such as excessive alcohol consumption, viral infections, or illicit drug use [4]. International guidelines suggest that lifestyle changes linked to nutrition should be a crucial component of NAFLD therapy, although there is no agreement on the pharmacological management of NAFLD [5]. Therefore, it is necessary to identify healthy dietary interventions to reduce the burden of NAFLD.
Dietary guidelines worldwide recommend milk and dairy products [6], but it remains controversial whether dairy products are associated with NAFLD. Several recent studies have shown that dairy product consumption is not associated with NAFLD (7–9). In contrast, the results of a case–control study showed that dairy product consumption was associated with a lower likelihood of NAFLD [10]. However, a cross-sectional study reported an increased likelihood of NAFLD associated with dairy consumption [11]. Numerous unique fatty acids found in dairy products, including short-chain fatty acids and palmitoleic acid, which resemble hormones, may have beneficial metabolic effects [12, 13]. In a previous study, dairy fat was found to enhance glucose tolerance, which may increase hepatic insulin sensitivity and systemically reduce liver fat deposition [14]. However, the chances of developing NAFLD were increased by dairy products (especially cheese) in another study, which could be due to saturated fatty acids in dairy products [15]. These inconsistent results highlight the diverse associations between different types of dairy intake and NAFLD.
To our knowledge, although a previous meta-analysis investigated the association between dairy intake and NAFLD, the inclusion of dairy product types was neither complete nor specific (e.g., ice cream was not included) [16]. Therefore, we aimed to more accurately and comprehensively estimate the impact of dairy consumption, including that of various dairy products, on NAFLD. To achieve this, we assessed the association between the consumption of dairy products, and their different types, and the development of NAFLD. Furthermore, we conducted a subgroup analysis based on the included studies’ characteristics, such as study design, dietary assessment tools, and of the study populations and regions.
## Database searches
Using the PubMed, Web of Science and Scopus databases, we conducted a thorough search of the literature published before January 5, 2023 (CRD42022357457) to identify observational studies examining the association between dairy product consumption and NAFLD in adult patients (age ≥ 18 years). The following words were combined to find results (Supplementary Table 1): “dairy” or “total dairy” or “dairy product” or “dairy products” or “milk” or “whole milk” or “low-fat milk” or “full-fat milk” or “yogurt” or “yoghurt” or “cream” or “ice cream” or “cheese” or “butter” or “kefir” combined with “fatty liver” or “NAFLD” or “non-alcoholic fatty liver disease” or “hepatocellular cancer” or “hepatic cancer” or “steatohepatitis” or “steatosis” or “nonalcoholic steatohepatitis.” A flowchart of literature selection is presented, in accordance with the Preferred Reporting Items for Meta-Analysis (PRISMA) guidelines [17].
## Inclusion and exclusion criteria
De-duplication of identical documents from multiple databases through literature management software such as Endnote. Selection criteria for duplicate literature. [ 1] Selection of the one with the largest sample size. [ 2] Selection of the one with the longest follow-up period. [ 3] Select the one with the most comprehensive study outcomes. The following were the conditions for inclusion: [1] adult population; [2] a study that explored the association between dairy product consumption and the likelihood of developing NAFLD, such as cohort studies, case–control studies or cross-sectional studies; [3] a diagnosis of NAFLD made by ultrasound, magnetic resonance imaging (MRI), controlled attenuation parameter (CAP), fibro-scan, fatty liver index, or liver biopsy; [4] consumption of dairy products: total dairy, milk, yogurt, cheese, or other types of dairy products; [5] outcome was NAFLD; [6] language was restricted to English. Exclusion criteria were as follows: [1] teenaged or pregnant participants; [2] surface antigens for hepatitis B, antibodies against hepatitis C, or HIV antibodies present; [3] abnormally high alcohol or drug intake that could be hazardous to the liver (tamoxifen, steroids, and amiodarone); and [4] reviews, comments, editorials, letters, interviews, or reports.
## Data extraction and quality assessment
From each study, the following information was extracted: first author, country, publication year, study design, sample size, number of cases, body mass index (BMI), participants’ mean age, NAFLD diagnostic method, dietary assessment tools, types of exposure, risk estimate (HRs, RRs, or ORs and its $95\%$ CI), and confounding factors adjusted in the final model. The Newcastle-Ottawa scale [18] and the Agency for Healthcare Research and Quality [19] were used to assess the quality of cohort studies, case–control studies, and cross-sectional studies. The Newcastle-Ottawa scale was used to rate the literature quality with a score out of 9 stars, with higher scores indicating higher quality studies; scores of ≥ 6 and < 6 indicated high- and low-quality studies, respectively. Scores of 0–3, 4–7 and 8–11 on the Agency for Healthcare Research and Quality indicated low-, moderate- and high-quality literature, respectively.
## Statistical analysis
The RRs and HRs used in this meta-analysis were assumed to be roughly equivalent to ORs. Heterogeneity was assessed using the I2 statistic [20], and I2 > $50\%$ indicated a high degree of heterogeneity between the studies. Depending on the degree of heterogeneity, we either used fixed-effects or random-effects models to construct ORs and $95\%$ CIs. ( I2 < 50, fixed-effects model; I2 ≥ 50, random-effects model). After excluding each article from the overall analysis, a sensitivity analysis was conducted to determine how each article contributed to the overall composite result. To evaluate publication bias, Egger’s and Begg’s tests were used [21, 22]. $p \leq 0.05$ was regarded as possibly biased due to publication.
In the subgroup analysis, the following factors were stratified: [1] study design (cross-sectional or case–control); [2] dietary assessment tools [food frequency questionnaire (FFQ) or dietary recall]; [3] regions (Europe or Asia); [4] sex (men or women); [5] BMI (18.5–24.9 kg/m2, ≥ 25 kg/m2, or ≥ 30 kg/m2); [6] types of exposure (milk, yogurt, cheese, or ice cream); and [7] NAFLD diagnostic criteria (CAP, ultrasonography, MRI, or NAFLD-related scores). Meta-regression analysis was conducted to determine possible associations between the above factors. Stata 17.0 software (Stata Corp, College Station, TX, United States) was used for the statistical analysis. In addition, the p value was two-tailed, with a value < 0.05 considered statistically significant.
## Literature search
A flowchart depicting the study selection process is shown in Figure 1. According to PubMed, Web of Science, and Scopus, 4,634 articles were identified. We excluded 310 articles that contained duplicate content and 3,812 articles for which the titles and abstracts did not match. The full text of the remaining 512 articles were read, and 498 were excluded because they were animal studies ($$n = 385$$), reviews or editorials ($$n = 101$$), or their data could not be extracted ($$n = 12$$). Ultimately, 14 studies (including seven cross-sectional studies, one cohort study and six case–control studies), including 25 estimates, met the inclusion criteria.
**Figure 1:** *The flowchart of the study inclusion process.*
## Study characteristics
Table 1 summarizes the characteristics of the study. The inclusion criteria were met by a total of 14 studies with 51,476 participants (33,159 in seven cross-sectional studies, 5,171 in one cohort study and 13,146 in six case–control studies) and 14,546 cases of NAFLD (9,640 in seven cross-sectional studies, 1,799 in one cohort study and 3,107 in six case–control studies). There was a wide range of publication years, ranging from 2015 to 2022. Among them, four studies were from Europe and nine from Asia. The results were divided into different subgroups based on exposure type, including milk, yogurt, cheese, and ice cream.
**Table 1**
| References | Country | Year | Study design | Sample size | Number of cases | BMI | Age | NAFLD diagnostic criteria | Dietary assessment tool | Exposures | OR/HR (95% CI) | Adjusted covariates |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Chan et al. (23) | China | 2015.0 | Cross-sectional | 797.0 | 220.0 | 25.5 ± 3.5 | 51.0 + 9.3 | MRI | FFQ | Milk and products | 1.53 (0.94–2.49) | Age, sex, BMI, current smoker status, current drinker status, central obesity, triglyceride >1.7 mmol/l, reduced HDL-cholesterol, hypertension, impaired fasting glucose, diabetes, and the PNPLA3 genotypes |
| Chiu et al. (24) | China | 2018.0 | Cross-sectional | 3400.0 | 1911.0 | 22.9 ± 3.0 | 55 | Ultrasonography | FFQ | Dairy | 1.02 (0.92–1.14) | Age, gender, education, history of smoking, history of alcohol drinking, total energy intake, vegetarian diet, and BMI |
| Charatcharoenwitth-aya et al. (25) | Switzer-land | 2021.0 | Cross-sectional | 252.0 | 41.0 | 28.8 ± 4.5 | 40.9 ± 10.5 | CAP | A food diary for seven consecutive days | Full-fat dairy | 0.42 (0.18–0.99) | Age, sex, healthcare profession, and daily calorie intake |
| Hao et al. (26) | China | 2021.0 | Cross-sectional | 4049.0 | 2602.0 | 26.9 ± 2.89 | 53.8 ± 7.46 | Ultrasonography | A self-questionnaire | Milk and products | 0.55 (0.43–0.70) | Age |
| Watzinger et al. (27) | German | 2020.0 | Cross-sectional | 136.0 | 72.0 | 32.5 ± 3.6 | 50.1 ± 8.0 | MRI | FFQ | High-fat dairy | 0.32 (0.10–0.98) | Age, sex, WC, calorie intake, and the ratio of energy intake/total energy expenditure |
| | | | | | | | | | | High-fat cheese | 0.55 (0.17–1.76) | |
| Mirizzi et al. (11) | Italy | 2019.0 | Cross-sectional | 136.0 | 136.0 | 33.41 ± 4.47 | 49.5 ± 10.1 | CAP | FFQ | Milk and Yogurt | 0.99 (0.99–1.00) | Age, sex, and energy intake |
| | | | | | | | | | | Sweet Products Milk-Based | 1.00 (0.97–1.03) | |
| | | | | | | | | | | Aged Cheeses | 0.98 (0.97–1.00) | |
| | | | | | | | | | | Cheeses | 0.99 (0.98–1.02) | |
| | | | | | | | | | | Local Aged Cheeses | 0.85 (0.74–0.98) | |
| | | | | | | | | | | Sweet Milk-Nowinter Icecream | 1.11 (1.01–1.21) | |
| | | | | | | | | | | Industrial Aged Cheeses | 1.17 (1.01–1.35) | |
| | | | | | | | | | | Winter Ice cream | 0.65 (0.47–0.89) | |
| Zhang et al. (28) | Tianjin, China | 2019.0 | Cross-sectional | 24389.0 | 4658.0 | 27.8 ± 0.1 | 42 | Ultrasonography | FFQ | Yogurt | 0.86 (0.76–0.98) | Age, sex, BMI, smoking status, alcohol drinking status, education level, working status, household income, physical activity, family history of disease, total energy intake, carbohydrate intake, total fat intake, EPA + DHA intake, soft drinks intake, vegetables intake, fruits intake, sweet foods intake, milk intake; hypertension, diabetes, hyperlipidemia, and WBC count |
| Kalafati et al. (15) | Italy | 2019.0 | Case–control | 351.0 | 134.0 | 31.11 ± 4.72 | 50.3 ± 10.5 | Ultrasonography | FFQ | Cheese full fat | 1.19 (1.00–1.42) | Age, gender, BMI, energy intake for the food groups, and pack-years |
| Ebrahimi et al. (7) | Iran | 2022.0 | Case–control | 243.0 | 121.0 | 30.5 ± 5.0 | 42.9 ± 11.5 | Ultrasonography | FFQ | Dairies | 1.61 (0.71–3.63) | Age, sex, energy intake, physical activity, marital status, education, supplement use, drug use, smoking status, BMI, and DDS |
| Sun et al. (10) | China | 2022.0 | Case–control | 11888.0 | 2529.0 | 23.7 ± 2.9 | 51.1 ± 14.6 | Ultrasonography | FFQ | Dairy | 0.85 (0.75–0.96) | / |
| Tutunchi et al. (9) | Iran | 2021.0 | Case–control | 210.0 | 105.0 | 33.8 ± 7.7 | 45.6 ± 9.1 | Ultrasonography | A three-day food diary | Low-fat dairy | 0.46 (0.16–1.32) | / |
| | | | | | | | | | | High-fat dairy | 2.01 (0.81–3.11) | |
| Dehghanseresht et al. (29) | Iran | 2020.0 | Case–control | 244.0 | 122.0 | 42.95 ± 11.46 | 30.53 ± 5.04 | Ultrasonography | FFQ | Dairy | 0.23 (0.09–0.58) | Age, sex, BMI, energy intake, smoking, educational status, and physical activity |
| Pasdar et al. (8) | Iran | 2019.0 | Case–control | 210.0 | 96.0 | 30.49 ± 5.89 | 43.82 ± 8.79 | Ultrasonography | FFQ | Dairy | 1.75 (0.84–3.67) | Age, sex, and physical activity |
| Lee et al. (30) | Korean | 2021.0 | Cohort | 5171.0 | 1799.0 | 23.5 ± 2.5 | 40–49 | NAFLD liver fat score | FFQ | Milk | Men: 0.85 (0.73–0.99) Women ≥50 years: 0.79 (0.66–0.95) Women <50 years: 0.94 (0.76–1.16) | Age, BMI, physical activity, smoking status, current drinker, daily protein intake per weight, daily carbohydrate intake per weight, daily calcium intake, daily vitamin E intake, MBP, plasma glucose level, serum total cholesterol level, and serum ALT level |
| | | | | | | | | | | Yogurt | Men: 0.91 (0.78–1.06) Women ≥50 years: 0.86 (0.73–1.01) Women <50 years: 0.97 (0.79–1.18) | |
The calibration of the included studies is reported in Supplementary Tables 2 and 3. All studies were of appropriate quality, based on two scales. The quality scores of the cohort studies and case–control studies ranged from seven to eight, while those of the cross-sectional studies ranged from seven to nine.
## Association between dairy products consumption and NAFLD
In total, 14 articles (including 25 effect groups) investigated the associations between the consumption of dairy products and NAFLD; the results of the pooled analysis are shown in Figure 2. The forest plot showed that dairy product consumption was associated with a lower likelihood of NAFLD (OR = 0.97, $95\%$ CI = 0.94–0.99).
**Figure 2:** *Overall pooled analysis of association between dairy products and non-alcoholic fatty liver disease.*
## Subgroup analysis
Several subgroup analysis were also performed (Table 2). In accordance with, we divided the patients into two subgroups according to the design of each study (cross-sectional and case–control). The cross-sectional studies (OR = 0.98, $95\%$ CI = 0.96–1.01) and case–control studies (OR = 1.02, $95\%$ CI = 0.73–1.42) showed no significant association between dairy product consumption and NAFLD. Subgroup analysis stratified by sex demonstrated that dairy product consumption was associated with a lower likelihood of NAFLD (OR = 0.98, $95\%$ CI = 0.95–0.99) in women, but not in men (OR = 0.83, $95\%$ CI = 0.40–1.72). Subgroup analysis stratified by BMI demonstrated that dairy product consumption was associated with a lower likelihood of NAFLD in patients with a BMI of 18.5–24.9 kg/m2 (OR = 0.88, $95\%$ CI = 0.83–0.93), but not in patients with a BMI ≥ 25 kg/m2 (OR = 0.78, $95\%$ CI = 0.52–1.16) and a BMI ≥ 30 kg/m2 (OR = 1.00, $95\%$ CI = 0.97–1.02). Based on the type of exposure, we divided all studies into four subgroups (milk, yogurt, cheese, or ice cream). The yogurt consumption (OR = 0.89, $95\%$ CI = 0.82–0.96) and milk consumption (OR = 0.94, $95\%$ CI = 0.87–1.00) was associated with a lower likelihood of NAFLD. However, cheese (OR = 0.99, $95\%$ CI = 0.96–1.03), and ice cream (OR = 0.87, $95\%$ CI = 0.52–1.47) were not associated with NAFLD. Subgroup analysis stratified by exposure assessment showed that consumption of dairy products assessed using FFQ was inversely associated with NAFLD (OR = 0.97, $95\%$ CI = 0.94–0.99), while no significant association was found when exposure was assessed using dietary recall (OR = 0.76, $95\%$ CI = 0.25–2.29). Subgroup analysis stratified by study region showed that dairy product intake was associated with a lower likelihood of NAFLD in Asian populations (OR = 0.90, $95\%$ CI = 0.77–0.97), but not in European populations (OR = 0.99, $95\%$ CI = 0.97–1.01). Finally, subgroup analysis stratified by assessments of NAFLD showed that dairy product intake was inversely associated with NAFLD when NAFLD-related scores (OR = 0.88, $95\%$ CI = 0.83–0.95) were used; however, no significant association was found when NAFLD was assessed using ultrasonography (OR = 0.91, $95\%$ CI = 0.73–1.13), MRI (OR = 0.72, $95\%$ CI = 0.26–2.02), and CAP (OR = 0.99, $95\%$ CI = 0.97–1.01).
**Table 2**
| Subgroup | Number of effects | OR (95% CI) | I2 statistics (%) | Meta-regression (P) |
| --- | --- | --- | --- | --- |
| Study design | | | | 0.26 |
| Cross-sectional | 15.0 | 0.98 (0.96, 1.01) | 77.60% | |
| Case–control | 7.0 | 1.02 (0.73, 1.42) | 79.50% | |
| Type of exposure | | | | 0.47 |
| Milk | 5.0 | 0.94 (0.87, 1.00) | 88.30% | |
| Yogurt | 2.0 | 0.89 (0.82, 0.96) | 0.00% | |
| Cheese | 7.0 | 0.99 (0.96, 1.03) | 62.70% | |
| Ice cream | 2.0 | 0.87 (0.52, 1.47) | 90.00% | |
| Region | | | | 0.04 |
| Europe | 11.0 | 0.99 (0.97, 1.01) | 70.70% | |
| Asia | 12.0 | 0.90 (0.77, 0.97) | 66.80% | |
| Sex | | | | 0.43 |
| Men | 20.0 | 0.83 (0.40, 1.72) | 78.50% | |
| Women | 5.0 | 0.98 (0.95, 0.99) | 80.30% | |
| BMI | | | | 0.01 |
| 18.5–24.9 | 5.0 | 0.88 (0.83, 0.93) | 0.00% | |
| ≥ 25 | 4.0 | 0.78 (0.52, 1.16) | 84.30% | |
| ≥ 30 | 16.0 | 1.00 (0.97, 1.02) | 71.70% | |
| Exposure assessment | | | | 0.91 |
| FFQ | 22.0 | 0.97 (0.94, 0.99) | 77.60% | |
| Dietary recall | 3.0 | 0.76 (0.25, 2.29) | 80.20% | |
| NAFLD diagnostic criteria | | | | 0.03 |
| CAP | 9.0 | 0.99 (0.97, 1.01) | 71.90% | |
| Ultrasonography | 10.0 | 0.91 (0.73, 1.13) | 78.60% | |
| MRI | 3.0 | 0.72 (0.26, 2.02) | 73.70% | |
| Scorea | 3.0 | 0.88 (0.83, 0.95) | 0.00% | |
## Sensitivity analysis and meta-regression
When successive articles were excluded from the sensitivity analysis, the results remained unchanged (Supplementary Figure 1). Meta-regression analysis (Table 2) showed that study design ($$p \leq 0.26$$), type of exposure ($$p \leq 0.47$$), sex ($$p \leq 0.43$$), and exposure assessment ($$p \leq 0.91$$) were not associated with heterogeneity, while region, NAFLD diagnostic criteria and BMI had a significant effect on heterogeneity ($p \leq 0.05$).
## Publication bias
Publication bias was evaluated using Egger’s test ($p \leq 0.05$), Begg’s test ($p \leq 0.05$), and funnel plots (Supplementary Figure 2), and these analysis revealed no publication bias.
## Discussion
The results showed that a lower likelihood of NAFLD was associated with dairy product consumption. However, when the types of dairy products were categorized, yogurt consumption and milk consumption were found to be significant among the factors associated with a lower likelihood of NAFLD. Subgroup analysis suggested that dairy product consumption was linked to a lower likelihood of NAFLD in subgroups of Asian populations, women, patients diagnosed using NAFLD-related scores, patients with a BMI of 18.5–24.9 kg/m2, and dairy product intake assessment using FFQ. In contrast, no significant associations were observed in the other subgroups. To explore possible sources of (expected) heterogeneity in the studies, we performed subgroup and meta-regression analysis, used sensitivity analysis to check the robustness of the results, and measured publication bias. Significant heterogeneity was present in most analysis, and this significant heterogeneity may reflect differences in the characteristics of the study population (region and BMI) and in the diagnostic approach to NAFLD. In a previous study, European populations were identified as a possible source of heterogeneity [31], which may be related to regional differences in the inclusion of participants. In addition, there were other factors such as study design and sample size that may also affect heterogeneity.
A previous study evaluated the association between dairy product intake and NAFLD [16], finding no significant correlation in cross-sectional studies. However, based on the results of case–control studies, dairy consumption was found to be positively associated with NAFLD. Despite this, as a result of the small sample size, the results of this previous study were limited. Moreover, the associations between dairy products and NAFLD in the different subgroups were not explored. Therefore, it was not possible to conclude from this previous study that dairy products caused NAFLD in a comprehensive manner. In this study, compared to previous meta-analysis, firstly, we conducted an updated search with more stringent inclusion criteria; whereas the previous meta-analysis was conducted in 2019, we conducted a thorough search for literature published before 5 January 2023. Secondly, ten additional articles were included in comparison to the previous article. Third, we included a more comprehensive range of dairy product types (e.g., ice cream) Fourth, we performed subgroup analysis and meta-regression based on study design, region, sex, BMI, type of exposure, NAFLD diagnostic criteria and exposure assessment tools. Fifth, we also performed a sensitivity analysis simultaneously. An analysis of 96 patients revealed that low-fat dairy intake was negatively correlated with NAFLD [32], a finding supported by a prospective cohort study of 101,510 Chinese participants demonstrating lower likelihood of NAFLD risk among consumers of dairy products [10]. Several possible mechanisms could explain why dairy product intake is negatively associated with NAFLD. First, dairy products offer crucial dietary micronutrients such as calcium, iron, and vitamins. [ 33]. A higher intake of dairy products may help prevent skeletal sarcopenia, an established risk factor for NAFLD [34]. Second, diabetes-related insulin resistance is a major cause of NAFLD. An epidemiological study showed that dairy intake is negatively correlated with insulin resistance [35]. Third, dairy consumption, particularly low-fat dairy products, had positive benefits on insulin resistance, waist circumference, and body weight, according to a recent meta-analysis [36], which was beneficial for NAFLD.
Our results found a negative association between yoghurt consumption and NAFLD. In line with our findings, a cross-sectional study of 24,389 adults found that participants who consumed yoghurt more than four times a week were less likely to develop NAFLD. Other similar studies have shown that yogurt consumption may have a preventive role in the development of other chronic diseases [28]. The following aspects may partially explain the observed results. First, probiotics are abundant in yogurt. Probiotics may prevent the onset of NAFLD by inhibiting the lipopolysaccharide and hepatic toll-like receptor 4 signaling pathway, according to animal studies [37]. Second, previous research has shown that probiotics from yogurt have anti-inflammatory, antioxidant, and immune-modulating properties, which may explain why people who consume more yogurt have a reduced prevalence of NAFLD [38, 39]. Finally, yogurt is one of the most nutrient-dense foods and is high in proteins, vitamins, and minerals (such as calcium, magnesium, and potassium). There is proof that consuming more calcium, which is present in yogurt, results in more fat being burned throughout the body [40].
Furthermore, our study found that dairy product intake was inversely associated with NAFLD in Asian but not European populations, which may be due to differences in the epidemiological features of NAFLD and the dairy consumption habits = in different regions. For example, cheese, which has a high saturated fatty acid content, is much more popular in European populations than in Asian populations. We also found a clear positive correlation between dairy intake and NAFLD in women, but not in men. Previous studies [41] have shown that dairy product consumption was more protective against NAFLD in women than in men. This may be because estradiol exerts a protective effect against liver injury by inhibiting lipid accumulation and liver fibrosis [42]. Lifestyle changed, including dietary habits and physical activity, should be the first line of treatment for NAFLD. Dietary modification therefore played a key role, as diets rich in carbohydrates, especially those high in fructose, were a major cause of obesity, insulin resistance and the development of NAFLD [43]. Following the Mediterranean diet can reduce liver fat, even without weight loss, and it is the most recommended dietary pattern for NAFLD. The Mediterranean diet is characterized by a reduced intake of carbohydrates, especially sugar and refined carbohydrates, and an increased intake of monounsaturated and omega-3 fatty acids [44]. The Mediterranean diet has also been found to improve metabolism and lower the risk of diabetes [45] and cardiovascular disease [46], two outcomes that are closely associated in people with NAFLD.
This study had several advantages. First, we conducted a comprehensive systematic search and applied comprehensive subgroup analysis to assess the associations between dairy consumption, including that of various dairy products, and NAFLD. Second, sensitivity and meta-regression analysis were performed to check the robustness of the results and explore potential heterogeneity.
However, the study also had several limitations. First, few articles were included in this study, and data on American populations were especially lacking. Second, all the included studies were observational, which has inherent limitations; for example, causality was uncertain. Third, the majority of patients with NAFLD in the included studies were diagnosed using ultrasonography, which is not the gold standard diagnostic method. However, liver biopsy is only performed when clinically indicated and is neither practical nor ethical for large epidemiological studies. Fourth, doogh is an important dairy product in parts of Asia, but we found no evidence for it in the available evidence, so further research is needed to explore this topic.
## Conclusion
In conclusion, a lower likelihood of NAFLD was associated with dairy product consumption, particularly milk consumption and yogurt consumption. Consumption of dairy products could help prevent the development of non-alcoholic fatty liver disease. However, given the few studies included, the results need further confirmation by more cohort studies and randomized controlled trials.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
WD and HL: conceptualization, formal analysis, visualization, and writing—original draft. TZ, QC, YZ, and CG: writing—review and editing. YX: conceptualization, resources, writing—review and editing, supervision, and funding acquisition. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the National Natural Science Foundation of China (grant number: 81903302), the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (grant number: YESS20200151), and the 345 Talent Project of ShengJing Hospital of China Medical University (grant number: M0294).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1119118/full#supplementary-material
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|
---
title: Salivary IgA subtypes as novel disease biomarkers in systemic lupus erythematosus
authors:
- Sandra Romero-Ramírez
- Víctor A. Sosa-Hernández
- Rodrigo Cervantes-Díaz
- Daniel A. Carrillo-Vázquez
- David E. Meza-Sánchez
- Carlos Núñez-Álvarez
- Jiram Torres-Ruiz
- Diana Gómez-Martín
- José L. Maravillas-Montero
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9992540
doi: 10.3389/fimmu.2023.1080154
license: CC BY 4.0
---
# Salivary IgA subtypes as novel disease biomarkers in systemic lupus erythematosus
## Abstract
### Introduction
Immunoglobulin A (IgA) is the main antibody isotype in body fluids such as tears, intestinal mucous, colostrum, and saliva. There are two subtypes of IgA in humans: IgA1, mainly present in blood and mucosal sites, and IgA2, preferentially expressed in mucosal sites like the colon. In clinical practice, immunoglobulins are typically measured in venous or capillary blood; however, alternative samples, including saliva, are now being considered, given their non-invasive and easy collection nature. Several autoimmune diseases have been related to diverse abnormalities in oral mucosal immunity, such as rheumatoid arthritis, Sjogren’s syndrome, and systemic lupus erythematosus (SLE).
### Methods
We decided to evaluate the levels of both IgA subtypes in the saliva of SLE patients. A light chain capture-based ELISA measured specific IgA1 and IgA2 levels in a cohort of SLE patients compared with age and gender-matched healthy volunteers.
### Results
Surprisingly, our results indicated that in the saliva of SLE patients, total IgA and IgA1 subtype were significantly elevated; we also found that salivary IgA levels, particularly IgA2, positively correlate with anti-dsDNA IgG antibody titers. Strikingly, we also detected the presence of salivary anti-nucleosome IgA antibodies in SLE patients, a feature not previously reported elsewhere.
### Conclusions
According to our results and upon necessary validation, IgA characterization in saliva could represent a potentially helpful tool in the clinical care of SLE patients with the advantage of being a more straightforward, faster, and safer method than manipulating blood samples.
## Introduction
Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a deleterious immune response affecting several organs and tissues [1], challenging diagnostic and treatment approaches. SLE is typically defined by the presence of high titers of circulating autoantibodies [2] with abnormal numbers of T and B lymphocytes [3]. Currently, the diagnostic criteria for SLE are predominantly based on the presence of clinical manifestations as well as the results of laboratory tests, such as low levels of C3/C4 complement components and the presence of anti-dsDNA or anti-Sm antibodies [4]. However, these currently available markers for SLE diagnosis remain suboptimal regarding either their sensitivity or specificity [5]. Consequently, more robust biomarkers for SLE are still needed to accurately diagnose patients, monitor disease progression and treatment effectiveness or predict future flares.
The common consensus in systemic lupus erythematosus (SLE) is that there is a general breakdown in lymphocyte tolerance. Consequently, autoreactive B cells are usually considered one of the central effector cell subsets responsible for maintaining inflammatory status in patients. Thus, B cells are considered among the main therapeutic targets of SLE treatment. Furthermore, B cell-targeted therapies have been applied to treat several autoimmune diseases such as pemphigus, multiple sclerosis, ANCA-associated vasculitis, and rheumatoid arthritis; therefore, hypogammaglobulinemia is recognized as a potential complication of these treatments [6, 7].
The European League Against Rheumatism (EULAR) and The American Academy of Asthma, Allergy, and Immunology (AAAAI) guidelines recommend assessing baseline immune function by testing serum immunoglobulins before or even after rituximab treatment in autoimmune disease (8–11). Nevertheless, only a small number of reports have emerged regarding aberrant immunoglobulin levels prior to starting B cell-depleting approaches in most autoimmune disorders; for example, polyclonal hypergammaglobulinemia is well documented in SLE. However, hypogammaglobulinemia has also been lupus-associated in patients exhibiting selective-isotype deficiencies [12].
Humoral immunity is essentially assessed by measuring titers of IgG-dominated serum antibodies, thus neglecting the contribution of IgA as the major immunoglobulin isotype in humans. To dimension that, it has been documented that the daily IgA production rates around 70 mg/kg of body weight exceed that of all other antibody isotypes combined (13–15). Moreover, IgA is the predominant antibody isotype in external secretions, including tears, intestinal mucous, colostrum, milk, and saliva (13–15), thus being well-known as the main mucosal immunoglobulin, playing a fundamental role as an immunological barrier that recognizes and excludes human pathogens (13–15). Beyond that, there are two human IgA subclasses: IgA1 and IgA2, both ubiquitously present but displaying a differential distribution in the body: IgA1 is dominant in serum or saliva, while IgA2 is most abundant in some other secretion fluids and the colon (16–20). In all body fluids, both IgA1 and IgA2 are mainly present as dimeric secretory IgA (SIgA) [15].
Numerous reports have documented a link between alterations in SIgA levels and inflammatory or autoimmune entities, nearly all using saliva for sampling purposes. In this way, patients with SLE, type I diabetes, oral lichen planus, overweight/obesity, oral submucous fibrosis, ankylosing spondylitis, rheumatoid arthritis, mixed connective tissue disease, and Sjögren’s syndrome have displayed significant higher total or antigen-specific salivary SIgA content than their healthy individuals’ counterparts (21–27). Although showing differences in SIgA titers, most of these studies have excluded salivary IgA1 and IgA2 subtypes assessment.
Information regarding the levels of salivary IgA subclasses in SLE is still lacking across existing reports. Most of the available efforts have been limited to the measurement of total IgA either in serum or saliva. Consequently, this study was undertaken to investigate the possible alterations in salivary IgA1 and IgA2 in a small cohort of patients with SLE and to characterize their association with disease features.
## Patients and Healthy individuals
The study population comprised 14 healthy individuals and 38 SLE subjects divided into the following groups: 27 inactive lupus (SLEDAI ≤6), 11 with active disease (SLEDAI >6) were recruited from the department of Immunology and Rheumatology of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán. All SLE patients fulfilled ACR/SLICC 2012 classification criteria [28], and disease activity was addressed by the SLE disease activity index (SLEDAI). We excluded subjects with ongoing acute or chronic infections (i.e., HIV or viral hepatitis), pregnancy, and patients with a diagnosis of other concomitant autoimmune diseases except for antiphospholipid (aPL) syndrome. None of the study participants received any B cell-depleting or other biological therapies. This study was approved by the Institutional Ethics and Research Committees of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (Ref. 2306). The patients/participants provided their written informed consent to participate prior to inclusion in the study. Demographic and Clinical characteristics of the study population are depicted in Table 1.
**Table 1**
| Features | Inactive SLE | Active SLE | P value |
| --- | --- | --- | --- |
| Gender - # (%) | Gender - # (%) | Gender - # (%) | Gender - # (%) |
| Male | 3 (11) | 2 (18) | |
| Female | 24 (89) | 9 (82) | |
| Age in years-median | 32 (20-65) | 34 (21-46) | 0.31 |
| Disease Activity-median | Disease Activity-median | Disease Activity-median | Disease Activity-median |
| SLEDAI score (min-max) | 2 (0-4) | 10 (6-25) | <0.0001 |
| Laboratory Values- median (IQR) | Laboratory Values- median (IQR) | Laboratory Values- median (IQR) | Laboratory Values- median (IQR) |
| Leukocytes (%) | 5.4 (4.4-6.2) | 5.9 (2.7-7.1) | 0.86 |
| Lymphocytes (%) | 29.3 (17.6-37.6) | 13.6 (11-20) | 0.0095 |
| Erytrocytes (%) | 4.7 (4.3-5.4) | 4.0 (2.5-5.1) | 0.3 |
| Monocytes (%) | 7.2 (6.4-11) | 7.8 (6-13.9) | 0.2 |
| Neutrophils (%) | 64.1 (53.4-69.6) | 71.5 (67.1-78.5) | 0.06 |
| Eosinophils (%) | 1.8 (0.95-3) | 0.9 (0-1.9) | 0.36 |
| Hemoglobin (g/dL) | 14.1 (13.5-14.9) | 10.9 (7.4-15.6) | 0.3 |
| Hematocrit (%) | 41.7 (40.6-47.9) | 33.5 (22.6-46.4) | 0.28 |
| Platelets (x109/L) | 262 (219-274) | 193.5 (135-304) | 0.24 |
| Glucose | 89 (84-93) | 92 (83-97) | 0.87 |
| Aspartate aminotransferase (U/L) | 21 (15-24) | 21 (19-24) | 0.21 |
| Alanine aminotransferase (U/L) | 18 (11.7-25) | 19 (8-25) | 0.21 |
| C-reactive protein (mg/dL) | 0.14 (0.06-0.26) | 0.5 (0.05-1.2) | 0.02 |
| Creatinine (mg/dL) | 0.78 (0.62-0.81) | 0.82 (0.6-0.9)) | 0.1 |
| C3 (mg/dL) | 105 (91-114) | 92 (58-97) | 0.053 |
| C4 (mg/dL) | 19 (11-23) | 8 (8-14) | 0.0083 |
| Anti-dsDNA (UI/mL) | 11.2 (6.5-139) | 90.4 (11.2-212) | 0.043 |
| Type of disease activity - # (%) | Type of disease activity - # (%) | Type of disease activity - # (%) | Type of disease activity - # (%) |
| Mucocutaneous | 1 (3.7) | 2 (18.1) | |
| Joint | 0 (0) | 2 (18.1) | |
| Serous | 0 (0) | 1 (9) | |
| Renal | 1 (3.7) | 5 (45.4) | |
| Hematological | 1 (3.7) | 1 (9) | |
| Nervous system | 0 (0) | 0 (0) | |
| Constitutional | 0 (0) | 2 (18.1) | |
| Treatments - # (%) | Treatments - # (%) | Treatments - # (%) | Treatments - # (%) |
| Mycophenolate Mofetil | 10 (30) | 3 (27.2) | |
| Cyclophosphamide | 1 (3.7) | 1 (9) | |
| Prednisone | 14 (51.8) | 8 (72.7) | |
| Hydroxychloroquine | 17 (62.9) | 4 (36.3) | |
| Methotrexate | 7 (25.9) | 1 (9) | |
| Azathioprine | 3 (11.1) | 3 (27.2) | |
## Saliva sampling
All recruited subjects briefly rinsed their mouth with purified water before were asked to deposit a total volume of around 2 mL of unstimulated whole saliva by passive drooling (letting the saliva drop) into a sterile 15 mL polyethylene centrifuge tube. Immediately after collection, the tubes were transported to the laboratory into an ice bucket and were centrifuged for 15 min at 10,000 g and 4°C to remove debris and cells. The supernatants were then separated and added with 2x protease inhibitors (Pierce, Protease Inhibitor Tablets, Thermo Scientific) and finally stored at -70°C until used. All samples (from patients and healthy donors) were treated with this same procedure, not delaying more than 5 min from collection to centrifugation to minimize the risk of protein degradation.
## IgA1 and IgA2 quantification in saliva
Detection of IgA1 and IgA2 in saliva was performed by developing a sandwich ELISA previously reported [29]. We used flat bottom microtiter plates (Thermo Scientific) that were coated overnight at 4°C with 1 µg/mL of anti-human Ig light chain antibody in 0.2 M Na2CO3/NaHCO3, pH: 9.4. Blocking was performed by using phosphate-buffered saline 1x containing $0.05\%$ Tween-20 at room temperature for 2 h. Fifty microliters of saliva samples (in triplicate) and standard samples were pipetted into the microtiter wells and incubated for 2 h at 37°C. Among the various incubation steps, the wells were washed five times with phosphate-buffered saline 1x containing $0.05\%$ Tween-20. Then, we added biotinylated anti-human IgA1 antibody (monoclonal mouse antibody, Abcam, Cat. Num. ab99796) diluted 1:2000 or biotinylated anti-human IgA2 antibody (monoclonal mouse antibody, Abcam, Cat. Num. ab128731) diluted 1:1000 in phosphate-buffered saline 1x containing $0.05\%$ Tween-20 and incubated for 1 h at 37°C. After washing, we added Streptavidin-HRP diluted 1:5000 in phosphate-buffered saline 1x and incubated for 1h at 37°C. Then we washed six times with phosphate-buffered saline 1x containing $0.5\%$ Tween-20. After a final wash, 50µL of the substrate solution, tetramethylbenzidine, was added, and the plates were incubated for 5min (to IgA1) and 10 min (to IgA2), and we used to stop enzyme reaction 50µL of 1N HCl. The absorbance was measured at 450nm using a microplate spectrophotometer. When indicated, the total IgA (tIgA) amount was obtained as the sum of measured IgA1 and IgA2 concentrations.
## Detection of salivary IgA anti-nucleosome and IgA anti-double stranded DNA antibodies
Salivary IgA anti-dsDNA antibodies and IgA anti-nucleosome were detected by ELISA kits QUANTA Lite ® HA dsDNA and HA Nucleosome, respectively, employing an HRP anti-tIgA antibody for detection and following manufacturer instructions. The plates were then read at 450nm on a Bio-Rad xMark spectrophotometer.
## Statistical analysis
As indicated, differences between groups were analyzed using two-way ANOVA followed by Tukey’s post hoc test, Kruskal-Wallis tests followed by Dunn’s multiple comparisons tests, or Mann-Whitney U tests. The correlation of IgA1, IgA2, tIgA, laboratory, and clinical features, were evaluated by calculating the Spearman’s rank correlation coefficient. A p value less than 0.05 was considered statistically significant. ROC curve analysis was performed to distinguish IgA between healthy individuals and SLE patients or between inactive and active patients. All statistical analysis was performed using GraphPad Prism 9 software. Additionally, sensibility and specificity parameters for ROC curves were assessed using MedCalc 20.215 software.
## IgA1 is the predominant subtype in SLE saliva
We evaluated the levels of IgA antibodies in saliva, considering that the levels of the immunoglobulin subtypes vary in the different mucosal surfaces [30]. A total of 52 individuals: 38 patients with SLE and 14 healthy individuals, were then evaluated to assess their concentration of SIgA salivary subtypes. As shown in Figure 1, the most dominant isotype in saliva was IgA1, but most importantly, healthy individuals showed significantly lower levels of IgA1 and IgA2 than patients with SLE.
**Figure 1:** *Differences in salivary IgA subtype levels in healthy individuals and SLE patients. Data were analyzed by an ordinary two-way ANOVA followed by Tukey’s post hoc test. ***p<0.001, ****p<0.0001.*
## SLE patients exhibit higher levels of salivary SIgA subtypes than healthy controls
Given the clinical differences between inactive and active patients, we segregated our patients into these two groups as described in Methods. Then we analyzed their salivary IgA1, IgA2, and total IgA (tIgA) levels, as shown in Figure 2. Interestingly, both IgA subtypes and tIgA are significantly elevated in inactive and active patients compared to healthy individuals. Nonetheless, only increased tIgA levels were significantly different according to disease activity.
**Figure 2:** *Increase of salivary IgA subtypes in inactive and active SLE patients. (A) Levels of IgA1 between healthy and inactive and active SLE patients. (B) Levels of IgA2 between healthy and inactive and active SLE patients. (C) Levels of tIgA (add of IgA1+IgA2) between healthy and inactive and active SLE patients. Data were assessed by Kruskall-Wallis tests followed by Dunn’s multiple comparisons tests. ns, not statistically significant, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.*
## Increasing concentrations in salivary subtypes of IgA correlate with titers of circulating anti-dsDNA antibodies in SLE
Trying to assess the importance of salivary IgA1 and IgA2 levels in SLE, we performed correlation analyses between these immunoglobulin isotype concentrations in saliva and different clinical and laboratory features of SLE activity (Figure 3A).
**Figure 3:** *Salivary IgA1, IgA2, and tIgA correlated with different clinical variables in SLE. (A) Correlation matrix showing a graphical representation of calculations between IgA1, IgA2, tIgA, and laboratory and clinical variables of SLE patients. The underlying color scale indicates Spearman’s coefficient values. Correlation analysis between antibodies anti-dsDNA with salivary IgA1 (B), salivary IgA2 (C), and salivary tIgA (D). Red slopes present a positive correlation. Correlations were assessed by calculating Spearman’s rank correlation coefficient (r) and p values (p) depicted. ***p<0.001, ****p<0.0001.*
Remarkably, we only found positive and highly significant correlations between circulating anti-dsDNA with the salivary concentration of IgA1 (Figure 3B), IgA2 (Figure 3C), and tIgA (Figure 3D) in patients with SLE.
## Anti-nucleosome IgA autoantibodies are present and increased in the saliva of patients with SLE
Since the clinical diagnosis of SLE usually involves the detection of different blood-circulating autoantibodies, including levels of anti-dsDNA (double-stranded DNA) IgG and anti-nucleosome IgG in serum, we become interested in finding analog autoantibodies of IgA isotype in other types of samples such as saliva. Therefore, we evaluate the presence of salivary IgA-ANAs with nuclear or cytoplasmic staining patterns by indirect immunofluorescence. As shown in Supplementary Fig. 1, we detected typical autoimmune recognition patterns over HEp-2 cells when an anti-IgA was used as a detection antibody.
To address if salivary IgA fraction recognizes specific nuclear antigens like nucleosomes or dsDNA in our cohort, we performed commercially available ELISA assays employing an anti-human IgA detection antibody instead of the regular anti-human IgG used. As depicted by Figure 4A, we did not detect differences in salivary anti-dsDNA IgA antibodies between samples of healthy donors and SLE patients. In contrast, when IgA anti-nucleosome were measured, we found significantly higher titers of these autoantibodies in SLE patients compared with healthy individuals (Figure 4B). To verify if IgA anti-nuclear antigens were also present in the circulation of these patients, we measured these same autoantibodies in their serum samples. As expected, we found both anti-dsDNA IgA and anti-nucleosome IgA significantly elevated in SLE patients (Supplementary Fig. 2).
**Figure 4:** *Levels of salivary IgA anti-dsDNA and salivary IgA anti-nucleosome in patients with SLE. (A) Salivary IgA anti-dsDNA in healthy individuals and SLE patients. (B) Salivary IgA anti-nucleosome in healthy individuals and SLE patients. Data were assessed by Mann-Whitney U tests. ns, not statistically significant, ****p<0.0001.*
## IgA subtypes as potential salivary biomarkers of SLE
Finally, to assess the usefulness of salivary IgA measurement as a potential SLE biomarker, we generated receiver operating characteristic (ROC) curves to determine the discriminative capacity of salivary IgA subtypes (IgA1 or IgA2) concentration in SLE patients vs. healthy donors (Figure 5A). We determined the corresponding areas under the curve (AUC), and we observed that IgA1 displayed an outstanding discriminative value (AUC of 0.855), but even IgA2 displayed a good value (AUC = 0.761).
**Figure 5:** *Receiver operator characteristics (ROC) curves of subtypes of salivary IgA for SLE discrimination and SLE activity prediction. (A) ROC curves of IgA subtypes for SLE discrimination. Data of n= 38 SLE patients and n=14 healthy individuals. (B) ROC curves of IgA subtypes for SLE activity prediction. Data of n= 27 inactive SLE patients and n=11 active SLE patients. Area under the curve (AUC) values and p values (p) are depicted.*
As mentioned, SLE diagnosis relies on composite data from clinical manifestations and biomarkers such as serum autoantibodies (anti-dsDNA or anti-nucleosome) or complement (C3/C4) levels. Interestingly, when we compared the diagnostic robustness of salivary SIgA concentrations through their displayed AUC or sensitivity at $95\%$ of specificity values with those from anti-dsDNA, anti-nucleosome, and complement levels in independent SLE cohorts reported previously [31, 32] as seen in Supplementary Table 1, these sensitivity/specificity values from SIgA are very similar to those displayed by traditional biomarkers, being IgA1 concentration the more robust.
Besides that, trying to distinguish between SLE inactive and active disease state contingent salivary IgA subclasses levels, we constructed an additional pair of ROC curves segregating these groups of patients. As shown in (Figure 5B), the discriminative capacity of both IgA subtypes in saliva was satisfactory but lower than the diagnostic approach, according to their displayed AUC values (above 0.6 in both cases).
## Discussion
*Humans* generate about 1.5 L of saliva, which is secreted by the three major types of salivary glands (parotid, submandibular, and sublingual glands) and the minor salivary glands. Among the most recognized components of this fluid, secretory IgA constitutes the predominant immunoglobulin isotype in this and other body secretions.
Since IgA is also the predominant antibody isotype produced by the human body [33], it possesses an essential role in health and several diseases. Interestingly, increases in serum IgA and IgA autoantibodies have been reported in different autoimmune disorders like Sjogren’s syndrome, rheumatoid arthritis, IgA nephropathy, inflammatory bowel disease, and SLE (34–37).
On the other hand, the evidence on salivary IgA in those autoimmune diseases is limited and mostly inconclusive (34–37). Most studies are focused on reporting individuals with low IgA levels since IgA deficiency’s mortality and morbidity rates correlate with SLE activity, mainly due to recurrent infections in these patients [38]. Conversely, only one previous report informs about significantly higher levels of salivary (total) IgA in patients with SLE compared to healthy individuals [39], a reason why we wanted to go further with this observation evaluating the IgA subclasses.
Beyond total IgA fraction, this antibody isotype consists of two subclasses in humans where their proportions vary depending on the mucosal site: in saliva, we usually detect around $60\%$ of IgA1 and close to $40\%$ of IgA2 in healthy individuals [30]; however, there was no previous data on salivary IgA subtypes in autoimmune diseases such as SLE.
Interestingly, we found that both salivary IgA1 and IgA2 are elevated in SLE patients compared to healthy individuals, being IgA1 levels significantly higher than those from IgA2. Besides that, when the SLE group was segregated into inactive and active patients, we observed a clear trend towards increased IgA subtype levels in active SLE individuals. Although these elevations did not exhibit significant differences when each IgA subtype was evaluated, the analysis of the levels of total IgA (tIgA=IgA1+IgA2) displayed a significant increase according to global disease activity, an observation that was not previously reported. Accordingly, increases in the antibody fraction (IgA+IgG) in the saliva of lupus patients were observed in a previous study [39], but it does not refer to any individual subtypes’ characterization. Regarding subtypes, the only prior published evidence comes from Roos Ljungberg et al., which found salivary IgA1 and IgA2 anti-citrullinated protein antibodies in rheumatoid arthritis patients [26]. The authors found a strong association between salivary IgA and disease activity, even better than serum IgA, suggesting effector mechanisms in this disease pathogenesis due to oral mucosal immune responses to citrullinated proteins [26]. However, the significance of altered salivary IgA subclasses was not yet elucidated in this or other autoimmune diseases.
Our results suggest that increased salivary IgA could be associated with disease activity in SLE. Interestingly, observing the absolute amounts of each antibody subclass, and although IgA2 is clearly elevated in both groups of patients, it becomes evident that the most dramatic increase is in the IgA1 fraction that displays a mean value below 2 μg/mL in healthy controls and close to 10 μg/mL in active SLE patients. This last observation is remarkable since, in the oral cavity, IgA1 would be prone to degradation mediated by bacterial proteases, given its particular structure with a larger hinge region [40, 41]. This rise in salivary IgA1 concentrations must result from an overproduction of this antibody that could probably be related to the increments in systemic IL-10 levels as previously reported in lupus patients [42, 43]. IL-10 is an anti-inflammatory cytokine that promotes B cell responses and plays a pathogenic role in SLE [44]. Beyond that, serum levels of this cytokine have been reported to correlate with lupus disease activity [45]. Furthermore, it has been proposed that the class switch to IgA1 is mediated by IL-10 and TGF-β [46, 47]. As IL-10 was also previously reported elevated in the saliva of SLE patients [48], the dysregulation exerted by that cytokine in the oral microenvironment could be a key element that supports our data.
Interestingly, both subclasses and the total amount of IgA showed robust and highly significant positive correlations with the circulating anti-dsDNA autoantibodies (defined as IgG isotype in serum). Anti-dsDNA antibodies represent a hallmark of SLE and constitute an effective parameter for diagnosing and classifying patients. Additionally, their fluctuating titers during the progression of the disease reflect its activity in many patients and even may predict disease relapse [49]. Still limited by our cohort size, these strong correlation values raise the feasibility of employing salivary IgA measuring, either total or subtypes, as a surrogate marker of SLE activity. This possibility needs further exploration in more significant and prospective cohorts.
So far, we have discussed changes in the amount of IgA regardless of its specificity. Thus, with these salivary antibodies correlating with a well-established biomarker as serum IgG anti-dsDNA, we determined if saliva could contain these and other IgA-isotype anti-nuclear antibodies (ANAs). So, beyond the qualitative findings depicted in our autoreactive IgA detection by immunofluorescence employing HEp-2 cells, we decided to perform a semi-quantification of specific salivary IgA ANAs. Surprisingly, we could not find any difference regarding IgA anti-dsDNA. However, we detected a highly significant increase of IgA anti-nucleosome antibody titers in the saliva of patients with SLE versus healthy individuals. To demonstrate that these IgA autoantibodies were also present in our patient’s blood, we measured their levels with the same approach in serum samples. In this regard, we found that circulating IgA anti-dsDNA or IgA anti-nucleosome levels were highly increased in SLE patients, as expected, since they have been reported as elevated previously [50, 51].
It is important to clarify that the method used to quantify these autoantibodies is limited by the unavailability of IgA standards, thus not making it possible to measure them beyond a semi-quantitative approach. Besides that, saliva constitution (in contrast with the serum used to develop and validate the commercially available tests employed here) could interfere with the adequate determination of these autoantibodies and partially explain why our measurements in this fluid seem to present such a high fluorescence background. Besides this problem, which requires an independent standardization of a new ELISA method for saliva, the significantly increased levels of salivary IgA anti-nucleosome antibodies are still of great interest. Several reports mention those anti-nucleosome antibodies as a disease activity marker in patients with SLE [52] and even correlate better than other conventional biomarkers (C3/C4 or anti-dsDNA) with SLE disease activity over time [53], making of the measurement of salivary IgA anti-nucleosome an attractive possibility for the routine monitoring of SLE patients in clinical practice due to the easy access and availability of the type of sample contingent on the validation of these observations in larger/multicenter cohorts and the development of specific antibody tests for this purpose.
To gain insight into the potential of IgA antibodies as diagnostic biomarkers in SLE, we constructed ROC curves for IgA subtypes in the saliva of these patients. Our results indicate outstanding discriminative values for IgA1, and a lower but still good for IgA2 when distinguishing patients with SLE from healthy individuals.
As depicted, the sensitivity/specificity values given by measuring IgA isotypes in saliva regarding SLE diagnostic are comparable to those exhibited by other clinically relevant markers such as serum anti-dsDNA, anti-nucleosome, or complement levels in independent cohorts. This observation makes these secretory antibodies (particularly salivary IgA1 concentration) appealing candidates to be included as other criteria in SLE diagnostic current approaches upon prior validation in a larger multiethnic cohort.
Interestingly, when we performed the same analysis for assessing salivary IgA subtypes’ predictive ability when distinguishing inactive from active disease states, we could only obtain satisfactory AUC values for both antibody subtypes concentrations, making these two variables not as robust for activity discrimination. However, it becomes evident that including more patients, particularly those with active disease, could improve predictive values.
Again, as the measurement of these immunoglobulins supports the discrimination of SLE patients, their potential employment as a clinically useful biomarker for this disease becomes evident. One more time, our study remains limited due to the cross-sectional nature of our approaches; hence, we propose to conduct longitudinal monitoring of SLE patients to assess and/or validate the utility of IgA subtypes as new clinical biomarkers. Beyond that, these measurements should also be performed considering their predictive potential for different SLE implications, such as different types of flares.
Finally, as SLE patients can be treated with B cell-depleting biological therapeutics that consequently could diminish immunoglobulin levels, our proposed measurement of SIgA towards establishing a useful biomarker would be limited. The potential development of hypogammaglobulinemia is among the main concerns after administering biological treatments for SLE; for example, one of the most common drugs of this type employed for lupus treatment, rituximab, depletes CD20+ peripheral B cells for an average of 6-12 months, including naive and unswitched B cells, both of which are direct precursors for IgM production, thus further reducing the circulating levels of this isotype [54].
Different studies have informed that rituximab administration apparently does not affect the circulating IgA baseline levels in patients with SLE in the long term [55, 56]. Conversely, other reports indicate only a slight initial decrease of IgA median levels that started recovering as early as two months after rituximab [54], a decrease that becomes significant upon cumulative cycles of treatment but only in a small percentage (around $3\%$) of patients [56]. Supporting these data, it has previously been described that circulating IgA+ plasmablasts can persist early after rituximab, suggesting resistance to depletion of switched IgA+ precursor B cells, likely in the mucosal microenvironment and/or due to an early replenishment [57].
Currently, there is little information about secretory IgA levels upon biological treatment administration in contexts of autoimmune disease. However, one case report [58] indicates that rituximab treatment leads to an unexpected increase in the percentage of IgA+ plasmablasts in parotid salivary glands, compared with a biopsy before treatment.
All the previously mentioned data support the idea that biological treatments do not significantly affect the levels of IgA, even the secretory one in saliva; however, this hypothesis needs to be corroborated, including these treated patients in an independent study.
In conclusion, as the saliva sample is easily accessible and non-invasive for the patients, its IgA measurement emerges as an attractive alternative to being proposed as a novel biomarker of SLE. Given our results, salivary IgA subtypes correlate with specific autoantibodies related to disease diagnosis or activity but also may allow us to differentiate healthy individuals and SLE patients through the measurement of total salivary IgA or individual salivary subclasses, independently of antibody specificity.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Institutional Ethics and Research Committees of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (Ref. 2306). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
SR-R, VS-H, and RC-D contributed to the design and performance of experiments, analysis, and interpretation of data. SR-R, VS-H, RC-D, DM-S, and CN-A performed experiments and analyzed data. DC-V and JT-R assisted in the processing and preservation of patient samples, collected patient data, and generated and organized our clinical database. JT-R and DG-M assisted in writing and editing the manuscript. SR-R, DG-M, and JM-M designed and performed experiments, supervised general work, wrote, and edited the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1080154/full#supplementary-material
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|
---
title: Developing an m5C regulator–mediated RNA methylation modification signature
to predict prognosis and immunotherapy efficacy in rectal cancer
authors:
- Rixin Zhang
- Wenqiang Gan
- Jinbao Zong
- Yufang Hou
- Mingxuan Zhou
- Zheng Yan
- Tiegang Li
- Silin Lv
- Zifan Zeng
- Weiqi Wang
- Fang Zhang
- Min Yang
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9992543
doi: 10.3389/fimmu.2023.1054700
license: CC BY 4.0
---
# Developing an m5C regulator–mediated RNA methylation modification signature to predict prognosis and immunotherapy efficacy in rectal cancer
## Abstract
### Background
Currently, a very small number of patients with colorectal cancer (CRC) respond to immune checkpoint inhibitor (ICI) treatment. Therefore, there is an urgent need to investigate effective biomarkers to determine the responsiveness to ICI treatment. Recently, aberrant 5-methylcytosine (m5C) RNA modification has emerged as a key player in the pathogenesis of cancer. Thus, we aimed to explore the predictive signature based on m5C regulator–related genes for characterizing the immune landscapes and predicting the prognosis and response to therapies.
### Methods
The Cancer Genome Atlas (TCGA) cohort was used as the training set, while GEO data sets, real-time quantitative PCR (RT-qPCR) analysis from paired frozen tissues, and immunohistochemistry (IHC) data from tissue microarray (TMA) were used for validation. We constructed a novel signature based on three m5C regulator–related genes in patients with rectal adenocarcinoma (READ) using a least absolute shrinkage and selection operator (LASSO)-Cox regression and unsupervised consensus clustering analyses. Additionally, we correlated the three-gene signature risk model with the tumor immune microenvironment, immunotherapy efficiency, and potential applicable drugs.
### Results
The m5C methylation–based signature was an independent prognostic factor, where low-risk patients showed a stronger immunoreactivity phenotype and a superior response to ICI therapy. Conversely, the high-risk patients had enriched pathways of cancer hallmarks and presented immune-suppressive state, which demonstrated that they are more insensitive to immunotherapy. Additionally, the signature markedly correlated with drug susceptibility.
### Conclusions
We developed a reliable m5C regulator–based risk model to predict the prognosis, clarify the molecular and tumor microenvironment status, and identify patients who would benefit from immunotherapy or chemotherapy. Our study could provide vital guidance to improve prognostic stratification and optimize personalized therapeutic strategies for patients with rectal cancer.
## Introduction
By blocking programmed cell death 1/programmed cell death ligand 1 (PD1/PDL1) axis, immune checkpoint inhibitors (ICIs) have introduced a new era of antitumor therapy that could elicit durable responses and significantly improve survival in several tumors [1, 2]. However, the contexture and organization of the immune environment can be highly heterogeneous among tumors, even within the same cancer type, leading to a complex crosstalk within the tumor immune microenvironment (TIME) [3]. The overall status of tumor-infiltrating lymphocytes (TILs) in TIME closely correlates with the efficacy of immunotherapy. According to the immune cell status in TIME, tumor immune infiltration pattern could be broadly classified into “hot tumor” (indicating presence of CD8+ and CD4+ T cells accompanied by high expression of immune checkpoint molecules) and “cold tumor” (representing the deficiency of immune cells within the tumor parenchyma) [4, 5]. The former has a potential antitumor efficacy, while the latter barely benefits from the ICI therapy [6]. At present, patients with deficient mismatch repair (dMMR)/microsatellite instability-high (MSI-H) have more immune cell infiltration accompanied by high tumor mutational burden (TMB), while microsatellite stable (MSS)/microsatellite instability-low (MSI-L) patients have low abundance of TILs and low TMB [7, 8]. Moreover, according to the KEYNOTE-016 study, $62\%$ of colorectal cancer (CRC) patients with MSI-H phenotype achieve an objective response, while patients with MSS/MSS-L tumors cannot achieve objective response, indicating a better efficacy of immunotherapy in patients with dMMR/MSI-H tumors [9]. Nonetheless, dMMR/MSI-H tumors account for only $15\%$ of all patients with CRC [7, 10]. Therefore, establishing effective predictive biomarkers is essential for the improvement of immunotherapeutic strategy.
RNA modification plays an important role in the regulation of gene expression. More than 150 RNA modifications containing N6-methyladenosine (m6A), 5-methylcytosine (m5C), and N1-methiadenosine (m1A) have been investigated [11, 12]. Among these modifications, m5C is one of the most intensively researched epigenetic modifications, and overall, 95391 m5C sites in the human genome have been identified [13]. The m5C methylation landscape is regulated by a dynamic process that integrates methyltransferases (“writer”), binding proteins (“readers”), and demethylases (“erasers”) [14, 15]. Although m5C is widely recognized for its essential function as an epigenetic marker for DNA, research into its functional roles in RNA is beginning to emerge. It has been shown that a vast majority of azactidine (5-AZA), widely used to treat hematologic malignancies, is incorporated into RNA instead of DNA of treated tumor cells [16]. Therefore, the potential use of m5C RNA modification as a novel therapeutic target for various types of cancers is a current topic of research.
RNA methylation impacts the efficacy of tumor immunotherapy by modulating immune activity in a range of tumors [17]. Recently, several studies have uncovered the close relationship between TIME-infiltrating immune cells and m5C RNA methylation. Pan et al. found that NOP2/Sun RNA methyltransferase 4 (NSUN4) and NOP2/Sun RNA methyltransferase 3 (NSUN3) were closely related to the infiltration by six major immune cells that could regulate TIME in lung squamous cell carcinoma [18]. Gao et al. showed that m5C RNA modification patterns could predict and affect TIME in oral squamous cell carcinoma [19]. Despite these facts, the relationship between RNA methylation and tumor immunotherapy is still in its infancy. In the current study, we integrated multiple data sets and developed a novel signature based on the expression of m5C RNA methylation regulators, which could be used to evaluate risk status and predict prognosis of patients with rectal adenocarcinoma. Furthermore, we comprehensively explored the correlations between the m5C RNA methylation regulator–based signature having immune characteristics, mutational burden, and immunotherapeutic and chemotherapeutic sensitivity in READ (rectal adenocarcinoma) patients. Our results suggested that the established signature based on m5c RNA methylation regulators could be used as a robust biomarker to predict the clinical prognosis and therapeutic effect among patients with rectal cancer.
## Acquisition and processing of data sets
The RNA-sequencing transcriptome data (TPM value) and corresponding clinical annotation were retrieved from The Cancer Genome Atlas (TCGA) database (http://gdc-portal.nci.nih.gov/). After patients without survival information were excluded, a total of 434 colon adenocarcinoma (COAD) and 157 READ samples were integrated for further analysis. The validation data set was retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE87211 ($$n = 190$$) [20] and GSE133057 ($$n = 17$$) [21]. The copy number variations (CNV) of READ used in our research were retrieved from the UCSC Xena browser (http://xena.ucsc.edu/), where genes with CNV values smaller than −0.3 were categorized as a “loss,” while CNV values larger than 0.3 were categorized as a “gain.” The messages of simple nucleotide variations (SNV) were retrieved from the TCGA database, R package maftools was used to analyze the level 4 mutation data, and the mafCompare function of maftools was used to identify the differentially mutated genes (DMGs) [22]. The neoantigens and mutation loads for READ were accessed from The Cancer Immunome Atlas (https://tcia.at/) database [23]. Information on CMS subtyping calls and sample annotations were retrieved from the Colorectal Cancer Subtyping Consortium Synapse [24]. The STRING database can predict the functional links between proteins based on a variety of algorithms. *The* genes with the highest confidence scores were identified as the functional partners of specific genes [25]. The Gene_DE module of Tumor Immune Estimation Resource (TIMER, cistrome.shinyapps.io/timer) can be utilized to examine the mRNA expression profiles between the tumor tissues and the normal tissues [26]. We used the Human Protein Atlas (HPA) database to analyze the protein expression levels of candidate genes in tumor tissues and corresponding normal tissues [27].
## Construction of gene signature and survival analysis
The least absolute shrinkage and selection operator (LASSO) model is a linear regression method applying L1-regularization, which could accurately contract some regression coefficients to zero to achieve sparseness and feature selection [28]. The LASSO model was generated through R package glmnet. At the penalty coefficient (λmin = 0.036), the optimal risk model was established based on three m5C regulatory genes. Next, the R package survival was used to calculate the risk scores for rectal cancer samples. The following formula was used: Patients from the TCGA training cohort were separated into a high-risk and a low-risk group according to the median value of the calculated risk score. Patients from the GEO validation data set were grouped based on the optimal cutoff decided by cutp function of the R package survMisc. The Kaplan–Meier method was employed to compare the survival probability between the two risk subgroups.
## Functional enrichment analysis
Differentially expressed genes (DEGs) between the subgroups were identified by R package limma. Metascape (http://metascape.org), a web tool comprising Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis [29], was used to identify the terms across different ontology sources enriched based on the screened DEGs. A GOCircle plot was depicted to show the enriched terms by R package Goplot [30]. To further investigate pathways enriched in specific subgroups, we performed Gene Set Variation Analysis (GSVA) by R package GSVA. GSVA is a gene set enrichment method that estimates variation of pathway activity over a sample population in an unsupervised manner [31]. *The* gene set of “c5.go.v7.4.symbols” was downloaded from MSigDB database, and gene markers of epithelial–mesenchymal transition (EMT) including EMT1, EMT2, and EMT3; angiogenesis; pan-fibroblast TGFβ; and type I IFN response were obtained from previous studies for GSVA analysis [32, 33].
## Immune cell infiltration analysis
A total of 28 immune cell types were collected for GSVA analysis [23]. A web server TIMER, integrating multiple algorithms (TIMER, Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts [CIBERSORT], European Prospective Investigation into Cancer and Nutrition [EPIC]) was used to estimate the abundances of immune cell types based on the gene expression profiles [26, 34, 35]. The ratios between immune-stimulatory signatures and immune-inhibitory signatures (CD8+/CD4+ regulatory T cells, pro-/anti-inflammatory cytokines, and M1/M2 macrophages) were also compared between the subgroups based on the average expression levels of the marker genes [36]. The immune system–related genes were obtained from previous studies (23, 37–39). The Pearson correlation was calculated and then depicted by R package corrplot.
## Prediction of the efficacy of immunotherapy and chemotherapy
A web platform named Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu) was used to evaluate the anti-PD1 and anti-CTLA4 immunotherapeutic response based on the gene expression profiles of the TCGA-READ cohort [40]. To validate the correlation between immunotherapeutic efficacy and three genes–based risk model, another data set was retrieved, which included 348 patients with metastatic urothelial cancer who were treated with an anti-PD-L1 agent [32]. The R package oncoPredict can be used to discover drug sensitivity in vitro and in vivo contexts [41]. The half-maximal inhibitory concentration (IC50) was calculated to predict the chemotherapeutic response in READ patients. The Cancer Therapeutics Response Portal (CTRP, https://portals.broadinstitute.org/ctrp/) [42] and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM, https://depmap.org/portal/prism/) [43] were both developed to access the associations between drug sensitivity and gene expression. The calcPhenotype function of R package oncoPredict was used to calculate the AUC (Area Under Curve) value of each drug based on the CTRP and PRISM databases. Lower AUC value indicates higher sensitivity to therapeutic drugs.
## Consensus clustering analysis
To further validate the reliability of discriminating patients with rectal cancer into two subgroups based on the three m5C regulatory genes, DEGs were identified through R package limma between the low- and high-risk groups. Furthermore, univariate Cox regression analysis was carried out by R package survival to filter prognostic genes on the basis of DEGs. Ultimately, unsupervised clustering analysis was conducted by using R package ConsensuClusterPlus, which was repeated 1,000 times to identify different risk gene clusters [44].
## Tissue microarray–based immunohistochemistry validation
From Superbiotek in Shanghai, China (#REC1601), we acquired a TMA of 80 paired rectal cancers and corresponding normal tissues. Surgical samples from the patients were taken between May 2008 and December 2012 through operations. The patients’ median survival duration was 81.5 months, ranging from 14 to 130 months. For every case, clinicopathological information including overall survival time, survival status, age, gender, tumor size, pathological T, N, and M stage, and grade was accessible. Based on this commercial TMA, we conducted a retrospective analysis.
For immunohistochemistry (IHC) process, the TMA slides were deparaffinized, rehydrated, and incubated by $3\%$ hydrogen peroxide to block the endogenous peroxidase activity for 10 min at room temperature. Antigens were restored by boiling in a pressure cooker containing sodium citrate buffer for 90 s. The slides were incubated in bovine serum albumin (BSA) for 30 min to reduce nonspecific background. Then, they were incubated with rabbit monoclonal NSUN4 antibody (HPA028489, Sigma), NSUN7 antibody (HPA020653, Sigma), and DNMT1 antibody (HPA002694, Sigma) at 4°C overnight. Next, secondary antibody was incubated with the slides for 1 h at 37°C. Finally, the slides were developed in 3, 3’-diaminobenzidine (DAB) and stained with hematoxylin.
The slides were assessed digitally with the APERIO ScanScope (Leica Biosystems, Germany) and the APERIO ImageScope (Leica Biosystems, Germany) using the positive pixel counting algorithm. The IHC staining results were interpreted by both the intensity of staining and the staining positive area. Each sample was assigned a score according to the intensity of the staining (0 = no staining; 1 = weak staining; 2 = moderate staining; and 3 = strong staining) and the proportion of stained cells (0 = $0\%$; 1 = $1\%$–$25\%$; 2 = $25\%$–$50\%$; 3 = $50\%$–$75\%$; 4 = $75\%$–$100\%$). The final score was calculated as the staining intensity multiplying positive area score, ranging from 0 to 12. The IHC results of TMA-rectal cancer were independently reviewed by two experienced pathologists who were blinded to the clinical parameters.
## Real-time quantitative PCR validation
For the RT-qPCR experiment, tissue samples from 26 rectal cancer patients and matched nearby normal tissue samples (proximity to the cancer larger than 5 cm) were collected at the Affiliated Hospital of Qingdao University. The inclusion requirements were as follows [1]: a pathological analysis and imaging-based diagnosis of rectal cancer; [2] radical resection; [3] available information on clinicopathological indexes, such as tumor size, pathological stage, and pathological TNM; [4] pathological TNM in accordance with the 8th edition of the American Joint Committee on Cancer; and [5] lack of a prior history of other malignancies. Patients with recurrent rectal cancer and nonprimary malignancies as well as those who had had neoadjuvant chemotherapy and/or radiation prior to surgery were disqualified. All of the included patients gave their informed permission. The Affiliated Hospital of Qingdao University’s Research Ethics Committee approved the study, and it was completed in conformity with the 1964 Helsinki Declaration and its later amendments.
Total RNA was extracted using RNeasy kit (Beyotime, Shanghai, China, R0027) in accordance with the manufacturer´s instructions. Then, total RNA (1 µg) was quantified, followed by reverse-transcription by the SuperScript II reverse transcriptase (Takara, Japan, RR047). Quantitative PCR analysis was operated using SYBR Green Mix (Takara, Japan, RR820) with ABI 7900 HT Real-Time PCR system. The primer sequences are listed below: NSUN4, 5’-CCAAACCCTGGCAAAAGGTG-3’, 5’- GCGTGCCGGTCATAGAAGAA-3’; NSUN7, 5’-CCAGATCATTTGAGCAGTCTTATT-3’, 5’- GGTTCTCTACTTCTTGAACTTCTGA-3’; DNMT1, 5’-ATCCGAGGAGGGCTACCTG-3’, 5’- ACTTCTTGCTTGGTTCCCGT-3’; GAPDH, 5’-CTGACTTCAACAGCGACACC-3’, 5’-TGAGCTTGACAAAGTGGTCGT-3’. mRNA levels were determined relatively according to the expression of GAPDH.
## Statistical analysis
The t-test or Wilcoxon test was adopted for comparisons of two groups, and one-way ANOVA or Kruskal–Wallis test was adopted for comparisons of three or more groups. The choice of t-test vs. Wilcoxon test, or one-way ANOVA vs. Kruskal–Wallis test, was based on the normality of the variables. Chi-squared tests were used to analyze the distribution of variables among different subgroups. Multivariate Cox regression analysis was carried out by R package survival. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive power of the established model. We constructed nomograms to predict survival probability using R package rms. P value less than 0.05 was recognized as significant in this research.
## Construction of m5C RNA methylation regulator–based signature for READ patients
The schematic diagram summarizes the study design of the current research (Figure 1). m5C RNA modification regulators (NOP2 nucleolar protein [NOP2], NOP2/Sun RNA methyltransferase [NSUN]2, NSUN3, NSUN4, NSUN5, NSUN7, DNA methyltransferase [DNMT]1, DNMT3A, DNMT3B, tRNA aspartic acid methyltransferase 1 [TRDMT1], Aly/REF export factor [ALYREF], and tet methylcytosine dioxygenase 2 [TET2]) were integrated in this research based on the previously published articles [40, 45]. To explore the function of these regulators, univariate Cox analyses were conducted for COAD and READ separately. Interestingly, we found that the m5C modification regulators mainly played their roles in READ contrasting with COAD; specifically, NOP2, NSUN4, NSUN7, DNMT1, and TRDMT1 functioned as protective factors for patients of READ (Figure 2A). Therefore, in the following research, we focused mainly on the functions of the m5C RNA modification regulators related to READ. Owing to the observation of the prognostic value of the m5C regulators, we explored the overall prognostic impact of these regulators on READ. We built a prognostic model based on the mRNA expression value of total m5C regulators multiplying hazard coefficients to predict the survival events of READ patients. Next, the patients were classified into two groups (Figure 2B). As expected, the high-risk group presented a worse survival rate than the low-risk group, which was observed both in TCGA and in GSE87211 data sets (Figure 2C). Correlations among the mRNA expression levels of the m5C modification regulators were analyzed by Pearson correlation analysis. The results exhibited a whole trend of positive correlation among m5C regulatory genes (Figure 2D), and protein–protein interactions were calculated using *String data* sets (Figure 2F), which demonstrated that the m5C regulators could play an integrated role in impacting the prognosis of patients with READ. The CNV events were also examined by retrieving the mutation data from the TCGA-READ cohort. NSUN4, NSUN7, and TET2 had a tendency to a loss of copy number, while the remaining regulators often showed copy number gain events. Specifically, DNMT3B showed the most frequent CNV events, followed by NSUN5 (Figure 2E), implying that m5C regulators play an important role in the process of m5C modification in READ. These results indicated the potential potency of the m5C regulators as prognostic biomarkers for READ patients.
**Figure 1:** *Schematic diagram of the study design.* **Figure 2:** *The prognostic value of m5C RNA methylation regulators. (A) Forest plot of the prognostic ability of the m5C regulator genes in COAD and READ separately. (B) The risk score distribution and patient survival status are shown in ranked dot and scattered plots based on the expression of m5C regulator genes. (C) The Kaplan–Meier curves for OS, PFS, and DFS between the high-risk and the low-risk groups in READ from the TCGA and GSE87211 cohorts. (D) Pearson correlation among the m5C regulators in READ patients. (E) The CNV variation frequency of the m5C regulators in the TCGA cohort. The orange rectangle = the amplification frequency; the blue rectangle = the deletion frequency. (F) The PPI network depicted for m5C regulators. CI, confidence interval; DFS, disease-free survival; HR, hazard ratio; OS, overall survival; PFS, progression-free survival. *P < 0.05.*
To promote the clinical application, LASSO-penalized *Cox analysis* was performed to enhance the forecast accuracy and explainability of the statistical model. In the current model, the optimal penalty coefficient (λ = 0.036, log λ = −3.33) was identified with the minimum criterion (Figure 3A). In Figure 3B, each curve indicates the track of a single gene, and the red dot represents the target lambda. We can see that three genes (DNMT1, NSUN4, and NSUN7) were retained after the shrinking process. Then, the produced three prognostic indicators were employed to predict clinical results.
**Figure 3:** *Prognostic significance of the m5C methylation-based signature in READ patients. (A) The process of LASSO regression based on the TCGA cohort and the identification of “lambda” for best selection of gene signature. (B) The curves indicate the tracks of single genes; the red dot line represents the target lambda. The blue track refers to NSUN4, pink track refers to NSUN7, and black line is DNMT1. (C) The Kaplan–Meier curves for OS and DSS between two categories in READ from the TCGA data set; the Kaplan–Meier curves for OS, 5-year survival based on the GSE87211 data set; the Kaplan–Meier curves for 7-year survival based on the GSE133057 data set. (D) The univariate and multivariate Cox analysis integrating risk score and clinicopathological indexes based on the TCGA cohort. (E) The prognostic ability of the risk score in distinguishing the overall survival status for READ patients with or without lymph node metastasis. The prognostic ability of the risk score in differentiating the overall survival status in patients with age less than 65 years or those with 65 years or more. CI, confidence interval; DFS, disease-free survival; DSS, disease-specific survival; HR, hazard ratio; OS, overall survival. *P < 0.05.*
## Prognostic significance of the m5C methylation–based signature in READ patients
To confirm the effectiveness of the established model, we carried out Kaplan–Meier survival analyses. We found a superior survival status in the low-risk group compared with the corresponding high-risk group in both the TCGA dataset and two GEO cohorts, illustrating that the built model could significantly predict the prognosis of READ patients (Figure 3C). Similar processes were applied to the samples of COAD patients, and no factor was retained after LASSO analysis (Figure S1A). The three factors identified in the READ patients were repurposed for COAD samples; as expected, the survival curves of the two groups were highly crossed (Figure S1B). To further explore the relationship of the prognostic risk model of the three m5C regulators and clinical features in READ, univariate and multivariate Cox regression analyses were conducted. To facilitate the understanding of the patients’ clinical and genetic background, a table including basic information about the low- and high-risk groups is displayed in Table S1. The results of Cox regression analysis revealed that the risk score was an independent prognostic factor for READ, unrelating to clinicopathological parameters, such as pathologic N and age (Figure 3D). We further investigated whether the risk score could further subdivide the pathological N and age parameters. The results showed that the established risk score further distinguished the risk pattern in subgroups differentiated by age, successfully stratified the patients in the N0 pathological stage, and exhibited a tendency to differentiate patients in the N1 pathological stage due to small sample size (Figure 3E). To visualize the expression pattern of m5C regulators, a heat map was depicted. To our expectation, the majority of the methylation regulators displayed a significant high expression module in the low-risk group (Figure S2), which is reasonable due to their protective ability in READ. Thus, this powerful and accurate model symbolized a potential clinical parameter for patients with READ.
## Construction and validation of a nomogram combined with clinical parameters
To make the m5C regulator–based risk signature more clinically adapted and available, a prognostic nomogram was depicted integrating the risk factors and independent identified parameters of READ. The aim was to establish a quantitative analytic algorithm that could be put into practice for survival prediction. In the current case, the pathologic N, age, and risk score were integrated to calculate the corresponding score, which could be used as an index for matching the one-, three-, and five-year death probabilities (Figure 4A). To reinforce the superior capability of the established nomogram, the ROC analyses were used to compare the prognostic accuracy and specificity. The results indicated that the nomogram was superior to other independent clinical factors for predicting the overall survival (OS) of READ patients in the TCGA cohort (AUC of one-year OS = 0.803; AUC of three-year OS = 0.855; AUC of five-year OS = 0.838; AUC of overall survival = 0.834; Figure 4B). The calibration curve was drawn to confirm the consistency between the nomogram-predicted and the actual probability. The calibration curves were close to the optimal performance in the one-, three-, and five-year nomogram (Figure 4C), indicating the accuracy of the constructed nomogram. These results implied that the three-gene signature was capable and reliable to make prediction for READ patients.
**Figure 4:** *Construction and validation of a risk model based on m5C methylation regulators. (A) The predictive nomogram integrating the risk score and clinicopathological parameters for 1-, 3-, and 5-year OS in READ patients from the TCGA cohorts. (B) The ROC for nomogram and independent clinical parameters for 1-, 3-, and 5-year OS based on the TCGA cohort in READ patients. (C) The calibration curve depicted for 1-, 3-, and 5-year nomogram in TCGA. OS, overall survival.*
## Functional enrichment analysis of m5C methylation–based signature between low- and high-risk READ patients
To explore the underlying molecular mechanisms of the m5C-based signature, GO, GSEA, and GSVA analyses were performed. DEGs were identified using the limma algorithm, and the result is displayed as a volcano plot (Figure S3). Next, the screened DEGs were put into the GO analysis. The GO pathway enrichment analysis revealed that the most significantly changed pathways in the high-risk subgroup were mainly related to cancer and immune-targeted processes, such as epithelial–mesenchymal transition, angiogenesis, hypoxia, regulation of leukocyte migration, and regulation of macrophage activation; however, cell cycle–related pathways, including G2M checkpoints, sister chromatid segregation, and signal transduction in response to DNA damage, were mainly converged in the low-risk group (Figure 5A). The GSEA analysis confirmed these findings and showed some extent of overlap with the GO analysis results (Figure 5B). In order to clarify the specific roles of these pathways according to the risk categories, a series of related gene sets were collected to further carry out the GSVA analysis. Importantly, the GSVA results revealed that the process of angiogenesis, EMT, and pan-fibroblast TGFβ were consistently upregulated in the high-risk category (Figure 5C). Meanwhile, the GSVA analysis indicated that many biological functions in the high-risk group primarily correlated with inflammatory responses and carcinogenic reactions, while in the low-risk group, RNA methylation process and drug response were significantly enriched (Figure 5D). These features gave the hint that cancer–immunity interaction is the potential mechanism of the m5C-based risk signature, and the efficacy of the established model was further validated by the above results.
**Figure 5:** *Functional enrichment analysis of the m5C methylation-based signature between low- and high-risk READ patients. (A) The enriched pathways including GO and HALLMARK terms are displayed by GOcircle plots. The red and blue dots represent the genes upregulated in the low-risk and high-risk categories separately. (B) GSEA enrichment plots for the two subgroups in the TCGA cohort. (C) The GSVA analysis for hallmarks of cancer in the TCGA cohort. (D) The heat map drawn for GSVA analysis based on GO terms. DEGs, differentially expressed genes. ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05; ns, not significant.*
## The immune characteristics of the m5C regulator–based signature in READ
Due to the close relationship between the built model and immune process, the detailed connection between the risk signature and immune cell abundance was studied. The GSVA and deconvolution algorithms including CYBERSORT, TIMER, and EPIC were used to evaluate the extent of infiltrating immune cells. CD4+ T cells, B cells, CD8+ T cells, dendritic cells, and T helper cells exhibited higher expression in the low-risk category; meanwhile, the abundance of myeloid-derived suppressor cells (MDSC) and regulatory T cells (Tregs) was elevated in the high-risk category compared with the low-risk group (Figures 6A–C, E). Furthermore, additional investigations were conducted to substantiate the above findings. The ratio between the immune stimulatory signatures (including CD8+ T cells, proinflammatory cytokines, and M1 macrophages) and the immune inhibitory signatures (integrating CD4+ regulatory T cells, anti-inflammatory cytokines, and M2 macrophages) was significantly increased in the low-risk category (Figure 6D), which was consistent with the above results, indicating an immune-inhibiting environment in the high-risk group and a proinflammatory status in the low-risk group. We collected the signatures of cancer–immunity cycle and immune stimulators. The heat maps showed that the majority of genes exhibited higher expression in the low-risk group (Figure 6F) and the established risk score correlated negatively with the expression of most of the immune stimulators (Figure 6G). According to the obtained evidence, the low-risk group belongs to activated immune microenvironment, while the high-risk group shows a suppressed immune phenotype.
**Figure 6:** *The immune characteristics of the m5C regulator–based signature in READ. The deconvolution algorithms of TIMER (A), EPIC (B), and CIBERSORT (C), which were applied to estimate the immune infiltration status between the high- and low-risk groups. (D) The ratios of CD8+ T cells to CD4+ regulatory T cells, pro- to anti-inflammatory cytokines, and M1 to M2 macrophages in the TCGA dataset. (E) GSVA analysis based on GO terms for the high- and low-risk groups. (F) The heat map depicts the expression of positive genes collected from cancer–immunity cycle based on the TCGA cohort. (G) Pearson correlation among immune stimulators was conducted and is shown in a correlation heat map. Correlations with P value > 0.05 are blank. ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05.*
## The mutational landscape for the m5C regulator–based signature in READ
Considering the evidence that hot tumor is more sensitive to immune therapy, we hypothesized that the low-risk group of our established model might be more readily responsive to immune therapies than the high-risk group. Previous studies have revealed that high somatic mutation and neoantigens represent a higher possibility to response. Thus, we investigated the differences in mutation status between the two groups. First, we identified the top 10 mutated genes in rectal cancer using the maftools R package (Figure S4A), and these genes were subsequently compared between the two subgroups. A significantly higher mutational rate of RYR2 was observed in the low-risk group, while the other genes showed no statistical differences (Figure S4B). Then, we used the mafCompare function to identify the DMGs. Interestingly, we found overall higher mutational rates in the low-risk group (Figure 7A), indicating that the built model did not affect the frequently mutated genes but exerted a cumulative effect of low-frequency mutations. We also found that the low-risk group was accompanied by more neoantigens. However, TMB only exhibited an elevated tendency (Figure 7B). Moreover, we combined the m5C-based model with neoantigens and TMB and found that neoantigens and TMB cannot effectively distinguish the survival status in patients with rectal cancer (riskscore-L + NEO-L vs. riskscore-L + NEO-H, $$P \leq 0.655$$; riskscore-L + TMB-L vs. riskscore-L + TMB-H, $$P \leq 0.748$$), although possessing high neoantigen levels showed a tendency of better overall survival compared with possessing low neoantigen levels (riskscore-H + NEO-L vs. riskscore-H + NEO-H, $$P \leq 0.083$$). The constructed risk score showed significant efficacy in stratifying patients with a same status of neoantigens and TMB (riskscore-L + NEO-L vs. riskscore-H + NEO-L, $$P \leq 0.012$$; riskscore-L + NEO-H vs. riskscore-H + NEO-H, $$P \leq 0.050$$; riskscore-L + TMB-L vs. riskscore-H + TMB-L, $$p \leq 0.005$$), confirming the superiority of this model over current biomarkers. In addition, riskscore-L + TMB-H vs. riskscore-H + TMB-H ($$P \leq 0.064$$) showed a strong tendency without significant difference. We also found that combining risk score with neoantigens (riskscore-L + NEO-H vs. riskscore-H + NEO-L, $$P \leq 0.002$$) could achieve a higher efficiency for predicting the prognosis of patients with rectal cancer (Figure 7C).
**Figure 7:** *The mutational landscape for the m5C regulator–based signature in READ. (A) The waterfall plot of differentiated somatic mutation features between the high- and low-risk groups using the TCGA-READ data set. (B) The neoantigens and mutation loads between the two subgroups are displayed. (C) Survival analyses for READ patients stratified by both the risk score and neoantigen burden or mutation loads using Kaplan–Meier curves. NEO, neoantigen burden; H, high; L, low. **P < 0.01; *P < 0.05.*
## Prediction of immunotherapeutic response for distinct subgroups in READ
The obtained findings promoted us to further examine the relationship between the m5C-based signature and immunotherapy. First, we compared the expression of the immune checkpoints in the two subgroups. No significant differences were found, as shown in Figure S5. Next, we investigated the relationship between model factors and immune infiltration cells. Interestingly, we found that DNMT1 and NSUN4 were moderately positively correlated with CD4+ T cells, natural killer cells, dendritic cells, and T helper cells; meanwhile, NSUN7 was weakly negatively correlated with MDSC and Tregs (Figure 8A), substantiating the close connection between the risk model based on the above three m5C regulatory genes and the tumor immune microenvironment. Next, we investigated the relationship between model factors and immune checkpoints. Higher expression of NSUN4 was accompanied by a higher expression of immune checkpoints, and patients with high DNMT1 expression showed a trend of elevated expression of immune checkpoints. However, low expression of NSUN7 was associated with only weakly elevated immune checkpoint expression (Figure 8B). A mature predicted method called TIDE was applied to anticipate the immunotherapeutic effect of PD1 administration. We found a higher proportion of responders in the low-risk group (Figure 8D), and the lower TIDE score, indicating a higher response rate, verified the obtained finding. Moreover, the T-cell dysfunction score and cancer-associated fibroblasts were elevated in the high-risk group. According to previous reports, tumors with MSI tend to more easily respond to immunotherapy. The finding of a higher MSI score in the low-risk group supports the expectation (Figure 8C). We further compared the low-risk patients with rectal patients with MSI-H phenotype to investigate which group would achieve a better objective response from ICI treatment. Due to a small proportion of MSI-H patients in the TCGA dataset ($\frac{4}{157}$), we evaluated the MSI score for each patient with READ by the TIDE algorithm. The patients with MSI score higher than the median value were characterized as the MSI-H group, the others were classified as the MSI-L group. The result showed that there was no significant difference between the low-risk group and MSI-H group ($$P \leq 0.354$$, Supplementary Figure S6), indicating that the m5C regulator–based signature could be utilized as an addition to the current MSI classification, the combining of two methods to evaluate the responsiveness of ICI treatment will provide a novel perspective for precision medicine. We then performed a direct investigation by adopting an additional data set with the therapeutic information. We compared the survival rates of two subgroups by conducting Kaplan-Meier analysis, and found that the low-risk group had prolonged survival compared with the high-risk group despite an insignificant P value ($$P \leq 0.121$$, Supplementary Figure S7). The expression of immune checkpoints was higher in the low-risk group, which represents higher sensitivity toward ICI treatment (Figure 8E). Accordingly, the proportion of complete response/partial response (CR/PR) was remarkably higher in the low-risk group (Figure 8F), and the risk score was lower in the CR/PR subgroup (Figure 8G). Interestingly, compared with the immune-excluded high-risk group, the low-risk group revealed an immune inflammation phenotype (Figure 8H). These results solidly certified that the established signature had the ability to efficiently predict the immunotherapeutic efficacy for READ patients.
**Figure 8:** *Prediction of immunotherapeutic response for distinct subgroups in READ. (A) Pearson correlation between three signature factors and 28 types of immune cells is illustrated by a correlation heat map. Correlations with P value > 0.05 are marked by a cross. (B) The differences in the three signature factors between distinct subgroups classified by the expression level of three immune checkpoints, including CTLA4, PD1, and PDL1. (C) The distribution of TIDE score, MSI score, T-cell dysfunction score, and abundance of CAF between the low- and high-risk categories. (D) The proportion of READ patients with response to ICI therapy in the high- and low-risk groups based on TIDE prediction. (E) The differential analysis for immune checkpoints between the two categories in IMvigor210 cohort. (F) The proportion of patients with response to PD-L1 treatment in the high- and low-risk groups based on IMvigor210 cohort. (G) Distribution of the risk score between CR/PR and SD/PD groups. (H) The proportion of immune phenotype in the high- and low-risk groups. CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease.*
## The transcriptomic characteristics of the m5C methylation–based gene clusters
To further investigate the heterogeneity of different m5C RNA methylation regulator patterns, we identified 950 DEGs between the high-risk and low-risk groups. Subsequently, univariate Cox regression analysis was conducted to certify the genes with prognostic value, and finally, a total of 173 m5C RNA methylation regulator risk model–related genes were identified (Figure 9A). Unsupervised clustering analysis based on the expression of these 173 genes separated READ patients into two clusters, which we referred to as m5C RNA methylation gene clusters (Figure 9B). Survival analysis indicated that cluster 2 had a better prognosis (Figure 9C). Moreover, we found that cluster 1 had a higher risk score than that in cluster 2 (Figure 9D), and chi-squared tests also revealed a significant difference between the two clusters (Figure 9E). CMS stratification is considered a robust classification system and is currently used for CRC with distinguished features; among the four CMS subtypes, CMS4 mesenchymal tumors display worse overall survival and relapse-free survival [24]. To evaluate the CMS status in different m5C regulator–based subgroups, we further compared the proportion of the CMS phenotypes by chi-squared tests. The high-risk group and cluster 1 category displayed a higher proportion of CMS4 compared with other categories (Figures 9F, G).
**Figure 9:** *The transcriptomic characteristics of the m5C methylation–based gene clusters. (A) The intersection of DEGs and prognostic genes. (B) The unsupervised consensus cluster of the identified 173 genes. (C) The Kaplan–Meier survival curve for two clusters in the TCGA cohort. (D) The m5C signature–based risk score distribution between two clusters. (E) The proportion of READ patients with different risk status in cluster 1 and cluster 2 from the TCGA cohort. The CMS distribution among the risk groups (F) and clusters (G) separately. (H) Sankey diagram depicting the relationship of survival status, risk groups, clusters, and CMS classification. (I) The functional enrichment analysis on GO terms of the two clusters. (J) The immune cells infiltration between different clusters. (K) The proportion of responsive patients in the two clusters based on TIDE prediction. The distribution of TIDE score (L), MSI score (M), T-cell dysfunction score (N), T-cell exclusion score (O), and abundance of CAF (P) in cluster 1 and cluster 2. (Q) Sankey diagram connecting the two classification systems with the immune response. ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05.*
In line with the previous findings, cluster 1 was enriched mainly in cancer and immune system–related pathways, while process related to the functions of RNA methylation played an important role in cluster 2 (Figure 9I). Patients of cluster 2 had higher abundance of CD4+ T cells and helper T cells, while cluster 1 exhibited higher amount of immune-inhibiting cells, such as Tregs and macrophages (Figure 9J). The relationship of the survival status, m5C regulator–based risk model, m5C regulator gene clusters, and CMS phenotypes is summarized in a Sankey diagram (Figure 9H). The TIDE algorithm was carried out to predict the immunotherapeutic response relating with the clustering system. Accordingly, there were more responders in cluster 2 (Figure 9K), and the index integrating the lower TIDE score, higher MSI score, lower extent of T-cell dysfunction and exclusion, and lower abundance of cancer-associated fibroblast (CAF) consistently indicated a better responsive rate for patients of cluster 2 (Figures 9L–P). To make the outline clear, a Sankey diagram connecting with both the risk classification and clustering system was depicted (Figure 9Q). Above all, these results reinforced the notion that there were indeed two different m5C regulator–based groups in READ, which represented different clinical and immune features.
## Validation of the m5C methylation–based signature by TMA in patients with rectal cancer
To demonstrate the robustness and repeatability of the prognostic value of the established model, different laboratory assays were adopted. RT-qPCR was conducted to detect the mRNA expression of the signature’s factors in 26 pairs of rectal cancer tissues and corresponding normal tissues. The results showed that NSUN4 was highly expressed in normal tissue (Figure S8A). Examination of the correlation between the risk score and clinical parameters revealed a higher proportion of patients with no lymph node metastasis in the low-risk group compared with the high-risk group (Figure S8B).
Next, we detected the protein expression levels of NSUN4, NSUN7, and DNMT1 via IHC staining in a tissue microarray containing 80 paired normal and tumor tissues. The clinical features of tissue microarray as the validation cohort are displayed in Table S2. The protein expression levels of the three m5C regulatory genes were analyzed using IHC staining, substantiating the findings obtained using the TCGA-READ dataset. The following analyses were based on the protein expression levels detected via IHC. The results revealed significant elevation of NSUN7 and DNMT1 in normal tissues compared with tumor tissues, while NSUN4 showed no obvious difference between the two groups (Figure 10A). Next, we investigated the relationship between the three genes using the Pearson correlation analysis. High correlation coefficients (> 0.7) shown in the correlation plot indicate that the protein expression levels of the three genes were closely associated (Figure 10B).
**Figure 10:** *Validation of the m5C methylation– based signature by rectal cancer tissue microarray (TMA). (A) The differential expression of NSUN4, NSUN7, and DNMT1 between normal and tumor tissue; the representative micrographs show NSUN4, NSUN7, and DNMT1 IHC staining of 80 pairs of rectal cancer and corresponding normal rectal tissue samples in the rectal cancer TMA. (B) The correlation among the expression levels of NSUN4, NSUN7, and DNMT1. (C) The heat map depicting the association of the risk score, gene expression, and clinicopathological parameters. (D) Kaplan–Meier curves of differential NSUN4, NSUN7, and DNMT1 expression in the TMA cohort of rectal cancer. ****P < 0.0001; **P < 0.01; *P < 0.05; ns, not significant.*
Importantly, the KM survival curves demonstrated that the survival probability was significantly increased in the high expression group compared to the low expression group, according to the protein expression of an individual gene in the risk model (Figure 10D). We constructed a signature based on the protein expression of the three genes, in which the low-risk group showed prolonged survival compared with the high-risk group (Figure 11A). Remarkably, based on the IHC protein expression data, the risk score was correlated with clinical characteristics including pathologic TNM, gender, grade, and clinical stage (Figure 10C); this was further confirmed by a Wilcoxon test between the two subgroups (Figure 11D). To examine the significance of the established risk score, univariate and multivariate Cox regression analyses were conducted. Risk score, grade, and pathologic M remained independent factors after the above tests (Figure 11C). ROC analysis was exploited to inspect the superiority of the built risk score over other indexes (AUC of risk score = 0.954; AUC of grade = 0.744; AUC of pathologic $$n = 0$.764$; AUC of pathologic $M = 0.639$; AUC of pathologic $T = 0.749$; Figure 11B). To validate the efficiency of the nomogram generated based on the TCGA-READ dataset, we integrated the model factors, including risk score, age, and pathological N, to construct a nomogram based on the IHC independent cohort (Figure 11E). The C-index of the nomogram was 0.840, indicating a stable and robust predictive power. The subsequent calibration plots also revealed high concordance between the predicted probability of three-, five-, and seven-year OS and actual OS (Figure 11F). These results reinforced that our classification based on the m5C methylation regulators was potent and reliable in terms of prognostic significance for patients with rectal cancer.
**Figure 11:** *Validation of the m5C methylation–based signature by tissue microarray (TMA). (A) Kaplan–Meier survival curves show overall survival for low- and high-risk patients based on the rectal cancer TMA cohort. (B) The ROC curves depicted for the risk score and common clinical diagnostic indexes. (C) The univariate and multivariate Cox analysis of the risk score and clinicopathological indexes in the rectal cancer TMA patients. (D) The distribution of the risk score among various parameters including pathological TNM, stage, grade, and gender. (E) A nomogram integrated age, pathological N, and risk score was constructed for 3, 5, and 7 years based on the rectal cancer TMA cohort. (F) The calibration curves show the discrepancy between actual and nomogram-predicted survival probability in 3-, 5-, and 7-year nomograms. ****P < 0.0001; **P < 0.01; *P < 0.05.*
In addition, the regulated genes associated with NSUN4, NSUN7 and DNMT1 using the STRING database were analyzed. Mitochondrial transcription termination factor 4 [MTERFD2], NOP14 nucleolar protein [NOP14] and RB transcriptional corepressor 1 [RB1] were identified to be closely related to NSUN4, NSUN7 and DNMT1 respectively with the highest predicted scores. As shown in Supplementary Figure S9A, NOP14 was significantly upregulated in rectal cancer tissues compared with normal tissues, while both MTERFD2 and RB1 showed no differences. Consistent with our results in TCGA, the immunohistochemistry results of the HPA database presented that the protein expression level of NOP14 was elevated in the tumor cells compared with the corresponding glandular cells, and mainly localized to the cytoplasmic and membranous nuclear (Supplementary Figure S9B). However, the protein expression of MTERFD2 or RB1 exhibited no difference between cancer tissues and normal tissues (Supplementary Figure S9B).
## Estimation of drug sensitivity for the m5C methylation–based signature
Based on the potential role played by the established m5C regulator signature in modulating the immunotherapies, we further investigated its clinical usefulness by measuring the IC50 value of different oncology drugs. According to the predictive model, we found that the effects of 10 commonly used drugs for READ were different between the two subgroups. Chemotherapeutic drugs, including camptothecin, 5-fluorouracil, cisplatin, oxaliplatin, and irinotecan, had a lower IC50 in the low-risk group.
Similarly, cediranib, sorafenib, and axitinib, which belong to VEGFR-targeted angiogenesis drugs, exhibited a lower IC50 in the low-risk group. EGFR/HER2 inhibitor lapatinib and BRAF inhibitor dabrafenib also followed that pattern (Figure 12A). To benefit high-risk patients, we further excavated both the CTRP and PRISM databases; two drugs specific to high-risk patients were found effective by intersecting the two sources (Figures 12B–D) and include chlorambucil and SKI.II. These results implied that our model could predict certain drug sensitivity that would be beneficial to different groups of READ patients.
**Figure 12:** *Estimation of drug sensitivity for the m5C methylation-based signature. (A) The evaluation of drug sensitivity including chemotherapeutics and small molecular drugs targeting VEGFR, EGFR/HER2, and BRAF. (B) Intersection of the identified drugs targeting high-risk patients between CTRP and PRISM databases. (C, D) The differential drug response analysis of CTRP- and PRISM-derived compounds targeting the high-risk group. IC50: half-maximal inhibitory concentration. ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05.*
## Discussion
Accumulating studies have revealed that colon and rectal cancer have distinct metastatic patterns, spread ratio, and drug response in patients [46]. In multiple trials, individuals with rectal or colon cancer who received bevacizumab-containing regimens have shown different survival rates (47–49). In order to systematically distinguish colon and rectal cancer, Liang et al. even profiled specific biomarker and identified a key factor to tailor the medical treatment of patients with colon and rectal cancer [50]. Available evidence indicates that colon and rectal cancer should be regarded as two specific cancers when considering clinical treatment. Therefore, we evaluated the prognostic significance of m5C regulators in COAD and READ separately. The results indicate that m5C might exert more impact on the prognosis of READ patients than COAD patients, which could be explained by the fact that colon and rectal cancer exhibit remarkably different genetic and epigenetic characteristics. A study enrolling 1,443 stage I–IV CRC patients revealed that the prevalence of MSI-high, BRAF mutations, and CIMP-high tumors rapidly decreased from the proximal colon to the rectum [51]. Moreover, proximal tumors were more frequently MSI, hypermutated, BRAF mutant, and densely infiltrated by TIL, whereas distal tumors were CIN, HER1, and HER2 amplified, with active EGFR signaling and mostly non-BRAF-like characteristics according to an analysis of molecular features along anatomical sites in colon carcinomas of patients enrolled in the Pan European Trial Adjuvant Colon Cancer-3 (PETACC3) chemotherapy trial [52], indicating a great heterogeneity within CRC. Overall, the observation of significant difference between two types of cancer led us to focus our research on patients with rectal cancer.
RNA epigenetic modification is a crucial biological process. There is increasing evidence that the malfunction of RNA epigenetic modification leads to the deterioration of cancers. For example, NUSN4 has been found to affect the expression of mitochondrial DNA, which leads to a cascade of changes relating with the regulation of mammalian oxidative phosphorylation, finally resulting in the progression of cancers (53–55). The dysfunction of NSUN7 has been reported to result in male infertility [56], and NSUN7 is downregulated in prostate cancer compared with normal prostate tissue, acting as a protective factor in patients with prostate cancer [57]. Additionally, DNMT1 is an important methyltransferase for the stable process of RNA methylation. It is associated with a series of cancers, including breast cancer, thyroid cancer, pancreatic cancer, and hepatocellular carcinoma (58–61). Here, based on the gene expression of m5C regulators (NSUN4, NSUN7, DNMT1), we established a signature that could effectively distinguish the prognosis of READ patients. A weak positive correlation was found between the three genes based on the TCGA-READ, indicating the independence of the three genes in the current model, and their cumulative effect can endow the model biological significance at the mRNA expression level. The constructed signature, age, and pathologic N act as independent prognostic factors in rectal cancer. Moreover, the signature could predict risk for patients of different age groups and N stages. Notably, the signature failed to distinguish the survival status of patients in the N1/N2 stage. At the advanced stage of the disease, colorectal cancer–associated immune infiltrates can be highly heterogeneous and can vary their phenotypes in a spatiotemporal manner [62, 63]. Moreover, various factors such as intestinal obstruction, gastro-intestinal bleeding, malnutrition, liver metastasis, and other maladies can cause death in advanced colorectal cancer. All the above uncertainties could account for the reason that risk score only exhibited a trend ($$P \leq 0.089$$) when stratifying the overall survival of patients in the pathological N1/N2 stage due to small sample size. Differentiating the survival status of N0 patients is significant for early intervention. Colorectal cancer develops asymptomatically, leading to the difficulty in diagnosis and thus progressing into the advanced stage, which requires considerable efforts to treat [64]. The established risk score could efficiently evaluate the hazards of patients in the pathological N0 stage and predict patients who are at high risk of developing advanced stage cancer, and the emphasis placed on these patients will benefit them clinically. Since IHC enables a pathologist to examine gene expression at the protein level within the context of histologically interpretable tissue sections, it is a reliable method for confirming expression signatures discovered by RNA sequencing. Therefore, to further substantiate the results of the bioinformatics analysis, TMAs from patients with rectal cancer were immunohistochemically stained for NSUN4, NSUN7, and DNMT1. The stained slides were evaluated for calculating risk score. In concordance with the TCGA data mining, the risk score was able to differentiate the prognosis of patients with rectal cancer well and determine their survival as an independent prognostic factor, and the nomogram integrating risk score, age, and pathological N could serve as a reliable indicator in predicting the survival probability of patients with rectal cancer. Since IHC is carried out on commonly processed clinical tissue samples, validated IHC assays could be easily applied in clinical diagnostics. To facilitate the clinical use, we developed a nomogram with high accuracy and robustness. Our findings together suggest that the built signature based on m5C RNA regulators is highly involved in the progression of rectal cancer and could serve for effective risk stratification in patients with rectal cancer.
There is increasing evidence relating the m5C modification with innate immunity as well as antitumor effect through a complex crosstalk among various m5C regulators. We found that the established signature could effectively determine the TIME infiltration patterns. The interplay between tumor and immunity begins when tumor antigens are presented by dendritic cells and activate CD8+ T cells and CD4+ T cells to exert cytotoxic effects [65]. Moreover, cancer cells can suppress immune system, leading to an inhibitory TIME to escape immune surveillance with the increase of Tregs and MDSC. As revealed in our analysis integrating CYBERSORT, TIMER, EPIC, and ssGSEA algorithms, the low-risk group was characterized by the activation of adaptive immunity, with the increasing abundance of CD4+ T cells, CD8+ T cells, B cells, and myeloid dendritic cells. The high-risk group was characterized by the suppression of immunity, accompanied by upregulation of Tregs and MDSC. The ratio analysis further explained that compared with the high-risk group, the scales of CD8+/CD4+ regulatory T cells and pro-/anti-inflammatory cytokines were higher in the low-risk group. According to different functions, macrophages could be classified into two categories: classically activated macrophages (M1), mainly acting as a tumor-killer role, and alternatively activated macrophages (M2), which function to promote tumor cells [66]. As indicated in our results, the ratio of M1 to M2 macrophages was elevated in the low-risk group. m5C RNA methylation regulators have already demonstrated the efficacy for predicting prognosis and regulating TIME in various cancers [18, 67, 68], suggesting the potential value in pan-cancer analysis. Consistent with the current knowledge, our model showed a predictive accuracy in prognosis and in TIME cell infiltration characterization among READ patients.
The signatures derived from m6A/m5C/m1A RNA methylation regulators were widely explored in recent studies. Commonly, the signatures could characterize the immune landscape of cancer patients and further predict the efficacy of immunotherapy [69, 70]. m6A modification is one of the most researched RNA methylation patterns. The “writer”, “reader”, and “eraser” of m6A modification correlated closely with immune infiltrating cells [71], giving rise to the application of m6A RNA methylation regulators in predicting immune efficacy. Two m6A RNA demethylases, FTO and ALKBH5 were targeted to develop inhibitors (72–74), providing insights into understanding the roles of m6A RNA methylation involved in multiple diseases. m5C RNA modification is regarded as a novel methylated process in eukaryotes. Small-molecular inhibitors targeting m5C RNA methylation regulators were conceived by proof-of-concept studies, while, specific m5C inhibitors have yet to be developed [75]. m5C RNA methylation regulators can impact the process of tumorigenesis by regulating TIME in cancers, so that inspecting the roles involved in the immune system will give hints to personalized immunotherapy strategies making. m1A methylation modification is a new form of modification of RNA, thus, studies on m1A modification in tumorigenesis are rarely reported. Although several signatures based on m1A modification were built to guild effective immunotherapy strategies [70, 76], controversies remained when detecting the m1A methylation sites [77, 78]. More efficient and accurate technologies need to be developed to uncover the m1A modification sites to fully exploit the value of m1A modification in anti-tumor immunotherapies. More effort is deserved to understand the complex network regulated by different kinds of RNA methylations in modulating tumor-immune interactions. However, in the current study, we focused on the prospects of m5C methylation regulator as the predictive biomarker for ICIs treatment.
The quantity of cancer mutations is reflected by TMB. Major histocompatibility complex proteins turn mutations into neoantigens and further present them to T cells. More neoantigens are produced by higher TMB, which in turn boosts the likelihood that T cells recognition will happen, clinically corresponding with improved ICI outcomes [79]. Several studies have shown that high TMB and neoantigens correlated with better prognosis in non-small-cell lung cancer (NSCLC) and melanoma (80–82). In this study, the low-risk group possessed more mutations and higher level of neoantigens than the high-risk group, suggesting a better response to immunotherapy within the low-risk group. We also identified the stratifying efficiency of the model in patients with same status of neoantigens and TMB. The prognostic power of the established model was superior to neoantigens or TMB. These results indicated that our model had the potential to combine with or modify existing biomarkers, achieving improved accuracy in prognostic prediction. In addition to using neoantigens and TMB, immune checkpoints can be inhibited to enable T cell functions. By allowing T-cell reactivation, ICIs have revolutionized cancer treatment [83]. The Food and Drug Administration (FDA) has approved six inhibitors of the programmed cell death protein pathway (PD1/PD-L1) and an inhibitor of the CTLA-4 for use in treating various cancers (84–86). In our study, we observed a weak correlation between model factors and immune checkpoints except for NSUN4. In fact, immune checkpoints alone are not sufficient to predict the efficiency of the immunotherapy due to a highly complex immune tumor microenvironment, which could be generalized by a cancer immunity cycle [87]. Several studies have suggested integrating multiple biomarkers to predict the immune response, including tumor-infiltrating lymphocytes, mutational burden, immune gene signatures, and multiplex immunohistochemistry [88, 89]. TIDE is a reliable surrogate biomarker that could accurately predict immune checkpoint blockade (ICB) response by measuring the tumor immune escape, and it even performed better than PD-L1 expression in melanoma; that is, a higher TIDE score is associated with worse ICB response and worse patient survival under anti-PD1 and anti-CTLA4 therapies [90]. According to our previous studies and others, the immune landscape is crucial in assessing the efficacy of immunotherapy and chemotherapy targeting CRC patients (91–94). However, the role of m5C RNA methylation regulators in patients with rectal cancer is still unclear. In the current research, we found that responders were proportionally more frequent in the low-risk group compared with the high-risk group. The lower TIDE prediction score, T-cell dysfunction score, CAF, and higher MSI score in the low-risk group indicate a good function of T cells with high infiltration by cytotoxic T lymphocyte (CTL), further explaining why the low-risk group was more sensitive to immunotherapy. In addition, in IMvigor210 cohort with the determined immune response, these results were well confirmed. Besides, drug sensitivity was examined between the low- and high-risk groups by performing the R package “oncoPredict”. Apparently, the majority of the chemotherapeutic agents achieved their efficacy among the low-risk group; nonetheless, drugs targeting specifically the high-risk group were also investigated by screening drugs of CTRP and PRISM databases. The AUC values between two risk groups were compared and drugs intended to the high-risk group were selected. Finally, chlorambucil and SKI.II were found in both the CTRP and PRISM databases. These results indicated the built risk model was a trustworthy and robust approach for a thorough evaluation of each patient’s therapeutic response, which could benefit the precision treatment combining immunotherapy and chemotherapy for patients with rectal cancer.
Furthermore, the mRNA transcriptome differences between the high- and low-risk groups have been investigated. They were highly involved in the cancer and immune system–related biological pathways. The DEGs with prognostic efficacy were considered m5C-related signature genes. Two genomic subgroups were discovered based on the m5C signature genes, which could significantly predict the survival and immune response of READ patients, and were substantially connected with immunological activity. These results were similar to the stratification of the risk model. This once again showed the power of the m5C regulator–based signature in shaping the landscapes of the READ patients. Thus, a thorough analysis of m5C alteration patterns will definitely improve the precision classification and therapeutic strategy for patients with READ.
Despite the encouraging findings, the current study included several limitations. First, the gathered data were analyzed retrospectively, and multicenter research and large-scale prospective investigation are required to confirm and rectify our model. Second, the specific crosstalk between these m5C methylation regulators and corresponding immune characteristics remains unrevealed. The regulatory network of the three genes in rectal cancer needs to be further investigated. As for now, the genes regulated by NSUN4 and NSUN7 still need to be identified. Research related to the regulatory role of the three genes could provide novel insights into the mechanisms of the built signature. Third, the ability of this signature to predict immunotherapeutic or chemotherapeutic response was assessed indirectly due to the lack of data from patients with rectal cancer receiving related treatments. Research focusing on the therapeutic effect of the current signatures should be done in vitro and in vivo in the future. Fourth, the sizes of clinical tissue specimens for TMA and RT-qPCR assay used in our independent validation cohorts were limited, and more samples are expected to verify the m5C methylation regulator –based signature in the future.
In conclusion, the established risk model could be used to comprehensively evaluate the prognosis and the clinical response to adjuvant chemotherapy and immunotherapy among patients with rectal cancer. Moreover, the complex characteristic of the TIME cell infiltration could be effectively illustrated by the built signatures based on m5C regulators, producing a number of novel insights for cancer immunotherapy. Our research offers fresh approaches for predicting survival status, enhancing immunotherapy outcomes, disclosing various tumor immune phenotypes, and conclusively, advancing tailored cancer treatment in the future.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Research Ethics Committee of The Affiliated Hospital of Qingdao University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MY and RZ conceived and designed the experiments. MY supervised the work. JZ, RZ, MZ, and ZY provisioned the study materials or patients. RZ, JZ, TL, and SL collected and assembled the data. RZ, ZZ, WW, and FZ analyzed and interpreted the data. MY and RZ wrote the article. The final manuscript was read and approved by all authors. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1054700/full#supplementary-material
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|
---
title: Effects of Lactobacillus plantarum fermented Shenling Baizhu San on gut microbiota,
antioxidant capacity, and intestinal barrier function of yellow-plumed broilers
authors:
- Weijie Lv
- Yimu Ma
- Yingwen Zhang
- Tianze Wang
- Jieyi Huang
- Shiqi He
- Hongliang Du
- Shining Guo
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC9992544
doi: 10.3389/fvets.2023.1103023
license: CC BY 4.0
---
# Effects of Lactobacillus plantarum fermented Shenling Baizhu San on gut microbiota, antioxidant capacity, and intestinal barrier function of yellow-plumed broilers
## Abstract
The current study focused on the effects of Shenling Baizhu San (SLBZS) fermented by *Lactobacillus plantarum* (L. plantarum) on gut microbiota, antioxidant capacity, and intestinal barrier function of yellow-plumed broilers. Our results showed that the content of ginsenoside Rb1 was the highest when SLBZS were inoculated with $3\%$ L. plantarum and fermented at 28°C for 24 h. One-day-old male broilers were divided into five treatment groups. Treatment consisted of a basal diet as a control (Con), $0.1\%$ unfermented SLBZS (U-SLBZS), $0.05\%$ fermented SLBZS (F-SLBZS-L), $0.1\%$ fermented SLBZS (F-SLBZS-M), and $0.2\%$ fermented SLBZS (F-SLBZS-H). On days 14, 28, and 42, six chickens from each group were randomly selected for blood collection and tissue sampling. The results showed that the addition of $0.1\%$ fermented SLBZS could significantly increase average daily feed intake (ADFI) and average daily gain (ADG), and decrease feed conversion ratio (FCR) of broilers. The addition of 0.1 and $0.2\%$ fermented SLBZS significantly increased the lymphoid organ index of broilers on day 28 and 42. The addition of 0.1 and $0.2\%$ fermented SLBZS could improve the antioxidant capacity of broilers. Moreover, the addition of 0.1 and $0.2\%$ fermented SLBZS could significantly increase the villus height/crypt depth of the ileum, and significantly increase the expression of tight junction. In addition, fermentation of SLBZS increase the abundance of Coprococcus, Bifidobacterium and Bilophila in the gut of broilers. These results indicate that the supplementation of fermented SLBZS in the diet could improve the growth performance, lymphoid organ index, antioxidant capacity, and positively affect the intestinal health of broilers.
## 1. Introduction
As early as 1940s, antibiotics were found to improve the growth efficiency of poultry and swine [1]. Since then, an increasing number of reports have corroborated the growth-promoting effects of antibiotics and its application to animal husbandry (1–3). The development of drug resistance in food animals was first reported in 1951 [4]. Some studies have shown that the use of antibiotics in food animals produce bacterial resistance and affect human health through the food chain, causing public health problems (5–7). Many countries have reduced or banned the addition of growth promoting antibiotics to animal feed, hence necessity of antibiotics substitute. Studies have shown that many substances have great potential to be substitutes for antibiotics additives, such as plant-derived products with known antibacterial properties derived from herbs and spices, probiotics, prebiotics, antimicrobial peptides and phages [8, 9]. Among them, herbal medicine attracts many scientists focusing on application and effectiveness of herbal medicine so as to provide scientific guidance for animal production. In addition to its positive effects and people's historical and traditional experience, herbal medicine has also been proved to be fewer side effects [10] and not easy to cause drug resistance [11], suitable and outstanding to animal feed addition.
Some herbs have been proved to have positive effects on animals when added to feed. The review by Mahmoud Alagawany et al. mentioned that licorice improve the growth performance, immune function and antioxidant capacity of poultry when added to poultry feed [12]. Fengjie Ji et al. found that adding Alpiniae Oxyphyllae (The Chinese name is Yizhi) to the feed improve the growth performance and maintain intestinal health of Cargill ducks [13]. In addition, herbs are deemed to enhance their original properties and/or produce new effects when fermented under appropriate conditions [14]. A formula named “Jian Ji San” fermented by *Bacillus subtilis* improved the growth performance of broilers [15]. The herbal blend fermented with Lactobacillus regulated gut microbiota and immune response, and improve growth performance [16]. The fermentation of sanguismus by *Lactobacillus plantarum* can effectively inhibit the growth of Aeromonas hydrophila, regulate the immune response and improve the survival rate of crucian carp against *Aeromonas hydrophila* [17]. Buzhongyiqi decoction, Sijunzi decoction, Shenlingbaizhu decoction and their fermented with *Lactobacillus plantarum* reduce the diarrhea symptoms caused by ceftriaxone sodium and improve intestinal flora and barrier function [18]. It can be seen that the application of fermented Chinese herbs in animal husbandry has a broad prospect.
Shenling Baizhu San (SLBZS) is a famous formula in “Tai Ping Hui Min He Ji Ju Fang”. In our previous studies, SLBZS alleviated antibiotic-associated diarrhea by regulating gut microbiota [19], relieved inflammatory bowel disease through multiple pathways [20], and improved DSS-induced colitis by inhibiting the pyroptosis pathway [21]. In addition, the theory of Traditional Chinese medicine believes that SLBZS has a good effect on appetizing and gaining weight. This may solve the problem of slow growth of some economic animals. However, there is no report about the application of SLBZS fermentation in broiler breeding.
This study explored the fermentation process of SLBZS and discovered the effect of fermentation SLBZS on growth performance and intestinal barrier function of broilers.
## 2.1. Chemical and reagents
Polymyxin B sulfate was purchased from Dalian Meilun Bio-Technology Co., Ltd (Dalian, China). Ginsenoside Rb1 (batch number: PRF20120442) was purchased from Chengdu Prufa Technology Development Co., Ltd (Chengdu, China). De Man Rogosa Sharpe (MRS) agar medium, MRS broth medium and Modified Chalmers (MC) agar medium were all purchased from Qingdao Haibo Biotechnology Co., Ltd (Qingdao, China). Chicken IgA kit and Chicken IgG kit were purchased from Shanghai Mlbio Co., Ltd (Shanghai, China). Glutathione Peroxidase (GSH-PX), Superoxide Dismutase (SOD), Total antioxidant capacity (T-AOC), Malondialdehyde (MDA) kits were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Trizol was purchased from Invitrogen (Shanghai, China). DEPC water, ChamQ Universal SYBR qPCR Master Mix, HiScript III RT SuperMix for qPCR, purchased from Vazyme Biotech Co., Ltd (Nanjing, China). The chemicals and reagents used in this study were all pure analytical grade.
## 2.2. Preparation of SLBZS
SLBZS is composed of Panax Ginseng, Wolfiporia cocos, Atractylodes macrocephala, Dioscorea opposita, Dolichos Lablab, Semen Nelumbinis, Semen Coicis, Fructus Amomi, *Platycodon grandiflorus* and *Glycyrrhiza uralensis* Fisch, all herbs were purchased from Beijing Tongrentang Guangzhou pharmaceutical chain Co., Ltd (Guangzhou, China). Panax Ginseng, Wolfiporia cocos, Atractylodes macrocephala, Dioscorea opposita, Dolichos Lablab, Semen Nelumbinis, Semen Coicis, Fructus Amomi, *Platycodon grandiflorus* and *Glycyrrhiza uralensis* Fisch at a ratio of 4:4:4:4:3:2:2:2:2:4, pulverize in a beater and pass through a sieve of 60 mesh [19, 20, 22]. 10g SLBZS powder was accurately weighed in a 250 ml conical flask, and $4\%$ (corn flour by weight/substrate by weight) of corn flour was added to it. High-temperature sterilization was performed at 121°C for 20 min, and cooling to room temperature for later use.
## 2.3.1. Chromatographic condition
Analysis of constituents in SLBZS was performed on an 1525-2707-2489 series HPLC system (Waters, United States) equipped with a binary solvent manager, sample manager, column compartment, UV detector with 280 nm, and LC-Solution software. The chromatographic column is Sun Fire C18 column (4.6 × 250 mm, 5 μm). Mobile phase A was $0.1\%$ phosphoric acid aqueous solution (1:999, V/V), and mobile phase B was acetonitrile, the gradient elution procedure is shown in Supplementary Table S1. The flow rate was 1 mL /min, the injection volume was 50 μL, the column temperature is 40°C, Uv detection wavelength was selected as 203 nm. HPLC diagram of ginsenoside Rb1 standard was shown in Supplementary Figure S1.
## 2.3.2. Preparation of reference solution
Accurately weigh 14 mg ginsenoside Rb1 in a 10 mL volumetric flask and add methanol to 10 mL to prepare a reference solution containing ginsenoside Rb1 1400 μg per 1 mL methanol. Seal the solution with a sealing film and store at 4°C for later use.
## 2.3.3. Preparation of the test solution
Accurately weigh 5 g powder to be tested and placed in a 50 mL conical flask, 10 mL of $70\%$ methanol was added, the plug was covered, its quality was recorded, and the water temperature was controlled at 40°C. The ultrasonic was 45 min and stirred every 15 min with ultrasonic power of 50 kHz. Let it sit overnight, ultrasound again for 45 min, cool it at room temperature, weigh the weight again, use $70\%$ methanol to make up the weight lost, mix well, transfer it to 15 mL centrifuge tube, centrifuged at 4,000 rpm for 10 min, the supernatant was filtered by 0.22 μm microporous membrane to obtain the test solution.
## 2.4. Bacterial cultures and growth conditions
Lactobacillus plantarum (L. plantarum) was grown in MRS broth medium at 37°C without shaking. After 24 h, the final concentration of the bacterial solution was 2 × 109 CFU/mL, which was inoculated into the new MRS *Broth medium* at the rate of $2\%$ for extended culture. After 24 h of culture in a shaker, centrifuged at 4000 rpm for 10 min, take the bacterial mud for use.
## 2.5. Exploration of fermentation conditions
SLBZS was fermented by L. plantarum, and the content of ginsenoside Rb1 in Shenlingbaizhu Powder after fermentation was used as the index. The bacterial inoculum, fermentation temperature and fermentation time were explored. Single factor levels are shown in Supplementary Table S2.
## 2.5.1. Effect of bacterial inoculum on fermentation
Add SLBZS to 5 mL of distilled water, the ammonium sulfate content (as a source of N) is $0.2\%$, the fermentation temperature was 28°C, the fermentation time was 36 h, the bacterial inoculum was 1, 3, 5, 7, $9\%$ (V/W bacterial mud volume/ substrate by weight), respectively. The experiment was repeated three times and the results were averaged. The content of ginsenoside Rb1 was obtained.
## 2.5.2. Effect of fermentation temperature on fermentation
Add SLBZS to 5 mL of distilled water, the ammonium sulfate content is $0.2\%$, the bacterial inoculum was $3\%$ (V/W bacterial mud volume/substrate by weight), the fermentation time was 36 h, and the fermentation temperature was 24, 28, 32, 36, 40°C, respectively. The experiment was repeated three times and the results were averaged. The content of ginsenoside Rb1 was obtained.
## 2.5.3. Effect of fermentation time on fermentation
Add SLBZS to 5 mL of distilled water, the ammonium sulfate content is $0.2\%$, the bacterial inoculum was $3\%$ (V/W bacterial mud volume/ substrate by weight), the fermentation temperature was 28°C, and the fermentation time was 12, 24, 36, 48, 72 h, respectively. The experiment was repeated three times and the results were averaged. The content of ginsenoside Rb1 was obtained.
## 2.5.4. Calculation of ginsenoside Rb1 yield
Calculation method of ginsenoside Rb1 yield: V: volume of test solution, in mL. ρn: mass concentration of ginsenoside Rb1, in μg/mL. M: mass of powder to be tested, in g. The unit of yield of ginsenoside Rb1 was mg/g.
## 2.6. Determination of pH value
Accurately weigh 2.0 g of powder to be measured in a beaker, add 1 mL distilled water, stir evenly, add 20 mL distilled water, stir for 5 min, then stand for 20 min, determine the pH value of supernatant with a precision pH meter.
## 2.7. Determination of L. plantarum concentration
The powder to be measured was precisely weighed 2.0 g in a 50 mL conical flask, and 18 mL sterilized normal saline was added. The powder was stirred and mixed with a glass rod, and was incubated at 37°C at 140 r/min and shaken for 45 min to obtain a dilution of 1:10. In the clean bench, dilute tenfold, successively to 10−9. Choose 3–4 suitable gradients, absorb 1 mL of diluent from each gradient into a sterile plate, Add 15 mL of modified MC medium (10,000 IU polymyxin B sulfate per 100 mL MC agar) in time. Shake the dish to mix thoroughly, and do two repetitions for each dilution. At the same time, 1 mL sterilized normal saline for dilution was used instead of diluent as blank control. Let stand for 40–50 min, turn the plate upside down, culture at 37°C for 36–48 h, and select colony count by transparent ring. Calculation formula: L. plantarum concentration = average colony × dilution ratio.
## 2.8. Animals and experimental design
One hundred and sixty healthy 1-day-old male yellow-feathered broilers were purchased from Guangdong Wens Dahuanong Biotechnology Co. Ltd (Guangdong, China). All experimental procedures used in this study were approved by the Animal Ethics Committee of the South China Agricultural University (approval number: SYXK 2019-0136, Guangzhou, China). The care and use of all animals were performed according to the Guidelines for Animal Experiments of the South China Agricultural University.
Broilers were randomly assigned into 5 treatment groups with 4 replicates (cages) per treatment and 8 birds per replicate. Treatments were as followed (Table 1): [1] control (Con) group, [2] unfermented SLBZS (U-SLBZS) group, [3] fermentation SLBZS low dose (F-SLBZS-L) group, [4] fermentation SLBZS medium dose (F-SLBZS-M) group, [5] fermentation SLBZS high dose (F-SLBZS-H) group. The experiment lasted for 42 days, with 1–14 days as the first stage, 15–28 days as the second stage, and 29–42 days as the third stage. Broilers were raised in cages without immunization and were free to eat and drink during the experiment, feed composition and nutrient level are shown in Supplementary Table S3. In the first week, the lighting time was controlled at 23 h, the darkness time was 1 h, and the temperature was controlled at 33–35°C. From the second week, the lighting time was controlled at 18 h, the darkness time was 6 h, and the temperature was gradually reduced from 33–35 to 25°C. Feed intake of broilers was recorded every day. On days 14, 28 and 42, 6 broilers were randomly selected from each group and fasted for 12 h. Body weight was recorded and blood was collected from jugular vein. The collected blood was centrifuged at 3000 rpm for 10 min, and serum was stored in −80°C for testing. After the broiler was euthanized, its viscera and cecal contents were collected and its weight was recorded, the viscera and cecal contents were stored in −80°C. Organ index = organ weight/body weight × $100\%$.
**Table 1**
| Group | Treatment |
| --- | --- |
| Control group (Con) | |
| Unfermented SLBZS group (U-SLBZS) | Add 0.1% unfermented SLBZS |
| Fermentation SLBZS low dos group (F-SLBZS-L) | Add 0.05% fermented SLBZS |
| Fermentation SLBZS medium dose group (F-SLBZS-M) | Add 0.1% fermented SLBZS |
| Fermentation SLBZS high dose group (F-SLBZS-H) | Add 0.2% fermented SLBZS |
## 2.9. ELISA
ELISA kit was used to quantify the contents of IgA and IgG in serum of broilers. The absorbance at 450 nm was measured at 37°C on a Mltiskan FC Automatic microplate reader (Thermo Fisher Scientific, Massachusetts, USA) to determine factor content levels.
## 2.10. Histological procedures
Tissue was collected and fixed in $10\%$ buffered formalin, embedded in paraffin and sliced into 5-μm-thick sections. Tissues were stained with H&E, and slides were assessed for villi and crypt structure.
## 2.11. Tissue RNA extraction and qRT-PCR analysis
Total RNA from the liver tissue was extracted using Trizol (Vazyme Biotech Co.,Ltd, Nanjing, China),and a Takara PrimeScript RT Kit (Vazyme Biotech Co., Ltd, Nanjing, China) was used to transcribe RNA into cDNA. ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd, Nanjing, China) was used for qRT-PCR following the manufacturer's instructions. NCBI Primer-BLAST (USA) was used to design the primers. The primer sequences are shown in the Supplementary Table S5.
## 2.12. 16S rRNA sequencing analysis
DNA extraction from broilers fecal samples was performed using DNeasy PowerSoil Kit (MoBio/QIAGEN) according to the manufacturer's instructions. The absorbance values of DNA at $\frac{260}{280}$ nm were measured using a fluorescence spectrophotometer to assess the concentration of sample DNA. The quality of DNA was detected by $1\%$ agarose gel electrophoresis. The V3-V4 region of the microbial 16S rRNA gene was amplified by PCR. The primer sequences were F: ACTCCTACGGGAGGCAGCA, R: GGACTACHVGGGTWTCTAAT. Sequencing was performed by Shanghai Paisano Biotechnology Co., Ltd. (Shanghai, China) using Illumina MiSeq gene sequencing platform.
## 2.13. Statistical analysis
The data were analyzed using SPSS 22.0 software (IBM, Armonk, NY, USA). GraphPad Prism 7.0 software (GraphPad; San Diego, CA, USA) was used to generate graphs. All data are presented as the mean value ± SD of at least three independent experiments. The data were ANOVA, Least-SignificantDifference (LSD) and Duncan's. $p \leq 0.05$ was considered significant.
## 3.1. L. plantarum fermentation improve the content of ginsenoside Rb1 in SLBZS
Probiotic fermented herbs have been proved to improve the effect. For example, Danggui Buxue Tang fermented by L. plantarum enhanced its anti-diabetes function [23], Cherries fermented with L. plantarum improved immunity in immunosuppressed mice [24]. Therefore, we explored the content changes of SLBZS before and after L. plantarum fermentation. Firstly, the effects of bacterial inoculation amount, fermentation temperature and fermentation time were investigated by single factor test. Single factor levels are shown in Supplementary Table S2. Ginsenoside Rb1 is one of the main components of SLBZS, and the content of ginsenoside Rb1 was taken as the index. Through single factor test, $5\%$ bacterial inoculation, 28°C fermentation temperature and 36 h fermentation time were selected as the fermentation conditions (Supplementary Figure S2). In order to continue to optimize fermentation conditions, we designed an orthogonal experiment (Table 2), and the levels of each factor in the orthogonal test are shown in Supplementary Table S4. According to the K-value in the orthogonal experiment (Table 2), the best combination of fermentation conditions was $3\%$ of bacterial inoculation, 28°C and fermentation for 24 h. Therefore, $3\%$ of bacterial inoculation, fermentation temperature at 28°C and fermentation time at 24 h were selected for subsequent tests.
**Table 2**
| Test group | A: Bacterial inoculation amount | B: Fermentation temperature | C: Fermentation time | Ginsenoside Rb1 yield (mg/g) |
| --- | --- | --- | --- | --- |
| 1 | 1.0 | 1.0 | 1.0 | 15.96 |
| 2 | 2.0 | 2.0 | 2.0 | 20.08 |
| 3 | 3.0 | 3.0 | 3.0 | 18.83 |
| 4 | 1.0 | 2.0 | 3.0 | 20.38 |
| 5 | 2.0 | 3.0 | 1.0 | 20.37 |
| 6 | 3.0 | 1.0 | 2.0 | 14.52 |
| 7 | 1.0 | 3.0 | 2.0 | 19.7 |
| 8 | 2.0 | 1.0 | 3.0 | 13.78 |
| 9 | 3.0 | 2.0 | 1.0 | 19.86 |
| K1 | 56.04 | 44.26 | 56.19 | |
| K2 | 54.23 | 60.33 | 54.3 | |
| K3 | 53.21 | 58.89 | 52.99 | |
| k1 | 18.68 | 14.75 | 18.73 | |
| k2 | 18.08 | 20.11 | 18.1 | |
| k3 | 17.74 | 19.63 | 17.66 | |
| R | 0.94 | 5.36 | 1.07 | |
Next, we used the above fermentation conditions to ferment SLBZS, and ginsenoside Rb1 was analyzed by HPLC. According to the chromatogram of ginsenoside Rb1 standard, the peak of ginsenoside Rb1 was around 84.261 min (Supplementary Figure S1). When compared the chromatograms of SLBZS before and after fermentation the composition and content of SLBZS changed after fermentation (Figures 1A, B). The results showed that compared with unfermentation, the content of ginsenoside Rb1 in SLBZS after L. plantarum fermentation was significantly increased (Figure 1C). In addition, the enrichment of L. plantarum was associated with decreased pH after fermentation (Supplementary Figure S3). In summary, these results suggested that L. plantarum fermentation improve the content of ginsenoside Rb1 in SLBZS and may enhance the effect of SLBZS.
**Figure 1:** *Effects of L. plantarum fermentation on SLBZS. (A) Chromatograms of unfermented SLBZS. (B) Chromatogram of fermented SLBZS. (C) The yield of ginsenoside Rb1 before and after SLBZS fermentation. Bars represent mean values ± SD (n = 3). **** indicate p < 0.0001 compared with the Unfermention.*
## 3.2. Fermentation of SLBZS improve the growth performance of broilers
Some probiotic-fermented herbal promoted the growth of chicks [16] or broilers [25]. In our experiment, ADFI, ADG and FCR of broilers were measured at days 1–14 (the first stage), 15–28 (the second stage), 29–42 (the third stage) and 1–42 (whole stage). The results showed that the supplementation of either unfermented SLBZS or fermented SLBZS had no significant effect on growth performance in the first stage (Supplementary Figure S4). In the second stage, medium dose and high dose of fermented SLBZS improved the ADFI (Figure 2A), and three doses of fermented SLBZS significantly improved the ADG (Figure 2D) and reduced FCR of broilers (Figure 2G). In the third stage, medium dose and high dose of fermented SLBZS improved the ADFI of broilers (Figure 2B), three doses of fermented SLBZS significantly improved the ADG of broilers (Figure 2E), only medium dose of fermented SLBZS significantly reduced the FCR of broilers (Figure 2H). For the whole stage, medium dose and high dose of fermented SLBZS significantly increased the ADFI of broilers (Figure 2C), and three doses of fermented SLBZS increased the ADG (Figure 2F) and reduced the FCR of broilers (Figure 2I). The results suggested that adding unfermented or fermented SLBZS into the feed improved the growth performance of broilers, and the effect of fermented SLBZS is more effective.
**Figure 2:** *Effects of fermented SLBZS on growth performance of broilers. (A) ADFI of broilers in the second stage. (B) ADFI of broilers in the third stage. (C) ADFI of broilers in the whole stage. (D) ADG of broilers in the second stage. (E) ADG of broilers in the third stage. (F) ADG of broilers in the whole stage. (G) FCR of broilers in the second stage. (H) FCR of broilers in the third stage. (I) FCR of broilers in the whole stage. Bars represent mean values ± SD (n = 3 for ADFI, n = 4–6 for ADG and FCR). #, ##, and ### indicate p < 0.05, p < 0.01 and p < 0.001 compared with the Con group, * and ** indicate p < 0.05 and p < 0.01 compared with the U-SLBZS group.*
## 3.3. Fermentation of SLBZS increased the lymphoid organ index and immune factors of broilers
Next, we tested whether adding fermented SLBZS to the feed had an effect on the immune function of broilers. The results showed the addition of medium and high doses of fermented SLBZS significantly increased the spleen index, thymus index and bursa of fabricius index of broilers on day 28 and 42 (Figures 3B, C, E, F, H, I). In addition, three doses of fermented SLBZS on day 14 significantly increased thymus index of broilers (Figure 3D), but had no significant effect on spleen index and bursa of fabricius index (Figures 3A, G). We wondered whether adding fermented SLBZS to the feed had an effect on the expression of antibody. To verify the conjecture, we tested the content of IgA and IgG in serum. On day 14, serum IgA in low dose and high dose of fermented SLBZS was significantly higher than that of normal diet (Supplementary Figure S5A), however, IgG content did not increase significantly (Supplementary Figure S5D). On day 28 and day 42, serum IgA content showed an increasing trend but was not significant (Supplementary Figures S5B, C). Serum IgG content was significantly increased on day 28 when adding low dose and medium dose of fermented SLBZS, and significantly increased on day 42 when adding unfermented SLBZS and high dose of fermented SLBZS (Supplementary Figures S5E, F). *In* general, the result suggested that adding fermented SLBZS can increase the lymphoid organ index and immune factors of broilers.
**Figure 3:** *Effects of fermented SLBZS on lymphoid organs weight of broilers. (A) Spleen index of broilers on day 14. (B) Spleen index of broilers on day 28. (C) Spleen index of broilers on day 42. (D) Thymus index of broilers on day 14. (E) Thymus index of broilers on day 28. (F) Thymus index of broilers on day 42. (G) Bursa of Fabricius index of broilers in on day 14. (H) Bursa of Fabricius index of broilers on day 28. (I) Bursa of Fabricius index of broilers on day 42. Bars represent mean values ± SD (n = 3). # and ## indicate p < 0.05 and p < 0.01 compared with the Con group, * indicate p < 0.05 compared with the U-SLBZS group.*
## 3.4. Fermentation of SLBZS enhance the antioxidant capacity of broilers
Researchers found that adding herbal ingredients or fermented preparations to broiler feed increased antioxidant capacity (26–28). In order to determine whether adding fermented SLBZS to the feed affect the antioxidant capacity of broilers, we tested the serum antioxidant factors of broilers according to the operating instructions. The current study exhibited a significantly increase of SOD level in broilers when adding medium and high dose of fermented SLBZS (Figure 4A), but an unapparent increasing trend of GSH-PX (Figure 4B). Adding medium dose of fermented SLBZS significantly increased T-AOC level of broilers (Figure 4C), and high dose of that significantly reduced MDA content (Figure 4D). Moreover, compared with unfermented SLBZS, adding fermented SLBZS significantly increased the T-AOC of broilers (Figure 4C). Therefore, our results indicated that adding fermented SLBZS to broiler feed can improve antioxidant capacity of broiler.
**Figure 4:** *Effects of fermented SLBZS on antioxidant factor content in broilers. (A) Content of SOD in serum of broilers. (B) Content of GSH-PX in serum of broilers. (C) Content of T=AOC in serum of broilers. (D) Content of MDA in serum of broilers. Bars represent mean values ± SD (n = 3-5). # and ## indicate p < 0.05 and p < 0.01 compared with the Con group, * and ** indicate p < 0.05 and p < 0.01 compared with the U-SLBZS group.*
## 3.5. Fermentation of SLBZS improve ileum villus morphology of broilers
To better understand differences between broilers added fermented SLBZS and unfermented SLBZS, we investigated the variety of ileum villus morphology in two groups since it plays a crucial role in nutrients absorption. The result showed that both unfermented SLBZS and fermented SLBZS could significantly increase ileum villi height (VH) of broilers (Figures 5A, B). There was no significant change in crypt depth (CD) (Figure 5C). In addition, medium and high dose of fermented SLBZS significantly increased the villi height to crypt depth (VH:CD) ratio in ileum of broilers (Figure 5D). The increase in VH:CD ratio indicated increase in the intestinal surface area for nutrient absorption, which may contribute to improved growth performance [29, 30]. The results suggested that adding fermented SLBZS can improve the broiler initiation morphology.
**Figure 5:** *Effects of fermented SLBZS on intestinal morphology of broilers. (A) H&E of broiler ileum. Green arrow: villi height, Yellow arrow: crypt depth. Scale: 200 μm. (B) Villi height in ileum of broilers. (C) Crypt depth of ileum in broilers. (D) Villi height to crypt depth (VH:CD) ratio ileum of broilers. Bars represent mean values ± SD (n = 5). # and ## indicate p < 0.05 and p < 0.01 compared with the Con group.*
## 3.6. Fermentation of SLBZS enhance the intestinal barrier function of broiler cecum
Based on the result that adding fermented SLBZS can improve the broiler intestinal morphology, we explored the effects of fermented SLBZS on the intestinal barrier by detecting the RNA relative expression of Claudin-1, Occludin and Zonula Occludens-1 (ZO-1). We noticed, on day 14, that medium dose of fermented SLBZS increased the mRNA expression of Claudin-1, while low, medium and high dose of fermented SLBZS increased the mRNA expression level of Occludin (Figures 6A, B). All three doses had no significant effect on the mRNA expression level of ZO-1 (Figure 6C). On day 28, both medium and high doses of fermented SLBZS increased the mRNA expression of Claudin-1, while all three doses of fermented SLBZS increased the mRNA expression of Occludin and ZO-1 (Figures 6D–F). On day 42, medium dose fermentation SLBZS increased the mRNA expression of Occludin, and medium dose and high dose fermentation SLBZS increased the mRNA expression of Claudin-1 and ZO-1 (Figures 6G–I). However, on day 42, the addition of unfermented SLBZS and low dose fermented SLBZS reduced the expression of Claudin-1, Occludin, and ZO-1 (Figures 6G–I). In conclusion, adding medium dose of fermented SLBZS to broiler feed can increase the mRNA expression of tight junction protein in cecum of broilers. The result suggested that fermented SLBZS can enhance the cecal barrier function of broilers.
**Figure 6:** *Effects of fermented SLBZS on ileum intestinal barrier in broilers. (A) Relative mRNA expression of Claudin-1 in broiler cecum on day 14. (B) Relative mRNA expression of Occludin in broiler cecum on day 14. (C) Relative mRNA expression of ZO-1 in broiler cecum on day 14. (D) Relative mRNA expression of Claudin-1 in broiler cecum on day 28. (E) Relative mRNA expression of Occludin in broiler cecum on day 28. (F) Relative mRNA expression of ZO-1 in broiler cecum on day 28. (G) Relative mRNA expression of Claudin-1 in broiler cecum on day 42. (H) Relative mRNA expression of Occludin in broiler cecum on day 42. (I) Relative mRNA expression of ZO-1 in broiler cecum on day 42. Bars represent mean values ± SD (n = 3). # and ## indicate p < 0.05 and p < 0.01 compared with the Con group, * and ** indicate p < 0.05 and p < 0.01 compared with the U-SLBZS group.*
## 3.7. Fermentation of SLBZS altered the gut microbiota of broilers
In view of the effect of fermented SLBZS on the gut, we examined the gut microbiota of broilers. Compared with Con and U-SLBZS, Chao1 and Shannon index in F-SLBZS group were increased, indicating that the microbiota species increased, and the abundance and evenness increased (Figure 7A). Unifrac-based PCoA analysis demonstrated that F-SLBZS was clustered separately compared with Con and U-SLBZS (Figure 7B). These results indicated that the addition of fermented SLBZS significantly change the gut microbiota structure of broilers, while the addition of SLBZS had a limited effect on it. According to between-groups difference analysis, the gut microbiota of broilers fed fermented SLBZS changed significantly (Figure 7C). The heatmap of species composition showed that the abundance of Coprococcus, Bifidobacterium, Bilophila, human and Blautia in F-SLBZS were higher than those in Con and U-SLBZS (Figure 7D).
**Figure 7:** *Fermented alters the structure of the gut microbiota in broilers. (A) Alpha analysis, including Chao1 and Shannon. (B) Principal Co-ordinates Analysis (PCoA). (C) Between-groups difference analysis. (D) Heatmap of species composition.*
## 4. Discussion
Antibiotic growth promoters could improve the growth performance of livestock and poultry, and have obvious benefits on the structure and function of intestinal epithelium, which has been considered as the “gold standard” to improve the performance of feed additives, and provided a direction for finding substitutes [31]. However, with the wide spread of multi-drug resistant bacteria, public health has been seriously threatened. Many countries have banned antibiotics in feed, hence the need for its substitutes. Over the years, there are growing studies propose phytogenic products the most likely substitutes for antibiotics added to livestock and poultry feed [8, 9]. For example, Pirgozliev et al. showed the growth performance, energy and nutrient retention and the intestinal cytokine expression of broilers could be improved by adding $5\%$ carvacrol, $3\%$ cinnamaldehyde and $2\%$ capenne oleoresin to their diets [32]. Besides, Galli et al. found that adding additives containing thymol, cinnamaldehyde and carvacrol to broiler feed could improve growth performance and meat quality without compromising intestinal health [33]. Gholami-Ahangaran et al. suggested that the addition of Gunnera (*Gundelia tournefortii* L.) extract and protein to broiler feed had a synergistic effect on feed efficiency and antioxidant status and reduced lipid levels, while having no effect on liver function of broilers [34]. Our previous studies showed that SLBZS could alleviate antibiotic diarrhea [19] and colitis induced by DSS (20–22). Keeping healthy of the gut facilitates the absorbtion of nutrition [35, 36]. Therefore, we proposed that adding SLBZS into broiler feed as a feed additive would promote the growth performance of broilers.
Besides, in the process of studying phytogenic products, the very promising fermented herbal medicines drew our attention for their enhancements to their original properties and/or generations of new effects [14]. Gao et al. found that the addition of Chinese medicine–probiotic compound microecological preparation, which is composed of 5 traditional Chinese medicine herbs (Galla Chinensis, Andrographis paniculata, Arctii Fructus, Glycyrrhizae Radix, and Schizonepeta tenuifolia) fermented by Aspergillus niger and a kind of compound probiotics (L. plantarum A37 and L. plantarum MIII), could improve the growth performance, serum parameters, immune function and intestinal health of broilers [25]. Wang et al. found that adding fermented herbal preparations, which is composed of 4 traditional Chinese medicine herbs (Astragalus, P.notoginseng, licorice and chickpeas) fermented by L. paracasei KL1 and L. plantarum Zhang-LL, can improve growth performance, regulate gut microbiota and enhance immunity of broilers [16].
Studies have reported that different probiotics were selected for fermentation, such as L. plantarum, Lactobacillus paracei and Bacillus [16, 18, 23, 37]. Based on these previous studies, we selected L. plantarum for fermentation and explored the best fermentation conditions. Ginsenoside Rb1 is often considered as one of the active components of SLBZS, so we chose ginsenoside Rb1 as the fermentation index. Bacterial inoculum on fermentation, fermentation temperature and fermentation time are often the variables that need to be controlled [15, 38]. Orthogonal experiments are often used to analyze the optimal combination of multifactor levels and to screen fermentation conditions [25]. Through orthogonal experiments, $3\%$ of bacterial inoculum, 28°C and 24 h of fermentation were selected as the conditions. To determine the occurrence of fermentation, we tested L. plantarum content and pH before and after fermentation. Lactobacillus fermented carbohydrate to lactic acid or other acids, and decrease the pH [39, 40]. The results showed the content of L. plantarum increased significantly and that the pH decreased significantly after fermentation, indicating that L. plantarum fermented SLBZS during this process. The results also showed the content of ginsenoside Rb1 in SLBZS increased significantly after fermentation. Ginsenoside Rb1 has been shown to have many benefits to the organism, including anti-oxidation, anti-stress, anti-inflammatory and so on [26, 41, 42]. We suggested that L. plantarum fermentation of SLBZS could enhance its efficacy.
Next, we added $0.1\%$ unfermented SLBZS and $0.05\%$, 0.1 and $0.2\%$ fermented SLBZS to broiler feed, respectively. As far as we know, there is no report about SLBZS or fermented SLBZS on the growth performance of broilers, so we tested the growth performance of experimental broilers. Our results showed that medium and high doses of fermented SLBZS could significantly improve the ADFI of broilers and all three doses of fermented SLBZS could significantly improve the ADG of broilers in the second, third and whole stage, compared with the control group. Accordingly, the three doses of fermentation SLBZS in the second and the whole stage significantly reduced FCR, while the medium dose of fermentation SLBZS in the third stage significantly reduced FCR. ADFI, ADG and FCR are often used as indicators to evaluate growth performance [43, 44], so it can be concluded that fermentation of SLBZS could improve the growth performance of broilers. In addition, the supplementation of unfermented SLBZS tended to improve the growth performance of broilers, but there was no significant difference. During fermentation, the macromolecule or polymers forms of the active ingredient can be cut down to smaller molecule, which favors the transmembrane transport and improve adsorption of the active ingredients by the tissues [45]. This may be the reason why the effect of SLBZS fermentation is better than that of unfermented SLBZS.
Broilers are vulnerable to pathogen invasion in early life, and pathogens preferentially attack lymphatic organs [46, 47]. Relative lymphoid organ weight and immune factor content are often used to evaluate immune performance of broilers [48, 49]. Next, relative lymphoid organ weight and serum IgA and IgG contents of broilers were measured. Higher relative lymphoid organ weight is considered to have greater immune performance [50, 51]. In our experiment, in the second and third stages, medium dose and high dose of fermentation SLBZS could improve spleen, thymus and bursa of fabricius index. We observed that all three doses increased thymus index in the first stage, but had no significant effect on spleen and bursa of fabricius index. Both ginsenoside Rb1 and its intestinal metabolite Rh1 have been shown to regulate the balance of immune cells to inhibit infectious responses [52, 53]. In the first stage, both unfermented and fermented SLBZS significantly increased the IgA content of broilers. However, we observed no significant change in IgA content in the second and third stages. IgA is the first line of defense for intestinal mucosal immunity [54], its increase is good for gut health. During the whole experiment, IgG content did not change significantly, which might be due to the fact that IgG was mainly involved in secondary immune response [55]. *In* general, SLBZS could be metabolized more ginsenoside Rb1 by gut microbial after fermentation, which stimulates lymphocyte proliferation and improves immune function.
To a certain extent, antioxidant capacity can reflect the immune function and anti-infection ability of the organism [56]. SOD, MDA, GSH-PX and T-AOC are commonly used to evaluate the antioxidant capacity of broilers [57, 58]. In our study, unfermented SLBZS did not significantly affect the antioxidant capacity of broilers. Medium and high doses of SLBZS significantly increase the content of SOD in serum. Medium dose of fermented SLBZS significantly increase T-AOC of broilers, and high dose of fermented SLBZS significantly reduce MDA content. Ginsenosides Rb1 and Rh1 could prevent liver injury induced by APAP in mice and improve their antioxidant capacity [59]. Ginsenosides Rg2 and Rh1 also enhance liver antioxidant capacity through Nrf2 pathway [60]. In addition, Lactobacillus and its metabolite 5-methoxy-indoleacetic acid also enhance the antioxidant capacity of liver through Nrf2 [61]. Therefore, we speculated that the release of more active components in fermented SLBZS and the presence of Lactobacillus could improve the antioxidant capacity of broilers.
The absorption function of small intestine is closely related to the growth performance of broilers [36], so we measured the intestinal morphology of ileum of broilers. We observed that medium and high doses of fermented SLBZS significantly increased VH:CD. Ji et al. found that SLBZS could significantly improve ileum villus density in diarrhea rats [62]. This was also demonstrated by the significant increase in ileum villus height when adding unfermented SLBZS to the feed. A study has found that SLBZS could regulate the composition of gut microbiota in broilers [63]. The increase of beneficial microbiota could produce more short-chain fatty acids (SCFA) [64] and promote the differentiation and proliferation of intestinal epithelial cells [65]. Therefore, we suggest that fermentation of SLBZS can improve ileum VH/CD of broilers.
Tight junction proteins play an important role in maintaining the integrity of the intestinal barrier, helping to resist pathogen invasion of intestinal epithelial cells [31]. Occludin-1 plays an important role in tight junction barrier function and signal transduction [66], and ZO-1 plays an important role in tight junction protein composition and maintenance of cell barrier permeability [67]. Our study found that fermentation of SLBZS increased the relative expression of tight junction proteins. Fermentation of SLBZS produced increased saponins, which can better promote the proliferation of beneficial bacteria and produce more SCFA such as butyric acid. SCFA can increase AMPK activity in intestinal epithelial cells and accelerate the recombination of tight junction proteins [68]. We hypothesized that SLBZS fermentation increased the expression of tight junction protein-related mRNA may cause by SCFA. Our previous studies have shown that SLBZS polysaccharide increase the abundance of SCFA-producing bacteria, increase the SCFA content in the intestine, and promote intestinal injury repair [22]. According to our results, the abundance of Coprococcus, Bifidobacterium, Bilophila, human and Blautia in F-SLBZS were higher than those in Con and U-SLBZS. Coprococcus and Bifidobacterium are both butyric-producing bacteria [69]. Bilophila wexlerae contains a high amount of starch (i.e., amylopectin) and produces carbohydrate metabolites (i.e., succinate, lactate, acetate) [70]. As one of the metabolites of gut microbiota, SCFA is involved in the maintenance of intestinal barrier function and immune homeostasis [71]. This may be one of the reasons for the better effect of fermented SLBZS.
In conclusion, this study optimized the fermentation conditions of L. plantarum for SLBZS, and fermented SLBZS could be a potential feed additive to promote the growth performance in broilers.
## Data availability statement
The sequences obtained by 16S rRNA sequencing were deposited in NCBI BioProject PRJNA906993, Short Read Archive (SRA SAMN31944550- SAMN31944561).
## Ethics statement
The animal study was reviewed and approved by Animal Ethics Committee of the South China Agricultural University.
## Author contributions
WL and SG designed the overall research experiments. WL, YM, YZ, TW, JH, SH, and HD performed the experiments. WL, YM, and HD analyzed the data. WL and YM wrote the manuscript. SG revised the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2023.1103023/full#supplementary-material
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|
---
title: Staphylococcus aureus ST1 promotes persistent urinary tract infection by highly
expressing the urease
authors:
- Kai Xu
- Yanan Wang
- Ying Jian
- Tianchi Chen
- Qian Liu
- Hua Wang
- Min Li
- Lei He
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC9992547
doi: 10.3389/fmicb.2023.1101754
license: CC BY 4.0
---
# Staphylococcus aureus ST1 promotes persistent urinary tract infection by highly expressing the urease
## Abstract
Staphylococcus aureus (SA) is a relatively uncommon cause of urinary tract infections (UTIs) in the general population. Although rare, S. aureus-induced UTIs are prone to potentially life-threatening invasive infections such as bacteremia. To investigate the molecular epidemiology, phenotypic characteristics, and pathophysiology of S. aureus-induced UTIs, we analyzed non-repetitive 4,405 S. aureus isolates collected from various clinical sources from 2008 to 2020 from a general hospital in Shanghai, China. Among these, 193 isolates ($4.38\%$) were cultivated from the midstream urine specimens. Epidemiological analysis showed UTI-derived ST1 (UTI-ST1) and UTI-ST5 are the primary sequence types of UTI-SA. Furthermore, we randomly selected 10 isolates from each of the UTI-ST1, non-UTI-ST1 (nUTI-ST1), and UTI-ST5 groups to characterize their in vitro and in vivo phenotypes. The in vitro phenotypic assays revealed that UTI-ST1 exhibits an obvious decline in hemolysis of human red blood cells and increased biofilm and adhesion in the urea-supplemented medium, compared to the medium without urea, while UTI-ST5 and nUTI-ST1 did not show significant differences between the biofilm-forming and adhesion abilities. In addition, the UTI-ST1 displayed intense urease activities by highly expressing urease genes, indicating the potential role of urease in UTI-ST1 survival and persistence. Furthermore, in vitro virulence assays using the UTI-ST1 ureC mutant showed no significant difference in the hemolytic and biofilm-forming phenotypes in the presence or absence of urea in the tryptic soy broth (TSB) medium. The in vivo UTI model also showed that the CFU of the UTI-ST1 ureC mutant rapidly reduced during UTI pathogenesis 72 h post-infection, while UTI-ST1 and UTI-ST5 persisted in the urine of the infected mice. Furthermore, the phenotypes and the urease expression of UTI-ST1 were found to be potentially regulated by the Agr system with the change in environmental pH. In summary, our results provide important insights into the role of urease in S. aureus-induced UTI pathogenesis in promoting bacterial persistence in the nutrient-limiting urinary microenvironment.
## Introduction
Urinary tract infections (UTIs) are common and recurrent infections that are often mild but can become life-threatening if left untreated. They are categorized as either uncomplicated or complicated and as either lower (bladder) or upper (pyelonephritis), based on their pathophysiology (Foxman, 2010). Although various species can cause UTI, most of the infections are caused by Gram-negative facultative anaerobic bacteria, such as Escherichia coli, Klebsiella pneumoniae, and Proteus vulgaris, and some infections are caused by Gram-positive bacteria, such as Enterococcus faecalis, Clostridium perfringens, and Staphylococci species (Donaldson, 1964; Hajdu et al., 2007; Foxman, 2010). Staphylococcus aureus (S. aureus, SA) may lead to complicated UTI in some diabetic, immunosuppressed, or catheter-applied patients (Zubair et al., 2019), while *Staphylococcus saprophyticus* (Flores-Mireles et al., 2015) may cause uncomplicated UTIs in sexually active females.
Staphylococcus aureus, a versatile and opportunistic pathogen, results in a wide range of infectious diseases, ranging from shallow skin infections to mortal endocarditis (Lowy, 1998). UTIs and Staphylococcal infections are common hospital-acquired infectious diseases (Ekkelenkamp et al., 2007; Hajdu et al., 2007; Foxman, 2010), although S. aureus-induced UTIs are uncommon, accounting for approximately $0.021\%$–$1.53\%$ of the UTIs (Ekkelenkamp et al., 2007; Foxman, 2010; Kitano et al., 2021). However, increased use of antibiotics has led to the emergence of methicillin-resistant S. aureus (MRSA) in the community and hospitals (Lee et al., 2018). Consequently, the incidence of MRSA-induced UTIs, which are resistant to routine antibiotic therapy, has increased in recent years, especially in immunocompromised or indwelling urinary catheter-applied patients (Karakonstantis and Kalemaki, 2018). Therefore, determining the molecular epidemiology, pathophysiology, and phenotypic characteristics of S. aureus-induced UTIs is necessary despite their relatively low prevalence.
Most Gram-negative bacteria initiate bladder infection through pili or biofilm formation by adhering directly to the tissues (Flores-Mireles et al., 2015). Moreover, a few uropathogens may secrete urease to survive the lower pH conditions in the bladder (Konieczna et al., 2012; Flores-Mireles et al., 2015; Zhou et al., 2019). Urease is the first metal-ion-containing enzyme to be isolated, and it is ubiquitous (Dixon et al., 1975; Konieczna et al., 2012). Many organisms, including plants, fungi, and bacteria, produce urease to hydrolyze urea, thus generating ammonia and carbonic acid (Krajewska and Ureases, 2009). Bacterial ureases show a wide range of compositions. Most bacterial ureases are composed of three subunits (α, β, and γ), nickel ions, and multiple accessory proteins, which are considered virulence factors (Rutherford, 2014). However, *Helicobacter pylori* urease lacks γ subunit but functions efficiently in elevating the pH of the stomach microenvironment (Konieczna et al., 2012). For urease-positive pathogens, urea hydrolysis can function as an acid response to elevate the acidic environment in the stomach or urine (Rutherford, 2014), thereby promoting epithelium destruction and renal stone formation (Rutherford, 2014; Schaffer et al., 2016). The majority of S. aureus (>$90\%$) strains can produce urease; however, the clinical urease detection test—*Christensen urea* agar—is qualitative and time-consuming. Therefore, in this study, we used a modified semi-quantitative assay (Duran Ramirez et al., 2022) to measure the urease activity and the underlying pathogenic mechanism of S. aureus-induced UTI. Recently, there has been an increase in studies on Gram-positive pathogens and S. aureus-induced UTIs. Studies have revealed that urease is an essential factor in promoting bacterial colonization (Hiron et al., 2010; Remy et al., 2013; Duran Ramirez et al., 2022) and that copper resistance plays a role in MRSA-associated urinary tract fitness (Saenkham-Huntsinger et al., 2021).
For our study, we collected a total of 4,405 S. aureus isolates from various clinical sources during 2008–2020 from a general Hospital in Shanghai, China. We performed multilocus sequence typing (MLST) to identify the sequence type (ST) of the isolates and analyzed the antibiotic resistance profile to characterize the strains. In addition, we explored the potential pathogenesis of urinary tract infection-derived-sequence type (UTI-ST)-1, which is one of the main STs of UTI-derived SA (UTI-SA).
## Bacterial strains and growth conditions
For this study, we collected 4,405 non-repetitive S. aureus isolates from various clinical sources during 2008–2020 from a general Hospital in Shanghai, China. Thereafter, we randomly selected 10 isolates of UTI-ST1, UTI-ST5, nUTI-ST1, UTI-ST7, and UTI-ST398 to perform the phenotypic experiments. All the strains were grown in TSB (Oxoid, United States) in the presence or absence of urea supplementation ($2\%$ w/v; Yeasen, Shanghai, China). A nutrient-deficient medium, containing 1 g of peptone (Diamond, Shanghai, China), 1 g of D(+)-glucose (Sangon Biotech, Shanghai, China), 2 g of potassium dihydrogen phosphate (BBI, Shanghai), and 5 g of sodium chloride (Diamond, Shanghai) in 1 L of sterilized water, with or without $2\%$ (w/v) urea-supplementation (Yeason), was prepared as described previously (Duran Ramirez et al., 2022), to conduct the urease activity test. The nutrient-deficient medium without phenol red was used to conduct the growth test.
## Growth curve
We selected 10 isolates of UTI-ST1 and UTI-ST5 for the growth curve test. The isolates were grown overnight in 3 mL TSB at 37°C and 220 rpm. The overnight cultures were then washed two times with phosphate-buffered saline (PBS) and diluted (1:10) in 3 mL fresh nutrient-deficient medium with or without urea supplementation ($2\%$ w/v). Thereafter, the diluted cultures were inoculated into sterile 96-well flat-bottom tissue culture plates (200 μL/well; Corning) and incubated at 37°C with shaking in Micro-ELISA Autoreader (Synergy 2, Bio-TeK, United States), and the OD was measured at 600 nm, every 30 min. The assay was performed in triplicate.
## Semi-quantitative biofilm assay
Semi-quantitative biofilm assay was performed as previously described (Vuong et al., 2003; He et al., 2019). Briefly, the overnight bacterial cultures were diluted with TSB, containing $0.5\%$ glucose, to obtain a final OD of 0.05. Then, the diluted cultures were aliquoted to 96-well plates (200 μL/well) and incubated at 37°C for 24 h. The wells were washed with PBS after the gentle removal of the culture supernatants. Thereafter, the Bouin fixative was added to the wells to treat the biofilms for 1 h. The fixative was then gently aspirated and the wells were washed thrice with PBS, and then stained with $0.4\%$ (w/v) crystal violet (Sangon). Biofilm formation was determined by measuring the OD at 570 nm using the Micro-ELISA autoreader.
## Hemolysis test
The hemolysis test was conducted as described previously (He et al., 2018; Ridder et al., 2021). Hemolytic activities were determined by incubating the overnight culture-supernatants with human red blood cells (RBCs; $2\%$ v/v in Dulbecco’s phosphate-buffered saline, DPBS) for 1 h at 37°C and measuring the OD at 540 nm using an enzyme-linked immunosorbent assay (ELISA) reader. The assay was performed in triplicate. MRSA strain USA300 was used as a reference, as it displayed relatively strong virulence. A standard curve was made by using serial dilutions (1,2, 1:4, 1:8, and 1:16) of the supernatants of the RBC lysates.
## Adhesion and invasion of UTI-ST1 and nUTI-ST1 to human bladder epithelial 5,637 cells
The adhesion and invasion test was performed as described previously (Wang et al., 2018). The human bladder carcinoma 5,637 cells were cultured in Roswell Park Memorial Institute (RPMI) medium, supplemented with $10\%$ fetal bovine serum (FBS) and $0.1\%$ (w/v) urea, at 37°C and $5\%$ CO2 and inoculated (2 × 105 cells/well) in a 24-well plate. The UTI-ST1 and UTI-ST5 isolates were cultivated in TSB, with or without urea supplementation, to the mid-logarithmic growth phase and washed twice with PBS. Thereafter, the epithelial cells were infected with the UTI isolates at a 1:10 ratio. The adhesion assay was performed by co-incubating bacterial cells and epithelial cells for 2 h at 37°C and $5\%$ CO2. The cells were washed three times with PBS to remove the planktonic bacteria and lysed with $0.1\%$ sodium deoxycholate (Sangon) to release the adhered bacteria. Bacterial CFU was determined by serial dilutions of epithelial cell lysates on TSA plates. For the invasion test, the cells were incubated with bacteria for 4 h, and gentamicin (100 μg/mL) was added to the culture for 30 min to digest the bacteria outside the cells. Thereafter, the bacterial CFU was enumerated to assess the invasion ability.
## Urease activity
The nutrient-deficient medium with phenol red was used to perform the bacterial urease activity test. The overnight cultures of different isolates were centrifuged at 4,000 rpm and washed with PBS to remove the secreted urease. Bacteria were resuspended in the nutrient-deficient medium, with or without urea supplementation, and diluted to a final OD of 0.03–0.04. The urease activity was measured by continuously monitoring the OD at 560 and 415 nm, every 30 min, on the Micro-ELISA autoreader. Proteus mirabilis and E. coli were used as urease-positive and -negative controls, respectively.
## Quantitative reverse transcription PCR
The transcription of urease (ureACD) and accessory gene regulator (agr) effector (RNAIII) genes in UTI-ST1 and UTI-ST5 was detected by Quantitative reverse transcription PCR (RT-qPCR). Complementary DNA (cDNA) was synthesized from total RNA using the PrimeScript™ reverse transcriptase kit (Takara), according to the manufacturer’s instructions. Thereafter, the cDNA samples were amplified using the FastStart Universal SYBR Green Master kit (Roche). The reactions were performed on a 7500 Sequence Detector (Applied Biosystems). Purified chromosomal DNA (0.005–50 ng/mL) was used to construct a standard curve. The reactions were performed in triplicate, and DNA gyrase subunit B (gyrB) was used as an internal reference.
## MLST of Staphylococcus aureus isolates
MLST was performed using seven housekeeping genes (arcC, areO, yqiL, glp, pta, tpi, and gmk). The sequences of the PCR products were compared with the references on the MLST website1 for S. aureus.
## Murine UTI model
The murine UTI model was constructed as described previously (Hagberg et al., 1983; Armbruster et al., 2018; Rashid et al., 2021). Female C57BL/6 mice (aged 6–8 weeks, housed 5/cage) were obtained from JSJ Laboratory Animals LTD (Shanghai, China) and subjected to bacterial infection after 3 days, after overnight water-deprivation, to void the bladders in case of immediate micturition after transurethral infection. Subsequently, the bacterial culture (5 × 108 CFU/100 μL) was transurethrally injected into the anesthetized mice with a catheter needle (28G). Each mouse was inoculated with one kind of bacterial culture. Urine was collected every 24 h by massaging the abdomen of the mice and 10 μL of urine was used for serial dilution to calculate bacterial CFU. Bladders and kidneys were harvested for the quantification of bacterial burden (CFU/mL) at 72 hpi, by serial dilution plating and colony enumeration of homogenized organs after 24-h incubation.
## Neutrophils isolation
To conduct the cytotoxicity detection test, the neutrophils were isolated from the venous blood of healthy individuals as described previously (Kremserova and Nauseef, 2020). The anticoagulant blood was mixed with $3\%$ dextran and incubated for 20 min to separate the erythrocytes from the leukocytes. Thereafter, the neutrophils were isolated by a discontinuous density gradient of Ficoll–Hypaque (Sigma, Germany). After centrifugation at 400 g for 40 min at room temperature, the supernatant was discarded and the erythrocytes were lysed. Finally, the neutrophils were washed with PBS three times and suspended in RPMI (Hyclone, United States).
## Intracellular bacterial persistence
The isolated neutrophils and bacteria were co-incubated at 1:100 for 2 or 5 h at 37°C and $5\%$ CO2, in 96-well plates. The plates were centrifuged at 400 g for 10 min to remove the supernatant and each well was washed three times with PBS. The samples were serially diluted to calculate bacterial CFU, as described earlier.
## LDH assay
The cytotoxicity was detected using the lactate dehydrogenase (LDH) cytotoxicity detection kit assay (Roche, Germany). For this assay, the isolated neutrophils and bacteria were co-incubated for 2 or 5h at a 1:100 ratio in RPMI. In addition, three cell samples without bacteria and three cell samples with $0.1\%$ Triton X-100 were set as negative and positive controls, respectively. The OD of the samples at 490 or 540 nm was recorded.
## pH measurement
The pH of the cultures was measured by using a pH meter (Leici, Shanghai, China) as described previously (Zhou et al., 2019). Tris–HCl solutions (pH = 8.0 and 6.0) were used as pH standard solutions for calibration.
## Molecular genetic techniques
To construct the UTI-ST1 ureC and agrA mutants, a homologous recombination procedure was performed as described previously (Liu et al., 2015; Dai et al., 2017), using pKOR1 plasmid. The mutated DNA fragments were PCR-amplified from chromosomal DNA of S. aureus isolate UTI-ST1-15-68, using ureC-A, -B, -C, and -D and agrA-A, -B, -C, and -D primers. These products were cloned into pKOR1 using clonase reaction and attB sites, yielding pKOR1-ureC and pKOR1-agrA plasmids. Thereafter, the recombinant plasmids were transferred first to S. aureus RN4220 and then to S. aureus isolate UTI-ST1-15-68 via electroporation. Proper integration was verified by analytical PCR and DNA sequencing of the PCR-derived regions, using pKOR-Up-ApiI-F and pKOR-Dn-KpnI-R primers. The primers used for amplification are listed in Supplementary Table S1. *For* genetic complementation, the ureC gene was amplified with a pair of primers, ureC-SmaI-F, and ureC-BamHI-R, and the agrA gene was amplified with agrA-SmaI-F and agrA-BamHI-R, using genome DNA of UTI-ST1-15-68 as a template. The amplified DNA fragment was connected to the vector pos1 with T4 ligase to generate a complementation plasmid. Then, the plasmid was transduced into the UTI-ST1 ureC mutant and UTI-ST1 agrA mutant strains with ϕ85. The detailed procedures were described earlier.
## Ethics approval
Animal experiments were performed in ABSL2 facilities following the Guide for the Care and Use of Laboratory Animal Sciences (CALAS) and approved by the ethics committee of Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China (Approval Number: KY2021-225-B).
## Statistical analysis
All statistical tests were performed with GraphPad Prism 8.0 software. Percentage values were analyzed pairwise by the two-tailed chi-square test or Fisher’s exact test. For comparisons of two groups, paired and unpaired two-tailed Student’s t-tests were applied. Error bars in all graphs indicated the standard error of the mean (mean ± SEM), and a p-value of <0.05 was considered statistically significant.
## Molecular characteristics of the UTI-SA
In this prevalence survey, a total of 4,405 non-repetitive S. aureus strains were collected from various clinical sources during 2008–2020 from a general Hospital in Shanghai, China (Table 1). Among these strains, UTI-ST5 ($33.37\%$) was the most prevalent ST, followed by UTI-ST239 ($12.10\%$), which has shown a gradual decline in recent years (Dai et al., 2019), and UTI-ST1 ($6.74\%$; Table 1). Screening of each ST by specimen type revealed that the midstream urine specimen isolations of UTI-ST1 ($11.59\%$, $p \leq 0.001$) were significantly higher than the other STs (Table 1). In addition, among the 193 UTI-SA strains, the isolation rate of UTI-ST5 ($29.53\%$) was the highest, followed by the isolation rates of UTI-ST1 ($18.13\%$) and UTI-ST7 ($9.84\%$; Figure 1A; Table 2). Furthermore, both UTI-ST1 and UTI-ST5 were prevalent in the elderly population (aged ≥65) compared to the young population ($88.57\%$ vs. $11.43\%$, $p \leq 0.05$ and $63.16\%$ vs. $36.84\%$, $p \leq 0.05$, respectively), while UTI-ST7, UTI-ST398, and UTI-ST188 were more frequently found in the younger populations (aged < 65; Table 2) and UTI-ST239 showed a slight advantage in elderly people (Table 2). Moreover, the majority of UTI-ST1 ($65.85\%$) and UTI-ST5 ($81.67\%$) isolates were recovered from men (Supplementary Figure S1). Therefore, owing to the high prevalence and isolation rates of UTI-ST1 and UTI-ST5, these samples were selected to further characterize the UTI-SA strains.
## Antibiotic resistance pattern among UTI-SA isolates
The antimicrobial pattern of UTI-ST1 was different from that of UTI-ST5 and other STs (Table 2). *In* general, UTI-ST5 and UTI-ST1 isolates were highly resistant to penicillin ($100\%$ and $97.14\%$, respectively) and cefoxitin ($94.74\%$ and $91.43\%$, respectively; Table 2). In contrast, gentamicin, erythromycin, and trimethoprim-sulfamethoxazole displayed potent antibiotic activity against UTI-ST1 ($8.57\%$, $11.43\%$, and $2.86\%$, respectively), but not against UTI-ST5 (Table 2). In addition, among the STs, only UTI-ST1 exhibited resistance to levofloxacin ($91.43\%$, $p \leq 0.05$; Table 2). The wide use of antibiotics in recent years has led to the generation of multiple antibiotic-resistance genes in bacteria, causing severe health problems. UTI-ST5 is a highly drug-resistant and less virulent ST, which is prevalent in the hospital, while UTI-ST398 and UTI-ST188 are community-associated STs with hypervirulence and low drug resistance. UTI-ST5 showed high drug resistance rates against most antibiotics, while UTI-ST1 displayed lower resistance to antibiotics, except for levofloxacin. Nitrofurantoin, trimethoprim-sulfamethoxazole, fosfomycin, and pivmecillinam are recommended for first-line therapy (Gupta et al., 2011) in UTI treatment, and β-lactams and fluoroquinolones are used as alternative treatments (Gupta et al., 2011, 2017), due to the prevalence of high antibiotic-resistance. This suggests that fluoroquinolone resistance in UTI-SA isolates is a major concern in UTI treatment.
## Urea supplement promotes UTI-ST1 to survive and persist in the urinary tract
The urinary tract is a urine-discharging system and human urine primarily consists of water ($95\%$) and urea ($2\%$; Sarigul et al., 2019; Volpin et al., 2019). The liver produces urea via the urea cycle, which is then released into the blood (Ha et al., 1947; Morris, 2002). The kidneys then filter the blood and concentrate urea and waste metabolites in the urine (Morris, 2002; Weiner et al., 2015). A previous study reported that urea supplementation in tryptic soy broth (TSB) supplemented with excess glucose, could reduce bacterial cell death during the stationary growth phase, due to ammonia generation and reduced intracellular reactive oxygen species levels (Zhou et al., 2019). To determine how UTI-ST1 and UTI-ST5 adapt to the high-urea environment of the bladder, we randomly selected 10 isolates from each group to test their growth, hemolytic ability, biofilm formation, and adhesion.
To investigate their growth competence, we conducted the growth assay in a modified nutrient-deficient medium, with or without urea supplementation ($2\%$), at 37°C and 220 rpm. During the 8-h monitoring, the optical density (OD) at 600 nm was recorded every 30 min. The UTI-ST1 isolates grew significantly faster in the urea-supplemented medium than in the urea-unsupplemented medium (Figure 1B). In contrast, UTI-ST5 isolates did not show growth improvement with urea supplementation until 6-h growth (Figure 1C). These results suggest that UTI-ST1 can utilize urea to promote survival and growth, while UTI-ST5 displays a delayed utilization of urea. Furthermore, to assess the effect of urea supplementation on the pathogenicity of UTI-ST1 and UTI-ST5, we performed hemolysis and biofilm formation tests in TSB, with or without urea supplementation. After 10–12 h of cultivation, the supernatants of UTI-ST1 and UTI-ST5 from the urea-supplemented and -unsupplemented media were collected and incubated with erythrocytes (Ridder et al., 2021). Our results revealed that the cytolytic abilities of the UTI-ST1 isolates were highly weakened in the presence of urea (Figure 1D), while the hemolytic abilities of the UTI-ST5 isolates were not impacted by urea supplementation (Figure 1D). Similarly, UTI-ST1 biofilm was thicker in the urea-supplemented media, compared to that in the unsupplemented media (Figure 1E), while no significant difference was observed in the biofilms of the UTI-ST5 isolates, in the presence or absence of urea supplementation (Figure 1E). Since the UTI-ST5 strain is characterized by aggressive biofilm formation and hypovirulence (Figures 1D,E), we compared the hemolytic and biofilm-forming abilities of the non-UTI-derived (nUTI)-ST1 strains, which showed no significant differences in the presence and absence of urea supplementation (Supplementary Figures S2A,B). These results demonstrate that UTI-ST1 shows reduced virulence and thicker biofilm formation on polystyrene surfaces, such as an implant in the urea environment. However, the weakened virulence, although may cause less severe inflammatory reactions (Gong et al., 2014), biofilm formation in the urinary tract can reduce the flow of urine and antimicrobial agents (Scotland et al., 2019), facilitating persistent infection. Considering the elevated adhesion of UTI-ST1 to polystyrene in the urea-supplemented medium, we hypothesized that the adhesion of UTI-ST1 to the urinary epithelium could be improved by urea supplementation. Thus, $0.1\%$ (w/w) urea was supplied to the cell medium to mimic the bladder microenvironment and it was observed that the UTI-ST1 isolates displayed stronger adhesion to the bladder epithelium 5,637 cells (Figure 1F), although their invasion ability was not improved (Supplementary Figure S2C).
We found that UTI-ST1 may shift to hypo virulent status and display increased biofilm formation on polystyrene, while the increased adhesion to epithelium helps UTI-ST1 to colonize and persist in the urinary tract. In addition, we hypothesized that UTI-ST5 may use its biofilm formation ability to survive in the urinary tract.
## Urease plays an important role in UTI-ST1 survival and persistence
Based on the previous results, we hypothesized that UTI-ST1 and UTI-ST5 may have disparate urease activities. However, the conventional urease detection assay—*Christensen urea* agar—is qualitative and has limited sensitivity. Therefore, in this study, we used a modified semi-quantitative method to evaluate the urease activity precisely and easily (Roberts et al., 1978; Duran Ramirez et al., 2022). We inoculated diluted overnight culture in a modified urea-supplemented ($2\%$, w/v) medium (pH 6.5) containing phenol red, a pH indicator, which would turn red under alkaline conditions, such as when ammonia is generated. The OD at 560 and 415 nm was recorded to monitor the color change of the solution and the results revealed that the urease activity of UTI-ST1 reached its peak at 2 h (Figure 2A), while all the selected ST5 strains, albeit harboring the wwurease operon confirmed by DNA sequencing (data not shown) and other strains did not show any obvious change in the urease activities (Figure 2A; Supplementary Figure S3). Thereafter, we quantified the urease activities of common UTI and nUTI strains at 2 h (Figure 2B) and found that UTI-ST1 and UTI-ST5 strains varied significantly in their urea-utilizing ability.
**Figure 2:** *Urease activities of urinary tract infection-derived-sequence type (UTI-ST)-1 and UTI-ST5 and urease gene transcription in UTI-ST1, UTI-ST5, and non-UTI-derived (nUTI)-ST1. (A,B) Urease activities of UTI-ST1 and UTI-ST5, determined by measuring optical density (OD) at 560 and 415 nm, represented by the red and yellow curves, respectively (A); OD was measured continuously for 2 h to quantitatively analyze the urease activities of UTI-ST1 and UTI-ST5 (B). (C–E) The transcriptional level of urease genes (ureA, ureC, and ureD) was measured by quantitative reverse-transcription PCR (RT-qPCR). 10 strains from each sequence type were performed in urease activities; 8 isolates were used in urease gene transcription. The strains used were listed in Supplementary Table S2. Each dot in RT-qPCR is an average of the triplicates of each strain. The primers used in RT-qPCR are listed in Supplementary Table 1. The transcription level of gyrB was used for normalization. Unpaired Student’s t-test was used for statistical analyses, *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.*
Thus, to determine the cause of the differences in the urease activities of the S. aureus strains, we selected 2 strains of UTI-ST1 and nUTI-ST1 each, for sequencing analysis. However, we found no variations in the sequences (mutations, insertions, or deletions) of urease genes between the two strains (data not shown). Therefore, we hypothesized that the variations in urease activities of the S. aureus strains may arise at the transcriptional level. S. aureus urease is a complex nickel-containing enzyme (Krajewska and Ureases, 2009). It consists of three subunits, α, β, and γ, which are encoded by UreC, ureB, and ureA, respectively, and coenzymes encoded by ureDEFH (Krajewska and Ureases, 2009; Konieczna et al., 2012). The results of quantitative reverse transcription PCR (RT-qPCR) revealed that the transcription of ureA, ureC, and ureD was significantly higher in UTI-ST1, compared to that in UTI-ST5, and nUTI-ST1 (Figures 2C–E), indicating the positive urease activities of UTI-ST1. Since the α-subunit, encoded by ureC, is the active site of the urease enzyme, we constructed the UTI-ST1 ureC mutant and a complemented strain to confirm the function of urease in ST1-induce UTI. The results revealed that the UTI-ST1 ureC mutant lost the urease activity (Figure 3A) and growth improvement in the urea environment (Figure 3B), indicating that the mutant could not utilize urea. However, the mutant showed no significant difference in the hemolytic and biofilm-forming phenotypes in the presence or absence of urea in the TSB medium (Figures 3C,D). While, the complemented strain recovered the urease activities (Figure 3A) and exhibited a similar hemolysis phenotype change as the WT strain (Figure 3C), confirming the function of urease. In addition, to verify the function of urease in vivo, we constructed a murine UTI model, by transurethrally injecting the same inoculum amount of UTI-ST1 WT, UTI-ST1 ureC mutant, and UTI-ST5 (Rashid et al., 2021). On the first day post-infection (24 hpi), both UTI-ST1 WT and UTI-ST1 ureC mutants were successfully established in most of the mice, with the mutant group displaying higher bacterial burden in the urine (Supplementary Figure S4), although the difference was not statistically significant. This was possibly because the mutant strain showed stronger virulence compared to the WT, leading to an intense immune response and discharge of more bacterial particles. Therefore, the CFU of the urine from the UTI-ST5-infected mice was slightly lower and relatively stable than that of the urine from the UTI-ST1-infected mice (Figure 3E; Supplementary Figure S4), which was consistent with its high isolation rate compared to all the STs. At 48 hpi, the bacterial burden of the ureC mutant-infected group declined rapidly (~10-fold) compared to that of the WT-infected group (Figure 3E), confirming the increased adhesion results that were obtained earlier. While at 72 hpi, the CFUs in the urine of UIT-ST1-WT dropped significantly probably because of the reduced virulence in urine for UTI-ST1 leading to phenotype like UTI-ST5. The bacterial burden of the bladder and kidney at 72 hpi suggested that the UTI-ST1 WT and UTI-ST1 ureC mutant mainly focused on the lower urinary tract, probably leading to cystitis (Figure 3F). However, UTI-ST5 displayed kidney colonization, suggesting that UTI-ST5 can cause ascending infections, such as pyelonephritis or bloodstream infection.
**Figure 3:** *Phenotypic differences between urinary tract infection-derived-sequence type (UTI-ST)-1 wildtype (WT), UTI-ST1 ureC mutant, and ureC complemented either in the presence or absence of 2% urea supplementation and murine UTI model. (A) The urease activities of UTI-ST1 WT, UTI-ST1 ureC mutant and ureC complemented strain were determined by measuring optical density (OD) at 560 and 415 nm; growth curves (B), hemolytic abilities (C), and biofilm-forming abilities (D) of UTI-ST1 WT and UTI-ST1 ureC mutant; (E,F) 15 female C57BL/6 mice were infected with UTI-ST1 WT, UTI-ST1 ureC mutant or UTI-ST5 for each group were used to construct murine UTI model; urine was collected every 24 h (E), and bladders and kidneys were harvested at 72 h and homogenized to calculate the CFU/mL (F). Statistical analyses were performed using unpaired Student’s t-test, *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.*
## Urease gene expression changes with the environmental pH
Previous studies have outlined urease as more than a virulence factor. Urease also acts as an acid resistance regulator to combat the low-pH environment (Cotter and Hill, 2003). With the growth of the bacteria, the pH of the culture drops, due to the accumulation of acid metabolites (Somerville and Proctor, 2009; Bronesky et al., 2016). We monitored the pH of the UTI-ST1 culture in urea-supplemented and unsupplemented TSB media for 8 h (Figures 4A,B) and found that in the absence of urea, the pH of the culture reduced to approximately 6.0 from 7.50 (Figure 4A). In the urea-supplemented media, the pH of the UTI-ST1 culture gradually rose to approximately 8.20 after 8 h, due to the urea hydrolysis caused by UTI-ST1 urease (Figure 4B); however, the pH of the UTI-ST5 and nUTI-ST1 cultures remained low (~6.5; Figure 4B). Thereafter, we monitored the continuous transcription of UTI-ST1 ureACD at 2, 4, and 6 h, on TSB without urea. At 4 h, when the culture pH was approximately 7.0, ureACD transcription was approximately 2–3 times higher than that at 2 h (pH 7.50). Furthermore, at 6 h, ureACD transcription declined 3–10 times compared to that at 4 h, with a drop in the culture pH (~6.0; Figures 4C–E). Meanwhile, both UTI-ST5 and nUTI-ST1 showed low urease gene transcription and low culture pH (Figures 4C–E). These results indicate that the urease gene transcription increases with the drop in pH and peaks in a neutral to mildly acidic environment. When the environmental pH continuously decreases, the transcription of urease would be repressed.
**Figure 4:** *Variations in urease gene expression with the environmental pH. (A) The pH and growth curve of urinary tract infection-derived-sequence type (UTI-ST)-1 in tryptic soy broth (TSB) without urea, as determined by optical density measurement at 600 nm; (B) the pH curve of UTI-ST1, UTI-ST5, and non-UTI-derived (nUTI)-ST1 in TSB medium supplemented with urea. (C–E) The transcription level of ureACD in UTI-ST1, UTI-ST5, and nUTI-ST1 at 2, 4, and 6 h, on TSB without urea. Each dot in (C–E) is an average of the triplicates of each strain. Unpaired Student’s t-test was used in these analyses, *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.*
## Agr regulates urease gene expression
*Urease* gene expression is a complex network, and ccpA (Zhou et al., 2019), codY (Huang et al., 2014), and agr (Queck et al., 2008; Bronesky et al., 2016) have been predicted to be involved in the regulation of the urease gene. A study has reported ccpA and codY as positive and negative regulators of the urease gene expression, respectively (Zhou et al., 2019). In addition, Agr, a critical quorum sensing system in S. aureus, has been proven to upregulate urease gene expression; however, its role in urease gene expression during UTI pathogenesis has not yet been determined. The Agr system consists of two adjacent transcripts, RNAII and RNAIII, whose expression is driven by P2 and P3 promoters, respectively. RNAII transcript is an operon of four genes (agrBDCA) encoding the machinery of the quorum sensing system, whereas RNAIII transcript encodes the primary effector that regulates the expression of most agr-dependent downstream target genes (Peng et al., 1988). Therefore, we evaluated the transcription of Agr system-associated genes and ureAD. At 4 h of incubation, the transcription of RNAIII and hla, a downstream regulatory virulence gene of the Agr system, was reduced by approximately 2-fold and 10-fold in the urea-supplemented media (pH ~7.7) compared to that in the urea-unsupplemented media (pH ~7.0; Figures 5A,B), indicating that the Agr system was inhibited in the alkaline environment, due to ammonia generation from urea degradation. Meanwhile, at 4 h of incubation, the transcription of ureA and ureD in the urea-supplemented media (pH ~7) also reduced by approximately 2.5-fold and 7.4-fold, respectively, compared to that in the urea-unsupplemented media (Figures 5C,D). Combined with the phenotypic changes in the urea-supplemented media, the Agr system senses the variations in the environmental pH to adapt to the environmental conditions during the bacterial growth process. The Agr system functions actively under neutral pH conditions and declines under alkaline and acidic conditions (Regassa and Betley, 1992), suggesting that it may downregulate virulence genes and upregulate biofilm-forming genes, during UTI pathogenesis (Queck et al., 2008; Arciola et al., 2012). Finally, we constructed UTI-ST1 agrA mutant and complemented strains and found that the ureACD expression of the mutant was significantly declined compared to that of the UTI-ST1 WT (Figures 5E–G), suggesting that agr plays a role in upregulating the urease genes, which might help in maintaining the optimal environmental pH (Figure 6).
**Figure 5:** *The regulation of urease gene transcription by the Agr system, with the change in environmental pH. (A–D) The transcription level of RNAIII (agr effector gene), hla, and ureAD (urease gene) in the presence or absence of urea-supplementation (2%) at 4 h. (E–G) The expression of ureACD in the urinary tract infection-derived-sequence type (UTI-ST)-1 wildtype (WT) and UTI-ST1 agrA mutant. Each dot in A–D is an average of the triplicates of each strain. Statistical analyses were performed using unpaired Student’s t-test, *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.* **Figure 6:** *Model of the pathogenesis process in the urinary tract of UTI-ST1 Staphylococcus aureus by highly expressing urease. The utilization of urea promoted the growth ability of UTI-ST1 S. aureus and the environmental pH was elevated by the hydrolysis products, ammonia. When pH rose to some degree, the urease expression would be down-regulated by the Agr system to maintain a relevant stable pH condition. As a result, the biofilm formation ability was promoted and the virulence was weakened, thus helping the UTI-ST1 to colonize the urinary tract with even persistence.*
## Discussion
An overarching objective of our research is to delineate the molecular epidemiology, phenotypic characteristics, and pathophysiology of S. aureus-induced UTIs. In this study, we collected 4,405 S. aureus isolates from various clinical sources during 2008–2020 from a general Hospital in Shanghai (China) and examined their STs, virulence characteristics, and antibiotic resistance. In this study, we also analyzed the role of urease in UTI-ST1-induced pathogenesis, since UTI-ST1 was one of the main STs of UTI-SA isolates. The antibiotic resistance profile revealed that most UTI-ST1 and UTI-ST5 isolates were MRSA strains, which can cause difficulties in clinical therapy. While UTI-ST1 isolates showed high resistance to levofloxacin (Quinolones), UTI-ST5 isolates showed resistance to aminoglycoside and macrolide antibiotics (Table 2). In a previous study, Cortes et al. analyzed the antimicrobial resistance traits of ST1 MRSA and found ST1 MRSA strains might be highly resistant to aminoglycosides and probably gained intrinsic ciprofloxacin resistance for amino acid substitution (Côrtes et al., 2021). While, the common HA-MRSA ST5 isolates were found to harbor a large number of resistance genes (Jian et al., 2021), which correlates with the antimicrobial profile in our study. Compared to the UTI-ST5 and nUTI-ST1 isolates, UTI-ST1 displayed declined hemolytic ability and increased biofilm formation and adhesion ability under urea supplementation. In addition, UTI-ST1 showed active urease gene expression and urease activity, while the other sequence types such as UTI-ST5, UTI-ST7, UTI-ST398, and nUTI-SA did not display a similar trend. Moreover, the UTI-ST1 ureC mutant displayed similar hemolytic ability and biofilm-forming phenotypes under urea-supplementation, and the urine CFU enumeration of the mutant reduced rapidly in the murine UTI model, while UTI-ST1 could not be eliminated so quickly. In addition, we found that UTI-ST1 phenotype and urease expression were regulated by the environmental pH and potentially regulated by the Agr system. Urea is the only substrate to urease and it is neutral itself, which means it may not impact the growth and pathogenesis of urease-negative bacteria. However, the urease-positive ones can hydrolyze the urea to produce ammonia, an alkaline product, to survive and persist in an acidic environment. With the change in pH, the Agr system regulates the expression of hemolysis, urease, and many other genes, resulting in phenotype changes (Figure 6). Moreover, our results highlight the role of urease, as a urea environment regulator, boosting S. aureus persistence in the nutrient-limiting urinary niche.
Chronic UTI is a persistent and frequent issue, especially in the elderly, female, and catheter-implanted patients (Flores-Mireles et al., 2015; Kitano et al., 2021). It is widely accepted that the female gender is a risk factor for UTIs (Foxman, 2010); however, the majority of UTI-ST1 ($65.85\%$) and UTI-ST5 ($81.67\%$) isolates were recovered from men, while $34.15\%$ and $18.33\%$ were recovered from women (Supplementary Figure S1), consistent with the previous reports that men are more susceptible to S. aureus infections (Gu et al., 2016; Dai et al., 2019; Thomsen et al., 2019). In this study, we found two common UTI-associated STs of S. aureus ST1 and ST5. Furthermore, by using a modified semi-quantitative urease detection assay, we discovered that the UTI-ST1 isolates displayed significantly higher urease activities than the other STs. Moreover, UTI-ST1 was found to degrade urea to generate ammonia, which promotes its growth and persistence by increasing its biofilm-forming ability and adhesion to the epithelium. Although UTI-ST5 did not show obvious urease activity, it displayed a strong biofilm-forming ability and mild virulence, which might facilitate initiating infection. As a conserved virulence factor, urease is widely present in plants, fungi, bacteria, and other organisms (Dixon et al., 1975; Rutherford, 2014; Gu et al., 2016). In previous studies, Gram-negative bacteria were the hotspots for UTI research (Nielubowicz and Mobley, 2010; Pitout, 2012; Foxman, 2014; Schaffer and Pearson, 2015; Clegg and Murphy, 2016; Terlizzi et al., 2017; Klingeberg et al., 2018). Therefore, S. aureus, with a relatively simple cell structure compared to the Gram-negative bacteria, was not considered a common cause of UTI. Fimbriae, pili, and secreted toxins are important virulence factors of uropathogenic E. coli (Terlizzi et al., 2017), and urease is an important virulence factor in Proteus mirabilis, which is associated with renal stone formation (Schaffer and Pearson, 2015; Clegg and Murphy, 2016). Owing to the frequent flush movement of the urine, the lack of nutrients, pH variations, etc., the urinary tract is a difficult environment for pathogens to colonize. However, the urease detection method is currently not common in routine clinical practice. In recent years, the 13C-urea breath test and the rapid urease test have been widely applied to detect H. pylori in the stomach (Ricci et al., 2007). In this study, we applied a modified semi-quantitative urease test (Duran Ramirez et al., 2022), which can be used to detect urease-producing bacteria. Both Gram-negative and Gram-positive urease-producing bacteria can hydrolyze urea to facilitate growth and regulate the environmental pH (Cotter and Hill, 2003). However, pH plays a complicated role in promoting renal stone formation (Henderson, 1993; Bihl and Meyers, 2001). Previous studies found that low urine pH may cause the deposition of calcium oxalate stones, while >7.0 pH of urine is found in struvite or triple phosphate stones, caused by Proteus and Klebsiella infections (Bihl and Meyers, 2001). However, a few studies hypothesized elevated pH as a lithogenic risk factor (Henderson, 1993; Schaffer et al., 2016). Different renal stone components are deposited in different pH environments. For those undergoing chronic UTI, the increased pH of urine can be a lithogenic risk factor for urease-positive bacteria and can be monitored.
Staphylococcus aureus contains various virulence genes and corresponding regulatory systems, with the *Agr quorum* sensing system being the most important (Regassa and Betley, 1992; Bronesky et al., 2016; Matsumoto et al., 2021). It is predicted and proven that urease is directly upregulated by the Agr system in an RNAIII-dependent manner (Queck et al., 2008; Arciola et al., 2012), which correlates with the RT-qPCR results (Figures 4C–E). In our study, we deeply studied this relationship combined with the phenotypic changes in urea-supplied medium and the potential pathogenesis of UTI-ST1. The variations in agr expression under different pH environments (Regassa and Betley, 1992) confirm the weakened virulence phenotype and enhanced biofilm-forming and adhesion ability of the UTI-ST1. However, we failed to observe the increased transcription of the biofilm regulator gene, ica (Kırmusaoğlu and Kaşıkçı, 2020). This could be because the biofilm is controlled by multiple genes, such as sarH1 (Queck et al., 2008), sasG (Geoghegan et al., 2010), and several ica-independent pathways. It has been proved that agr mutant is susceptible to evading the cytotoxic effects of neutrophils (Zhou et al., 2019; Matsumoto et al., 2021), which was also observed in the urea-supplemented and unsupplemented UTI-ST1 groups (Supplementary Figure S5); however, the underlying mechanism is yet to be revealed. In previous studies, the expression of urease genes was found to be induced by a mildly acid environment (Zhou et al., 2019). However, the transcription of urease gens dropped at pH 6 in our study (Figures 4C–E). It might be because we applied a different method to demonstrate the expression of urease genes. We directly detect the RNA level by RT-PCR while the researchers quantified the urease expression in translation level in previous studies, which might include additive effects. In addition, the change of mRNA is usually prior to protein expression so that our opinions did not conflict with others.
In conclusion, our data indicate that UTI-ST1 is the main strain type of UTI-SA and that UTI-ST1 highly expresses urease, which hydrolyzes urea to produce ammonia and elevate the environmental pH. The alkaline environment further declines the expression of the Agr system, resulting in weakened virulence and improved biofilm and adhesion of UTI-ST1. Therefore, our findings demonstrate the importance of urease in the survival and persistence of ST1 S. aureus in the urinary tract during UTI pathogenesis.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The animal study was reviewed and approved by the Ethics Committee of Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
## Author contributions
LH and ML: conceptualization, funding acquisition, supervision, and writing—review and editing. KX, LH, and ML: methodology. LH, KX, YW, TC, and YJ: investigation. KX, LH, ML, QL, and HW: visualization. KX: writing—original draft. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the National Natural Science Foundation of China [grant numbers 82272395 (to LH), 81974311 (to LH), 81873957 (to ML), 82172325 (to ML), and 82102455 (to YW)], the Shanghai Pujiang Program [grant number 2019PJD026 (to LH)], and the Shanghai Sailing Program [21YF1425500 (to YW)].
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1101754/full#supplementary-material
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|
---
title: The gut microbiota in experimental abdominal aortic aneurysm
authors:
- Jie Xiao
- Zhanjie Wei
- Chuanlei Yang
- Shilin Dai
- Xiancan Wang
- Yuqiang Shang
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC9992639
doi: 10.3389/fcvm.2023.1051648
license: CC BY 4.0
---
# The gut microbiota in experimental abdominal aortic aneurysm
## Abstract
### Background
Abdominal aortic aneurysm (AAA) is a life-threatening disease and there are no effective treatments to inhibit aneurysm progression and rupture. The gut microbiota has been increasingly recognized, as a new therapeutic target, because of its role in host homeostasis. However, the role of the gut microbiota in AAA has not been clarified. Therefore, we performed 16S rRNA analysis to determine and compare the composition of the gut microbiota between AAA and control groups.
### Methods
We used the classical angiotensin-II induced AAA mouse model to investigate the role of gut microbiota and abdominal aortic aneurysm. The mice were randomly assigned to 2 groups: the control ($$n = 7$$) group received saline (vehicle), while the AAA ($$n = 13$$) group received solutions of Ang II. Aortic tissue and fecal samples were harvested 28 days after infusion. Fecal samples were analyzed by 16S rRNA sequencing.
### Results
The levels of Oscillospira, Coprococcus, Faecalibacterium prausnitzii, Alistipes massiliensis, and *Ruminococcus gnavus* were increased in the AAA group, while those of Akkermansia muciniphila, Allobaculum, and *Barnesiella intestinihominis* were increased in the control group. Furthermore, network analysis and ZiPi score assessment highlighted species in the phylum Bacteroidetes as the keystone species. PICRUSt2 analysis revealed that PWY-6629 (a super pathway of L-tryptophan biosynthesis), PWY-7446 (sulfoglycolysis), and PWY-6165 [chorismate biosynthesis II (archaea)] may-be involved in the metabolic pathways that contribute to AAA formation, and E. coli/Shigella may be the key bacteria that influence those three pathways.
### Conclusion
Alterations in the gut microbiota may be associated with the formation of AAA. Akkermansia and Lactobacillus were significantly decreased in the AAA group, but the keystone species in the phylum Bacteroidetes and the metabolic products of these bacteria should be given more attention in AAA formation research.
## 1. Introduction
Multiple compelling pieces of evidence have demonstrated that the composition and function of the gut microbiota can alter host homeostasis and result in obesity, inflammation, cardiovascular disease, and other diseases [1, 2]. The gut microbiota is regarded as a human organ since it forms a complex community of interacting organisms and communicates with distal host organs through microbial metabolites [3]. Recently, Tian et al. [ 4] reported that gut microbiota dysfunction plays key roles in the formation of abdominal aortic aneurysm (AAA). They found that *Roseburia intestinalis* and its metabolite butyrate significantly reduce neutrophil infiltration and the formation of NOX2-dependent neutrophil network structures, thereby reducing inflammation and promoting the formation of AAA. Moreover, Shinohara and his teammate [5] found that depleting the gut microbiota by oral antibiotic treatment can suppress macrophage accumulation and alleviate AAA development. These results have highlighted the role of the gut microbiota in the development of AAA.
AAA usually occurs in the infrarenal part of the aorta and is usually described as a weakening and dilatation of the abdominal aorta [6]. AAA is usually asymptomatic unless complications occur; such complications lead to 150,000–200,000 deaths each year worldwide [7]. Either open operation or endovascular repair is an effective method for treating patients with large, asymptomatic AAAs or symptomatic or ruptured AAAs of any size [8]. However, few effective non-invasive therapy strategies can prevent the progression of abdominal aortic aneurysms.
Herein, we provide new insights into the gut microbiota associated with AAA in an Ang II-induced experimental abdominal aortic aneurysm (EAAA) mouse model to elucidate alterations in the composition of the gut microbiota and the potential mechanisms of action related to AAA formation.
## 2.1. Ang II-induced EAAA model
All animal studies were approved by the Ethical Approval for Formation Review of Experimental Animal Welfare and Ethics, Zhongnan Hospital of Wuhan University (ZN2022173). A total of 20 male apolipoprotein E-deficient C57BL/6 mice (ApoE–/–) aged 12 weeks and weighing 28–30 g were obtained from Beijing HFK Bioscience Co., Ltd. (HFK). All mice were housed under environmentally controlled specific pathogen-free conditions with a 12:12-h light-dark cycle and fed standard laboratory chow and tap water ad libitum. Ang II (Sigma-Aldrich; 1,000 ng/min/kg) or saline was administered to these ApoE–/– mice through an osmotic mini-pump (model 2004, ALZET Osmotic Pumps) for up to 4 weeks. The mice were randomly assigned to 2 groups: the control ($$n = 7$$) group received saline (vehicle), while the AAA ($$n = 13$$) group received solutions of Ang II. Aortic tissue and fecal samples were harvested 28 days after infusion. Sample specimens were immediately flash frozen and subsequently stored at −80°C.
## 2.2. Assessment of EAAA
A high-resolution Vevo 2100 microimaging system (Visualsonic) was used to measure the aortic diameter in each group of mice on days 0 and 28. A suprarenal aortic diameter increase of ≥$50\%$ or the occurrence of aortic dissection (AD) in the mice was considered aneurysmal. Furthermore, the survival ratios were monitored daily, and a Softron BP-2010 Series system (a non-invasive tail-cuff system) was used to measure the systolic blood pressure on days 0 and 28.
## 2.3. DNA extraction and 16S rRNA gene-based analysis
Bacterial DNA extraction and sequencing of the 16S rRNA gene were conducted by Personalbio Technology Co., Ltd. (Shanghai, China). Briefly, fecal samples of approximately 200 mg per mouse were collected and used for DNA extraction with a QIAamp DNA stool Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s protocol. A NanoDrop 2000 (Thermo Scientific) was used to evaluate the DNA concentration and purity. The V3-V4 hypervariable regions of the 16S rRNA gene were amplified from the DNA extracts with primers (forward primer: ACTCCTACGGGAGGCAGCA and reverse primer: GGACTACHVGGGTWTCTAAT). The samples were sequenced on an Illumina NovaSeq6000 PE 250 system to obtain raw data. QIIME2 and DADA2 were used to denoise the raw data and obtain clean amplicon sequence variants (ASVs)/operational taxonomic units (OTUs). Each ASV/OTU sequence was annotated using QIIME2 (version 2019.4). R software (version 4.0.2) was used to draw taxonomic trees in packed circles (R ggraph, ggplot2 package). Krona software provides an interactive display of the taxonomic composition of the community. QIIME2 (version 2019.4) was used to calculate the alpha diversity index (e.g., Chao1 and Shannon diversity index) and beta-diversity. R software (version 4.0.2) was used to draw the rank-abundance curves (R ggplot2 package), to carry out principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) based on the Bray-Curtis distance (R ape package and vegan package), to generate the heatmap of genus abundance (R pheatmap package), and to compare the sample groups in pairs with the MetagenomeSeq method (R metagenomeSeq package). Furthermore, linear discriminant analysis (LDA) effect size (LefSe) [9] and random forest analysis (QIIME2, version 2019.4) were used to analyze the difference between the control and AAA groups. Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUST2) [10] was used to predict the function of the gut microbiota. Network analysis [11] was used to determine the key species based on the composition distribution of species in each sample. Moreover, network connectivity based on the ZiPi score is often used to indicate keystone species [12]. The ZiPi score was calculated with R software (version 4.0.2) using the igraph package. R was used to calculate the Zi and Pi score values of each node in the network according to the modular cutting results of the co-occurring network. The Zi value refers to within-module connectivity, while the Pi value refers to among-module connectivity. Then, the role of each node in the associated network was determined according to the Zi and Pi score values. Nodes in a network (ASV/OTU) can be divided into four parts using Zi and Pi values, which are peripherals, connectors, module hubs and network hubs. In ecological research, peripherals represent specialists in microbial networks, module hubs and connectors primarily represent species that are close to generalists, and network hubs represent supergeneralists in microbial networks.
## 2.4. Statistical analysis
All data are presented as the means ± SEMs, and a P value <0.05 was considered to indicate statistical significance. The χ2 test was used to analyze the incidence of AAA. Two-tailed Student’s t test (for parametric data) was used to analyze differences between two groups. In addition, one-way analysis of variance (ANOVA) with the Bonferroni correction was used to compare multiple groups of Ang II/ApoE mice. Furthermore, an adjusted $P \leq 0.05$ was deemed to be significant depending on specific cases. The statistical analyses were performed using Prism 5.0 (GraphPad Software, La Jolla, CA).
## 3.1. Ang II-induced experimental abdominal aortic mouse model
After 28 days of perfusion, 10 of 13 mice ($76.9\%$, Figure 1D) in the AAA group were diagnosed with AAA (2 died from AD on days 8 and 17), while none of the mice in the control group were diagnosed with AAA ($P \leq 0.05$) (Figures 1A, B). In addition, the aortic diameter in the AAA group was significantly larger than that in the control group (1.703 ± 0.40 mm vs. 1.03 ± 0.08 mm, $P \leq 0.05$, Figure 1C), and the systolic blood pressure of mice in the AAA group on day 28 was significantly higher than that at baseline (on day 0, $P \leq 0.05$, Figure 1E). Furthermore, the survival ratio (Figure 1F) was notably higher in the control group ($100\%$) than in the AAA group ($84.6\%$, 2 died from AD on days 8 and 17).
**FIGURE 1:** *Ang II-induced experimental abdominal aortic aneurysm mouse model. (A) Typical ultrasound images of the abdominal aortic aneurysm from each group 28 days after infusion. (B) Representative photographs showing the visible changes in each group. (C) Aortic diameter (AAA group vs. control group, 1.703 ± 0.40 mm vs. 1.03 ± 0.08 mm, P < 0.05). (D) AAA incidence (AAA group: 2 died from AD on days 8 and 17, control group: 0, P < 0.05). (E) Systolic blood pressure (systolic blood pressure of mice in the AAA group on day 28 was significantly higher than that at baseline on day 0, P < 0.05). (F) Survival percentage (AAA group: 2 died from AD on days 8 and 17, control group: 0, ***represent P < 0.01, ****represent P < 0.001).*
## 3.2. Comparison of the gut microbiota between the AAA and control groups
After bioinformatic analysis was performed with QIIME2, we found that Bacteroidetes, Firmicutes, Verrucomicrobia, and Proteobacteria were the dominant phyla in both the AAA group and the control group (Figures 2A, C). However, the genera Akkermansia and Lactobacillus, which are always regarded as beneficial bacteria in human beings, were significantly decreased in the AAA group (Figures 2B–E).
**FIGURE 2:** *16S rRNA sequencing revealed alterations in the gut microbiota between the AAA and control groups. (A) Phylum level; (B) genus level; (C) taxonomic tree in packed circle; (D,E) Krona analysis of the bacterial community structures of the AAA and control groups.*
## 3.3. The alpha and beta diversities of the gut microbiota
The Chao1 estimator and Shannon diversity index were used to calculate the richness and diversity of the gut microbiota, respectively (Figures 3A, B). In addition, we drew a rank-abundance curve to verify the richness and evenness of the gut microbiota (Figure 3C). These results demonstrated the low alpha diversity of the AAA group compared with that of the control group ($P \leq 0.05$).
**FIGURE 3:** *Alpha and beta diversities of the microbiota. QIIME2 (version 2019.4) was used to calculate the alpha diversity index and beta-diversity. (A) Chao1 estimator; (B) Shannon diversity index; (C) rank-abundance Curve; (D) PCoA analysis; (E) NMDS analysis, stress value, 0.137; (F) ANOSIM analysis, R = 0.32, *represent P < 0.01.*
Moreover, beta diversity was calculated by PCoA and non-metric multidimensional scaling (NMDS) analysis (Figures 3D, E). The stress value of NMDS was 0.137, which indicates the reliability of the NMDS analysis. The ANOSIM analysis demonstrated that the differences between groups were greater than the differences within groups ($R = 0.32$, $P \leq 0.01$, Figure 3F).
Taken together, these results demonstrated that the gut microbiota may play an essential role in the progression of AAA.
## 3.4. Species differences and marker species analysis
Having explored the differences in microbial community composition (i.e., beta diversity), we also needed to determine which species were primarily responsible for these differences. A species composition heatmap, metagenomeSeq analysis, LEfSe analysis (Linear discriminant analysis Effect Size), and random forest analysis were used to filter out the dominant species in the AAA and control groups. The heatmap demonstrated the differential expression of the intestinal microbes in each group (Figure 4A). In the MetagenomeSeq analysis, we found that the AAA group was enriched in Oscillospira and Coprococcus (Figure 4B), while the control group was enriched in Akkermansia and Allobaculum (Figure 4C). The LFfSe analysis demonstrated that Faecalibacterium prausnitzii, Alistipes massiliensis, and *Ruminococcus gnavus* were significantly increased in the AAA group, and that *Akkermansia muciniphila* and *Barnesiella intestinihominis* were increased in the control group (Figures 4D, E). The random forest analysis revealed the degree of importance of the genera Allobaculum and Akkermansia in the control group and the genus Faecalibacterium in the AAA group (Figure 4F).
**FIGURE 4:** *Species differences and marker species analysis. (A) Species composition heatmap, red bar represents the control group, blue bar represents the AAA group; (B,C) MetagenomeSeq analysis, red bar represents Bacteroidetes, blue bar represents Firmicutes; (D,E) linear discriminant analysis (LDA) effect size (LEfSe) analysis was used to analyze the difference between the control and AAA groups, red bar represents control group, blue bar represents AAA group; and (F) random forest analysis, red color represents high abundance in each group, blue color represented low abundance in each group.*
## 3.5. Network analysis and ZiPi score to reveal the keystone species
We performed network analysis to identify the relationship between the microbiome and host environment. The network analysis demonstrated that the phyla Firmicutes and Bacteroidetes may play an essential role in influencing the host environment (Figures 5A, B). Topological index analysis revealed that the distribution of the gut microbiota fits a scale-free network, which means that the main factor influencing host homeostasis is the dominant microbiota (Figure 5C). Additionally, the ZiPi score verified that the keystone species that influenced host homeostasis belonged to the phylum Bacteroidetes (Figure 5D), however, the genus level was not identified (Figure 5E).
**FIGURE 5:** *Network analysis and ZiPi score to determine the keystone species. (A,B) Network analysis was used to determine the key species based on the composition distribution of species in each sample; (C) topological index analysis; (D,E) ZiPi score was used to indicate keystone species.*
## 3.6. PICRUSt2 analysis to predict the potential role of the gut microbiota in AAA formation
We used PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) to predict the potential role of the gut microbiota in AAA formation. The function of the gut microbiota predicted to play a primary role in host homeostasis was biosynthesis; other predicted functions included degradation, utilization, assimilation, detoxification, generation of precursor metabolites and energy, glycan pathways, macromolecule modification, metabolic clusters, and so on (Figure 6A). In addition, we found that the PWY-6629 (a super pathway of L-tryptophan biosynthesis), PWY-7446 (sulfoglycolysis), and PWY-6165 [chorismate biosynthesis II (archaea)] metabolic, pathways differed between the AAA and control groups ($P \leq 0.05$) (Figure 6B). Through species composition analysis, we found that E. coli/Shigella may be the key bacterial group that influences those three pathways (Figure 6C).
**FIGURE 6:** *PICRUSt2 analysis to predict the potential role of the gut microbiota. (A) The gut microbiota participating in metabolic pathways; (B) different metabolic pathways between the AAA and control groups; (C) species composition analysis.*
## 4. Discussion
The gut microbiota has gained recognition as a result of its role in homeostasis and is considered as a new treatment target for many diseases [13]. In this study, we performed 16S rRNA analysis to identify alterations in the gut microbiota between the EAAA and control groups. We hypothesized that the gut microbiota may participate in the progression of abdominal aortic aneurysms. For example, different alpha and beta diversities revealed that the gut microbiota in the AAA and control groups differed.
The species composition map demonstrated that the genus levels of Akkermansia and Lactobacillus were significantly decreased in the AAA group compared with the control group. Furthermore, thorough investigations via MetagenomeSeq analysis, LEfSe analysis, and random forest analysis showed that the levels of Oscillospira, Coprococcus, Faecalibacterium prausnitzii, Alistipes massiliensis, and *Ruminococcus gnavus* were increased in the AAA group, while the levels of Akkermansia muciniphila, Allobaculum, and *Barnesiella intestinihominis* were increased in the control group. Akkermansia was first described in 2004 and many studies have been conducted to investigate its potential role in humans [14]. Previous studies have demonstrated that *Akkermansia is* a promising target for treating intestinal microbiota-related diseases, such as colitis, metabolic syndrome, and immune diseases [15]. Recently, He et al. [ 16] reported that *Akkermansia muciniphila* inhibited the formation of AAA by restoring gut microbiota diversity, and altered the expression of peripheral immune factors. Barnesiella intestinihominis is also regarded as a beneficial bacterium for host homeostasis. A study published in 2016 found that *Barnesiella intestinihominis* accumulated in the colon and promoted the infiltration of γδT cells to produce IFN-γ in cancer lesions and ultimately ameliorated the efficacy of cyclophosphamide (CTX) to inhibit the progression of cancer [17]. However, Oscillospira may play a bidirectional role in host metabolism. Zha et al. revealed that Oscillospira was likely to drive the microbiome in patients with type 2 diabetes mellitus to a more dysbiotic status [18]. Another study demonstrated that the increased abundance of the genus Oscillospira may exert protective actions by enhancing short-chain fatty acids, NAD + metabolism, and sirtuin activity to increase fatty acid oxidation and ultimately inhibit the progression of non-alcoholic steatohepatitis [19]. Therefore, further studies are needed to clarify the mechanism of action of Oscillospira in AAA progression. Moreover, Coprococcus has been demonstrated to be positively related to obesity, and a recent study found that inulin can decrease the level of the genus Coprococcus and ameliorate the moods of obese patients [20]. Faecalibacterium prausnitzii is usually regarded as a probiotic for human metabolism [21]. However, Filippis et al. enrolled 120 children (90 children with allergies and 30 age-matched healthy controls) to investigate alterations in the gut microbiota and found a high abundance of *Faecalibacterium prausnitzii* in children with allergies [22]. In the human Faecalibacterium complex, eleven clades have been identified (clade A through clade K) as F. prausnitzii [23]. Filippis et al. [ 23] reported that *Faecalibacterium prausnitzii* is associated with a Westernized lifestyle, and a Western diet has a different prevalence of Faecalibacterium clades. For example, the prevalence of clade A and clade D was higher in a Western diet group, while the other clades were higher in a non-Western diet group. It is known that Western diet is associated with the formation of abdominal aortic aneurysm [24]. In our study, we found that the abundance of *Faecalibacterium prausnitzii* was increased in the AAA group, and the potential explanation may be that *Faecalibacterium prausnitzii* clade A or clade D was detected. We believe this phenomenon is very interesting, and we will intend to clarify the role of *Faecalibacterium prausnitzii* in the formation of AAA in future studies. Alistipes massiliensis belongs to the Alistipes genus, which is a relatively newly identified genus of bacteria. There is a bidirectional function of Alistipes that may have protective effects against some diseases, such as cancer, colitis, and liver fibrosis. In contrast, other studies have indicated that Alistipes may participate in the formation of colorectal cancer and depression [25]. Ruminococcus gnavus is an anaerobic, gram-positive bacterium that can digest intestinal mucus and produce an inflammatory substance. A recent cohort study found that *Ruminococcus gnavus* was directly associated with percent body fat and induced metabolic syndrome [26].
Although the studies above highlighted notable species of the gut microbiota, our network analysis and ZiPi score assessment demonstrated that the keystone species involved in AAA formation were those in the phylum Bacteroidetes. Bacteroidetes are gram-negative, anaerobic bacteria that are resident flora in humans. They have an outer membrane, a peptidoglycan layer, and a cytoplasmic membrane. Their main products include acetic acid, isovaleric acid, and succinic acid [27]. The *Bacteroidetes phylum* may play a key role in host homeostasis, and its impact on host health and disease is complex, as it involves the catabolism of ingested complex polysaccharides, colonization resistance to pathogens, the production of B vitamins, and support for other anaerobic microorganisms [28]. Recently, Yao et al. [ 29] found that the gut bacterial phylum Bacteroidetes which expresses selective bile salt hydrolase (BSH) activity, plays an essential role in maintaining host homeostasis. Germ-free mice colonized with hydrolase-deleted bacteria maintained a better weight and lower levels of fat in their blood and liver. Furthermore, those mice shifted to burning fat instead of carbohydrates for energy and regulated immune pathways in the gut. Therefore, the *Bacteroidetes phylum* may play an essential role in AAA formation, and further studies are needed for clarification.
In addition, PICRUSt2 analysis was conducted to predict the potential role of the gut microbiota. We found that PWY-6629 (a super pathway of L-tryptophan biosynthesis), PWY-7446 (sulfoglycolysis), and PWY-6165 [chorismate biosynthesis II (archaea)] may contribute to the different metabolic pathways between the AAA and control groups. Through species composition analysis, we found that E. coli/Shigella may be the key bacterial taxon that influences these three pathways. Future studies are needed to clarify the potential mechanism that influences AAA formation.
Some limitations should be considered. First, human samples are needed for sequence analysis. Although the Ang II-induced EAAA mouse model could imitate the pathology of human AAA, the gut microbiota of humans and mice are different. Second, shotgun metagenomic sequencing is better than 16S rRNA analysis, and more data should be obtained through shotgun metagenomic sequencing. Shotgun metagenomic sequencing is better for functional profiling, taxonomic resolution (bacterial species, sometimes strains and single nucleotide variants, if sequencing is deep enough), coverage of all taxa (including viruses), bioinformatics requirements, high sensitivity to host DNA contamination, and lower bias, but usually costs too much compared with 16S rRNA [30].
## 5. Conclusion
Alterations in the gut microbiota may be associated with the formation of abdominal aortic aneurysm. Akkermansia and Lactobacillus were significantly decreased in the AAA group, but the keystone species were found to belong to the phylum Bacteroidetes, and the metabolic products of these bacteria should be given more attention in AAA formation research.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://dataview.ncbi.nlm.nih.gov/object/PRJNA890908?reviewer=6gvcn9j2pb4d7drg85e4jpjhb, PRJNA890908.
## Ethics statement
The animal study was reviewed and approved by Institutional Animal Care and Use Committee of Tongji Medical College, Huazhong University of Science and Technology.
## Author contributions
JX, ZW, and CY designed and performed the experiments, analyzed the data, and wrote the manuscript. YS reviewed the manuscript. SD and XW performed the experiments. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Development and validation of a risk prediction model for early diabetic peripheral
neuropathy based on a systematic review and meta-analysis
authors:
- Xixi Liu
- Dong Chen
- Hongmin Fu
- Xinbang Liu
- Qiumei Zhang
- Jingyun Zhang
- Min Ding
- Juanjuan Wen
- Bai Chang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9992641
doi: 10.3389/fpubh.2023.1128069
license: CC BY 4.0
---
# Development and validation of a risk prediction model for early diabetic peripheral neuropathy based on a systematic review and meta-analysis
## Abstract
### Background
Early identification and intervention of diabetic peripheral neuropathy is beneficial to improve clinical outcome.
### Objective
To establish a risk prediction model for diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM).
### Methods
The derivation cohort was from a meta-analysis. Risk factors and the corresponding risk ratio (RR) were extracted. Only risk factors with statistical significance were included in the model and were scored by their weightings. An external cohort were used to validate this model. The outcome was the occurrence of DPN.
### Results
A total of 95,604 patients with T2DM from 18 cohorts were included. Age, smoking, body mass index, duration of diabetes, hemoglobin A1c, low HDL-c, high triglyceride, hypertension, diabetic retinopathy, diabetic kidney disease, and cardiovascular disease were enrolled in the final model. The highest score was 52.0. The median follow-up of validation cohort was 4.29 years. The optimal cut-off point was 17.0, with a sensitivity of 0.846 and a specificity of 0.668, respectively. According to the total scores, patients from the validation cohort were divided into low-, moderate-, high- and very high-risk groups. The risk of developing DPN was significantly increased in moderate- (RR 3.3, $95\%$ CI 1.5–7.2, $$P \leq 0.020$$), high- (RR 15.5, $95\%$ CI 7.6–31.6, $P \leq 0.001$), and very high-risk groups (RR 45.0, $95\%$ CI 20.5–98.8, $P \leq 0.001$) compared with the low-risk group.
### Conclusion
A risk prediction model for DPN including 11 common clinical indicators were established. It is a simple and reliable tool for early prevention and intervention of DPN in patients with T2DM.
## 1. Introduction
The number of people with diabetes mellitus (DM) and its comorbidities is growing rapidly worldwide. Diabetic peripheral neuropathy (DPN) is a major complication of diabetes, and at least $50\%$ of patients with diabetes will develop DPN in their lifetime [1, 2]. There is a large amount of evidence revealing that DPN is an important factor in the development of diabetic foot ulcer, Charcot neuroarthropathy [3], and even non-traumatic lower-limb amputation [1, 4]. Besides, dysfunction of small and large fibers leads to abnormal foot temperature, pain sensation, and proprioception, which ultimately result in repeated foot damage and imbalance, increasing the risk of falls and fractures [5, 6]. Furthermore, DPN is a predictor of mortality in patients with diabetes. A 13-year prospective study of patients with diabetes showed that DPN was significantly associated with cardiovascular and all-cause mortality [7]. Considering the devastating consequences, DPN has been a public health problem that pose a significant challenge to social, financial and health care systems [8, 9]. Unfortunately, there is currently a lack of early diagnosis and effective clinical intervention. DPN is usually insidious and is missed in the onset until it is well-established, at the point it seems to be irreversible [10]. So early prevention is critical to tackle this issue.
Hyperglycemia has been recognized as the most important risk factor for DPN in patients with type 2 diabetes mellitus (T2DM). However, the UK prospective diabetes study (UKPDS) [11], the intensified multifactorial intervention in patients with type 2 diabetes (steno-2) study [12] and other large clinical intervention trials (13–15) did not find the benefit of glucose control on the occurrence and development of DPN in patients with T2DM. Antidiabetic treatment alone is insufficient to prevent DPN in individuals with T2DM [16]. Besides hyperglycemia, T2DM coexists with other metabolic disorders, such as obesity, dyslipidemia, and hypertension, etc. Recent evidence suggests that these multiple metabolic disorders are involved in DPN onset. Early recognition and comprehensive assessment of these related risk factors allow for an earlier identification of high-risk individuals and an earlier management of DPN. Therefore, a risk prediction model for DPN including related risk factors is developed in this study, and it may be a more effective strategy for preventing DPN.
## 2.1. Study registration
The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CRD42021246320.
## 2.2.1. Derivation cohort
The derivation cohort patients came from a systematic review and meta-analysis. We searched the electronic databases including Pubmed, Embase, and Cochrane Library from inception to May 2020, using the following medical subject heading terms and their keywords: “diabetes mellitus, type 2,” “diabetic neuropathies,” “risk factors,” and “cohort studies.” Ultimately, a total of 95,604 patients with T2DM from 13 prospective cohorts and 5 retrospective cohorts were included. The research subjects were mainly from 19 countries and regions, of which $50\%$ were from Asia, $22.22\%$ were from Europe, $22.22\%$ were from America, and $5.56\%$ were from Oceania. All the 18 cohort studies reported the risk ratio (RR) with $95\%$ confidence interval (CI) of each risk factor, and they were of high quality as assessed by the Newcastle-Ottawa Scale (NOS; provided in Supplementary Table 2). A flow diagram of literature selection process is shown in Figure 1. Details of literature search strategy, inclusion and exclusion criteria, data extraction, publication bias, and quality assessment are shown in Supplementary material.
**Figure 1:** *Flow diagram of literature selection process.*
## 2.2.2. Validation cohort
In total, 2,608 patients with T2DM who were admitted to Tianjin Medical University Metabolic Diseases Hospital at least twice (baseline from September 2010 to September 2020) were considered for our study. We further selected patients aged 35–79 years old, without DPN at baseline, and with a follow-up of more than 12 months for inclusion in the validation cohort. Exclusion criteria included the presence of acute complications, serious infection, myocardial infarction, stroke, and cancer. We excluded 81 patients aged <35 or >79 years old, 715 patients with a follow-up for <12 months, 942 patients with DPN at baseline, 358 patients with acute diabetic complications or serious infection, and 50 patients with incomplete data. Finally, 462 patients were selected as the retrospective validation cohort. The flowchart is shown in Figure 2.
**Figure 2:** *Process for the selection of patients in the validation cohort.*
## 2.3. Outcome
The outcome was the occurrence of DPN. DPN was diagnosed by a combination of symptoms, signs and nerve conduction function consistent with the guideline provided by the 2010 Toronto Consensus [17].
## 2.4. Definitions
Smoking was defined as a total number of ≥100 cigarettes in their lifetime [18]. Low high-density lipoprotein (HDL-c) was defined as HDL-c <1.3 mmol/L. High triglyceride (HTG) was defined as TG ≥ 1.7 mmol/L. Hypertension (HTN) was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg. Diabetic retinopathy (DR) was confirmed by ophthalmoscopy. Diabetic kidney disease (DKD) was identified clinically by an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and/or urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g caused by diabetes mellitus for ≥3 months [19]. Cardiovascular diseases (CVD) included angina, previous myocardial infarction, or electrocardiographic manifestations of coronary ischemia.
## 3.1. Meta-analysis
The RR value and $95\%$ CI of each risk factor were extracted from the included cohorts, and then pooled to screen out risk factors according to the heterogeneity across studies. Heterogeneity test was analyzed by Q-test, and measured by I2-value. When there was statistically significant heterogeneity (P-value < 0.10 or I2-value > $50\%$), the pooled RR and $95\%$ CI were generated by a random effects model, otherwise by a fixed effects model. Subgroup analyses were performed according to the magnitude of the increase in continuous variables. Continuous variables included age (years, increment by 1 vs. 5–10), BMI (kg/m2, increment by 1–5), and duration of DM (years, increment by 1 vs. 5–10). Sensitivity analyses were conducted to evaluate the robustness of the results after a single study was omitted. Publication biases were determined using Begg's and Egger's linear regression tests, and the latter one prevailed if the two results were inconsistent. All tests were considered statistically significant at two-tailed P-value < 0.05, except for heterogeneity test and publication bias were at P-value < 0.1. Statistical analyses were performed with Stata software (version 12.0 StataCorp, College Station, TX).
## 3.2. Model development
We developed a risk score system, which is simple and convenient for clinical practice. First, all the risk factors with appropriate stratifications from the above systematic review and meta-analysis were incorporated into the model. We selected appropriate RR value and $95\%$ CI to calculate the corresponding β-coefficient (β-coefficient), which represents the multiple increase in the risk of an individual developing a certain disease when each risk variable increases by one level. Second, multiplying β-coefficient by 10, and then rounding it to one decimal place [20], we further obtained the respective score of each risk factor. At last, all risk factors were stratified and assigned scores to construct a risk prediction model for DPN according to meta-analysis and clinical practice guidelines. The total score was calculated by adding up the score of each risk factor [21]. For individuals, the higher the cumulative score, the higher risk of DPN in the future.
## 3.3. Model validation
Continuous variables with normal distributions were expressed as mean ± standard deviation, and those with skewed distributions were described as median (interquartile range). Categorical variables were represented by frequency (percentage). A receiver operating characteristic (ROC) curve was performed based on the total scores. The sensitivity, specificity, the area under ROC curve (AUC) values, and optimal cut-off point were calculated. The AUC means prediction accuracy, with the value ranging from 0.5 to 1.0. The higher the AUC value, the better the prediction accuracy. The optimal cut-off point with higher sensitivity and a certain specificity was determined according to the Youden index. According to the optimal cumulative score, patients were segmented into four risk groups, including low-, moderate-, high-, and very high-risk. Kaplan–Meier curves were conducted to evaluate the cumulative risk of morbidity in different groups. Statistical analyses were performed using the SPSS 26.0 (IBMCorp, Armonk, NY, USA) and Stata software version 12.0 (StataCorp, College Station, TX).
## 4.1.1. Derivation cohort
We roughly analyzed the baseline data of participants from the included cohorts. A total of 95,604 patients with T2DM were included in the derivation cohort, with age between 35 and 79 years old, male accounting for $49.6\%$, and duration of DM ranging 1–19 years. The follow-up was 1–13 years, equivalent to 95,604 to 1,242,853 person-years. Among the patients, the mean body mass index (BMI) ranged 24.9–31.0 kg/m2, mean hemoglobin A1c (HbA1c) ranged 7.0–$8.7\%$ (53.0–71.6 mmol/mol), mean SBP ranged 135–143 mmHg, mean DBP ranged 77–87 mmHg, mean HDL-c ranged 1.30–3.40 mmol/L, and mean TG ranged 1.37–9.91 mmol/L. 6.6–$80.2\%$ of participants were smokers. 23.4–$88.1\%$ were with hyperlipidemia, 41.5–$77.8\%$ were with hypertension, 12.0–$32.1\%$ were with DR, 14.6–$43.4\%$ were with DKD, and 6.0–$44.6\%$ were with CVD. Across the studies, 54.2–$88.9\%$ received oral antidiabetic drugs (OAD), 1.0–$47.8\%$ received insulin injection, 7.8–$80.0\%$ received stains, and 18.5–$82.8\%$ received angiotensin converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB). During follow-up, 19,399 DPN events were observed, with an estimated incidence of $20.3\%$. There were 24 risk factors available from these studies, including age, gender, marital status, smoking, height, BMI, waist circumference, duration of DM, fasting plasma glucose (FPG), HbA1c, total cholesterol (TC), TG, HDL-c, low density lipoprotein (LDL-c), SBP, DBP, C-reactive protein, eGFR, hypertension, DR, DKD, CVD, insulin, and statins. Baseline characteristics and risk factors of the 18 cohorts are provided in Supplementary Tables 1, 3.
## 4.1.2. Validation cohort
A total of 462 patients with T2DM were enrolled, including 315 males ($68.2\%$). The median follow-up time was 4.29 years, and 249 patients (162 males and 87 females) developed DPN at the end of follow-up. The incidence was $53.8\%$. Among all patients at baseline, the mean age was 52.4 ± 12.2 years old, duration of DM was 6.0 (3.0–11.0) years, mean BMI was 27.49 ± 4.43 kg/m2, mean HbA1c was 8.52 ± $1.88\%$ (69.5 ± 20.5 mmol/mol), mean SBP was 136 ± 66 mmHg, mean DBP was 81 ± 11 mmHg, mean HDL-c was 1.19 ± 0.28 mmol/L, and TG was 1.63 (1.15, 2.56) mmol/L. Two hundred ($43.3\%$) participants were smokers. Two hundred and thirty-six ($51.1\%$) had hypertension, 109 ($23.6\%$) had DR, 120 ($26.0\%$) had DKD, and 224 ($48.5\%$) had CVD. Four hundred and twenty-seven ($92.4\%$) patients received OAD, 269 ($58.2\%$) received insulin, 183 ($39.6\%$) received statins, and 186 ($40.3\%$) received ACEI or ARB. Baseline data of the validation cohort are shown in Supplementary Table 5.
## 4.2. Model development
Of the 24 risk factors identified from the above meta-analysis, 11 risk factors were involved in DPN onset. The risk stratification methods were carefully selected by subgroup or sensitivity analyses, which were most reasonable considering the feasibility and convenience of clinical practice. These 11 risk factors included in the final model were as follows: age incremented by 1 year (RR 1.02, $95\%$ CI 1.01–1.03, $$P \leq 0.001$$; β-coefficient 0.020, score 0.2), smoking (RR 1.43, $95\%$ CI 1.29–1.59, $P \leq 0.001$; β-coefficient 0.358, score 3.0), BMI incremented by 1–5 kg/m2 (RR 1.18, $95\%$ CI 1.02–1.37, $$P \leq 0.030$$; β-coefficient 0.166, score 1.5), duration of diabetes incremented by 5–10 years (RR 1.39, $95\%$ CI 1.21–1.60, $P \leq 0.001$; β-coefficient 0.329, score 3.0), HbA1c incremented by $1\%$ (RR 1.14, $95\%$ CI 1.08–1.19, $P \leq 0.001$; β-coefficient 0.131, score 1.5), low HDL-c (RR 1.34, $95\%$ CI 1.13–1.59, $$P \leq 0.001$$; β-coefficient 0.293, score 3.0), high triglyceride (HTG; RR 1.34, $95\%$ CI 1.19–1.51, $P \leq 0.001$; β-coefficient 0.293, score 3.0), hypertension (RR 1.35, $95\%$ CI 1.08–1.68, $$P \leq 0.008$$; β-coefficient 0.300, score 3.0), DR (RR 2.05, $95\%$ CI 1.25–3.37, $$P \leq 0.005$$; β-coefficient 0.718, score 7.0), DKD (RR 1.91, $95\%$ CI 1.32–2.77, $$P \leq 0.001$$; β-coefficient 0.647, score 6.5), and CVD (RR 1.66, $95\%$ CI 1.33–2.08, $P \leq 0.001$; β-coefficient 0.507, score 5.0). A forest plot of heterogeneity test of 11 risk factors is presented in Figure 3A, and subgroup and sensitivity analyses are shown in Figure 3B. These risk factors, risk stratification, RRs, $95\%$ CIs, β-coefficients, and risk scores are shown in Supplementary Table 6.
**Figure 3:** *(A) Pooled RR (95% CI) and heterogeneity test of the risk factors for developing DPN. (B) Subgroup or sensitivity analyses of the risk factors for DPN. BMI, body mass index; HbA1c, Hemoglobin A1c; HDL-c, high density lipoprotein cholesterol; HTG, high triglyceride; HTN hypertension; DR, diabetic retinopathy; DKD, diabetic kidney disease; CVD, cardiovascular disease.*
According to the stratifications and scores of the above risk factors, a simple DPN risk prediction model was developed as follows: age (years, 35–49 = 0, 50–59 = 2.0, 60–69 = 4.0, 70–79 = 6.0), smoking (no = 0, yes = 3.5), BMI (kg/m2, <24.00 = 0, 24.00–27.99 = 1.5, ≥28.00 = 3.0), duration of DM (years, <5.0 = 0, 5.0–9.9 = 2.5, 10.0–19.9 = 5.0, ≥20.0 = 7.5), HbA1c (%, <7.0 = 0, 7.0–7.9 = 1.5, 8.0–8.9 = 3.0, ≥9.0 = 4.5), HDL-c (mmol/L, ≥1.30 = 0, <1.30 = 3.0), TG (mmol/L, <1.70 = 0, ≥1.70 = 3.0), hypertension (no = 0, yes = 3.0), DR (no = 0, yes = 7.0), DKD (no = 0, yes = 6.5), and CVD (no = 0, yes = 5.0; shown in Table 1).
**Table 1**
| Risk factors of DPN | Risk stratification | Score |
| --- | --- | --- |
| Age (year)## | 35–49 | 0.0 |
| | 50–59 | 2.0 |
| | 60–69 | 4.0 |
| | 70–79 | 6.0 |
| Smoking### | No | 0.0 |
| | Yes | 3.5 |
| BMI (kg/m2)#### | <24.00 | 0.0 |
| | 24.00–27.99 | 1.5 |
| | ≥28.00 | 3.0 |
| Duration of DM (year) | <5.0 | 0.0 |
| | 5.0–9.9 | 2.5 |
| | 10.0–19.9 | 5.0 |
| | ≥20.0 | 7.5 |
| HbA1c (%) | <7.0 | 0.0 |
| | 7.0–7.9 | 1.5 |
| | 8.0–8.9 | 3.0 |
| | ≥9.0 | 4.5 |
| HDL-c (mmol/L) | ≥1.30 | 0.0 |
| | <1.30 | 3.0 |
| TG (mmol/L) | <1.70 | 0.0 |
| | ≥1.70 | 3.0 |
| HTN | No | 0.0 |
| | Yes | 3.0 |
| DR | No | 0.0 |
| | Yes | 7.0 |
| DKD | No | 0.0 |
| | Yes | 6.5 |
| CVD##### | No | 0.0 |
| | Yes | 5.0 |
## 4.3. Model validation
In the validation cohort, the AUC value of this model was 0.831 ($95\%$ CI 0.794–0.868, $P \leq 0.001$). The ROC curve is provided in Figure 4A. The purpose of constructing this model was to early identify the high-risk population of DPN, so the sensitivity should be as high as possible on the premise of ensuring a certain degree of specificity when selecting the cut-off point. Therefore, a score of 17.0 was finally selected as the best predictive cut-off point, with a higher sensitivity of 0.846, a specificity of 0.668, and a maximum Youden index value of 0.531. Sensitivity, specificity and Youden indexes of different cut-off scores are shown in Supplementary Table 7. Based on the frequencies of the total risk scores, 462 patients were divided into four groups: low- ($$n = 97$$, 0–12.5 scores), moderate- ($$n = 81$$, 13.0–16.5 scores), high- ($$n = 149$$, 17.0–24.5 scores), and very high-risk ($$n = 135$$, 25.0–52.0 scores), and the corresponding numbers of patients who developed DPN at the end of the follow-up were 11 ($11.3\%$), 24 ($29.6\%$), 99 ($66.4\%$), and 115 ($85.2\%$), respectively. The risk of developing DPN was significantly increased in moderate- (RR 3.3, $95\%$ CI 1.5–7.2, $$P \leq 0.020$$), high- (RR 15.5, $95\%$ CI 7.6–31.6, $P \leq 0.001$), and very high-risk groups (RR 45.0, $95\%$ CI 20.5–98.8, $P \leq 0.001$) compared with the low-risk group. The Kaplan-Meier curves for these four groups are shown in Figure 4B. The cumulative risk for each group was provided in Table 2.
**Figure 4:** *(A) Receiver operating characteristic curve for the DPN risk prediction model. The AUC and $95\%$ CI were 0.831 (0.794–0.868). (B) Kaplan-Meier curve of DPN end point for four risk group: moderate- (RR 3.3, $95\%$ CI 1.5–7.2, $$P \leq 0.020$$), high- (RR 15.5, $95\%$ CI 7.6–31.6, $P \leq 0.001$), and very high-risk groups (RR 45.0, $95\%$ CI 20.5–98.8, $P \leq 0.001$).* TABLE_PLACEHOLDER:Table 2
## 5. Discussion
Recent evidence suggests that multiple metabolic disorders are all involved in DPN onset, but the results of different studies were not entirely consistent. Given that high-quality meta-analysis is at the top of the evidence-based medicine level pyramid, and it helps to establish a more robust prediction model than a single study. Meta-analysis was applied to integrate relevant cohort studies (22–39). We screened out the risk factors of DPN from a meta-analysis, and constructed a simple risk prediction model for DPN by quantitatively evaluating risk factors. This model provided quantitative standards for early identifying high-risk groups, thus we can develop comprehensive and individualized prevention and intervention strategies.
We included 18 cohort studies with a total of 95,604 patients, and screened out 11 risk factors of DPN, including age, smoking, BMI, duration of DM, HbA1c, HDL-c, TG, hypertension, DR, DKD, and CVD. Except age and duration of DM were non-modifiable, the remaining factors can be modifiable by lifestyle optimization and drug intervention. We recommended that patients at low-risk improve self-monitoring and conduct regular risk assessment; High-risk groups, based on risk assessment, actively improve lifestyle, such as quitting smoking, optimizing diet structure, participating in moderate physical exercise, and weight control, etc. They should optimize the basic controlling of blood glucose, lipids and blood pressure, and further intervene in diabetic complication under the guidance of professional doctors. It should be noted that once a patient is classified as more than low risk, neurological examination for DPN should be performed, especially the sensitive tests for small fiber neuropathy, to monitor the stage and progression of DPN. Through systematic clinical prevention strategies, we can reduce the incidence of DPN, improve life quality of patients, and save costs of clinical management.
In the risk factors for DPN from our meta-analysis, DR, DKD, and CVD were the most powerful, and they usually share common factors and are often co-morbid. Our study suggested that both DR and DKD were increased the risk of DPN by nearly 2 times, and CVD increased the risk by ~1.7 times. Duration of DM, smoking, low HDL-c, high TG and hypertension were the relatively moderate risk factors. Previous cross-sectional and cohort studies shown that the incidence of DPN reached $50\%$ after 8 years of diagnosis in diabetic patients [34]. We indicated that the risk of DPN increased by $39\%$ for every 10-year increment in the duration of DM. Smoking is an independent risk factor for DPN in diabetic patients [40]. Consistent with previous researches, our study reported the risk increased by about $40\%$, whether quitting smoking or not. Smoking was defined as a total number of ≥ 100 cigarettes in their lifetime [18]. Dyslipidemia is closely correlated with the progression of DPN, especially high TG and low HDL-c levels [34, 37]. As TG ≥ 1.70 mmol/L or HDL-c <1.30 mmol/L, the risk for DPN increased by $34\%$ in this study. We found no statistical significance when it came to LDL-c, and there is still a lack of research data. Considering that patients with T2DM are prone to comorbid dyslipidemia, the widespread use of statins at baseline may affect the results. Hypertension plays a crucial role in the occurrence and development of DPN. Our result reported that hypertension increased the risk of DPN by $35\%$, but no correlation was found between SBP, DBP, and DPN. The relationship between blood pressure and DPN risk still needs to be investigated in large-scale prospective studies. Age, HbA1c, and BMI were also major risk factors of DPN. Prospective researches have shown that age was independently related with DPN. For every 10 years increment in age, the risk of DPN was increased by $20\%$ in this study. According to study population included in our meta-analysis, the results were mainly applicable to patients with T2DM aged 35–79 years old. Patients with type 1 diabetes mellitus tend to be younger, and there is currently a lack of data on DPN risk in patients with early-onset (age <35 years old) T2DM. Further prospective studies with large samples are needed. With HbA1c incremented by $1\%$, the risk of DPN increased by $14\%$ in this study. We did not find a correlation between FPG and DPN. FPG is the blood glucose level at a certain time point, while HbA1c reflects the average level in the recent 2–3 months. Therefore, HbA1c could better reflect blood glucose control. BMI is the most common measure of obesity and plays a crucial role in initiation of DPN. Obesity is often associated with insulin resistance and dyslipidemia, which are components of cardiovascular disease risk factors, and their interaction will also increase the risk of DPN [41]. When BMI incremented by 1–5kg/m2, the risk of DPN increased by $18\%$ in our study. The difficulty lied in the excessive stratification of a certain risk factor and the occurrence of extreme value, which led to its excessive weight. In order to avoid this influence, appropriate results and stratification methods were selected for some risk factors. For example, considering the weight of BMI is too high, we converted it into categorical variable. According to Chinese standards for BMI [42], the population was divided into three groups: normal (<24.00 kg/m2), overweight (24.00–27.99 kg/m2), and obese (≥28.00 kg/m2). We defined BMI < 24.00 kg/m2 as the normal group with a 0 score, BMI ranged 24.00–27.99 kg/m2 as the overweight group with a score of 1.5, and BMI ≥28.00 kg/m2 as the obese group with a score of 3.0.
Some investigators have established prediction models for DPN (43–46). However, those models were mostly constructed based on cross-sectional studies, small sample cohort studies or post-hoc analysis of randomized controlled trials. A prior model developed by Basu et al. [ 44] used complex computer algorithms, which is not convenient for clinical promotion. Hence, based on meta-analysis from 18 cohort studies with 95,604 patients with T2DM, we established a simple and robust risk prediction model for DPN that consisting of lifestyle and clinical data, including age, smoking, BMI, duration of DM, HbA1c, low HDL-c, high TG, hypertension, DR, DKD and CVD.
Furthermore, patients with T2DM from China were included as an external cohort to verify the predictive performance of this model. AUC of our model was 0.831, indicating good predictive performance. Since this model aimed at early identification of high-risk population of DPN, the sensitivity should be improved as far as possible on the premise of ensuring a certain degree of specificity when selecting cut-off point. Finally, a score of 17.0 with a higher sensitivity of 0.846 and a specificity of 0.668 was selected as the optimal cut-off point. Sensitivity represents the true positive rate, our model achieved a high sensitivity, indicating that the authenticity of positive prediction is relatively high, which is in line with the purpose of the prediction model. Specificity stands for the true negative rate. There is a shortcoming that the true negative rate is slightly low. The reason may be that some indicators which may affect the occurrence and development of DPN were not included due to the meta-analysis, such as C-reactive protein, erythrocyte sedimentation rate, fibrinogen, and D-dimer, etc. Few related cohort researches were available in this meta-analysis, so we failed to include them in our model. Further prospective studies on these indicators are still needed, and we expect to include them to improve the model's specificity. Patients with a cumulative score of ≥17.0 were at high-risk of DPN onset. According to the total scores of participants, we further divided them into four groups, namely, low-, moderate-, high- and very high-risk groups. Compared with the low-risk group, the moderate-, high- and very high-risk groups had 3.3-, 15.5-, and 45.0-fold increases in the rate of developing DPN, respectively. Through the application of this model to assess the risk factors and make targeted measures, it is expected to transform high-risk individuals into lower-risk groups, so as to achieve dynamic management and ultimately reduce the occurrence of DPN.
Nevertheless, the study presented here faces some limitations. First, heterogeneity among literatures is inevitable because of differences in study design and diversities in race and sex compositions of the included studies. Although subgroup analysis and sensitivity analysis were further conducted to minimize heterogeneity, the causes for heterogeneity of some factors were still not explicit. Second, the number of included researches on some risk factors is small. Some other clinical indicators, such as C-reactive protein, erythrocyte sedimentation rate, fibrinogen and D-dimer, may involve in the progression of DPN, but few related cohort studies were retrievable, so we failed to include them in our model. All these confounding factors may bias the results. We expect more prospective studies on these factors to be explored and included them to update our model in the future. Third, participants in the derivation cohort were from several countries and regions, while the validation cohort was only consisted of Chinese patients. Therefore, multicenter external cohorts remained to verify the predictive performance of this model.
## 6. Conclusion
Based on meta-analysis, we developed a simple and reliable risk prediction model for DPN in combined with lifestyle and clinical data, including age, smoking, BMI, duration of DM, HbA1c, low HDL-c, high TG, hypertension, DR, DKD, and CVD. This model is important for early prevention and individual intervention of DPN.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of Tianjin Medical University Metabolic Diseases Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
XixL and DC conducted the research, collected the data, statistical analysis, and wrote the manuscript. HF, XinL, QZ, JZ, MD, and JW contributed to the discussion, performed study quality assessment, and reviewed the manuscript. BC contributed to concept, design, and manuscript revision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1128069/full#supplementary-material Systematic review and meta-analysis to identify the risk factors of diabetic peripheral neuropathy onset in patients with type 2 diabetes mellitus is available in the Supplementary material.
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|
---
title: Defining metabolic migraine with a distinct subgroup of patients with suboptimal
inflammatory and metabolic markers
authors:
- Elena C. Gross
- Niveditha Putananickal
- Anna-Lena Orsini
- Jean Schoenen
- Dirk Fischer
- Adrian Soto-Mota
journal: Scientific Reports
year: 2023
pmcid: PMC9992685
doi: 10.1038/s41598-023-28499-y
license: CC BY 4.0
---
# Defining metabolic migraine with a distinct subgroup of patients with suboptimal inflammatory and metabolic markers
## Abstract
Emerging evidence suggest migraine is a response to cerebral energy deficiency or oxidative stress in the brain. Beta-hydroxybutyrate (BHB) is likely able to circumvent some of the meta-bolic abnormalities reported in migraine. Exogenous BHB was given to test this assumption and, in this post-hoc analysis, multiple metabolic biomarkers were identified to predict clinical improvements. A randomized clinical trial, involving 41 patients with episodic migraine. Each treatment period was 12 weeks long, followed by eight weeks of washout phase / second run-in phase before entering the corresponding second treatment period. The primary endpoint was the number of migraine days in the last 4 weeks of treatment adjusted for baseline. BHB re-sponders were identified (those with at least a 3-day reduction in migraine days over placebo) and its predictors were evaluated using Akaike’s Information Criterion (AIC) stepwise boot-strapped analysis and logistic regression. Responder analysis showed that metabolic markers could identify a “metabolic migraine” subgroup, which responded to BHB with a 5.7 migraine days reduction compared to the placebo. This analysis provides further support for a “metabolic migraine” subtype. Additionally, these analyses identified low-cost and easily accessible biomarkers that could guide recruitment in future research on this subgroup of patients.
This study is part of the trial registration: ClinicalTrials.gov: NCT03132233, registered on 27.04.2017, https://clinicaltrials.gov/ct2/show/NCT03132233
## Introduction
Migraine is a common, complex and debilitating neurological disorder1, but its primary pathogenic mechanisms are not yet completely understood. Despite having been referred to as a “hypoglycemic headache” in 1935 already2, the focus of clinical and basic research in the last decades was primarily on (neuro-) vasculature, cerebral excitability, and neurotransmission. In recent years, metabolism and mitochondrial (dys-)function have regained interest. Various lines of evidence suggest migraine is—at least partially—a metabolic as much as a neurological disease, in which the migraine attack is a warning signal to increased oxidative stress and / or (cerebral) hypometabolism3.
Magnetic resonance spectroscopy (MRS) studies in migraineurs consistently show decreased ATP levels or hypometabolism4–15. Mitochondrial function and oxidative stress markers have also been shown to be abnormal in higher-frequency migraine16. Additional support comes from early studies demonstrating metabolic changes induced by fasting, glucose or insulin administration, which were shown to be able to even trigger migraine attacks in susceptible patients16–22.
Several nutraceuticals23, such as riboflavin at high dose (200–400 mg/day)30–36; coenzyme Q10 (400 mg capsules or 300 mg liquid suspension)24–29, magnesium37 and alpha-lipoic acid (600 mg)38–40 have shown to prevent migraine attacks also suggesting a link between migraine and metabolism/or mitochondrial functioning.
Oxidative stress seems to be the common denominator of most migraine triggers41,42 and apart from clearly “metabolic” triggers (such as physical exercise, fasting, and stress), many of the seemingly unrelated triggers (like ovarian hormone changes, alcohol, weather changes, intense light, and strong odors) can negatively impact mitochondrial metabolism and/or oxidative stress (see reviews3,41). Mechanistically, nitrosative, oxidative, and electrophilic stress can activate transient receptor potential channels, expressed in meningeal nociceptive nerve terminals43,44, thereby providing a mechanism by which known migraine trigger factors which increase oxidative stress could lead to migraine pain.
Metabolic approaches to migraine prevention, such as a ketogenic diet (KD), which to some extent mimics the state of fasting, have been shown to be migraine protective44–49. The KD was developed over 100 years ago, after the observation that prolonged fasting has antiepileptic properties50. Like fasting, it promotes the hepatic production of ketone bodies (KBs). Recently the KD has received renewed interest, due to the observation that KBs could be beneficial for a variety of other neurological disorders as51–53 all brain cells have the capacity to use KBs as respiratory substrates54.
Out of the three physiologically relevant KBs β-hydroxybutyrate (BHB) constitutes up to $70\%$55 and acts also as a signaling molecule56. Consequently, it has the potential to positively influence other pathways commonly believed to be part of migraine pathophysiology57.
In complex and heterogenous diseases such as migraine, a therapy that can simultaneously target multiple possible pathogenic pathways seems advantageous and elevated KB levels have been shown to be well tolerated for extended periods of time, even up to several years47,58–70. However, a very strict KD, may be difficult to adhere to longer-term.
Our research group wanted to examine whether exogenously raised KBs would also be able to attenuate migraine frequency, if ingested daily, and carried out the first RCT exploring the effect of BHB as a prophylactic agent in episodic migraine patients71 where a non-statistically significant reduction of 1.9 migraine days over placebo was documented, however, some patients clearly reduced more days than other. In line with the already outline evidence supporting the existence of a “metabolic migraine subgroup”, we aimed to evaluate if metabolic health markers could identify patients responded to BHB supplementation.
## Trial design
The trial conducted was a double-blind, randomized, placebo-controlled trial with a crossover design with 41 migraine patients meeting the ICHD-3 (International Classification of Headache Disorders version 3 Beta) Classification criteria73. The trial was registered at ClinicalTrials.gov (NCT03132233), approved by the local ethics committee Swissethics (EKNZ 2015-304) and the National Swiss Drug Agency (2016DR2109). The detailed methods can be found in the published study protocol72. In brief, the trial consisted of a four-week run-in period followed by randomization. Then a first treatment period of 12 weeks, followed by a washout period of 4 weeks. Afterward, a second run-in phase of 4 weeks and finished with the second treatment period of 12 weeks.
## Study medication
The investigational medicinal product (IMP) used in this clinical trial was 9 gr of D-BHB (from 18 gr racemic BHB) in powdered calcium (Ca2 +)–magnesium (Mg2 +)–salt form (Ca–Mg–BHB) divided into three servings per day. The mineral load determined the maximal IMP dose. The placebo group received sachets containing Mannitol.
## Clinical measures
At pre- and post- intervention visits the following assessments were additionally conducted: Migraine Disability Test (MIDAS)74, Headache Impact Test, version 6 (HIT-6)75 and blood draw for biomarker and safety analysis (albumin, Calcium, cortisol, alanine aminotransferase, pancreatic-amylase, alkaline phosphatase, aspartate aminotransferase, beta-hydroxybutyrate, bilirubin, creatine kinase, Chloride, Cholesterol, Cholesterol Quotient, Cortisol basal, high sensitivity-C reactive protein, globulin, fasting glucose, gamma-glutamyl transferase, glomerular filtration rate, uric acid, HbA1c, High density lipoprotein, urea, Potassium, creatinine, lactate plasma, lactate dehydrogenase, low-density lipoprotein (calculated with Friedewald’s equation), Magnesium, Sodium, Phosphate, total protein, triglycerides, leukocyte count, erythrocytes, hemoglobin, hematocrit, mean corpuscular volume, platelets, T3, T4, Insulin and thyroid stimulating hormone (TSH). All blood samples were taken after an overnight fast between 8 and 11 am and all markers were considered in the responder analysis. Further details on data collection are provided in the published study protocol72.
## Statistical analysis
Data wrangling and statistical analyses for this purpose were performed using R version 4.0.3. and the packages: tidyverse, readxl, performance, tableone, gtools, MASS, bootStepAIC, lmtest, rpart and car. When relevant, data and all linear model residuals were tested for normality using stats::shapiro.test. A Friedman Test was used to analyze pharmacokinetics data and differences between responders and non-responders were analyzed using Mann–Whitney and Kruskal–Wallis’s rank sum tests. Baseline vs follow-up metabolite changes were analyzed using Wilcoxon tests.
To identify factors associated with positively responding to KBs supplementation, we evaluated the relevance of different combinations of independent predictors according with the explanatory capacity of each model Akaike’s Information Criterion (AIC), the consistency of their coefficient signs, and the consistency of their statistical relevance. This procedure was performed via a bootstrap AIC consistency diagnosis in which 100 independent samples drawn at random from the dataset using bootStepAIC::boot.stepAIC. To avoid collinearity, we analyzed potential models by grouping blood markers according with their corresponding physiological system (thyroid markers, liver function markers, blood cells markers, etc.) to identify the best predictor from each system and test its predictive contribution to different potential models. For evaluating the all-around performance of the combined models for predicting BHB response, we used performance::compare_performance which allows for simultaneously comparing AIC, Bayes Information Criterion (BIC), Root mean squared error (RMSE), and Tjur’s R2. Linear assumptions of the models we corroborated using performance::check_model.
Finally, we used supervised machine-learning regression trees to identify potentially useful cutoffs for relevant predictors using rpart::rpart.
## Local ethical approval and consent to participate
All participants provided informed consent to participate in the trial. Ethical approval was granted by Swissethics, EKNZ PB 2016-00497. Also, all methods were performed in accordance with the relevant guidelines and regulations.
## Study population
A total of 9 out of 32 patients ($28.13\%$) met our conservative criteria for BHB treatment response, ranging from 3- to 12-day reduction in migraine days from baseline compared to placebo. Treatment responders had an average of 5.78 (SD = 2.63) less migraine days compared to placebo.
To evaluate is pharmacokinetic differences were likely responsible for the differences in therapeutic success, a Friedman test was conducted between responders and non-responders for glucose and BHB (Fig. 1).Figure 1Blood BHB and glucose levels with IMP (panels (A) and (C)) placebo (panels (B) and (D)) in non-responders (blue) & responders (red). Error bars depict the standard deviation (SD).
Table 1 describes the distribution and demographic and metabolic differences between responders and not responders at baseline. Table 1Differences in metabolites between responders and non-responders at baseline. Non-responder ($$n = 23$$)Responders ($$n = 9$$)p valueMeanStd. devp50p25p75MeanStd. devp50p25p75HS-CRP1.72.01.40.51.733.372.40.83 < 0.001TSH20.81.91.32.52.52.531.91.430.182Albumin40.23.040384239.82.594039410.778ALT23.410.019152516.24.63151319 < 0.001BHB0.20.20.20.10.20.20.120.20.10.30.822Cortisol363.9200.0327263363.9266.389.39264211303 < 0.001Glucose4.90.54.84.65.150.4854.75.40.056HbA1c50.354.85.25.20.275.155.3 < 0.001HDL1.60.31.61.41.81.60.21.61.51.80.338Lactate1.20.710.81.41.41.150.90.71.50.894LDH173.720.0171156.8188.2161.733.33156141172 < 0.001LDL2.30.82.31.72.92.60.852.31.92.90.211Mg0.80.10.80.80.90.80.060.80.80.90.803Na140.42.0140139142140.32.211401391420.866Pi1.10.21.111.210.1710.91.1 < 0.001Triglycerides10.40.80.61.20.90.280.80.710.440FT34.90.84.94.45.34.60.754.44.24.8 < 0.001FT415.82.015.814.716.8163.3615.314.116.40.373Insulin9.25.08.65.910.98.72.98.36.810.20.888Age36.41033284544104535530.08BMI23.44.0022.420.825.124.5423.921.225.40.52SexFemale27 ($87.1\%$)Male4 ($12.9\%$)Female8 ($88.9\%$)Male1 ($11.1\%$)0.06P-values were obtained using tableone::CreateTableOne which uses Kruskal–Wallis’s rank sum tests for non-parametric hypothesis testing.
After 3 months of BHB supplementation many of these markers changed into the direction of the non-responder levels. TSH dropped by $15\%$, triglycerides by $12\%$, fasting glucose by $7\%$, hs-CRP by $53\%$ and endogenous BHB levels increased by $56\%$ in the responder group. In addition, fasting insulin dropped by $11\%$ and cortisol levels by $18\%$. Furthermore, ALT increased by $29\%$, phosphate by $12\%$, LDH by $7\%$ and magnesium by $5\%$ (see Fig. 2). Only the change in CRP was statistically significant ($$p \leq 0.002$$) after using Wilcoxon’s hypothesis testing. Figure 2Metabolite changes in responders after 3 months with the IMP in percent difference from baseline values. ( $$n = 9$$).
After comparing multiple logistic regression models with 100 bootstrapped samples from all available measurements, we concluded that, because of their cost, availability and Beta coefficient consistency, C-reactive protein, Phosphorus and HbA1c are the most useful predictors of BHB supplementation responsiveness. All were statistically significant below the 0.05 threshold in > $95\%$ of the bootstrap simulations and the sign of their estimate was consistent $100\%$ of the times. Only variables with a $100\%$ coefficient sign consistency were selected for the candidate models and, to avoid collinearity and physiological “redundance”, only one predictor per system was used. Table 2 summarizes the all-around performance of the candidate models according with their AIC, BIC, RMSE and R2.Table 2Performance comparison of candidate logistic regression models. ModelAICBICR2RMSEAll metabolites113.6207.70.780.19Pi + HS − CRP + FT3 + HbA1c209.2226.00.180.39Pi + HS − CRP + FT3 + ALT192.3209.20.260.37Pi + HS − CRP + HbA1c218.3231.70.130.40Pi + HS − CRP + FT3 + ALT + HbA1c + LDH173.3196.80.350.34Performance of logistic regression models for the outcome “responder”. AIC Akaike’s information criterion, BIC Bayes information criterion, R2 Tjur’s R2, RMSE root mean square error.
The model including Pi + HS − CRP + FT3 + ALT accounts for $26\%$ of the variance using only 4 predictors. Adding 2 more predictors; HbA1c and LDH, improved the explained variance and accounts for almost half of the variance explained by the model using all metabolites. After identifying potentially useful markers, we used a regression tree to then identify their potentially useful cut-off points.
As suggested by Fig. 3, using these cut-off points, the least and apparently most useful markers for identifying whether a person belongs to the responder group were inorganic phosphorous, hs-CRP (a marker for inflammation) and Hba1C (long-term blood sugar). This model’s linear assumptions were corroborated (Supplemental Information).Figure 3Regression tree of using Pi + LDH + HS − CRP + FT3 + ALT + HbA1c and the R function rpart::rpart. Color blue = ” responders” and color red = ” non-responders”.
## Discussion
Classifying sub-groups of migraineurs based on objective biomarkers is essential for improving clinical study designs, developing novel treatments and ultimately, improving clinical care. Hereby, we documented metabolic markers in BHB supplementation responders differed from those who did not respond. To our knowledge, this is the first work proposing blood biomarkers for predicting treatment response to migraine prophylaxis and could help pave the way not only for testing future anti-inflammatory/metabolic interventions, but also for reassessing already existing solutions76.
However, there were several limitations to this analysis. At trial onset, only the racemic BHB was available, which has ½ of the potency of the bioidentical D- BHB77. In addition, the dose of D-BHB (9 g per day) was low compared to the 185 g of KBs produced by the liver during fasting78. The upper limit was determined by the mineral’s upper daily intake requirements that the BHB was bound to. Due to these two factors, the potency of the current formulation was so low that nutritional ketosis (> 0.5 mmol/l BHB) was never reached79. Furthermore, mannitol was used as a placebo, but it may be not a be an ideal placebo because it shares one migraine relevant mechanism as it increases brain tissue oxygenation80,81. Not surprisingly, we identified 4 responding to mannitol.
Despite the trial’s limitations, we could find potentially useful and easily available predictors (and their potentially useful cut-offs) for identifying whether a person belongs to the responder group or not based on the independent markers of inorganic phosphorous, HS-CRP (a marker for inflammation) and Hba1C (long-term blood sugar) and this result is highly significant—despite our small sample size but should be prospectively validated in future studies.
In contrast, several markers of metabolism and inflammation were worse in the BHB responder group while no pharmacokinetic differences were found. Additionally, most of these markers improved or started looking more like the non-responder group after the intervention. Most notably hs-CRP (inflammation) more than halved and this change was significant, despite the small sample size ($$n = 9$$), thus, it is likely the rest of the paired analyses were underpowered. Together, these findings suggest that responders were responding because of their baseline pathophysiological differences and not because they were exposed to higher doses of BHB.
While the connection to energy metabolism with glucose, insulin, KBs, HbA1c and triglycerides seems evident, the other markers also have a strong connection to metabolism. Hs-CRP has been found to be a marker of impaired energy metabolism, in addition to a marker of acute systemic inflammation82, the thyroid is well known for its signalling role in energy homeostasis and energy metabolism83, and ALT has been shown to play a role in metabolic disease84. The exact mechanisms by which KB improve or prevent migraine can be multiple and additive ranging from restoring energy utilisation to ameliorating inflammation85.
It is necessary to mention we are not the first ones to find an association between treatment response to a metabolic migraine therapeutic and a biomarker. Over a decade ago the therapeutic response to high-dose riboflavin was shown to be associated with specific mitochondrial DNA (mtDNA) haplogroups (non- H mitochondrial DNA haplotypes)36, which is also indicative of a “metabolic migraine subgroup”. However, mtDNA haplogroups are, however, not as easily identified as the common three laboratory markers that we propose. These biomarkers could be used to guide the inclusion criteria of future clinical trials and aid the selection of “metabolic migraineurs” for future trials.
We should highlight that the utility of these individual predictors is context dependant and we provided data for cost–benefit assessments. For example, as shown in Table 2, adding FT3 and ALT to Pi + HS − CRP + HbA1c doubles the R2 but would likely complicate recruitment as there are not many point-of-care options for measuring them. Importantly, the identified cut-offs in this work need to be validated prospectively but, the fact that they are already close to those currently used in clinical practice suggest.
Finally, it is possible that these potentially relevant metabolic predictors are not migraine specific and could be useful for studies on other illnesses for which ketosis has been hypothesized to be beneficial such as diabetes, heart failure and epilepsy.
## Conclusion
This study provides further support for a distinct “inflammatory/ metabolic migraine” subgroup with unique metabolic and inflammatory signatures. Three easy to measure blood markers (hs-CRP, HbA1c and phosphorus) could assist personalized metabolic migraine treatments and prophylactic interventions.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-28499-y.
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|
---
title: Effects of wetted inner clothing on thermal strain in young and older males
while wearing ventilation garments
authors:
- Ken Tokizawa
journal: Frontiers in Physiology
year: 2023
pmcid: PMC9992724
doi: 10.3389/fphys.2023.1122504
license: CC BY 4.0
---
# Effects of wetted inner clothing on thermal strain in young and older males while wearing ventilation garments
## Abstract
The present study examined the effect of wearing a water-soaked inner t-shirt with a ventilation garment on thermal and cardiovascular strain in eight young (26 ± 4 years) and eight older (67 ± 3 years) men undertaking moderate-intensity work (metabolic rate: 200–230 W m−2) in a hot environment (37°C, $50\%$ RH, 2.8 kPa). While intermittent walking in hot conditions for 60 min, as a control (CON), the subject wore a dry inner t-shirt (long-sleeved) without fanning of a ventilation jacket (single-layered cotton, 0.21 clo). On separate days, under a fanned ventilation jacket, the subject wore a dry inner t-shirt (DRY) or an inner t-shirt soaked with 350 mL of tap water (WET). In the young group, increases in rectal temperature from pre-exercise baseline in the WET trial (0.7°C ± 0.2°C) were lower than in the CON (1.3°C ± 0.3°C) and DRY (1.1°C ± 0.2°C) (both $p \leq 0.05$) trials during exercise in hot conditions. In the older group, the increases were also attenuated in WET (0.7°C ± 0.4°C) compared with CON (1.3°C ± 0.4°C) and DRY (1.1°C ± 0.4°C) (both $p \leq 0.05$) without differences between age groups. Heart rate and whole-body sweat loss were lowest in the WET, followed by DRY, and then CON conditions in both groups (all $p \leq 0.05$). These findings demonstrate that wearing a water-soaked inner t-shirt while using a ventilation garment is an effective and practical cooling strategy to mitigate thermal and cardiovascular strains in young and older individuals during moderate-intensity work in hot conditions.
## Introduction
Climate change and global warming cause increased human exposure to more prolonged and intense heat, which hampers labor productivity in physically demanding work (Flouris et al., 2018). At the same time, labor force demographics are rapidly changing and the unprecedented aging of populations and workforce in most developed and many developing countries has significant implications for employees, human resource management, organizations, and societies (Hertel and Zacher, 2018). Older individuals are vulnerable to heat stress, which is associated with an alteration in thermoregulatory function both in hot environmental conditions and during exercise (Kenney et al., 2014). Reduced sweating response and cutaneous vasodilation impair heat loss (Inoue and Shibasaki, 1996; Kenney and Munce, 2003), which induces hyperthermia, higher levels of cardiovascular strain (Kenney et al., 2014), and increases the risk of heat-related illness during exposure to a hot environment and exercise (Kenney and Munce, 2003). To protect an aging workforce, especially in physically demanding jobs in hot conditions, countermeasures for inhibiting an elevation of core temperature (Tcore) by body cooling strategies should be reconsidered or developed, because cooling effects (e.g., fanning) might be less effective in older adults than in young ones due to the reduced heat dissipation (Gagnon et al., 2016; Gagnon et al., 2017).
Personal cooling garments have been found to attenuate thermal and cardiovascular strains during working, such as ambient or cold air ventilation, liquid cooling, and phase-change material garments (Yazdi and Sheikhzadeh, 2014; Chan et al., 2015). These garments have inevitable ergonomic problems such as the additional weight and layers, and restriction of body movement, but an ambient-air ventilation garment can be practical. The attached fans and mobile battery are light, and the garments do not bother the wearer during work activities. However, a field study showed that the efficacy of ventilation garments was limited to reducing skin temperature (Tskin), then Tcore, heart rate (HR), and labor productivity were not affected by their use in hot conditions (Ioannou et al., 2021).
In a laboratory, Shapiro et al. [ 1982] showed that ventilation garments attenuated increases in Tcore and HR during exercise at 35°C ($75\%$ RH) while wearing a protective semipermeable overgarment. Since then, ventilation units have been improved and evaluated in human subjects (Muza et al., 1988; Chen et al., 1997; Chinevere et al., 2008; Hadid et al., 2008; Barwood et al., 2009) and by modeling using thermal manikins (Xu and Gonzalez, 2011; Zhao et al., 2013; del Ferraro et al., 2021; Yang et al., 2022); the efficacy of using ventilation garments might be potentially augmented. Although evaporation depends on the wearer’s sweat, the volume of sweat varies between individuals (Bain et al., 2011) and with age (Shoenfeld et al., 1978; Inoue et al., 1991). To this end, soaking inner shirts with water can supplement sweat production while using ventilation garments, which might be an ideal strategy for older individuals.
Some studies have demonstrated that supplemental skin wetting is less effective for mitigating thermal and cardiovascular strains during climatic heat waves. Song et al. [ 2019] reported that soaking a t-shirt and short pants inhibited increases in the Tcore by ∼0.2°C while resting at 43°C ($57\%$ RH) for 90 min. Morris et al. [ 2019] showed that self-dousing with water to the skin attenuated increases in HR by ∼5 beats/min and sweat losses resting at 40°C ($50\%$ RH) and 47°C ($10\%$ RH) for 120 min without affecting Tcore. Cramer et al. [ 2020] reported that soaking a t-shirt attenuated the increases in Tcore by ∼0.2°C, HR by ∼5 beats/min, and sweat losses in resting at 42°C ($34\%$ RH) for 120 min. However, they also showed that electric fan use with a water-soaked t-shirt abolished these mitigating effects. Because the Ta exceeded the Tskin, it seems that fanning in such conditions produced convective heat gain and offset any improvement in evaporative heat loss (Cramer et al., 2020). A recent study demonstrated that the beneficial thresholds for electric fan use were for temperatures below 37°C (Morris et al., 2021). Although previous studies targeted daily living in a house designed to resist heat waves (Morris et al., 2019; Song et al., 2019; Cramer et al., 2020), workers rarely experience extreme heat or avoid working in such conditions. Daytime hot weather conditions in most countries worldwide were under the threshold in recent years with a broad range of humidity ($5\%$–$80\%$ RH) (Morris et al., 2021). Thus, the threshold of Ta of 37°C and the middle of the RH range of $50\%$ were adopted in the present study for assessing the effects of combining a wet-inner t-shirt with ventilation garments.
The aim of this study was to evaluate whether a water-soaked t-shirt while wearing ventilation garments would attenuate exercise-induced thermal and cardiovascular strains in young and older men at moderate-intensity work (200–230 W m−2, the average metabolic rate of physically demanding occupations) in hot and moderately humid conditions. It was hypothesized that 1) wearing a water-soaked t-shirt in combination with a ventilation garment would attenuate thermal and cardiovascular strains compared with a dry t-shirt/ventilation garment combination in both age groups; and 2) mitigation of thermal and cardiovascular strains would be greater in the older than in the young group.
## Participants
Sixteen healthy men were recruited for this study from two age ranges: eight young (age 26 ± 4 years, range 21–30) and eight older (age 67 ± 3 years, range 62–70). There were no differences between groups for height (young, 171 ± 5 vs. older, 166 ± 5 cm; $$p \leq 0.14$$), body mass (young, 61.8 ± 7.9 vs. older, 62.9 ± 10.9 kg; $$p \leq 0.83$$), and body surface area (BSA) (young, 1.7 ± 0.1 vs. older, 1.7 ± 0.2 m2; $$p \leq 0.80$$). There were significant differences in body fat percentage (young, 14 ± 3 vs. older, $19\%$ ± $5\%$; $$p \leq 0.04$$) and predicted VO2peak (young, 49.8 ± 8.7 vs. older, 33.3 ± 3.3 mL kg-1 min-1; $$p \leq 0.001$$). Baseline mean arterial pressure was higher in the older than in the young groups (young, 85 ± 7 vs. older, 94 ± 9 mmHg; $$p \leq 0.04$$).
All participants were recreationally active and free from illness and injury. Participants were non-smokers and had no history of cardiovascular disease, skin/sweat-related conditions, or neuromuscular disorders. All participants in the older age group were semiretired and worked two or three times per week (e.g., pruning, delivery, cleaning).
All experimental procedures were approved by the Human Research Ethics Committee of the National Institute of Occupational Safety and Health, Japan (2021N-1-8). The participants were informed of the experimental procedures and potential risks, and all signed consent forms. The study was conducted in accordance with the latest version of the Declaration of Helsinki, except for registration in a database.
## Experimental design
The volunteers visited the laboratory on four separate occasions which included a preexperimental/familiarization session and three experimental trials. The three trials (CON, DRY, and WET, for short, details in Experimental protocol) were randomized (at least 72 h between each one) and conducted between 10:00 to 15:00 h.
The volunteers refrained from consuming any beverages containing caffeine or alcohol from the night before the day of the experiment. They ate a light meal and drank ∼ 500 mL of water 2 h before the experiment and then reported to the laboratory. After voiding completely, each volunteer entered the thermoneutral room with an ambient temperature (Ta) of 25°C and $50\%$ RH.
## Preexperimental session
In this pre-session, all volunteers completed a submaximal exercise test on a treadmill (SportsArt Fitness T650, Woodinville, WA, USA) at a Ta of 25°C and $50\%$ RH using a metabolic analyzer (AE-310 s, Minato Medical Science, Osaka, Japan) and electrocardiography (BSM-3400; Nihon Koden, Tokyo, Japan). The test was a modified ramp protocol consisting of a 2 min warmup of 2.5 km h−1 at $0\%$ gradient followed by an increase in speed by 0.5 km/h every 2 min. After reaching 4.5 km h-1, the gradient was increased by $3\%$ every 2 min until up to $15\%$. Then, the speed was increased by 0.5 km h−1 every 2 min at the $15\%$ gradient. To ensure the safety of the older volunteers, the test was terminated when the subject’s steady-state HR reached $85\%$ of the age-predicted maximum (220—age). The VO2 and HR data collected during the submaximal exercise test were extrapolated to estimate VO2peak (American College of Sports Medicine, 2013). The same procedure was applied to the young volunteers to allow the comparison between the age groups.
To prescribe walking speeds and gradients for the main trials, the rate of metabolic energy expenditure (M) and heat production were estimated for each participant using data from the submaximal exercise test, as estimated by the following equation (Cramer et al., 2014): M=(VO2 ec R – $\frac{0.7}{0.3}$+ef 1 – R/0.3)×1000 / 60 W, Heat production=M−W / BSA W m−2 where R is the respiratory exchange ratio, e c is energy equivalent of carbohydrate (21.13 kJ) per L of O2 consumed (L min−1), and e f represents the energy equivalent of fat (19.69 kJ) per L of O2 consumed (L min−1). Heat production was estimated as the difference between M and the external work (W) and divided by BSA (W m−2). External work was calculated as follows (Gibson et al., 1979): $W = 9.81$ v×gradient×mass W where v is the selected treadmill speed (in m s−1), gradient is the treadmill gradient expressed as a decimal (e.g., $5\%$ = 0.05), and mass is the body mass (in kilograms).
For each participant, measures of height in cm (YG-200P, Yagami, Nagoya, Japan), body mass in kg (F150S, Sartorius, Goettingen, Germany, Resolution 1 g), and body fat percentage via bioelectrical impedance (Body composition analyzer, InBody-270, Biospace, Seoul, Korea) were recorded.
## Experimental protocol
Participants wore a long-sleeved t-shirt ($100\%$ cotton, 160 g), a ventilation jacket ($100\%$ cotton, 430 g), undershorts ($100\%$ polyester, 80 g), work pants ($100\%$ cotton, 315 g), socks ($100\%$ cotton, 40 g), and shoes in all three trials. The intrinsic clothing insulation value of the jacket was 0.21 clo and that for the total clothing was 0.53 clo, was determined using a thermal manikin (Thermal manikin, Kyoto Electronics Manufacturing Co., Ltd., Tokyo, Japan). The size of the t-shirt was selected for each participant to adhere tightly to their body surface. To increase the wetted surface area, the long-sleeved type of t-shirt was selected. The ventilation jacket (KU91400, Kuchofuku, Tokyo, Japan) was long sleeved and equipped with two fans (8 cm blade diameter, 194 g) on the lower back (Supplementary Figure S1). The fans used a battery box (4 AA nickel-metal hydride, 140 g) and took in outside air. The airflow rate of the fan was 15.1 L s−1, which was set at the maximum of the apparatus because heat loss increases with ventilation flow rates (Yang et al., 2022). The jacket was tightened around the waist with an elastic band, and then, the air was exhausted from the cuff and neck openings. The airflow velocity underneath clothing was 4.5, 2.5, and 7.0 m s−1 at the cuff, in front of the neck, and behind the neck, respectively (measured using an anemometer; 6006, Kanomax Japan Inc., Suita, Japan).
After baseline measurements at 25°C ($50\%$ RH, <0.3 m/s air velocity), the t-shirt was soaked with 350 ± 5 mL of tap water (37°C) using an electric vaporizer in the WET trial, and water was never added later. This amount of water was chosen to saturate the t-shirt without leaving dry spots or dripping. To ensure the volume of water, the clothed body weight was measured before and immediately after the soaking (±5 g error) because the participants had trouble wearing a pre-determined and pre-soaked t-shirt. At the same time, the fans of the ventilation jacket were turned on in the DRY and WET trials. In the CON trial, the fans remained off. Immediately after each preparation of the clothing, the room temperature was elevated to 37°C ($50\%$ RH, <0.3 m/s air velocity) and stabilized within 10 min. The participants remained seated during the Ta transition, then they performed three 20-min bouts of walking exercise (Ex1, Ex2, and Ex3) separated by 10-min breaks (B1, B2, and B3). The walking was conducted at a predefined speed (all participants, 4.5 km h−1) and inclines (young, $6.6\%$ ± $2.4\%$; older, $3.4\%$ ± $2.1\%$) for a target heat production of 200 W m-2 on the treadmill (%VO2peak, young $39\%$ ± $9\%$, older $54\%$ ± $8\%$, as averaged across trials). During the breaks, drinking water (37°C) was provided ad libitum.
## Measurements
Rectal temperature (Trec) was measured continuously using a thermistor probe (701J, Nikkiso-Therm, Tokyo, Japan) self-inserted by the participant to 13 cm beyond the rectal sphincter. Skin temperature (Tskin) at the chest, abdomen, back, forearm, upper arm, thigh, and lower leg were measured every 10 s using a temperature/humidity logger (DS1923 iButton Hydrochron, Maxim Integrated, San Jose, CA, USA) (MacRae et al., 2018; Kato et al., 2021). The temperature/humidity loggers were placed on the skin. The temperature sensor was located at the bottom the logger, measuring the skin surface temperature. The humidity sensor was located at the top of the logger, estimating the air humidity ca. 6 mm above the skin surface. The mean Tskin was calculated using the Ramanathan weighting system (Ramanathan, 1964). Clothing microclimate temperature and RH between the ventilation jacket and t-shirt around the chest were also measured using the two loggers placed on the inside of the jacket: one was set with the temperature sensor up and another was set with the humidity sensor up. To prevent touching the t-shirt, each sensor was covered with a 12-mm high plastic mesh cage. Sweat rate (SR) on the chest, back, forearm, and thigh were monitored by dew point hygrometry (OKS-04HM, ASE Giken, Nagoya, Japan). A ventilated plastic capsule was attached to each skin region. An index of skin blood flow on the chest, back, and forearm was measured using laser Doppler flowmetry (LDF; FLO-C1, Omegawave, Tokyo, Japan) located adjacent to the ventilated capsule at a sampling rate of $\frac{1}{1}$,000 s. To avoid movements of the arms and trunk during the assessment of LDF, the participants held handrails for 30 s every 5 min. Because the large movements of the legs during walking disturb the LDF reading, measurements on the legs were not recorded. HR was recorded from an electrocardiogram (BSM-3400; Nihon Koden). Arterial blood pressures were measured using an electrosphygmomanometer (EBP-330, Minato Medical Science) every 5 min, with mean arterial pressure subsequently calculated as $\frac{1}{3}$ (pulse pressure) + diastolic blood pressure. Cutaneous vascular conductance (CVC) was calculated as the LDF value divided by the mean arterial pressure and is expressed as a percentage change from the baseline value.
Metabolic rate was measured using the metabolic analyzer during exercise. Heat production during exercise was calculated using 5–20 min data in each walking period. Body mass was measured using a precision balance with ±1 g accuracy (F150S, Sartorius, Goettingen, Germany) before and after the experiment with the participant wearing only undershorts. Total sweat loss was calculated as follows: body mass loss + the weight of water ingested (kg). On completion of the experiment, participants removed the clothing immediately, and the weight of each clothing was measured. The moisture content of each clothing was calculated as the weight at the end of the experiment (g)—the weight of dry clothing before dressing (g).
Participants rated their thermal sensation, thermal comfort, thirst sensation, and fatigue separately by drawing a cross line on a visual analog scale (VAS). The subjective ratings of thermal sensation and comfort used a 20 cm VAS; “coldest” or “most uncomfortable” were scored as −10 cm, “hottest” or “most comfortable” were scored as 10 cm, and “neutral” was scored as 0 (Nakamura et al., 2008). For evaluating thirst and fatigue, the minimal VAS rating was scored as 0 (not thirsty or fatigued at all) and the maximal rating as 10 cm (extremely thirsty or fatigued). The ratings were conducted at baseline, immediately before Ta elevation and Ex1, and at the end of Ex1–Ex3 and B1–B3.
## Statistical analysis
An a priori sample-size calculation was performed (G*Power 3.1.9.6; Faul et al., 2007) using data from my previous investigations undertaken employing the similar experimental model (Trec, Tskin, and Sweat loss). This indicated that we would need ≥8 participants per group to find statistical significance with an effect size of 0.4, a power of 0.9 and alpha set to 0.05.
Physical characteristics were compared between groups (i.e., young and older) using independent samples t tests (two-tailed). Single time point data (percentage of maximum HR, total sweat loss, the total volume of water ingested, body mass loss, and clothing mass) were evaluated by two-way analysis of variance (ANOVA) for repeated measurements with trials (CON, DRY, and WET) and groups (young and older). All other data were analyzed using a three-way repeated-measures ANOVA with trials, groups, and time. If a significant interaction was observed, post hoc paired-sample t tests were used to analyze further the effects of trials or groups by the Bonferroni method (physiological data) and Friedman’s test (perceptual data). Statistical significance was assumed at $p \leq 0.05.$ Analyses were performed using commercially available software (IBM SPSS Statistics v. 21, IBM Corp., Armonk, NY, USA). All variables are reported as the mean ± standard deviation (SD).
## Results
The main effect of time was detected for Trec, mean Tskin, regional SR, CVC, HR, heat production, the rating scores, and clothing microclimate temperature and RH in the young and older groups (all $p \leq 0.05$). The main effect of trial was significant for Trec, mean Tskin, regional SR, CVC, HR, heat production, whole-body sweat loss, the total volume of water ingested, body mass loss, the rating scores of thermal sensation and comfort, the moisture content of t-shirts and other clothing, and clothing microclimate temperature and RH in both groups (all $p \leq 0.05$). A significant main effect of group was detected for mean Tskin and percentage of maximum HR (both $p \leq 0.01$). Furthermore, a significant interaction effect of time*trial (all $p \leq 0.05$) was detected for Trec, mean Tskin, regional SR, CVC, HR, heat production, and the rating scores of thermal sensation and comfort, and clothing microclimate temperature and RH in both groups. The interaction effect of trial*group was detected for percentage of maximum HR, whole-body sweat loss, the total volume of water ingested, body mass loss, and the moisture content of t-shirts and other clothing (all $p \leq 0.05$). A time*trial*group interaction effect was detected only for mean Tskin ($p \leq 0.05$).
Trec was lower in the DRY than in the CON trials at 110 min (Figures 1A, C, $p \leq 0.05$). Trec in WET was lower than in both CON at 70–110 min ($p \leq 0.05$) and DRY at 50–110 min ($p \leq 0.05$). The mean Tskin was lower in WET than in CON and DRY throughout the experiments (Figures 1B, D, $p \leq 0.01$). The mean Tskin in DRY was lower than in CON from 40 to 110 min ($p \leq 0.05$). In WET, the mean Tskin was lower in the older than in the young groups from 40 to 110 min ($p \leq 0.01$). The mean Tskin responses were similar to Tskin on the chest, back, abdomen, upper arm, and forearm (Supplementary Figure S2). SR was lower in the WET than in the CON trials on the chest (Figures 2A, E, 40–110 min, $p \leq 0.05$), back (Figures 2B, F, 30–110 min, $p \leq 0.05$), forearm (Figures 2C, G, 35–110 min, $p \leq 0.01$), and thigh (Figures 2D, H, 75–110 min, $p \leq 0.05$). On the forearm, SR was lower in the WET than in the DRY trials (Figures 2C, G, 35–110 min, $p \leq 0.05$). Considering the changes in the CVC on the chest, back, and forearm (Supplementary Figure S3), before walking (5–20 min), changes in the CVC were lower in the WET than in the CON and DRY trials in all regions (all $p \leq 0.01$). During walking and breaks (25–110 min), changes in the CVC in all regions were lower in the WET than in the CON trials (all $p \leq 0.01$).
**FIGURE 1:** *Rectal (A,C) and mean skin temperature (B,D) responses during three bouts of intermittent exercise (Ex1–3) in hot conditions (37°C, 50% RH) interspersed by three breaks (B1–3) in the young (left) and older (right) groups while wearing a dry inner t-shirt without ventilation garment fanning in the control (CON, open circles), a dry inner t-shirt with ventilation garment fanning (DRY, opened triangles), and a wetted inner t-shirt with ventilation garments fanning (WET, closed triangles). The arrow indicates the start of fanning (DRY) and wetting and fanning (WET). *Significantly different for CON vs. WET trial, p < 0.05. #Significantly different for DRY vs. WET trial, p < 0.05. §Significantly different for CON vs. DRY trial, p < 0.05. †Significantly different for young vs. older in the WET trial, p < 0.05. Data are expressed as the mean ± SD for eight participants.* **FIGURE 2:** *Regional sweating rate (SR) responses on the chest (A, E), back (B, F), forearm (C, G), and thigh (D, H) during three bouts of intermittent exercise (Ex1–3) in hot conditions (37°C, 50% RH) interspersed by three breaks (B1–3) in the young (left) and older (right) groups while wearing a dry inner t-shirt without ventilation garment fanning in the control (CON, open circles), a dry inner t-shirt with ventilation garment fanning (DRY, opened triangles), and a wetted inner t-shirt with ventilation garment fanning (WET, closed triangles). The arrow indicates the start of fanning (DRY) and wetting and fanning (WET). *Significantly different for CON vs. WET trial, p < 0.05. #Significantly different for DRY vs. WET trial, p < 0.05. §Significantly different for CON vs. DRY trial, p < 0.05. Data are expressed as the mean ± SD for eight participants.*
HR was lower in the DRY than in the CON trial (Figures 3A, B, 75–110 min, $p \leq 0.05$). In the WET trial, HR was lower than in the CON and DRY trials (30–110 min, both $p \leq 0.05$). The percentage of maximum HR during walking in each trial was higher in the older group (CON $85\%$ ± $10\%$; DRY $79\%$ ± $10\%$; WET $70\%$ ± $10\%$) than in the young group (CON $73\%$ ± $14\%$, DRY $67\%$ ± $10\%$, WET, $60\%$ ± $12\%$; all $p \leq 0.05$). In all trials, heat production in Ex2 and Ex3 was greater than in Ex1 (Figures 4A, B, all $p \leq 0.05$). In Ex3, heat production was lower in the WET trial than in both the CON and DRY trials (all $p \leq 0.05$).
**FIGURE 3:** *Heart rate (HR) responses during three bouts of intermittent exercise (Ex1–3) in hot conditions (37°C, 50% RH) interspersed by three breaks (B1–3) in the young (A) and older (B) groups while wearing a dry inner t-shirt without ventilation garments fanning in the control (CON, open circles), a dry inner t-shirt with ventilation garment fanning (DRY, opened triangles), and a wetted inner t-shirt with ventilation garment fanning (WET, closed triangles). The arrow indicates the start of fanning (DRY) and wetting and fanning (WET). *Significantly different for CON vs. WET trial, p < 0.05. #Significantly different for DRY vs. WET trial, p < 0.05. §Significantly different for CON vs. DRY trial, p < 0.05. Data are expressed as the mean ± SD for eight participants.* **FIGURE 4:** *Heat production during three bouts of intermittent exercise (Ex1–3) in hot conditions (37°C, 50% RH) in the young (A) and older (B) groups while wearing a dry inner t-shirt without ventilation garments fanning in the control (CON, open circles), a dry inner t-shirt with ventilation garment fanning (DRY, opened triangles), and a wetted inner t-shirt with ventilation garment fanning (WET, closed triangles). *Significantly different for CON vs. WET trial, p < 0.05. #Significantly different for DRY vs. WET trial, p < 0.05. Data are expressed as the mean ± SD for eight participants.*
Whole-body sweat loss in the CON trial was greater than in the DRY and WET trials, with values being greater in the DRY than in the WET trial in both groups (young: CON 1.03 ± 0.28 kg, DRY 0.80 ± 0.23 kg, WET 0.48 ± 0.22 kg; older: CON 1.04 ± 0.25 kg, DRY 0.85 ± 0.22 kg, WET 0.56 ± 0.14 kg; all $p \leq 0.05$). The total volume of water ingested during three break periods was greater in the CON than in the DRY and WET trials in the older group (CON 582 ± 217 mL, DRY 385 ± 163 mL, WET 366 ± 122 mL; both $p \leq 0.05$). No difference among the three trials was observed in the young group (CON 600 ± 308 mL, DRY 453 ± 160 mL, WET 445 ± 137 mL). In each break (B1–3), the volume did not differ among the three trials and between age groups (Supplementary Figure S4). Body mass loss was lower in the WET than in the CON and DRY trials in both groups (young: CON 0.42 ± 0.17 kg, DRY 0.35 ± 0.19 kg, WET 0.04 ± 0.11 kg; older: CON 0.46 ± 0.35 kg, DRY 0.47 ± 0.26 kg, WET 0.20 ± 0.22 kg; all $p \leq 0.05$).
The thermal sensation rating in the WET trial decreased immediately after soaking the inner t-shirt (Figures 5A, E, $p \leq 0.05$). In the CON and DRY trials, the thermal sensation increased in Ex1–3 and B1–3 (all $p \leq 0.05$), but the rating scores in WET increased only in Ex3. The thermal sensation was lower in the DRY than in the CON from Ex2 to B3 ($p \leq 0.05$). The rating scores in WET was lower than in the CON throughout the experiment ($p \leq 0.05$) and DRY trials until Ex2 ($p \leq 0.05$). The thermal comfort rating decreased (i.e., discomfort increased) in Ex1–3 and B1–3 in the CON and DRY trials (Figures 5B, F; all $p \leq 0.05$), but only in Ex1–3 in the WET trial (all $p \leq 0.05$). The decreases in CON were greater than in DRY (B2 and B3, both $p \leq 0.05$) and WET (Ex1–3 and B1–3, all $p \leq 0.05$). The thirst sensation rating increased in Ex1–3 in all trials (Figures 5C, G; all $p \leq 0.05$). The fatigue rating increased in Ex1–3 and B1–3 in all trials (Figures 5D, H; all $p \leq 0.05$).
**FIGURE 5:** *Perceptual responses of thermal sensation (A, E), thermal comfort (B, F), thirst (C, G), and fatigue (D, H) during three bouts of intermittent exercise (Ex1–3) in hot conditions (37°C, 50% RH) interspersed by three breaks (B1–3) in the young (left) and older (right) groups while wearing a dry inner t-shirt without ventilation garment fanning in the control (CON, open circles), a dry inner t-shirt with ventilation garment fanning (DRY, opened triangles), and a wetted inner t-shirt with ventilation garments fanning (WET, closed triangles). The arrow indicates the start of fanning (DRY) and wetting and fanning (WET). *Significantly different for CON vs. WET trial, p < 0.05. #Significantly different for DRY vs. WET trial, p < 0.05. §Significantly different for CON vs. DRY trial, p < 0.05. Data are expressed as the mean ± SD for eight participants.*
The moisture content of the t-shirts was greater in the CON than in the DRY and WET trials in the young group (CON 178 ± 64 g, DRY 44 ± 45 g, WET 58 ± 29 g; $p \leq 0.05$). In the older group, the moisture content of the t-shirts was greater in the CON than in the DRY and WET trials, with values being greater in the WET than in the DRY trials (CON 224 ± 88 g, DRY 83 ± 47 g, WET 111 ± 57 g; $p \leq 0.05$). The moisture content of other clothing (jacket, pants, undershorts, socks) was also greater in CON than in DRY and WET, with values being greater in WET than in DRY in the young group (CON 74 ± 40 g, DRY 19 ± 6 g, WET 18 ± 7 g; $p \leq 0.05$) and the older groups (CON 97 ± 41 g, DRY 29 ± 15 g, WET 42 ± 17 g; $p \leq 0.05$).
Clothing microclimate temperature increased immediately after room temperature was elevated to 37°C in all trials (Supplementary Figure S5). The increases in the DRY trial were greater than in the CON trial ($p \leq 0.05$), and the increases in the WET trial were lower than in both the CON and DRY trials (both $p \leq 0.05$). The RH of the clothing microclimate increased after the room temperature was elevated to 37°C in all trials (Supplementary Figure S5). The increases, which were greater than in the DRY and WET trials (all $p \leq 0.05$), were maintained throughout the experiment in the CON trials. The increases rapidly changed to decreases in the DRY trial but changed more slowly in the WET trial.
## Discussion
The present study evaluated whether wearing a water-soaked inner t-shirt with a fanned ventilation jacket would attenuate thermal and cardiovascular strains during exercise performed at moderate intensity work in hot and moderately humid conditions (37°C, $50\%$ RH) in young and older individuals of work clothing worn. Consistent with our first hypothesis, wearing a water-soaked inner t-shirt while the ventilation jacket’s fans were on (WET) attenuated the increases in Trec, HR, and sweat loss during the 60-min intermittent exercise separated by three breaks in hot conditions compared with wearing a dry inner t-shirt with (DRY) or without (CON) ventilation jacket fanning in both age groups. Contrary to our second hypothesis, the mitigation of thermal and cardiovascular strains in WET did not differ between the young and older groups. These findings advance our understanding regarding effective and practical cooling strategies to reduce thermal and cardiovascular strains in hot occupations while wearing work clothing.
Differences in Trec responses (Figures 1A, C) among the three trials likely reflected degrees of evaporative heat loss, while the precise heat loss values of a clothed person (Bröde et al., 2007; Havenith et al., 2008; Havenith et al., 2013) could not be calculated in the present study. Evaporative heat loss changes with a skin-air vapor pressure gradient, skin wettedness, the evaporative heat transfer coefficient, and air velocity (Gagge, 1937; Nelson et al., 1947). In the DRY conditions, natural sweating enhanced a certain degree of evaporative heat loss; attenuating thermal and cardiovascular strains by only using ventilation garments. This finding is in line with previous studies (Muza et al., 1988; Chen et al., 1997; Chinevere et al., 2008; Hadid et al., 2008; Barwood et al., 2009). In the WET trial, the prior moisture trapped in the inner t-shirt could supplement sweat production and lead to greater evaporative heat loss and mitigation of thermal and cardiovascular strains. Heat production in Ex3 was greater in CON and DRY relative to WET (Figure 4), although constant work intensity (walking speed and incline) for each volunteer was set at a target heat production (200 W m−2) that was determined in the preexperimental exercise test. This excess of heat production could be also related to the differences in Trec responses. The water in the inner t-shirt in the WET trial dried gradually, the clothing microclimate temperature and RH reached the levels of the CON and DRY trials respectively in Ex3 (Supplementary Figure S5). Had the present study reapplied moisture to the inner t-shirt at the breaks, the benefits of wearing a water-soaked inner t-shirt while using ventilation garments could have been even greater. In practice, reapplying water is required to carry a worker through an entire day of labor.
Regional sweating was inhibited in the WET trial, largely on the chest, back, and forearm in both age groups (Figures 2A–C; Figures 2E–G). Thermoregulatory sweating is primarily controlled by Tcore (Nadel, 1979), Tskin (Bullard et al., 1967), and skin blood flow (Wingo et al., 2010). All these factors were inhibited in the WET compared with both CON and DRY conditions throughout the experiment (Figure 1). Furthermore, because skin wettedness may affect the sweat gland response (Nadel and Stolwijk, 1973; Candas et al., 1979), the wetted inner t-shirt itself would reduce sweating on the chest, back, and forearm. Supplemental cooling by soaking an inner t-shirt before heat exertion can reduce natural sweat production on the torso and upper extremities covered by the ventilation jacket (∼$55\%$ coverage of the total body surface), which reduced total sweat loss in the WET trial. Workers in hot conditions often begin a working shift in a state of hypohydration and maintain a depleted hydration status throughout the day (Meade et al., 2015; Piil et al., 2018), although the American Conference of Governmental Industrial Hygienists (ACGIH) recommends frequent intake of a small amount of fluid, such as one cup (∼200 mL) every 20 min when working in warm environments (ACGIH, 2007). Here, the volunteers drank water ad libitum in the three break periods, while thirst sensations did not differ among the three trials in either group (Figures 5C, G). As a result, the ingested volume was not enough to balance the sweat lost in the CON and DRY trials in both groups (∼0.4 kg, total body mass loss including the ingested water). However, in the WET trial, body mass loss became nearly zero in the young group and was attenuated in the older group (∼0.2 kg). This mixed method of cooling (i.e., soaking an inner t-shirt and using ventilation garments) could be useful to prevent dehydration for workers in a hot environment. Because blood and urine samples could not be obtained in the present study, future studies should evaluate osmolarity and urine specific gravity.
Thermal sensation was less warm and thermal discomfort was lower in the WET than in the CON trial throughout both groups (Figures 4A, B, E, F). In the DRY trial, thermal perceptions were lower than in the CON trial after Ex2. Differences between the DRY and WET trial in thermal perceptions were observed from before exercise to Ex2. Thermal sensation is the information that pertains to external objects or the environment and is obtained only through warm or cold receptors in the skin; thermal comfort is affected by information from the skin and body core temperatures (Cabanac, 1979) and non-thermal factors (Havenith et al., 2002; Nagashima et al., 2018). Differences in Trec and mean Tskin between the CON and WET trials were evident throughout (Figure 1). Increases in Trec differed between the DRY and WET trials throughout (Figures 1A, C), but differences in mean Tskin between these trials diminished gradually toward the end of the experiment (Figures 1B, D). Although the rating scores of fatigue did not differ among the trials (Figures 4D, H), the relief of thermal sensation/discomfort by using ventilation garments might contribute to mitigation of workers’ psychological burden in hot conditions. Because the humidity sensation was not assessed in the present study, in the future, it should be evaluated how the soaking the inner t-shirt negatively affect humidity sensation in hot conditions.
Contrary to the hypothesis, thermal and cardiovascular responses in each trial were not different between the young and older groups, though mean Tskin responses in the WET trial were lower in the older than in the young group (Figures 1B, D). The mitigation effects of the mixed cooling method were similar between the two age groups. In the literature, it is unclear whether the effects of electric fan use on thermal strain differ between young and older individuals. Gagnon et al. ( Gagnon et al., 2016; Gagnon et al., 2017) demonstrated that electric fan use in older individuals resulted in greater thermal and cardiovascular strains compared with young individuals during resting exposure to extreme heat and humidity (42°C, $30\%$–$70\%$ RH) while wearing only shorts. They presumed that the age-related differences were associated with a reduced sweating capacity in older adults (Gagnon et al., 2016; Gagnon et al., 2017). On the other hand, Wright Beatty et al. [ 2015] showed that mitigations in Trec and Tskin responses by increasing airflow during exercise in hot conditions (35°C, $60\%$ RH) were similar between young and older adults while wearing standard work clothing. They observed that sweating responses during exercise did not differ between the young and older groups, although heart rate and cutaneous vascular responses were attenuated by increasing airflow in the young compared to older groups (Wright Beatty et al., 2015). The discrepancy between the two studies might relate to a reduced level of sweating capacity in older adults. Here, sweat loss and regional SR were not different between the young and older groups in each trial. If sweating responses were attenuated in the older groups more than in the young group, the effects of ventilation garments use only (i.e., the DRY trial) would be smaller in the older groups. However, supplemental sweating by soaking an inner t-shirt compensated the age-related decline in sweating, thus the mixed-method cooling (i.e., the WET trial) could not lead to differences in effectiveness between the two age groups.
## Considerations
These findings are limited to the environmental conditions tested. If the air temperature surpasses Tskin to a great extent, ventilation garment fanning would enhance convective heat gain and exceed evaporative heat loss. In fact, while wearing a wetted t-shirt, facing an electric fan at 42°C ($34\%$ RH) caused greater Tcore increases than no fanning (Cramer et al., 2020). Foster et al. [ 2022] showed the critical environmental limits for electric fan use during work in hot conditions. In their models, RH is also an important factor and not only air temperature for occupational heat strain. When the air temperature is ≥35°C, fans are ineffective and potentially harmful when RH is below $50\%$ (∼2.8 kPa), i.e., in dry-heat climatic conditions. A high wind would be beneficial against thermal strain for humid-heat climatic conditions (RH ≥ $50\%$). Furthermore, the critical environmental limits were modified by SR; beneficial environmental zones broadened in elevated SR (1,500 g h−1) and were narrower in cases of depressed SR (500 g h−1) (Foster et al., 2022). Evaluating the critical environmental limits of using ventilation garments with a wetted inner t-shirt represents an important issue for future research because cooling efficiency may differ between an electric fan and ventilation clothing uses.
Second, the present study selected for the control condition of wearing the ventilation jacket without fanning as standard work clothing. Although the intrinsic clothing insulation of the jacket was relatively low (0.21 clo, single-layered $100\%$ cotton), wearing it can restrict heat dissipation from the trunk and arms in hot conditions. Because there are work situations where light clothing is permitted for heat safety, a trial without the jacket (i.e., wearing only a t-shirt) should have been evaluated.
Third, the older participants were healthy with no history of cardiovascular disease and were not taking any medications at the time of participation. Notley et al. [ 2021] reported that type 2 diabetes and hypertension in older adults attenuated the tolerance to prolonged work in hot conditions, whereas no differences in thermoregulatory responses were observed compared with healthy older adults. Future studies should explore the cooling strategies during work in heat for older adults with common chronic disease. In addition, because sex, fitness level, hydration and heat acclimatization state influence thermoregulatory function in hot conditions (Ioannou et al., 2022), future studies should evaluate whether cooling interventions for individuals with each factor are effective in reducing thermal strain.
Finally, the present findings are restricted to laboratory settings that do not include sun exposure, natural wind flow, and job task requirements and complexities. Furthermore, some industrial sectors need more severe work intensities (higher metabolic demand) and extended work shifts. Future research should consider investigating whether strategies to supplement sweat production and ventilation garments can effectively mitigate thermal strain in real occupational fields of observational and intervention studies (Ioannou et al., 2021).
## Conclusion
For both young and older individuals during moderate-intensity work in hot conditions while wearing work clothing, the combination of wearing a water-soaked inner t-shirt and ventilation garments mitigated the rise in core temperature and heart rate and reduced skin temperature and whole-body sweat losses.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Human Research Ethics Committee of the National Institute of Occupational Safety and Health, Japan (2021N-1-8). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
KT contributed to all of the study and manuscript.
## Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2023.1122504/full#supplementary-material
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|
---
title: A neural circuit for gastric motility disorders driven by gastric dilation
in mice
authors:
- Xi-yang Wang
- Xiao-qi Chen
- Guo-quan Wang
- Rong-lin Cai
- Hao Wang
- Hai-tao Wang
- Xiao-qi Peng
- Meng-ting Zhang
- Shun Huang
- Guo-ming Shen
journal: Frontiers in Neuroscience
year: 2023
pmcid: PMC9992744
doi: 10.3389/fnins.2023.1069198
license: CC BY 4.0
---
# A neural circuit for gastric motility disorders driven by gastric dilation in mice
## Abstract
### Introduction
Symptoms of gastric motility disorders are common clinical manifestations of functional gastrointestinal disorders (FGIDs), and are triggered and exacerbated by stress, but the neural pathways underpinning them remain unclear.
### Methods
We set-up a mouse model by gastric dilation (GD) in which the gastric dynamics were assessed by installing strain gauges on the surface of the stomach. The neural pathway associated with gastric motility disorders was investigated by behavioral tests, electrophysiology, neural circuit tracing, and optogenetics and chemogenetics involving projections of the corticotropin-releasing hormone (CRH) from the paraventricular nucleus of the hypothalamus (PVN) to acetylcholine (ChAT) neurons in the dorsal motor nucleus of the vagus (DMV).
### Results
We found that GD induced gastric motility disorders were accompanied by activation of PVNCRH neurons, which could be alleviated by strategies that inhibits the activity of PVNCRH neurons. In addition, we identified a neural pathway in which PVNCRH neurons project into DMVChAT neurons, modulated activity of the PVNCRH→DMVChAT pathway to alleviate gastric motility disorders induced by GD.
### Discussion
These findings indicate that the PVNCRH→DMVChAT pathway may mediate at least some aspects of GD related gastric motility, and provide new insights into the mechanisms by which somatic stimulation modulates the physiological functions of internal organs and systems.
## 1. Introduction
Functional gastrointestinal disorders (FGIDs) are widespread, and constitute a major personal and socio-economic burden (Drossman, 2016). A recent global epidemiological study (Sperber et al., 2021) has found that over $40\%$ of the world’s population suffers from FGIDs. The disease is characterized by chronic abdominal discomfort without structural or biochemical causes, and its etiology and pathophysiology are multifactorial and still incompletely defined. Stress is widely relevant to the pathophysiology and treatment of digestive disorders. The latest Rome IV guidelines identify FGIDs as a disorder of gut–brain interaction (Drossman and Hasler, 2016), and stress contributes dynamically to various pathways of brain–gut communication, including the autonomic nervous system, the HPA axis, the local immune system, and brain mechanisms (Labanski et al., 2020). Changes in brain–gut interactions may underlie the symptoms of several FGIDs, including functional dyspepsia (FD) and irritable bowel syndrome (Mayer et al., 2006; Drossman, 2016).
The gastrointestinal tract and its enteric nervous system are innervated by the autonomic nervous system, which provides an efferent pathway for the stress-induced modulation of the gastrointestinal sensorimotor function, with the role of the vagus nerve in gastrointestinal sensitivity and motility having interesting clinical implications (Bonaz et al., 2018). From a neuro-gastrointestinal perspective, the functions of the upper gastrointestinal tract, including gastric tone and motility, are regulated by the activity of pacing neurons in the dorsal motor nucleus of the vagus (DMV), and its activity is regulated in turn by the tonic GABAergic input from the adjacent nucleus tractus solitarius (NTS) (Travagli and Anselmi, 2016) as well as inputs from higher centers, including projections from the paraventricular nucleus of the hypothalamus (PVN) (Browning and Travagli, 2014). PVN is an important autonomous control center due to the secretion of multiple peptides (Ferguson et al., 2008), and is closely related to the stomach. Peptidergic neurons in PVN release related transmitters after activation, and take part in the adjustment of physiological functions of the stomach by nerve conduction and neuro-endocrine systems (Benarroch, 2005). The corticotropin-releasing hormone (CRH, also known as the corticotropin-releasing factor, CRF) is a typical stress neuropeptide that is mainly distributed in the PVN. It coordinates the autonomic response of the gastrointestinal tract to stress (Czimmer and Tache, 2017).
Resolving the interactions between peripheral alterations and brain changes remains a challenging task. In recent years, optogenetic and chemogenetic techniques have provided high spatial and temporal resolutions, and have been used to target the manipulation of the activity of specific types of neurons. This provides a more comprehensive and precise understanding of the nervous system involved in regulating various organismal functions. In this study, we established a mouse model of stress-induced gastric motility disorder by using gastric dilation (GD). We dissected the functional organization of the PVN → DMV pathway, and investigated the principles of pathway acclimation in a mouse model of GD-induced gastric motility disorder via behavioral tests, neural circuit tracing, and electrophysiological, optogenetic, and chemogenetic techniques.
## 2.1. Resting-state functional magnetic resonance imaging (rS-fMRI) trial
We recruited 24 healthy subjects for the study, of which 12 were males and 12 females. They were all 20–25 years of age, with a mean of 22.7 ± 1.9 years. The exclusion criterion for all participants was related to any contraindications to FMRI scanning. The procedures followed were in accordance with the World Medical Association’s Declaration of Helsinki and the Clinical Experimentation Ethics Committee of Anhui University of Chinese Medicine (ChiCTR2200055920).
All subjects were treated with acupuncture interventions performed at the abdomen and stomach (Cai et al., 2018) for 20 min under water-loaded GD conditions. rS-fMRI (GE 3.0T, GE Medical System, Milwaukee, Wisconsin) and gastric electromyography (Abbreviated as EGG, EGEG-2D6B type, Hefei Huake Electronic Technology Research Institute) were performed before and after acupuncture. For water-loaded GD (van Dyck et al., 2016), all subjects fasted for 6–8 h, then drank 100 ml of water within 20 s, and continued to drink pure water at about 37°C until they began to feel full. They continued to drink water in this mode until they felt completely full or could not continue owing to epigastric symptoms. We recorded the water intake at this time, that is, the maximum threshold of gastric satiety.
The Data Processing Assistant for rS-fMRI (DPARSF) 4.2 software based on the MATLAB R2013b platform and Statistical Parametric Mapping (SPM) 12 software were used to pre-process the raw data. In this study, the correlation analysis method based on the seed point region of interest (ROI) was used. By using the built-in WFU-Pick-Atlas tool in SPM12 software, two spherical seed points with a radius of 2 mm were constructed as bilateral hypothalamic ROIs based on the Montreal Neurological Institute (MNI) coordinates defined in the literature (Lips et al., 2014). The coefficients of functional connectivity between the ROI and the brain voxels were obtained by REST 1.8 software and voxel-wise analysis. The calculated values of the coefficient of functional connectivity r were converted into Z-values by Fisher’s Z, and statistically significant changes in functional connectivity were presented as images by using Alphasim correction.
## 2.2. Ethical approval and animals
We used 8–10-week-old C57BL/6J and CRH-Cre male mice, obtained from the Charles River or The Jackson Laboratory. The mice were housed in groups of five per cage in a stable environment (23–25°C, $50\%$ humidity, 12-h light–dark cycles) for rearing, except for the mice used for surgery. After gastric balloon implantation, the mice were provided with liquid food. All experiments were conducted in accordance with the ARRIVE guidelines (Percie du Sert et al., 2020) and the Animal Experimentation Ethics Committee of Anhui University of Chinese Medicine (Reference no. AHUCM-mouse-2021-85).
## 2.3. Animal models
The mice were anesthetized with 1.5–$3.0\%$ isoflurane, and the surgical area was shaved and disinfected. Mice were fasted for 24 h prior to gastric surgery (with water provided). A catheter with a balloon (7 cm long, 0.6 mm outer diameter, 0.3 mm inner diameter) was used. A skin incision approximately 5 mm long was made at the head–neck junction on the back of the mouse, and a skin incision was made below the xiphoid cartilage at an intersection of 0.5 cm next to the midline of the abdomen. The two incisions were connected by inserting a sterile catheter. The stomach was exposed along the abdominal incision, the balloon was placed in the stomach, and then the gastric wall, abdominal muscles, and skin incisions were sutured in sequence. The other end of the catheter was passed from the abdominal incision to the neck incision, and was fixed at the neck incision with sutures. After surgery, mice were housed individually access to liquid diet. To avoid surgical stress reactions, GD and related experiments were performed 3 days after surgery.
In the GD experiment, 500 μl of 37°C saline was injected into the balloon with a rate of 0.5 mL/s for GD (McConnell et al., 2008; Liu et al., 2019; Kim et al., 2020) and lasting 20 min. Control mice receive the same gastric surgery (placement of the balloon in the stomach) as the GD group mice, but without GD. We checked the connection between the balloon and catheter at the end of the experiment. If leakage had occurred, the relevant data were excluded.
## 2.4. Animal electroacupuncture (EA) process
The site of animal electroacupuncture intervention was consistent with those used in previous experiments (Wang et al., 2015). Zhong-wan-acupoint (RN12) was located on the intersection of the upper $\frac{1}{3}$ and lower $\frac{2}{3}$ of the line connecting the xiphoid process and the upper border of the pubic symphysis, wei-shu-acupoint (BL21) was 2 mm adjacent to the spinous process of the 12th thoracic vertebra. Using disposable sterile acupuncture needles (0.25*13 mm, Yunlong Medical Co., Ltd., China) and electrical stimulator (G6805, Qingdao Xinsheng, China). A 20 min EA procedure was performed at a frequency of $\frac{2}{100}$ Hz by using current with an intensity of 0.1–1 mA.
## 2.5. Virus microinjection
An intraperitoneal injection of pentobarbital (20 mg per kg) was used to induce anesthesia for stereotaxic brain injection by using a stereotactic frame (RWD Co., Ltd., China). Throughout the procedure, a heating pad was used to maintain the animals’ body temperature at 36°C.
Through calibrated glass microelectrodes connected to an infusion pump (micro4, WPI Co., Ltd., USA), 200 nl of the virus was injected (depending on the virus titer and expression strength) at a rate of 35 nl min–1 (unless otherwise stated). Virus overflow was prevented by leaving the pipette for a minimum of 10 min at the injection site. Three coordinates were used: anterior–posterior (AP) from the bregma, medial–lateral (ML) from the midline, and dorsal–ventral (DV) from the brain surface. All viruses used in this study were obtained from BrainVTA Co., Ltd. (Wuhan, China).
## 2.5.1. Anterograde tracing
To allow EYFP expression in the downstream fibers, rAAV-Ef1α-DIO-hChR2 (H134R)-EYFP-WPRE (abbreviation rAAV-DIO-ChR2-EYFP, AAV$\frac{2}{9}$, 2.0E + 12 vg/mL, BrainVTA Co., Ltd) was injected into the PVN (−0.62 mm AP, ± 0.25 mm ML, −4.55 mm DV) of the CRH-Cre mice. After 3 weeks, their brain slices were co-stained with acetylcholine-specific antibodies (abbreviation ChAT) to track EYFP+ signals in the DMV.
## 2.5.2. Retrograde tracing
The C57BL/6J mice were injected with scAAV2/R-hSyn-EGFP-WPREs (AAV2R, 5.09E + 12 vg/mL, BrainVTA Co., Ltd) into the DMV (−7.83 mm AP, ± 0.24 mm ML, −3.6 mm DV), this virus could be absorbed by the terminals at the injection site and transported retrogradely to the soma to express the EGFP. After 3 weeks of virus injection, the brain sections were prepared to follow the EYFP+ signals and co-stained with CRH-specific antibodies.
## 2.5.3. Optogenetic manipulation
Two Cre-dependent viruses were delivered to the PVN of the CRH-Cre mice, respectively: rAAV-Ef1α-DIO-hChR2(H134R)-EYFP-WPRE-hGh-pA (abbreviation rAAV-DIO-ChR2-EYFP, AAV$\frac{2}{9}$, 2.0E + 12 vg/mL, BrainVTA Co., Ltd) and rAAV-Ef1α-DIO-eNpHR3.0-EYFP-WPRE-hGh-pA (abbreviation rAAV-DIO- eNpHR3-EYFP, AAV$\frac{2}{9}$, 2.0E + 12 vg/mL, BrainVTA Co., Ltd), rAAV-DIO-EYFP-WPRE-pA (abbreviation rAAV-DIO–EYFP, AAV$\frac{2}{9}$, 1.95 × 1,012 vgml/mL, BrainVTA Co., Ltd) viruses were used as the controls. Optical fibers (200 μm OD,0.37 NA, Inper Co., Ltd., Hangzhou, China) were embedded in the ipsilateral DMV after injection of optogenetic activation virus in the right PVN; two optical fibers (200 μm OD,0.37 NA, Inper Co., Ltd., Hangzhou, China) were embedded in the bilateral DMV after injection of optogenetic suppression virus in bilateral PVN, and fixed with dental cement.
## 2.5.4. Chemogenetic manipulation
The Cre-dependent viruses rAAV-Ef1α-DIO-hM3d(Gq)-mCherry-WPREs (abbreviation rAAV-DIO-hM3Dq-mCherry, AAV$\frac{2}{9}$, 2.0E + 12 vg/mL, BrainVTA Co., Ltd) and rAAV-Ef1α- DIO-hM4D(Gi)-mCherry-WPREs (abbreviation rAAV-DIO- hM4Di-mCherry, AAV$\frac{2}{9}$, 2.0E + 12 vg/mL, BrainVTA Co., Ltd) were delivered to the PVN of the CRH-Cre mice, respectively. The virus rAAV-Ef1α-DIO-mCherry-WPRE-pA (abbreviation rAAV-DIO–mCherry, AAV$\frac{2}{8}$, 8.93 × 1012 vgml/mL, BrainVTA Co., Ltd) were used as controls for the chemogenetic virus (rAAV-DIO-hM4Di-mCherry/rAAV-DIO-hM3Dq-mCherry, BrainVTA Co., Ltd), and the chemogenetic virus + saline group was used as a control for the chemogenetic virus + Clozapine-N-oxide (CNO) group.
For this part of the experiment, activating viruses were injected into the right PVN and inhibiting viruses were injected into both PVNs. Confocal microscopy (LSM880, Zeiss, Germany) was used to acquire signals associated with virus injection in regions of the mouse brain.
## 2.6.1. Optogenetic manipulation
Optogenetic activation or inhibition experiments were performed 3 weeks after viral expression. Chronically implantable fibers were connected to a laser generator using optic fiber sleeves. A Master-8 pulse stimulator (Shanghai Fiblaser Technology Co., Ltd., China) was used to deliver a 5-min pulse of blue (473 nm, 10 Hz, 5–8 mW) or yellow light (594 nm, 5–8 mW, constant). In optogenetic inhibition experiments, yellow light was given immediately after establishing the GD model and gastric motility was recorded for 20 min; The control group was not given yellow light after establishing the GD model. In the optogenetic activation experiment, gastric motility was recorded for 20 min immediately after the application of blue light in the light group and in the absence of light in the control group; in the EA group, electroacupuncture was performed for 20 min immediately after the blue light and gastric motility was recorded at the end of the electroacupuncture treatment.
## 2.6.2. Chemogenetic manipulations
Chemogenetic activation or inhibition experiments were performed 3 weeks after viral expression. In chemogenetic inhibition experiments, after clozapine-N-oxide (CNO, 5 mg/kg, Sigma) or saline was injected intraperitoneally for 40 min, the GD model was established and maintained 20 min, within which time (20 min) the gastric motility was detected. In chemogenetic activation experiments, after 40 min of CNO/saline injection, gastric motility was recorded (Supplementary Figure 1).
## 2.7. Optical fiber-based Ca2+ signal recording
A total of 200 nL of rAAV-CRH-GCaMP6s-WPRE-hGH (abbreviation rAAV-CRH-GCaMP6s, AAV$\frac{2}{9}$, 2.0E + 12 vg/mL, BrainVTA Co., Ltd) was microinjected unilaterally into the PVN of the C57BL/6J mice. An optical fiber (200 μm OD,0.37 NA, Inper) was implanted roughly 0.2 mm above the site of viral injection. 3 weeks after the mice had received the viral injection and optical fiber implantation, they were subjected to fiber photometry recording. A special balloon catheter was implanted in the stomach (refer to Method 2.3) of the mice 2 days prior to recording.
GCaMP6s fluorescence intensity was recorded before and during mechanical stimuli (GD). The values of fluorescence change ΔF/F (%) were derived by calculating ΔF/F (%) = (Fsignal-Fbaseline)/Fbaseline × 100, where *Fbaseline is* the mean of the GCaMP6s signal for 5 s before time zero (stimulus initiation), *Fsignal is* the GCaMP6s signal for the entire session (Zhu et al., 2021). Typical Ca2+ traces and thermograms were generated with InperPlot software (Inper Technology). We retroactively validated the reliability of fiber optic insertion and viral infection.
## 2.8.1. Brain section preparation
The pentobarbital-anaesthetized mice were intracardially perfused with 20–30 ml of ice-cold oxygenated N-methyl-d-Glucosamine artificial cerebrospinal fluid (NMDG ACSF) (solution components provided in the Supplementary Data 1). Coronal sections (300 μm) containing PVN or DMV were sectioned on a vibrating microtome (VT1200s, Leica, Germany) at a speed of 0.14 mm/s. The brain sections were first incubated in NMDG ACSF at 33°C for 12 min and then transferred to N-2-hydroxyethylpiperazine-N-2-ethanesulfonic acid (HEPES) ACSF (solution components provided in the Supplementary Data 1) at 25°C for 1 h. The brain sections were then placed in a sectioning chamber (Warner Instruments, USA) for whole-cell recording while being continuously perfused with standard ACSF (solution components in the Supplementary Data 1) at 2.5–3 ml/min at 32°C.
## 2.8.2. Whole-cell patch-clamp recordings
Whole-cell patch-clamp recordings were performed on visualized PVN and DMV neurons using an infrared-differential interference contrast (IR/DIC) microscope (BX51WI, Olympus, Japan) with a 40x water-immersion objective. Patch pipettes (3–5 MΩ) were pulled from borosilicate glass capillaries (VitalSense Scientific Instruments Co., Ltd) using a four-stage horizontal micropipette puller (P1000, Sutter Instruments, USA), patch pipettes were filled with intracellular solution (solution components in the Supplementary Data 1) were used for voltage-clamp recording. Signals were amplified with a Multiclamp 700B amplifier, low-pass filtered at 2.8 kHz, digitized at 10 kHz, and recorded in a computer for offline analysis using Clampfit 10.7 software (Molecular Devices) (Zhou et al., 2022).
The current-evoked firing of PVNCRH neurons was recorded in current-clamp mode ($I = 0$ pA). The threshold current of the action potential was defined as the minimum current to elicit an action potential. To visualize the PVN neurons, we injected rAAV-DIO-EYFP into the CRH-Cre mice so that green fluorescent EYFP was expressed only in the CRH neurons.
For validation of chemogenetic virus function. After 3 weeks of chemogenetic virus expression, electrophysiological brain slices were prepared by the above process. The PVN neurons expressing m-Cherry were visualized by using a vertical microscope in Mercury lamp mode, and neuronal responses were recorded before and after CNO administration.
In the vitro electrophysiological recordings of light-evoked response, brain slices were prepared by the above process after 3 weeks of optogenetic virus expression, blue light was delivered through an optical fiber (diameter of 200 μm, Inper) that was positioned 0.2 mm above the surface of the target areas. To characterize the function of rAAV-DIO-ChR2-EYFP in the PVN, ChR2-EYFP+ neurons in PVN were visualized by a vertical microscope in Mercury lamp mode, and the responses elicited by different frequencies of blue light stimulation (473 nm, 5–8 mV, pulse width 10 Mm, stimulation frequencies 5 Hz, 10 Hz, 20 Hz) were recorded. For recording light-evoked postsynaptic currents (Fang et al., 2020; Zhou et al., 2022), DMV expressing ChR2-EYFP+ fibers were visualized by a vertical microscope in Mercury lamp mode. The membrane potentials were held at −70 mV for recording the excitatory postsynaptic currents and at 0 mV for recording inhibitory postsynaptic currents, and these recordings were immediately terminated once the series resistance changed more than $10\%$. To eliminate the polysynaptic components, tetrodotoxin (TTX; 1 μM, Dalian Refine Biochemical Items Co., Ltd.) and 4-aminopyridine (4-AP; 2 mM, Sigma) were added to the standard ACSF to block sodium channels and augment light-induced postsynaptic currents, respectively.
## 2.9. Immunohistochemistry and imaging
Mice were deeply anesthetized with an intraperitoneal injection of pentobarbital sodium and then perfused with ice-cold $0.9\%$ saline followed by $4\%$ PFA. The brain tissue was embedded into dehydrated paraffin and cut into 5 μm-thick sections, alternatively, coronal sections were cut to a thickness of 40 μm using a cryostat (CM1860, Leica). For immunofluorescence, the sections were incubated with blocking buffer ($0.3\%$ Triton X-100, $10\%$ donkey serum in phosphate buffer saline) for 1 h at room temperature, and then they were treated with primary anti-bodies diluted with blocking solution, including anti-c-Fos (1:100, mouse, Santa Cruz), anti-CRH (1:500, rabbit, Abcam), anti-acetylcholine (1:500, goat, Merck), anti-GABA (1:500, rabbit, Sigma), anti-c-Fos (1:500, goat, Santa Cruz), at 4°C for 24 h. The sections were treated for 2 h at room temperature with the matching fluorophore-coupled secondary anti-body (1:500, Invitgen), or secondary anti-body sheep anti-mouse IgG (1:500, Beyotime). After rinsing, the slices were treated with 4,6-diamidino-2-phenylindole (DAPI; 1:2,000, Sigma) at the last stage. To display the fluorescent signals, the sections were photographed and scanned using the confocal microscopy (LSM880, Zeiss).
## 2.10. In vivo gastric recordings
Following the same surgical procedure as was performed on the model group, two incisions were made in the scapula and the abdomen, and they were connected by inserting a sterile catheter. The stomach was exposed and a custom strain gauge transducer (120 Ω, GuangCe Co., Ltd) was sticked on the surface of the gastric wall on the gastric antrum (Kawachi et al., 2011; Gao et al., 2021; Supplementary Figure 2). Another skin incision is made along the dorsal midline. The lead from the transducer is passed through the abdominal wall and extended posteriorly under the skin. Strain gauge signals were amplified by a bridge (ML-301, AD Instruments) and digitizer (Powerlab $\frac{26}{04}$, AD Instruments) to provide automatic collection of gastric motility data. Each mouse was recorded for at least 20 min and the average amplitude and frequency were automatically calculated using Labchart 8 software (AD Instruments) with low-pass filtering and high-pass filtering. After surgery, mice were housed individually access to liquid diet.
## 2.11. Statistical analysis
The data were analyzed by investigators blind to the treatments. Owing to missing targets, such as the injection of the viruses or the positioning of the optical fiber, the viral tracing, in vivo recording, and behavioral data on some animals were removed from subsequent examination. The paired t-test was used to compare the results before and after the treatment of the same subject. For experimental groups with multiple comparisons, the data were analyzed by using one-way and two-way analysis of variance (ANOVA). Tukey’s method was used for comparisons between groups within multiple groups. The data were reported as the mean ± standard deviation and significance was defined as $p \leq 0.05.$
## 3.1. Functional connectivity between hypothalamus and brainstem increased in subjects with water-loaded GD state following EA
The correlation between changes in the functional connectivity of the brain regions and changes in gastric motility was analyzed based on the seed points (regions of interest). The hypothalamus was used as the ROI and its functional connectivity with other brain regions was examined. Interestingly, it was found that the fMRI in water-loaded GD subjects showed enhanced functional connectivity between the hypothalamus and the brainstem after EA intervention (Figures 1A, B) and the changes in this functional connectivity were positively correlated with changes in EGG amplitude ($r = 0.583$, Correlation Analysis) (Figure 1C). This phenomenon suggests an association between hypothalamic–brainstem connectivity and gastric motility in water-loaded GD subjects.
**FIGURE 1:** *Functional connectivity between hypothalamus and brainstem was increased in subjects with water-loaded-GD state following EA. (A) Representative images of brain regions (brainstem) with changes in hypothalamic functional connectivity before and after EA. (B) The specific values of changes in hypothalamic-brainstem functional connectivity before and after EA. (C) EA-induced hypothalamic-brainstem (r = 0.583, P = 0.004) was positively correlated with changes in EGG amplitude, EGG, electrogastrogram.*
## 3.2. GD increased PVNCRH neuronal activity in mice
The CRH neurons are mainly distributed in the PVN, and coordinate the autonomic response of the gastrointestinal tract to stress. We focused on the PVNCRH neurons (Rosenberg, 1989). To investigate whether they were sensitive to GD stimulation, we performed fiber optic photometric recordings in mice receiving the infusion of a fluorescent Ca2+ indicator in the PVN for the CRH promoter (rAAV-CRH-GCaMP6s) (Figures 2A, B). The Ca2+ signals increased rapidly following GD stimulation (Figures 2C, D). The immunofluorescence experiments (Figures 2G, H) showed an increased co-labeling rate of CRH and C-fos within PVN in the GD group compared with the normal mice ($t = 6.581$, ****$P \leq 0.0001$, one-way ANOVA). Whole-cell recordings of the PVNCRH neurons were performed in acute brain sections, and we found increased current-evoked action potentials in the GD mice [F[1, 18] = 13.50, **$$P \leq 0.0017$$, two-way ANOVA]. These results suggest that the excitability of the PVNCRH neurons is enhanced in GD situations (Figures 2I, J).
**FIGURE 2:** *GD increases PVNCRH neuronal activity in mice. (A) Schematic of the fiber photometry setup. Ca2+ transients were recorded from GCaMP6s-expressing PVNCRH neurons in mice. (B) Typical images showing the injection site within the PVN by rAAV-CRH–GCaMP6s, scale bar 100 μm. (C,D) The mean (left) and the heatmaps (right) show that Ca2+ signals rapidly increased in gastric dilation (GD) state compared with normal state in mice. The colored bar on the right indicates ΔF/F (%). Each line in the heat map represents one experiment with one mouse. (E,F) The mean (left) and heat map (right) show that the Ca2+ signal decreases rapidly in GD mice upon acupuncture stimulation. EA is the abbreviation for acupuncture stimulation. (G) Representative images of C-fos and corticotropin-releasing hormone (CRH) expression in the PVN of various groups of mice. GD: gastric dilation group; EA: electroacupuncture group, scale bar 50 μm. (H) The C-fos and CRH co-labeling rate statistics for each group, n = 6 mice per group, one-way ANOVA, Control vs. GD (t = 6.581, P < 0.0001); EA vs. GD (t = 2.524, P = 0.0226). (I,J) Sample traces (I) and data (J) of firing rates recorded from PVNCRH neurons of mice treated in control group, GD group and EA group. n = 10 cells from six mice per group, two-way ANOVA, Control vs. GD [F(1,18) = 13.50, P = 0.0017]; EA vs. GD [F(1, 18) = 4.703, P = 0.0438]. *P < 0.05, **P < 0.01, ****P < 0.0001.*
Meanwhile, we found that EA intervention inhibited PVNCRH neuronal excitability in GD mice. Ca2+ fiber optic recording showed a rapid decrease in Ca2+ signal in GD mice after acupuncture stimulation (Figures 2E, F); immunofluorescence showed a decrease in co-labeling rate in the GD + EA group compared with the GD group ($t = 2.524$, *$$P \leq 0.0226$$, one-way ANOVA; Figures 2G, H); and membrane clamp recording showed a decrease in current-evoked action potentials in GD + EA mice compared with the GD group [F[1, 18] = 4.703, *$$P \leq 0.0438$$, two-way ANOVA]. Hence, EA intervention reduces excitability of PVNCRH neurons activated by GD (Figures 2I, J).
## 3.3. Gastric motility disorders induced by GD were alleviated by inhibition of PVNCRH neuronal activity in mice
The above experiments revealed that the excitability of PVNCRH neurons was closely related to GD. We modulated the neuronal activity for further observation.
We injected the Cre-dependent inhibitory chemogenetic virus (rAAV-DIO-hM4Di-mCherry) into the bilateral PVN of the CRH-Cre mice to selectively inhibit PVNCRH neurons (Figures 3A, B). Prior to behavioral assays, the function of the chemogenetic virus was verified by the membrane clamp of the brain slice, and its combination with CNO was effective in inhibiting PVNCRH neurons [F[1, 8] = 48.89, ***$$P \leq 0.0001$$, two-way ANOVA; Figure 3C; Supplementary Figure 3A]. After 40 min of the intraperitoneal injection of CNO, the GD mice showed a significant increase in the amplitude of gastric motility ($t = 4.064$, *$$P \leq 0.0181$$, one-way ANOVA). However, no significant changes in this amplitude were observed in the mCherry + CNO group or the hm4Di-mCherry + saline group (Figures 3D, E). These results suggest that the inhibition of PVNCRH neuronal activity alleviates gastric motility disorders in mice under stressful conditions.
**FIGURE 3:** *Chemogenetic inhibition of PVNCRH neuronal activity alleviates GD-induced gastric motility disorders. (A) Schematic of chemogenetic experiments in CRH-Cre mice. (B) Typical images showing the injection site within the PVN by inhibition chemogenetic virus. Scale bars, 100 μm. (C) Membrane potential change induced by CNO, n = 5 cells from five mice for each group, two-way ANOVA, F(1, 8) = 48.89, P = 0.0001. Action potentials induced by CNO in neurons with red fluorescence in the PVN region were recorded on brain slices containing hm4Di-mCherry and those containing mCherry, respectively. (D) Representative graphs of gastric motility in various groups of mice. (E) Effects of chemogenetic inhibition of PVNCRH neurons on gastric motility in GD mice, n = 6 mice in each group, one-way ANOVA, t = 4.064, P = 0.0181. (F) Schematic of chemogenetic experiments in CRH-Cre mice. (G) Typical images showing the injection site within the PVN by activated chemogenetic virus. Scale bars, 100 μm. (H) Membrane potential change induced by CNO, n = 5 cells from five mice per group, two-way ANOVA, F(1, 8) = 45.52, P = 0.0001. (I) Representative graphs of gastric motility in various groups of mice. (J) Effect of chemogenetic activation of PVNCRH neurons on gastric motility in normal mice. n = 6 mice in each group, one-way ANOVA, t = 3.901, P = 0.023. *P < 0.05, ***P ≤ 0.0001.*
Given the increased excitability of PVNCRH neurons in the GD model mice, we injected the Cre-dependent excitatory chemogenetic virus (rAAV-DIO-hM3Dq-mCherry) into the blank CRH-Cre mice to activate PVNCRH neurons (Figures 3F, G). Prior to behavioral experiments, the function of the chemogenetic activation virus was verified, and its combination with CNO was effective in activating PVNCRH neurons [F[1, 8] = 45.52, ***$$P \leq 0.0001$$, two-way ANOVA; Figure 3H; Supplementary Figure 3B]. After 40 min of intraperitoneal injection, this manipulation of activation of the PVNCRH neurons reduced the amplitude of gastric motility in the mice ($t = 3.901$, *$$P \leq 0.023$$, one-way ANOVA; Figures 3I, J). These results provide evidence of the functional causal relationship between the PVNCRH neurons and gastric motility.
## 3.4. Dissecting the PVNCRH-to-DMVChAT pathway
The DMV is one of the key centers of gastrointestinal regulation, and past evidence (Sawchenko, 1983; Llewellyn-Smith et al., 2012) suggests a direct fiber link between the PVN and the DMV. Moreover, more than $90\%$ of the DMVs are cholinergic neurons (Travagli and Anselmi, 2016). To confirm the PVNCRH → DMV projection, we applied a cell type-specific anterograde tracking system and injected the anterograde rAAV-DIO-ChR2-EYFP virus into the PVN of the CRH-Cre mice (Figure 4A). 3 weeks later, neurons positive for the yellow fluorescent protein (EYFP+) were observed in the PVN (Figure 4B). EYFP+ signals were observed in the DMV, which surrounded the acetylcholine-positive (ChAT+) neurons with red fluorescence (Figure 4C). To further resolve the PVNCRH-to-DMVChAT connection, we injected a broad-spectrum retrograde non-trans-synaptic virus (scAAV2/R-hSyn-EGFP) into the DMV (Figure 4D). 3 weeks later, EGFP+ fibers and EGFP+ neurons were found on the DMV, and the results of immunofluorescence showed that most of the EGFP+ neurons were cholinergic (red fluorescence in Figure 4E). In addition, a large number of EGFP+ neurons appeared in the PVN, and the results of immunofluorescence revealed multiple EGFP+ neurons co-labeled as CRH+ neurons (Figure 4F). These findings suggest a PVNCRH → DMVChAT pathway.
**FIGURE 4:** *Dissection of the PVNCRH to DMVChAT pathway. (A) Schematic of PVN injection of rAAV-DIO-ChR2-EYFP in CRH-Cre mice. (B) Representative image of EYFP labeling neurons by PVN infusion of rAAV-DIO-ChR2-EYFP. Scale bar, 100 μm. (C) Images representative of ChR2-EYFP+ fibers in DMV of CRH-Cre mice with PVN injection of rAAV-DIO-ChR2-EYFP (left) and ChR2-EYFP+ fibers co-localized with acetylcholine neuronal markers (ChAT) immunofluorescence within the DMV (right). Scale bars, 50 μm (left) or 50 μm (right-top) or 20 μm (right-bottom). (D) Schematic of DMV injection of scAAV2/R-hSyn-EGFP in C56BL/6 mice. (E) Representative image of scAAV2/R-hSyn-EGFP+ neurons and fibers, which co-localized with acetylcholine neuronal markers (ChAT) immunofluorescence in DMV. Scale bars, 100 μm (top) or 50 μm (bottom). (F) Representative image of EGFP+ neurons in PVN, which co-localized with CRH immunofluorescence. Scale bars, 100 μm (top) or 20 μm (bottom). (G) Schematic of PVN injection of rAAV-DIO-ChR2-EYFP and the recording configuration in acute slices. (H) Representative image injection site and viral expression within the PVN of CRH-Cre mice with PVN infusion of rAAV-DIO-ChR2-EYFP. Scale bar, 100 μm. (I) Sample traces of action potentials evoked by blue light (473 nm, 5–8 mV, pulse width 10 Mm, stimulation frequencies 5 Hz, 10 Hz, 20 Hz) recorded from PVN EYFP+ neurons in acute brain slices. (J) Schematic of PVN injection of rAAV-DIO-ChR2-EYFP in CRH-Cre mice and the recording configuration in acute slices. (K) Representative traces of light-evoked currents (473 nm, 20 ms, blue bar) before and after PTX (10 μM) treatment recorded from the DMV neurons. (L) Summarized data of light-evoked currents (473 nm, 20 ms) before and after PTX (10 μM) treatment recorded from the DMV neurons, n = 6 cells from six mice per group, paired t-test, t = 9.997, P = 0.0002. ***P < 0.001.*
To examine the functional connections of the PVNCRH → DMVChAT pathway, optogenetic experiments were performed. We first verified the activity of rAAV-DIO-ChR2-EYFP viruses by the membrane clamp, and then recorded action potentials induced by irradiation from 473 nm blue light (5, 10, and 20 Hz) in CRH neurons expressing the ChR2-EYFP+ in the PVN region of the brain slice (Figures 4G–I). All CRH neurons in the PVN of the injection site were found to exhibit action potentials as induced by the blue light. As shown by the whole-cell membrane clamp combined with the optogenetic techniques, the brief stimulation of efferent fibers of ChR2-containing PVN neurons by blue light in the DMV elicited inhibitory postsynaptic currents in the DMV neurons that were eliminated by the GABA receptor antagonist picronectin [picro-toxin (PTX)] ($t = 9.997$, ***$$P \leq 0.0002$$, paired t-test; Figures 4J–L). After whole-cell membrane clamp recording, we performed immunofluorescence detection on the brain slice and found that PVNCRH neurons labeled with ChR2-EYFP were co-localized with GABAergic antibodies (Supplementary Figure 4A), and ChAT+ neurons in DMV were encapsulated by EYFP+ fibers (Supplementary Figure 4B). These data support the hypothesis that PVNCRH neurons send monosynaptic projections to DMVChAT neurons.
## 3.5. PVNCRH neurons control the DMVChAT neurons to alleviate gastric motility disorder induced by GD
To investigate the role of the PVN → DMV pathway in GD-induced gastric motility disorders, optogenetics experiments were performed. We injected the cre-dependent inhibitory optogenetic virus rAAV-DIO-eNpHR3-EYFP into the bilateral PVN and buried optical fibers in the bilateral DMV (Figures 5A, B). The optical inhibition of DMV-carrying eNpHR3 in the GD mice led to a significant increase in the amplitude of gastric motility [F[1, 10] = 13.33, **$$P \leq 0.0045$$, two-way ANOVA; Figures 5C, D], which suggests that the optogenetic inhibition of the PVNCRH → DMVChAT pathway significantly promoted gastric motility in the GD mice.
**FIGURE 5:** *Optogenetic modulation of the PVNCRH → DMVChAT pathway alleviates GD-mediated gastric motility disorders. (A) Schematic of optogenetic experiments in CRH-Cre mice. (B) Representative image of EYFP+ neurons by PVN infusion of rAAV-DIO-NpHR3-EYFP or rAAV-DIO-EYFP. Scale bar, 100 μm. (C) Representative graphs of gastric motility in various groups of mice. (D) Effects of optogenetic inhibition of PVNCRH neurons on gastric motility in GD mice, n = 6 mice in each group, two-way ANOVA, F1,10 = 13.33, P = 0.0045. (E) Schematic of optogenetic experiments in CRH-Cre mice. (F) Representative image of EYFP+ neurons by PVN infusion of rAAV-DIO-ChR2-EYFP or rAAV-DIO-EYFP. Scale bar, 100 μm. (G) Representative graphs of gastric motility in various groups of mice. (H) Effects of optogenetic activation of PVNCRH neurons on gastric motility in normal mice, n = 6 mice in each group, two-way ANOVA, F(1,10) = 20.51, P = 0.0011. (I) Effect of EA intervention on gastric motility based on optogenetic activation of PVNCRH neurons in normal mice. n = 6 mice in each group, paired t-text, t = 2.683, P = 0.0437. *P < 0.05, **P < 0.01.*
In light of the findings of the previous step, we injected Cre-dependent rAAV-Ef1a-DIO-ChR2-EYFP in naïve CRH-Cre mice and optically activated the end of the ChR2-containing PVNCRH fiber in the DMV (Figures 5E, F). We found that the activation of this pathway attenuated gastric motility in the mice [F[1,10] = 20.51, **$$P \leq 0.0011$$, two-way ANOVA; Figures 5G, H]. Based on this, the amplitude of gastric motility increased after 20 min of EA intervention ($t = 2.683$, *$$P \leq 0.0437$$, paired t-text; Figures 5G, I). This result also proved, inversely, that EA may improve gastric motility by inhibiting the activity of the PVNCRH → DMVChAT pathway. Interestingly, this result is consistent with findings from human fMRI.
## 4. Discussion
This study defined the PVNCRH → DMVChAT pathway, which is involved in the generation of gastric motility disorders in case of GD and plays an important role in the regulation of gastric motility. Central to these processes are mechanisms of the neural pathway that involve increased excitability of PVNCRH neurons and increased inhibition from them to DMVChAT neurons under acute stress-related conditions.
The PVN is a key node in the regulation of physiological stress responses, and receives multiple afferent messages about external stress and internal physiological states. It plays an important role in the regulation of gastrointestinal functions under stress. The PVN contains many stress-responsive neuron types, with a dense distribution of the CRH as a central player in the stress response (Sawchenko, 1983; Browning et al., 2014). These CRF neurons are thought to be glutamatergic or GABAergic (Tache et al., 2001). Some evidence has shown that the acute release of CRF, such as in response to a stressful event, induces plasticity within neural circuits of the vagal brainstem, which has the potential to alter the vagal output to the gastrointestinal tract (Browning et al., 2014). Moreover, pre-treatment through the injection of CRF peptide receptor antagonists into the ventricles blocks the inhibition of the gastric motor function induced by various stressors (Tache et al., 2001). The central role of the CRF in delaying gastric transit is mediated not by the stimulation of the associated HPA axis, but instead by the autonomic nervous system, as gastric inhibition of motor responses is still observed in adrenalectomized or hypophysectomized rats, but not in vagotomized rats (Lenz et al., 1988). The major structures affecting the autonomous flow to the stomach, namely, the PVN and the dorsal vagal complex (DVC) of the brain stem, were identified as the brain nuclei responsible for CRF-induced gastric emptying and motor inhibition in rats (Monnikes et al., 1992). In line with previous reports, we confirmed that GD mice exhibited significant deficits in gastric motility with activation of CRH neurons in the PVN.
The DMV is the origin of vagal efferent fibers that regulate gastric motility and other visceral functions (e.g., the vagal circuit and its effect on gastric motility) (Travagli and Anselmi, 2016). The efferent fibers of the DMV form synapses with postganglionic neurons located in the stomach to regulate gastric motility (Travagli and Anselmi, 2016), and the DMV has a direct fiber connection to the gastrointestinal tract. The vast majority of neurons in the DMV are cholinergic, and activate nicotinic cholinergic receptors on postganglionic neurons within the target organ (Browning and Travagli, 2010). There is a well-known projection between the PVN and the DMV (Willett et al., 1987), and we verified this result with anterograde and retrograde monosynaptic tracing. In line with previous reports (Browning et al., 2014), the CRF increased inhibitory GABAergic synaptic transmission to the identified corpus-projecting DMV neurons. In this study, we observed that subjects with water-loaded GD state following EA showed enhanced functional connectivity between hypothalamus and brainstem in fMRI, and the change in this functional connectivity was positively correlated with the change in EGG amplitude, then we mapped the PVN → DMV pathway in animal experiments, found through optogenetic techniques that PVNCRH neurons projecting to acetylcholine neurons in the DMV were inhibitory, and alleviated gastric motility disorders in GD mice by inhibiting PVNCRH → DMVChAT excitability.
Owing to the complex and multifactorial pathophysiology of FGIDs, the effectiveness of current treatments is still unsatisfactory. In this case, nearly $50\%$ of FGIDs patients have exhibited a tendency to seek complementary and alternative medicine (Lahner et al., 2013). Many clinical studies and evidence-based evaluations have shown that acupuncture treatment alleviates the symptoms of FGIDs (Yuan-yuan et al., 2019; Guo et al., 2020; Wang et al., 2021) and mitigates symptoms and anxiety in FD patients. The central nervous system is an important site for the integration of acupuncture information and disease-related information. Acupuncture treatment involves the insertion of fine needles into the skin and underlying muscle layers, where the methods of stimulation may be manual or electric. Many previous studies (Koizumi et al., 1980; Kagitani et al., 2005, 2010; Takahashi, 2013) have reported that acupuncture stimulates somatic afferent nerves in the skin and muscles, when the mechanical force generated by acupuncture directly or indirectly acts on the acupoint area. Mechanical stimulation is transformed into neurochemical signals that induce afferent signals from the body. Many experiments have shown that the effect of acupuncture may be achieved by somatosensory autonomic reflexes or the modulation of the nervous system (Yu, 2020; Liu et al., 2021). The neural mechanisms involved in the modulation of gastrointestinal movement by acupuncture feature several aspects of acupuncture signaling, the sympathetic and parasympathetic nervous systems, the enteric nervous system, and the central nervous system (Wang et al., 2021). With the development of neuroimaging technology, the study of the acupuncture effect is not limited to animal experiments, non-invasive and high spatial and temporal resolution techniques, such as fMRI, support acupuncture effect based on human beings. Several neuroimaging studies have shown that acupuncture treatment improves not only clinical symptoms (postprandial fullness, epigastric distention, etc.) but also significantly modulates abnormal brain functions such as medial prefrontal cortex, brainstem, thalamus, caudate, and hippocampus in FD patients (Zeng et al., 2015; Teng et al., 2022; Yin et al., 2022). The central neural mechanism of the acupuncture effect is closely related to its modulation of neural circuits or neural networks, our study found after EA intervention, fMRI in GD subjects showed changes in functional connectivity between hypothalamus and brainstem. It also reflects the central nervous system provides exogenous neural input to control gastrointestinal motility in a broader and more integrated manner involving the spinal cord, medulla oblongata, thalamus, and so on.
The modulatory effects of acupuncture on gastrointestinal motility require the involvement of the CNS by altering the activity of nuclei associated with gastrointestinal motility, including the DMV, the NTS, the nucleus of the middle suture, the lateral hypothalamic area (LHA), and the PVN. All of these have been identified following the injection of neuro-anatomical tracers into the stomach and ST36 (Lee et al., 2001; Wang et al., 2013). The NTS and DMN form the main neuro-anatomical structure of the vagus nerve, the DVC, the role of which in acupuncture-mediated regulation of gastrointestinal function is supported by multiple pieces of evidence from several studies. The electro-acupuncture points ST36 and ST37 modulate the electrical activity of the stomach while regulating the firing of NTS and DMV neurons (Liu et al., 2004; Wang et al., 2007; Gao et al., 2012). The PVN is particularly important in the regulation of the gastrointestinal function, especially in case of stress-induced changes in gastrointestinal dynamics. Our previous study (Wang et al., 2015) found that the RN12 and BL21 signals of electroacupuncture converge in the PVN, and increase the expression of gastrointestinal hormones as well as their receptors in the PVN and the gastric antrum. Previous studies (Zhao et al., 2021) have suggested that the improvement in stress-induced jejunal motility disorders by the EA of ST36 may be related to the deregulation of CRF-R2. However, information on the acupuncture-mediated regulation of gastric motility by the CRH function is scarce. In this study, we found that EA modulates gastric motility by inhibiting the excitability of PVNCRH neurons in GD mice. We used viral tracking nuclear electrophysiology to identify an inhibitory neural circuit of the PVNCRH → DMVChAT pathway. Following this, the activity of this circuit was modulated by using optogenetics, and the results suggested that EA possibly improves gastric motility by inhibiting the activity of the PVNCRH → DMVChAT pathway. This result also proved, inversely, that EA may improve gastric motility by inhibiting the activity of the PVNCRH → DMVChAT pathway. Interestingly, this result is consistent with findings from human fMRI.
In summary, this study explored the significance of the PVNCRH → DMVChAT pathway in GD-induced gastric motility disorders. We found that the alleviation of its symptoms through the inhibition of the pathway may involve a hypothalamic paraventricular nucleus-mediated system of autonomic control. As options for the pharmacological treatment for functional gastric motility disorders remain limited, these findings suggest the potential for non-pharmacological therapeutic approaches, and provide new insights into the mechanisms by which somatic stimulation modulates the physiological function of internal organs and systems.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Clinical Experimentation Ethics Committee of Anhui University of Chinese Medicine. The patients/participants provided their written informed consent to participate in this study. This animal study was reviewed and approved by Animal Experimentation Ethics Committee of Anhui University of Chinese Medicine.
## Author contributions
G-MS and X-YW: conceptualization. X-YW, R-LC, and HW: methodology and formal analysis. X-YW, G-QW, and X-QC: software, validation, and writing—original draft preparation. X-YW, G-QW, SH, HW, and X-QP: investigation. R-LC, H-TW, and G-MS: resources. X-YW, G-QW, X-QC, X-QP, and H-TW: data curation. R-LC, HW, and G-MS: writing—review and editing. X-QP, M-TZ, and H-TW: visualization. G-MS: supervision and project administration. G-MS, M-TZ, and X-YW: funding acquisition. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023.1069198/full#supplementary-material
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|
---
title: Altered GnRH neuron and ovarian innervation characterize reproductive dysfunction
linked to the Fragile X messenger ribonucleoprotein (Fmr1) gene mutation
authors:
- Pedro A. Villa
- Nancy M. Lainez
- Carrie R. Jonak
- Sarah C. Berlin
- Iryna M. Ethell
- Djurdjica Coss
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992745
doi: 10.3389/fendo.2023.1129534
license: CC BY 4.0
---
# Altered GnRH neuron and ovarian innervation characterize reproductive dysfunction linked to the Fragile X messenger ribonucleoprotein (Fmr1) gene mutation
## Abstract
### Introduction
Mutations in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene cause Fragile X Syndrome, the most common monogenic cause of intellectual disability. Mutations of FMR1 are also associated with reproductive disorders, such as early cessation of reproductive function in females. While progress has been made in understanding the mechanisms of mental impairment, the causes of reproductive disorders are not clear. FMR1-associated reproductive disorders were studied exclusively from the endocrine perspective, while the FMR1 role in neurons that control reproduction was not addressed.
### Results
Here, we demonstrate that similar to women with FMR1 mutations, female Fmr1 null mice stop reproducing early. However, young null females display larger litters, more corpora lutea in the ovaries, increased inhibin, progesterone, testosterone, and gonadotropin hormones in the circulation. Ovariectomy reveals both hypothalamic and ovarian contribution to elevated gonadotropins. Altered mRNA and protein levels of several synaptic molecules in the hypothalamus are identified, indicating reasons for hypothalamic dysregulation. Increased vascularization of corpora lutea, higher sympathetic innervation of growing follicles in the ovaries of Fmr1 nulls, and higher numbers of synaptic GABAA receptors in GnRH neurons, which are excitatory for GnRH neurons, contribute to increased FSH and LH, respectively. Unmodified and ovariectomized Fmr1 nulls have increased LH pulse frequency, suggesting that Fmr1 nulls exhibit hyperactive GnRH neurons, regardless of the ovarian feedback.
### Conclusion
These results reveal Fmr1 function in the regulation of GnRH neuron secretion, and point to the role of GnRH neurons, in addition to the ovarian innervation, in the etiology of Fmr1-mediated reproductive disorders.
## Introduction
Mutations in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene lead to the most common genetic form of intellectual disability and autism, called Fragile X Syndrome (FXS) [1, 2]. In addition to intellectual impairment, FMR1 gene mutations are also associated with reproductive disorders, such as early menopause in females, and macroorchidism in males (3–6). The FMR1 gene encodes FMR protein (FMRP), an mRNA binding protein that regulates protein levels of its target genes [7, 8]. Target mRNAs bound by FMRP encode a variety of proteins, including transcription factors that regulate other genes (9–11). Mutation of this gene that causes FXS entails full expansion of the unstable CGG trinucleotide repeats (>200) that leads to hypermethylation, silencing of the gene and the loss of FMRP. Premutation, in a range of 50-200 repeats, causes Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) and exhibits reduced FMRP levels. While the mechanisms of intellectual impairments following FMRP loss are beginning to emerge, mechanisms of reproductive disorders are not known. Although FMRP is ubiquitous, it is highly abundant in the nervous system. In the brain, FMRP binds mRNAs that encode synaptic proteins, contributing to cognitive dysfunctions in FXS (9–11). The effect of FMR1 mutations on the cortex and hippocampus have been analyzed [12, 13], however, how mutations affect hypothalamic functions have not been examined. Herein, we investigated the effects of FMRP loss in reproduction, specifically on a population of hypothalamic neurons that regulate the hypothalamus-pituitary-gonadal axis.
Given that FMR1 gene mutations are also associated with reproductive disorders (3–6), combined with increasing infertility rates [14, 15], it is critical to examine FMR1 role in the reproductive axis. Reproduction is controlled by gonadotropin-releasing hormone (GnRH)-secreting neurons from the hypothalamus [16, 17]. GnRH is secreted in a pulsatile fashion into the hypophysial-portal system, to regulate synthesis and secretion of pituitary gonadotropin hormones, luteinizing hormone (LH) and follicle-stimulating hormone (FSH), which in turn regulate gonadal function [18]. Synchronization of GnRH secretion is determined by an upstream regulatory network. One population of such afferent neurons are GABAergic neurons from the mediobasal hypothalamus (19–25). Although GABA is the primary inhibitory neurotransmitter in the brain, GABA is excitatory for GnRH neurons (20, 26–28). Changes in GnRH neuron connectivity and its innervation control neuropeptide pulsatile secretion and consequently gonadotropin hormone levels.
Contrary to the hypothalamus, gonadal roles of FMRP have been analyzed in several reports. Macroorchidism in men affected with FXS [5] is caused by increased Sertoli cell proliferation during development [29]. In women, mutations in the FMR1 gene comprise the largest number of cases with the known genetic causes of early cessation of reproductive function [4, 30, 31]. FMR1 exhibits X-linked dominant inheritance pattern and associated disorders have a high penetrance [32], which contributes to their high incidence [33]. Premature ovarian failure is an infertility disorder affecting $1\%$ of reproductive age women that lose ovarian function before the age of 40 [34, 35]. Women with early menopause experience not only early infertility, but increased risk of cardiovascular disease, osteoporosis and depression [36]. Premutations of the FMR1 gene are primarily associated with the early loss of reproductive function, which may be due to the much higher prevalence of premutations compared to full mutations. Although to a lesser degree, premutation also causes lower levels of FMRP, and thus, lack of FMRP may cause pathologies in both premutation and full mutation [37]. Contrary to men, premature ovarian failure in women with FMR1 mutations is not a developmental disorder. Previous studies in mice [31] and humans (6, 38–40), did not find differences in primordial follicle numbers, indicating that early follicular development is not affected. Women affected with mutations have elevated FSH [40, 41], which stimulates cyclic recruitment of a wave of ovarian follicles into the growing pool in each menstrual or estrous cycle [42]. However, it is still not clear how FRM1 mutations lead to early infertility.
With a high prevalence of FMR1 mutations in women of child-bearing age, investigating FMRP-mediated effects on reproductive function will help us understand the mechanism of early menopause. Previous studies that examined ovarian hormone levels or follicle development, failed to explain early infertility. In this study, we first examined the reproductive function in female Fmr1 KO mice and determined that the mouse model lacking the *Fmr1* gene mimics the reproductive deficits observed in women with FMR1 mutations associated with reduced FMRP levels. We further observed that Fmr1 KO mice had larger litters and more corpora lutea in the ovaries, but normal primordial follicle count, indicating increased recruitment. In addition, we found an increase in gonadotropin levels, inhibin B and steroid hormone levels, which indicated that ovarian feedback is present. We further analyzed ovarian vascularization and innervation, and observed changes that may explain increased FSH. Ovariectomy however, revealed a hypothalamic contribution to increased LH. We then analyzed alteration in gene expression and protein levels of critical hypothalamic molecules, and examined the consequence of FMRP loss on hypothalamic GnRH neurons, that have been neglected for their potential role in the etiology of early menopause, and show changes in GABAergic innervation. We show higher LH pulse frequency, before and after ovariectomy, which indicates higher GnRH neuron activity, correlating with alterations in GnRH neuron connectivity. Therefore, central mechanisms, most likely via alterations in the activity of GnRH neurons, in addition to ovarian effects, can contribute to increased gonadotropins and higher recruitment of follicles leading to reproductive dysfunction in women with FMR1 mutations.
## Animals
All animal procedures were performed with the approval from the University of California (Riverside, CA) Animal Care and Use Committee and in accordance with the National Institutes of Health Animal care and Use Guidelines. Breeding pairs of FVB.129P2-Fmr1tm1Cgr/J (Fmr1 KO) and their congenic controls (WT) mice were obtained from Jackson Laboratories and bred in-house. Mice were maintained under a 12-h light, 12-h dark cycle and received food and water ad libitum. Since we were interested in determining the mechanisms of premature ovarian failure in women with mutations in the FMR1 gene, only female mice were used for our studies. Estrous cycle stage was determined with vaginal smears and females were collected in a specific estrous cycle stage, as indicated for each analysis. For fertility studies, Fmr1 KO females and WT controls were housed with WT males and their litters and numbers of pups per litter were recorded. Ovariectomy was performed, as described before, using 8-week-old mice [43]. Animals were allowed to recover and seven days later blood was collected for hormone analyses as described below.
## Hormone assay
Previous studies using FXS mouse models demonstrated that these mice have heightened response to stress and altered levels of glucocorticoids [44], which can lead to a decrease in luteinizing hormone (LH) levels [45, 46]. To reduce stress, animals were acclimated by daily handling and tail massage for two weeks prior to hormone measurements. For LH measurements, blood was collected from the tail and analyzed by an in house ultra-sensitive ELISA. The capture monoclonal antibody (anti-bovine LH beta subunit, 518B7) was provided by Janet Roser, University of California. The detection polyclonal antibody (rabbit LH antiserum, AFP240580Rb) was provided by the National Hormone and Peptide Program (NHPP). HRP-conjugated polyclonal antibody (goat anti-rabbit) was purchased from DakoCytomation (Glostrup, Denmark; D048701-2). Mouse LH reference prep (AFP5306A; NHPP) was used as the assay standard. Assay sensitivity is 0.016 ng/ml, while intra-assay coefficient of variation is $2.2\%$ and inter-assay coefficient of variation was $7.3\%$ at the low end of the curve. Other hormone assays were performed by the University of Virginia, Ligand Core using serum that was obtained from the inferior vena cava and serum prepared per their instructions. The University of Virginia Center for Research in Reproduction Ligand Assay and Analysis *Core is* supported by the Eunice Kennedy Shriver NICHD/NIH Grant R24HD102061. FSH was assayed by RIA using reagents provided by Dr. A.F. Parlow and the National Hormone and Peptide Program, as previously described [47]. Mouse FSH reference prep AFP5308D was used for assay standards. Inhibin and steroid hormone levels were analyzed using validated commercially available assays, information for which can be found on the core’s website: http://www.medicine.virginia.edu/research/institutes-and-programs/crr/lab-facilities/assay-methods-page and reported in [48]. Limits of detection were 2.4 ng/ml for FSH, 300 pg/ml for progesterone, and 100 pg/ml for testosterone. Intra- and inter-assay coefficients of variation were $6.9\%$/$7.5\%$, $6.0\%$/$11.4\%$ and $4.4\%$/$6.4\%$ for FSH, progesterone (P) and testosterone (T), respectively. Each animal used for each hormone analysis is represented as a dot in the figure.
## Pulsatile LH levels
Pulsatile secretion of LH strictly corresponds to GnRH secretion [49, 50]. LH is used as an indicator of GnRH secretion, since GnRH secretion into median eminence cannot be measured in mice. To ascertain if LH or GnRH neuron secretion is affected, we measured LH pulses and used an ultrasensitive ELISA assay for LH that allows for LH measurement in 5 µL of whole blood [51]. Mice were acclimated for 2 weeks by daily tail massage. 10µL of blood was collected every 8 min for 3 hours from the tail vain [45, 52]. LH levels were analyzed using ELISA described above. LH amplitude was determined by subtracting the LH value at the peak from the basal value prior to the onset of the pulse and averaged for each mouse. Mean LH concentration was calculated by averaging LH values, while pulse frequency was determined using freeware DynPeak algorithm [53].
## Nanostring analysis of hypothalamic gene expression
8-week old female mice in diestrus were perfused with ice cold PBS, brains rapidly removed and flash frozen in isopentane on dry ice. Coronal brain sections of 500 μm were obtained using vibratome, hypothalamus dissected, RNA isolated using the RNAqueous®-Micro Kit (Ambion) and quantified using Nanodrop. Gene expression in 50 ng RNA per sample was analyzed using the Nanostring instrument as described before [54], according to manufacturer’s instruction, with the nCounter Mouse Neuroinflammation Panel (770 genes, gene list available at the manufacturer’s website). The panel was customized with an addition of 30 custom probes for: Gnrh1, Kiss1, Kiss1r, Pdyn, Tac2, Tac3r, Agrp, Npy, Pomc, Cart, Mc3r, Mc4r, Hcrt, Ghrh, Crh, Trh, Oxt, Avp, Prl, Prlr, Adcyap1, Slc6a3, Slc32a1, Slc17a6, Th, Lif, Lifr, Gabra5, Gabra1, Gabrg2. Only samples with an RNA integrity number RIN over 7 were used after passing QC, with no imaging, binding, positive control, or CodeSet content normalization flags. Data analysis was performed using nSolver Analysis Software 4.0, including nCounter Advanced Analysis (version 2.0.115). Genes with the expression lower than the limit of detection after background subtraction, and compared to negative controls included in the panel, were excluded. Seven housekeeping control genes that are included in the panel, were used for normalization. A heatmap of differentially expressed genes (DEG) was created using Heatmapper software from University of Alberta (Edmonton, Canada [55];). Results are plotted in the Volcano plot as log fold change vs. log p-value, and genes with changes higher than $20\%$ were indicated with colors in the figures: red indicates genes higher in KO compared to WT, while green indicates genes that are higher in the WT compared to KO. Genes with significant changes in expression are indicated above the dashed line. Gene ontology (GO) enrichment analysis of the DEG genes was performed using the ShinyGo 0.76.3 platform (South Dakota State University [56]). False discovery rate (FDR) cutoff was 0.05, with the pathway minimum set to 10. Data is deposited in GEO repository (accession number GSE222723).
## qPCR analysis
Total RNA was extracted from ovaries using Trizol (Invitrogen, CA), and from hypothalamus and pituitary using the RNAqueous®-Micro Kit (Ambion), quantified using Nanodrop and the same amount per sample reverse transcribed using Superscript IV (Invitrogen, CA). To dissect the hypothalamus, coronal brain sections of 300 μm were obtained using vibratome. Sections containing anterior hypothalamus and posterior hypothalamus were used to dissect the 1 mm x 1 mm mediobasal portion for RNA isolation. Conditions and primers were reported before (57–61).
## Histological analyses and immunohistochemistry
WT controls and Fmr1 KO mice were anesthetized, perfused with 20 ml PBS and 20 ml $4\%$ paraformaldehyde; and tissues were collected. Ovaries were fixed in $4\%$ paraformaldehyde, embedded in paraffin, and cut to 20 μm sections. Slides were deparaffinized in xylene, rehydrated and H&E stain was performed to count ovarian follicles. For ovarian vasculature and innervation studies, frozen floating sections were stained with antibody to CD31 (1:2000 dilution, 553370, BD Biosciences) or with antibody to tyrosine hydroxylase (TH, 1:5000, ab112, Abcam) for 48 hours at 4°C, followed by overnight incubation with goat anti-rat IgG-Alexa 488 (1:2000, A11006, Vector Laboratories, Burlingame, CA) or goat anti-rabbit IgG-Alexa 488 (1:1000, A11034, Vector Laboratories, Burlingame, CA), respectively. Vascularization of corpora lutea and antral follicles was quantified by the mean fluorescent intensity (MFI) using Fiji ImageJ. Ovarian innervation was quantified by counting the number of neuronal projections in direct contact with follicles or corpora lutea.
Hypothalami were sectioned to 100 μm sections. Sections containing organum vasculosum laminae terminalis (OVLT) where GnRH neurons are located, were blocked and stained for GnRH using rabbit anti-GnRH antibodies (1:10,000 dilution) kindly provided by Greg Anderson (University of Otago; Dunedin, New Zealand [62]), GABAγ2 receptor subunit (1:10,000 dilution, guinea pig anti-GABAγ2, Synaptic systems 224 004), VGAT (1:5,000, mouse anti-VGAT, Synaptic systems 131 011) for 72 hours at 4°C. After PBST washes, slides were incubated overnight at 4°C with secondary antibodies goat anti-rabbit IgG-Alexa 488 (1:1000, A11034, Vector Laboratories, Burlingame, CA); anti-mouse IgG-Alexa 594 (1:1000, A11032, Vector Laboratories, Burlingame, CA); anti-guinea pig–biotin (1:1000, BA-7000) followed by streptavidin-Cy5 (1:1000, 434316, Vector Laboratories, Burlingame, CA). Secondary antibody-only controls were performed to determine antibody specificity. To determine puncta density, we followed our established protocol as previously published (60, 63–66). Puncta were counted in the individual neurons, by an investigator blinded to the group, where at least 45 μm of the axon proximal to soma can be observed using z-stack acquired by confocal Leica SP2 microscope. At least 15-20 individual neurons from 4-5 different sets of mice were counted. 3–D reconstruction was performed by Imaris software (Bitplane, Inc; Concord, MA).
Immunostaining for FMRP was performed using antigen retrieval methods, as previously described [67]. Slices were stained overnight with mouse anti-FMRP (1:1000; Developmental Studies Hybridoma Bank, catalog #2F5-1-s, RRID: AB_10805421). Secondary antibody was donkey anti-mouse Alexa 594 (1:300; Molecular Probes, A-21202). Slices were mounted on slides with Vectashield mounting medium containing DAPI (Vector Laboratories, H-1200).
## Western blot
Whole cell lysates were obtained from the dissected hypothalami from WT controls and Fmr1 knockout mice. The same amount of protein from each sample, determined by Bradford assay, was resolved on SDS-PAGE, transferred on nitrocellulose membrane and probed for: GABAγ2 receptor subunit (1:1000, 14104-1-AP, Proteintech), NMDAR1 (1:1000, 32-0500 Invitrogen), NMDAR2B (1:1000, 07-632 EMD Millipore), postsynaptic density protein 95 (PSD-95; 1:1000, 3409, Cell Signaling), microtubule-associated protein 2 (MAP2; 1:5000, ab5392, Abcam) or β-tubulin (1:1000, sc-9104, Santa Cruz Biotechnology). Bands were quantified using ChemiDoc imaging system (Bio-Rad, Hercules, CA).
## Statistical analyses
Statistical differences between WT control and Fmr1 KO mice ($p \leq 0.05$) were determined by t-test, or ANOVA when appropriate, followed by Tukey’s post-hoc test for multiple comparisons using Prism software (GraphPad, CA).
## Fmr1 knockout female mice experience early cessation of reproductive function
Since women with FMR1 mutation experience increased risk of early menopause, to begin investigating the role of FMRP in reproductive function, we first determined if the lack of FMRP in female Fmr1 KO mice can mimic reproductive dysfunctions observed in women with reduced levels of FMRP due to FMR1 mutation. In affected people, full expansion of the unstable CGG trinucleotide repeats (>200) leads to the loss of FMRP and FXS. CGG expansion in a range of 50-200 repeats, or FMR1 premutation, exhibits reduced FMRP levels and FXTAS. Due to differential methylation between human and mouse genes, the Fmr1 KO is a widely used mouse model to study Fragile X Syndrome and is considered a better model than putative mimics of the CGG repeat expansion [68, 69]. Our study showed that Fmr1 KO female mice experienced early vaginal opening, an external sign of puberty in mice (Figure 1A, FMR1, Fmr1 knock-out (KO); WT, wild-type control; each point represents a mouse, while bars represent group average). Fmr1 KO mice demonstrated vaginal opening at postnatal day 29 (p29), compared to p31 in WT controls. At 8 weeks of age, we paired Fmr1 KO females and control WT females, with control males and recorded birth dates and number of litters, and the number of pups in each litter, until they stopped reproducing. There was no difference in the length of time between litters (Figure 1B) or in the estrous cycle duration (Figure 1C). We determined that Fmr1 KO female mice exhibited early cessation of reproductive function, which is similar to early menopause in women with FMR1 mutation or premutation (Figure 1D, each point represents a mouse) [70, 71]. Fmr1 KO mice stopped having litters at 5.5 months of age and an average age of the last litter was p163 (FMR1, black squares), compared to p263 for WT control females (WT, open circles). Similar to the penetrance in women with mutations in the FMR1 gene [32], we observed different degrees of premature cessation of reproductive function. Fmr1 KO females split into two groups based on the age at last litter: females more severely affected with the mutation that stopped reproducing at p97, and less severely affected females that had the last litter at p221. Nonetheless, the difference between controls (p263) and less affected females (p221) was also statistically significant. We counted the number of pups in the litters and determined that Fmr1 KO females had larger first three litters (Figure 1E). The average litter size of the first litter was 10.6 pups for Fmr1 KO and 7.5 pups for controls; the average size of the second litter was 11.4 pups for Fmr1 KO and 8.8 pups for controls, and the average size of the third litter was 10.8 pups for Fmr1 KO and 7.1 pups for controls. However, while control females continued to produce litters for at least 10 litters, the number of Fmr1 KO females that continued to produce litters decreased after each litter, and only 5 out of 11 Fmr1 KO females had a fourth litter, while none had eighth litter. There was no difference in weight at any age between KO and control females and thus, change in reproductive function does not stem from a difference in weight. Therefore, similar to women with a mutation in the FMR1 gene, Fmr1 KO female mice experience early cessation of reproductive function.
**Figure 1:** *Fmr1 knockout (KO) females stop reproducing early. (A)
Fmr1 KO females (FMR1, white bars represent group mean +/- standard error, and each black square represents one animal) experience early vaginal opening, an external sign of puberty, at postnatal day (p) 29, compared to wild type controls at p31 (WT, gray bars represent mean +/- standard error, open circles represent each animal); (B)
Fmr1 KO females have litters at the same rate as WT controls; (C) No difference in the length of the estrous cycle; (D) Determined by the age at the last litter, Fmr1 KO females stop reproducing early at p163, compared to WT controls at p263; Each point represents one animal, and bars represent group means +/- standard error. Statistical significance (*, p < 0.05) was determined with t-test followed by Tukey’s post hoc test. (E)
Fmr1 KO females have more pups per litter in the first three litters. Each circle represents one litter produced by WT females, while each square represents one litter produced by Fmr1 KO females, with number of pups in 1st – 8th litter indicated. The number of Fmr1 KO females that continue to produce litters, indicated by squares, decreases gradually. Bars represent group means +/- standard error, * indicates statistically significant difference between WT and KO.*
To examine if early cessation of reproductive function is due to initially diminished ovarian reserve or to an accelerated loss of follicles, we counted the number of primordial follicles in pre–pubertal females at 3 weeks of age (p21, Figure 2A count, and Figure 2D representative images). Three equal size areas of the ovarian cortex were selected per mouse, primordial follicles counted, and the numbers were averaged for each mouse. There was no difference in the number of primordial follicles between Fmr1 KO and WT females, indicating that development of the initial pool is not affected by the Fmr1 loss. The analysis of the number of corpora lutea (CL) in the ovaries at 6 weeks of age (p42) showed that Fmr1 KO females had over 4 times more corpora lutea than WT controls. Fmr1 KO had 10.2 average number of corpora lutea per ovary compared to 2.2 corpora lutea per ovary in controls (Figure 2B count, Figure 2E representative images). Given that Fmr1 KO females exhibited early vaginal opening, to confirm that the increase in corpora lutea in KO females does not stem from early puberty, we counted the number of corpora lutea at 9 weeks of age (at p63; Figure 2C). At p63, an average number of corpora lutea per ovary was significantly higher in Fmr1 KO females (8.6 corpora lutea) compared to control WT mice (5.4 corpora lutea). Together, these results demonstrate that Fmr1 KO female mice stop reproducing early, have the same primordial follicle pool, but increased number of corpora lutea corresponding to the larger litter size in young animals, indicating potentially higher recruitment to the growing pool in young animals.
**Figure 2:** *Ovarian histology demonstrates more corpora lutea in young Fmr1 KO mice. Fmr1 KO females (FMR1, white bars represent group mean +/- standard error while each black square represents one animal) were compared to wild type controls (WT, gray bars represent mean +/- standard error, each open circle represents one animal). (A) Primordial follicles were counted at 3 weeks of age. Each point represents one mouse, and an average of 4 separate 1x10-8 m2 areas in the ovary cortex of each mouse. (B, C) corpora lutea were counted at 6 (B) and 9 weeks of age (C) throughout each ovary. (D) representative images of ovaries from 3-week old mice to observe primordial follicles (630x). (E) representative images of ovaries at 6 weeks of age to observe numbers of corpora lutea (40x). Statistical significance, indicated with * (p < 0.05) was determined with t-test followed by Tukey’s post hoc test.*
## Fmr1 KO females exhibit increased gonadotropin and ovarian hormone levels
Given that ovarian function is regulated by gonadotropin hormones from the pituitary, we analyzed the hormone levels in female mice in diestrus at 9 weeks of age (Figure 3). LH and FSH levels were significantly higher in diestrus Fmr1 KO. LH doubled in KO to 0.84 ng/ml from 0.42 ng/ml in controls. Serum FSH was also higher with 4.2 ng/ml in KO, compared to 2.3 ng/ml in diestrus controls (Figure 3A). These results may demonstrate that high FSH leads to higher recruitment of follicles in the growing pool, which together with high LH results in more corpora lutea. Since LH and FSH β-subunit transcription, that is unique for each hormone, precedes changes in hormone concentration in the circulation, and fluctuations in mRNA levels in the gonadotrope correlate with concentration of the hormones [18], we analyzed pituitary mRNA levels (Supplemental Figure 1). Both Lhb (LHβ) and Fshb (FSHβ) expression was increased in Fmr1 KO mice, while expression of the common Cga (αGSU, Glycoprotein hormones common subunit alpha), Gnrhr (GnRH receptor) or other pituitary hormones was unchanged. These results indicate that concentrations of LH and FSH in the circulation correlate with β-subunit mRNA levels.
**Figure 3:** *Fmr1 KO mice have higher LH and FSH. Serum levels of LH and FSH, (A) testosterone (T), progesterone (P) and inhibin B (Inh B) (B) in diestrus females in WT controls and Fmr1 KO. Each point represents one animal, while bars represent group means +/- standard error. LH was sampled from the tail tip after acclimatization to handling, to minimize stress and prevent exposure to isoflurane that may affect LH levels. FSH, T, P, Inh B samples are obtained from inferior vena cava. Statistical significance, indicated with a * (p < 0.05) was determined with t-test followed by Tukey’s post hoc test.*
Previous studies postulated that ovarian impairment contributed to diminished negative feedback, which in turn caused increased FSH observed in affected women [40, 41]. To address this possibility, we analyzed ovarian hormones that provide feedback to the hypothalamus and pituitary in 8-week-old diestrus females before cessation of reproductive function. Steroid hormones primarily provide feedback to the hypothalamus, while inhibin regulates FSH levels (72–77). Testosterone was significantly increased in Fmr1 KO female mice, 279 pg/ml in KO compared to 200 pg/ml in controls (Figure 3B, T). Progesterone was elevated as well to 3 ng/ml in Fmr1 KO from 1.7 ng/ml in controls (Figure 3B, P), demonstrating that negative feedback is present and increased LH cannot be explained by reduced negative feedback. We also analyzed inhibin B levels in diestrus females in the circulation, and determined that inhibin B was higher in KO mice, 1.9 ng/ml compared to 1.5 ng/ml in controls (Figure 3B, Inh B). Our results demonstrate that inhibin B is higher in young animals, which may be a result of larger number of follicles, or alternatively that is stimulated by higher FSH levels. This means that inhibin feedback is also present, and cannot explain elevated FSH. This implicates central mechanisms, rather than ovarian insufficiency in the reproductive phenotype of Fmr1 KO mice. Together, our results demonstrate elevated gonadotropin hormone levels, higher testosterone and progesterone, more corpora lutea, larger litters in young animals, and early cessation of reproductive function.
## Ovariectomy reveals hypothalamic and ovarian contribution to endocrine changes
To discern ovarian contribution from the hypothalamic origin of the disorders, we ovariectomized (OVX) the mice and a week later analyzed LH and FSH levels. Over 10-fold higher LH and over 20-fold higher FSH confirmed the successful OVX (compare levels in Figures 3, 4). Interestingly, LH remained significantly higher in OVX KO mice (8 ng/ml) compared to OVX WT mice (6 ng/ml), while there was no difference in FSH levels between WT and Fmr1 KO females after OVX. Therefore, increased LH in unmodified animals likely stems from central dysregulation, while increased FSH involves ovaries.
**Figure 4:** *Ovariectomized Fmr1 KO mice have higher LH. One week after ovariectomy, LH and FSH samples were collected as in
Figure 3
. Each point represents one animal, while bars represent group means +/- standard error. Statistical significance, indicated with a * (p < 0.05) was determined with t-test followed by Tukey’s post hoc test.*
## Increased innervation and vascularization in the ovaries
To address seemingly discordant results, that Fmr1 KO females exhibit higher levels of ovarian hormones than WT controls, which can provide negative feedback, and also increased FSH, which as revealed by ovariectomy is due to ovarian dysregulation, we analyzed ovarian vascularization and innervation. Using an endothelial cell marker, CD31 we stained ovarian sections and analyzed vascularization around follicles and corpora lutea. Follicles from WT and Fmr1 KO had the same degree of vascularization (Figure 5A, representative image; Figure 5B, quantification). However, corpora lutea (CL) were more highly vascularized in Fmr1 KO than in WT mice (Figure 5C low magnification, top). Higher magnification revealed more abundant and thicker vasculature in the Fmr1 KO CL (Figure 5C, bottom, Figure 5D, quantification).
**Figure 5:** *Fmr1 KO have increased corpus luteum vascularization. Ovaries were sectioned and stained with antibodies to CD31 (PECAM-1, Platelet endothelial cell adhesion molecule) to visualize vascularity. Mean fluorescent intensity (MFI) was determined using Fiji imageJ. Consistent areas were used to quantify fluorescence intensity. (A) Representative images of antral follicles. (B) MFI quantification. (C) Corpora lutea representative images; top, 1.6 mm x 1.6 mm area, CD31 green; bottom, 300 μm x 300 μm area, CD31, green, DAPI, blue. (D) MFI quantification. Statistical significance, indicated with a * (p < 0.05) was determined with t-test followed by Tukey’s post hoc test.*
Although FMRP is expressed at high level in neurons, ovarian innervation was not previously examined to possibly explain ovarian phenotype observed in Fmr1 KO mice. Using antibodies to tyrosine hydroxylase, the rate-limiting enzyme in catecholamine synthesis, we counted numbers of neuronal fibers that reach the theca layer of growing follicles. Secondary follicles in Fmr1 KO ovaries had significantly more neuronal fibers than WT follicles; 4.8 average fibers per secondary follicle in KO compared to 2.3 average fibers in WT (Figure 6A, representative images; Figure 6B quantification). Innervation of CLs was very variable within each animal and between animals, and we did not identify significant differences between WT and KO mice (Figure 6C bottom left quarter of CL with innervation presented, Figure 6D, quantification). Together, ovarian histology demonstrated increased vascularization of corpora lutea and increased innervation of secondary follicles in Fmr1 KO mice.
**Figure 6:** *Fmr1 KO have higher innervation of growing follicles. Ovaries were sectioned and stained with antibodies to tyrosine hydroxylase (TH, green) to visualize innervation around secondary follicles (A) and corpora lutea (C). Fibers that surround the follicles and penetrate theca layer were counted (B, D). * indicates significant difference.*
## Altered levels of synaptic molecules in the hypothalamus of Fmr1 KO mice
Our results demonstrate elevated LH before and after ovariectomy, which is regulated by GnRH secretion. To address the mechanisms of changed LH, we examined changes in the hypothalamus, first focusing on gene expression changes caused by the loss of *Fmr1* gene. Although FMRP is an RNA-binding protein that regulates protein levels of its targets, genome-wide changes in the RNA expression signatures can identify pathogenic pathways that may be indirectly regulated by FMRP. Especially since FMRP regulates levels of several transcription factors and other transcriptional regulators, which may exhibit extensive changes on the transcriptome. Previous analyses of Fmr1 KO transcriptomes focused on embryonic hippocampus and cortex, and identified overexpression of immune-related genes and downregulation of genes implicated in behavioral phenotype [78]. For that reason, we used Nanostring neuroinflammation panel, which contains 770 genes implicated in neurological disorders, neuronal injury, neurotransmission, neuron-glia interactions, neuroplasticity, cell integrity, neuroinflammation, and metabolism; and added 30 custom probes for hypothalamic neuropeptides and their receptors. The complete list of genes that changed in KO compared to WT is presented by the heatmap (Figure 7A). There were 59 genes that were upregulated >$120\%$ from WT levels, and 39 genes that were downregulated <$80\%$ of WT levels, delineated with a dashed line. Significant changes in expression of neuropeptides and other genes of interest were highlighted in the volcano plot (Figure 7B, log fold change vs. log p-value, statistically significant change in expression indicated with a dashed line; red, upregulated genes in KO compared to WT; green, downregulated gene in KO compared to WT). Immediate early gene, transcription factors Egr1, Fos and Jun, that are used as markers of neuronal activation, were upregulated in Fmr1 KO mice. Genes encoding GABAA receptor γ2 subunit, (GABARγ2, Gabrg2) which is the obligatory subunit of the pentameric GABAA receptor [79]; and PSD-95 (Dlg4), a postsynaptic scaffolding protein anchoring glutamate receptors, were upregulated in KO mice. Ppfia4, involved in neurotransmitter release, and Opalin, important for oligodendrocyte differentiation, were also upregulated. On the other hand, genes correlated with DNA repair, Ercc2; neurodegenerative disorders, Serpina3n; hypoxia, Hif1a; and apoptosis, Hcar2 and Bag4, were downregulated. Importantly, genes encoding GLAST, Slc1a3, and VGLUT2, Slc17a6, were also downregulated. Of interest, neuropeptide gene encoding GnRH, Gnrh1, was upregulated, while genes for kisspeptin, Kiss1, neurokinin B, Nkb, Tac3; and cocaine and amphetamine regulated transcript, Cart, were downregulated. We confirmed changes in Gnrh1 and Kiss1 expression by qPCR of hypothalamic lysates (Figure 7C). GO pathway analysis indicated that pathways such as AP1 complex, comprised of Fos and Jun, myelin adaxonal regulation, spine and dendrite development were upregulated, while neuropeptide binding, ubiquitin ligase binding, and receptor signaling pathways were downregulated (Figure 7D).
**Figure 7:** *Nanostring analysis demonstrates significant changes in hypothalamic gene expression in Fmr1 KO mice. (A) Hypothalami from 3 mice per group were dissected and 50 ng RNA used in Nanostring analysis. Heatmap indicates gene expression changes in Fmr1 KO hypothalami that are <0.8-fold and >1.2-fold over WT. (B) Data were plotted as log fold change on x-axis vs. log p value on y-axis, and dashed line indicates significance. Red indicates genes that are increased in KO compared to WT mice, while green indicates genes that are decreased in KO compared to WT. Genes below the dotted line, light red and light green did not reach significance. (C) qPCR of the hypothalamus confirms changes in GnRH and kisspeptin expression. Each point represents one animal and bars represent group average. Statistical significance is indicated with *, determined by t-test followed by Tukey’s post hoc test. (D) Gene ontology pathway analysis indicates upregulated (top) and downregulated (bottom) pathways in the Fmr1 KO mice.*
Considering changes in excitation/inhibition synaptic balance in the cortical neurons of Fmr1 KO mice [80], we next investigated the levels of several synaptic proteins in hypothalamus at the protein level. Although previous studies showed changes in neurotransmitter receptor levels, such as GABAA receptor for GABA, and NMDA receptor for glutamate, in several brain regions of male Fmr1 KO mice, no studies were done in females or in the hypothalamus. Hypothalami were dissected from diestrus female brains, and levels of synaptic molecules were measured by western blotting. We analyzed GABARγ2 (Gabrg2) levels in the hypothalami of KO and control female mice in diestrus, since GABA transmission activates GnRH neurons, via activation of GABAA receptor [26, 81], and determined that Fmr1 KO females had significantly higher levels of GABARγ2 GABAA receptor than controls (Figure 8A, representative western blots; Figure 8B quantification). Protein levels correlate with gene expression analysis, indicating that GABARγ2 may be indirectly regulated by Fmrp. We then analyzed levels of the glutamatergic NMDA receptors (NMDARs, or NRs) [82, 83], since 30-$50\%$ of GnRH neurons respond to NMDA [84, 85]. NR1 is an obligatory subunit that forms a heterotetramer with either 2A or 2B subunits (other isoforms are less frequent). We determined that NR1 (Nmdar1, GluN1) levels were increased in the hypothalami of KO mice compared to controls (Figure 8C). Depending on the NR2 isoform, NMDAR is localized at synapses or extrasynaptically with different effects on long-term potentiation or negative feedback, respectively [64, 86]. NR2B is localized extrasynaptically, and we determined that the levels of NR2B are lower in the KO mice compared to controls (Figure 8D). Genes encoding these proteins did not change at the transcriptional level, which indicates that they may be regulated by Fmrp at the protein level. We also analyzed levels of PSD-95, since PSD–95 anchors glutamate receptors [87], and determined that PSD-95 protein levels were the same in Fmr1 KO and controls, which is contrary to gene expression studies and again points to direct regulation by Fmrp (Figure 8E). Taken together, an increase in the synaptic GABAA and NR1 receptors, and a decrease in extrasynaptic NR2B, may contribute to altered activity of hypothalamic neurons in Fmr1 KO female mice.
**Figure 8:** *Fmr1 KO females have higher levels of the obligatory GABAA receptor subunit in hypothalami. Hypothalami were dissected and protein levels analyzed by western blotting. Representative blots are shown in (A). Protein levels from 6 mice per group were quantified using Chemidoc and levels of neuronal proteins normalized to β-tubulin (B–E). Statistical significance (p < 0.05), determined with t-test followed by Tukey’s post hoc test, is indicated with a *.*
## Lack of Fmrp changes GnRH neuron connectivity
To determine if Fmrp loss alters neurotransmitter receptor levels specifically in GnRH neurons, we first confirmed that $82\%$ of GnRH neurons express Fmrp protein (Figure 9A, left image, confocal microscopy, right image, 3D reconstruction to demonstrate Fmrp inside GnRH neurons). Antibody specificity was determined by staining the hypothalami from Fmr1 KO mice (Supplemental Figure 2). We also analyzed GnRH neuron number in Fmr1 KO mice to compare to WT to ascertain if increased in GnRH neuron number contributes to higher LH. There was no difference in the number of GnRH neurons in WT and KO mice (Supplemental Figure 2).
**Figure 9:** *GnRH neurons of Fmr1 KO females have more GABAA receptors. (A) GnRH neurons (green) express Fmrp (red, top panels). (B) Representative GnRH neurons (green) from WT control (top panel) and Fmr1 KO mice (bottom panel) co-stained with GABAγ2 antibody (red). (C) Quantification of (B) Each point represents one animals and an average of 15-20 neuron per animal, and bars represent group means. Panels represent counts in the whole soma, along the first 15 μm segment of the process (1-15 μm) and second 15 μm segment (16-30 μm) from the soma, as indicated above. Statistical significance (p < 0.05), determined with t–test followed by Tukey’s post hoc test, is indicated with a *.*
Since GABA can regulate GnRH neuron activity [26, 81], and we determined elevated GABAγ2 subunit of the GABAA receptor in the hypothalami of KO mice compared to controls by western blot, we analyzed if GABAA receptor immunoreactivity is increased specifically in GnRH neurons. To determine GABAA receptor distribution in GnRH neurons, we immunostained 100 μm coronal sections of the preoptic area of the hypothalamus for GABAγ2 and GnRH. After staining, sections were imaged with high-resolution confocal microscopy. GABAγ2 receptors showed puncta-like distribution in GnRH neurons. GABAγ2 receptor puncta colocalized with GnRH immunoreactivity were identified by closely apposed puncta when no black pixels were visible between two signals in optical slices, and counted blind to condition by scrolling through the series of captured z-stack images for each GnRH soma and along the process, at 15-μm intervals, for each GnRH neuron. At least 15 neurons were counted from each mouse, and the average for each mouse was calculated (represented by a dot in the Figure 9C with bars representing group average). We determined that GABAγ2 puncta numbers increased significantly in the GnRH neuron soma and in the first 15 μm of the process proximal to the soma (Figure 9C quantification, Figure 9B, representative images, top WT, bottom Fmr1 KO). The increase in GABAergic inputs in this area is significant, since this region of the neuron is the region where action potentials are initiated [88], and it exhibits synaptic plasticity during development and in different hormonal milieu (89–91). Since GABA is excitatory for GnRH neurons (20, 26–28), the alteration of the receptor levels may enhance GnRH neuron responsiveness and neuropeptide secretion, which in turn would cause changes in gonadotropin levels.
To determine GnRH innervation, we also analyzed appositions of GABAγ2 subunit of the GABAA receptor with vesicular GABA transporter (VGAT), presynaptic marker of GABAergic terminals. We performed a triple stain for GnRH, GABAγ2 and VGAT, and as above, counted number of puncta where VGAT was in a close opposition to GABAγ2 in GnRH neurons (Figure 10A). We counted at least 15-20 neurons from each mouse, 5 pairs of mice; and used Imaris software to perform 3-D modeling of VGAT-GABAA receptor appositions (Figure 10B). Fmr1 KO mice had a higher number of GABAergic appositions in GnRH neuron soma and proximal process, in the segment 1–15 μm and segment 16–30 μm from the soma, than WT controls (Figure 10C). The increase in synaptic GABAA receptors in the proximal process indicates higher innervation of GnRH neurons in the area that is plastic and receives synaptic input.
**Figure 10:** *GnRH neurons of Fmr1 KO females have more synaptic GABAA receptors. (A) Representative images of triple stain for presynaptic VGAT (green), GABAγ2 receptor (red) and GnRH (white); (B) 3-D GnRH neuron models using Imaris software. VGAT (green) appositions to GABAγ2 receptor subunits (red) in GnRH neurons (white) were counted in 15-20 neurons per mouse, five mice from each group. (C) Quantification from different regions of the neuron, as above, is presented in panels, and significance indicated with *.*
## Fmrp regulates LH pulsatility
Pulsatile secretion of LH strictly corresponds to GnRH secretion [49, 50]. LH is used as an indicator of GnRH secretion, since GnRH secretion into median eminence cannot be measured in mice. To ascertain whether GnRH neuron secretion was affected, we measured LH pulses and used an ultrasensitive ELISA assay for LH [51] that allows for LH measurement in 5 µl of whole blood. Mice were acclimated for 2 weeks by daily tail massage. Serial sample collection every 8 min for 3 hours from the tail vein was performed [45, 52]. Representative LH pulse profile from unmodified WT and KO mice are presented in Figure 11A, right side (WT control, top; Fmr1 KO, bottom). Number of LH pulses per 2.5 hours of measurement were determined using DynPeak algorithm [53] and compared between genotypes. LH, and therefore GnRH, pulse frequency was significantly higher in Fmr1 KO mice compared to WT controls (Figure 11A, left side). Amplitude was determined by subtracting the highest LH value from the basal value prior to the onset of the pulse and averaged for each mouse (Figure 11A, middle).
**Figure 11:** *Higher LH/GnRH pulse frequency in Fmr1 KO females before and after ovariectomy. LH pulse frequency reflects GnRH neuron activity. (A) Frequent tail-tip whole blood sampling over 3 hours demonstrate higher pulse frequency of LH in Fmr1 KO female mice in diestrus. Representative profiles from WT control in the top and Fmr1 KO at the bottom (right side), pulse frequency calculated from pulse profiles using DynPeak (left); amplitude was determined by subtracting the LH value at the peak from the basal value prior to the onset of the pulse and averaged for each mouse (middle). (B) LH pulse in ovariectomized (OVX) animals. Statistical significance (p < 0.05), determined with t-test followed by Tukey’s post hoc test, is indicated with a *.*
Pulsatile LH analyses were performed using ovariectomized animals as well. Frequency of LH secretion was faster in OVX Fmr1 KO animals compared to OVX WT mice (Figure 11B, left; representative profiles right), while pulse amplitude was the same. Representative pulse profiles are shown on the right side. These experiments determined that the lack of Fmrp increased LH pulse frequency, indicating higher GnRH neuron activity corresponding to higher GABAergic innervation of GnRH neuron. It is possible the lack of Fmrp alters GnRH neuropeptide secretion leading to the faster GnRH pulse frequency and elevated LH, which contributes to ovarian dysregulation in young animals.
## Discussion
We sought out to uncover the effects of FMRP loss on hypothalamic GnRH neurons and ovarian function, which may help elucidate the mechanisms of early cessation of reproductive function in females with a mutation in the FMR1 gene. Women with FMR1 mutations comprise the majority of known genetic causes of premature ovarian failure [3, 35, 92]. Premature ovarian failure or primary ovarian insufficiency is the most extreme manifestation of premature ovarian senescence and affects about $1\%$ of women [34]. Premature reproductive senescence affects approximately $10\%$ of women and is characterized by an early depletion of ovarian follicles [34]. Molecular causes of premature cessation of reproductive function in women with FMR1 mutations and mechanisms underlying reproductive dysfunctions are still unknown. The hypothalamus was especially neglected in previous studies addressing FMR1 function, or etiology of premature reproductive senescence. Our study is the first to examine the hypothalamic function of the FMR1 gene. We analyzed mechanisms of reproductive disorders associated with FMR1 mutations using the Fmr1 KO female mice, since knockout mice lack Fmrp mimicking the loss of FMRP in humans with FMR1 mutation. Fmr1 KO female mice are useful model to study reproductive disorders in women with a mutation in the FMR1 gene, as they exhibit early cessation of reproductive function similar to women with FMR1 mutations. The main findings of this study implicate central mechanisms and ovarian innervation in reproductive disorders associated with the FMRP loss. We demonstrate that Fmr1 KO female mice show higher GnRH neuron and ovarian follicle innervation, increased GnRH neuron secretion, and augmented gonadotropin levels, all of which may contribute to increased recruitment of ovarian follicles to the growing pool, corresponding to a higher number of corpora lutea and larger litters in young animals. These growing follicles exhibit increased innervation, which is associated with higher steroidogenesis. Together, our results point to hypothalamic mechanisms, specifically GnRH neuron connectivity, and ovarian innervation in the reproductive disorders associated with FMRP loss that have not been considered before.
Women with early menopause face not only infertility, but an increased risk of heart disease and osteoporosis (93–97). Most women are only diagnosed after their ovarian function has ceased, since they seek care due to infertility or amenorrhea. In that case, it is difficult to predict earlier hormonal changes, when ovarian reserves are relatively normal. As of yet, there are no screening strategies to detect women with increased risk before they are symptomatic [98]. Most studies analyzing a role of FMRP have focused on males, while females are rarely included. Furthermore, cortical mechanisms attracted the attention of the investigators, because FXS is the most common monogenic cause of intellectual disability and autism. However, the mechanism underlying the dysregulation of reproductive function in FMR1 mutations was not extensively studied. Several reports demonstrate elevated FSH in women affected with FMR1 mutations showing early menopause [40, 41], which are consistent with our observations in mice. Since the primary reproductive defect in females with FMR1 mutation is premature ovarian failure, gonadal origin was proposed. We demonstrate that primordial follicle number is unaffected by Fmr1 loss, which agrees with previous studies that showed that women with FMR1 mutation and mouse models of premutation have normal pool of primordial follicles [31, 38, 39, 70]. This indicates that early ovarian development is not adversely affected by the loss of FMRP. Our results using complete KO model show larger litters and more corpora lutea, while premutation mouse models have smaller litters [31, 38], but reasons for differences are not clear. Our results do not preclude other intra-ovarian defects that may contribute to the early loss of follicles and early depletion, such as increased atresia as suggested in [38]. Given that there is no difference in primordial follicle development, inappropriate ovarian response to gonadotropin stimulation, compounded by changes in gonadotropin levels, likely contributes to early cessation of reproductive function in FMR1 mutations.
Removal of ovaries demonstrated that increased FSH depends on ovarian feedback. However, the increase in FSH is not a result of the lack of negative feedback as we show that in females, ovarian hormones, inhibin or steroid hormones, are higher than in controls, indicating that hormonal feedback to the pituitary and hypothalamus was present. Studies in women with FMR1 mutations are inconclusive, with one study reporting unchanged inhibin [6], while the other found decreased inhibin [40]. The latter study is the only one, to our knowledge, that analyzed steroid hormone levels and found decreased progesterone [40]. The discrepancy may arise due to the age of the subjects, as discussed above. Increase in inhibin B levels in our results, implies that higher FSH occurs irrespective of inhibin feedback, in fact, may lead to increased inhibin, and thus, higher FSH may be the cause rather than the consequence of reproductive disorders.
Since ovarian hormones did not provide adequate explanation, we examined ovarian innervation and vascularization, and detected increased vascularization of corpora lutea which may correlate with higher progesterone levels [99]. Increased progesterone may lead to higher FSH [100, 101]. Fluorescent studies with endothelial cells marker reveal that large follicle vascularization is not changed, but corpora lutea exhibit increased vascularization. Developing corpus luteum is a site of rapid angiogenesis, under the influence of the vascular endothelial growth factor (VEGF) [99, 102, 103]. Studies in several species determined that angiogenesis and VEGF induction is stimulated by LH (104–106). Thus, increased LH in our study likely contributes to increased vascularization of the corpus luteum. Treatment with VEGF antagonist demonstrated decreased progesterone [107]. Therefore, increased progesterone in Fmr1 KO mice we report here may be due to increased vascularization of corpora lutea. Several studies determined that progesterone could increase FSH at the transcriptional level, which is thought to be important for the specific secondary rise of FSH during luteal phase. Progesterone treatment in combination with estrogen, increased FSH, while antiprogestins blocked FSH secretion and mRNA expression during the preovulatory surge [108] and during the secondary rise [109]. Therefore, our studies postulate that increased LH may cause increased angiogenesis during luteinization, which leads to higher progesterone, which in turn increases FSH, and together, may explain why increased LH is of hypothalamic origin, while increased FSH requires ovaries.
Ovaries receive sympathetic innervation via two routes (110–112). The superior ovarian nerve projections innervate the secretory component of the ovary. The fibers surround the developing follicles, but do not penetrate the granulosa layer or the corpus luteum. We determined that innervation of the follicles with fibers that originate from the superior ovarian nerve is increased. This innervation is required for steroidogenesis, since transection of the nerve reduced steroid hormone levels (113–115). Thus, higher steroid hormone levels may be a result of increased innervation. As discussed above, this may contribute to higher FSH levels in KO animals.
Increased LH likely stems from higher GnRH secretion, via increased GnRH pulse frequency, since GnRH from the hypothalamus strictly regulates LH secretion. We determined that GnRH neurons have increased GABAergic innervation, which is excitatory for GnRH neurons (20, 26–28). Therefore, it is possible that increased GABA tone leads to altered responsiveness of GnRH neurons to the pulse generator and the upstream regulatory network. Alternatively, constantly increased GABA input may lead to increased activity of GnRH neurons. The idea of enhanced activation of GnRH neurons is also supported by the observed increases in neurotransmitter receptor levels in the hypothalamus and specifically GnRH neurons. FMRP binds mRNAs that encode synaptic proteins (9–11) and previous studies reported altered levels of GABAA receptors and NMDA receptors in several brain areas. Here, we further determined that GABAA receptor abundance changes at the transcriptional level, while NMDARs change on the protein level, which may elucidate direct versus indirect regulation by FMRP. Contrary to the studies in the cortex and hippocampus, which detected decreased GABAA receptor, we determined that GABAA receptor levels are increased in the hypothalamus at the mRNA and protein levels. $30\%$-$50\%$ of GnRH neurons respond to NMDA [84, 85], and our studies in the hypothalamus agree with previous studies in the cortex that demonstrate increased NMDA receptors in Fmr1 KO mice. Therefore, the increase in NMDA and GABA receptors, that are both excitatory for GnRH neurons, likely changes GnRH neuron function and GnRH neuropeptide secretion.
Therefore, both the hypothalamus and ovaries contribute to endocrine disruption that may lead to larger litters in young animals. Hypothalamic contribution to the etiology of early menopause has been underappreciated. We propose that changes in GnRH neuron and ovarian innervation contribute to changes in gonadotropin hormones, LH and FSH, levels. This may cause early depletion of ovarian follicles and premature cessation of reproductive function, which will be addressed in future studies. Together, our results point to hypothalamic mechanisms and ovarian innervation in the reproductive function disorders associated with FMRP loss that have not been considered before.
## Data availability statement
The data presented in the study are deposited in the Gene Expression Omnibus GEO repository, accession number GSE222723. link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222723 these are fluorescent images and the background is meant to be black to demonstrate specific staining of the area of interest.
## Ethics statement
The animal study was reviewed and approved by UCR IACUC.
## Author contributions
PV and NL performed most of the experiment reported herein and data analyses. CJ determined reproductive phenotype. SB performed analyses of ovarian vasculature. IE provided valuable insight in Fragile X Syndrome pathology. DC conceived and guided the study and wrote the manuscript. PV: Conceptualization, Investigation, Formal Analysis, Visualization, Writing – Review and editing. NL: Investigation, Formal Analysis, Visualization, Writing – review and editing. CJ: Investigation, Formal Analysis, Visualization, Writing – review and editing. SB: Investigation, Formal Analysis, Visualization, Writing – review and editing. IE: Resources, Validation, Supervision, Writing – review and editing. DC: ORCid: 0000-0003-0692-1612 Conceptualization, Formal Analysis, Funding Acquisition, Project Administration, Supervision, Visualization, Writing – original draft and preparation. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1129534/full#supplementary-material
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|
---
title: K‐medoids clustering of hospital admission characteristics to classify severity
of influenza virus infection
authors:
- Aleda M. Leis
- Erin McSpadden
- Hannah E. Segaloff
- Adam S. Lauring
- Caroline Cheng
- Joshua G. Petrie
- Lois E. Lamerato
- Manish Patel
- Brendan Flannery
- Jill Ferdinands
- Carrie A. Karvonen‐Gutierrez
- Arnold Monto
- Emily T. Martin
journal: Influenza and Other Respiratory Viruses
year: 2023
pmcid: PMC9992770
doi: 10.1111/irv.13120
license: CC BY 4.0
---
# K‐medoids clustering of hospital admission characteristics to classify severity of influenza virus infection
## Abstract
### Background
Patients are admitted to the hospital for respiratory illness at different stages of their disease course. It is important to appropriately analyse this heterogeneity in surveillance data to accurately measure disease severity among those hospitalized. The purpose of this study was to determine if unique baseline clusters of influenza patients exist and to examine the association between cluster membership and in‐hospital outcomes.
### Methods
Patients hospitalized with influenza at two hospitals in Southeast Michigan during the $\frac{2017}{2018}$ ($$n = 242$$) and $\frac{2018}{2019}$ ($$n = 115$$) influenza seasons were included. Physiologic and laboratory variables were collected for the first 24 h of the hospital stay. K‐medoids clustering was used to determine groups of individuals based on these values. Multivariable linear regression or Firth's logistic regression were used to examine the association between cluster membership and clinical outcomes.
### Results
Three clusters were selected for $\frac{2017}{2018}$, mainly differentiated by blood glucose level. After adjustment, those in C171 had 5.6 times the odds of mechanical ventilator use than those in C172 ($95\%$ CI: 1.49, 21.1) and a significantly longer mean hospital length of stay than those in both C172 (mean 1.5 days longer, $95\%$ CI: 0.2, 2.7) and C173 (mean 1.4 days longer, $95\%$ CI: 0.3, 2.5). Similar results were seen between the two clusters selected for $\frac{2018}{2019.}$
### Conclusion
In this study of hospitalized influenza patients, we show that distinct clusters with higher disease acuity can be identified and could be targeted for evaluations of vaccine and influenza antiviral effectiveness against disease attenuation. The association of higher disease acuity with glucose level merits evaluation.
## INTRODUCTION
Infectious respiratory diseases caused by influenza virus, respiratory syncytial virus, and SARS‐CoV‐2 can cause significant illness and are responsible for hundreds of thousands of hospitalizations in the United States annually. 1 Data on in‐hospital progression of disease and treatment course are broadly available and used to evaluate severity of illness, 2, 3 or the impact of vaccination 4, 5 and treatment. 6, 7, 8 However, the primary cause of admission, particularly in those with baseline multimorbidity, might be due to causes either exacerbated by milder respiratory tract infection (e.g., asthma) or possibly unrelated to infection (e.g., dehydration) rather than acute illness. This might bias results of vaccine or antiviral effectiveness against prevention or attenuation of severe disease. Differences in general health and health care seeking behaviour are difficult to directly measure, 9, 10 and individuals may present and be admitted to the hospital at different stages in their disease course with varying disease severity. These patterns vary by population, health system, and specific aetiology. 11, 12, 13, 14 While patients hospitalized with respiratory diseases such as influenza have historically been older with significant comorbidity, 11, 15 the pattern has differed in various phases of the COVID‐19 pandemic. 16 The heterogeneity of the hospitalized population at admission creates challenges when examining events occurring during hospitalization. Differential baseline comorbidity and presenting symptomology can significantly confound hospital data used as a surveillance metric for respiratory disease severity and can bias estimates of the effectiveness of interventions to reduce influenza morbidity or progression of disease.
Unsupervised machine learning algorithms provide a way to derive and characterize different groups of patients independent of an outcomes or treatment framework. 17, 18 When applied to clinical data, this methodology can help identify distinct phenotypes of individuals driven by underlying relationships between health metrics. The aims of the current study were to develop clinically distinct clusters of patients based on laboratory and physiologic measurements within the first 24 h of hospitalization, to determine if cluster membership was associated with worse in‐hospital outcomes, and to evaluate the association of influenza vaccination on in‐hospital outcomes within a given cluster.
## METHODS
Institutional review board approval for the US Hospitalized Adult Influenza Vaccine Effectiveness Network (HAIVEN) study was obtained from the University of Michigan. Cases were identified from 2017 to 2018 and 2018 to 2019 enrolees from a single site of the HAIVEN study, encompassing two major hospital systems in southeast Michigan (Michigan Medicine Hospital, Ann Arbor and Henry Ford Hospital, Detroit). Inclusion criteria for the HAIVEN cohort have been described elsewhere. 15, 19 Briefly, adult patients ≥18 years of age were eligible for participation if presenting to the hospital within 10 days of symptom onset or worsening with a diagnosis or chief complaint broadly consistent with an acute respiratory illness such as influenza or pneumonia. Patients were prospectively recruited and completed a brief interview and research‐assistant performed specimen collection to determine laboratory‐confirmed influenza illness. Only individuals who tested positive for influenza were included in our analysis.
Participants were interviewed in‐person at the time of study enrolment. Data obtained from the electronic health record (EHR) via trained research staff chart review or database queries included demographics and comorbid conditions, acute illness characteristics such as symptom duration/type, laboratory and physiologic measures, and outcomes including ICU admission and hospital length of stay. 15, 19
## Physiologic characteristics of interest
Minimum and maximum values for physiologic and laboratory variables of interest were collected from the EHR for the first 24 h of hospitalization. Physiologic data included heart rate, respiratory rate, systolic blood pressure (SBP), temperature, and oxygen saturation. 20 *Laboratory data* included non‐fasting blood glucose, haematocrit, haemoglobin, blood urea nitrogen (BUN), sodium, pH, total white blood cell (WBC) count, creatinine, platelets, bilirubin, and lactic acid. 20 Estimated glomerular filtration rate (eGFR) was computed using the maximum creatinine value within the 24‐h window. 21
## Clustering of data
Variables were selected for clustering algorithm inclusion based on clinical relevance to indicating illness severity. Specific variables selected were as follows. Both minimum and maximum values were included unless otherwise specified: Temperature, heart rate (maximum), SBP, blood glucose, creatinine (maximum), haematocrit (minimum), sodium, WBC, platelets (minimum), respiratory rate (maximum), oxygen saturation (minimum), eGFR, and time from symptom onset to admission. Missing data for all selected physiologic measures were imputed using the study population mean stratified by age group (18–49, 50–64, 65+) and hospital. A table of selected metrics can be found in Table S1.
Prior to the creation of clusters, Hopkin's statistic was used to assess the randomness of the distribution of the data in relation to a uniform distribution. Values of.5 for this statistic indicate data are similar to the univariate distribution, while values closer to 1 indicate the data may contain clusters. The use of this statistic helps reduce the risk of a machine learning algorithm detecting clusters when the data do not actually have clusters within. 22 Data were classified separately for each influenza season using the k‐medoids partitioning around the medoids (PAM) algorithm with Manhattan distance. Briefly, k‐medoids clustering assigns groups to a set of data based on the distance to an assigned central data point of a cluster. 23 To start, these medoids are randomly assigned, and the algorithm iterates through selection of data centroids and cluster groupings until the distance from the centroid is minimized to all other data points in the cluster. K‐medoids clustering is more robust in the presence of outliers than other centroid‐based clustering algorithms such as k‐means because the chosen centroid is an observed data point. Additionally, this algorithm assigns all data observations to a cluster; this is preferred in a cohort of hospitalized individuals where biologically plausible data outliers are of interest. The appropriate number of clusters to be assigned for a given season was chosen using the largest average silhouette width, a measure of the distance from points in one cluster to another, with a maximum of 10 clusters tested.
The k‐medoids clustering was performed using the “pam” function in R. Following group assignment, the silhouette width of each cluster was computed using the “silhouette” function. An average silhouette width close to 1 indicates perfect clusters, and an average silhouette width around 0 indicates clusters lie close together. A negative silhouette width for a given observation indicates that the data point may have been misclassified.
## Additional covariates
Additional covariates for adjusted analyses included age group (18–49, 50–64, 65+ years), sex, BMI, Charlson Comorbidity Index (CCI), admitting hospital, influenza strain and subtype, and influenza vaccination status.
## Hospitalization severity metrics
Outcomes considered for severity of illness during the hospitalization included intensive care unit (ICU) admission, mechanical ventilator use, and total hospital length of stay (continuous, and prolonged defined as ≥8 days 24).
## Statistical analysis
General descriptive statistics were computed separately for each influenza season ($\frac{2017}{2018}$ and $\frac{2018}{2019}$) and were reported as means with standard deviations, medians with interquartile range, or frequency and percentage, as appropriate. The normality of data and presence of outliers were assessed using histograms and box‐and‐whisker plots. Data clustering was performed as above within each year using the PAM algorithm. Characteristics between clusters were compared using Chi‐squared or Fisher's exact tests for categorical variables and independent t tests, ANOVA, Mann–Whitney U, or Kruskal–Wallis tests for continuous variables, as appropriate.
To determine if different classes of early hospitalization characteristics were associated with severe hospital sequelae, a series of models were constructed separately for each influenza season. For binary outcomes (ICU admission, ventilator use, and prolonged hospital length of stay), Firth's logistic regression models were constructed. Generalized linear models were used for the continuous outcome of hospital length of stay. Variables chosen a priori for model inclusion were k‐medoids cluster, age, sex, CCI (continuous), hospital, and influenza vaccination status. An exploratory analysis was conducted as above with the removal of outliers prior to clustering, with an outlier conservatively defined as a value <1st quartile‐1.5 * (interquartile range) or >3rd quartile + 1.5 * (interquartile range). Outliers were then imputed to the mean of remaining values stratified by age group and hospital. To maintain comparability with the primary analysis, the same number of clusters were implemented within a given influenza year.
Analysis was conducted using RStudio version 1.2.5042 and SAS v9.4 (SAS Institute, Cary, NC). A P value of.05 was considered statistically significant.
## RESULTS
There were 242 individuals who met inclusion criteria from the $\frac{2017}{2018}$ influenza season and 115 individuals from the $\frac{2018}{2019}$ season (Table S2). Overall, patients were predominantly female, obese, and vaccinated against influenza. More individuals experienced ICU admission ($13.0\%$ vs. $6.2\%$; $$P \leq .031$$) and mechanical ventilation ($16.5\%$ vs. $9.9\%$; $$P \leq .074$$) in the $\frac{18}{19}$ influenza season than the preceding season. Of those with a reported vaccination type, $\frac{56}{147}$ ($38.1\%$) in $\frac{17}{18}$ and $\frac{19}{73}$ ($26.0\%$) in $\frac{18}{19}$ received the trivalent vaccine. Overall characteristics of values included in the clustering algorithm are shown in Table 1.
**TABLE 1**
| Unnamed: 0 | 2017/2018 season (N = 242) | 2018/2019 season (N = 115) |
| --- | --- | --- |
| Clustering metrics measured within 24 H of admission | Clustering metrics measured within 24 H of admission | Clustering metrics measured within 24 H of admission |
| Time from symptom onset to admission (days) | 2.4 (2.1) | 2.6 (2.0) |
| Temperature | | |
| Min | 97.9 (0.6) | 98.1 (0.6) |
| Max | 100.0 (1.5) | 100.1 (1.5) |
| Heart rate (max) | 107.0 (18.3) | 110.7 (19.2) |
| Systolic blood pressure | | |
| Min | 111.5 (16.7) | 105.1 (15.6) |
| Max | 137.0 (30.3) | 126.3 (32.3) |
| Glucose | | |
| Min | 117.0 (49.1) | 118.2 (56.3) |
| Max | 159.5 (83.5) | 175.0 (112.4) |
| Creatinine (max) | 1.3 (1.6) | 1.7 (2.1) |
| Haematocrit (min) | 35.6 (5.4) | 35.6 (6.2) |
| Sodium | | |
| Min | 135.7 (3.1) | 135.3 (4.0) |
| Max | 138.1 (3.3) | 138.1 (3.9) |
| White blood cells | | |
| Min | 6.5 (3.7) | 7.6 (8.4) |
| Max | 8.6 (7.6) | 10.2 (12.9) |
| Platelets (min) | 178.6 (67.2) | 193.8 (119.4) |
| Respiratory rate (max) | 24.2 (5.5) | 26.2 (8.2) |
| Oxygen saturation (min) | 91.6 (4.4) | 90.6 (5.0) |
| Estimated glomerular filtration rate | 75.0 (39.8) | 73.6 (40.8) |
## 2017/2018 cohort
The Hopkin's statistic for $\frac{2017}{2018}$ was 0.810. The 3‐cluster model was selected with the highest average silhouette width of 0.15 (Figure 1). The silhouette plots indicate the possibility of minor misclassification of some individuals. There were significant differences in race, age, CCI, and diabetes between clusters (Table 2). For the variables included in the PAM algorithm, those in Cluster 1 for the $\frac{2017}{2018}$ season (C171) had significantly higher mean glucose (minimum mean 210.4 mg/dL, SD 66.9) than those in Cluster 2 (C172) (minimum mean 90.5 mg/dL, SD 16.4) and Cluster 3 (C173) (minimum mean 110.0 mg/dL, SD 26.7). Those in C172 had significantly lower maximum heart rate, maximum systolic blood pressure, minimum white blood cell count, minimum platelets, and estimated glomerular filtration rate than the other two clusters. Those in C172 also had significantly higher maximum creatinine than the other clusters. The rate of being mechanically ventilated was higher in C171 than C172 ($22.6\%$ vs. $6.9\%$), and the overall hospital length of stay was longer for those in C171 than C173 (mean 4.5 days [SD 4.4] vs. mean 2.8 days [SD 2.4]).
**FIGURE 1:** *Clustering metrics for the $\frac{2017}{2018}$ influenza season, including the silhouette plot of k‐medoids clusters (A) and the top two principal components of data in the k‐medoids clustering algorithm (B), with cluster membership highlighted.* TABLE_PLACEHOLDER:TABLE 2 After adjustment for age group, sex, hospital, continuous CCI, and influenza vaccination status, those in C171 had 5.6 times the odds of having a mechanical ventilator than those in C172 ($95\%$ CI: 1.49, 21.1; Figure 2). Additionally, those in C171 had a significantly longer model‐adjusted mean hospital length of stay than those in both C172 (mean 1.5 days longer, $95\%$ CI: 0.2, 2.7) and C173 (mean 1.4 days longer, $95\%$ CI: 0.3, 2.5). There were no significant differences between clusters for the outcomes of ICU stay or prolonged hospital stay. Vaccination status was not associated with adverse outcomes in the fully adjusted models.
**FIGURE 2:** *Adjusted odds ratios (A,C) and difference in model‐adjusted means (B,D) with 95% confidence intervals for outcomes. * indicates statistically significant differences between comparison groups. Models were adjusted for age, sex, hospital, continuous CCI, and influenza vaccination status.*
## 2018/2019 cohort
The Hopkin's statistic for $\frac{2018}{2019}$ was 0.837. The 2‐cluster model was selected with the highest average silhouette width of 0.27 (Figure 3). The silhouette plots indicate the possibility of moderate misclassification of some individuals in Cluster 1 for the $\frac{2018}{2019}$ season (C181). There were significant differences in underlying comorbidity between clusters, though there were no major differences in demographics (Table 2). For the variables included in the PAM algorithm, those in C181 had significantly higher mean glucose (minimum mean 157.2 mg/dL, SD 82.2) than those in Cluster 2 (C182) (minimum mean 99.7 mg/dL, SD 21.4). Additionally, those in C181 had significantly lower minimum oxygen saturation than those in C182 (mean 88.5 [SD 6.7] vs. mean 91.6 [SD 3.7]). The C181 group had higher rates of mechanical ventilation ($32.4\%$ vs. $9.0\%$) and prolonged hospital stay ≥8 days ($16.2\%$ vs. $3.9\%$) than those in C182, as well as a longer hospital stay (mean 5.3 days [SD 5.7] vs. mean 2.8 days [SD 2.0]).
**FIGURE 3:** *Clustering metrics for the 2018/2019 influenza season, including the silhouette plot of k‐medoids clusters (A) and the top two principal components of data in the k‐medoids clustering algorithm (B), with cluster membership highlighted.*
After adjustment for age group, sex, hospital, continuous CCI, and influenza vaccination status, those in C181 had 4.9 times the odds of being mechanically ventilated than those in C182 ($95\%$ CI: 1.7, 14.3; Figure 2), and 4.3 times the odds of having a prolonged hospital stay ($95\%$ CI: 1.2, 16.4). After adjustment, those in C181 had a significantly longer model‐adjusted mean hospital length of stay than those in C182 (mean 2.5 days longer, $95\%$ CI: 1.1, 3.9). Vaccination status was not associated with adverse outcomes in the fully adjusted models.
## Sensitivity analysis
Imputation of outliers in both influenza cohorts and their effect on the sample are shown in Table S3. After model adjustment with the new clusters, there were no statistically significant differences in the odds or model‐adjusted means of outcomes for the $\frac{2017}{2018}$ influenza season (Figure S1). For the $\frac{2018}{2019}$ influenza season, after adjustment those in new cluster 1 had significantly higher odds of an intensive care unit stay compared with those in new cluster 2 (OR 4.62, $95\%$ CI: 1.34, 15.97; Figure S1).
## DISCUSSION
In this cohort of individuals in the $\frac{2017}{2018}$ and $\frac{2018}{2019}$ influenza seasons, we created clinically meaningful groups using k‐medoids clustering to improve the analysis of severity in a population of patients hospitalized with influenza. Our results suggest that those who were in clusters with hyperglycaemia and lower oxygen saturation at admission had higher risk of adverse in‐hospital sequelae and are thus potential cohorts of interest for further study of vaccine or antiviral effects.
We found glucose to be significantly different between clusters, with one cluster having significantly higher glucose in both years. The distribution of diabetes was also consistent across years, with approximately $70\%$ prevalence in the high‐glucose clusters and $30\%$ prevalence in the non‐hyperglycaemic clusters. Together, these results highlight that the use of simple dichotomous classifications for complex conditions such as diabetes may not accurately indicate a patient's risk for adverse outcomes. Indeed, controlling for such complex confounding has long been problematic within infectious disease severity research, most recently when examining treatments and hospital outcomes related to infection with SARS‐CoV‐2, leading to inconsistent results. 25, 26, 27 This challenge is due in part to differential measurement and management of confounding, including analyses at the point of hospitalization admission, given model limitations in the number of confounders that can be included, and their often‐complex interrelationships. The use of techniques such as k‐medoids clustering to simultaneously account for multiple measures of comorbidity and group like patients together independent of outcomes‐based analysis provides a tool to increase homogeneity within groups and heterogeneity across groups for a more robust confounding adjustment.
More traditional dimensional reduction methods such as the use of propensity score matching have often been used to account for differential patterns of comorbidities between groups of interest. While propensity score matching is useful in reducing heterogeneity in the presence of a single exposure of interest, it becomes complex in instances where multiple treatments or exposures are being compared simultaneously. Additionally, there is inherent reduction in sample size when matching, limited by the number of individuals with and without the exposure having similar propensity scores; individuals in either group with uncommon comorbidity profiles may be overlooked and excluded from the matching if their propensity score does not align. For example, a 2020 study by Groeneveld et al examining the effective of oseltamivir lost $36\%$ of oseltamivir patients and $65\%$ of controls when matching, reducing the sample size to 88 pairs. 6 While use of propensity score matching has been shown to reduce bias, 28 such significant loss of data, especially in a rare‐outcomes setting, may lead to an increase in Type II error, and thus incorrect conclusions, due to inadequate power. 29, 30 K‐medoids clustering can be used to identify subgroups that are biologically different without such restrictions, maintaining sample size for more robust analysis of effect modification by multiple treatment types. It should be noted that outliers within the range of biologically normal values are of great clinical significance, as these individuals may be at higher risk for adverse outcomes. K‐medoids clustering is robust to such outliers through use of data‐derived centroids for the clusters, rather than an arbitrary mean.
This study has several strengths, most notably that the cohort was nested within a large prospective two‐centre study of influenza vaccine effectiveness across multiple seasons, allowing for a robust and diverse analytic cohort. Both case definition and EHR data capture were standardized across sites, reducing heterogeneity of data quality. Additionally, the use of two hospitals within our region allowed for a more generalizable analysis. The biggest limitation of the study is small sample size and small number of outcomes, and due to missing data we were unable to adjust for vaccination type in modelling; however, we believe our analysis has minimized some of the bias from these limitations. Finally, while the k‐medoids clusters presented here may not generalizable to other cohorts, the methodology has many direct and current applications in severity analysis.
One of the most immediate applications can be for evaluating the effectiveness of new and existing antivirals for severe respiratory disease. Previous studies of such treatments have utilized traditional methods of covariate adjustment, which may contribute to heterogeneity of study findings. 31 The use of this clustering method to phenotype baseline presentation can reduce this confounding and can be quickly implemented for these analyses. Such a technique will be needed as we continue understand how new antiviral treatments affect severity, and how vaccination impacts severity in instances of low vaccine effectiveness.
## CONCLUSIONS
In conclusion, we found it was possible to cluster adult patients hospitalized with influenza into clinically distinct groups by baseline characteristics independent of a clinical outcome. Those with hyperglycaemia and lower oxygen saturation at admission were more likely to experience adverse events in our cohort, including prolonged hospitalization. The k‐medoids algorithm is a promising approach to disentangling the heterogeneity surrounding hospital admissions.
## AUTHOR CONTRIBUTION
Aleda M. Leis: Conceptualization (equal), formal analysis (lead), methodology (lead), software (lead), writing—original draft preparation (lead), writing—review and editing (equal). Erin McSpadden: Data curation (supporting), writing—review and editing (equal). Hannah E. Segaloff: Conceptualization (equal), methodology (supporting), writing—review and editing (equal). Adam S. Lauring: Data curation (supporting), writing—review and editing (equal). Caroline Cheng: Data curation (lead), formal analysis (supporting), software (supporting), writing—review and editing (equal). Joshua G. Petrie: Conceptualization (supporting), writing—review and editing (equal). Lois E. Lamerato: Data curation (supporting), writing—review and editing (equal). Manish Patel: Conceptualization (equal), methodology (supporting), writing—review and editing (equal). Brendon Flannery: Conceptualization (supporting), methodology (supporting), writing—review and editing (equal). Jill Ferdinands: Conceptualization (equal), data curation (supporting), writing—review and editing (equal). Carrie A. Karvonen‐Gutierrez: Conceptualization (supporting), supervision (supporting), writing—review and editing (equal). Arnold Monto: Conceptualization (supporting), writing—review and editing (equal). Emily T. Martin: Conceptualization (equal), funding acquisition (lead), methodology (supporting), supervision (lead), writing—original draft preparation (supporting), writing—review and editing (equal).
## CONFLICT OF INTEREST STATEMENT
ASL reports receiving research funding from the Centers for Disease Control and Prevention, National Institutes for Health, Burroughs Wellcome Fund, and FluLab, and receiving consulting fees from Sanofi (oseltamivir) and Roche (baloxavir) outside of submitted work. LEL reports receiving funding from the Centers for Disease Control and Prevention. AM reports receiving research funding from the Centers for Disease Control and Prevention. ETM reports receiving research funding from the Centers for Disease Control and Prevention and grant funding from Merck.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1111/irv.13120.
## DATA AVAILABILITY STATEMENT
Data are available upon reasonable request to study investigators.
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|
---
title: Usefulness of phase angle on bioelectrical impedance analysis as a surveillance
tool for postoperative infection in critically ill patients
authors:
- Gyeo Ra Lee
- Eun Young Kim
journal: Frontiers in Medicine
year: 2023
pmcid: PMC9992789
doi: 10.3389/fmed.2023.1111727
license: CC BY 4.0
---
# Usefulness of phase angle on bioelectrical impedance analysis as a surveillance tool for postoperative infection in critically ill patients
## Abstract
### Purpose
Bioelectrical impedance analysis (BIA) has advantages of obtaining results quickly, safely, reproducibly, and non-invasively. Phase angle (PhA) is one of the parameter of BIA, its values represent the permeability or integrity of cell membrane. With the exception of C-reactive protein (CRP), few studies have estimated an association between PhA and these conventional biomarkers. Herein, we aimed to investigate the association between the PhA value and the conventional inflammatory markers in postoperative patients in intensive care unit (ICU). Also, the correlation between the change in PhA and the occurrence of infectious complication were determined.
### Methods
From July 2020 to February 2022, retrospective observation study conducted in 221 patients who admitted to ICU after abdominal surgery. BIA measurements and blood sampling were routinely performed the next morning. The relationship between PhA and the inflammatory markers were assessed after adjusting for age and body mass index. Univariate and multivariate logistic regression analysis was performed to examine the predisposing factors for postoperative infections.
### Results
Among 221 patients admitted to ICU after abdominal surgery, infectious complications occurred in 62 cases. CRP, procalcitonin, or presepsin levels were negatively correlated with PhA in both gender. ( −0.295, −0.198 or −0.212 of partial correlation coefficients, respectively in males, and 0.313, −0.245 or −0.36 of partial correlation coefficients, respectively in females) But, white blood cell did not show significant association with PhA in both genders. For males, increased level of CRP on postoperative day 1 (POD1) was revealed as the significant predicting factor for postoperative infectious complication [odds ratio (OR): 1.184, $95\%$ confidence interval (CI): 1.090–1.285, $p \leq 0.001$]. For females, increased Acute Physiology and Chronic Health Evaluation II score at admission (OR: 1.457, $95\%$ CI: 1.068–1.987, $$p \leq 0.018$$), increased level of presepsin on (OR: 1.003, $95\%$ CI: 1.001–1.006, $$p \leq 0.016$$) and decreased value of PhA on POD1 (OR: 0.980, $95\%$ CI: 0.967–0.993, $$p \leq 0.003$$) were revealed as the significant predicting factors.
### Conclusion
Phase angle obtained through BIA can be used as a predictor of infection as it shows a significant association with inflammatory markers. Phase angle measurements through BIA could improve patient prognosis after abdominal surgery through the careful observation of infections and early, appropriate treatment.
## Introduction
Postoperative infections frequently occur in patients who undergo abdominal surgery, [1, 2] and its rate is relatively high, ranging from about $20\%$ to $40\%$. Various conventional markers such as C-reactive protein (CRP) and white blood cell (WBC) counts are commonly used to detect infections in clinical practice, and recently, some novel markers such as procalcitonin (PCT) and presepsin were also proposed. However, those markers need blood sampling to obtain the results, and the results cannot be confirmed in real-time. Additionally, there is a decisive limitation in that it is impossible to perform the laboratory tests of these markers in all hospitals because special equipment and professional personnel are essential to obtaining the results.
Bioelectrical impedance analysis (BIA) measures the resistance and reactance of body components by recording the voltage drop according to a given electric current [3, 4]. The phase angle (PhA) is a parameter obtained from BIA through the relationship between the reactance and resistance of body tissues and represents the permeability or integrity of the cell membranes as a biological marker of cellular health [5, 6]. A previous study reported that lower PhA values in critically ill hospitalized patients were associated with increased mortality rates and complications [7, 8]. Pena et al. [ 9], Barros et al. [ 10], and Roccamatisi et al. [ 11] reported that low preoperative PhA values measured by BIA were associated with increased rates of infections after elective surgery. Several studies have reported a relationship between the PhA and various inflammatory markers (12–17). Moreover, the PhA from BIA is more advantageous than conventional markers as a diagnostic tool because the results are obtained safely, non-invasively, and more quickly. However, few studies have estimated an association between the PhA and conventional markers in postoperative patients who are vulnerable to infectious complication.
Herein, we aimed to investigate the association between the PhA value obtained through BIA surveillance and the conventional inflammatory markers in the acute phase of postoperative patients in the intensive care unit (ICU). Also, the relationship between changes in the PhA value during the postoperative period and the occurrence of infections was determined.
## Study design and patient enrollment
From July 2020 to February 2022, we performed a prospective observational study in a 22-bed ICU of a single tertiary hospital. All patients aged over 18 years who were admitted to the ICU after abdominal surgery performed under general anesthesia were eligible for inclusion, regardless of the surgical technique, such as laparotomy, laparoscopic or robotic surgery. If the patient underwent endovascular surgery or percutaneous transluminal angioplasty, they were excluded from enrollment. However, cases of open thrombectomy or emergent exploration due to abdominal aortic aneurysmal rupture were enrolled. Patients who met any one of these criteria were excluded from study enrollment: [1] those who had any contraindication or significant confounders of BIA, such as any prosthetic medical devices including an implanted cardiac defibrillator, pacemaker, or metallic intravascular device, or any bone fixation implants or limb amputation; [2] pregnant women; [3] those who underwent extracorporeal membrane oxygenation treatment before surgery; [4] those who were readmitted within 48 h after discharge from the ICU or died within 72 h after surgery; [5] those who were admitted to the ICU only for medical causes without surgery, and [6] those lacking or missing essential BIA data. Patients such as those receiving hemodialysis for end-stage renal disease or severe acute kidney injury that could be significant confounders of laboratory inflammation tests such as presepsin [18, 19], were also excluded from the study analysis. Written informed consent was obtained from each patient and the recruiting data, including demographics, disease profile, and laboratory results, were reviewed retrospectively. This study was approved and carefully monitored by our Institutional Review Board (No. IRB; KC22RISI0346), and was performed in accordance with the 1964 Declaration of Helsinki and its later amendments.
## BIA measurement
BIA measurements were routinely performed the morning after the patients were admitted postoperatively to the ICU. The body composition status was assessed using a commercial portable BIA device with 50-kHz alternating current (InBody S10®, InBody Corp., Seoul, Korea), [20] and was designed using touch- or adhesive-type electrodes attached to four limbs, as described in a study of Lee et al. [ 20] (Figure 1). This device has an intrinsic impedance of 1.6 Ω when measured at 50 kHz. However, since the electrode-skin contact impedance appears very different depending on the skin condition, it is difficult to calculate an absolute value or absolute range. All participants had refrained from eating or drinking for 6 hours before BIA measurement. And, participants are placed in a supine position, with the extremities in a relaxed position. Two pairs of electrodes were placed, hand electrode are inserted to thumb and middle finger, and foot electrodes are placed between the malleolus and the heel. Regarding the environmental condition that could affect the BIA value, the ICU of our institution is specially controlled to maintain a relatively constant environment at a temperature of 24°C and humidity between $35\%$ and $40\%$ [21]. To minimize measurement errors, BIA measurements were performed by the same well-trained physician and supervised by another senior physician. The time required for BIA measurement was about 2 min for each patient, and the body composition status data were immediately analyzed and printed in real-time. For each patient, the following BIA parameters were obtained: intracellular water, extracellular water (ECW), total body water, ECW ratio, which was defined as the ratio of ECW to total body water, whole-body and segmental PhA, impedance, and reactance. PhA was defined as the physiological index of cell membrane integrity and vitality to reflect the quantity and quality of the soft tissues. *In* general, a higher PhA value indicates greater cellularity, cellular function, and cell membrane integrity [10]. It was calculated using the following formula:
**Figure 1:** *Electrodes placement for bioelectrical impedance analysis.*
(where Ø is the phase angle, *Xc is* reactance, and R is resistance). The coefficient of variation (CV) of repeated R and Xc measurements at 50 kHz was assessed in 10 patients (7 males and 3 females) by the same physician. The CVs were $0.37\%$ for R and $1.49\%$ for Xc.
## Other data variables and clinical outcome assessment
Laboratory tests on blood samples obtained from all participants were routinely conducted at the same time as the BIA measurements, and inflammatory markers such as CRP, PCT, and presepsin were also measured. The following data were obtained from the electronic medical records: demographics, surgical profiles, including the site of surgery, disease characteristics, and severity index using Acute Physiology and Chronic Health Evaluation II (APACHE II) and sequential organ failure assessment (SOFA) scores at ICU admission, and the presence of shock. Any development of postoperative morbidities during hospitalization was monitored and recorded. Postoperative morbidities were classified from grade 0 to 5 according to the Clavien-Dindo classification [22]. According to the definition by the “Bulletin of the American College of Surgeons,” [23] infection complications included operative wound dehiscence with openings greater than 3 cm, surgical site infections defined by the Centers for Disease Control guidelines for the prevention of SSI, pneumonia, bacteremia, urinary tract infection, sepsis or septic shock, and a sustained fever over 38°C with infection. A simple hematoma or seroma, such as an SSI that did not require any additional treatment, was not considered an infection-related morbidity. Postoperative mortality was defined as mortality within 30 days of surgery or within the same hospitalization as the surgery.
## Statistical analysis
All statistical analyses were conducted using SPSS statistical package software (version 24.0 for Windows; SPSS, Inc., Chicago, IL, USA). Continuous data are presented as the mean ± standard deviation, and the overall differences were tested by the Student’s t-test or analysis of variance. Whether the variables were normally distributed was tested using the Kolmogorov–Smirnov test, and in the case of variables that were not normally distributed, a nonparametric test was performed using the Mann–Whitney test. The sample size was obtained through the Bland & Altman method. The probability of rejecting the null hypothesis as known as a type I error was set to 0.05, and the probability of accepting the null hypothesis when in fact it is false as known as type II error was set to 0.20. For males, a minimum required size of calculated sample was smaller than our recruited population of 132, and for females, it was smaller than our population of 89. The categorical variables were calculated using Fisher’s exact test or Chi-squared (χ2) test. The relationship between the PhA on BIA and inflammatory markers such as CRP, PCT, or presepsin measured by blood tests, was assessed using Pearson’s correlation coefficients (r) after adjusting for age and body mass index (BMI). Linear regression analysis was used to assess the relationship between the PhA on BIA and inflammatory markers, and the Bland–Altman plot was used to assess the agreement between those parameters. The differences were regarded as statistically significant for p-values <0.05. Differences in the incidence of postoperative infections were analyzed according to the PhA value on BIA measured postoperative day 1 (POD1). Univariate logistic regression analysis was performed to examine the predisposing factors of postoperative infections, and based on these, the significantly correlated variables were analyzed by multivariate logistic regression.
## Results
During the study period, a total of 606 patients were admitted to the ICU after abdominal surgery. As shown in Figure 2, a total of 221 patients were finally analyzed according to our inclusion criteria. Given that the normal reference range of BIA data differs according to gender, we divided the patients into males (132 patients, $59.7\%$) and females (89 patients, $40.3\%$), and analyzed the results, respectively. The patients were also subdivided into groups with infections (IC group) and without infections (NC group). As shown in Table 1, there was no significant difference in age, BMI, or underlying disease between the IC group and the NC group in either gender. For females, disease severity represented by SOFA score or APACHE II score was significantly higher in the IC group than in the NC group ($$p \leq 0.001$$ and $p \leq 0.001$, respectively). Regarding the inflammatory markers measured on the first POD1, the WBC count did not differ between the IC group and the NC group. However, CRP and PCT levels were significantly higher in the IC group than in the NC group. Presepsin levels were also significantly higher in the IC group of females. In the comparison of BIA measurements, in males, there were no significant differences between the two groups, but the PhA was slightly lower in the IC group than in the NC group (4.22 ± 1.92 vs. 4.68 ± 1.07, $$p \leq 0.085$$). The value of p was greater than 0.05 but within the upper limit of 0.1. In females, the PhA was significantly lower in the IC group than in the NC group (3.18 ± 1.02 vs. 4.25 ± 1.02, $p \leq 0.001$). Also, ECW and the ECW ratio were significantly higher in the IC group. Regarding clinical outcomes, the length of hospital stay was significantly longer in the IC group of males. In females, the length of mechanical ventilation, length of ICU stay and length of hospital stay was significantly longer in the IC group.
**Figure 2:** *Schematic diagram of study enrollment. *IC group: group with infections; ICU: intensive care unit; NC group: group without infections.* TABLE_PLACEHOLDER:Table 1
## Relationship between the PhA measured at 50 kHz on BIA and various parameters
Bivariate correlation analysis was performed on the association between PhA measured at 50 kHz on BIA and various variables by gender, and the correlations between the variables were similar regardless of gender. Firstly, anthropometric parameters, such as BMI, were positively correlated with the PhA in both genders, whereas age was negatively correlated with the PhA. Additionally, inflammatory markers, such as CRP, PCT, and presepsin levels, were also negatively correlated with the PhA (−0.262, −0.22, and −0.22 correlation coefficients, respectively, in males, and −0.361, −0.277, and −0.347 correlation coefficients, respectively, in females). WBC counts were not associated with PhA values. After adjusting for age and BMI, a partial correlation analysis was performed, as shown in Table 2. CRP, PCT, and presepsin levels were negatively correlated with the PhA in both genders (−0.295, −0.198, and −0.212 partial correlation coefficients, respectively, in males, and 0.313, −0.245, and −0.36 partial correlation coefficients, respectively, in females). WBC counts were not significantly associated with the PhA in either gender. Also, compared inflammatory markers and phase angle on BIA through the Bland and Altman plot (Figures 3, 4). All of them were distributed between the $95\%$ limit of agreement. For all variables except for presepsin, it was uniformly distributed around the bias, the plot shows a significant correlation between inflammatory marker and phase angle on BIA. Only presepsin showed an upward sloping pattern, but since there are values between the $95\%$ limit of agreement limit, it is impossible to assured that there is no correlation.
## Determination of predictive factors of postoperative infections
Table 3 demonstrates the results of logistic regression analysis for infections that occurred after surgery in males. After univariate analysis, the increased CRP levels on POD1 and increased PCT levels on POD1 were significant predictive factors for infections in males admitted to the ICU after surgery. After multivariate analysis, only increased levels of CRP on POD1 were revealed as significant predictive factors for postoperative infections in males [odds ratio(OR) = 1.184, $95\%$ confidence interval (CI): 1.090–1.285, $p \leq 0.001$]. In females, as shown in Table 4, increased SOFA scores and APACHE II scores at admission, and increased CRP, PCT, and presepsin levels on POD1 were significant factors in univariate analysis. Decreased PhA values on POD1 (OR = 0.354, $95\%$ CI: 0.206–0.608, $p \leq 0.001$) were significant factors in univariate analysis. After multivariate analysis, increased APACHE II scores at admission (OR = 1.457, $95\%$ CI: 1.068–1.987, $$p \leq 0.018$$), increased presepsin levels on POD1 (OR = 1.003, $95\%$ CI: 1.001–1.006, $$p \leq 0.016$$), and decreased PhA values on POD1 (OR = 0.980, $95\%$ CI: 0.967–0.993, $$p \leq 0.003$$) were revealed as significant predictive factors for post-surgical infections in females.
## Discussion
Our results showed that the PhA on BIA was strongly negatively correlated with CRP, PCT, and presepsin in both males and females who underwent abdominal surgery. In multivariate analysis, decreased PhA values on POD1, increased APACHE II scores at admission, and increased presepsin levels on POD1 were revealed as significant predictors of post-surgical infections in females. In males, only increased CRP levels on POD1 were a significant predictor of infections.
BIA is a tool that can indirectly identify cellular damage by assessing the quality of whole-body cell membranes. Among the parameters of BIA, the PhA has been reported to be closely related to the degree of inflammation in vivo [6, 24]. Oxidative stress resulting from an imbalance between oxidants and antioxidants, with increased reactive oxygen species, occurs in severe inflammation. It can lead to cellular injury by damaging cellular components such as proteins or lipids. These alterations can cause the cellular membrane to rupture, and the breakdown of the membrane’s phospholipid structure causes the transformation of cell shape and fluid imbalance by promoting the migration of intracellular water molecules to the extracellular environment. As a result, the ECW ratio is increased due to a fluid shift from intra cellular water to ECW, and this causes a decrease in cell mass that eventually leads to a decrease in the PhA value. Thus, a high PhA value might indicate a high proportion of healthy cell membranes, and conversely, low PhA could be associated with cell death or decreased cell integrity [25, 26]. In fact, the low PhA observed after surgery that associated with cell loss and cell integrity was also described in the study of Petro et al. [ 27]. Therefore, low PhA values are expected to sensitively detect oxidative stress, which is closely related to inflammation. It is noteworthy that oxidative stress may be more pronounced in infectious environments with severe inflammation, where typically, the host’s metabolic response is increased, resulting in the increased production of oxygen metabolites. In fact, our results also showed a significant correlation between PhA values and various inflammatory markers, such as CRP, PCT, and presepsin, commonly used to detect infections in clinical practice. As shown in Table 5, PhA values were significantly lower in the IC group with postoperative infections compared to the NC group without infections, regardless of gender. Additionally, in univariate analysis, PhA values were negatively associated with postoperative infections in both genders, and these values were a significant predictor of post-surgical infections in females. Therefore, we expect that the PhA value on BIA could be useful as a monitoring tool to detect infections accompanied by severe inflammation after abdominal surgery, where oxidative stress is increased due to severe inflammation and enhancement of the immune response and metabolic processes in the body.
**Table 5**
| POD 1 | Males (n = 132, 59.7%) | Males (n = 132, 59.7%).1 | Males (n = 132, 59.7%).2 | Females (n = 89, 40.3%) | Females (n = 89, 40.3%).1 | Females (n = 89, 40.3%).2 |
| --- | --- | --- | --- | --- | --- | --- |
| POD 1 | + (n = 37, 28%) | − (n = 95, 72%) | p-value | + (n = 25, 28%) | − (n = 64, 72%) | p-value |
| Laboratory markers | | | | | | |
| WBC * 103/mL | 12.38 ± 7.42 | 14.32 ± 7.67 | 0.186a | 10.38 ± 6.33 | 12.29 ± 5.33 | 0.189a |
| C-reactive protein (mg/dl) | 12.07 ± 9.57 | 4.68 ± 3.65 | <0.001a | 12.77 ± 10.16 | 4.91 ± 3.65 | <0.001a |
| Procalcitonin (ng/mL) | 12.32 ± 19.44 | 4.03 ± 14.01 | 0.007a | 18.72 ± 33.46 | 0.87 ± 1.26 | <0.001a |
| Presepsin (pg/mL) | 731.6 ± 465.6 | 711.4 ± 731.9 | 0.851a | 1,253.8 ± 1,754.5 | 433.4 ± 322.2 | <0.001a |
| BIA data | | | | | | |
| Phase angle _ WB (°) | 4.36 ± 1.29 | 4.63 ± 1.4 | 0.288a | 3.18 ± 1.02 | 4.25 ± 1.02 | <0.001a |
| Phase angle _ RA (°) | 4.52 ± 3.75 | 4.33 ± 1.32 | 0.775a | 3.1 ± 0.88 | 3.65 ± 0.74 | 0.009 |
| Phase angle _ LA (°) | 3.83 ± 1.08 | 4.17 ± 1.21 | 0.13a | 2.89 ± 0.68 | 3.65 ± 0.62 | <0.001a |
| Phase angle _ TR (°) | 3.88 ± 1.49 | 4.12 ± 1.68 | 0.428 | 3.44 ± 1.27 | 3.92 ± 1.41 | 0.128 |
| Phase angle _ RL (°) | 4.65 ± 1.32 | 5.18 ± 2.01 | 0.08a | 3.24 ± 1.49 | 5.15 ± 2.21 | <0.001a |
| Phase angle _ LL (°) | 4.37 ± 1.46 | 5.09 ± 1.99 | 0.024a | 3.11 ± 1.42 | 5.03 ± 2.19 | <0.001a |
| ECW (L) | 14.6 ± 2.6 | 15.3 ± 2.5 | 0.207a | 12.5 ± 1.7 | 11.5 ± 1.6 | 0.023 |
| ICW (L) | 22.1 ± 4.7 | 23.5 ± 4.2 | 0.124a | 17.6 ± 2.1 | 17.7 ± 2.5 | 0.870 |
| ECW ratio | 0.39 ± 0.02 | 0.39 ± 0.01 | 0.194 | 0.41 ± 0.02 | 0.39 ± 0.02 | <0.001a |
| At 36 h postoperatively | + (n = 18, 41.9%) | − (n = 25, 58.1%) | p-value | + (n = 8, 40%) | − (n = 12, 60%) | p-value |
| Laboratory markers | | | | | | |
| WBC * 103/mL | 11.67 ± 4.99 | 13.56 ± 5.12 | 0.063a | 12.62 ± 6.77 | 12.31 ± 6.42 | 0.914a |
| C-reactive protein (mg/dl) | 16 ± 7.95 | 14.33 ± 6.16 | 0.202 | 13.93 ± 7.64 | 10.71 ± 5.62 | 0.059a |
| Procalcitonin (ng/mL) | 9.74 ± 20.5 | 15.65 ± 110.35 | 0.181a | 9.28 ± 22.48 | 0.61 ± 0.85 | 0.173a |
| Presepsin (pg/mL) | 805.11 ± 897.57 | 719.61 ± 568.7 | 0.745a | 875.27 ± 1161.84 | 484.45 ± 309.3 | 0.217a |
| BIA data | | | | | | |
| Phase angle _ WB (°) | 3.79 ± 1.17 | 4.39 ± 1.31 | 0.123 | 3.74 ± 0.73 | 4 ± 0.72 | 0.437 |
| Phase angle _ RA (°) | 3.84 ± 1.16 | 4.3 ± 1.22 | 0.215 | 3.14 ± 0.83 | 3.6 ± 0.64 | 0.173a |
| Phase angle _ LA (°) | 3.69 ± 1.18 | 3.98 ± 1.11 | 0.433 | 3.29 ± 0.46 | 3.48 ± 0.53 | 0.405 |
| Phase angle _ TR (°) | 3.61 ± 1.97 | 3.66 ± 1.72 | 0.928 | 3.46 ± 1.29 | 3.44 ± 1.49 | 0.998 |
| Phase angle _ RL (°) | 3.74 ± 1.36 | 4.8 ± 1.94 | 0.42 | 3.93 ± 0.85 | 4.83 ± 1.18 | 0.080 |
| Phase angle _ LL (°) | 3.82 ± 1.32 | 4.62 ± 1.69 | 0.087 | 3.9 ± 1.04 | 4.68 ± 1.11 | 0.134 |
| ECW (L) | 15.9 ± 2 | 15.1 ± 2.5 | 0.209 | 12.4 ± 1.5 | 11.7 ± 2 | 0.377 |
| ICW (L) | 23.4 ± 3.9 | 22.6 ± 3.7 | 0.503 | 17.3 ± 1.7 | 17.9 ± 3 | 0.515 |
| ECW ratio | 0.41 ± 0.01 | 0.4 ± 0.02 | 0.157 | 0.41 ± 0.01 | 0.39 ± 0.01 | 0.151 |
| At 72 h postoperatively | + (n = 4, 44.4%) | − (n = 5, 55.6%) | p-value | + (n = 2, 50%) | − (n = 2, 50%) | p-value |
| Laboratory markers | | | | | | |
| WBC * 103/mL | 10.28 ± 5.07 | 12.3 ± 5.98 | 0.034a | 11.78 ± 6.77 | 10.22 ± 5.11 | 0.407a |
| C-reactive protein (mg/dL) | 12.87 ± 6.59 | 13.61 ± 6.54 | 0.571 | 13.98 ± 9.98 | 9.36 ± 4.78 | 0.115a |
| Procalcitonin (ng/mL) | 6.11 ± 12.88 | 15.24 ± 114.57 | 0.496a | 6.41 ± 15.58 | 0.38 ± 0.5 | 0.229 |
| Presepsin (pg/mL) | 817.6 ± 722.9 | 866 ± 773.9 | 0.741a | 1,059.67 ± 916.37 | 592.18 ± 413.51 | 0.025a |
| BIA data | | | | | | |
| Phase angle _ WB (°) | 2.32 ± 0.52 | 3.4 ± 0.58 | 0.053 | 2.68 ± 0.72 | 2.8 ± 0.7 | 0.859 |
| Phase angle _ RA (°) | 2.38 ± 0.44 | 3.06 ± 1.25 | 0.303 | 2.72 ± 0.82 | 2.25 ± 1.34 | 0.709 |
| Phase angle _ LA (°) | 2.55 ± 0.26 | 2.96 ± 0.86 | 0.359 | 2.44 ± 0.96 | 2.8 ± 0.57 | 0.577 |
| Phase angle _ TR (°) | 2.65 ± 0.92 | 3.24 ± 1.28 | 0.448 | 2.92 ± 0.82 | 3.2 ± 1.28 | 0.811 |
| Phase angle _ RL (°) | 2.15 ± 0.78 | 2.88 ± 1.48 | 0.376 | 2.6 ± 0.86 | 3.5 ± 0.1 | 0.079 |
| Phase angle _ LL (°) | 2.18 ± 0.51 | 2.72 ± 1.33 | 0.435 | 3.11 ± 1.42 | 2.5 ± 0.86 | 0.716 |
| ECW (L) | 14.9 ± 3.9 | 15.1 ± 1.9 | 0.931 | 13.6 ± 2.2 | 10.8 ± 1.4 | 0.132 |
| ICW (L) | 19.7 ± 4.7 | 21.8 ± 2.9 | 0.475 | 18.7 ± 2.4 | 15.3 ± 1.1 | 0.59 |
| ECW ratio | 0.43 ± 0.01 | 0.41 ± 0.01 | 0.051 | 0.42 ± 0.01 | 0.42 ± 0.01 | 0.657 |
Interestingly, our results demonstrated a significant negative association between PhA values and other inflammatory markers, but not with WBC counts. WBCs are inflammatory markers, but they are also increased in a variety of conditions, such as allergic disorders, parasitic infections, systemic autoimmune diseases, and aseptic inflammation. This non-specificity of WBCs, an indirect indicator of the degree of inflammation, may have been the reason no significant correlation with PhA values was found. However, other conventional markers including CRP, PCT, and presepsin showed significant associations with PhA. But, in order to obtain laboratory results, conventional inflammatory markers require blood sampling from the patient, and the results take more than an hour to receive. Furthermore, testing for these markers is not available in all healthcare facilities. Therefore, these limitations may reduce their usefulness as markers for the early diagnosis of postoperative infections. In contrast, the PhA on BIA can always be measured in a simple, easy, and non-invasive way, and the results can be obtained in just 5 minutes. As a result, when it is necessary to quickly diagnose postoperative infections, the measurement of PhA using BIA could avoid time-consuming, invasive, and unnecessary blood sampling, and could be used as an indicator to quickly and indirectly assess an infection. Of course, further studies should be conducted for comparing the diagnostic accuracy between the PhA and other inflammatory markers. However, we expect that the PhA on BIA could be an additional tool to monitor and evaluate the development of infections in patients after abdominal surgery with non-invasiveness and simplicity of measurement, and will be more useful for postoperative patients who experience frequent blood draws, time-consuming invasive procedures, and severe wound pain.
In the current study, lower PhA values measured at 50 kHz were identified as a risk factor for postoperative infections in females. Therefore, a low PhA value may help in the early recognition of a developing infection, and the physician can perform additional culture tests to identify the bacterial species and escalate the administration of empirical antibiotics accordingly, and imaging tests, such as computed tomography scans, can be performed early and quickly for a more reliable diagnosis and confirmation of the infection source. Consequently, this may facilitate the drainage of a contaminated fluid collection or surgical treatment of the infection as soon as possible, thereby facilitating the treatment of subsequent infections and improving clinical outcomes. Our results also showed that higher CRP levels measured on POD1 in males, and higher presepsin levels and APACHE II scores in females were predictors of postoperative infections. Using these various risk factors in addition to the PhA measured by BIA will be helpful in the prediction, early recognition, and proper treatment of patients with postoperative infections. However, only risk factors for diagnosing the occurrence of postoperative infections were analyzed in this study, and whether they ultimately affected clinical outcomes, such as mortality, was not analyzed. Therefore, additional research related to the interpretation of the findings is needed. Nevertheless, our study may be helpful in the early diagnosis of postoperative infections through PhA measurement.
The results of the current study should be interpreted with caution due to various limitations. Firstly, as in other previous studies [13, 16, 17, 28], the cross-sectional design of the current study could not allow for identifying causality between PhA values and other inflammatory markers, which limits the external validity for other populations. Therefore, there are inevitable limitations that cannot draw conclusions due to research methodological limitations. Additionally, we retrospectively reviewed and analyzed data from a single institution composed of a relatively uniform race. Based on the independence of the various common confounding variables included in our study, our results could be valid for our populations. However, considering that the standard values for body composition can differ by factors such as race, the reliability, reproducibility, and universality of the results should be demonstrated in a multicenter study based on a large sample that includes various races in the future. Secondly, baseline phase angle values that was known to related to the nutritional status of patients, a risk factor for postoperative infection were not included. As per our institutional policy, our team is performing treatment after surgery and admission to the ICU, there are limitations to performing bioelectrical impedance analysis before surgery. However, the baseline nutrition status was indirectly assessed using the subjective global assessment score, and there was no significantly difference in SGA score between two groups as shown in Table 1. In the next study, the data of baseline phase angle should be included and analyzed to confirm the influence of baseline phase angle value for postoperative infection. [ 29] And, we actually measured BIA several times such as 36, 72 h postoperatively, but it was not able to conclude in this study because the number of participants analyzed at that time was insufficient to analyze the relationship between phase angle change and clinical outcomes. Authors suppose that additional analysis with a sufficient data of phase angle measured serially should be conducted in the next study for determining the relationship between the change of phase angle and clinical results. Thirdly, bioelectrical impedance vector analysis (BIVA) could not be performed. BIVA is known to detect changes in tissue hydration status or soft tissue mass regardless of body weight. Unfortunately, we did not collect and failed to analyze data using BIVA in the current study. In the near future, BIVA should be analyzed and presented together. Fourthly, we did not analyze some inflammatory markers such as IL-6 or TNF-a, because various important cytokines such as IL-6, TNF-α or IFN-γ are not covered by health insurance in our country, and the cost of testing these cytokines is much higher than that of other conventional markers. Nevertheless, our results included all major inflammatory markers that are most commonly used in clinical practice and are well-known to have significant associations with various pathologic processes. [ 30] Finally, various factors in the ICU environment, such as skin temperature, ambient air, or seating, that could affect BIA measurements, were difficult to fully control. However, we conducted BIA measurements under specially controlled conditions, including constant temperature and humidity, and we believe that this may have helped to minimize the bias from environmental factors in the ICU. Nevertheless, this study is meaningful in that it differs from previous studies. This was a prospective cohort study of patients who underwent major abdominal surgery. In addition, this study focused on critically ill patients who underwent abdominal surgery, who were particularly vulnerable to infection. So, it is important to predict postoperative infection and to promptly diagnose and treat infection for the patient’s prognosis. However, inflammatory markers used to diagnose infection had the disadvantage of blood sampling from patient and taking more than an hour to get results. Through this our study, we revealed the significant association between inflammatory markers and phase angle, and also the significant differences in the phase angle according to the occurrence of postoperative infections. Phase angle measurement through BIA, which can be performed non-invasively within 5 min, could serve a role as an important and rapid indicator for ICU patient infection treatment. Also, in the case of abdominal surgery, there are massive tissue injury during surgery and the inflammatory response in the abdominal cavity that normally occurs after surgery. Therefore, clinical symptoms that are not truly pathogenic are very common, usually include inflammatory symptoms such as leukocytosis, fever, and abdominal pain. Because of this, it is easy to confuse early postoperative clinical symptoms with early onset of infectious complication results in clinical deterioration. Also, it is difficult to detect the occurrence of infection at an early stage, which delays diagnosis and appropriate examination and treatment, that is a vulnerability that can worsen the prognosis. Therefore, the study results of analyzing the phase angle as a surveillance tool in patients after abdominal surgery, which is more difficult to detect infectious complications, have clinical significance. In particular, this study is distinct from the majority of existing studies that analyzed the relationship between phase angle and infectious complications for nonsurgical patients. So, it is expected to be more useful for patients who are admitted to ICU after surgery.
In conclusion, the phase angle obtained through BIA showed a significant association with inflammatory markers and could be used as a predictor of infections. Phase angle measurements through BIA could improve patient prognosis after abdominal surgery through the careful observation of infections and early, appropriate treatment. Further studies with larger samples through prospective cohort observational study should be needed to clarify our findings drawn in current study.
## Data availability statement
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
## Ethics statement
The studies involving human participants were reviewed and approved by Institutional Review Board of the College of Medicine of the Catholic University of Korea. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
GL performed literature search, wrote the manuscript, collected data, and performed the statistical analysis. EK collected the data, designed the study, and revised the manuscript. GL and EK helped to perform the research. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Dual red and near-infrared light-emitting diode irradiation ameliorates LPS-induced
otitis media in a rat model
authors:
- Yoo-Seung Ko
- Eun-Ji Gi
- Sungsu Lee
- Hyong-Ho Cho
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC9992796
doi: 10.3389/fbioe.2023.1099574
license: CC BY 4.0
---
# Dual red and near-infrared light-emitting diode irradiation ameliorates LPS-induced otitis media in a rat model
## Abstract
Objective: *Otitis media* (OM) is an infectious and inflammatory disease of the middle ear (ME) that often recurs and requires long-term antibiotic treatment. Light emitting diode (LED)-based devices have shown therapeutic efficacy in reducing inflammation. This study aimed to investigate the anti-inflammatory effects of red and near-infrared (NIR) LED irradiation on lipopolysaccharide (LPS)-induced OM in rats, human middle ear epithelial cells (HMEECs), and murine macrophage cells (RAW 264.7).
Methods: An animal model was established by LPS injection (2.0 mg/mL) into the ME of rats via the tympanic membrane. A red/NIR LED system was used to irradiate the rats ($\frac{655}{842}$ nm, intensity: 102 mW/m2, time: 30 min/day for 3 days and cells ($\frac{653}{842}$ nm, intensity: 49.4 mW/m2, time: 3 h) after LPS exposure. Hematoxylin and eosin staining was performed to examine pathomorphological changes in the tympanic cavity of the ME of the rats. Enzyme-linked immunosorbent assay, immunoblotting, and RT-qPCR analyses were used to determine the mRNA and protein expression levels of interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α). Mitogen-activated protein kinases (MAPKs) signaling was examined to elucidate the molecular mechanism underlying the reduction of LPS-induced pro-inflammatory cytokines following LED irradiation.
Results: The ME mucosal thickness and inflammatory cell deposits were increased by LPS injection, which were reduced by LED irradiation. The protein expression levels of IL-1β, IL-6, and TNF-α were significantly reduced in the LED-irradiated OM group. LED irradiation strongly inhibited the production of LPS-stimulated IL-1β, IL-6, and TNF-α in HMEECs and RAW 264.7 cells without cytotoxicity in vitro. Furthermore, the phosphorylation of ERK, p38, and JNK was inhibited by LED irradiation.
Conclusion: This study demonstrated that red/NIR LED irradiation effectively suppressed inflammation caused by OM. Moreover, red/NIR LED irradiation reduced pro-inflammatory cytokine production in HMEECs and RAW 264.7 cells through the blockade of MAPK signaling.
## Introduction
Otitis media (OM) is the most common cause for a preschool-aged child to visit the hospital and is the most frequent infectious disease that leads to antibiotic prescription worldwide (Rosenfeld et al., 2001; Ahmed et al., 2014). OM causes fever and otalgia and may result in complications such as meningitis or hearing loss (Kwak et al., 2014). Acute otitis media (AOM) is usually related to Eustachian tube dysfunction (Tysome and Sudhoff, 2018). Inflammation in the pharyngeal mucosa can be refluxed into the middle ear through the Eustachian tube. This unwanted secretion in the middle ear may result in an infection and cause AOM. AOM often recurs, resulting in repeated or long-term use of antibiotics.
As OM is often characterized by recurrent intractable infection, several strategies to facilitate treatment have been proposed in addition to antibiotics. Instead of the systemic administration of antibiotics, trans-tympanic local drug delivery by increasing the tympanic membrane permeability has been attempted (Al-Mahallawi et al., 2017; Abdelbary et al., 2019). Biofilm formation is the major issue in intractable OM, and methods for biofilm inhibition have been suggested. The use of anti-DNAB II Fab was reported to reduce eDNA, one of the components of biofilm (Novotny et al., 2021). In addition, the Hydrodebrider system was used to disrupt the biofilm (Abi Hachem et al., 2018). The photosensitizer Chlorin e6 was used to enhance bactericidal activity against biofilm (Luke-Marshall et al., 2020).
Photobiomodulation (PBM) was first discovered by Endre Mester, which is known as “low-level laser therapy” (Mester et al., 1968). Kovacs et al. observed hair growth by applying ruby laser (694 nm) to the skin. Subsequently, they discovered that HeNe laser (632.8 nm) can stimulate wound healing (Kovacs et al., 1974). Currently, similar to lasers, LEDs are used as a light source (de Freitas and Hamblin, 2016). Red (600–700 nm) and near-infrared (NIR) (770–1,200 nm) wavelengths are the most commonly used, which have shown positive biologic effects. The effects of blue and green light have also been investigated; however, there are issues due to the short penetration depth (Hamblin, 2017). PBM has been found to have anti-inflammatory properties with positive effects on wound healing, arthritis, brain and muscle injury healing, inflammatory pain, and autoimmune diseases (Hamblin, 2017). As a non-invasive method, PBM has also shown anti-microbial effects by generating heat (photothermal effect) and reactive oxygen species (ROS) (Han et al., 2021).
In the current study, we investigated the effect of simultaneous red and NIR wavelengths for treating OM. We used a dual red and NIR LED and evaluated its effect on an in vivo lipopolysaccharide (LPS)-induced AOM rat model as well as human middle ear epithelial cells (HMEECs) and murine macrophage cells (RAW 264.7) in vitro.
## Animals and experimental OM model
Sprague-Dawley rats (age, 7–8 weeks old; gender, male; weight, 200–250 g) were purchased from Damul Science (Daejeon, Korea). All rats were provided with adequate food and water. Animal experiments were conducted in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of Chonnam National University and the protocol approved by the Committee on the Ethics of Animal Experiments of Chonnam National University (CNUHIACUC-20027). All rats were alive during the research period, and normal tympanic membranes were observed prior to LPS injection. In this study, ketamine (100 mg/kg) and xylazine (10 mg/kg) were intraperitoneally injected for the anesthesia. Anesthesia were performed before LPS injection or LED irradiation. Animals were also anesthetized before decapitation for euthanasia. The OM animal model was established by injecting LPS (2.0 mg/ml; L9143, Sigma, St. Louis, MO, United States) from *Pseudomonas aeruginosa* 10 into the ME of the rats through the tympanic membrane. Intra-tympanic injection was used to deliver LPS since it was the least invasive method compared to other surgical approaches opening the bulla. Rats with PBS injection served as the control. After LPS exposure for 24 h, rats with OM were randomly divided into two groups (Red/NIR LED irradiated group versus none-irradiated group).
## Light source and irradiation
In this study, the continuous-wave dual red and NIR LED irradiation system (HK HEALTHCARE CO., LTD., Korea) with wavelengths of 655 nm and 842 nm consisted of a control module and a battery and was connected to an LED light source with a power cable (Supplementary Figure S1). The LED light source unit was composed of an LED light source and an optical fiber. The power intensity of the LED light was 102 W/m2. To examine the therapeutic effect of the red/NIR LED on LPS-induced AOM, rats were irradiated with the red/NIR LED through the ear canal for 30 min for 3 days after LPS injection. Optical fiber (diameter = 3 mm) was tightly placed in the external auditory canal cartilaginous portion. The direction of the optical fiber was monitored to always face the tympanic membrane during the irradiation. Since the optical fiber itself might cause otitis externa, the same optical fiber was place into the external auditory canal (under same anesthesia) without turning on the LED light for the control LPS group. The red/NIR LED system for cell experiments consisted of a power connector and a power supply unit, and the upper LED light source was composed of six LEDs. HMEECs and RAW 264.7 cells were irradiated with red and NIR wavelengths of 653 nm and 842 nm, respectively, and an intensity of 49.4 mW/m2 for 3 h after LPS stimulation.
## Cell lines
HMEECs (ScienCell, Carlsbad, CA, United States) and murine macrophage RAW 264.7 cells (Korean Cell Line Bank, Seoul, Korea) were grown with EPiCM-2 and RPMI 1640, respectively. HMEECs and RAW 264.7 cells were treated with LPS at a concentration of 10 μg/mL or 1 μg/mL immediately after serum starvation.
## Histopathological analyses
After the rats were euthanized, tympanic bullae were harvested and fixed in $4\%$ paraformaldehyde for 24 h at 4°C, rinsed with PBS, and decalcified in Calci-Clear Rapid (National Diagnostics, Atlanta, GA, United States) for 5 days. The softened bullae were dehydrated and embedded in paraffin. The paraffin-embedded bullae were sectioned into 7 µm longitudinal sections for staining. The sectioned bullae were deparaffinized, rehydrated, and stained with hematoxylin and eosin (H&E) to visualize the ME mucosa.
## Immunohistochemistry
Immunohistochemical detection of myeloperoxidase (MPO), 7 µm-thick sections were deparaffinized and rehydrated. Citrate buffer solution was used for antigen retrieval, followed by incubation at room temperature for 10 min using $0.3\%$ hydrogen peroxide and the removal of the endogenous peroxidase. The sections were incubated with a primary antibody at 4°C overnight and horseradish peroxidase was then applied for 1 h. Signals were developed in 3,3′-diaminobenzidine (DAB) tetrahydrochloride solution containing $0.1\%$ H2O2 and observed under the microscope. The number of positive MPO neutrophils in each group was calculated by randomly selecting six fields of view at magnification ×40.
## Real-time polymerase chain reaction (qPCR)
Total RNA was extracted from the ME mucosa of rats or cells using the TRIzol reagent (Invitrogen, Carlsbad, CA, United States) following the manufacturer’s instructions. Extracted RNA was used to synthesize complementary DNA with a reverse transcription kit (Takara, Kyoto, Japan). qPCR was performed with SYBR Green (Takara) and monitored using Thermal Cycler Dice® Real Time System III (Takara). Primers were designed to target endogenous genes, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH and Gapdh) or β-actin (ACTB and Actb) was used as the endogenous control. The crossing point of target genes with GAPDH and Gapdh was calculated using formula 2—(target gene − GAPDH and Gapdh or ACTB and Actb), and the relative amounts were quantified. The qPCR primers are listed in Supplementary Table S1.
## Antibodies and western blotting
IL-1β, IL-6, TNF-α, MPO antibodies were purchased from Santa Cruz Biotechnology (Dallas, TX, United States) or Cell Signaling Technology (Danvers, MA, United States). Total ERK, pERK, JNK, pJNK, p38, pp38 (Cell Signaling Technology), and actin (Sigma) were used with the appropriate secondary antibodies from MBL (Shirley, NY, United States). For western blotting, proteins were separated by $12\%$ PAGE and then electrophoretically transferred onto PVDF membranes. The membranes were incubated with primary antibodies, which were diluted according to the manufacturer’s instructions at 4°C overnight. Horseradish peroxidase-conjugated anti-mouse or anti-rabbit IgGs were added as secondary antibodies. The blots were reprobed with anti-actin antibody as the loading control. Finally, immunoreactive proteins were visualized using an enhanced chemiluminescence (ECL) protocol. The protein levels of IL-1β, IL-6, and TNF-α were measured by densitometry, and the relative protein levels were compared with that of actin and depicted as bar graphs (mean ± SEM, $$n = 9$$).
## ELISA
The concentrations of the cytokines IL-1β, IL-6, and TNF-α in the tympanic bullae of rats were determined by ELISA using human ELISA kits from R&D Systems (Minneapolis, MN, United States), mouse ELISA kits from Elabscience (Houston, TX, United States), and rat ELISA kits from MyBioSource (San Diego, CA, United States). The results are expressed as picograms per milliliter in accordance with the manufacturer’s instructions. The results for the samples following triplicate experiments are depicted as bar graphs (mean ± SEM, $$n = 3$$).
## Cell viability assay
The cell viability of HMEECs and RAW 264.7 cells was analyzed using the EZ-Cytox cell viability assay kit from DoGenBio (Seoul, Korea). In brief, the cells were plated and cultured in 96-well plates (5 × 103 cells/well). Next, 10 µL of EZ-Cytox reagent was added to each well and incubated at 37°C for 2 h, and cell viability was examined. The absorbance was measured using an ELISA microplate reader with a wavelength of 450 nm.
## Statistical analysis
We determined the statistical significance of differences by non-parametric analysis, Mann-Whitney U and Kruskal Wallis tests. When the data met normal distribution (among sample size $$n = 9$$), one-way ANOVA with post-hoc Tukey HSD or Student’s t-test was performed. Assessment of normality of data was done using Kolmogorov-Smirnov and Shapiro-Wilk test P values less than 0.05 were regarded as statistically significant. Statistical analysis was performed using SPSS 27(Chicago, IL, United States).
## Establishment of LPS-induced AOM in rats
To verify the successful establishment of LPS-induced AOM in a rat model, we observed the tympanic membrane by otoscopy and the histologic sections by H&E staining. As shown in Figures 1A–E, the tympanic membrane of the normal control group was transparent, and there were no signs of an inflammatory reaction; however, the LPS-treated group showed an inflammatory response at 2 days after LPS treatment, and the inflammation disappeared after 7 days. The ME mucosal thickness was significantly increased on the first day after LPS treatment and recovered after 7 days (Figures 1F–O).
**FIGURE 1:** *Establishment of a rat model of OM induced by LPS injection through the tympanic membrane. Otoscope images of the tympanic membrane after LPS injection (A–E). ME mucosa stained with H&E, magnification ×2.5 (F–J) and ×20 (K–O). Scale bars indicate 500 µm (F–J) and 100 µm (K–O). MH: malleus handle, TM: tympanic membrane, E: exudates.*
## Reduction of ME mucosal thickness by red/NIR LED irradiation
To investigate the therapeutic effect of the red/NIR LED on LPS-induced AOM, rats were irradiated through the ear canal using the red/NIR LED with wavelengths of 655 nm and 842 nm and an intensity of 102 mW/m2 for 3 days after LPS injection. As shown in Figures 2A–C, otoscopy revealed that red/NIR LED irradiation reduced the LPS-induced inflammation of the tympanic membrane. In comparison with the control group, the ME mucosal thickness of the LPS-treated group with OM was much thicker with increased inflammatory cell deposits (Figures 2D–I). Compared with the control group, higher degree of MPO positive cell infiltration was observed in the LPS-treated group with OM (Figures 2J–L). The ME mucosal thickness of the control group was 21.5 ± 3.53 μm, which was increased to 102.2 ± 17.81 μm in the LPS-treated OM group. On the other hand, the ME mucosal thickness was significantly reduced to 40.7 ± 8.82 μm in the red/NIR LED-irradiated OM group (Figure 2M). The number of MPO positive neutrophils was significantly decreased in the red/NIR LED-irradiated OM group (Figure 2N). The results indicated that LPS induced AOM in the rats, and the increase in the ME mucosal thickness and inflammatory cell deposits was markedly reversed by red/NIR LED irradiation.
**FIGURE 2:** *Reduction of ME mucosal thickness by LED irradiation. Representative otoscope images of the tympanic membrane of LPS-treated rats with or without LED irradiation on day 3 (A–C). Representative H&E staining images of the ME histopathology of LPS-treated rats with or without LED irradiation on day 3, magnification ×2.5 (D–F) and ×40 (G–I). Scale bars represent 500 µm (D–F) and 50 µm (G–I). Immunohistochemical detection of MPO positive cells in LPS-treated rats with or without LED irradiation on day 3 (no positive cells infiltration was observed in the control) (J–L). Scale bars represent 50 µm (J–L). Histograms of the ME mucosal thickness measured in chosen areas (n = 9) (M). One-Way Anova: *p < 0.05, **p < 0.01, and ***p < 0.001. Corresponding quantification of MPO positive cells in chosen areas (n = 9) (N). Kruskal Wallis test: (p) = 0.00006. Mann-Whitney U test: *p < 0.05, **p < 0.01, and ***p < 0.001. Data are expressed as the mean ± SEM. MH: malleus handle, TM: tympanic membrane.*
## Reduced expression of pro-inflammatory cytokines in rats with LPS-induced AOM following red/NIR LED irradiation
To investigate the in vivo effects of red/NIR LED irradiation on LPS-induced AOM in rats, we designed an experiment as shown in Figure 3A. The inflammatory response is known to be crucial in ME damage under various conditions, such as bacterial exposure (Ferenbach et al., 2007). Therefore, we attempted to examine whether OM amelioration by the red/NIR LED is associated with inflammation suppression and analyzed the expression of pro-inflammatory cytokines by western blotting and ELISA in the tympanic bullae obtained from rats after LPS treatment. The results showed that the increase in inflammation of the tympanic membrane was markedly reduced by LED irradiation (Figures 3B–D), and the protein expression of the pro-inflammatory cytokines IL-1β, IL-6, and TNF-α, which was increased by LPS, was significantly decreased by LED irradiation (Figure 3E). ELISA results were consistent with those of western blotting (Figures 3F–H). OM is associated with ROS levels, and ROS is associated with mitogen-activated protein kinase (MAPK) signaling. Therefore, we subsequently investigated whether MAPK signaling is inhibited by LED irradiation. The phosphorylation of ERK, p38, and JNK, which was increased in the ME tissue of rats after LPS treatment, was inhibited by LED irradiation (Figure 3I). The results demonstrated the efficacy of red/NIR LED irradiation in inhibiting pro-inflammatory cytokine expression induced by LPS, and the inhibition was highly correlated with the suppression of the MAPK signaling pathway.
**FIGURE 3:** *Amelioration of LPS-induced inflammation in the ME of rats by LED irradiation via blockade of MAPK signaling. LED irradiation reduced pro-inflammatory cytokine release in LPS-induced OM rats. Experimental protocol for LPS exposure in a rat model of OM (50 μL emulsion of 2 mg/ml LPS) (A). Representative images of the tympanic membrane of LPS-treated rats with or without LED irradiation (B–D). Immunoblot analysis of the expression levels of IL-1β, IL-6, and TNF-α in the tympanic bullae of rats. IL-1β, IL-6, and TNF-α ·protein levels were quantified following immunoblotting (n = 9) (E). One-Way Anova: *p < 0.05, **p < 0.01, and ***p < 0.001. ELISA of pro-inflammatory cytokines including IL-1β (F), IL-6 (G), and TNF-α (H) in the tympanic bullae of rats (n = 3). Kruskal Wallis test: (p) = 0.001. Reduction of pro-inflammatory cytokines in the tympanic bullae of rats by LED irradiation through the blockade of MAPK signaling (n = 6) (I). Kruskal Wallis test: (p) = 0.001. Mann-Whitney U test: *p < 0.05, **p < 0.01, and ***p < 0.001. Data are expressed as the mean ± SEM.*
## Reduced expression of inflammatory cytokines in HMEECs and RAW 264.7 cells following red/NIR LED irradiation
We examined the in vitro effect of red/NIR LED irradiation on the expression of pro-inflammatory cytokines. The HMEEC line has been widely used in OM studies; thus, we used this cell line to investigate the effect of LED irradiation on LPS-induced cytokine elevation. The experimental design for LED irradiation with HMEECs is illustrated in Figure 4A. We first confirmed the cell viability of HMEECs after LED irradiation. The number of viable cells did not show any difference over time in the LED-irradiated group compared with the control group (Figures 4B–G). As shown in Figures 4H–J, IL-1β, IL-6, and TNF-α mRNA levels were significantly increased with LPS stimulation at 50 μg/ml for 3 h, which were greatly reduced by LED irradiation in HMEECs. Furthermore, western blot results revealed that the expression levels of IL-1β, IL-6, and TNF-α were increased by LPS exposure, and this upregulation was reversed by LED irradiation in HMEECs (Figure 4K). Additionally, the concentrations of IL-1β, IL-6, and TNF-α were determined by ELISA, which were consistent with the western blot results (Figures 4L–N). The phosphorylation of ERK, p38, and JNK, which was increased after LPS stimulation, was inhibited by LED irradiation in HMEECs (Figure 4O). Important inflammatory mediators in OM are produced by infiltrating immune cells such as macrophages (Juhn et al., 2008). LPS can induce various inflammatory mediators and stimulate local macrophages to produce soluble mediators including IL-1β, IL-6, and TNF-α. Therefore, we investigated the effect of red/NIR LED irradiation on the expression of pro-inflammatory cytokines in RAW 264.7 cells, a mouse macrophage cell line. The experimental design for RAW 264.7 cells was similar to that for HMEECs, as shown in Figure 5A. LED irradiation showed no cytotoxicity towards RAW 264.7 cells (Figures 5B–G). Likewise, the mRNA and protein expression levels of IL-1β, IL-6, and TNF-α, which were increased by LPS treatment, were markedly inhibited by LED irradiation in RAW 264.7 cells (Figures 5H–N). Moreover, the phosphorylation of ERK, JNK, and p38, which was increased by LPS, was attenuated by LED irradiation in RAW 264.7 cells (Figure 5O). These results indicated that red/NIR LED irradiation suppressed the expression of pro-inflammatory cytokines induced by LPS in the macrophage cell line RAW 264.7 through the blockade of MAPK signaling. Overall, our study demonstrated that red/NIR LED irradiation effectively suppressed inflammation caused by OM. Moreover, red/NIR LED irradiation reduced pro-inflammatory cytokine production in HMEECs and RAW 264.7 cells by blocking MAPK signaling (Figure 6).
**FIGURE 4:** *Reduction of the expression of pro-inflammatory cytokines in HMEECs by LED irradiation. Treatment of HMEECs with LPS (50 μg/ml or 10 μg/ml) for 3 h (A). Cell morphology (B–F) and viability (G) of HMEECs following LED irradiation. The scale bar represents 200 µm (B–F). qPCR assay of the mRNA levels of IL-1β (H), IL-6 (I), and TNF-α (J) in HMEECs. Data are presented as the relative fold (n = 6). Kruskal Wallis test: (p) = 0.001. Western blotting of the protein expression levels of IL-1β, IL-6, and TNF-β (n = 9) (K). One-Way Anova: *p < 0.05, **p < 0.01, and ***p < 0.001. ELISA of pro-inflammatory cytokines including IL-1β (L), IL-6 (M), and TNF-α (N) in HMEECs (n = 3). Kruskal Wallis test: (p) = 0.001. Reduction of pro-inflammatory cytokines by LED irradiation through the blockade of MAPK signaling (n = 6) (O). Kruskal Wallis test: (p) = 0.002 for pERK and (p) = 0.001 for pp38 and pJNK. Mann-Whitney U test: *p < 0.05, **p < 0.01, and ***p < 0.001. Data are expressed as the mean ± SEM.* **FIGURE 5:** *Reduction of the expression of pro-inflammatory cytokines in RAW 264.7 cells by LED irradiation. Treatment of RAW 264.7 cells with LPS (50 μg/ml or 10 μg/ml) for 3 h (A). Cell morphology (B–F) and viability (G) of RAW 264.7 cells following LED irradiation. The scale bar represents 200 µm (B–F). qPCR assay of the mRNA levels of IL-1β (H), IL-6 (I), and TNF-α (J) in RAW 264.7 cells (n = 6). Kruskal Wallis test: (p) = 0.001. Data are presented as the relative fold. Western blotting of the protein expression levels of IL-1β, IL-6, and TNF-α (n = 9) (K). One-Way Anova: *p < 0.05, **p < 0.01, and ***p < 0.001. ELISA of pro-inflammatory cytokines including IL-1β (L), IL-6 (M), and TNF-α (N) in RAW 264.7 cells (n = 3). Kruskal Wallis test: (p) = 0.001. Reduction of pro-inflammatory cytokines by LED irradiation through the blockade of MAPK signaling (n = 6) (O). Kruskal Wallis test: (p) = 0.00039 for pERK and pp38 and (p) = 0.001 for pJNK. Mann-Whitney U test: *p < 0.05, **p < 0.01, and ***p < 0.001. Data are expressed as the mean ± SEM.* **FIGURE 6:** *Representative schematic of OM amelioration by LED irradiation. LED reduced the expression of pro-inflammatory cytokines induced by LPS exposure in a rat model of OM, HMEECs, and RAW 264.7 cells via the inhibition of MAPK signaling.*
## Discussion
OM with effusion is a common infectious disease caused by several factors such as viruses, bacteria, and Eustachian tube dysfunction. In OM, the inflammatory process in the ME caused by bacteria involves macrophages and several cytokines (Barzilai et al., 1999). Endotoxins are known to trigger inflammation in the ME and stimulate macrophages to secrete TNF-α and IL-1 (Willett et al., 1998). A previous study has reported that the expression of IL-6 and TNF-α plays an important role in the development of OM in an animal model with effusion (Johnson et al., 1994). In particular, the treatment for OM consists of vaccination and antibiotics. The incorporation of pneumococcal conjugate vaccines has reduced the incidence of OM. However, some serotypes of pneumococci are not covered by the current vaccines, are increasing (Kaur et al., 2017). Moreover, multidrug-resistant pneumococci may lead to recurrent AOM (Gavrilovici et al., 2022). Consequently, finding an effective and safe therapeutic strategy for OM is an ongoing process.
Recently, the efficiency of LED irradiation in increasing tissue blood perfusion, ameliorating post-inflammatory hyperpigmentation, reducing the wound diameter, and promoting the healing of inflammatory conditions such as arthritis has been reported (Dall Agnol et al., 2009; Li et al., 2010; Wu and Persinger, 2011; Kuboyama et al., 2014). Furthermore, LED irradiation was found to decrease the formation of inflammatory cells and the expression of inflammatory cytokines in a collagen-induced *Achilles tendonitis* rat model (Xavier et al., 2010). The LED device used in this study has a structure that directly irradiates light to the inflamed site of the ME at wavelengths highly effective for inflammation. Phototherapy has been introduced for treating otologic diseases. A 632 nm diode laser effectively killed S. pneumonia, H. influenzae, and M. catarrhalis in OM (Jung et al., 2009). An 808 nm diode laser was used to prevent noise-induced hearing loss (Lee et al., 2019). There are several advantages in using LEDs compared with lasers, such as safety, portability, ability to irradiate large tissue areas at the same time, incorporation of wearable technology, and a much lower cost per mW.
PBM utilizes red and red/IR irradiation to stimulate healing, relieve pain, and reduce inflammation. Previously, LED irradiation has shown anti-inflammatory effects on collagen-induced arthritis in mice (Kuboyama et al., 2014) and P. gingivalis LPS-treated human gingival fibroblasts (Choi et al., 2012). In the present study, rats were administrated with LPS to induce OM, and the ability of red/NIR LED irradiation in reducing inflammation was examined. We confirmed that AOM was successfully established in the rats based on the presence of inflammation in the tympanic membrane and the thickened ME mucosa. Red/NIR LED irradiation significantly reduced the inflammatory response in the tympanic membrane and the ME mucosal thickness, demonstrating that red/NIR LED irradiation ameliorated LPS-induced AOM in the ME of the rats.
Pro-inflammatory cytokines regulate the pathogenesis of OM by initiating an inflammatory response to infection and may cause the transformation from acute to chronic OM. Elevated concentrations of IL-1β, IL-6, and TNF-α in children may be correlated with OM; in particular, IL-6, a marker of a bacterial infection, can induce C-reactive protein (CRP) production and appears to play a vital role in inflammation in OM (Yellon et al., 1995). LED irradiation has been found to be effective for the suppression of pro-inflammatory cytokines, suggesting that LED therapy could be an effective strategy in ameliorating diseases associated with inflammation (Nomura et al., 2001; Kuboyama et al., 2014). Therefore, restricting the secretion of pro-inflammatory cytokines may be an effective approach for the treatment of OM. In the present study, the expression levels of IL-1β, IL-6, and TNF-α were significantly increased in the LPS group, which was in agreement with histological findings; red/NIR LED irradiation significantly inhibited the secretion of these pro-inflammatory cytokines, demonstrating that red/NIR LED irradiation could treat OM by impeding pro-inflammatory cytokine release.
Severe diseases can emerge due to an inadequate cytokine response, and cytokines are recognized as regulators in the immunopathology of an ever-increasing number of diseases including OM. Adequate cytokine production is essential for the development of protective immunity (Choi et al., 2012). Therefore, regulation of cytokines is essential for all infectious diseases including OM.
Excessive ROS production is associated with inflammation, aging, and human diseases (Snezhkina et al., 2019). In several human diseases, OM is correlated with ROS levels, and the IL-17 pathway is known to be associated with ROS levels in OM with effusion (Abdelhafeez and Mohamed, 2021). Specifically, ROS is associated with MAPK signaling, which is known as a master regulator of inflammation. JNK and p38 are activated by ROS, and ERK$\frac{1}{2}$ promotes ROS generation (Genestra, 2007). In agreement with previous findings, we found that the MAPK signaling pathway was activated after LPS stimulation, resulting in the high expression levels of phosphorylated p38, ERK$\frac{1}{2}$, and JNK, which were reduced by red/NIR LED irradiation. Therefore, the study demonstrated that the amelioration of AOM by LED irradiation may be associated with MAPK suppression.
The use of MAPK inhibitors has been regarded as an attractive treatment strategy because of their ability in reducing both the synthesis and signaling of pro-inflammatory cytokines. In particular, potent anti-inflammatory drugs inhibiting both p38 MAPK and JNK can suppress macrophage inflammatory cytokines, including IL-1β, IL-6, TNF-α, and macrophage inflammatory protein 1α and 1β (Bianchi et al., 1996; Cohen et al., 1996). Taken together, our results showed that red/NIR LED irradiation significantly inhibited pro-inflammatory cytokine levels and MAPK activity upregulated by LPS, suggesting that red/NIR LED irradiation may be an effective method for the treatment of OM.
## Conclusion
In the present study, owing to the anti-inflammatory effects of dual red and NIR LED irradiation, inflammatory cytokines were inhibited in an LPS-induced AOM rat model, as well as HMEECs and RAW 264.7 cells. Moreover, the decrease in pro-inflammatory cytokine production following LED irradiation was mediated by the blockade of MAPK signaling. Collectively, these findings suggest that red/NIR LED irradiation may be an effective therapeutic strategy for the treatment of OM.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by Committee on the Ethics of Animal Experiments of Chonnam National University (CNUHIACUC-20027).
## Author contributions
Conception and design: Y-SK, SL, H-HC. Development of methodology: Y-SK, E-JG, SL, H-HC. Acquisition of data: Y-SK, E-JG. Writing, review, and/or revision of the manuscript: Y-SK, SL, H-HC. Study supervision: SL, H-HC.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1099574/full#supplementary-material
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|
---
title: The biomarkers related to immune infiltration to predict distant metastasis
in breast cancer patients
authors:
- Chengsi Ren
- Anran Gao
- Chengshi Fu
- Xiangyun Teng
- Jianzhang Wang
- Shaofang Lu
- Jiahui Gao
- Jinfeng Huang
- Dongdong Liu
- Jianhua Xu
journal: Frontiers in Genetics
year: 2023
pmcid: PMC9992813
doi: 10.3389/fgene.2023.1105689
license: CC BY 4.0
---
# The biomarkers related to immune infiltration to predict distant metastasis in breast cancer patients
## Abstract
Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis.
Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism.
Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC ($$p \leq 0.01$$, HR = 0.63), and it has been validated in internal ($$p \leq 0.023$$, HR = 0.58) and external ($$p \leq 0.0065$$, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful.
Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.
## Introduction
Currently, breast cancer (BC) has been the highest cause of cancer death for women (Sung et al., 2021). Approximately $90\%$ of patient death come from metastatic complications although only $20\%$–$30\%$ of patients suffer from metastatic recurrence, which makes the treatment schedules of metastatic patients different from those of non-metastatic patients (Chen et al., 2018; Medeiros and Allan, 2019). Therefore, accurate identification of distant metastasis (DM) in each individual patient with BC is crucial for determining individualized follow-up strategy and the optimal treatment regimen. The further exploration of new biomarkers is of great significance.
BC is a heterogeneous disease at molecular levels, which is associated with distinct patterns of metastatic spread (Liang et al., 2020). As a result of different molecular subtypes, accurate prediction of DM is still a great challenge. Notably, gene expression-based molecular subtyping appears to have clinical implications for the treatment of patients with BC (Masuda et al., 2013; Prat et al., 2015). Moreover, previous studies have shown that molecular subtypes of BC could be considered as a risk factor for distant recurrence (Gnant et al., 2014; Tobin et al., 2017), This suggests that gene expression profiling has a great value in predicting the probability of DM (Ellis et al., 2011; Filipits et al., 2014). However, what pattern of gene expression causes this difference still remains unclear. In this study, we aim to find gene biomarker associated with metastasis and construct a gene signature that could accurately predict distant metastasis–free survival (DMFS).
Nomogram, a simple devices for predicting the likelihood of disease, is widely used in the field of oncology (Balachandran et al., 2015). However, there is no literature that has applied gene signature to a nomogram for predicting DMFS in BC, although multiple predictive nomograms have been constructed for patients with BC (Rouzier et al., 2005; Wang et al., 2019; Huang et al., 2020). Therefore, the aim of this study was to provides a reliable nomogram to predict the risk of metastasis development based on gene signature in patients with BC.
## Data collection and pre-processing
Data of the cancer and adjacent normal tissues of samples with metastasis were downloaded from the Gene Expression Omnibus (GEO) database: GSE183947 and GSE184717. Meanwhile, GSE20685 and GSE6532 were singled out to construct a gene signature and a nomogram. Samples without complete clinical information or based on different platforms were regarded as substandard samples in the present study. Subsequently, batch effects were removed using Combat from R package SVA (Johnson et al., 2007).
## Acquisition of differentially expressed genes (DEGs)
An R package limma (Ritchie et al., 2015) was applied to identify DEGs between cancer and adjacent normal tissues. The threshold was set to |logFC| >2 and the adjusted $p \leq 0.05.$ Then, we use Venn diagrams to find the intersection of DEGs that simultaneously upregulated or downregulated in both metastatic tumor and primary tumor.
## Screening of optimal predictive biomarkers and development of signature
GSE20685 was randomized 1:1 and split into a training cohort and an internal validation cohort. For reproducibility, the random seed was used and set to 3. To find optimal predictive biomarkers, univariate and multivariate Cox regression analysis was performed in the training cohort with a p-value cutoff of 0.05. Then, the regression coefficient was defined according to the multivariate Cox regression model and the formulas were described as follows: risk score=∑$i = 1$NExpi∗Coei Finally, results were visualized using the “forestplot” package in R.
## Construction and assessment of the nomogram
In order to make better use of the signature, a nomogram was constructed using the “RMS” package. The Harrell’s Concordance index (C-index) values range from 0 to 1, which is positively correlated to the predictive performance of the nomogram (Harrell et al., 1996). The nomogram was subjected to bootstrapping validation (1,000 bootstrap resamples) to calculate a relatively corrected C-index. To assess the consistency of DMFS at 3-, 5-, and 10-year between the nomogram predicted probabilities and observed rates, calibration curves were plotted.
## Evaluation of predictive value
Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/) is an online tool, which contains massive RNA sequencing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx) (Tang et al., 2017). We analyze the differential gene expression and correlation between BC tissues ($$n = 1$$,085) and normal tissues ($$n = 291$$) using GEPIA.
In terms of the signature, the optimal cutoff value was calculated according to the median value of the signature in the training cohort. Then we use it to divide patients into two groups and predict the DMFS in the training, internal validation, and external validation cohort by means of plotting survival curves based on the Kaplan-Meier method. To date fifty-five dataset have been included in Breast Cancer Gene-Expression Miner v4.7 (bc-GenExMiner) (http://bcgenex.centregauducheau.fr/), which can be used to improve gene prognostic analysis performance (Jézéquel et al., 2012). Candidate biomarkers were validated again using bc-GenExMiner.
Compared with ROC curves, decision curve analysis (DCA) can integrate patient and doctor preference into analysis, which is increasingly being utilized in clinical practice (Huang et al., 2016). To evaluate the clinical utility of models, DCA curves were developed using the “stdca. R” package in R.
## Gene set enrichment analysis
The degree of differential gene expression was reordered by high- and low-score groups instead of definite differential gene thresholds, which was used to screen significantly enriched KEGG pathways. This method helps minimize losses of original gene expression data (Subramanian et al., 2005). We performed Gene set enrichment analysis (GSEA) to analyze the difference in anti-metastatic potential between two groups.
GSEA was performed to analyze the causes of BC metastasis risk difference between high and low score groups. Figure 7A showed that upregulated genes in the high score group were significantly enriched in immune related signal pathways, such as T cell receptor signaling and chemokine signaling pathway, suggesting that our signature may be an immune infection related signature (IIRS).
**FIGURE 7:** *Functional analysis of the CD74 and TSPAN7. (A) GSEA identified gene sets significantly enriched in the phenotype of high score patients based on the IIRS. (B) Correlations between CD74 or TSPAN7 expression and immune infiltration levels. (C) Different levels of immune infiltration were observed between the high score (n = 212) and the low score (n = 202) groups.*
## Immune infiltration analysis
The relationship between optimal predictive biomarkers and immune infiltrating levels was analyzed using Timer (https://cistrome.shinyapps.io/timer/) (Li et al., 2017). An algorithm named quanTIseq was used to estimate the fraction of immune cell subsets infiltrating the tissue from GSE20685 and GSE6532 (Finotello et al., 2019). Student’s t-test was used to test for significance between high-score group and low-score group.
We further utilized TIMER to analyze the possible correlation between CD74, TSPAN7 expression and levels of immune infiltration in BC (Figure 7B). Both CD74 and TSPAN7 are associated with multiple immune cell infiltration, especially T cells. Therefore, we want to know whether the correlation between the signature and immune infiltration is applicable to all our cohorts. We integrated three cohorts and calculated the fraction of immune cell subsets using quanTIseq. As shown in Figure 7C, the high score group had a higher fraction of B cells ($p \leq 0.001$), M2 Macrophage ($p \leq 0.001$), Neutrophil ($$p \leq 0.009$$), CD8+T cell ($p \leq 0.001$), Tregs ($p \leq 0.001$). On the contrary, the fraction of NK cell ($$p \leq 0.012$$) and uncharacterized cells were found to increase significantly in the low score group. It is worth noting that the analysis showed that there was the most significant difference in CD8+T cells between groups.
In view of myeloid cells playing an important role in the recruitment and concentration of CD8+ T cells (Jiang et al., 2022), we attempted to evaluate the correlation between myeloid cell levels and the IIRS in BC patients. CD33 is widely used as a marker for tumor infiltrating myeloid cells (Choi et al., 2020; Toor et al., 2021), so we collected FFPEs from 28 consecutive pairs of BC patients and performed IHC for CD74 and CD33, respectively. Interestingly, CD74 protein mainly existed in some CD33+ cells (Figure 8A), and was positively correlated with CD33 levels in three cohorts (Figure 8B), IHC (Figure 8C) and TCGA (Figure 8D), respectively. In addition, there is a significant positive correlation between CD74 and CD8 in BC (Figure 8E), which means that CD74+ myeloid cells will not affect the recruitment of CD8+T cells.
**FIGURE 8:** *Gene expression and correlation. (A) IHC for CD74 and myeloid marker CD33. (B–D) Correlation between the expression of CD74 and CD33 in cohorts (Panel B), IHC (Panel C) and TCGA (Panel D) (Pearson correlation coefficient). (E) Correlation between the expression of CD74 and CD8A in TCGA. (F) Western blot analysis of TSPAN7 in cell lysates of myeloid cells and breast cancer cells. (G–K) Correlation between CD33 and TSPAN7 expression of different PAM50 subtypes in bc-GenExMiner. Basal-like subtype (G); HER2-enriched subtype (H); Luminal A subtype (I); Luminal B subtype (J); Nromal-like subtype (K). (L, M) TSPAN7 mRNA (L) and protein (M) expression was upregulated after treatment with the demethylation reagent 5-Aza. *p < 0.05,**p < 0.01, by Student’s t-test.*
We detected TSPAN7 protein expression in three common BC cell lines (MCF-7, ZR-75-1, MDA-MB-231) and a myeloid cell line K562 (Figure 8F). TSPAN7 was almost undetectable in BC cell lines, contrary to the myeloid cell line. Importantly, we found that the expression of TSPAN7 was significantly correlated with CD33 in a variety of molecular subtypes, which has been proven to exist mainly in myeloid immune cells (Figures 8G–K). It suggested that the differential expression of TSPAN7 may affect the prognosis by reflecting the infiltration of myeloid cells. However, TSPAN7 expression in BC tissues is lower than that in normal tissues without immune invasion. To explain this paradoxical phenomenon, we hypothesized that TSPAN7 methylation occured and treated three TSPAN7-silenced cells with demethylation agent 5-Aza, respectively. TSPAN7 expression was subsequently restored in all treated BC cells (Figures 8L,M). TSPAN7 methylation in BC cells and myeloid cells infiltration together result in the difference of TSPAN7 expression.
## Immunohistochemistry
28 tissue samples of BC were collected in Shunde Hospital of Guangzhou University of Chinese Medicine to analyze the correlation between CD74 and myeloid cells. To reduce error, Immunohistochemistry (IHC) for CD74 (1:200 dilution; EPR4064, Abcam) and CD33 (1:200 dilution; EPR23051-101, Abcam) were performed by an autostrainer system (Lumatas Titan, LumatasBiosystem Inc.) on 3-μm-thick,formalin-fixed and paraffin-embedded (FFPE) Sections. Counterstaining was done with hematoxylin. The average optical density (AOD) was calculated with Image Pro Plus6.0 to determine the protein expression level.
## Cell culture
BC cell line MCF-7,ZR-75-1, MDA-MB-231 and myeloid leukemia cell line K562 were all obtained from the Experimental Center, Shunde Hospital of Guangzhou University of Chinese Medicine (Foshan, China). Three breast cell lines were maintained in DMEM medium with $10\%$ fetal bovine serum (FBS). Cells were seeded in 6-well plates and, 12 h after plating, demethylation was induced with 10 μM 5-aza for 24 h. Stable expression of TSPAN7 was confirmed by RT-qPCR and Western blot.
## qRT-PCR
Total RNA was extracted using TRIzol (Invitrogen) and a two-step reverse transcription-quantitative PCR (RT-qPCR) protocol was performed using PrimeScript RT Master Mix (Takara) and TB Green Premix Ex Taq (Takara), following manufacturer’s instructions. GAPDH was used as a loading control. The sequences of TSPAN7 primers were as follows: 5′-CTGGCTGTTGGAGTCTGG-3′ (forward); 5′-CCGATGAGCACATAGGGA' (reverse). The sequences of GAPDH primers were:5′-CGGATTTGGTCGTATTGGG-3′ (forward); 5′-CTGGAAGATGGTGATGGGATT-3′ (reverse). The experiment was repeated three times biologically for statistical analysis. The relative expression was quantified using the 2−ΔΔCT method.
## Western blot analysis
Cells were placed on ice and lysed with RIPA buffer (Beyotime, China) containing protease inhibitors Cocktail (MCE,China). Proteins were resolved on SDS-PAGE gels, transferred to PVDF membranes (Millipore, United States), and incubated at 4°C overnight with TSPAN7 primary antibodies diluted at 1:1,000 (ProteinTech, 18695-1-AP) and β-tubulin loading control antibodies diluted at 1:1,000 (Servicebio, GB11017) and the secondary antibody diluted at 1:10,000 (Abcam,ab6721). Bands were visualized using an ECL kit.
## Screening of DEGs
A flow chart of the study design is shown in Figure 1. The R package “limma” was used to screen DEGs between tumor and normal tissues in GSE184717 and GSE183947, where a total of 1967 (1,494 upregulated and 473 downregulated) and 351 (180 upregulated and 171downregulated) DEGs were obtained, respectively. The distribution of each gene was visualized by volcano plots (Figures 2A,B). The resulting list of DEGs was the intersection between the above datasets and a total of 62 overlapping DEGs were obtained (Figure 2C).
**FIGURE 1:** *A flowchart of the study procedure.* **FIGURE 2:** *Identification of differentially expressed genes (DEGs). (A) Volcano plot of DEGs between primary tumor and paired normal tumor in GSE183947. (B) Volcano plot of DEGs between metastasis tumor and paired normal tumor in GSE184717. (C) Venn diagram showing the intersection of the DEGs in GSE 183947 and GSE 184717.*
## Two genes were screened out as potential predictive biomarkers
The clinicopathologic characteristics are summarized in Table 1. To identify DEGs that are associated with metastasis, univariate Cox regression analysis was performed. A total of nine candidate genes (CD74, TSPAN7, COL11A1, FLG, MMP11, CHRDL1, FNDC1, MELK, PITX1) with an adjusted $p \leq 0.05$ were screened out (Figure 3A). Detailed information for each gene was listed in Table 2.
We further analyzed the differential gene expression between BC tissues ($$n = 1$$,085) and normal tissues ($$n = 291$$) using GEPIA. As shown in Figure 4, eight genes were found to be differentially expressed in BC tissues, including six upregulated genes (CD74,MMP11,MELK,COL11A1,PITX1,FNDC1) and two downregulated genes (TSPAN7, CHRDL1).
**FIGURE 4:** *Validation of the expression of potential predictive biomarker in TCGA and GETx (n = 1,376). (A) TSPAN7, (B) CD74, (C) MMP11, (D) MELK, (E) COL11A1, (F) CHRDL1, (G) PITX1, (H) FNDC1. T: tumor; N: normal. Green and blue, respectively indicate tumor and normal groups. *p < 0.05.*
Interestingly, breast tissues with low CD74 expression were related to poorer DMFS although CD74 commonly upregulated in BC patients.
Additionally, bc-GenExMiner was utilized to validate the relationship between predictive biomarkers and DMFS. The expression levels of seven genes, including TSPAN7(HR:0.75; $95\%$CI:0.69-0.83; $p \leq 0.0001$), CD74(HR:0.88; $95\%$CI:0.81-0.97; $$p \leq 0.0094$$), MMP11 (HR:1.33; $95\%$CI:1.21-1.46; $p \leq 0.0001$), MELK(HR:1.87; $95\%$CI:1.70-2.06; $p \leq 0.0001$), COL11A1(HR:1.13; $95\%$CI:1.03-1.24; $$p \leq 0.0108$$), CHRDL1(HR:0.81; $95\%$CI:0.73-0.89; $p \leq 0.0001$), PITX1(HR:1.17; $95\%$CI:1.07-1.29; $$p \leq 0.0010$$), were found able to estimate DMFS (Figure 5).
**FIGURE 5:** *Kaplan-Meier curves for Distant metastasis-free survival (DMFS) of TSPAN7 (A), CD74 (B), MMP11 (C), MELK (D), COL11A1 (E), CHRDL1 (F), and PITX1 (G)in breast cancer patients using bc-GenExMiner.*
Finally, CD74 (HR:0.61; $95\%$CI:0.4-0.92; $$p \leq 0.019$$) and TSPAN7(HR:0.51; $95\%$CI:0.29-0.9; $$p \leq 0.021$$) were screened out to develop a signature according to the results of multivariate *Cox analysis* (Figure 3B).
## Development and validation of the signature
We calculated the signature based on the expression of CD74 and TSPAN7 as follows: Signature = (0.7,697,433 X expression of CD74) + (0.6633031 X expression of TSPAN7).
Of note, the expression of CD74 and TSPAN7 was negatively correlated with DMFS, suggesting that patients with low scores have a higher probability of distant metastasis.
To be better applied in clinical diagnosis, a constant cutoff value was determined by the median of the training cohort (15.488). Survival analysis, using the Kaplan-Meier method, indicated that the low score group portends a worse DMFS (HR:0.63; $95\%$CI:0.49-0.82; $$p \leq 0.01$$; Figure 6A). Subsequently, survival analysis was performed twice with the same results in the internal validation cohort (HR:0.58; $95\%$CI:0.36-0.96; $$p \leq 0.023$$; Figure 6B), and the external validation cohort (HR:0.67; $95\%$CI:0.47-0.95; $$p \leq 0.0065$$; Figure 6C), respectively. To individually show the differences between low score and high score groups, we visualized the scores, distant metastasis, and gene expression profiles in the three cohorts (Figures 6D–F). The above results indicated that the signature could predict the risk of metastasis development as an independent risk feature.
**FIGURE 6:** *Kaplan-Meier curves of DMFS according to signature and the prognosis of patients with BC. (A–C) Kaplan-Meier curves of DMFS based on the signature (15.488) in training cohort (A), internal validation cohort (B) and external validation cohort (C). (D–F) The distribution of risk score (top), metastatic status (middle) and expression heatmap (bottom) of the two biomarkers in training cohort (D), internal validation cohort (E) and external validation cohort (F). M, metastasized; NM, unmetastasized.*
## Construction and assessment of predictive nomogram
In order to increase clinical utility, a predictive nomogram was constructed (Figure 9) based on the risk feature verified by univariate and multivariate Cox analysis, including the IIRS, age, T stage, and N stage (Figures 3C,D). Interestingly, we found that younger patients are at higher risk of metastasis, which was consistent with previous findings (Purushotham et al., 2014).
**FIGURE 9:** *Nomogram to predict for 3-year,5-year and 10-year probabilities of DMFS for breast cancer patients. Age: older: ≥60,young: <60; T_stage: 0: T0; 1: T1; 2: T2; 3: T3; 4: T4; N_stage: 0: N0; N+: N1, N2, N3; signature, IIRS.*
Subsequently, calibrate curves were plotted to identify the consistency between ideal outcome and actual observation in prediction of 3-,5- 10-year distant metastasis-free survival times. The calibration curves showed good performance in training cohort (Figure 10A), internal validation cohort (Figure 10B), and external validation cohort (Figure 10C), especially for long term survival rate (10-year DMFS).
**FIGURE 10:** *Performance of the nomogram to predict DMFS. (A–C) Calibration curves of 3-year, 5-year and 10-year DMFS in training cohort (A), internal validation cohort (B), and external validation cohort (C). (D) Decision curve analysis for the nomogram with/without signature.*
To assess the predictive performance of this model, the nomogram was internally validated by 1,000 bootstrap resamples in three cohorts. Pleasantly, and surprisingly, the nomogram yielded a C-index of 0.742 ($95\%$ CI, 0.715–0.769) for training cohort, 0.801 ($95\%$ CI, 0.754 to 0.0.848) for internal validation cohort and 0.695 ($95\%$ CI, 0.643 to 0.0.747) for external validation cohort. Therefore, we demonstrated that the nomogram could predict the DMFS of BC patients effectively.
We subsequently performed a decision curve analysis to evaluate the clinical net benefit in predicting the probability of 10- year DMFS. As shown in Figure 10D, if the threshold probability of a patient or doctor is <$48\%$ or >$63\%$, using the nomogram with IIRS to predict DMFS adds more benefit than the other without IIRS.
## Discussion
It is well known that almost all metastatic BC has poor overall survival and incurable nature (Gobbini et al., 2018). In recent years, it is gratifying to note that the median survival time after diagnosis of metastatic BC has been increasing due to improved treatment (Mariotto et al., 2017). Therefore, early assessment and diagnosis of distant metastasis are still meaningful strategies for improving the prognosis of BC patients.
Nowadays multiple biomarkers, such as circulating miRNA (Baldasici et al., 2022), circulating tumor DNA (Page et al., 2021) and circulating tumor cell (Ring et al., 2022), can be used to detect metastasis of BC. What’s more, several serum biomarkers related to metastasis, including soluble POSTN (Jia et al., 2022), PTHrP (Washam et al., 2013), S100P (Peng et al., 2016), have been found. Non-etheless, when these biomarkers increase, it is very likely that BC has undergone distant metastasis (Al-Mahmood et al., 2018). Therefore, improved methods based on gene expression to predict the risk of BC metastasis in advance are needed.
Unfortunately, there is no effective way to achieve this goal. Although PAM50 signature has proven to be able to provide prognostic information from the lymph node metastasis of BC patients, only a few subtypes are associated with metastasis (Tobin et al., 2017; Wang et al., 2019), suggesting that individualized accurate prediction according to PAM50 subtypes is still difficult.
In this study, we sought to identify metastasis-related genes to predict the probability of distant metastasis in BC patients. A total of 62 genes were screened out in GSE184717 and GSE183947.7 genes (CD74,TSPAN7,COL11A1, MMP11, CHRDL1, MELK, PITX1) proved to be associated with DMFS in BC patients, and seven of them (TSPAN7, CD74, MMP11, MELK, COL11A1, CHRDL1, PITX1) were verified by bc-GenExMiner. Two potential biomarkers (CD74, TSPAN7) of metastasis development were used to construct a gene signature by multivariate analysis. The signature proved to be associated with distant metastasis–free survival in three cohort.
The CD74 gene, responsible for producing a protein associate with class II major histocompatibility complex (MHC) is implicated in an effective intratumor immune response (Wang et al., 2017). It also serves as a cell surface receptor for the cytokine macrophage migration inhibitory factor, which may play a pro-oncogenic role in promoting BC cell-stroma interactions (Verjans et al., 2009). Of note, CD74 was observed to be related to triple-negative breast cancer, which is the most aggressive subtype of breast cancer (Tian et al., 2012). The TSPAN7 is a cell-surface protein coding gene, and the coding protein of which plays a role in the regulation of cell development, activation, growth and motility (Perot and Ménager, 2020). For this reason, TSPAN7 is also known as CD231. Our study is the first to link CD74 and TSPAN7 expression with distant metastasis–free survival in breast cancer, highlighting gene signature based on CD74 and TSPAN7 as a predictor of metastasis development in BC, with a strong effect on patients’ distant metastasis–free survival.
In past studies, multiple prognostic models have been constructed because of the differential expression of CD74 (Wang et al., 2020) or TSPAN7 (Wu et al., 2020) between tumor and normal tissues. However, the reason for this difference is still unclear. As a result, much effort has been expended in trying to explore differential expression of CD74 or TSPAN7 in cancer cell strains, ignoring the influence of immune infiltration (Greenwood et al., 2012; Wang et al., 2018; Qi et al., 2020; Yu et al., 2021). Notably, heterogeneity of CD74 expression has been confirmed in tumor tissues (Richard et al., 2014), indicating that characterization of immune landscape cannot be discounted. In this study, GSEA revealed a statistical enrichment of immune-related signaling pathways in the high score group. Thus we speculated that the signature we constructed may reveal the levels of immune infiltration. The results of TIMER database confirmed this conjecture. CD74 and TSPAN7 expression was negatively correlated with tumor purity while positively correlated with the level of multiple immune cells. Furthermore, the fraction of immune cells in high score group was significantly higher than that in low score group, especially CT8+ T cells. According to the results presented above, we identified a novel cause of differential expression of CD74 and TSPAN7. The levels of CD74 and TSPAN7 reflected the ability of immune cells to infiltrate tumors. To the best of our knowledge, this is the first study to show that high CD74 and TSPAN7 expression is associated with tumor-infiltrating immune cells. Further studies on Intra-tumoral heterogeneity are warranted, in order to analyze the relationship between survival time and the level of immune cell infiltration.
Our study has some potential limitations. First, pathologic features were unavailable, so the correlation between our signature and pathological features could not be evaluated. Second, quanTIseq is a prediction tool of immune cell fraction based on deconvolution algorithm, as a result, it will predict a minimal amount of immune cells even though they are absent (Sturm et al., 2019).
In conclusion, we identified seven genes related to distant metastasis–free survival, namely, TSPAN7, CD74, MMP11, MELK, COL11A1, CHRDL1, PITX1, and a signature based on CD74 and TSPAN7 expression that may have potential of predicting DMFS. We found that methylation of TSPAN7 in BC cells inhibits the recruitment of CD74 positive immune cells, which may be associated with a lower risk of metastasis. The present study was the first to propose that high CD74 expression may be derived from tumor-infiltrating immune cells. Given the favorable discrimination of the signature, we developed a nomogram for clinical applications. This is the first nomogram based on gene signature that can be used to facilitate the individualized prediction of DMFS in BC patients.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethnic Committee of Shunde Hospital of Guangzhou University of Chinese Medicine. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
All authors listed have made direct intellectual contributions to the work, and approved it for its publication. CR made a substantial contribution when drafting the manuscript.
## Conflict of interest
The reviewer MY declared a shared parent affiliation with the author(s) DL to the handling editor at the time of review.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Challenging inhibitory control with high- and low-calorie food: A behavioural
and TMS study'
authors:
- Valentina Bianco
- Domenica Veniero
- Alessia D’Acunto
- Giacomo Koch
- Silvia Picazio
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9992824
doi: 10.3389/fnut.2023.1016017
license: CC BY 4.0
---
# Challenging inhibitory control with high- and low-calorie food: A behavioural and TMS study
## Abstract
Most people are often tempted by their impulses to “indulge” in high-calorie food, even if this behaviour is not consistent with their goal to control weight in the long term and might not be healthy. The outcome of this conflict is strongly dependent on inhibitory control. It has already been reported that individuals with weaker inhibitory control consume more high-calorie food, are more often unsuccessful dieters, overweight or obese compared to people with more effective inhibitory control. In the present study, we aimed at investigating inhibitory control in the context of human eating behaviour. A sample of 20 healthy normal-weight adults performed a $50\%$ probability visual affective Go/NoGo task involving food (high- and low-calorie) and non-food images as stimuli. Single-pulse transcranial magnetic stimulation (TMS) was administered over the right primary motor cortex (M1) either 300 ms after image presentation to measure corticospinal excitability during the different stimulus categories or 300 ms after the appearance of a fixation point, as a control stimulation condition. The experimental session consisted of a food target and a non-food target block. Behavioural outcomes showed a natural implicit inclination towards high-calorie food in that participants were faster and more accurate compared to the other categories. This advantage was selectively deleted by TMS, which slowed down reaction times. MEPs did not differ according to the stimulus category, but, as expected, were bigger for Go compared to NoGo trials. Participants judged high-calorie food also as more appetising than low-calorie food images. Overall, our results point to a differential modulation when targeting inhibitory control, in favour of the more palatable food category (high-calorie). Present data suggest that the activity of the motor system is modulated by food nutritional value, being more engaged by appetising food. Future work should explore to what extent these processes are affected in patients with eating disorders and should aim to better characterise the related dynamics of cortical connectivity within the motor network.
## Introduction
Eating and managing caloric intake is essential for our survival. Food-related choices mediate a large part of a person’s well-being from many points of view such as health, social life, and self-esteem. Since the dawn of neuroscience, the salience of food stimuli for the nervous system has been clearly recognised. The eminent Russian physiologist Pavlov has in fact demonstrated how food generates an unconditioned response and how other stimuli become salient if repeatedly associated with it [1].
Most people, are often tempted by their impulses to “indulge” in high-calorie food, even if this behaviour is not consistent with their goal to control weight in the long term (e.g., 2). This conflict is exacerbated by our social environment, where the abundance of appetising high-calorie food can trigger overconsuming in individuals with enhanced food cue reactivity [3]. The outcome of this conflict between short-term gratification and long-term goal is strongly dependent on inhibitory control, i.e., the ability to withhold pressing responses [4]. In line with this view, previous studies have shown that individuals with weaker inhibitory control consume more high-calorie food, are more often unsuccessful dieters, and are more often overweight or obese compared to people with more effective inhibitory control (5–8). In contrast, individuals with abnormal inhibitory control can exhibit a dysfunctional restriction of food intake and weight loss [9, 10].
Previous work acknowledged that reactivity to food cues is part of a trait that combines increased appetitive drive and reduced inhibitory control, which in turn would explain why some individuals are more prone to uncontrolled eating or at the opposite restrictive eating behaviour [9, 11, 12]. The ability to control impulses is challenged by appetizing stimuli. This does not seem to be related to the need to procure the food necessary for the sustenance of the organism (homeostatic drive), but rather resembles a mechanism similar to addiction (hedonic drive) [13, 14], which in some cases can become very harmful [15].
A large body of research showed that inhibitory control plays a crucial role in balancing food behaviour and in the psychopathology of eating disorders (16–21).
However, studies that have investigated food-related inhibitory control in healthy participants are few and have shown inconsistencies [22].
Inhibitory control can be assessed through the Go/NoGo task. Participants are instructed to respond to a target (Go trial) and withhold response to a non-target (NoGo trial), whilst response speed and accuracy are measured. Previous studies used a classic version of this task with abstract stimuli to investigate the relationship between inhibitory control and eating behaviour [23]. However, both top-down inhibitory control and bottom-up drive to food stimuli interact to determine eating behaviour [24]. Therefore, Go/NoGo tasks incorporating food stimuli are likely to be more informative.
Previous studies already used this task including food stimuli with promising results, but with some limitations. For instance, Batterink et al. [ 25] developed a Go/NoGo task using healthy food as Go stimuli and unhealthy food images as NoGo stimuli but lacked a control stimulus, such as non-food pictures, and the sample was limited to female participants. A following study [26], measured response inhibition in food and non-food trials in males and females, but the task used words rather than images, with the undesired involvement of reading ability and abstract thought.
Although inhibitory control has been traditionally considered to rely exclusively on the prefrontal cortex, recent findings using transcranial magnetic stimulation (TMS) have shown that other areas are involved. Not only does the prefrontal cortex send its inhibitory command to the primary motor cortex (M1) but other nodes of the motor system, such as the cerebellum, play an active role in inhibitory control [27, 28].
The aim of this study was twofold. First, we wanted to study inhibitory control when different food stimuli (high-calorie vs. low-calorie) are presented in a design that would overcome previous studies’ limitations, by using images of food rather than words and by comparing the obtained response to a control stimulus (non-food). Second, we wanted to investigate the involvement of the motor system in inhibitory control and food-related environmental cues. Specifically, we used a Go/NoGo task with food and non-food images as Go and/or NoGo stimuli and concurrently investigate the excitability of the primary motor cortex (M1) collecting motor-evoked potentials (MEPs) elicited by Transcranial Magnetic Stimulation (TMS) in healthy eaters. We targeted M1 as a part of the inhibitory system because it can be easily accessed by TMS and because it provides a direct measure of the system excitability via MEP amplitude.
Since other factors such as current hunger or body weight may interfere with inhibitory mechanisms [22, 29], all participants were healthy eaters and they were tested under the same satiety state. Finally, we evaluated the relation between individual impulsivity traits and behavioural measures. We hypothesised that participants were faster and more accurate when presented with high-calorie, compared to low-calorie and non-food images, because they were adaptively and implicitly prompted to react at targets with good nutritive value.
## Participants
A gender-balance sample of 20 healthy participants (10 females: age 27.9 ± 3.8; years of education >13; body mass index – BMI 23.3 ± 2.9) was recruited. The sample size for the main mixed-design ANOVA (Stimulus type × TMS) was determined with G*power software [30]. The effect size was estimated from a previous study [31]. We set the expected effect size f(U) at 0.38, the α level at 0.05 and the desired power (1 − β) at $80\%$.
Inclusion criteria were the absence of any reported neurological or psychological disorders and the absence of eating disorders as measured by EDE-Q global score [32, 33]. Moreover, vegetarian or vegan participants were excluded, as well as those who claimed to have particular food preferences or restrictions related to intolerances, allergies to foods or metabolic compromissions (e.g., diabetes and celiac disease). All participants were right-handed (Edinburgh Handedness Inventory, 34), reported normal or corrected-to-normal vision and were naïve about the aim of the study. Written informed consent was obtained from all participants according to the Declaration of Helsinki. The project was approved by the Santa Lucia Foundation IRCCS of Rome Ethical Committee.
## Experimental procedure
Participants were seated in a comfortable armchair in a dimly illuminated, electrically shielded, and sound-proof room. Since fasting levels might have an impact on food related inhibitory performance [35], the experimental procedure consisted of a single session that was programmed at least 2 h after the last meal. Participants first performed the Go/NoGo task whilst TMS was delivered to the right primary motor cortex. At the end of the session, they were asked to rate the palatability of the food images presented during the task and to fill out questionnaires (see below for details).
## Food Go/NoGo task
A $50\%$ probability visual Go/NoGo task with food and non-food images was used. Images were selected from the extended food-pics database [36]. Food stimuli included high-calorie and low-calorie food pictures whereas non-food images represented pleasant inedible objects. The experimental task consisted of a food-target and a non-food target block. In the food-target block, participants had to respond by pressing the space bar of a QWERTY keyboard with the right index finger when they recognised a food (Go trials) and refrain from responding when they saw a non-food picture (NoGo trials). In the non-food target block, the Go/NoGo categories were reversed (Figure 1A).
In each block, a total of 384 trials were presented, including 192 ($50\%$) non-food images and 192 ($50\%$) food images. Of the 192 food stimuli, 96 were high-calorie and 96 low-calorie. Each trial started with a fixation point (a yellow point at the centre of a white screen) presented for a variable interval between 500 ms and 1,500 ms (mean = 975 ms). The timing of the fixation point was varied pseudo-randomly to prevent the predictability of the stimulus. This was followed by a food or non-food image, presented for 500 ms. The duration of each trial ranged from 1,500 to 2000 ms. The duration of each block was approximately 10 min interspersed with two short breaks. Overall, the task lasted around 25 min. It is worth noting that having an equal probability of Go/NoGo trials could be less effective to elicit a clear inhibitory activity compared to Go/NoGo tasks with rare NoGo trials [37]. However, the present choice was motivated by the need to have two equal blocks only differing in the instructions, and complicated by the existence of two (high-, low-calorie) food categories to be balanced with the non-food condition. Moreover, this choice was based on previous results by our group showing that a simple $50\%$ probability Go/NoGo task can effectively modulate frontocentral cortical activity that is related to inhibitory control (Figure 1A; 28, 38).
The task was programmed and run through E-Prime 2.0 Professional software and stimuli were shown on a 23 inches monitor. For each task condition, reaction times (RTs) in ms and accuracy scores (the percentage of correct responses, i.e., button press for Go trials and no button press for NoGo trials) were collected. Both speed and accuracy were encouraged for task performance (Figure 1B).
**Figure 1:** *Experimental setting and behavioural task. (A) Each participant performed the task responding with the right hand whilst single pulse transcranial magnetic stimulation (spTMS) was applied over the right primary motor cortex (M1) and motor evoked potentials (MEP) were collected from the left hand. Each participant self-reported height and weight for BMI calculation, filled the Barratt-Impulsivity scale 11, and expressed its judgement about the palatability of task food images in a 5-point scale. (B) The Go/NoGo task was composed of two blocks in which participants had to alternatively respond to food or non-food stimuli.by pressing the spacebar. In FT trials spTMS was delivered 300 ms following the fixation point onset, in TT trials spTMS was delivered 300 ms following the target food/non-food target image onset. In NT trials spTMS was not delivered.*
## Transcranial magnetic stimulation (TMS)
Single-pulse transcranial magnetic stimulation (TMS) was administered throughout the experiment over the right primary motor cortex (M1) to measure corticospinal excitability during the different task conditions (high-calorie, low-calorie, non-food). Namely, in a subset of 128 trials “TARGET TMS” (TT) [64 food (32 high-calorie, 32 low-calorie), 64 non-food stimuli] TMS was applied after 300 ms from the food/non-food picture appearance. In a subset of 128 trials “FIXATION TMS” (FT) [64 food (32 high-caloric, 32 low-caloric), 64 non-food stimuli] TMS was applied during the presentation of the fixation point, to have a control condition. TMS pulse was always released with a stimulus onset asynchrony (SOA) of 300 ms. This SOA has been previously used when a single TMS pulse was combined with the execution of a task in healthy participants (Figure 1; 39–42). Finally, in a subset of 128 “NO TMS” – NT – trials [64 food (32 high-calorie, 32 low-calorie) 64 non-food stimuli] TMS was not applied, to observe participants’ behaviour in absence of any TMS interference.
TMS was performed using a MagStim Super Rapid magnetic stimulator (Magstim Company, Whitland, Wales, United Kingdom) connected to a figure-of-eight coil with a diameter of 70 mm. The magnetic stimulus had a biphasic waveform with a pulse width of about 300 μs. The coil over M1 was always placed tangentially to the scalp at the 45° angle from the midline of the central sulcus, inducing a posterior–anterior current flow. Electromyographic (EMG) traces were recorded from the left first dorsal interosseous (FDI) muscle by using 9-mm-in-diameter surface cup electrodes. The active electrode was placed over the muscle belly and the reference electrode over the metacarpophalangeal joint of the index finger. The ground electrode was placed over the left wrist. The TMS intensity was adjusted to evoke an MEP of ~1 mV peak to peak in the relaxed FDI [43]. The average TMS intensity was 65 ± $12\%$ of the maximum stimulator output.
Responses were amplified with a Digitimer D360 amplifier through filters set at 20 Hz and 2 kHz, with a sampling rate of 5 kHz and then recorded by a computer with the use of Signal software.
The average MEP peak-to-peak amplitude was calculated for each stimulus type (high-calorie, low-calorie, non-food) and TMS TT and FT conditions. MEPs above and below 2 standard deviations of the mean were removed from the analysis [44]. The left FDI relaxation during the experiment was visually monitored by the experimenter who checked both the position of the hand and the EMG traces online. Participants responded to the task with their right hand, whilst the left hand, from which the MEPs were collected, was comfortably placed on an armrest. All participants were informed prior to the start of the task that their left hand could make small involuntary movements in response to the TMS. As specified above, MEPs above the 2 standard deviations were removed from the analysis to exclude trials where the muscle was not relaxed. The number of MEPs excluded for each condition was: 10.4 ± $6.5\%$ of High-calorie Go; 12.5 ± $11.3\%$ of Low-calorie Go; 7.3 ± $4.5\%$ of Non-Food Go; 5.2 ± $4.8\%$ of High-calorie NoGo; 10.4 ± $1.8\%$ of Low-calorie NoGo; 12 ± $7.7\%$ of Non-Food NoGo.
MEP amplitude for each stimulus type was then normalised using the MEP obtained for the FT condition, i.e., MEP amplitude obtained in the TT condition was expressed as a percentage of the amplitude recorded in FT trials.
## Palatability of the images
After the Go/NoGo task, all high-and low-calorie food images were presented again in random order and participants were asked to score their palatability on a five-level Likert scale, from 1 (unappetising) to 5 (very appetising).
## Impulsivity assessment
All participants filled out the Barratt Impulsiveness Scale-11 (BIS-11), a commonly used 30-item self-report questionnaire designed to assess impulsiveness [45]. All items are measured on a four-point Likert-type scale. In the scoring procedure, the items are summed and the higher scores indicate greater impulsivity (ranging between 30 and 120). A summary of the BIS-11 results is reported in Table 1.
**Table 1**
| Attention | Motor impulsiveness | Self-control | Cognitive complexity | Perseverance | Cognitive instability | Total score |
| --- | --- | --- | --- | --- | --- | --- |
| 9.55 ± 0.63 | 11.6 ± 0.61 | 11.95 ± 0.61 | 10.8 ± 0.37 | 6.8 ± 0.42 | 6.45 ± 0.39 | 57.15 ± 1.84 |
## Eating behaviour assessment
The body Mass Index (BMI; kg/m2) was calculated according to the self-reported weight and height values [46]. All participants completed the latest edition of the Eating Disorder Examination Questionnaire (EDE-Q 6.0 – 32, 33). The questionnaire has been extensively studied, and its psychometric properties have been demonstrated to distinguish healthy participants from patients with eating disorders. Furthermore, the EDE-Q has shown high internal consistency in both nonclinical and clinical samples. The EDE-Q provides a global score based on four subscales (Restraint, Eating Concern, Shape Concern and Weight Concern). Participants with clinical value in EDE-Q global score were excluded from the study.
## Statistical analysis
Two-way 3 × 3 repeated measures ANOVAs were performed for each behavioural measure of interest (reaction times and accuracy scores) with factors Stimulus type (High-Calorie, Low-Calorie, Non-food), TMS (NT, FT, and TT). A two-way repeated measure ANOVA was performed on MEP amplitude with factors Stimulus type (High-Calorie, Low-Calorie, and Non-food), and Trial type (Go vs. NoGo). Statistical analyses were performed in STATISTICA 8.0 using two-tailed alpha levels of <0.05 for defining significance. Post-hoc comparisons were performed by paired t-tests (Bonferroni corrected). The effect size was indicated as partial eta square (ηP2). The relationship between BMI and BIS-11 total scores was also investigated using Spearman’s rho coefficient.
## Palatability
Participants judged high-calorie food as being the most appetising, with the exception of image 5 in the high-and low-calorie food categories, which were scored differently from other images of their same category, i.e., both were perceived as halfway between high-and low-palatable food (average score = 3). For this reason, all measurements collected for these images were removed from the final analysis (Figure 2).
**Figure 2:** *Palatability rating. Each participant judged the palatability of each food image used in the main task on a 5-point Likert’s scale. The mean palatability score is shown on the y-axis, for each high/low-calorie stimuli used (x-axis). With the exception of high-calorie and low calorie item 5, which were excluded from the final analysis, participants considered high-calorie food images as being more appetising than low-calorie images.*
## Food Go/NoGo
The ANOVA on Go RTs showed significant main effects of Stimulus type (F2,38 = 21.44, $p \leq 0.001$, ηP2 = 0.53) and TMS (F2,38 = 7.86, $$p \leq 0.001$$, ηP2 = 0.29) and a significant Stimulus type × TMS interaction (F4,76 = 8, $p \leq 0.001$, ηP2 = 0.3). We first investigated the effect of Stimulus type in the NT condition. Post-hoc comparisons indicated that, in the absence of TMS, participants were faster in response to high-calorie (mean = 428 ms) compared to low-calorie (mean = 445 ms; $p \leq 0.002$) and non-food images (mean = 465 ms; $p \leq 0.001$). Participants were also faster to respond to low-calorie food images compared to non-food images ($p \leq 0.001$). Post-hoc comparisons on the FT condition revealed the same pattern, with faster RTs to high-calorie (mean = 422 ms) compared to low-calorie food (mean = 450 ms; $p \leq 0.001$) and non-food images (mean = 463; $p \leq 0.001$). Finally, when TMS was delivered during the image presentation (TT), we found an effect specific to high-calorie food with RTs significantly slower compared to the NT condition (high-calorie TT = 448 ms vs. high-calorie NT = 428 ms; $p \leq 0.001$). A summary of these results is shown in Figure 3A and Tables 2, 3.
**Figure 3:** *Effects of image type and TMS over MI. (A) Mean go reaction times (RTs) for High/Low-Calorie and Non-food trials for NT (No TMS), FT (Fixation TMS) and TT (Target TMS). In NT and FT, high-calorie RTs were faster than RTs collected for low-calorie and non-food images. Low-calorie RTs were also faster compared to non-food RTs. A specific increase of high-calorie RTs emerged between NT and TT conditions. (B) Mean accuracy for High/Low-Calorie and Non-food trials for NT, FT and TT. Participants were more accurate for high-calorie compared to low calorie NT trials. Accuracy decreased in TT conditions, regardless of stimulus type. (C) Mean normalised amplitude of motor evoked potentials (MEPs). MEP amplitude collected during the target presentation (TT) for High/Low-Calorie and Non-food trials in Go and NoGo conditions is expressed as percentage of the MEP amplitude collected at baseline (FT). As expected, corticospinal excitability is lower in NoGo conditions ($$p \leq 0.036$$). In each panel, black dots represent individual data. The box and wiskers show the Median ± 1.5 interquartile range (IQR) and asterisks represent significance.* TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 The ANOVA performed on accuracy showed significant main effects of Stimulus type (F2,38 = 7, $$p \leq 0.003$$, ηP2 = 0.27) and TMS (F2,38 = 4.4, $$p \leq 0.019$$, ηP2 = 0.19) but not significant Interaction. Post-hoc comparisons indicated that participants were generally more accurate to respond to high-calorie (mean = $98.84\%$) than low-calorie (mean = $97.59\%$; $$p \leq 0.002$$) and non-food images (mean = $98.43\%$; $p \leq 0.05$). We also found that TMS caused a deterioration of the accuracy in TT compared to NT conditions ($$p \leq 0.019$$), regardless of Stimulus type. A summary of these results is shown in Figure 3B and Tables 2, 4. No significant correlation between BIS-11 total scores and self-reported BMI was found ($R = 0.197$; $$p \leq 0.414$$).
**Table 4**
| TMS | NT | FT | TT | Stimulus type | High | Low | Non-food |
| --- | --- | --- | --- | --- | --- | --- | --- |
| NT | | 0.16 | 0.02 | High | | 0.002 | 0.702 |
| FT | 0.16 | | 1.0 | Low | 0.002 | | 0.056 |
| TT | 0.02 | 1.0 | | Non-food | 0.702 | 0.056 | |
## Motor-evoked potentials (MEPs)
The repeated measure ANOVA performed to investigate changes in MEP amplitude revealed no significant main effect of Stimulus type (F2,38 = 0.21, $$p \leq 0.809$$), Trial type (F1,19 = 2.64, $$p \leq 0.121$$) and no significant Interaction (F2,38 = 0.11, $$p \leq 0.897$$). To further investigate changes in M1 excitability due to the Go/NoGo condition, we decided to run an additional ANOVA, again with factors Stimulus type and Trial type, but only considering food vs. non-food stimuli, without distinguishing between high-and low-calorie images. We found a significant main effect of Trial type (F1,19 = 5.129, $$p \leq 0.036$$, ηP2 = 0.21), explained by a greater MEP amplitude in Go trials (mean Go amplitude = $104\%$ vs. mean NoGo amplitude = $99\%$). No other significant effect was found. A summary of these results is shown in Figure 3C and Table 5.
**Table 5**
| Unnamed: 0 | High | Low | Non-food |
| --- | --- | --- | --- |
| Go | 104 ± 20 | 103 ± 23 | 105 ± 27 |
| NoGo | 98 ± 30 | 102 ± 25 | 99 ± 24 |
## Discussion
In the present study, we investigated whether there is a relationship between the visual appearance of food (high-calorie, low-calorie food) and inhibitory control in healthy individuals, in light of the growing interest for the topic in the eating-disorder literature [47]. To this aim, we used an affective Go/NoGo task manipulating the stimulus category and coupled the behavioural measures with measures of corticospinal excitability (i.e., MEPs) to gain further insight on the contribution of the motor system. Overall, the designed task was innovative when compared to previous investigations because it used food stimuli rather than abstract stimuli [23], included a neutral condition rather than limiting the comparison to high-and low-calorie food [25] and prevented the undesired influence of reading ability or abstract thought on performance [26]. Crucially, whilst previous investigations were limited to female participants [25] our sample included both normal-weighted male and female participants.
In line with a neuroimaging study using similar paradigms [48], we showed that RTs to high-calorie food were faster than RTs to low-calorie and non-food stimuli. High-calorie food images were considered also more appetising than low-calorie food images by our participants confirming the already reported correlation between the calorie content and the perceived palatability [49, 50].
This result suggests that the visual appearance of high-calorie food generates a state of heightened arousal in the observer, which in turn contributes to promptly responding to appetising food pictures and that we are naturally more inclined to respond to rewarding stimuli such as high-calorie food [48]. Crucially, participants were not tested under conditions of starvation (i.e., they were invited to consume breakfast or lunch 2 or 3 h prior to the experimental session); therefore, the evidence of reduced RTs to high-calorie food was specifically powerful, even in absence of a starvation state. Collectively, these observations reinforce the view that high-calorie foods have high incentive value, prompt response [51] and increase arousal independently from satiety levels [52].
An interesting result is the increase of high-calorie RTs when a single TMS pulse was delivered over the primary motor cortex during the image presentation (TT condition). This effect is specific to high-calorie stimuli and therefore cannot be explained by a generic interference of TMS on task execution. The present result suggests that the motor system might be particularly engaged in movements aimed at approaching high-calorie and therefore high-nutritious/appetising food. In this sense, the response of the motor system, which we measure here by testing the excitability of the primary motor area, in reacting to high-calorie food compared to food with little (low-calorie) or no nutritional value (non-food) would be greater. The finding that the caloric content of food did shape task performance is particularly interesting because participants were unaware of the distinction between high-and low-calorie stimuli during the task. They were simply instructed to go or not to go in response to food or non-food images. The challenge posed by appetising foods to inhibitory control mechanisms could explain why often during a diet we cannot stop right in front of high calorie foods. Most of the attempts so far have targeted the dorsolateral prefrontal cortex [47, 53]. A different strategy could target and modulate the activity of other areas of the motor circuit, such as the primary motor cortex or the cerebellum.
We also found that accuracy was increased for high-compared to low-calorie food. This result corroborates previous studies (e.g., 54) suggesting a more efficient response to foods with greater salience. It is worth noting that the accuracy reflects the ability to effectively respond as well as refrain from when a stimulus is presented. Therefore, the selection of the appropriate response (go or no go, depending on the instructions) to palatable foods might reflect a fine-tuning of cognitive control processes as a result of the increased nutritive value and the appetising nature of high-calorie food. Accordingly, several studies have found that accuracy for high-calorie food during inhibitory control tasks is reduced in overweight population (55–57), suggesting a dysregulation of the inhibitory system that is specific for palatable foods. In addition, He et al. [ 48] showed that the ability to inhibit response to high-calorie foods is more difficult for individuals with higher BMI and who reported to consume more high-calorie foods. However, in the present study we did not find a direct correlation between response inhibition and individual BMI or impulsivity assessment. The lack of a correlation might be due to measurement errors, since weight and height were self-reported and not objectively measured in the laboratory. Furthermore, a sample size of 20 normal-weighted participants with very low variations in BMI might not be powerful enough to unveil a possible correlation. Future studies including participants with abnormal BMI (i.e., excessively high or low) are needed to clarify this relationship.
Last, neurophysiological results (MEPs) showed an effect of trial type, with higher corticospinal excitability for Go compared to NoGo trials, independently from the stimulus category. This is in line with previous studies [58, 59] showing that the decrease in MEP amplitude is due to the inhibition of the corticospinal pathway after the NoGo decision or to the increase of corticospinal excitability following Go stimuli, in line with premovement facilitation [60]. However, the present MEP results are in contrast to what already reported in a preceding TMS study showing that the additional excitatory drive triggered by salient cues counteracts the presence of inhibitory influences to M1 [61].
One possible explanation of the null result regarding the modulation of MEP amplitude according to high, low-calorie, or non-food category could be the timing of the pulse delivery, i.e., 300 ms after the image presentation which could be too late to target the dynamics of corticospinal excitability. According to this view, in a previous study, the time course of corticospinal excitability changes during a similar task found effects on MEP amplitude up to 200 ms following the onset of a simple Go/NoGo visual cue [38]. In the present study, we reasoned that more complex visual stimuli (images of food or objects instead of geometric shapes), would require a longer processing time and therefore we increased the cue to TMS interval to 300 ms. However, the average RTs to the food Go/NoGo task (high: 428 ms; low: 445 ms; non-food: 465 ms) are comparable to those of the simple Go/NoGo task used in Picazio et al. [ 38] (428 ms). It is therefore possible that using the same cue to TMS interval in the present study could have shown differences in the corticospinal activity depending on stimulus type. Therefore, we might have missed the relevant window for MEP modulation but were still able to interfere with RTs which are the final output of the motor process involved. Another explanation of this negative result for the MEPs could be the Go/NoGo trial ratio ($\frac{50}{50}$). This could be less effective in evoking a clear motor inhibition compared to Go/NoGo tasks with rarer NoGo trials [37].
The present data do not allow us to distinguish between externally-driven action inhibition, which is typically triggered by Go/NoGo tasks and internally-driven motivational factors that here could be elicited by the affective/nutritional component of the task [62]. This aspect should be explored in future studies in which participants could choose to respond or not when images are presented according to their preferences.
Finally, the present study has some limitations that could be addressed in future. First, larger sample sizes are needed to investigate any correlation between food-related inhibitory control and individual measures of body weight and impulsivity; second, subjective measures of weight and height should be replaced with objective measures; third, corticospinal excitability should be tested at different time points from stimulus presentation.
In the present study, we only tested healthy participants, but it has been previously reported that eating disorders are associated with altered responses in the control system. For example, in anorexia nervosa high-calorie food elicits an enhanced activation of cognitive control regions, explaining the persistent food avoidance and starvation [63]. On the contrary, in obesity, high-calorie food is associated with abnormal activation of the impulsive system, which leads to excessive food consumption [64, 65]. Therefore, in future studies should address the relationship between food-related inhibitory control mechanisms including also patients with eating disorders.
In conclusion, our findings show that the calorie content of food frequently corresponds to the perceived palatability in healthy participants and that the sight of high-calorie food triggers an implicit drive to approach and is characterised by a stronger activation of the primary motor cortex. This enhanced involvement of the motor circuit coupled with reduced reaction times and improved performance for high-calorie food might reflect the existence of adaptive mechanisms aimed to approach food with high nutritive value in healthy participants.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Fondazione Santa Lucia IRCCS. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AD’A, SP, and VB collected the data. DV, SP, and VB analysed and interpreted the data. SP and VB wrote the paper. GK and SP designed the experiment. All authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work.
## Funding
This work was supported by grant of the Italian Ministry of Health (grant number GR-2019-12369640 to SP).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: The SLC25A47 locus controls gluconeogenesis and energy expenditure
authors:
- Jin-Seon Yook
- Zachary H. Taxin
- Bo Yuan
- Satoshi Oikawa
- Christopher Auger
- Beste Mutlu
- Pere Puigserver
- Sheng Hui
- Shingo Kajimura
journal: Proceedings of the National Academy of Sciences of the United States of America
year: 2023
pmcid: PMC9992842
doi: 10.1073/pnas.2216810120
license: CC BY 4.0
---
# The SLC25A47 locus controls gluconeogenesis and energy expenditure
## Body
The role of mitochondria extends far beyond adenosine triphosphate (ATP) generation. Mitochondria serve as an essential organelle that supplies a variety of important metabolites to the cytosolic compartment and nucleus. An example is in the liver, wherein the mitochondria export phosphoenolpyruvate (PEP) and malate, which serve as gluconeogenic precursors in response to fasting. Under a fed condition, the mitochondria supply citrate that contributes to de novo lipogenesis [1, 2]. In addition, mitochondrion-derived alpha-ketoglutarate (α-KG) functions as a cofactor of Jumonji C domain demethylases and ten-eleven translocation enzymes in the nucleus, thereby controlling the transcriptional program, a.k.a., retrograde signaling [3].
Mitochondrial flux in the liver is tightly regulated by hormonal cues, such as insulin and glucagon, and dysregulation of these processes profoundly impacts the maintenance of euglycemia, as often seen under the conditions of hyperglycemia and type 2 diabetes (4–6). For instance, elevated protein expression or activity of pyruvate carboxylase (PC), a mitochondrial matrix-localized enzyme that catalyzes the carboxylation of pyruvate to oxaloacetate (OAA), is associated with hyperglycemia [7, 8]. On the other hand, liver-specific deletion of PC potently prevented hyperglycemia in diet-induced obese mice [9]. Another example is the mitochondrion-localized phosphoenolpyruvate carboxykinase (PCK2, also known as M-PEPCK) that is expressed highly in the liver, pancreas, and kidney, where it catalyzes the conversion of OAA to PEP [10, 11]. It has been demonstrated that activation of PCK2 in the liver enhanced the PEP cycle and potentiated gluconeogenesis [12, 13]. In turn, depletion of PCK2 in the liver impaired lactate-derived gluconeogenesis and lowered plasma glucose, insulin, and triglycerides in mice [14]. Accordingly, a better understanding of mitochondrial metabolite flux in the liver may provide insights into therapeutic strategies for the management of hyperglycemia and type 2 diabetes.
Of note, the mitochondrial inner-membrane is impermeable to metabolites relative to the outer membrane. As such, a variety of carrier proteins in the mitochondrial inner-membrane play essential roles in the regulation of metabolite transfer between the matrix and the cytosolic compartment [15, 16]. As an example, mitochondrial pyruvate carrier (MPC) mediates the import of pyruvate into the matrix [17, 18]. It has been demonstrated that liver-specific deletion of MPC1 or MPC2 reduced mitochondrial tricarboxylic acid (TCA) flux and impaired pyruvate-driven hepatic gluconeogenesis in diet-induced obese mice (19–21). Recent studies also reported the identification of SLC25A39, which is responsible for glutathione import [22], SLC25A44 for branched-chain amino acids import [23, 24], and SLC25A51 for nicotinamide adenine dinucleotide (NAD+) import (25–27).
Because of their essential roles, nearly all mitochondrial metabolite carriers (e.g., SLC25A family members) are ubiquitously expressed in mammalian tissues. However, there are two exceptions: uncoupling protein 1 (UCP1, also known as SLC25A7) that is selectively expressed in brown/beige fat [28], and an orphan carrier, SLC25A47, which is expressed selectively in the liver (Fig. 1A). SLC25A47 was previously described as a mitochondrial protein of which expression was down-regulated in hepatocellular carcinoma and that could reduce mitochondrial membrane potential in cultured Hep3B cells, a liver-derived epithelial cell line [29]. In yeast, SLC25A47 overexpression elevated mitochondrial electron transport chain uncoupling, implicating its protective role against hepatic steatosis [30]. In contrast, a recent study showed that genetic loss of Slc25a47 led to mitochondrial dysfunction, mitochondrial stress, and liver fibrosis in mice [31]. Given these apparently inconsistent reports, this study aims to determine the physiological role of SLC25A47 in systemic energy homeostasis.
**Fig. 1.:** *SLC25A47 is a liver-specific mitochondrial carrier that links to human metabolism. (A) Relative mRNA levels in indicated human tissues. The data obtained from Human Protein Atlas were analyzed. (B) Upper: The assay of transposase accessible chromatin sequencing (ATAC)-seq analysis of the Slc25a47 gene locus in the liver, heart, and lung [data from gene expression omnibus (GEO) (GSE111586)]. Lower: The recruitment of HNF4α to the Slc25a47 gene based on the chromatin immunoprecipitation sequencing (ChIP-seq) data of HNF4α [data from GEO (GSE90533)]. (C) The mRNA expression of liver Slc25a47 in HNF4 null mouse embryos and controls (18.5-dpc). The data were obtained by analyzing the Affymetrix Mouse Genome 430 2.0 Array data (GSE3126). n = 3 for both groups. ***P < 0.001 by unpaired Student’s t test. (D) The phenome-wide association plot shows significant associations of SLC25A47 for available traits generated by bottom-line metaanalysis across all datasets in the Common Metabolic Diseases Knowledge Portal. (E) The genetic associations between SLC25A47 snips (SNPs) and indicated metabolic phenotypes.*
## Significance
Given the impenetrable nature of the mitochondrial inner-membrane, most of the known metabolite carrier proteins, including SLC25A family members, are ubiquitously expressed in mammalian tissues. One exception is SLC25A47, which is selectively expressed in the liver. The present study showed that depletion of SLC25A47 reduced mitochondrial pyruvate flux and hepatic gluconeogenesis under a fasted state, while activating energy expenditure. The present work offers a liver-specific target through which we can restrict hepatic gluconeogenesis, which is often in excess under hyperglycemic and diabetic conditions.
## Abstract
Mitochondria provide essential metabolites and adenosine triphosphate (ATP) for the regulation of energy homeostasis. For instance, liver mitochondria are a vital source of gluconeogenic precursors under a fasted state. However, the regulatory mechanisms at the level of mitochondrial membrane transport are not fully understood. Here, we report that a liver-specific mitochondrial inner-membrane carrier SLC25A47 is required for hepatic gluconeogenesis and energy homeostasis. Genome-wide association studies found significant associations between SLC25A47 and fasting glucose, HbA1c, and cholesterol levels in humans. In mice, we demonstrated that liver-specific depletion of SLC25A47 impaired hepatic gluconeogenesis selectively from lactate, while significantly enhancing whole-body energy expenditure and the hepatic expression of FGF21. These metabolic changes were not a consequence of general liver dysfunction because acute SLC25A47 depletion in adult mice was sufficient to enhance hepatic FGF21 production, pyruvate tolerance, and insulin tolerance independent of liver damage and mitochondrial dysfunction. Mechanistically, SLC25A47 depletion leads to impaired hepatic pyruvate flux and malate accumulation in the mitochondria, thereby restricting hepatic gluconeogenesis. Together, the present study identified a crucial node in the liver mitochondria that regulates fasting-induced gluconeogenesis and energy homeostasis.
## SLC25A47 Is a Liver-Specific Mitochondrial Carrier That Links to Human Metabolic Disease.
The SLC25A solute carrier proteins comprise 53 members in mammals, constituting the largest family of mitochondrial inter-membrane metabolite carriers [32]. Among these 53 members, SLC25A47 is unique because this is the sole SLC25A member that is expressed selectively in the liver of mice [29, 31]. We independently found that SLC25A47 is selectively expressed in the liver of humans (Fig. 1A) and in mice (SI Appendix, Fig. S1A). The publicly available single-cell RNA-seq dataset [33] shows that hepatocytes are the primary cell type that expresses SLC25A47, while Kupffer cells also express SLC25A47 that account for approximately $10\%$ of total transcripts in the liver (SI Appendix, Fig. S1B).
We next examined the genetic mechanism through which Slc25a47 is selectively expressed in the liver. The analysis of assay of transposase accessible chromatin sequencing (ATAC-seq) data (GSE111586) found an open chromatin architecture in the Slc25a47 gene locus (chromosome 12: 108,815,740 to 108,822,741) specific to the liver, whereas the same region appeared to form a heterochromatin structure in the heart and lung (Fig. 1 B, Upper). Notably, the euchromatin region of the Slc25a47 gene contained binding sites of hepatocyte nuclear factor 4 alpha (HNF4α), to which HNF4α is recruited in the liver (Fig. 1 B, Lower). This result caught our attention because mutations of HNF4α are known to cause maturity-onset diabetes of the young 1, and it plays a central role in the regulation of hepatic and pancreatic transcriptional networks [34, 35]. Importantly, HNF4α is required for the hepatic expression of Slc25a47, as the analysis of a previous microarray dataset [36] found that genetic loss of HNF4α significantly attenuated the expression of Slc25a47 in the mouse liver (Fig. 1C).
Another important observation is in human genetic association studies from the Type 2 Diabetes Knowledge Portal (type2diabetesgenetics.org), wherein we found significant associations between SLC25A47 and glycemic and lipid homeostasis. The notable associations include fasting glucose levels adjusted for body mass index (BMI), random glucose levels, HbA1c levels adjusted for BMI, high-density lipoprotein (HDL) cholesterol levels, and aspartate aminotransferase (AST)–alanine aminotransferase (ALT) ratio (Fig. 1D). One of the strongest single nucleotide polymorphisms (SNIPs) was located in the intronic region of SLC25A47 (rs1535464), which showed significant associations with lower levels of fasting and random glucose, lower HbA1c levels adjusted for BMI, and higher HDL cholesterol levels (Fig. 1E). Similarly, another SNIP (rs35097172) in the regulatory region of SLC25A47 was associated with lower levels of fasting/random glucose, HbA1c levels adjusted for BMI, and higher HDL cholesterol levels. These data indicate that SLC25A47 is involved in the regulation of glucose and lipid homeostasis, although how these snips (SNPs) affect SLC25A47 expression remains unknown.
## Liver-Specific Depletion of SLC25A47 Protects against Body-Weight Gain and Lowers Plasma Cholesterol Levels.
To determine the physiological role of SLC25A47 in energy homeostasis, we next developed mice that lacked SLC25A47 in a liver-specific manner by crossing Slc25a47flox/flox mice with Albumin-Cre (Alb-Cre; Slc25a47flox/flox, herein Slc25a47Alb-Cre mice). We validated that the liver of Slc25a47Alb-Cre mice expressed significantly lower levels of Slc25A47 messenger RNA (mRNA) than littermate control mice (Slc25a47flox/flox) by 80 % (Fig. 2A). The remaining mRNA in Slc25a47Alb-Cre mice could be attributed to inefficient Cre expression or the transcripts in nonhepatocytes, such as Kupffer cells. The expression of the Slc25a47 neighboring genes, including Wdr25, Begain, Dlk1, Meg3, Slc25a29, Yy1, and Degs2, was not altered in the liver of Slc25a47Alb-Cre mice relative to control mice (SI Appendix, Fig. S2A).
**Fig. 2.:** *Metabolic characterization of liver-specific SLC25A47 deletion mice. (A) Relative liver Slc25a47 mRNA levels in Slc25a47Alb-Cre (n = 10) and littermate controls (n = 6). (B) Changes in body weight of Slc25a47Alb-Cre mice and control on a regular chow diet and on a high-fat diet. Regular chow diet; n = 16 for Slc25a47Alb-Cre, n = 10 for controls. High-fat diet; n = 6 for Slc25a47Alb-Cre, n = 11 for controls. P-value determined by two-way ANOVA followed by unpaired Student’s t test. (C) Body composition of mice at 16 wk of regular chow diet and at 6 wk of high-fat diet. Regular chow diet; n = 11 for Slc25a47Alb-Cre, n = 10 for controls. High-fat diet; n = 6 for Slc25a47Alb-Cre, n = 11 for controls. (D) Indicated tissue weight of mice in (B). (E) Serum cholesterol levels of mice at 12 wk of age on a regular chow diet and after 6 wk of high-fat diet. Regular chow diet; n = 16 for Slc25a47Alb-Cre, n = 10 for controls. High-fat diet; n = 6 for Slc25a47Alb-Cre, n = 10 for controls. (F) Serum TG levels of mice in (E). ns, not significant. A–E, *P < 0.05, **P < 0.01, ***P < 0.001 by unpaired Student’s t test.*
At birth, there was no difference in the body weight and body size between Slc25a47Alb-Cre mice and littermate control mice (SI Appendix, Fig. S2B). However, Slc25a47Alb-Cre mice gained significantly less weight than controls at 3 wk of age and thereafter on a regular-chow diet (Fig. 2 B, Left). This phenotype was more profound when mice at 6 wk of age were fed on a high-fat diet (HFD, $60\%$ fat) (Fig. 2 B, Right). The difference in body weight arose from reduced adipose tissue mass and lean mass both on a regular-chow diet and a high-fat diet (Fig. 2C). At tissue levels, adipose tissue and liver mass were lower in Slc25a47Alb-Cre mice relative to control mice (Fig. 2D).
Additionally, we found significantly lower serum levels of total cholesterol in Slc25a47Alb-Cre mice than those in controls both on regular-chow and high-fat diets (Fig. 2E). On the other hand, we observed no difference in serum triglyceride (TG) levels between the two groups both on regular-chow and high-fat diets (Fig. 2F). We found no difference in serum ALT, AST, and albumin levels on a high-fat diet, although serum ALT and AST levels were higher in Slc25a47Alb-Cre mice at 12 wk of age on a regular-chow diet (SI Appendix, Fig. S2 C–E).
## Depletion of SLC25A47 Led to Elevated Whole-Body Energy Expenditure.
Given the difference in body weight between Slc25a47Alb-Cre mice and control mice, we examined the whole-body energy expenditure using metabolic cages. Regression-based analysis of energy expenditure by CaIR-analysis of covariance (ANCOVA) [37] showed that Slc25a47Alb-Cre mice exhibited significantly higher whole-body energy expenditure (kcal/day) independent of body mass at 23 °C. The difference remained significant when mice were kept at 30 °C (Fig. 3A). On the other hand, there was no difference in their food intake and locomotor activity between the genotypes (Fig. 3 B and C).
**Fig. 3.:** *Depletion of SLC25A47 elevated whole-body energy expenditure. (A) CaIR-analysis of covariance (ANCOVA) analysis of Slc25a47Alb-Cre mice and littermate controls at 23 °C after 1 wk on a high-fat diet and at 30 °C after 3 wk on a high-fat diet. n = 6 for Slc25a47Alb-Cre, n = 11 for controls. (B) Food intake of mice in (A). (C) Locomotor activity of mice in (A). (D) Serum FGF21 levels of mice at 12 wk of age on a regular chow diet and a high-fat diet for 6 w. Regular chow diet; n = 16 for Slc25a47Alb-Cre, n = 10 for controls. High-fat diet; n = 16 for Slc25a47Alb-Cre, n = 10 for controls. (E) Relative hepatic Fgf21 expression of mice in (D). (F) Relative hepatic Fgf21 expression of mice on a regular chow diet at indicated ages. n = 6 for Slc25a47Alb-Cre, n = 4 for controls (2 wk), n = 8 for Slc25a47Alb-Cre, n = 14 for controls (4 wk), n = 14 for Slc25a47Alb-Cre, n = 14 for controls (7 wk), n = 16 for Slc25a47Alb-Cre, n = 9 for controls (12 wk). (G) Correlation between serum FGF21 levels and serum AST levels of mice at 2 wk (Left) and 4 wk of age (Right) on a regular chow diet. n = 6 for Slc25a47Alb-Cre, n = 4 for controls (2 wk), n = 8 for Slc25a47Alb-Cre, n = 11 for controls (4 wk). ns, not significant, by simple linear regression. B–F, ns, not significant, *P < 0.05, **P < 0.01, ***P < 0.001 by unpaired Student’s t test.*
A possible explanation for the high energy expenditure might be the enhanced thermogenic capacity of brown adipose tissue (BAT) or its sensitivity to β3-adrenergic receptor (β3-AR) signaling. Accordingly, we tested the hypothesis by examining BAT thermogenesis in response to a β3-AR agonist (CL316,243) at 30 °C. This is a gold-standard method to determine BAT thermogenic responses to β3-AR stimuli, while excluding the contribution of shivering thermogenesis by skeletal muscle [38]. We found that a single administration of β3-AR agonist (CL316,243) at 0.5 mg/kg (high dose) potently increased whole-body energy expenditure both in Slc25a47Alb-Cre and littermate controls to a similar degree (SI Appendix, Fig. S3A). This result suggests that the cell-intrinsic thermogenic capacity of BAT, if maximumly activated by a β3-AR stimulus, appears comparable between the two groups. Accordingly, we asked if there was any change in circulating hormonal factors that influenced whole-body energy expenditure of Slc25a47Alb-Cre mice. In this regard, FGF21 is a probable candidate because it is a well-established endocrine hormone that increases energy expenditure by activating the sympathetic nervous system [39]. Consistent with the recent work [31], we found that serum levels of FGF21 in Slc25a47Alb-Cre mice were significantly higher relative to littermate controls both on regular-chow and high-fat diets (Fig. 3D). The increase in circulating FGF21 levels was due to elevated Fgf21 transcription in the liver (Fig. 3E). This is in agreement with the previous work demonstrating that the liver is the primary source of circulating FGF21 [40].
Of note, elevated *Fgf21* gene expression in Slc25a47Alb-Cre mice was already observed at 2 wk of age, a time point in which there was no difference in body weight, serum ATL/AST levels, and mitochondrial stress-related genes in the liver (Fig. 3F and SI Appendix, Fig. S3 B–D). Importantly, there was no correlation between serum FGF21 levels and AST levels in control and Slc25a47Alb-Cre mice at 2 and 4 wk of age (Fig. 3G). The results indicate that the stimulatory effect of SLC25A47 loss on FGF21 expression is not merely a consequence of liver damage. We addressed this point further in the following sections.
## SLC25A47 Is Required for Pyruvate-Derived Hepatic Gluconeogenesis In Vivo.
We next examined the extent to which SLC25A47 regulates systemic glucose homeostasis. This is based on the observation that fasting glucose levels of Slc25a47Alb-Cre mice were consistently lower than littermate controls both on regular-chow and high-fat diets (Fig. 4A). At 4 wk of high-fat diet, we found no major difference in glucose tolerance between the two groups, although fasting glucose levels were lower in Slc25a47Alb-Cre mice than control mice (Fig. 4B). In contrast, Slc25a47Alb-Cre mice exhibited significantly higher insulin tolerance than controls in response to insulin at a low dose (0.4 U/kg) (Fig. 4C). It is notable that Slc25a47Alb-Cre mice remained hypoglycemic (<70 mg/dL) following insulin administration.
**Fig. 4.:** *SLC25A47 is required for pyruvate-derived hepatic gluconeogenesis. (A) Fasting blood glucose levels (6 h) in Slc25a47Alb-Cre mice and littermate controls at 7 wk of age on a regular chow diet and at 4 wk on a high-fat diet. Regular diet; n = 16 for Slc25a47Alb-Cre, n = 10 for controls. High-fat diet; n = 6 for Slc25a47Alb-Cre, n = 11 for controls. *P < 0.05, ***P < 0.001 by unpaired Student’s t test. (B) Glucose tolerance test in Slc25a47Alb-Cre mice (n = 6) and littermate controls (n = 11) at 4 wk of high-fat diet. 6-h-fasted mice received glucose (2 g kg−1 body weight, i.p.). Right: Area under the curve (AUC) of the data. ns, not significant. (C) Insulin tolerance test in Slc25a47Alb-Cre mice (n = 7) and littermate controls (n = 11) at 4 wk of high-fat diet. 6-h-fasted mice received insulin (0.4 U kg−1 body weight, i.p.). (D) Pyruvate tolerance test in Slc25a47Alb-Cre mice (n = 6) and littermate controls (n = 11) on a high-fat diet for 3 wk. 16-h-fasted mice received pyruvate (2 g kg−1 body weight, i.p.). (E) Pyruvate tolerance test in male Slc25a47Alb-Cre mice (n = 16) and littermate controls (n = 10) on a regular chow diet. (F) Pyruvate tolerance test in female Slc25a47Alb-Cre mice (n = 8) and littermate controls (n = 12) on a regular chow diet. (G) Schematic illustration of tracer experiments. Fasted mice on a regular chow diet were infused with indicated 13C-labeled tracers via the catheter. (H) Direct contribution of 13C-labeled tracers to glucose, lactate, and glycerol in (G). n = 6 for Slc25a47Alb-Cre, n = 6 for controls. *P < 0.05 by unpaired Student’s t test. (I) The relative contribution of 13C-labeled lactate to circulating levels of glucose, pyruvate, and lactate in (G). **P < 0.01. B–F, P-value determined by two-way ANOVA followed by Fisher's least significant difference (LSD) test. AUC: *P < 0.05, **P < 0.01, ****P < 0.0001 by unpaired Student’s t test.*
Pyruvate tolerance tests found that Slc25a47Alb-Cre mice at 3 wk of high-fat diet exhibited significantly lower hepatic gluconeogenesis than control mice (Fig. 4D). Of note, the difference in pyruvate tolerance was independent of diet and sex, as we observed consistent results both in male and female mice on a regular-chow diet (Fig. 4 E and F). On the other hand, there was no difference in glucose-stimulated serum insulin levels and hepatic glycogen contents between the two groups (SI Appendix, Fig. S4 A and B). These results led to the hypothesis that the lower fasting glucose levels seen in Slc25a47Alb-Cre mice are attributed to reduced hepatic gluconeogenesis, rather than impaired glycogenolysis or elevated insulin sensitivity in the skeletal muscle.
To test the hypothesis, we next examined the contribution of hepatic gluconeogenesis to circulating glucose by infusing fasted mice with U-13C-labeled lactate or 13C-labeled glucose. To examine the relative contribution of other gluconeogenic precursors to blood glucose, we also infused fasted mice with U-13C-labeled glycerol and U-13C-alanine (Fig. 4G). During the infusion, we collected and analyzed serum from fasted mice using liquid-chromatography–mass spectrometry (LC–MS), as described in recent studies [41, 42]. We used 13C-lactate as a gluconeogenic precursor instead of pyruvate because circulating lactate is the primary contributor to gluconeogenesis and in rapid exchange with pyruvate [43].
The analyses showed that glucose production from 13C-lactate was significantly lower in Slc25a47Alb-Cre mice than in control mice (Fig. 4H orange bars). Notably, this impairment was selective to the lactate-to-glucose conversion, as we found no significant difference in glucose production from 13C-glycerol between the two groups (Fig. 4H blue bars and SI Appendix, Fig. S4C). The relative contribution of alanine to serum glucose was far less than lactate, with no statistical difference between the genotypes (Fig. 4H red bars). The lactate-to-pyruvate conversion was unaffected in Slc25a47Alb-Cre mice, suggesting that impaired gluconeogenesis from lactate is attributed to reduced pyruvate utilization in the liver (Fig. 4I). We also found no difference in the conversion from 13C-glucose to pyruvate and lactate (SI Appendix, Fig. S4D). These results indicate that SLC25A47 is required selectively for gluconeogenesis from lactate under a fasted condition, whereas it is dispensable for gluconeogenesis from other substrates.
## Acute Depletion of SLC25A47 Improved Glucose Homeostasis without Causing Liver Damage.
A recent work suggested the possibility that the metabolic changes in Slc25a47Alb-Cre mice, such as elevated FGF21 and impaired glucose production, were merely secondary to general hepatic dysfunction and fibrosis [31]. To exclude metabolic complications caused by chronic deletion of SLC25A47, particularly during the prenatal and early postnatal periods, we aimed to acutely deplete SLC25A47 in adult mice. To this end, we acutely depleted SLC25A47 in adult mice by delivering adeno-associated virus (AAV)-thyroxine binding globulin (TBG)-Cre or AAV-TBG-null (control) into the liver of Slc25a47flox/flox mice via tail-vein (Fig. 5A). AAV-Cre administration successfully reduced Slc25a47 mRNA expression by approximately $50\%$ (Fig. 5B). Although the depletion efficacy of AAV-Cre was less than the genetic approach using Albumin-Cre, this model gave us an opportunity to determine the extent to which acute and partial depletion of SLC25A47 in adult mice sufficiently affect hepatic glucose production and energy expenditure, while avoiding metabolic complications associated with chronic SLC25A47 deletion.
**Fig. 5.:** *Acute depletion of SLC25A47 improves glucose homeostasis independent of liver damage. (A) Schematic illustration of acute depletion of Slc25a47 study. Slc25a47flox/flox mice at 7 wk of age on a regular chow diet received AAV-Cre or AAV-null (control) via tail-vein. (B) Relative hepatic Slc25a47 mRNA levels of mice at 2 wk after AAV injection. n = 5 for both groups. *P < 0.05 by Mann–Whitney U test. (C) Body weight of mice in (B). *P < 0.05 by unpaired Student’s t test. (D) Serum FGF21 levels of mice after 2 wk of AAV injection. n = 16 for controls, n = 18 for AAV-Cre. ****P < 0.0001 by unpaired Student’s t test. (E) Relative expression of hepatic Fgf21 of mice in (B). **P < 0.01 by Mann–Whitney U test. (F) Fasting blood glucose levels (6 h) of mice in (B). ****P < 0.0001 by unpaired Student’s t test. (G) Fasting insulin levels of mice in (A). n = 16 for controls, n = 18 for AAV-Cre. *P < 0.05 by unpaired Student’s t test. (H) Pyruvate tolerance test at 2 wk after AAV injection. Fasted mice received pyruvate (2 g kg−1 body weight, i.p.). n = 15 for controls, n = 16 for AAV-Cre. P-value determined by two-way ANOVA followed by Fisher's LSD test. Right: AUC. **P < 0.01 by unpaired Student’s t test. (I) Insulin tolerance test in Slc25a47flox/flox mice at 6 wk. Fasted mice received insulin (0.4 U kg−1 body weight, i.p.). n = 11 for controls, n = 13 for AAV-Cre. Right: AUC. *P < 0.05 by unpaired Student’s t test. (J) Representative liver Picro-Sirius Red staining of mice in (B). Scale = 200 μm. (K) Relative liver expression of fibrosis marker genes of mice in (B). ns, not significant (L) Correlation between serum AST levels and hepatic Slc25a47 expression of mice in (B).*
After 2 wk of AAV administration, we found that acute SLC25A47 depletion led to reduced body-weight gain (Fig. 5C and SI Appendix, Fig. S5A) and increased serum FGF21 levels (Fig. 5D). The increase in serum FGF21 levels was associated with elevated hepatic FGF21 mRNA expression (Fig. 5E). Consistent with the observations in Slc25a47Alb-Cre mice, acute SLC25A47 depletion resulted in reduced fasting serum glucose levels (Fig. 5F) and insulin levels (Fig. 5G). Importantly, acute SLC25A47 depletion improved systemic pyruvate tolerance (Fig. 5H) and insulin tolerance (Fig. 5I). In contrast, acute SLC25A47 did not alter systemic glycerol tolerance, although there was a modest change at later time points after glycerol administration (SI Appendix, Fig. S5B). The difference in glycerol tolerance at later time points is likely because glycerol-derived glucose is converted to lactate in peripheral tissues, which is eventually utilized as a gluconeogenic substrate [44].
Next, we examined whether such metabolic changes were associated with liver injury in vivo. Histological analyses by Picro-Sirius Red staining did not find any noticeable sign of liver fibrosis (Fig. 5J). Similarly, histological analyses by hematoxylin and eosin (H&E) staining found no difference between control vs. AAV-Cre injected mice (SI Appendix, Fig. S5C). Furthermore, acute SLC25A47 depletion did not alter the expression of liver fibrosis marker genes (Fig. 5K). Also, we found no significant correlation between serum AST levels and hepatic SLC25A47 expression (Fig. 5L) and between serum AST levels and FGF21 levels (SI Appendix, Fig. S5D). Moreover, we observed no significant difference in the Complex I and II activities of isolated liver mitochondria between the two groups (SI Appendix, Fig. S5E). These data suggest that acute SLC25A47 depletion sufficiently enhanced hepatic FGF21 expression, pyruvate tolerance, and insulin tolerance independent of liver damage and hepatic mitochondrial dysfunction.
## SLC25A47 Is Required for Mitochondrial Pyruvate Flux and Malate Export.
We next asked which steps of the lactate-derived hepatic gluconeogenesis were altered in Slc25a47Alb-Cre mice. To this end, we took unbiased omics approaches—RNA-seq and mitochondrial metabolomics analyses—in the liver of Slc25a47Alb-Cre mice and littermate controls under a fasted condition. The summary of the results is shown in Fig. 6A. The RNA-seq data analysis found that the liver of Slc25a47Alb-Cre mice expressed significantly higher levels of Pkm, Eno3, Aldoa, Fbp1, Gpi1, and G6pc3 (Fig. 6B), suggesting a compensatory upregulation of gluconeogenic gene expression in Slc25a47Alb-Cre mice.
**Fig. 6.:** *SLC25A47 is required for mitochondrial pyruvate efflux and malate export. (A) Summary of liver RNA-seq and mitochondrial metabolomics data in Slc25a47Alb-Cre mice and littermate controls on a regular chow diet. Red letters: up-regulated in Slc25a47Alb-Cre relative to controls. Blue letters: down-regulated in Slc25a47Alb-Cre. Black letters: no statistical changes. (B) Heatmap displaying liver mRNA levels of cytosolic gluconeogenic enzymes and mitochondria localized enzymes in fasted 12-wk-old Slc25a47Alb-Cre mice (n = 7) and controls (n = 6). Data transcript per million (TPM) are expressed as z-scores; blue (low expression), red (high expression). P-value determined by unpaired Student’s t test. (C) Relative metabolite levels in the liver mitochondria of fasted Slc25a47Alb-Cre mice (n = 14) and controls (n = 14). Data were normalized to mitochondrial protein levels (ug/mL). P-value determined by unpaired Student’s t test. (D) Schematic illustration of tracer experiments in isolated mitochondria. (E) The relative contribution of 13C-labeled pyruvate to indicated metabolites in the mitochondria. n = 14 for Slc25a47Alb-Cre, n = 14 for lcontrols. Data were normalized to mitochondrial protein. P-value determined by unpaired Student’s t test.*
A notable finding is the distinct regulation of mitochondrial matrix-localized enzymes vs. cytosolic enzymes: We found that the expression of the mitochondrial TCA cycle enzymes, such as citrate synthase (Cs), the mitochondrial form of isocitrate dehydrogenase (Idh2), and Suclg2 (the subunits of succinate-CoA ligase) was significantly up-regulated in the liver of Slc25a47Alb-Cre mice relative to controls. In addition, the expression of Pck2, the M-PEPCK that converts OAA to PEP within the mitochondria, was up-regulated in the liver of Slc25a47Alb-Cre mice. In contrast, the expression of the cytosolic form of PEPCK (Pck1) was unchanged. Similarly, the expression of Mdh2, which catalyzes the conversion between OAA and malate in the mitochondria, was significantly elevated in the liver of Slc25a47Alb-Cre mice, whereas the expression of Mdh1, the cytosolic form, showed a trend of down-regulation. These results suggest that SLC25A47 loss leads to a distinct gene expression pattern of mitochondrial vs. cytosolic enzymes that control hepatic gluconeogenesis.
The mitochondrial metabolomics analysis revealed that the liver mitochondria of Slc25a47Alb-Cre mice accumulated significantly higher levels of isocitrate, fumarate, and malate than those of control mice (Fig. 6C). In contrast, mitochondrial PEP contents were lower in Slc25a47Alb-Cre livers relative to controls. We found no difference in the mitochondrial contents of pyruvate, citrate, α-KG, succinyl CoA, succinate, and OAA between the two groups. Additionally, there was no difference in the mitochondrial contents of cofactors required for the TCA cycle reactions, such as coenzyme A, reduced nicotinamide adenine dinucleotide (NADH), nicotinamide adenine dinucleotide phosphate (NADP+), NADPH, and flavin adenine dinucleotide (FAD), although mitochondrial NAD+ and guanosine triphosphate (GTP) levels were higher in Slc25a47Alb-Cre mice than controls (SI Appendix, Fig. S6A).
The above data led to the hypothesis that SLC25A47 controls either pyruvate import to the mitochondrial matrix or pyruvate flux within the mitochondria. To test this, we isolated mitochondria from the liver of Slc25a47Alb-Cre mice and littermate controls under a fasted condition. The isolated mitochondria were incubated with [U-13C] labeled pyruvate and subsequently analyzed by LC–MS/MS (Fig. 6D). We found no difference in the mitochondrial contents of 13C-pyruvate levels between the two groups, suggesting that mitochondrial pyruvate uptake per se was not altered in the liver of Slc25a47Alb-Cre mice (Fig. 6E). This is in agreement with the data that the expression of MPC1 and MPC2 was not different between the genotypes (Fig. 6B). On the other hand, the enrichments of 13C-labeled citrate, isocitrate, succinate, fumarate, and malate were significantly lower in the mitochondria of Slc25a47Alb-Cre mice than those in controls (Fig. 6E). There was no difference in 13C-labeled OAA and PEP between the groups. Together, these results suggest that genetic loss of SLC25A47 impaired mitochondrial pyruvate flux, leading to an accumulation of fumarate, malate, and isocitrate in the liver mitochondria. Impaired export of malate from the mitochondria into the cytosolic compartment leads to reduced lactate-derived hepatic gluconeogenesis under a fasted condition.
## Discussion
Mitochondrial flux in the liver is highly nutrition-dependent. Under a fed condition, malate is imported into the mitochondrial matrix in exchange for α-KG via mitochondrial α-KG/malate carrier (SLC25A11) as a part of the malate-aspartate shuttle, a mechanism to transport reducing equivalents (NADH) into the mitochondrial matrix [45]. In addition, mitochondrial dicarboxylate carrier SLC25A10 can mediate the import of malate into the mitochondrial matrix in addition to malonate, succinate, phosphate, sulfate, and thiosulfate [46]. Under a fasted state, when liver glycogen is depleted, malate is exported from the mitochondrial matrix into the cytosolic compartment, where it is converted to OAA by MDH1 and utilized as a gluconeogenic substrate. However, what controls the nutrition-dependent mitochondrial malate flux remains elusive. The present work showed that SLC25A47 depletion led to an accumulation of mitochondrial malate and reduced hepatic gluconeogenesis, without affecting gluconeogenesis from glycerol. The results indicate that SLC25A47 mediates the export of mitochondrion-derived malate into the cytosol. However, the present study could not exclude the possibility that SLC25A47 mediates the transport of cofactors needed for mitochondrial pyruvate flux, although we found no difference in the mitochondrial contents of coenzyme A and NADH between the genotypes. Our future study aims to determine the specific substrate of SLC25A47 by biochemically reconstituting this protein in a cell-free system, such as liposomes.
The present work showed that depletion of SLC25A47 reduced mitochondrial pyruvate flux, thereby restricting lactate-derived hepatic gluconeogenesis and preventing hyperglycemia. This is in alignment with several mouse models with impaired mitochondrial pyruvate flux in the liver. For instance, liver-specific depletion of pyruvate carboxylase (PC limits the supply of pyruvate-derived OAA in the mitochondria, leading to reduced TCA flux and hepatic gluconeogenesis [9]. Similarly, liver-specific depletion of the MPC1 or MPC2 or the M-PEPCK reduces hepatic gluconeogenesis and protects mice against diet-induced hyperglycemia (14, 19–21). A recently developed noninvasive method, positional isotopomer NMR tracer analysis, would be instrumental to determine how SLC25A47 loss alters the rates of hepatic mitochondrial citrate synthase flux vs. PC flux [35].
It is worth pointing out that elevated energy expenditure and reduced body weight are unique to Slc25a47Alb-Cre mice. Indeed, no changes in energy expenditure and body weight were seen in mice that lacked MPC$\frac{1}{2}$ or M-PEPCK relative to the respective controls. Elevated energy expenditure of Slc25a47Alb-Cre mice appears to be attributed to elevated FGF21 as recent work demonstrated that deletion of FGF21 abrogated the effects of SLC25A47 on energy expenditure and body weight [31]. Importantly, our results suggest that partial SLC25A47 depletion was sufficient to stimulate FGF21 production independently from liver damage. It is conceivable that changes in mitochondrion-derived metabolites, such as malate and others, control the transcription of FGF21 via retrograde signaling [3]. Our future study will explore the mechanisms through which SLC25A47-mediated mitochondrial signals control the nuclear-coded transcriptional program in a nutrition-dependent manner. In addition, genetic rescue experiments, such as ectopically reintroducing SLC25A47 into the liver of Slc25a47Alb-Cre mice will determine the direct vs. indirect actions of SLC25A47 on gluconeogenesis and energy expenditure.
With these results in mind, we consider that SLC25A47 is a plausible target for hyperglycemia and type 2 diabetes for the following reasons. First, excess hepatic gluconeogenesis is commonly seen in human hyperglycemia and type 2 diabetes (4–6). Notably, genome-wide association studies (GWAS) data found significant associations between SLC25A47 and glycemic homeostasis in humans—particularly, several SNPs in the SLC25A47 were significantly associated with lower levels of glucose and HbA1c adjusted for BMI, although how these SNPs affect SLC25A47 expression awaits future studies. Second, SLC25A47 is exceptionally unique among 53 members of the mitochondrial SLC25A carriers, given its selective expression in the liver. This tissue specificity makes SLC25A47 an attractive therapeutic target, considering the recent successful examples in which liver-targeting mitochondrial uncouplers protected mice against type 1 and type 2 diabetes, hepatic steatosis, and cardiovascular complications (47–49). A potential caveat is the detrimental effect associated with chronic SLC25A47 deletion, such as mitochondrial stress, lipid accumulation, and fibrosis [31]. However, our data showed that acute depletion of SLC25A47 by ~$50\%$ sufficiently restricted gluconeogenesis and enhanced insulin tolerance in adult mice without causing liver fibrosis and mitochondrial dysfunction. Thus, it is conceivable that temporal and partial inhibition of SLC25A47 using small-molecule inhibitors or antisense oligos would be effective in restricting excess hepatic gluconeogenesis while avoiding the detrimental side effects.
## Animal Study.
All the animal experiments in this study were performed in compliance with protocols approved by the Institutional Animal Care and Use Committee at Beth Israel Deaconess Medical Center. The Slc25a47 floxed (Slc25a47flox/flox) mouse was generated by in vitro fertilization of homozygous sperm (UC David) from Slc25a47tm1a (EUCOMM)Hmgu targeting exons 5 and 6 of the Slc25a47 gene in C57BL/6J background. A floxed LacZ-neomycin cassette on the Tm1a allele was removed using a flippase (FLP)/Frt deletion by breeding Slc25a47flox/flox with FLP deleter mice (Jackson Laboratory, Stock No. 009086). Slc25a47flox/flox mice were bred with Albumin Cre mice (Jackson Laboratory, Stock No. 003574) to generate liver-specific Slc25a47 deletion mice (Slc25a47Alb-Cre). Mice were kept under a 12-h:12-h light–dark cycle at ambient temperature (22 to 23 °C) and had free access to food and water. Mice were maintained on a regular chow diet or fed with a high-fat diet ($60\%$ fat, D12492, Research Diets) starting from 6 wk of age for 6 wk. All mice were fasted for 6 h before killing. To acutely deplete Slc25a47, we injected 7-wk-old Slc25a47flox/flox mice with 1.5 × 1011 genome copies of AAV8-TBG-Cre (Addgene, 107787-AAV8) or AAV8-TBG-null (control, Addgene, 105536-AAV8) through tail vein injection.
## Human SNP Analyses.
Data were obtained from the Type 2 Diabetes Knowledge Portal (type2diabetesgenetics.org) and reconstructed. We used the SLC25A47 gene as the primary locus and expanded 5,000 bp proximal and distal to the total gene distance in order to identify regions of interest that may be outside of the coding sequence, i.e., promoters or enhancers.
## 13C-Glucose and 13C-Lactate Infusion Study.
Jugular vein catheters (Instech Labs) were implanted in the right jugular vein of 10-wk-old mice ($$n = 6$$ per group) under aseptic conditions. The catheter was connected to a vascular access button (Instech Labs) into which the tracer was infused. After 1 wk of the recovery period, mice were fasted for 6 h, and then infused for 2.5 h with U-13C-glucose (0.2 M, CLM-1396), U-13C-sodium lactate (0.49 M, CLM-1579), 0.2 M 13C-alanine (0.2 M, CLM-2184-H), and U-13C-glycerol (0.1M, CLM15101), respectively, at 2 to 3-d interval. The infusion rate was 0.1 µL g−1 min−1, and mice moved freely in a cage during the intravenous infusions. Blood (~ 10 µL) was collected from the tail into microvettes with coagulating activator (Starstedt Inc, 16.440.100). Blood samples were kept on ice, and serum was separated by centrifugation at 3,000 g for 10 min at 4 °C. 4 µL serum was added to 60 µL ice-cold extraction solvent (methanol: acetonitrile: water at 40:40:20), vortexed vigorously and incubated on ice for at least 5 min. The samples were centrifuged at 16,000 g for 10 min at 4 °C, and the supernatant was transferred to LC–MS tubes for analyses.
## Calculation of Direct Contribution Fraction of Gluconeogenic Substrates to Glucose.
The calculation follows the method as prior reported [50]. Briefly, for a metabolite with carbon number C, the labeled isotopologue is noted as [M+i], and its fraction is noted as L[M+i], with i being the number of 13C atoms in the isotopologue. The overall 13C labeling Lmetabolite of the metabolite is calculated as the weighted average of atomized labeling of all isotopologues, or mathematically,Lmetabolite=∑i=oniCL[M+i] The normalized labeling Lmetabolite←tracer is defined as the labeling of a metabolite normalized by the labeling of the infused tracer, asLmetabolite←tracer=LmetaboliteLtracer As such, the direct contribution of gluconeogenic substrates to glucose production is algebraically calculated by solving the matrix equation1Lgly←lacLala←lacLlac←gly1Lala←glyLlac←alaLgly←ala1 flacfglyfala=Lglu←lacLglu←glyLglu←ala Specifically, let M be the matrix and f the vector on the left side, and L the vector on the right side. The operation seeks tomin M·f-L, subject to vector f≥vector 0
The equation is solved using the R package limSolve [51]. The error was estimated using Monte Carlo simulation by running the matrix equation 100 times, each time using randomly sampled Lmetabolite←tracer values drawn from a normal distribution based on the mean and SE of entries in M and f. The calculated f 's were pooled to calculate the error. This scheme was extended to calculate the mutual interconversions among the metabolites. The peak intensity of each measured isotope was corrected by natural abundance. To calculate the fraction of 13C-labeled carbon atoms of glucose, pyruvate, lactate, glutamine, and alanine derived from 13C-glucose and 13C-lactate, percent 13C enrichment (%) was first calculated from the data corrected by natural abundance and then normalized based on the serum tracer enrichment.
## 13C-Tracers in the Liver Mitochondria.
Fifty microliters isolated mitochondrial suspension was added into 450 uL modified KPBS (136 mM KCL, 10 mM KH2PO4, 10 mM HEPES, pH 7.25) containing 2 mM U-13C pyruvate (Cambridge Isotopes, CLM-2440-0.1) and incubated on ice for 5 min. After incubation, samples were centrifuged at 10,000 g × 30 s, and washed three times by adding 1 mL ice-cold KPBS. Subsequently, the supernatant was removed and 1 mL ice-cold LC/MS $80\%$ methanol was added. To completely extract metabolites from the mitochondria, the sample was homogenized using TissueLyser II (Qiagen, 85300) for 5 min at 30 Hz, followed by centrifugation at 20,000 g for 15 min at 4 °C. The supernatant was kept on dry ice, and the pellet was resuspended with 500 uL $80\%$ LC/MS-grade methanol, vortexed vigorously, and allowed to extract on ice. The samples were then centrifuged at 20,000 g for 10 min at 4 °C. The extraction was vacuum dried using a vacuum concentrator (Eppendorf, concentrator Plus 5305). Dried samples were solubilized in 50 µL LC/MS water. Metabolite analysis was conducted at the BIDMC metabolomics core. The data were normalized by protein concentration.
## Author contributions
J.-S.Y., Z.H.T., and S.K. designed research; J.-S.Y., Z.H.T., B.Y., S.O., C.A., and B.M. performed research; S.H. contributed new reagents/analytic tools; J.-S.Y., Z.H.T., B.Y., S.O., C.A., B.M., P.P., S.H., and S.K. analyzed data; S.K. conceived the project; and J.-S.Y. and S.K. wrote the paper.
## Competing interests
The authors declare no competing interest.
## Data, Materials, and Software Availability
Previously published data were used for this work [36]. Human SNP data were obtained from the Type 2 Diabetes Knowledge Portal (type2diabetesgenetics.org). scATAC-seq and ChIP-seq data were obtained from GEO (GSE111586 and GSE90533, respectively). For the analysis of SLC25A47 gene expression in human tissues and single cells of the human liver, the data was obtained from Human Protein Atlas (https://www.proteinatlas.org/ENSG00000140107-SLC25A47/tissue and https://www.proteinatlas.org/ENSG00000140107-SLC25A47/single+cell+type/liver, respectively). The data for mouse Slc25a47 expression in tissues was obtained from GTEx portal (https://www.gtexportal.org/home/gene/SLC25A47).
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|
---
title: Conserved reduction of m6A RNA modifications during aging and neurodegeneration
is linked to changes in synaptic transcripts
authors:
- Ricardo Castro-Hernández
- Tea Berulava
- Maria Metelova
- Robert Epple
- Tonatiuh Peña Centeno
- Julia Richter
- Lalit Kaurani
- Ranjit Pradhan
- M. Sadman Sakib
- Susanne Burkhardt
- Momchil Ninov
- Katherine E. Bohnsack
- Markus T. Bohnsack
- Ivana Delalle
- Andre Fischer
journal: Proceedings of the National Academy of Sciences of the United States of America
year: 2023
pmcid: PMC9992849
doi: 10.1073/pnas.2204933120
license: CC BY 4.0
---
# Conserved reduction of m6A RNA modifications during aging and neurodegeneration is linked to changes in synaptic transcripts
## Body
Decades after it was first described, the posttranscriptional modification of mRNA has recently become an area of intense research interest in multiple fields [1]. N6-methyladenosine (m6A) is the most abundant internal RNA modification and has been studied in the context of multiple cellular functions (2–4). The deposition of m6A modifications on targeted mRNAs is mediated by the activity of a m6A methylation complex composed of methyltransferases (METTL) METTL3 and METTL14 with the adaptor protein WTAP (5–7). m6A sites are commonly located within the DRACH consensus sequence (where D = A, T, or G, R = A or G, and H = A, T, or C) and are found across the entire transcript but often concentrate close to the stop codon and in the 3′UTR [2, 3, 8]. m6A modifications are removed by demethylases such as fat mass and obesity-associated protein (FTO) and alpha-ketoglutarate–dependent dioxygenase AlkB homologue 5 (ALKBH5), making the regulation of m6A levels a complex and dynamic process [9, 10]. Several reader proteins recognize m6A-labeled transcripts affecting a broad array of biological processes associated with mRNA metabolism, including nuclear export, transport, degradation, and translation (7, 11–14). There is also emerging evidence that such epitranscriptomic mechanisms play a role in the onset and progression of various diseases including malignant neoplasms [15]. m6A RNA methylation has been linked to synapse function, memory consolidation, fear extinction, and recovery from brain injury (16–23). More recently, the expression levels of the methylation machinery and m6A levels have been investigated in the context of neurodegenerative diseases, particularly in Alzheimer’s disease (AD) (24–27). While changes in m6A levels have been observed in AD, the magnitude of these changes and their consequences for disease progression are not fully understood (28–30). Thus, more research on the epitranscriptome in the healthy and diseased brains is needed.
In this study, we analyze the m6A epitranscriptome in adult mouse and human brains. We found a conservation of methylation sites in transcripts that are linked to synapse function. Differential methylation analyses of brain regions of aged mice and human AD patients revealed a substantial reduction of m6A RNA methylated transcripts specifically within transcripts involved in synapse functioning such as CAMKII or Glua1. In line with these data, we show that reducing m6A levels impairs synaptic plasticity and results in reduced synaptic translation of these CAMKII and Glua1 transcripts into the corresponding protein. These data suggest that loss of m6A modifications on transcripts associated with synaptic function and plasticity could be an early event in cognitive diseases.
## Significance
The precise role of m6A RNA methylation in the adult brain is not well understood. In our study, we describe the genome-wide m6A epitranscriptome in the healthy and diseased brains of mice and humans. Our data demonstrate that a substantial amount of m6A transcripts are conserved. These transcripts are linked to the regulation of synaptic processes and are localized to synapses. We detected decreased m6A RNA methylation in brain tissue from an AD mice model and in human brain tissue from the cingulate gyrus in individuals with Alzheimer’s disease. At the mechanistic level, we provide evidence that supports that reduced m6A-modified transcripts are linked to impaired synaptic protein synthesis.
## Abstract
N6-methyladenosine (m6A) regulates mRNA metabolism. While it has been implicated in the development of the mammalian brain and in cognition, the role of m6A in synaptic plasticity, especially during cognitive decline, is not fully understood. In this study, we employed methylated RNA immunoprecipitation sequencing to obtain the m6A epitranscriptome of the hippocampal subregions CA1, CA3, and the dentate gyrus and the anterior cingulate cortex (ACC) in young and aged mice. We observed a decrease in m6A levels in aged animals. Comparative analysis of cingulate cortex (CC) brain tissue from cognitively intact human subjects and Alzheimer’s disease (AD) patients showed decreased m6A RNA methylation in AD patients. m6A changes common to brains of aged mice and AD patients were found in transcripts linked to synaptic function including calcium/calmodulin-dependent protein kinase 2 (CAMKII) and AMPA-selective glutamate receptor 1 (Glua1). We used proximity ligation assays to show that reduced m6A levels result in decreased synaptic protein synthesis as exemplified by CAMKII and GLUA1. Moreover, reduced m6A levels impaired synaptic function. Our results suggest that m6A RNA methylation controls synaptic protein synthesis and may play a role in cognitive decline associated with aging and AD.
## m6A Landscape in the Adult Mouse Brain.
We started our analysis by characterizing the landscape of m6A RNA modifications in the healthy adult mouse brain. The brains of ten (C57BL/6J) 3-mo-old (young) wild-type (WT) mice were extracted and dissected to obtain hippocampal subregions CA1, CA3, and dentate gyrus (DG) and the anterior cingulate cortex (ACC), which have been implicated with learning and memory processes and cognitive disease (Fig. 1A) [31]. Methylated RNA immunoprecipitation sequencing (meRIP-seq) was performed to determine the subregion-specific epitranscriptomic landscape in young adult mice.
**Fig. 1.:** *The m6A epitranscriptome in the adult mouse brain. (A) Experimental scheme. (B) Upper: Pie chart showing the percentage of m6A methylated transcripts in the hippocampal CA1, CA3, and DG regions when calculated against the corresponding input. Lower: m6A peak location is shown for the annotated transcript regions. The percentages are calculated from the total number of m6A peaks. (C) Guitar plot showing the distribution of m6A peaks along mRNAs in the hippocampal subregions. (D) Venn diagram comparing m6A methylated transcripts across hippocampal subregions. (E) GO term (biological function) analysis of the m6A methylated transcripts commonly detected in all hippocampal regions.(G) Guitar plot showing the distribution of m6A peaks along mRNAs in the ACC. (F) Upper: Distribution of m6A RNA methylation across the transcriptome in the ACC. Lower: m6A peak location is shown for the annotated transcript regions. The percentages are calculated from the total number of m6A peaks. (H) Venn diagram comparing m6A commonly methylated transcripts in the hippocampus and ACC. (I) Sunburst plots showing the enrichment of synapse-specific GO terms within commonly m6A methylated transcripts in the hippocampus and ACC. (J) Pie chart showing the percentage of m6A methylated transcripts within RNA-seq datasets obtained from hippocampal synaptosomes or synapses isolated from microfluidic chambers. ACC - anterior cingulate cortex, DG - dentate gyrus, 5UTR - 5′ untranslated region, 3UTR - 3′ untranslated region, CDS - coding sequence, ncRNA - noncoding RNA.*
The analysis of methylated regions revealed a large number of m6A peaks in the hippocampal brain subregions, with 18,270 peaks detected in the CA1, 20,415 in the CA3, and 16,686 in the DG (SI Appendix, Fig. S1A). These data are in agreement with previous findings from genome-wide analysis of m6A levels in cerebellar and cortical mouse tissues [16, 20, 22]. When we compared the transcripts carrying m6A modifications to the entire transcriptome, we observed that $40.27\%$ of the expressed genes in the DG were modified by m6A RNA methylation, while in the CA1 and CA3 regions, $42.81\%$ and $44.38\%$ were detected, respectively (Fig. 1B and Dataset S1). On average, every methylated transcript had 2.4 to 2.7 m6A methylated regions per transcript depending on the hippocampal subregion (SI Appendix, Fig. S1B). Motif enrichment analyses of the detected m6A peaks showed a strong overrepresentation of the m6A DRACH consensus motif, confirming that meRIP-seq had successfully enriched for m6A sites (SI Appendix, Fig. S1C). The m6A modifications were detected across the gene bodies and enriched in the vicinity of the stop codon and 3′UTR (Fig. 1 B and C), which is in agreement with previous data [2]. About $60\%$ (5,206 transcripts) of the transcripts with m6A modifications could be detected in all hippocampal subregions (Fig. 1D and Dataset S2). Gene ontology (GO) term analysis revealed that the m6A-modified transcripts detected in all hippocampal subregions showed a significant enrichment for genes associated with synaptic transmission, neurogenesis, synapse assembly, and RNA metabolism (Fig. 1E and Dataset S3). For example, more than 332 transcripts were linked to chemical synaptic transmission including genes such as CAMKIIa, or Glua1 (also known as Gria1) that were methylated in all hippocampal subregions. We also detected transcripts that were exclusively m6A modified in only one of the hippocampal subregions. In detail, 725 transcripts were specifically methylated in the CA3 region, 624 in the DG region, and 343 in the CA1 region (Fig. 1D and Dataset S4). GO term analysis revealed that CA1-specific transcripts are enriched for only two processes, namely negative regulation of protein complex assembly and regulation of sodium ion transport (SI Appendix, Fig. S1D and Dataset S5). CA3-specific transcripts with m6A modifications were linked to biological processes such as nuclear export, protein localization to the cellular periphery, or regulation of autophagy (SI Appendix, Fig. S1E and Dataset S6). Analysis of DG-specific transcripts revealed a number of interesting GO terms of which noncoding (nc) RNA processing was the most significant (SI Appendix, Fig. S1F and Dataset S7).
To further understand how the epitranscriptomic landscape varies across brain subregions, we performed meRIP-seq analysis from ACC tissue obtained from the same young mice. In the ACC, 11,816 m6A peaks were detected corresponding to 4,160 consistently methylated transcripts (2.83 peaks per transcript), which represented $27.3\%$ of the expressed genes (Fig. 1F and SI Appendix, Fig. S1 A and B).
The distribution of m6A modifications across transcripts detected in the ACC was similar to the pattern observed for the hippocampal tissues (Fig. 1 F and G). Considering all transcripts methylated in the hippocampus and ACC, $61.29\%$ were methylated in both (Fig. 1H and Dataset S8). Methylation sites also displayed similar m6A levels in hippocampal brain regions, especially when comparing the CA1 and CA3 regions, while the DG was more distant. The ACC was more distant from the hippocampal regions, and all investigated brain regions substantially differed from the heart, another mainly postmitotic and excitable organ (SI Appendix, Fig. S1 G and H). GO term analysis revealed that transcripts commonly methylated in the hippocampus and ACC showed a strong enrichment for pathways associated with synaptic assembly, organization and signaling, and learning and memory (SI Appendix, Fig. S2A and Dataset S9). Using SynGO [32], an experimentally annotated database for synaptic location and functional GO, we observed that transcripts commonly methylated in the hippocampus and ACC are highly enriched for proteins known to be localized to synapses (Fig. 1I). ACC-specific transcripts also displayed a significant enrichment for synaptic proteins, and the most significant GO terms linked to the m6A transcripts specific to the ACC were also related to synapse function (SI Appendix, Fig. S2B and Dataset S10). While chemical synaptic transmission was the most significant GO term when transcripts common to the ACC and hippocampus were analyzed, modulation of chemical synaptic transmission and chemical synaptic transmission were the most enriched GO terms for m6A transcripts specific to the ACC (SI Appendix, Fig. S2 A–C and Datasets S9–S11). These data may suggest that some of the transcripts specifically methylated in the ACC act in similar pathways as in the hippocampus. A closer look at the transcripts provides some evidence for this interpretation. For example, synaptotagmins (syt) control synaptic vesicle endocytosis. While the Syt2, Syt4, and Syt13 transcripts carry m6A modifications in the ACC and hippocampus, in the ACC, Syt 1, Syt5, and Syt7 were additionally methylated (Dataset S8). In contrast, hippocampus-specific transcripts showed no significant synaptic enrichment (SI Appendix, Fig. S2D).
These data suggest that common m6A transcripts might be specifically enriched at synapses, which is in agreement with previous data [20, 23]. To further explore this notion, we made use of a recently published dataset containing a high-confidence hippocampal synaptic RNAome and compared it to our hippocampal epitranscriptome data. This synaptic RNA dataset was generated from purified synaptosomes of WT mice and primary neurons grown in microfluidic chambers to isolate their synaptic compartments, making it a robust resource of synapse-localized RNAs [33] (SI Appendix, Fig. S2 E and F). In both datasets, we observed a strong enrichment of methylated transcripts in synapse-localized mRNAs with more than $70\%$ of the synaptosomes and $64\%$ of the microfluid chamber transcriptome having at least one m6A peak (Fig. 1J and Dataset S12).
These data support previous findings suggesting m6A RNA modification as a crucial regulatory process of synaptic function in the adult brain [20, 23]. However, it has to be mentioned that our interpretation is currently based on cross-intersecting our lists of transcripts with other databases.
## m6A Landscape in the Adult Human Brain Reveals a Conserved Enrichment of Transcripts Linked to Synaptic Function.
Next, we decided to profile the m6A distribution across transcripts of the human brain employing postmortem tissue of the cingulate cortex (CC) from five nondemented individuals. We observed that $22.8\%$ of all expressed transcripts [3,625] carried at least one m6A peak (Fig. 2A). This corresponded to 11,672 detected m6A peaks, with an average of 3.17 peaks per methylated transcript (SI Appendix, Fig. S3A). Similar to our observation in mice, m6A peaks were detected across the CDS with an enrichment in the vicinity of the stop codon and toward the 3′UTR (Fig. 2 A and B). The methylation peaks were enriched for the DRACH consensus motif (Fig. 2C). GO term analysis of the methylated transcripts revealed various molecular pathways, such as gene expression regulation, RNA metabolism, neural development, and synaptic function (SI Appendix, Fig. S3B and Dataset S13).
**Fig. 2.:** *Conserved m6A modifications between mouse and human. (A) Upper: Pie chart showing the percentage of m6A methylated transcripts in the human CC calculated against the corresponding input. Lower: m6A peak location is shown for the annotated transcript regions. The percentages are calculated from the total number of m6A peaks. (B) Guitar plot showing the distribution of m6A modifications along mRNAs in the human CC. (C) Motif analysis within the m6A peaks identifies the m6A DRACH consensus motif (D = A, T, or G, R = A or G, and H = A, T, or C). (D) Left: Doughnut chart showing the mouse/human genes with known homologues in human/mouse, respectively, that were used to compare methylated transcripts across species. Right: Venn diagram comparing m6A methylated homologue transcripts in the adult mouse ACC to the human CC. (E) GO categories (biological process) for m6A methylated transcripts common to the mouse ACC and human CC. (F) Sunburst plot showing synapse-specific location GO term enrichment for m6A methylated transcripts common to the mouse ACC and human CC. (G) Representative coverage tracks showing conserved m6A modifications along the 3′ end of homologous transcripts, in this case Camk2b in the mouse ACC and human CC (CAMKIIb/CAMKIIB). Tracks show coverage values for m6A-RIP normalized for the corresponding inputs and library size. Scale in RPM. (H) GO categories (biological process) detected when m6A methylated transcripts specific to the human CC are analyzed. (I) Heat map showing the odds ratio for the association between conserved transcripts (commonly detected in mice and humans) and human- and mouse-specific transcripts in comparison to synaptic RNAs, as published in ref. 34. Color scale represents the numerical value of enrichment (odds ratio), numbers in orange correspond to the P value for the corresponding overlap, and numbers in parentheses refer to the number of overlapping genes. N.S. = not significant. SC = RNAs detected in the synaptic compartments of microfluidic chambers; Syn = RNAs detected in synaptosomes (34). Random corresponds to 2,000 randomly selected brain-expressed human genes. ACC - anterior cingulate cortex, CC - cingulate cortex.*
We used the dataset generated from the mouse ACC for comparison with the human CC since both represent cortical brain regions. First, we identified all transcripts with an assigned homologue in the corresponding species, which accounted for the vast majority of all m6A transcripts ($86\%$ in mouse and $78\%$ in human, Fig. 2D). More than half ($55\%$) of these methylated transcripts detected in human were also methylated in the mouse ACC (Fig. 2D and Dataset S14). These conserved transcripts were strongly enriched for GO pathways linked to synaptic plasticity such as transsynaptic signaling, regulation of synapse organization, or learning and memory (Fig. 2E). Furthermore, SynGO analysis revealed that such transcripts are enrichment for synaptic location (Fig. 2F) and function (SI Appendix, Fig. S3C).
Further analysis revealed that also the location of methylated regions was strongly conserved since the annotated m6A peaks were detected in the same region of the corresponding homologous human/mouse transcripts (Fig. 2G and SI Appendix, Fig. S3D).
While the commonly methylated transcripts were linked to synaptic function and localization, we also detected transcripts uniquely methylated in either the human CC or the mouse ACC. The mouse-specific transcripts corresponded to genes involved with neurogenesis, the regulation of signal transmission, and synaptic function, although with considerably less significant enrichment as observed for the transcripts conserved in both species (SI Appendix, Fig. S3E and Dataset S15). Transcripts uniquely methylated in the human CC were enriched for genes associated with the regulation of gene expression, chromatin organization, and RNA metabolism (Fig. 2H) and did not show enrichment for synaptic localization (Fig. 2I and SI Appendix, Fig. S3F). In line with these observations, the experimentally confirmed synaptic transcripts were significantly enriched within the methylated transcripts detected in mice and humans. In contrast, methylated transcripts specific to humans showed comparatively low or no enrichment for synaptic mRNAs (Fig. 2I). Of course, care has to be taken when interpreting data comparing brain-specific gene expression between mice and humans. This is due to species differences but in some cases also due to a lack of consensus on anatomical definitions. In our study, we analyzed the CC region of the human brain corresponding to the *Brodmann area* (BA) 24, while it has been suggested that the ACC in mice best compares not only to human BA24 but also 25 and 36 [35]. Moreover, we compared 3-mo-old mice to healthy elderly humans, and one may argue that it might be more suitable to use tissue from older mice for comparison. Therefore, we also compared data obtained from the ACC of 16-mo-old mice to the CC of healthy elderly humans which yielded similar results (SI Appendix, Fig. S3 G–I).
## m6A RNA Changes in Mouse Models of Cognitive Decline and Human AD Patient Brain Tissue.
Our data support the view that the regulation of synaptic organization, function, and plasticity through m6A modifications might be a conserved mechanism in the adult mammalian brain. To further explore this, we studied the m6A landscape in a model of cognitive decline and chose age-associated memory impairment in mice as a model system. Previous studies have reported that age-associated memory impairment can be observed already in 16-mo-old mice, while at this stage, only minor changes in neuronal gene expression are detected (36–38). We reasoned that the comparison of 3- vs. 16-mo-old mice would allow us to test whether changes in m6A RNA methylation may precede changes in gene expression. To this end, we collected the ACC, CA1, CA3, and DG from 3- (young) and 16- (old) mo-old mice and performed meRIP-seq analysis (Fig. 3A). In line with previous RNA-seq data from bulk hippocampal tissue, differential gene expression analysis between samples from old and young mice revealed comparatively mild changes (FC > 1.2 and FDR ≤ 0.05) ranging from 39 differentially expressed genes (DEGs) in the CA1 to 115 in the ACC (Fig. 3B, SI Appendix, Fig. S4A, and Dataset S16). DEGs were not significantly enriched for any GO categories. In contrast to the transcriptome, m6A RNA methylation substantially differed when comparing tissue samples from young vs. old mice. Using the same cutoffs as for the differential expression analysis, 1,971 transcripts were differentially methylated in the DG, followed by the CA1 with 1,557, ACC with 1,373, and CA3 with 743 transcripts with significantly altered m6A modifications (Fig. 4B and Dataset S17). On average, 1.15 methylated regions, meaning individual peaks, were significantly altered per affected transcript (SI Appendix, Fig. S4). Since a transcript can carry multiple m6A methylation peaks, it is possible that such transcripts exhibit at the same time m6A peaks that increase, while others could decrease. Our data show that only in a few cases, increased and decreased m6A peaks were detected within the same transcript (named mixed transcripts, Fig. 3C). Throughout the analyzed brain regions, the majority of the transcripts characterized by altered m6A modifications exhibited either consistently decreased or increased changes in m6A methylation, which we refer to as hypo- or hypermethylated, respectively. The vast majority of significantly altered m6A modifications showed hypomethylation ranging from $94\%$ in the CA1 to $70\%$ in the DG (Fig. 3C). In contrast, only a small fraction of transcripts was hypermethylated (Fig. 3C). Interestingly, when considering the fold change (FC) and P value, the magnitude of hypomethylation changes across brain subregions differed, with the CA1 and ACC displaying a more pronounced reduction in m6A levels when compared to the CA3 and DG (SI Appendix, Fig. S5A).
**Fig. 3.:** *Tissue-specific m6A changes in the aging mouse brain. (A) Experimental scheme. (B) Bar graph showing the number of differentially expressed and differentially methylated genes detected in the corresponding brain subregion, applying equal cutoffs for FC and adjusted P value (FC > 1.2 and padj ≤ 0.05). (C) Pie charts showing the proportion of methylated transcripts containing peaks with only reduced methylation levels in aging (hypomethylated) and only increased methylation (hypermethylated) or a mixture of decreased and increased (mixed) m6A peaks in the analyzed brain subregions. (D) Bar chart showing the annotated distribution of significantly hypomethylated m6A peaks across transcripts for the investigated brain regions. (E) Venn diagram comparing the hypomethylated transcripts across hippocampal subregions and the ACC. The dark red rectangle refers to the 87 transcripts detected in all hippocampal subregions. The corresponding Right/Upper panel shows the GO term (biological process) analysis for these transcripts. The black rectangle refers to the 33 transcripts commonly hypomethylated in the hippocampus and ACC, and the Right/Lower panel shows the corresponding GO term analysis. (F) qPCR validation of two differentially methylated genes (hypo- and hypermethylation). The graphs show the FC in methylation as detected by meRIP-seq and meRIP–qPCR. For the meRIP-Seq side, columns show the mean FC with the FDR displayed above, as calculated by ExomePeak. For the qPCR data, the columns show the mean ± SEM of four independent replicates per condition. Statistical significance was determined by Student’s t test, and the P value is displayed above the comparison. ACC - anterior cingulate cortex, DG - dentate gyrus, 5UTR - 5′ untranslated region, 3UTR - 3′ untranslated region, CDS - coding sequence, ncRNA - noncoding RNA.* **Fig. 4.:** *Epitranscriptomic changes in neurodegeneration and aging. (A) Bar plot showing the number of differentially expressed and differentially m6A methylated transcripts in AD vs. control samples applying equal cutoffs for FC and adjusted p value (FC > 1.2 and padj ≤ 0.05). (B) Pie chart showing the proportion of hypomethylated, hypermethylated, and mixed transcripts in AD samples compared to control. (C) Enriched GO categories (biological process) for m6A hypomethylated transcripts (FC > 1.5) in AD compared to control. (D) Bar chart showing the distribution of m6A peaks across transcripts hypomethylated in AD. (E) Venn diagram comparing significantly hypomethylated transcripts in the aged mouse ACC and CC of human AD patients. Highlighted are the CaMKII isoforms. (F) Image showing the KEGG pathway LTP (hsa04720). Highlighted in purple are transcripts that are commonly hypomethylated in the aging mouse ACC and human CC of AD patients. CAMKII also belongs to this group but is highlighted in red since its hypomethylation was confirmed via qPCR (H). (G) Bar plots showing the m6A hypomethylation for the CamkII isoforms compared in the young vs. aged mouse brains and the human CC in control vs. AD patients. Each bar represents a methylation site in the 3UTR of the corresponding transcript closest to the stop codon. (H) qPCR validation of a hypomethylated region in the 3UTR of the different CamkII isoforms. The graphs show the FC in m6A methylation detected via meRIP-seq and meRIP–qPCR. Error bars show the mean ± SEM of 6/4 (young/old) independent replicates; P value is displayed above the comparison. Statistical significance was evaluated by Student’s t test with Welch’s correction for unequal variances. AD – Alzheimer’s disease, ACC - anterior cingulate cortex, DG - dentate gyrus, 5UTR - 5′ untranslated region, 3UTR - 3′ untranslated region, CDS - coding sequence, ncRNA - noncoding RNA.*
The location of the m6A modifications within a transcript has been associated with different functional consequences. Therefore, we wondered whether the observed m6A methylation changes would concentrate at specific regions within the affected transcripts. Similar to the distribution observed in young mice (Fig. 1), hypomethylated peaks were detected along the gene body and showed an enrichment toward the stop codon and 3′UTR (Fig. 3D). Only in the ACC a slight enrichment of hypomethylated peaks within the 5′UTR was observed (Fig. 3D and SI Appendix, Fig. S5B). The significantly hypermethylated peaks showed a considerably larger variability in their location within mRNAs (SI Appendix, Fig. S5 B and C). The possibility remained that alternative splicing may confound the detected changes in m6A peaks. However, we could not detect any relation of the comparatively few alternative splicing events to the differences in m6A modifications (SI Appendix, Fig. S6).
When comparing the hippocampus, 87 mRNAs were commonly hypomethylated in all subregions (Fig. 3E), while two transcripts (Rtn1 and Pfn1) were consistently hypermethylated (SI Appendix, Fig. S5D). GO term analysis of the 87 common transcripts showed that these genes are, for example, associated with cognition and synaptic organization (Fig. 3E and Dataset S18). Of note, differentially methylated regions detected at the sequencing level could be validated independently by meRIP–qPCR for selected transcripts (Fig. 3F). When further comparing the hypomethylated transcripts among the hippocampal subregions and the ACC, 33 common transcripts were detected, and GO term analysis revealed that these genes are associated with synapse organization (Fig. 3E and Dataset S19). While these data suggest that aging distinctly affects m6A RNA methylation in the hippocampal subregions and the ACC at the transcript level, the regulation of synaptic function appears to be a commonly deregulated process. This view is supported by a GO term analysis performed for each of the analyzed regions individually. Despite the limited overlap of the affected transcripts across brain regions, identical GO terms such as chemical synaptic transmission, regulation of synaptic plasticity, neurogenesis, and modulation of chemical synaptic transmission were detected when we analyzed the hypomethylated transcripts in the hippocampal regions and the ACC (Fig. 3E, SI Appendix, Fig. S5E, and Datasets S20–S23). In addition, many GO terms, although not identical, were related to synapse assembly that was detected in the ACC, CA1, and DG, while in the CA3 region, we found the GO term regulation of presynapse organization among the top 10 enriched processes (Datasets S20–S23). A number of GO terms were also specific to the individual hippocampal subregion. For example, GO terms linked to RNA biology including mRNA processing or mRNA splice site selection were significantly enriched when hypomethylated transcripts of the CA1 were analyzed, while these GO terms were not detected in any of the other hippocampal subregions or the ACC (SI Appendix, Fig. S5E and Datasets S20–S23).
Taken together, these data suggest that the onset of age-associated memory impairment, which is first detected in 16-mo-old mice [36, 39], is accompanied by m6A hypomethylation within transcripts linked to synaptic function and plasticity. Since this interpretation is based on GO term analysis, further experiments are required to study the impact of m6A hypomethylation on synaptic transcripts.
Next, we analyzed the m6A epitranscriptome in the brains of AD patients, the most common form of age-associated dementia in humans. Postmortem human cortex samples from AD patients (Braak and Braak stage IV) were matched with those of corresponding nondemented controls (NDC, Braak and Braak stage I/II) and analyzed via meRIP-seq. At the gene expression level, we detected 185 DEGs (100 up-regulated and 85 down-regulated; FC > 1.2 and FDR ≤ 0.05, Fig. 4A and SI Appendix, Fig. S7A). GO term analysis showed that the up-regulated genes were enriched for regulators of the Wnt signaling pathway, whereas down-regulated genes were not associated with any GO term. It is worth noting that no genes associated with the m6A machinery were differentially expressed in this dataset (SI Appendix, Fig. S7A).
When we performed differential methylation analysis of the meRIP-seq dataset using the same FC and FDR cutoffs as for the differential expression analysis, more than 2,500 transcripts exhibited significantly altered m6A modifications (Fig. 4A and Dataset S24). This corresponded to 3288 differentially methylated peaks, with an average of 1.26 m6A peaks affected per differentially methylated transcript (SI Appendix, Fig. S7 B and C). The majority of these changes represented hypomethylation, namely $81\%$ of the affected transcripts were exclusively hypomethylated, while $14\%$ showed exclusive hypermethylation. The remaining $5\%$ of the transcripts displayed hypo- and hypermethylated regions (mixed transcripts, Fig. 4B). GO term analysis showed that the hypomethylated transcripts were mainly associated with neuronal function and the regulation of synaptic plasticity (Fig. 4C and Dataset S25). The location of the significantly altered m6A modifications did not favor any specific region within the affected transcripts (Fig. 4D and SI Appendix, Fig. S7D).
These data show some similarity to the corresponding changes observed in the aging mouse brain. There was a considerable overlap between the populations of hypomethylated transcripts in the aged mouse brain and the human AD brain, and more than 1,000 transcripts characterized by m6A hypomethylation were detected in both species (Fig. 4E and Dataset S26). GO term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of these common transcripts showed that they play a role in the regulation of synaptic function and plasticity (SI Appendix, Fig. S7 E and F and Datasets S27 and S28). Especially, pathways associated with the regulation of plasticity—like long-term potentiation (LTP)—were highly overrepresented (Fig. 4F, SI Appendix, Fig. S7F, and Dataset S28). Furthermore, there was a significant overlap between these transcripts and previously described synaptic mRNAs, as well as synaptosomal transcripts that were found to carry m6A modifications (SI Appendix, Fig. S8) [20, 33]. In addition, several key regulators of LTP could be found within this group of transcripts (Fig. 4F, shown in purple and red). Among them were multiple isoforms of one of the best described subfamilies of synaptic plasticity-associated proteins, the calcium/calmodulin-dependent protein kinase II (CAMKII), namely CCAMKIIa, CAMKIIb, and CAMKIIg (Fig. 4 E–G). CAMKII, and especially the α and β isoforms, is central for memory formation and learning [40]. The corresponding transcripts were characterized by a consistent hypomethylation in the aging mouse brain and human AD brain (Fig. 4G and Dataset S29). This finding was confirmed by qPCR (Fig. 4H) and is in line with previous reports showing that loss of the m6A reader YTHDF1 reduces synaptic CAMKII levels [21]. Moreover, m6A RNA methylation of the CAMKIIb transcript was detected in the human parahippocampus [23]. We also detected hypomethylated transcripts that differed between the aged mouse brain and the brain of AD patients (Fig. 4E and Dataset S26), which is likely due to species differences and the fact that the mechanisms underlying aging and AD are not identical. GO term analysis revealed that transcripts hypomethylated specifically in the cortex of AD patients were linked to the regulation of neuronal projection development. Other transcripts were linked to protein modification process, covalent chromatin modification, mRNA splicing, or intracellular protein transport (SI Appendix, Fig. S7G and Dataset S30). The transcripts specifically hypomethylated in the ACC of aging mice were linked to GO terms important for neuronal functions such as neurogenesis, regulation of nervous system development, axonogenesis, chemical synaptic transmission, and in addition to processes such as regulation of transcription by RNA polymerase II (SI Appendix, Fig. S7H and Dataset S31).
## Decreased m6A Levels Affect the Local Synthesis of the Plasticity-Related Protein CAMKII.
The fact that we see comparatively few changes in gene expression while substantial m6A hypomethylation is observed in aged mice and the analyzed AD brains suggests that the m6A changes detected in our experimental settings do not affect transcript stability, a process that has been linked to m6A RNA methylation [41]. In line with this hypothesis, there was no obvious correlation between m6A modifications and expression changes in the corresponding transcript in any of the analyzed tissues (SI Appendix, Fig. S9 A–E). We also tested histone 3 trimethylation at lysine 36 (H3K36me3). We observed that the levels of H3K36me3, a repressive histone mark that had been linked to transcription-dependent changes in m6A RNA methylation [34], were similar when comparing hippocampal tissue samples from young and old mice via ChIP-sequencing (SI Appendix, Fig. S9 F and G). These data do not question previous findings showing that neuronal m6A RNA methylation regulates mRNA levels and stability [42]. Although we cannot exclude the possibility that m6A RNA hypomethylation may affect mRNA stability in our experimental setting, our data suggest that this is unlikely the main biological consequence of the detected hypomethylation.
m6A modifications on mRNA are also known to play a role in the regulation of neuronal mRNA transport and translation of plasticity-related genes [14, 19, 20]. To determine whether these mechanisms could be affected by m6A hypomethylation observed during cognitive decline, we first isolated synaptosomal compartments from the hippocampi of young and old mice and performed RNA-seq on the resulting synaptic mRNA population (SI Appendix, Fig. S10A). Similar to the analysis of bulk tissue (SI Appendix, Fig. S4), we detected comparatively few differentially expressed transcripts in synaptosomes when comparing 3- vs. 16-mo-old mice (three transcripts were up-regulated and none down-regulated; SI Appendix, Fig. S10B). Moreover, there was no correlation between the changes in synaptic transcript levels and the changes in their m6A methylation status in our experimental setting (SI Appendix, Fig. S10C). These data suggest that synaptic localization may not be the major consequence of m6A hypomethylation within the transcripts detected in our analysis.
Another process linked to m6A RNA methylation is mRNA translation [13]. Thus, we performed polysome sequencing on young and old mouse hippocampal tissue samples. Differential binding analysis identified 83 genes that were differentially translated during aging (SI Appendix, Fig. S10 D and E and Dataset S32). However, there was no significant overlap with the transcripts affected by differential m6A methylation (SI Appendix, Fig. S10F).
Previous studies hypothesized that m6A RNA methylation plays a role in local synaptic protein synthesis [19, 23]. Moreover, m6A was shown to control axonal protein synthesis in motoneurons [43]. Thus, our analysis of bulk tissue via polysome sequencing might not be sensitive enough to detect changes in protein synthesis if specifically synaptic compartments are affected. To address this experimentally, we opted to use a primary neuronal cell culture model to evaluate the effect of reduced m6A levels on local protein synthesis at the synapse. Since there are no suitable high-throughput methods to assay synaptic protein synthesis, we decided to evaluate its rate and location by studying the synthesis of CAMKII via a puromycin-based proximity ligation assay (puro-PLA). We chose CAMKII since it is a well-described synaptically located transcript that is known to be synaptically translated. Moreover, CAMKII plays a key role in memory function [44], can undergo m6A modifications [23], and was hypomethylated in the aging mouse and human AD brains as shown in this study.
To reduce m6A levels, the methyltransferase Mettl3 was knocked down (KD) in primary mouse neurons. The KD of Mettl3 via siRNA has been reported to be challenging in primary neurons [20]. Indeed, we observed only partial effects despite high concentration of siRNA probes (SI Appendix, Fig. S11 A and B). Therefore, we decided to employ another technology, namely LNA GAPmers, at lower doses and for longer treatment periods. Primary neurons treated with a LNA GAPmer targeting Mettl3 packaged in lipid nanoparticles (LNPs) at day in vitro (DIV) 7 showed an almost complete reduction of Mettl3 mRNA levels (> $95\%$) when measured 3 d later (Fig. 5A and SI Appendix, Fig. S11C). However, further 3 d of culture were necessary to sufficiently decrease METTL3 protein and m6A levels (Fig. 5 B–D). Having established the successful reduction of m6A levels, we studied the rate of protein synthesis for CAMKII via the puro-PLA. Puro-PLA depends on the use of the antibiotic puromycin for the labeling of nascent protein chains and N-terminal primary antibodies to detect sites of translation through proximity ligation (Fig. 5E and SI Appendix, Fig. S11D) [45]. A cycloheximide pretreatment was also applied to improve the spatial localization of sites of protein synthesis [46]. Puromycin labeling and translational arrest were confirmed in neurons (SI Appendix, Fig. S11E). DIV 13 primary neurons that had been treated at DIV 7 with either a Mettl3 KD or control GAPmer were processed for puro-PLA using an antibody that detects the N terminus of CAMKII α, β, and γ (Fig. 5 E and F). Puro-PLA–treated neurons were imaged by a confocal microscope, and the PLA punctae were automatically detected and quantified. The synaptic marker synaptophysin (SYP) was used to determine the synaptic localization of the detected PLA punctae (Fig. 5F). Neurons with reduced levels of m6A (Mettl3 KD) showed a reduction of PLA punctae in dendritic projections (Fig. 5F and SI Appendix, Fig. S12A). Quantitative analysis revealed that the total number of PLA punctae in the whole neuron was not significantly reduced (SI Appendix, Fig. S12B). The number of synapses detected via SYP staining was also not significantly changed in response to decreased m6A levels (SI Appendix, Fig. S12 C and D). However, when looking at the proportion of CAMKII-PLA punctae detected in the vicinity of SYP+ synaptic compartments, the Mettl3 KD-treated neurons showed significantly decreased numbers (Fig. 5 F and H). We furthermore confirmed that the reduced local translation of CAMKII resulted in reduced synaptic protein levels (SI Appendix, Fig. S13 A and B). Although our analysis of synaptosomes in young and aged mice indicated that changes in m6A modifications do not affect the total levels of synaptic transcript (SI Appendix, Fig. S10), we wanted to further test the possibility that the differences observed in Mettl3 KD-treated neurons might be a consequence of decreased mRNA transport to synaptic compartments. We used a previously established custom-made microfluidic chamber culture system to isolate synapse-localized transcripts (SI Appendix, Fig. S2E) [33]. Mettl3 KD treatment on the somas of the cultured neurons showed no significant effect on the amount of CAMKIIa, CAMKIIb, and CAMKIIg mRNA located in synaptic compartments (Fig. 5I and SI Appendix, Fig. S12E). To confirm that the effect of reduced m6A modifications on synaptic mRNA translation is not exclusive to CAMKII, we performed puro-PLA for another key synaptic protein, which we also found to be hypomethylated in aging and AD. We selected GLUA1, which is known to be locally synthesized and has been linked to aging and neurodegenerative diseases [47]. Synaptic GLUA1 synthesis was significantly decreased in Mettl3 KD neurons when compared to control (SI Appendix, Fig. S14). In line with these data, we found that Mettl3 KD-mediated loss of m6A RNA methylation impaired the network activity of neurons in culture, which is highly dependent on synaptic function and plasticity when measured via a multielectrode array (SI Appendix, Fig. S13).
**Fig. 5.:** *m6A changes influence the synaptic protein synthesis of CAMKII. (A) Bar plot showing the qPCR results for Mettl3 expression in neuronal cultures treated with a GAPmer targeting Mettl3 (Mettl3 KD) or a corresponding control oligonucleotide (control). (B) Representative immunoblot showing METTL3 protein level in response to GAPmer-mediated knockdown of Mettl3. (C) Quantification of (B). (D) Analysis of bulk m6A levels upon GAPmer-mediated knockdown of Mettl3. Graphs in A, C, and D display the mean ± SEM of each condition. Each data point represents one independent replicate; statistical significance was determined by Student’s t test. (E) Schematic illustration of the puro-PLA labeling used to quantify the synthesis of CAMKII in primary neurons. (F) Representative images of primary hippocampal neurons treated with either control or Mettl3 KD GAPmers. (Scale bar, 20 μm.) CAMKII-PLA signal is shown in green, SYP in red, and Map2 in blue. Right panel shows high-magnification images of a representative dendrite. (Scale bar, 10 μm.) Arrowheads indicate sites of CAMKII synthesis in the close vicinity of synapses. (G) Violin plot showing the total number of detected PLA punctae in treated neurons. Negative control (NegC) was not treated with puromycin before being processed for PLA. (H) Violin plot comparing the synaptically located CAMKII-PLA punctae in control and Mettl3 KD-treated neurons. Graphs in G and H show the mean of three independent experiments; for each experiment, 7 to 13 neurons were imaged and analyzed. Quartiles are marked by gray lines. (I) Normalized CAMKIIa mRNA levels in the synaptic compartments of primary neurons grown in microfluidic chambers. Dots in I represent individual independent replicates of neuronal cultures. Statistical significance was determined by Student’s t test. P values are displayed in the corresponding figure panels.*
## Discussion
In this study, we aimed to dissect the role of m6A RNA methylation in the brain and analyzed the m6A epitranscriptome in mice and humans in the context of cognitive decline associated with aging and AD. We found that 40 to $44\%$ of all transcripts in the different hippocampal subregions in mice carried m6A modifications. These data are in line with recent studies in which different brain regions or bulk hippocampal tissue had been analyzed for m6A RNA methylation [30, 48, 49]. m6A-labeled mRNAs in all of the hippocampal subregions were strongly enriched for genes associated with the regulation of synaptic function and plasticity. Moreover, $60\%$ of the transcripts with m6A modifications were common to all hippocampal subregions. Similarly, when comparing the ACC and hippocampus, $61\%$ of the m6A-modified transcripts could be detected in both brain regions. These results are in agreement with previously published data comparing m6A-modified transcripts across different tissues [16, 48]. The commonly m6A-modified transcripts across brain regions were mainly involved in synaptic function and structure and were overrepresented for mRNAs that are localized to synapses. These data are in line with previous findings suggesting that m6A transcripts are detected at synapses [20, 23] and support the view that m6A-dependent mechanisms play an important role in synaptic plasticity. Our data also support a recent study in which a reporter transcript was fused to the 3′UTR of selected synaptic RNAs such as CAMKIIa to show that synaptic localization is reduced when the adenosine of the m6A sites within the corresponding 3′UTR region is changed for a guanine [50].
Interestingly, processes linked to synaptic function were not among the most significant GO terms when m6A transcripts specific to the individual hippocampal subregions were analyzed. For example, protein complex assembly, nuclear export, and ncRNA processing were the most significant GO terms associated with m6A transcripts specific to the CA1, CA3, and DG regions, respectively. These data suggest that the m6A transcripts specific to the hippocampal subregions represent distinct molecular processes. However, further research is required to fully understand the molecular and cellular consequences of this observation. This is also true for the comparison of m6A transcripts in the mouse ACC and hippocampus. While the transcripts specific to the hippocampus could not be linked to any significant GO term, the most significant GO terms for m6A transcripts specific to the ACC were associated with chemical synaptic transmission. These data may suggest that synaptic processes in the ACC are even more tightly controlled via m6A RNA methylation when compared to the hippocampus.
When comparing the m6A landscape of the mouse ACC and human CC, we observed that $56\%$ of the methylated transcripts found in humans were also detected in mice. This is remarkable when considering that a similar degree of conservation is observed when the ACC is compared to the hippocampus within the same species ($61\%$ in mice). These data are also in agreement with a previous study reporting a $62\%$ overlap between m6A-modified transcripts in the mouse and human cerebella [48]. The commonly methylated transcripts detected in the mouse and human cortices were mainly linked to the regulation of synaptic function and plasticity and showed a strong overrepresentation of transcripts found at synapses. Interestingly, the m6A transcripts specific to mice were also enriched for GO terms related to synaptic plasticity, while the methylated transcripts specific to the human CC were also enriched for GO terms linked to RNA processing and gene expression control. These data may suggest that the m6A-mediated orchestration of synaptic plasticity could be an evolutionary conserved mechanism in mammals, while a role of m6A RNA modification in gene expression control became specifically important in the human brain. This is however speculative, and care must be taken when performing these kinds of interspecies comparisons also because of the complex relationships between homologous brain regions in mice and humans [35].
To study the epitranscriptome in the context of cognitive decline, we analyzed the brains of aging mice and human AD patients. During aging, the onset of significant cognitive impairment is believed to represent the transition between normal aging and pathology [51, 52]. Previous data demonstrated that mice start to display first memory impairment at 16 mo of age [36, 53]. When we compared 3- vs. 16-mo-old mice, we observed a massive m6A hypomethylation across multiple transcripts in all investigated brain regions. This was accompanied by comparatively mild changes in gene expression, which is in agreement with previous studies showing that only minor changes in gene expression are observed in the hippocampus and cortex when comparing 3- vs. 16-mo-old mice [36]. Our findings are also in line with observations from other postmitotic and excitable tissues, namely the heart. Here, during the pathogenesis of heart failure, massive changes in m6A hypomethylation preceded changes in gene expression [54].
While about $60\%$ of the m6A-modified transcripts were common to all investigated brain regions in 3-mo-old mice, aging had more specific effects on the epitranscriptome within the individual hippocampal subregions and the ACC. Yet, the comparatively few commonly hypomethylated transcripts were all associated with synapse function. Thus, our data suggest that the different brain regions undergo distinct changes in m6A RNA methylation during aging, while the GO term analysis revealed that many transcripts specifically altered in the analyzed brain regions are similarly linked to processes directly associated with synaptic function. Interestingly, processes related to RNA regulation such as mRNA processing or mRNA splice site selection were only detected in the CA1 region of aged mice which might indicate that gene expression control in the CA1 region is most sensitive to the aging process and under tight control of m6A RNA methylation. Thus, it would be interesting to see whether gene expression changes would be more severe in the CA1 region when mice older than 16 mo are analyzed. Future research should address this question.
In summary, these data suggest that loss of m6A RNA methylation coincides with the onset of memory impairment in the aging brain and may contribute to cognitive decline. This view is supported by previous data showing that a knockdown of the m6A demethylase FTO in the prefrontal cortex of mice results in an improved consolidation of fear memories [42]. Similarly, loss of the m6A reader YTHDF1, which has been linked to enhanced translation, leads to impairment of hippocampal LTP and memory formation in mice [21].
Another recent study analyzed m6A levels in the brains of 2-wk-old and 1-, 1.5-, 6.5-, and 13-mo-old mice. In comparison to our data, the authors observe comparatively milder changes, and the affected transcripts were mainly characterized by increased m6A modifications within the UTR when comparing 1.5- vs. 13-mo-old mice [30]. These data are however difficult to compare since 1.5-mo-old mice could still be considered juvenile. Moreover, animals at 13 mo of age do not exhibit detectable memory impairment when compared to their younger counterparts [53]. Longitudinal studies in mice showed that memory impairment manifests between 15 and 16.5 mo of age [53]. It is therefore also possible that the increased m6A RNA modification observed when comparing 1.5- vs. 13-mo-old mice represents compensatory mechanisms. In line with this interpretation, the affected genes were linked to pathways such as cellular stress signaling [30]. The same study also analyzed m6A levels in the brains of 6-mo-old 5xFAD mice, a mouse model for amyloid deposition. Here, decreased m6A levels were observed when comparing WT to 5xFAD mice, and the affected transcripts were linked to GO terms such as synaptic transmission [30]. Since previous data showed that 5xFAD mice start to display memory impairment around 6 mo of age [55], these data are in agreement with our observations. Nevertheless, more research is needed to elucidate the dynamics of m6A modifications across the transcriptome of the aging and diseased brains.
Additional support for the hypothesis that cognitive decline is accompanied by m6A hypomethylation of transcripts important for synaptic function stems from our observation that m6A-modified transcripts display massive hypomethylation in the postmortem human cortex of AD patients. Moreover, there was a significant overlap between the hypomethylated transcripts in the aging mouse ACC and the human CC of AD patients. In line with this observation, the commonly hypomethylated mRNAs were associated with processes such as synaptic plasticity, LTP, or multiple pathways linked to neurodegeneration. These data are in line with the fact that cognitive aging is an important risk factor of AD and that both processes exhibit similarities, such as synapse loss, inflammation, or oxidative stress, which have led to the hypothesis that AD reflects—at least in part—accelerated aging [56]. Nevertheless, several hypomethylated transcripts were specifically altered in either the aging mouse brain or in human AD patients. Although the transcripts differed, GO term analysis revealed that processes linked to neuronal function and gene expression control were affected in aging mice and in AD patients. Other GO terms such as chromatin modification were specific to humans. This is likely due to the anatomical differences between species [35] but may also reflect the fact that, besides the abovementioned similarities in age-associated cognitive decline and AD, the course of AD differs from physiological brain aging at various levels including distinct neuropsychological changes and specific gray and white matter alterations [57] [58].
The finding that AD is associated with m6A hypomethylation is in agreement with recent studies showing decreased mRNA and protein levels of the m6A methyltransferase METTL3 in hippocampal and cortical tissues of AD patients [24, 27]. Reduced expression of m6A writers could indeed be one mechanism to explain lower levels of m6A RNA modification in AD. In line with this view, knockdown of Mettl3 exacerbated Tau pathology in a Drosophila model for AD [39] and neurodegenerative phenotypes in a mouse model for amyloid deposition [27]. It should be mentioned that the role of m6A RNA methylation in neurodegenerative disease may be more complex. For example, a recent study observed increased m6A levels in a mouse model for Tau pathology and in the brains of human AD patients [35]. However, these data are based on a semiquantitative analysis of m6A immunostaining within the soma, which is difficult to compare to sequencing-based approaches.
Similarly, another recent study reported an increase in bulk m6A and METTL3 levels, while FTO protein levels were decreased in the hippocampus and cortex of 9-mo-old APP/PS1 mice [28]. The fact that the analysis of sequencing based vs. bulk m6A levels currently appear contradictory may indicate that there is an RNA species, which undergoes hypermethylation in neurodegenerative diseases, that is not captured by the current sequencing approaches but dominates the analysis of bulk m6A levels. For example, recent evidence hints to an important role of m6A methylation of pre- and mature microRNAs [59]. It will be interesting to study microRNA methylation in brain diseases.
In addition, it will be important to study m6A modifications in neuronal subcompartments. In fact, in our experimental settings, m6A hypomethylation occurred mainly within synapse-localized transcripts, pointing to a role of m6A RNA methylation in the synaptic translation of mRNAs, a well-known phenomenon that ensures the supply of key proteins necessary for synaptic function and plasticity in response to stimuli [60, 61]. The function of m6A in the regulation of local protein synthesis has been shown in the axons of motoneurons [43], and recent data suggest a role in synaptic protein synthesis [23]. We analyzed the synaptic protein synthesis of two transcripts that code for key regulators of synaptic plasticity and undergo m6A hypomethylation in the aging mouse brain and in the brains of AD patients, namely Camk2 and Glua1. Camk2 was also among the list of transcripts that underwent hypomethylation in the cortex of 6-mo-old 5xFAD mice [30]. Our results showed that m6A RNA hypomethylation achieved by the knockdown of Mettl3 expression significantly impaired synaptic translation of Camk2 and Glua1 mRNAs. In line with this observation, knockdown of Mettl3 was associated with decreased neuronal activity. These data support the view that a m6A-dependent mechanism orchestrates synaptic protein synthesis and contributes to impaired synaptic function when deregulated. To further substantialize this view, future research is needed. For example, it would be important to explore the role of m6A hypomethylation in synaptosomes of young and aged mice and in AD models via a combination of meRIP-seq and Ribo-Seq methods. It is also important to reiterate that in addition to local mRNA translation, m6A modification of neuronal transcripts was shown to affect other processes such as mRNA stability [42]. Since in our experimental settings, changes in m6A levels exceeded by far changes in transcript levels, mRNA stability does not seem to be the major process affected in the aging mouse brain in the analyzed human AD brains. However, we cannot exclude the possibility that process such as mRNA stability will be affected by changes in m6A levels when time points or different brain regions are analyzed.
More research is also needed to further elucidate the exact mechanism by which m6A levels control the synaptic translation of mRNA transcripts and better understand the processes that underlie decreased in m6A RNA methylation during aging and in AD. In this context, it is noteworthy that the m6A demethyltransferase FTO was shown to be present in cytoplasmic regions near to synapses [17] but decreased in protein abundance during learning [42]. At the same time, the m6A reader YTHDF1 is also located in synaptic compartments, and its protein levels increase significantly following fear conditioning in the hippocampus [21]. Furthermore, immunohistochemical analysis suggests that NMDA or KCL treatment of differentiated neuroblastoma or medulloblastoma cells enhances the synaptic colocalization of the m6A signal with YTHDF1 [23]. YTHDF1 was shown to promote translation [21]. This might be a mechanism by which a reduction of m6A RNA modifications affects synaptic protein synthesis. Consistently, the knockdown of Ythdf1 was shown to negatively affect spine formation, LTP, and hippocampus-dependent learning in mice [21]. In addition, recent data also implicate the m6A reader YTHDF3 and the m6A eraser ALKBH5 with the regulation of m6A RNA methylation at the synapse providing additional evidence for a key role of m6A modifications in synaptic plasticity [23]. Of course, other m6A readers may also play a role in the regulation of synaptic mRNA translation via processes like mRNA degradation, transport, or phase separation, as proposed by several previous studies [20, 23, 26]. In summary, changes in the localization and function of m6A writers, readers, and erasers may underlie the deregulation of m6A RNA methylation observed during aging and AD. In addition, metabolic changes may contribute since the methyl donor S-adenosyl methionine (SAM) was shown to be decreased in the brains of AD patients [62]. Moreover, a meta-analysis of AD mouse models treated with SAM-supplemented food confirmed the beneficial effects on cognitive function [63]. It will be interesting to investigate whether this effect is linked to m6A RNA methylation.
In conclusion, our data provide an important resource to the field and further elucidate the function of m6A RNA modification in young and aged mouse brains and in the brains of cognitively intact humans and AD patients. Since decreased m6A RNA methylation of synaptic genes is observed in brain aging and in AD, targeting the m6A RNA methylation machinery might be a promising strategy to prevent cognitive decline.
## Human AD Tissue.
A total of 12 postmortem samples from the ACC (BA 24) were obtained from the Netherlands Brain Bank (NBB). The samples corresponded to six diagnosed AD patients (age 89.33 ± 4.42 y, Braak and Braak stage IV, and postmortem delay (PMD) 6:34 ± 1:00) and 6 NDC (age 86.33 ± 3.25 y, Braak and Braak stages I–II, and PMD 6:16 ± 1:38). Braak and Braak staging is an established approach to define the degree of AD pathology. While stage I/II refers to early stages with altered pathology in the brain stem, at stage IV, cortical regions are affected [64]. All individuals, except one AD patient, were female. All experiments were approved by an ethics committee.
## meRIP.
RNA samples were processed as previously described for meRIP-seq [54].
## Library Preparation and Sequencing.
Samples were prepared for sequencing using the TruSeq Stranded Total RNA Library Prep Kit (Illumina) or the SMARTer Stranded Total RNA-Seq Kit v2—Pico Input Mammalian (Takara) according to the manufacturer’s instructions. For more information, see SI Appendix, Supplementary Methods. Additional metadata is also available via the GEO database (GSE198526).
## Bioinformatic Analysis of meRIP-seq and RNA-seq.
Raw reads were processed and demultiplexed using bcl2fastq (v2.20.2), and low-quality reads were filtered out with Cutadapt v1.11.0 [65]. Filtered reads were mapped to the human (hg38) or mouse (mm10) genome using the STAR aligner v2.5.2b [66]. The resulting bam files were sorted and indexed, and the unmapped reads removed using SAMtools v1.9.0 [67]. Methylation sites were determined using MeTPeak v1.0.0 [68], and differential methylation (hypo- and hypermethylated regions) was assessed with ExomePeak v2.16.0 [69] using young samples as control and old as treatment for the mouse data, while in the case of human data, healthy individuals were used as control and AD patients as treatment. An adjusted P value (padj, also termed as FDR [false discovery rate]) cutoff of 0.05 and FC cutoff of 1.2 or 1.5 were used as indicated in the text. For mouse samples, only consistently significantly differentially methylated peaks were used, unless indicated; for human samples, significantly differentially methylated peaks were used.
For RNA-seq analyses, read counts were obtained with subread’s featureCounts v1.5.1 [70] from the bam files of input samples. *Differential* gene expression was determined by DESeq2 v3.5.12 [71] using normalized read counts and correcting for covariates detected by RUVseq v1.16.1 [72]. Cutoffs of padj ≤ 0.05, FC ≥ 1.2, and BaseMean ≥ 50 were applied to the results. Background expressed genes were determined for each region as those genes with a BaseMean > 50 in the corresponding input sample.
For visualization, bam files of both IP and input samples were collapsed for PCR duplicates using SAMtools, and IP samples were normalized to their corresponding inputs and to their library size using deeptools’ v3.2.1 [73] bamCompare. The resulting normalized tracks were visualized in the IGV Browser 2.9.2 [74].
## GO Analysis.
GO term enrichment analyses were performed using the App ClueGO v2.5.3 in Cytoscape 3.7.2 [75], with GO Term Fusion enabled to collapse terms containing very similar gene lists and using a custom background corresponding to expressed genes in the corresponding species as obtained from RNA-seq results of the corresponding input samples of the meRIP experiments. GO term tables for biological process, cellular component, pathways, and KEGG were produced and are labeled accordingly in the figures. Resulting enriched GO terms were visualized with a custom script using ggplot2 v3.3.5 [76] displaying the adjusted p value (padj) for the GO term, the number of genes from the list that belong to said term, and the percentage of the total genes in the GO term that are present in the list. Synaptic GO enrichment analyses were performed with SynGO (v1.1, syngoportal.org) [32].
## Additional Bioinformatic Packages and Tools.
Scripts and analysis pipelines were written in R (3.5.2) [77]. Peak annotation was performed with Homer v4.10.4 [78] and Annotatr v1.8.0 [79]. Guitar plots were produced with the Guitar v1.20.1 [80] R package. Volcano plots were generated with plot.ly/orca v4.9.4.1 [81]. Area-proportional Venn diagrams were produced with BioVenn (www.biovenn.nl), and multiple list comparisons performed with Intervene/UpSet (asntech.shinyapps.io/intervene/). Mouse/human homologues were determined by their annotation in NCBI’s HomoloGene database using the HomoloGene (v1.4.68.19.3.27) R package. Odds ratios and p values to determine significance in overlapped datasets were calculated with the GeneOverlap R package v1.18.0 [82]. De novo motif analyses were performed with Homer’s findMotifsGenome, and motifs containing the DRACH consensus sequence out of the top 10 most significant are displayed. KEGG pathway enrichment was produced with KEGG Mapper (www.genome.jp/kegg/mapper/) [83]. Microscopy images were preprocessed with Fiji, and quantification was automated in Cell Profiler (cellprofiler.org) [84]. Graphs, heat maps, and statistical analyses were performed on GraphPad Prism version 9.3.1 for Mac. Some custom figures were created with BioRender (biorender.com).
## qPCR.
qPCR was performed as described before [53]. Primer sequences are available as Dataset S33.
## Hippocampal Primary Neuronal Culture.
Primary neurons were prepared as described recently [53].
## Western Blot/Immunofluorescence.
Antibodies used for western blot, immunofluorescence, and other applications, as well as the dilutions used, are described in Dataset S33.
## Puro-PLA.
Puromycin-based proximity ligation assay (puro-PLA) was performed as previously described with minor alterations [45]. DIV 13 mouse primary hippocampal neurons were pretreated with 100 ug/mL cycloheximide for 30 min to arrest translational elongation. Cells were then treated with 3 μM puromycin for 10 min to label nascent polypeptide chains. This treatment time was chosen to balance labeling intensity with the propensity of labeled peptides to diffuse away from their synthesis sites [46, 85]. Puromycin incorporation and cycloheximide pretreatment were validated by western blot.
## Polysome Sequencing.
Polysomes were prepared from the DG of five young and five old animals as described previously [54].
## Synaptosome Isolation for Sequencing.
Synaptosomes were isolated from the hippocampi of 3- and 16-mo-old mice as recently described [33].
## H3K36me3 ChIP.
Cell-type–specific chromatin isolation and ChIP sequencing were performed as previously described [86]. 3 CA1 were pooled for each replicate, and nuclei were FACS sorted by NeuN expression. 300 ng of chromatin and 1 µg of H3K36me3 antibody (Abcam, ab9050) were used for each ChIP.
## Author contributions
R.C.-H. and A.F. designed research; R.C.-H., T.B., M.M., R.E., J.R., L.K., R.P., M.S.S., S.B., M.N., K.E.B., M.T.B., and I.D. performed research; I.D. contributed new reagents/analytic tools; R.C.-H., T.B., T.P.C., L.K., R.P., and A.F. analyzed data; and R.C.-H. and A.F. wrote the paper.
## Competing interests
The authors declare no competing interest.
## Data, Materials, and Software Availability
GEO database: GSE198526. Datasets S1–S33 can be accessed via the following link: https://doi.org/10.6084/m9.figshare.21966983.v1 [87].
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|
---
title: The association between living altitude and serum leptin concentrations in
native women
authors:
- Jiayu Cheng
- Yingying Luo
- Lihui Yang
- Yufeng Li
- Fang Zhang
- Xiuying Zhang
- Xianghai Zhou
- Linong Ji
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992867
doi: 10.3389/fendo.2023.1107932
license: CC BY 4.0
---
# The association between living altitude and serum leptin concentrations in native women
## Abstract
### Background
Lower diabetes prevalence and cardiovascular mortality have been observed in residents at a higher altitude. Leptin is associated with incident diabetes and adverse cardiovascular outcomes, and our aim was to investigate the association of living altitude with serum leptin concentrations.
### Methods
Two cross-sectional surveys were used in this study, including native populations living at Tibet (high altitude) and Beijing (low altitude). A propensity score was conducted for matching age and body mass index (BMI) between native women at high and low altitude. Pearson’s correlation analysis was performed to evaluate the correlation of leptin with other variables.
### Results
A total of 1414 native women were included in this study, including 594 at high altitude and 820 at low altitude. The serum leptin concentrations of native women living at high altitude were 13.74 ± 11.03 ng/ml, which was significantly lower than that of native women living at low altitude (20.90 ± 12.91 ng/ml). After matching age and BMI, women living at the high altitude still had lower serum leptin concentrations. After adjusting for the potential confounding factors, the correlation coefficient between Ln (leptin) and BMI of women at high altitude was significantly lower than that of women at low altitude (0.228 versus 0.559; $P \leq 0.0001$). The serum leptin concentrations of each BMI subgroup (<18.5, 18.5 to <25, 25 to <30, ≥ 30 kg/m2) in women at high altitude were lower than that in women at low altitude.
### Conclusions
Serum leptin concentrations were significantly decreased in native women living at high altitude, and living altitude may alter the correlation of BMI and leptin. The findings of our study support that residents at high altitude have a protective effect with regards to improving cardiovascular and metabolic outcomes.
## Introduction
Obesity is a complex multifactorial disorder. Over the past decades, the prevalence of overweight and obesity has increased substantially, and one third of the world’s population is categorized into overweight or obese [1]. In 2021, a nationally representative data reported that an estimated 85 million adults (48 million men and 37 million women) aged 18–69 years were obese in China [2]. Obesity is a major risk factor for many diseases, including diabetes [3], cardiovascular diseases [4], renal diseases [5], and cancers [6].
Of note, overweight and obesity have divergent geographic prevalence and trends. Compared with the population living at low altitude, the counterparts living at high altitude has different metabolic characteristics, lower prevalence of obesity and diabetes [7, 8], and lower mortality of cardiovascular diseases [9]. A previous study indicated that residents living at < 500 m had five times the risk of obesity than the residents living > 3000 m [10]. Consistently, a cross-sectional study conducted at different altitudes reported that body mass index (BMI), waist circumference (WC) and waist-to-height ratio decreased with an increasing level of altitude [11]. Therefore, the prevalence of obesity is inversely associated with altitude, which is independent of lifestyle and ethnicity [7]. In addition, Americans living at high altitude is associated with lower adjusted risk of having diabetes compared to those living at low altitude [8]. Furthermore, a longitudinal study showed that the mortality from coronary heart disease significantly decreased with increasing altitude (-$22\%$ per 1000 m), while adjusting for multiple risk factors [9]. However, the mechanisms underlying the above interesting findings remain unknown.
Leptin is a peptide hormone mainly synthesized in white adipose tissue [12], and its circulating concentrations are typically proportional to the mass of body and fat [13]. Leptin regulates food intake, body mass, glucose, lipid, and protein metabolism, and plays a vital role in cardiovascular disorders and proinflammatory immune responses [14, 15]. A large prospective study indicated the association of high serum leptin concentrations with high risk of diabetes, and showed that serum leptin concentrations could predict incident diabetes [16]. Besides, hyperleptinemia is positively correlated with adverse outcomes in cardiovascular diseases [15].
Few study have collected leptin values and other clinical data in the community at high altitude [17]. In addition, it is unclear whether serum leptin concentrations and the association of leptin with other factors are the same for indigenous communities at high altitude as for other populations. Thus, our aim was to determine serum leptin concentrations and related factors of the native women at high altitude, and to find the supporting evidence of cardiovascular and metabolic health by comparing with the native women at low altitude.
## Study population
We used two cross-sectional surveys, including native populations living at Tibet (>3500m above sea level; high altitude) and Beijing (0-100m above sea level; low altitude). In 2014, a study of endocrine disorders was carried out in community population of Tibet by two-stage cluster random sampling, and 1499 adults participated in the study. From September 2013 to July 2014, the Pinggu metabolic disease study was carried out in community population of Beijing by the same sampling, and 4002 adults aged 26-76 years old took part in the study. Details of the Pinggu metabolic disease study have been published in our previous publication [18]. Pregnant women were not recruited in both studies. The exclusion criteria are as follows: (a) participants with diabetes history ($$n = 136$$ at high altitude; $$n = 386$$ at low altitude); (b) participants with newly diagnosed diabetes and prediabetes (fasting plasma glucose ≥ 6.1 mmol/L, and/or 2-h plasma glucose after a 75-g oral glucose tolerance test ≥ 7.8 mmol/L, and/or hemoglobin A1c ≥ $5.7\%$; $$n = 507$$ at high altitude; $$n = 1822$$ at low altitude); (c) participants with missing data on serum leptin concentrations ($$n = 18$$ at high altitude; $$n = 215$$ at low altitude). After excluding participants with the above criteria, 838 healthy individuals at high altitude and 1579 at low altitude were left. However, there was a large difference in the sample size of men between the two places ($$n = 244$$ at high altitude; $$n = 759$$ at low altitude), and there were few overweight and obese people at high altitude after BMI stratification, which may not be representative. Besides, there are substantial differences in fat mass and serum leptin concentrations between men and women. Thus, we finally enrolled 594 native women at high altitude and 820 native women at low altitude in the current analysis.
## The collection of clinical variables and biochemical parameters
Both the participants at high and low altitude underwent interviews with questionnaires, physical examinations and laboratory tests. We collect the data on demographic characteristics, height, weight, and blood pressure. BMI was calculated as weight divided by height in meters squared (kg/m2). After 10 min of rest, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured.
Fasting blood samples for the measurement of the following biochemical parameters were collected. The biochemical parameters included fasting plasma glucose (FPG), and 2-hour post-prandial plasma glucose (2h-PPG) after a 75-g oral glucose tolerance test, hemoglobin A1c (HbA1c), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum uric acid (UA) and serum leptin. HbA1c was measured by high-performance liquid chromatography (Adams A1c HA-8160; Arkray, Japan). Plasma glucose, TG, HDL-C, LDL-C, and serum UA were measured by an automated routine laboratory analyzer (UnicelDxC 800; Beckman Coulter, USA). Serum leptin concentrations were measured by a commercial ELISA kit (EMDMilipol, Billerica, MA, USA).
## Statistical analysis
Continuous variables are expressed as mean ± standard deviation (SD) or median (25th and 75th percentiles), and the differences between the two groups were compared by the t test or the Mann–Whitney test. Categorical variables are expressed as n (%), and the differences were compared by the chi-square test. To adjust the effect of age and BMI on serum leptin concentrations, we developed a propensity score matching for native women at high and low altitude. The propensity score through nearest neighbor matching was calculated using a multivariate logistic regression [19]. Age and BMI were included in the logistic regression model. Due to the skewed distribution of serum leptin concentrations, its value was logarithmically transformed to Ln (leptin). Pearson’s correlation analysis was performed to evaluate the correlation between Ln (leptin) and other variables. Furthermore, serum leptin concentrations were calculated by BMI subgroups with the standard WHO criteria (<18.5, 18.5 to <25, 25 to <30, ≥ 30 kg/m2). The statistical significance level was set at $P \leq 0.05.$ *Statistical analysis* was carried out using SPSS Statistics software (version 27.0).
## The characteristics of native women
As shown in Table 1, a total of 1414 native women were included in this study, including 594 at high altitude and 820 at low altitude. The mean age of women at high altitude was 35.6 ± 14.5 years and the BMI was 22.5 ± 3.8 kg/m2, while the mean age of women at low altitude was 44.0 ± 10.8 years, and the BMI was 24.6 ± 3.5 kg/m2. The women at high altitude had lower proportion of individuals with BMI ≥25 kg/m2 than those at low altitude. Compared with the women at low altitude, the women at high altitude had lower values of SBP, FPG, 2h-PPG, HbA1c, and LDL-C, but higher levels of TG, HDL-C, and serum UA (all $P \leq 0.001$). The serum leptin concentrations of native women living at high altitude were 13.74 ± 11.03 ng/ml, which was significantly lower than that of native women living at low altitude ($P \leq 0.001$). There was no significant difference of DBP between women at high and low altitude ($P \leq 0.05$).
**Table 1**
| Unnamed: 0 | High altitude | Low altitude | P value |
| --- | --- | --- | --- |
| N | 594 | 820 | – |
| Age, y | 35.6 ± 14.5 | 44.0 ± 10.8 | <0.001 |
| BMI, kg/m2 | 22.5 ± 3.8 | 24.6 ± 3.5 | <0.001 |
| BMI ≥25 kg/m2, n (%) | 132 (22.2) | 335 (40.9) | <0.001 |
| SBP, mmHg | 112.2 ± 15.9 | 121 ± 17 | <0.001 |
| DBP, mmHg | 74.2 ± 11.1 | 74 ± 10 | 0.582 |
| FPG, mmol/L | 4.2 ± 0.5 | 5.2 ± 0.4 | <0.001 |
| 2h-PPG, mmol/L | 5.0 ± 1.1 | 6.1 ± 1.0 | <0.001 |
| HbA1c, % | 5.1 ± 0.4 | 5.3 ± 0.2 | <0.001 |
| TG, mmol/L | 0.94 (0.69, 1.41) | 0.85 (0.56, 1.28) | <0.001 |
| HDL-C, mmol/L | 1.46 ± 0.45 | 1.26 ± 0.30 | <0.001 |
| LDL-C, mmol/L | 2.07 ± 0.79 | 2.72 ± 0.72 | <0.001 |
| Serum UA, μmol/L | 248 ± 60 | 234 ± 55 | <0.001 |
| Leptin, ng/ml | 13.74 ± 11.03 | 20.90 ± 12.91 | <0.001 |
To adjust the effect of age and BMI on serum leptin concentrations, an age and BMI-matched analysis was conducted between the native women at high and low altitude. Table 2 shows that the age and BMI are comparable between the women at high and low altitude ($P \leq 0.05$). However, compared with the women at low altitude, the women at high altitude had significantly lower serum leptin concentrations (13.50 ± 11.71 versus 16.01 ± 10.06 ng/ml).
**Table 2**
| Unnamed: 0 | High altitude | Low altitude | P value |
| --- | --- | --- | --- |
| N | 162 | 162 | – |
| Age, y | 40.3 ± 11.7 | 41.1 ± 11.0 | 0.535 |
| BMI, kg/m2 | 23.2 ± 3.6 | 23.3 ± 3.2 | 0.872 |
| Leptin, ng/ml | 13.50 ± 11.71 | 16.01 ± 10.06 | 0.039 |
## The correlation between leptin and different variables
Figure 1 shows the scatter plot of BMI and Ln (leptin) of women living at high and low altitude. Pearson correlation analysis indicated that women at high altitude had a mild and positive correlation of Ln (leptin) with BMI ($r = 0.092$, $$P \leq 0.026$$). The Ln (leptin) was strongly and positively correlated with BMI in women living at low altitude ($r = 0.625$, $P \leq 0.001$).
**Figure 1:** *Scatter plot of BMI and Ln (leptin) (A)The native women living at high altitude. Pearson correlation analysis indicated that Ln (leptin) was mildly and positively correlated with BMI. (B) The native women living at low altitude. Pearson correlation analysis showed that Ln (leptin) was strongly and positively correlated with BMI.*
In women at high altitude, the correlation coefficients indicated that the Ln (leptin) was positively correlated with HbA1c ($r = 0.237$, $P \leq 0.001$) and TG ($r = 0.122$, $$P \leq 0.003$$), while no significant correlations were found between Ln (leptin) with SBP, DBP, FPG, 2h-PPG, and UA (all $P \leq 0.05$) (Table 3). In women at low altitude, the correlation coefficients suggested that the Ln (leptin) was positively correlated with DBP, FPG, 2h-PPG, HbA1c, TG, LDL-C, and UA (all $P \leq 0.01$), while was negatively correlated with age (r = -0.120, $$P \leq 0.001$$), and HDL-C (r = -0.207, $P \leq 0.001$) (Table 3). After adjusting for the potential confounding factors, the correlation coefficient between Ln (leptin) and BMI of women at high altitude was significantly lower than that of women at low altitude (0.228 versus 0.559; $P \leq 0.0001$) (Table 3).
**Table 3**
| Unnamed: 0 | High altitude | Unnamed: 2 | Low altitude | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | Coefficient (r) | P value | Coefficient (r) | P value |
| Without adjustment | Without adjustment | Without adjustment | Without adjustment | Without adjustment |
| Age | -0.296 | <0.001 | -0.120 | 0.001 |
| BMI | 0.092 | 0.026 | 0.625 | <0.001 |
| SBP | -0.017 | 0.682 | 0.033 | 0.349 |
| DBP | -0.010 | 0.800 | 0.168 | <0.001 |
| FPG | -0.058 | 0.157 | 0.143 | <0.001 |
| 2h-PPG | -0.058 | 0.161 | 0.172 | <0.001 |
| HbA1c | 0.237 | <0.001 | 0.092 | 0.008 |
| TG | 0.122 | 0.003 | 0.237 | <0.001 |
| HDL-C | -0.204 | <0.001 | -0.207 | <0.001 |
| LDL-C | -0.121 | 0.003 | 0.125 | <0.001 |
| UA | -0.031 | 0.452 | 0.265 | <0.001 |
| After adjustment* | After adjustment* | After adjustment* | After adjustment* | After adjustment* |
| BMI | 0.228 (r1) | <0.001 | 0.559 (r2) | <0.001 |
## Serum leptin concentrations by BMI subgroups and menstruation
According to the BMI levels, the women at high and low altitude were categorized into four subgroups: BMI <18.5, 18.5 to <25, 25 to <30, ≥ 30 kg/m2. Table 4 shows the mean ± SD, 2.5th, 25th, 50th, 75th, and 97.5th percentile of serum leptin concentrations by BMI subgroups. The serum leptin concentrations of women with BMI of 18.5 to <25 kg/m2 at high altitude were 13.64 ± 9.65 ng/ml, while that of women with BMI of 18.5 to <25 kg/m2 at low altitude were 15.84 ± 9.04 ng/ml. Generally, the serum leptin concentrations of each BMI subgroup in women at high altitude were lower than that in women at low altitude. As shown in Table 5, both premenopausal women and menopausal/perimenopausal women at high altitude had lower serum leptin concentrations than those at low altitude.
## Discussion
We found that native women living at high altitude had significantly lower serum leptin concentrations than the counterparts at low altitude, and the result was consistent even after matching age and BMI. In addition, the correlation between BMI and leptin of native women living at high altitude was weaker than that of women at low altitude. To the best of our knowledge, this is the first study with a large sample of representative population to compare serum leptin concentrations between local people at high and low altitude in China.
Some previous studies focused on the relationship between leptin and altitude. In a study of fifty-five healthy volunteer men, the data showed that there were no significant differences between plasma leptin concentrations in three populations of dwellers at different altitude (sea level, 3250 m, 4550 m) [17]. Another study indicated that elevated plasma leptin concentrations were found after exposure to high altitude for 7 days in a group of 30 lowlanders [20]. On the contrary, in a cross-sectional cohort of 889 subjects, the study reported an inverse correlation of serum leptin concentration with altitude [21]. However, the above-mentioned studies have some limitations, including small sample size, unadjusted for BMI, and a relatively low altitude (200-1020 m) [17, 20, 21]. After adjusting for age and BMI, this large sample study found lower serum leptin concentrations of native women at high altitude (>3500 m above sea level) compared to native women at low altitude (0-100 m above sea level). Besides, our study supports that women at high altitude may have favorable cardiovascular and metabolic profiles (7–9).
There are several potential mechanisms for the lower serum leptin concentrations at higher altitude. Hypoxia is an important feature of high altitude, which may be involved in the change of serum leptin concentrations. A study in rats showed that hypoxia exposure significantly reduced serum leptin concentrations, both in the exercise group and the non-exercise group [22]. In addition, a study of healthy humans indicated that altitude-induced hypoxia suppressed plasma leptin concentrations [23]. The increase in neural sympathetic activity at high altitude, partly induced by hypoxia, could inhibit leptin gene expression [24]. Cold temperature also could play a role in the regulation of serum leptin concentrations. Recently, a prospective study reported that significant decrease of serum leptin concentrations were observed in healthy adults after short-term exposure to cold temperature [25]. Of note, there are many confounders that have not been adequately addressed, which may have a combined effect on the relationship of leptin and altitude [26].
The data in our study reported that the correlation coefficient between BMI and leptin of native women living at high altitude was significantly lower than that of women at low altitude. We speculate that people at high altitude have less body fat with the same lean body mass, leading to the lower serum leptin concentrations [27]. In concordance with our study, a previous study suggested that the high-altitude group had significantly higher HDL-C levels and lower BMI than the low-altitude group [28]. Beside, compared with residents living below 500 m, clinically healthy residents living between 3000 and 4500 m had lower FPG [7]. Lower prevalence of obesity and diabetes has been reported among people residing at high altitudes [29]. Animal study showed that blockade of leptin signaling could decrease blood pressure [30], supporting that low serum leptin concentrations contribute to the favorable cardiometabolic outcomes.
Our study has several limitations. Firstly, the participants living at high altitude were Tibetans, and the participants living at low altitude were Han Chinese. the ethnic variation in our study may contribute to the difference of serum leptin concentrations. However, a previous study indicated that ethnicity may not explain the favorable metabolic profiles of residents at higher altitudes [8]. Beside, Tibetans have lived at Tibet for generations, while Han Chinese have lived at low altitude for generations, and we speculate that they have genetic homogeneity. Secondly, our study did not have the information of dietary pattern and physical exercise. Of note, the dietary habits and physical exercise could lead to the change of BMI levels, but we adjusted for age and BMI when we analyzed the leptin concentrations. Thirdly, we did not collect the data on fat distribution, visceral fat and subcutaneous fat, which may help us understand the relationship between BMI and leptin. Fourthly, our study analyzed the data of native women. In a study of women and men, the authors reported an inverse correlation of leptin and altitude [21], but it needs more studies to verify.
In conclusion, our study found that serum leptin concentrations were significantly decreased in native women living at a higher altitude, and living altitude may alter the correlation of BMI and leptin. The findings of our study support that residents at high altitude have a protective effect with regards to improving cardiovascular and metabolic outcomes. Future studies are required to verify the findings in our study and clarify the potential explanations between leptin, altitude and cardiometabolic outcomes.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. Requests to access these datasets should be directed to XiaZ, [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by the medical ethics committees of Tibet Autonomous Region People’s Hospital and Peking University Health Science Center. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XiaZ and LJ contributed to the study concept and design. LY, YuL, FZ, and XiuZ contributed to the acquisition of data. JC performed the statistical analysis. JC and YiL were involved in interpretation of the data. All authors contributed to drafting, modifying and approving the manuscript, and take responsibility for accuracy and integrity of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Lifestyle intervention reduces risk score for cardiovascular mortality in company
employees with pre-diabetes or diabetes mellitus – A secondary analysis of the PreFord
randomized controlled trial with 3 years of follow-up
authors:
- Christian Brinkmann
- Hannah Hof
- Detlef-Bernd Gysan
- Christian Albus
- Stefanie Millentrup
- Birna Bjarnason-Wehrens
- Joachim Latsch
- Gerd Herold
- Karl Wegscheider
- Christian Heming
- Melchior Seyfarth
- Hans-Georg Predel
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992873
doi: 10.3389/fendo.2023.1106334
license: CC BY 4.0
---
# Lifestyle intervention reduces risk score for cardiovascular mortality in company employees with pre-diabetes or diabetes mellitus – A secondary analysis of the PreFord randomized controlled trial with 3 years of follow-up
## Abstract
### Aim
To evaluate the effects of a multimodal intervention (including exercise training, psychosocial interventions, nutrition coaching, smoking cessation program, medical care) on the health and long-term cardiovascular disease (CVD) mortality risk of company employees with pre-diabetes or diabetes mellitus (DM) at high CVD risk.
### Methods
In the PreFord study, German company employees ($$n = 4196$$) participated in a free-of-charge CVD mortality risk screening at their workplace. Based on their European Society of Cardiology – Systematic Coronary Risk Evaluation score (ESC-SCORE), they were subdivided into three risk groups. High-risk patients (ESC-SCORE≥$5\%$) were randomly assigned to a 15-week lifestyle intervention or usual care control group. Data from patients with pre-DM/DM were analyzed intention-to-treat (ITT: $$n = 110$$ versus $$n = 96$$) and per protocol (PP: $$n = 60$$ versus $$n = 52$$).
### Results
Body mass index, glycated hemoglobin, total cholesterol, low-density lipoprotein, triglyceride levels as well as systolic and diastolic blood pressure improved through the intervention (ITT, PP: $p \leq 0.001$). The ESC-SCORE markedly decreased from pre- to post-intervention (ITT, PP: $p \leq 0.001$). ESC-SCORE changes from baseline differed significantly between the groups, with the intervention group achieving more favorable results in all follow-up visits 6, 12, 24 and 36 months later (at each time point: ITT: $p \leq 0.001$; PP: p ≤ 0.010).
### Conclusion
The study demonstrates the feasibility of attracting employees with pre-DM/DM at high CVD mortality risk to participate in a multimodal lifestyle program following a free CVD mortality risk screening at their workplace. The lifestyle intervention used in the PreFord study shows high potential for improving health of company employees with pre-DM/DM in the long term. ISRCTN23536103.
## Graphical Abstract
## Introduction
Cardiovascular diseases (CVDs) are the leading cause of premature death [1, 2]. Thus, reducing the incidence of CVDs is of high public health importance. A meta-analysis from observational studies has shown that a healthy lifestyle can reduce the risk of developing CVDs by up to $66\%$ [3]. Preventive measures aimed at lifestyle changes can therefore be helpful to reduce individual mortality risk. In the PreFord study [4], German company employees of the Ford Motor Company ($$n = 4196$$) participated in a free-of-charge CVD mortality risk screening at their workplace. The participants were then subdivided into three risk groups based on their risk factors, quantified by the European Society of Cardiology – Systematic Coronary Risk Evaluation score (ESC-SCORE), which is an established metric to estimate the risk of fatal cardiovascular events with a high accuracy for Germans and other Europeans [5, 6]. Employees with a high risk score (ESC-SCORE ≥ $5\%$) were randomly assigned to a multimodal lifestyle intervention group (receiving exercise training, psychosocial interventions, nutrition coaching, smoking cessation program, medical care) or to a usual care group (receiving medical care only).
Large-scale observational studies show that patients with diabetes mellitus (DM) have a drastically increased risk of cardiovascular events and CVD mortality (7–9). As lifestyle changes can help reduce cardiovascular risk, patients with pre-DM and DM should optimize their lifestyle as early as possible. Unfortunately, these patients are often very difficult to motivate for lifestyle changes; moreover, there might be several psycho-social barriers [10]. When they participate in an intervention program, achieving sustainable effects is usually challenging, due to low program adherence and high drop-out rates [11].
This secondary analysis of the PreFord study data explores the direct effects of the study’s 15-week multimodal lifestyle intervention on the ESC-SCORE and other health-related variables in the pre-DM/DM subgroup. Long-term effects on the individual cardiovascular risks and the program’s efficiency for patients with pre-DM/DM are discussed, considering that aggressive programs for lifestyle changes are urgently needed to account for an increasing incidence and prevalence of DM [12, 13].
## Study design
The PreFord trial was designed as a randomized controlled, multicenter clinical study. The study design has already been described in detail [4]. The study protocol in line with good clinical practice has been approved by the Ethics Committee of the University of Cologne (ref: 03-217) and the Ethics Committee of the North Rhine Medical Association (Ärztekammer Nordrhein, ref: 2004079). Subjects gave their written informed consent prior to the start of the study.
## Subjects
Employees of the Ford Motor Company Germany (>15.000) were invited to participate in a free-of-charge cardiovascular medical check-up (T0) and to determine their ESC-SCORE which reflects personal risk of cardiovascular events. The score was calculated by an independent statistics institution (Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne). Age, blood pressure, smoking habits and total cholesterol values were recorded for risk assessment. Inclusion criteria were defined as follows: an ESC-SCORE ≥ $5\%$ (high-risk group) and the ability to exercise. Exclusion criteria were defined as follows: exercise-limiting diseases, history of cardiovascular disease, cancer, pregnancy or severe mental disorders.
The secondary data analysis is reported in accordance with the CONSORT statement [14]. Only employees diagnosed with diabetes mellitus (and receiving pharmacological treatment) and/or with glycated hemoglobin (HbA1c) levels ≥ $5.7\%$ were included in this analysis (Figure 1). In total, the datasets of $$n = 142$$ persons with pre-DM (HbA1c levels ≥ $5.7\%$ and < $6.5\%$ without anti-diabetic medication) and $$n = 64$$ patients with manifest DM (HbA1c levels ≥ $6.5\%$ and/or treated with anti-diabetic medication) were considered. The HbA1c thresholds correspond to the American Diabetes Association cutoffs for the diagnoses of pre-DM and DM [15].
**Figure 1:** *Study flow chart.*
## Lifestyle intervention
Subjects were randomly assigned to the intervention (INT) group or the usual care control (CON) group by block randomization 1:1. The computer-generated random list was provided by the Clinical Trial Center Cologne. Study personnel assigned participants to the INT or CON group according to this random list. The 15-week multimodal lifestyle intervention (Table 1) was supervised by professional health care specialists (medical doctors, exercise physiologists, psychologists, and nutritional coaches). The intervention was performed in small groups twice a week for 2.5-3 hours per session in two rehabilitation centers in Cologne, Germany. Further details about the program are available in the publication of Gysan et al. [ 4]. All employees who participated in the intervention program were examined immediately after the intervention (T1). The CON group participants received usual care from their general practitioners.
**Table 1**
| Components | Hours planned | Subgroup data: Actual time spent (percentage of planned hours) |
| --- | --- | --- |
| Aerobic endurance and resistance training | 37.0 | 90.0% |
| Nutrition coaching Information/Education in Mediterranean-style diet and practical training in preparing a meal | 11.0 | 73.1% |
| LifeSkills according to Williams and Williams | 13.5 | 74.5% |
| Progressive relaxation training | 6.0 | 90.7% |
| Smoking cessation program | 0.45 | 11.1%(5 persons) |
| Medical care with guideline-based pharmacotherapy | 4.75 | 90.7% |
| Information/Education Healthy lifestyle management | 8.0 | 121.9% |
## Follow-up
All company employees who participated in the study, in either the INT or ON group, were invited for follow-up medical check-ups 6 (T2), 12 (T3), 24 (T4) and 36 (T5) months after start of the study. The study ended after completion of the last follow-up.
## Primary and secondary outcomes
The ESC-SCORE was defined as the primary outcome. It was determined in the INT and CON group at every follow-up examination and thus helped assess the long-term effectiveness of the intervention. The same ESC-SCORE algorithm in its initially published form was used throughout the study [5]. The ESC-SCORE provides an accurate prediction of cardiovascular events in Europeans without a history of severe cardiovascular diseases (e.g., coronary heart disease, stroke, peripheral artery disease, heart failure, heart arrhythmia).
To determine the intervention’s direct effectiveness, body weight, body mass index (BMI), waist circumference, glycated hemoglobin (HbA1c), high-sensitive C-reactive protein (hsCRP), total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, systolic and diastolic blood pressure and exercise capacity pre- and post-intervention were defined as secondary outcomes.
## Statistical analyses
Data are presented as mean values ± standard deviations (SD) and $95\%$ confidence intervals ($95\%$-CI). The “SPSS” program (v. 28.0, IBM Corporation, Armonk, New York, USA) was used for the statistical analyses. Parametric tests were used throughout. When assumptions were violated and when appropriate, non-parametric (rank-based) hypotheses tests were conducted. For baseline comparisons of interval-scaled variables between the two groups, the Student’s t test or the Mann-Whitney U test for unpaired samples were performed. The Chi2 test was used to assess differences in the distribution of nominal-scaled variables between the groups. For pre-post-comparisons of interval-scaled variables within the INT group, the Student’s t test or the Wilcoxon signed rank test for paired samples were used. For follow-up analyses within each group (INT and CON), the Friedman test was carried out. To compare changes from baseline between the two groups at the different follow-up time points, the Student’s t test or the Mann-Whitney U test for unpaired samples were used. Data were analyzed intention-to-treat and per protocol. The intention-to-treat cohort included all patients. Missing values in the intention-to-treat analysis were replaced by the last observation carried forward ($29.9\%$ missing ESC-SCORE data, $33.2\%$ missing HbA1c data). The per protocol cohort included only those patients who fully adhered to the study protocol. In addition, data from all measurement time points had to be available. Significance was considered at p ≤ 0.05.
## Sample size and power calculation
A sample size calculation was performed for the original study a priori [4]. For this subgroup data analysis, a second power analysis was performed for the ESC-SCORE as the primary outcome a posteriori using G Power (v. 3.1.9.7., University of Düsseldorf, Düsseldorf, Germany). For the intention-to-treat analysis, a power of $100\%$ was calculated for the comparison between the ESC-SCORE pre- and post-intervention of the INT group and a power of $94\%$ for the comparison of ESC-SCORE changes from baseline between the INT and CON groups during the follow-up medical check-up 36 months later. For the per protocol analysis, statistical power values of $100\%$ and $83\%$ were calculated, respectively.
## Baseline data
The baseline (T0) data of the subjects of the INT and CON groups are presented in Table 2. The ratio of men and women roughly reflects the ratio of employees in the company. The groups were almost perfectly matched for the ESC-SCORE and also did not significantly differ in any other variable, except BMI.
**Table 2**
| Intervention group n=110 | Intervention group n=110.1 | Usual care group n=96 | p-value |
| --- | --- | --- | --- |
| ESC-SCORE result [%] | 8.07 ± 5.17 (7.09-9.05) | 8.03 ± 4.83 (7.06-9.01) | 0.959 ◊ |
| Sex [m/f, n] | 96/14 | 86/10 | 0.606 † |
| Age [years] | 60.1 ± 8.7 (58.4-61.7) | 60.2 ± 7.7 (58.6-61.7) | 0.994 ◊ |
| Body weight [kg] | 89.6 ± 15.3 (86.7-92.4) | 86.4 ± 14.3 (83.5-89.3) | 0.076 ◊ |
| BMI [kg/m2] | 29.62 ± 4.55 (28.76-30.48) | 28.19 ± 3.85 (27.41-28.97) | 0.019 ◊ |
| Waist circumference [cm] | 103.8 ± 10.6 (101.8-105.8)n=109 | 101.0 ± 12.4 (98.5-103.5)n=95 | 0.083 # |
| HbA1c [%] | 6.41 ± 0.86 (6.25-6.57) | 6.18 ± 0.57 (6.06-6.29) | 0.103 ◊ |
| hsCRP [mg/l] | 0.31 ± 0.56 (0.20-0.41)n=108 | 0.33 ± 0.54 (0.22-0.44)n=95 | 0.752◊ |
| Total cholesterol [mg/dl] | 238.9 ± 48.5 (229.7-248.1) | 237.4 ± 48.5 (227.6-247.2) | 0.908 ◊ |
| HDL [mg/dl] | 53.5 ± 12.3 (51.1-55.8) | 54.8 ± 12.8 (52.2-57.4) | 0.338 ◊ |
| LDL [mg/dl] | 150.6 ± 34.4 (144.1-157.1) | 149.7 ± 33.6 (142.9-156.5) | 0.928 ◊ |
| Triglycerides [mg/dl] | 218.0 ± 160.2 (187.7-248.2) | 204.5 ± 138.6 (176.5-232.6) | 0.982 ◊ |
| Systolic BP [mmHg] | 139.8 ± 17.7 (136.4-143.1) | 138.1 ± 15.0 (135.1-141.2) | 0.417 ◊ |
| Diastolic BP [mmHg] | 87.9 ± 11.1 (85.8-90.0) | 88.7 ± 9.9 (86.7-90.7) | 0.720 ◊ |
| Exercise capacity [W/kg] | 1.73 ± 0.47 (1.64-1.82)n=103 | 1.69 ± 0.43 (1.60-1.78)n=86 | 0.715 # |
| Smokers | Smokers | Smokers | Smokers |
| Non-smokers | 42 (38.2%) | 39 (40.6%) | |
| Current smokers | 23 (20.9%) | 21 (21.9%) | 0.866 † |
| Ex-smokers | 45 (40.9%) | 36 (37.5%) | |
| Anti-diabetic drugs | Anti-diabetic drugs | Anti-diabetic drugs | Anti-diabetic drugs |
| Insulin | 8 (7.3%) | 10 (10.4%) | 0.425 † |
| Oral antidiabetic agents | 19 (17.3%) | 9 (9.4%) | 0.099 † |
| Other drugs | Other drugs | Other drugs | Other drugs |
| ASS | 13 (11.8%) | 20 (20.8%) | 0.078 † |
| Statins | 26 (23.6%) | 15 (15.6%) | 0.151 † |
| Anti-hypertensive agents | 55 (50.0%) | 36 (37.5%) | 0.072 † |
## Direct effects of the multimodal lifestyle intervention on the ESC-SCORE and important health variables
Pre-post-intervention data (T0-T1) are presented in Table 3. The ESC-SCORE decreased significantly, irrespective of the type of analysis conducted (intention-to-treat or per protocol). Nearly all other health-related variables (body weight, BMI, waist circumference, HbA1c, total cholesterol, LDL, triglycerides, systolic and diastolic blood pressure, exercise capacity) also improved significantly. HDL levels remained unchanged and hsCRP levels increased significantly, but very slightly.
**Table 3**
| Intention-to-treat analysisIntervention groupPre-intervention n=110 | Intention-to-treat analysisIntervention groupPre-intervention n=110.1 | Intention-to-treat analysisIntervention groupPost-intervention n=110 | p-value | Per protocol analysisIntervention groupPre-intervention n=69 | Per protocol analysisIntervention groupPost-intervention n=69 | p-value.1 |
| --- | --- | --- | --- | --- | --- | --- |
| ESC-SCORE result [%] | 8.07 ± 5.17 (7.09-9.05) | 6.33 ± 4.31 (5.52-7.15) | <0.001 ○ | 8.62 ± 5.29 (7.35-9.89) | 5.85 ± 3.87 (4.92-6.78) | <0.001 ○ |
| Body weight [kg] | 89.6 ± 15.3 (86.7-92.4) | 87.8 ± 15.1 (85.0-90.7) | <0.001 ▪ | 86.8 ± 14.7 (83.2-90.3) | 84.0 ± 13.7 (80.7-87.3) | <0.001 ▪ |
| BMI [kg/m2] | 29.62 ± 4.55 (28.76-30.48) | 29.02 ± 4.36 (28.20-29.84) | <0.001 ○ | 29.08 ± 4.46 (28.01-30.16) | 28.13 ± 3.97 (27.17-29.08) | <0.001 ○ |
| Waist circumference [cm] | 103.8 ± 10.6 (101.8-105.8)n=109 | 101.9 ± 10.4 (99.9-103.9)n=109 | <0.001 ▪ | 102.0 ± 10.7 (99.4-104.6)n=68 | 99.0 ± 9.7 (96.6-101.3)n=68 | <0.001 ▪ |
| HbA1c [%] | 6.41 ± 0.86 (6.25-6.57) | 6.26 ± 0.87 (6.09-6.42) | <0.001 ○ | 6.29 ± 0.80 (6.09-6.48) | 6.04 ± 0.75 (5.86-6.22) | <0.001 ○ |
| hsCRP [mg/l] | 0.31 ± 0.56 (0.20-0.41)n=108 | 0.32 ± 0.64 (0.20-0.41)n=108 | 0.035 ○ | 0.24 ± 0.33 (0.16-0.32)n=67 | 0.26 ± 0.52 (0.14-0.39)n=67 | 0.035 ○ |
| Total cholesterol [mg/dl] | 238.9 ± 48.5 (229.7-248.1) | 219.7 ± 49.5 (210.3-229.0) | <0.001 ○ | 232.2 ± 44.2 (221.6-242.9) | 201.5 ± 36.6 (192.8-210.3) | <0.001 ○ |
| HDL [mg/dl] | 53.5 ± 12.3 (51.1-55.8) | 53.8 ± 13.0 (51.3-56.2) | 0.625 ○ | 54.8 ± 12.8 (51.7-57.9) | 55.3 ± 13.8 (52.0-58.6) | 0.625 ○ |
| LDL [mg/dl] | 150.6 ± 34.4 (144.1-157.1) | 134.3 ± 34.3 (127.9-140.8) | <0.001 ○ | 147.6 ± 33.6 (139.5-155.6) | 121.6 ± 26.4 (115.3-128.0) | <0.001 ○ |
| Triglycerides [mg/dl] | 218.0 ± 160.2 (187.7-248.2) | 188.6 ± 149.5 (160.3-216.8) | <0.001 ○ | 191.2 ± 130.2 (159.9-222.4) | 144.3 ± 90.7 (122.5-166.1) | <0.001 ○ |
| Systolic BP [mmHg] | 139.8 ± 17.7 (136.4-143.1) | 132.7 ± 13.9 (130.1-135.3) | <0.001 ▪ | 142.5 ± 19.2 (137.8-147.1) | 131.2 ± 13.6 (127.9-134.5) | <0.001 ▪ |
| Diastolic BP [mmHg] | 87.9 ± 11.1 (85.8-90.0) | 84.5 ± 9.6 (82.7-86.3) | <0.001 ○ | 88.5 ± 11.7 (85.7-91.3) | 83.1 ± 9.1 (80.9-85.3) | <0.001 ▪ |
| Exercise capacity [W/kg] | 1.73 ± 0.47 (1.64-1.82)n=103 | 1.89 ± 0.50 (1.80-1.99)n=103 | <0.001 ○ | 1.78 ± 0.51 (1.66-1.91) n=65 | 2.04 ± 0.50 (1.92-2.17) n=65 | <0.001 ▪ |
## Follow-up and long-term effects of the multimodal lifestyle intervention on the ESC- SCORE
There was a significant overall time effect for the ESC-SCORE in each group (INT and CON) from T0 across all follow-up time points (T2, T3, T4, T5) (Friedman test: $p \leq 0.001$), which was evident in both the intention-to-treat and per protocol analyses (Supplemental Data File: ESM 1). It appears that the ESC-SCORE of the INT group increased only very slightly in the long term after the intervention, while it increased more in the CON group. This is reflected in the changes from baseline. The delta values differed significantly between the groups (INT and CON) at each time point (T2, T3, T4, T5), with the intervention group achieving more favorable results in the intention-to-treat (Figure 2) as well as the per protocol analysis (Figure 3).
**Figure 2:** *Delta values of the European Society of Cardiology – Systematic Coronary Risk Evaluation score (ESC-SCORE) – Intention-to-treat analysis. Means with 95% confidence intervals.* **Figure 3:** *Delta values of the European Society of Cardiology – Systematic Coronary Risk Evaluation score (ESC-SCORE) –Per protocol analysis. Means with 95% confidence intervals.*
To clarify whether there is a difference in the primary outcome between pre-DM and DM patients, a further subgroup analysis was performed for ESC-SCORE changes (Supplemental Data File: ESM 2). The intention-to-treat analysis revealed that the pre-DM patients’ (INT: $$n = 72$$, CON: $$n = 70$$) results were quite similar to those of all patients (pre-DM/DM patients). Delta values differed significantly between the groups (INT and CON) at each time point (T2, T3, T4, T5), with the intervention group achieving more favorable results. In DM patients (INT: $$n = 38$$, CON: $$n = 26$$), a significant difference in ESC-SCORE changes was evident after the lifestyle intervention, with better results in the INT group. However, from T3 onward, there was no longer a significant difference in delta values between the groups (INT and CON). It should be noted that ESC-SCORE baseline values were significantly higher in pre-DM than in DM patients in both groups (INT: pre-DM: 9.16 ± $4.99\%$ ($95\%$-CI: 7.99-$10.33\%$), DM: 6.00 ± $4.95\%$ ($95\%$-CI: 4.38-$7.63\%$), U test: $p \leq 0.001$; CON: pre-DM: 8.54 ± $4.12\%$ ($95\%$-CI: 7.55-$9.52\%$), DM: 6.67 ± $6.24\%$ ($95\%$-CI: 4.15-$9.20\%$), U test: $$p \leq 0.017$$). Due to the small number of included DM patients (INT: $$n = 15$$, CON: $$n = 12$$), no subgroup analysis was performed in the per protocol cohort.
## Follow-up and long-term effects of the multimodal lifestyle intervention on glycemic control
There was a significant overall time effect for the HbA1c levels in the INT group from T0 across all follow-up time points (T2,T3,T4,T5) (Friedman test: $p \leq 0.001$), which was evident in both the intention-to-treat and per protocol analyses (Supplemental Data File: ESM 3). There were no significant HbA1c changes in the CON group. Of all pre-DM patients from the per protocol cohort, $5\%$ developed manifest DM in the INT and $22\%$ in the CON group (from T0 to T5). Half of them started treatment with anti-diabetic medication.
## Adverse events during the intervention
There were no adverse events during the intervention.
## Discussion
DM can drastically increase the risk of CVDs. The INTERHEART study, which collected data from more than 27,000 subjects in 52 countries, identified DM as a strong risk factor for acute myocardial infarction [9]. Other famous large-scale studies such as the Framingham study or the San Antonio Heart Study found increased CVD mortality rates in DM patients compared with non-diabetic subjects from the general population [7, 8]. Furthermore, the understanding of the pathogenesis of CVDs in the context of DM improves continuously, with hyperglycemia, hyperinsulinemia and hypercoagulability playing important roles in increased CVD risk and mortality [16, 17]. Lifestyle interventions that can prevent the development of CVDs or that have a positive effect on their progression should therefore be strongly recommended as preventive measures not only for patients with manifest DM, but also for those with pre-DM [18, 19].
The secondary data analysis of the PreFord study shows that the cardiovascular risk of persons with pre-DM/DM can be substantially reduced through the multimodal lifestyle program applied in the study. There was a direct effect on several health variables and the ESC-SCORE after 15 weeks. Over the next 3 years of follow-up, there were more favorable results in the INT group.
The ESC-SCORE reflects the probability of dying in the next 10 years from a cardiovascular event [5]. The ESC-SCORE used in this study is calculated based on age, systolic blood pressure, smoking habits and total cholesterol values [5, 6]. Although the algorithm does not consider pre-DM or diabetes status, the ESC-SCORE is nonetheless suitable for a rough assessment of the cardiovascular risk in the subgroup studied, because the relationship of the other risk factors with CVDs are almost parallel in individuals with and without DM [5, 20]. However, the risk of persons with DM is generally higher. According to the ESC- SCORE’s instructions, it should be considered for the interpretation that the calculated risk at every risk factor combination can be at least twice as high in men and up to 4-fold higher in women with manifest DM [5]. It must therefore be assumed that the actual CVD risk tends to be underestimated by the ESC-SCORE value for the subgroup studied, but because many more pre-DM patients than patients with manifest DM were included in the analysis, the underestimation should not be too far-reaching.
The overall results suggest a clear positive health effect of the intervention for the subgroup studied, which is very similar to the effect for the entire study cohort group [4]. Persons in the intervention group generally benefited from the multimodal lifestyle program, which was reflected in more favorable ESC-SCORE changes compared to those in the usual care control group over the course of the study. Multimodal interventions that also target self-empowerment, such as the program in the PreFord study, promise long-term effectiveness, which in turn may also be cost-effective [21]. Kähm et al. [ 22] estimated the costs for diabetic complications in German patients. End-stage renal disease, amputations, stroke, myocardial infarction and ischemic heart disease were deemed very cost-intensive. Indirect costs related to lost productivity and work ability due to diabetes and its complications are also very high [23]. Magliano et al. [ 24] demonstrated that “productivity-adjusted life years” were reduced by $11.6\%$ and $10.5\%$ among men and women with DM, respectively. Interventions that focus on persons with pre-DM and DM and which are initiated early in working life could thus help reduce work absenteeism and protect the workforce by preventing the development of disease complications.
A closer look at the long-term effects on the ESC-SCORE changes (intention-to-treat analysis) implies that pre-DM patients in particular benefited from the lifestyle intervention. Further measures may be necessary to achieve more beneficial effects in patients with manifest DM. However, it should be noted that the pre-DM patients already had higher values at the beginning of the study, so that possible improvements may be more pronounced in them than in the DM patients. However, the result should not be overestimated, as only $42\%$ of the DM patients of the intention-to-treat analysis fully adhered to the study protocol.
The strategy for raising awareness of CVD risk at the workplace through flyers and offering a quick medical check-up free of charge could—as demonstrated in the present study—motivate workers with pre-DM and DM to participate in multimodal therapy. Despite the noted drop-out rate during the intervention of $37\%$ among those with pre-DM and DM (for all study participants, the rate was $32\%$), the intention-to-treat analysis nevertheless indicated significant and clinically meaningful improvements post-intervention, underscoring the program’s overall efficacy.
The PreFord study has some limitations, which have already been pointed out in the initial publication [4]. One limitation, for example, is the fact that there could be concerns against the employer who pushed the study, so that some employees did not participate in the CVD mortality risk screening due to concerns that their health data could be misused. Therefore, the representativeness of the results for the entire company cannot be guaranteed. Another limitation is that very few women were included. Therefore, the question is to what extent the results are gender-specific. This cannot be clarified based on the present data.
An additional point that might be of interest, especially for the secondary analysis, is that no distinction was made between the types of DM. Among the 13 insulin-dependent patients, some patients with type 1 DM may have been included. However, Juutilainen et al. [ 25] showed in an 18-year observational study that there was no major difference between middle-aged individuals with T1DM and T2DM in terms of their CVD mortality risk (onset of the disease was > 30 years in both groups). However, other data suggest a greater mortality risk for T2DM patients compared with T1DM patients when the age of onset of the diabetic disease is earlier in both groups (15-30 years) [26].
Furthermore, there was a minor, but statistically significant difference in BMI values between the INT and CON group, which might have affected the development of health values. However, for the primary outcome (ESC-SCORE), the groups were almost perfectly matched.
## Conclusion
In conclusion, attracting company employees who are at high CVD mortality risk to participate in a multimodal lifestyle program following a free CVD mortality risk screening at their workplace may be a successful strategy for CVD prevention, particularly in patients with pre-DM/DM. The multimodal intervention used in the PreFord study was suitable for improving the health of company employees with pre-DM/DM and for reducing their CVD mortality risk in the long term.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Cologne. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
CB had the idea for this paper. CB and HH performed the statistical analyses. CB wrote and revised the manuscript. All other authors (D-BG, CA, SM, BB-W, JL, GH, KW, CH, MS, H-GP) have contributed substantially to the design, acquisition, analysis and interpretation of study data from the PreFord study and gave their intellectual input to the present manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
CB is a member of the Abbott Diabetes Care Advisory Board and has received research grants and honoraria from Abbott. GH was employed by the Health Service of the Ford Motor Company GmbH.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1106334/full#supplementary-material
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|
---
title: Identifying advanced MAFLD in a cohort of T2DM and clinical features
authors:
- Ana Maria Sanchez-Bao
- Alfonso Soto-Gonzalez
- Manuel Delgado-Blanco
- Vanesa Balboa-Barreiro
- Diego Bellido
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992874
doi: 10.3389/fendo.2023.1058995
license: CC BY 4.0
---
# Identifying advanced MAFLD in a cohort of T2DM and clinical features
## Abstract
### Background
MAFLD is the most common cause of chronic liver disease, affecting $25\%$ of the global population. Patients with T2DM have an increased risk of developing MAFLD. In addition, patients with T2DM have a higher risk of advanced forms of steatohepatitis and fibrosis. Identifying those patients is critical in order to refer them to specialist and appropriate management of their disease.
### Aims and Objectives
To estimate advanced fibrosis prevalence in a cohort of patients with T2DM and to identify possible predictors.
### Methods
subjects with T2DM during regular health check-up were enrolled. Demographic and general characteristics were measured, including metabolic parameters and homeostasis model assessment of insulin resistance (HOMA2-IR). Four non-invasive fibrosis scores (NAFLD fibrosis scores, FIB-4, APRI, *Hepamet fibrosis* score) were measure and compared with transient elastography (TE).
### Results
96 patients ($21\%$) presented risk of significant fibrosis (≥F2) measured by TE and 45 patients ($10\%$) presented with risk of advanced fibrosis F3-F4. Liver fibrosis was related to BMI, AC, HOMA2-IR. The results of the non-invasive fibrosis scores have been validated with the results obtained in the TE. It is observed that the index with the greatest area under the curve (AUC) is APRI (AUC=0.729), with a sensitivity of $62.2\%$ and a specificity of $76.1\%$. However, the test with better positive likelihood ratio (LR+) in our study is NAFLD fibrosis score.
### Conclusions
Our results show that in a general T2DM follow up, $10\%$ of patients were at risk of advanced fibrosis. We found a positive correlation between liver fibrosis and BMI, AC and HOMA2-IR. Non-invasive fibrosis markers can be useful for screening, showing NAFLD Fibrosis score a better LHR+ compared to TE. Further studies are needed to validate these results and elucidate the best screening approach to identify those patients at risk of advanced MAFLD.
## Introduction
Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly named non-alcoholic fatty liver disease (NAFLD), is the most common cause of chronic liver disease, affecting $25\%$ of the global population [1]. It is nowadays a major health and economic burden worldwide. MAFLD is diagnosed in patients when they have both hepatic steatosis and any of the following three metabolic conditions: overweight/obesity, diabetes mellitus, or evidence of metabolic dysregulation (MD) in lean individuals [2].
While simple steatosis generally has a benign course, it is well known that advanced forms, especially when fibrosis is present, may progress to cause cirrhosis, liver failure and hepatocellular carcinoma (HCC) [3].
Patients with type 2 diabetes mellitus (T2DM) have an increased risk of developing MAFLD, with reported prevalence ranging from 49 to $74\%$. Moreover, patients with T2DM have a higher risk of developing advanced stages of steatohepatitis and fibrosis [4]. On the other hand, patients without T2DM at the time of MAFLD diagnosis, run a high risk of future T2DM development [5].
MAFLD remains asymptomatic in a significant proportion of patients. Measurements of hepatic aminotransferase levels in plasma and liver ultrasonography are commonly used screening tools but lack sensitivity for diagnosis. Liver biopsy remains the gold standard however is not free of risk as it is an invasive procedure [6]. On the other hand, non-invasive fibrosis scores and transient elastography (TE) can be a first-line tool to identify low-risk patients since they are easy to apply and highly available and reproducible, making it easier to identify those patients who do not need more advanced diagnostic methods [7]. MAFLD patients with evidence of nonalcoholic steatohepatitis and advanced fibrosis are at markedly increased risk of adverse outcomes, including overall mortality, and liver-specific morbidity and mortality, respectively. Identification of this cohort of patients is paramount, given the associated poorer outcomes, to target resources to those who need it most [1].
The aim of the study is to estimate the prevalence of advanced fibrosis in a cohort of patients who attend an endocrinology clinic for their T2DM follow up and to identify possible predictors of advanced fibrosis.
## Study design and population
We selected patients seen at the Department of Endocrinology of University Clinical Hospital of A Coruña (Spain) during 2016 who fulfilled the following inclusion criteria: a medically confirmed diagnosis of T2DM according the American Diabetes *Association criteria* and acceptance of participation in the study, signing the corresponding informed consent document.
Exclusion criteria were 1) patients with type 1 DM (T1DM), latent autoimmune diabetes in adults (LADA), monogenic diabetes and other types of diabetes rather than T2DM. 2) patients with alcohol consumption > 40 g/day in men and > 20 g/day in women. 3) coexistence of liver disease 4) treatment with hepatotoxic drugs.
The sample size was calculated to cover the primary objective, estimating advanced fibrosis, with acceptable precision by $95\%$ confidence interval. Considering a prevalence of hepatic fibrosis between 5-$25\%$ in patients with T2DM, a sample size of 450 patients was calculated with a precision between 2- $4\%$ respectively by $95\%$ confidence interval.
The study was approved by the Ethics and Clinical Research Committee (register number $\frac{2016}{172}$), in accordance with the Declaration of Helsinki. All clinical data were obtained from the Electronic Medical Record System of the University Clinical Hospital of A Coruña, Spain, and patient anonymity was preserved.
## Demographic and clinical variables
Study parameters included demographic variables (age, sex), anthropometric variables (height, weight, BMI, abdominal circumference), past medical history, time form T2DM diagnosis, anti-diabetic medications, and non-anti-diabetic medications, glucose level, HbA1c, insulin level, lipidic profile (total cholesterol, LDL cholesterol, non-HDL cholesterol, HDL cholesterol and triglycerides), creatinine concentration, aspartate transaminase (AST) and alanine transaminase (ALT) levels. Insulin Resistance (IR) was determined by the Homeostasis Model Assessment of IR (HOMA2-IR). HOMA2-IR was calculated by the following formula: [plasma glucose (mg/dL) ∗ plasma insulin (μU/mL)]/405). The HOMA2-IR provides a surrogate estimate of IR [8] and a cut-off point of 3.8 was selected, based on previously studied populations with similar characteristics [9].
## Risk ok fibrosis: Non-invasive fibrosis scores
AST/platelet ratio index (APRI) was calculated as follows: (AST level/AST upper level of normal/platelet counts) × 100, considering a result of <0.5 as low risk, a result between 0.5 and 1.5 as intermediate risk, and a result > 1.5 as high risk [10]; FIB-4 as (age × AST level/platelet count × √ALT), considering a result < 1.3 as low-risk, a result between 1.3 and 2.67 as intermediate risk, and high risk a result > 2.67 [11]; NAFLD fibrosis score as follows: [-1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m2) + 1.13 × impaired fasting glucose/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio - 0.013 × platelet count - 0.66 × albumin], considering a result of < -1.455 as low risk, a result between -1.455 and 0.676 as intermediate risk, and a result > 0.676 as high risk [12]. Hepamet score was calculated using a free online application (https://www.hepamet-fibrosis-score.eu/), considering a result < 0.12 as low risk, results from 0.12 to 0.47 as intermediate risk, and results above 0.47 as high risk [13].
## Fibrosis evaluation with transient elastography
All patients were studied with transient elastography (TE) (FibroScan; Echosens, Paris, France). TE was performed by a single operator (with experience in more than 1000 exams before the start of the study). M or XL probe was used, in the lobe right liver, through the intercostal spaces, in the supine position, with the right arm in maximum abduction, suspended breathing and after fasting for 2 hours, according to the Boursier et al. criteria [14]. The examination was considered valid when the interquartile range (IQR) did not exceed $30\%$ of the total value obtained and the reference values that have been used are 7.0–8.1 kPa (F0-F1), 8.2–9.6 kPa (F2), 9.7–13.5 kPa (F3 or advanced fibrosis) and >13.6 (F4 or cirrhosis). These cutoff levels have been chosen as they are known to have a high positive predictive value to confirm the existence of clinically relevant fibrosis and cirrhosis, as has been shown in previous studies (15–17).
## Statistical analysis
A descriptive study of the variables included in the work was carried out. For the quantitative variables, the estimate of the mean is provided, together with the standard deviation (SD). The qualitative variables are expressed as absolute value and percentage, with the estimation of their confidence interval at $95\%$.
The comparison of means between two groups was performed using the non-parametric Mann Whitney U test after checking for normality with the Kolmomorov Smirnov test. The association between qualitative variables will be concluded with the Chi-square statistic or Fisher’s test.
To determine possible factors related to the presence of fibrosis, a univariate logistic regression analysis was carried out.
In the case of the results of the FIB-4, NAFLD fibrosis score, APRI and *Hepamet fibrosis* score biochemical indices, the results were compared with the diagnosis of fibrosis using the M and XL probes, and the accuracy of these indices was calculated by the area under the ROC curves. In each case, the estimate of the cut-off points were assessed using the Youden index. The Youden index (IJ) is defined as the maximum vertical distance between the ROC curve and the diagonal or line change and is calculated as IJ= max (sensitivity + specificity -1). In turn, the parameters of sensitivity, specificity, predictive values, and probability ratios (or likelihood ratios) were calculated to determine the validity of the procedures.
ROC curve comparison was performed following the procedure described in DeLong et al. [ 18], using the algorithm of Sun and Xu [19].
Statistical analysis was carried out with the IMB SPSS Statistics 24.0 and RStudio programs (2022.2.0.443 version).
## Study variables
Although 577 patients were included, 448 patients were finally analyzed. The loss of cases is due to a significant loss of data in those cases, and therefore they were not considered for the analysis. Table 1 shows the demographic and clinical characteristics of the study population were 231 (51,$56\%$) of participants were women. It is observed that the mean age at the time of the interview was 65 ± 7.8 years, with the minimum age observed being 37 years. Regarding the time of evolution of the disease, a mean time of approximately 13 years was observed, reaching 40 years in some cases, with an interquartile range between 7 and 17 years, and HbA1c levels were 7.0 ± $1.15\%$ while mean BMI was 31.5 ± 5.36 kg/m2.
**Table 1**
| Unnamed: 0 | Mean (SD) |
| --- | --- |
| Age (years) | 65.44 (7.81) |
| Duration of T2DM (years) | 12.97 (8.57) |
| Abdominal circumference (cm) | 105.60 (12.84) |
| BMI (kg/m2) | 31.50 (5.36) |
| DBP (mmHg) | 81.87 (10.22) |
| SBP (mmHg) | 141.23 (17.89) |
| HbA1c (%) | 7.00 (1.15) |
| Total cholesterol (mg/dl) | 178.06 (36.03) |
| HDL cholesterol (mg/dl) | 48.36 (12.55) |
| LDL cholesterol (mg/dl) | 100.33 (30.21) |
| Triglycerides (mg/dl) | 149.83 (79.71) |
| AST (UI/L) | 22.57 (9.15) |
| ALT (UI/L) | 26.96 (14.84) |
| HOMA2IR | 6.17 (14.03) |
| FIB4 score | 1.37 (0.75) |
| NAFLD Fibrosis Score | -0.17 (1.05) |
| APRI score | 0.27 (0.18) |
| Hepamet score | 0.20 (0.15) |
Findings of TE show that 96 patients ($21\%$) presented risk of significant fibrosis (≥F2) measured by TE and 45 patients ($10\%$) presented with risk of advanced fibrosis F3-F4 (see Table 2).
**Table 2**
| Transient Elastography (Fibroscan) | n (%) | CI 95% | CI 95%.1 |
| --- | --- | --- | --- |
| F0 | 201 (44.90) | 40.29 | 49.51 |
| F1 | 151 (33.70) | 29.32 | 38.08 |
| F2 | 51 (11.40) | 8.46 | 14.34 |
| F3 | 28 (6.30) | 4.05 | 8.55 |
| F4 | 17 (3.80) | 2.03 | 5.57 |
| Total valid tests | 448 (100) | | |
## Factors associated to fibrosis
Table 3 shows the factors associated with liver fibrosis measured with TE. It can be highlighted that body mass index (BMI) was identified as a risk factor ($$p \leq 0.013$$); patients with a higher BMI have a higher risk of fibrosis. In relation to normal weight patients there was no increased risk in the overweight category, but there was an increased risk of advanced fibrosis in patients with obesity in all categories, so that patients with type I obesity have an OR of 4.95 ($95\%$CI 1.42, 17.29), increasing this estimate of OR up to grade III-IV obesity, with an OR 9 ($95\%$CI 1.62, 50.12). Abdominal circumference (AC) shows a similar behavior, with higher levels being observed in patients with fibrosis (108.99 ± 11.02 vs. 101.93 ± 10.74 cm, $p \leq 0.001$), obtaining an OR=1.059 CI$95\%$ (1.028-1.09).
**Table 3**
| Unnamed: 0 | F0-F1-F2 | F3-F4 | Unnamed: 3 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| Variable | n=403 | n=45 | OR (95% CI) | p-value |
| Anthropometric Data | Anthropometric Data | Anthropometric Data | Anthropometric Data | Anthropometric Data |
| Abdominal circumference (cm) | 101.9 (10.74) | 109,0 (11,02) | 1,06 (1,03; 1,09) | <0,001 |
| BMI (kg/m^2) | 29.9 (4.35) | 33.0 (5.31) | 1.13 (1.07; 1.21) | <0.001 |
| Normal weight - Overweight class I | 99 (24.90%) | 3 (7.50%) | Ref | |
| Overweight class II | 123 (31.50%) | 9 (22.50%) | 2.42 (0.64; 9.16) | |
| Obesity class I | 120 (30.70%) | 18 (45%) | 4.95 (1.42; 17.29) | |
| Obesity class II | 38 (9.70%) | 7 (17.50%) | 6.08 (1.49; 24.73) | |
| Obesity class III – IV | 11 (2.80%) | 3 (7.50%) | 9 (1.62; 50.12) | |
| Biochemical parameters | Biochemical parameters | Biochemical parameters | Biochemical parameters | Biochemical parameters |
| Total Cholesterol (mg/dl) | 181.41 (37.10) | 164.46 (25.6) | 0.98 (0.97; 0.99) | 0.004 |
| HDL Cholesterol (mg/dl) | 49.37 (12.76) | 42.16 (10.74) | 0.94 (0.91;0.97) | <0.001 |
| LDL Cholesterol (mg/dl) | 103.15 (30.57) | 87.17 (22.15) | 0.97 (0.96; 0.99) | 0.002 |
| Triglycerides (mg/dl) | 148.23 (82.27) | 180.54 (89.20) | 1.02 (1,01; 1,03) | 0.009 |
| HbA1c (%) | 6.96 (1.17) | 7.15(0.91) | 1.15 (0.86;1.52) | 0.092 |
| HbA1c ≤ 7.5% | 251 (73.60%) | 22 (59.50%) | 1 | |
| Hb1Ac>7.5% | 90 (26.40%) | 15 (40.50%) | 1.902 (0.94;3.80) | |
| HOMA2IR | 5.706 (14.05) | 7.07 (6.76) | 1.01 (0.98;1.02) | <0.001 |
Total cholesterol, on the other hand, is shown to be a protective factor, as total cholesterol and HDL increase, the risk of fibrosis decreases. The opposite occurs with LDL cholesterol; higher levels of LDL cholesterol are related with hepatic fibrosis severity. Concerning insulin resistance, measured by HOMA2-IR, we found statistically significant correlation with the risk of liver fibrosis with an increase of 1.36 for patients with a degree of fibrosis F3-F4. Further details are shown in Table 3. Conversely, we found no evidence of significant statistical association between hepatic fibrosis and age, sex, hypertension or HbA1c levels (data not shown).
Regarding the non-invasive fibrosis scores, FIB-4, NAFLD Fibrosis Score, APRI and Hepamet Fibrosis Score were measured (see Table 4). We found that FIB-4 and APRI showed a statistically significant relationship with the TE in this cohort of patients. Those patients with higher FIB4 index, have 1.43 times more risk (OR=1.43, CI$95\%$= (1.0-22.19)) while patients with higher levels of APRI have 12 times more risk (OR=12.59, CI$95\%$= (2.60; 60.89)) of advanced fibrosis.
**Table 4**
| Unnamed: 0 | F0-F1-F2 | F3-F4 | Unnamed: 3 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | n=403 | n=45 | OR (95% CI) | p-value |
| Non-invasive fibrosis scores | Non-invasive fibrosis scores | Non-invasive fibrosis scores | Non-invasive fibrosis scores | Non-invasive fibrosis scores |
| FIB4 score | 1.33 (0.72) | 1.67 (1.09) | 1.43 (1.01; 2.01) | 0.049 |
| NAFLD Fibrosis Score | 0.34 (0.99) | 0.04 (1.31) | 1.34 (0.94; 1.92) | 0.419 |
| APRI | 0.24 (0.17) | 0.38 (0.25) | 12.59 (2.60; 60.89) | <0.001 |
| Hepamet | 0.19 (0.14) | 0.22 (0.19) | 3.34 (0.43-25.79) | 0.51 |
The results of the non-invasive fibrosis scores have been validated with the results obtained in the TE (Table 5; Figure 1). It is observed that the index with the greatest area under the curve (AUC) is APRI (AUC=0.729), with a sensitivity of $62.2\%$ and a specificity of $76.1\%$ and the test with higher maximum positive likelihood ratio (LR+: 7.45) in this study was NAFLD fibrosis score. Table 6 shows the pairwise comparisons of ROC curves (AUC) between these four tests for the diagnosis of fibrosis.
## Discussion
In this observational study, among a population of T2DM patients with an average glycemic control of HbA1c $7\%$ and average duration of disease of 13 years, $21\%$ of patients had risk of significant hepatic fibrosis (TE ≥ F2) in $10\%$ of patients there was suspicion of advanced hepatic fibrosis (TE ≥ F3), being consistent with similar studies [20]. Neither control of diabetes nor time of evolution were predictors of advanced fibrosis, consistent with what has been described previously [21].
We found that hepatic fibrosis was associated to BMI and to AC with a growing trend, being in tune with known studies [22]. Obesity is a known major risk factor for both hepatic steatosis and fibrosis, since adipose tissue dysfunction causes intrahepatic triglyceride accumulation through increased hepatic lipid flow, IR and pro-inflammatory adipokines release; moreover the oxidative stress and inflammation associated with excess adiposity promotes dysregulation of the genes involved in liver tumorigenesis, increasing the risk of hepatocarcinoma [23]. This seems to be related to the distribution of body fat, presenting those patients more visceral adiposity. In this study we found that as the AC increases by one unit, the risk of hepatic fibrosis increases by $6\%$. AC is predictive of increased visceral adipose tissue (VAT) among people with the same BMI and has been shown to be more strongly associated with amount of VAT than waist‐to‐hip ratio [24]. There has been increasing interest in recent years in the role of VAT in MAFLD. Studies have demonstrated that VAT, which was originally considered a passive depot for energy storage, is an active endocrine tissue that releases many peptides and hormones that regulate metabolism, inflammation, and immunity, thus participating in the pathogenesis of MAFLD [25]. In line with that, we found that HOMA2-IR is associated with the presence of advanced hepatic fibrosis in adults with T2DM, and it is known that HOMA2-IR has a significant positive correlation to visceral adipose tissue [26, 27].
Concerning the non-invasive fibrosis scores, they are a widely available, inexpensive, tool for first line identification of patients at risk. Nonetheless, data are still emerging regarding the optimal way to use these tests. Due to the generally low pretest probability of advanced fibrosis and cirrhosis in the general population, the positive predictive value (PPV) of a result above the high cut-off is typically modest, and often not sufficient to be diagnostic in the absence of additional supportive clinical information. In contrast, the negative predictive value (NPV) is generally very high, allowing the clinician to be confident that advanced fibrosis or cirrhosis has been excluded [28].
In this study, although APRI has a better AUROC related to TE, also the higher estimation of OR. On the other hand, NAFLD fibrosis score has the higher LR+, showing a theoretical better result for a screening test. Nevertheless, these tests are still being refined and recent diagnostic algorithms propose a two steps screening, using TE in second line to confirm those patients at risk of advanced fibrosis [29].
The large global impact of MAFLD and T2DM on healthcare systems requires a paradigm shift to early identification and risk stratification of MAFLD in primary care and diabetes clinics. Establishing a diagnosis may be especially important in patients with T2DM, not only because of its high prevalence, in this described cohort a $10\%$ of patients with F3-F4 score of fibrosis, but also because it has been shown that patients with liver fibrosis and T2DM are at a high risk of serious hepatic pathology, including cirrhosis and hepatocellular carcinoma, increasing all-cause and liver-related mortality and morbidity, even after adjustment for potential confounding factors [30]. In addition, a recent American Heart Association (AHA) Statement highlights that it may potentially worsen cardiovascular disease risk (CVD), independently of other components of the metabolic syndrome [31]. Thus, the identification of MAFLD in this population may have important management implications beyond hepatic disease, including intensive therapy to reduce CVD risk.
This study has several limitations. Firstly, the diagnosis of hepatic fibrosis was based on TE rather than liver biopsy, which may have resulted in misclassification of some patients. Liver biopsy remains the gold standard for MAFLD diagnosis. However, cost, procedure related complications, and intra- and inter-observer variations in reporting the histology are the major draw backs of liver biopsy, and, therefore, it is usually not recommended in clinical practice for general screening [6]. Secondly, although our study had sufficient power to identify a significant association of hepatic fibrosis with anthropometric data as BMI and AC, we lack body composition data. Further studies analyzing total body fat and abdominal fat would be interesting, as recent data suggest they are strongly related to the risk of MAFLD [32].
This study has also strengths. We used data from a well-characterized sample of T2DM patients, based on direct measurements collected by Endocrinologist and Hepatologist in a tertiary-level hospital in Spain. In addition, the most appropriate TE probe was selected in each patient (M or XL probe) to avoid measurement errors.
## Conclusion
In conclusion, our results show that in a cohort of T2DM patients, $10\%$ were at risk of advanced fibrosis. We found a positive correlation between liver fibrosis and BMI, AC and HOMA2-IR. Non-invasive fibrosis markers can be useful for screening, showing NAFLD Fibrosis score a better LR+ compared to TE. Further studies are needed to validate these results and elucidate the best screening approach to identify those patients at risk of advanced MAFLD.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Autonomic Committee of Clinical Research Ethics of Galicia, Spain (register number $\frac{2016}{172}$). The authors also certify that formal approval to conduct the experiments described has been obtained from the human subjects review board of their institution and could be provided upon request. The participants did not receive economic profit. Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper.
## Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Incidence and risk factors of postoperative nausea and vomiting following
laparoscopic sleeve gastrectomy and its relationship with Helicobacter pylori: A
propensity score matching analysis'
authors:
- Yali Song
- Jie Zhu
- Zhiyong Dong
- Cunchuan Wang
- Jia Xiao
- Wah Yang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992875
doi: 10.3389/fendo.2023.1102017
license: CC BY 4.0
---
# Incidence and risk factors of postoperative nausea and vomiting following laparoscopic sleeve gastrectomy and its relationship with Helicobacter pylori: A propensity score matching analysis
## Abstract
### Background
Postoperative nausea and vomiting (PONV) are common after laparoscopic sleeve gastrectomy (LSG), affecting patient satisfaction and postoperative recovery. The purpose of this study was to investigate the incidence and severity of PONV after LSG and the relationship between *Helicobacter pylori* (HP) and PONV.
### Methods
Patients undergoing LSG in our center from June 1, 2018, to May 31, 2022, were divided into HP-positive and HP-negative groups for retrospective analysis. The independent risk factors of PONV were determined by univariate and binary logistic regression analysis using a 1:1 propensity score matching (PSM) method.
### Results
A total of 656 patients was enrolled, and 193 pairs of HP-positive and negative groups were matched after PSM. Both groups of patients had similar clinical features and surgical procedures. PONV occurred in 232 patients ($60.1\%$) after LSG, and the incidence of PONV in HP-positive patients was $61.10\%$. The incidence and severity of PONV were statistically similar in both groups ($$P \leq 0.815$$). Multivariate analysis showed that the female sex (OR=1.644, $$P \leq 0.042$$), postoperative pain (OR=2.203, $$P \leq 0.001$$) and use of postoperative opioid (OR=2.229, $$P \leq 0.000$$) were independent risk factors for PONV after LSG, whereas T2DM (OR=0.510, $$P \leq 0.009$$) and OSAS (OR=0.545, $$P \leq 0.008$$) independently reduced the incidence rate of PONV. There was no difference either in smoking ($$P \leq 0.255$$) or alcohol drinking ($$P \leq 0.801$$). HP infection did not affect PONV ($$P \leq 0.678$$).
### Conclusions
The incidence of PONV following LSG was relatively high. Female sex, postoperative pain and use of postoperative opioid predicted a higher incidence of PONV. Patients with T2DM and OSAS were less likely to have PONV. There was no clear association between HP infection and PONV after LSG.
## Introduction
Overweight and obesity are defined as an excess of body fat accumulation that threatens health. According to the updated data from the World Health Organization, in 2016, more than 1.9 billion adults were overweight globally. Of these, over 650 million were in obesity [1, 2]. According to epidemiological studies, obesity can progressively cause and/or exacerbate a wide spectrum of chronic diseases, which include type 2 diabetes mellitus, chronic kidney disease [3], cardiovascular disease [3, 4], a range of musculoskeletal disorders [5, 6], and even certain types of cancer [7]. Bariatric surgery becomes necessary for people with severe obesity who cannot sustain weight loss by non-surgical means (e.g., diet and exercise). Laparoscopic sleeve gastrectomy (LSG) has become the most common bariatric surgery because of its simple operation, fewer complications, and good effect in reducing weight and alleviating obesity metabolism-related complications (8–11). Of note, there are a variety of side effects and post-op risks related to bariatric surgery, including acid reflux, dilation of the esophagus, obstruction of the stomach, weight gain or failure to lose weight, infection, and postoperative nausea and vomiting (PONV) [12].
PONV, defined as nausea, vomiting, or retching occurring within 24 h following anesthesia, is the most common adverse reaction after LSG. Without preventive antiemetic treatment, its incidence can reach $80\%$ [13]. PONV will induce postoperative discomforts and cause serious complications, such as water-electrolyte disorder and aspiration pneumonia, resulting in prolonged hospitalization and increased medical expenses [14]. Previous studies have identified the factors affecting the incidence of PONV came from three aspects: patient factors (e.g., female sex, anxiety, infection, metabolic disease, and gastrointestinal disease), medication/anesthesia factors (e.g., opioids, volatile agents, and nitrous oxide), and surgery factors (e.g., surgical time, procedure, and technique) [15, 16]. In adults, the known risk factors for PONV include female sex, non-smoking status, use of postoperative opioids, younger age, and history of PONV or motion sickness [17]. However, for obese patients, several factors may contribute to the high susceptibility to PONV. Because patients who undergo bariatric surgery are usually younger women and non-smokers, with laparoscopic or robotic surgery lasting more than one hour, and receive perioperative opioid analgesia, all these are risk factors for PONV. Besides, impaired splanchnic perfusion during pneumoperitoneum and gastric volume reduction (especially after LSG) may further lead to PONV (18–20).
Studies have shown that *Helicobacter pylori* (HP) infection is closely related to digestive tract diseases such as peptic ulcer, gastric cancer, gastric lymphoma, and chronic gastritis [21]. There are many studies on the mechanism, prevention, and treatment, but few on the relationship between HP infection and gastrointestinal adverse reactions such as PONV. Several researches have shown that there is an association between HP and hyperemesis gravidarum, which indicates that HP can exacerbate nausea and vomiting during pregnancy (22–25). Thus, the aims of this retrospective study were to investigate the incidence and risk factors of PONV after LSG and to explore whether HP infection affects PONV in subjects receiving LSG using a propensity score matching (PSM) analysis.
## Study population
This study was conducted at the Department of Metabolic and Bariatric Surgery in the First Affiliated Hospital of Jinan University. A preliminary assessment determined surgical qualifications by a multidisciplinary team including surgeons, endocrinologists, anesthesiologists, nutritionists, and nurses. This retrospective study included all patients with obesity who underwent LSG at our bariatric surgery center from June 1, 2018, to May 31, 2022. The exclusion criteria were: [1] age less than 18 years, [2] patients did not undergo HP examination before the operation, [3] patients who were transferred to the intensive care unit (ICU) immediately after the operation, [4] the revision surgery (a repeated surgery due to complications or unsatisfactory results after initial bariatric surgery), [5] patients received HP eradication treatment before the operation, [6] patients received antibiotic treatment within four weeks before the operation, [7] nausea or vomiting before anesthesia.
All Bariatric surgeries were performed by the same well-experienced surgical team. The surgical techniques of LSG and postoperative management were introduced previously [26]. On the basis of PONV prophylaxis guidelines, we routinely gave palonosetron and dexamethasone at the end of the operation [13, 27]. After surgery, we transferred the patients to post-anesthesia care unit (PACU) until complete recovery and monitored vital signs according to standard clinical practice. In the ward, we used a visual analogue scale (VAS) to evaluate nausea and vomiting or pain (least: 0–10: worst). Depending on the severity of PONV, we decided whether to use antiemetics. For the patients with PONV or cases were intolerable, we usually offered rescue antiemetic agent (including: 5 mg tropisetron, 10 mg metoclopramide or 4 mg ondansetron). On the basis of the level of pain, subjects with postoperative pain received analgesic management, such as flurbiprofen 50 mg, parecoxib 40 mg or tramadol 100 mg [26].
Since [1] we had informed all participants receiving LSG that the clinical data which were acquired during the perioperative period may be retrospectively analyzed and published; And [2] in our study, all data were collected as a regular part of surgical care, and none were designed to collect data specifically for the research, so there was no need for written informed consent. This study protocol was approved by the Ethical Committee of the First Affiliated Hospital of Jinan University (no. KY-2021-070).
## Anesthesia protocol
All procedures were finished under general anesthesia following a standardized clinical routine. Routine monitoring of electrocardiogram, blood pressure, and pulse oximetry were carried out. General anesthesia was induced with propofol, remifentanil, and rocuronium, and the dosage of drugs depended on the body weight of the patient. The maintenance of anesthesia was implemented by the use of remifentanil and propofol, oxygen, and air [26]. In accordance with the PONV prevention guidelines, we routinely provided dexamethasone and palonosetron at the end of surgery [13, 27].
## Study outcomes
Nausea is defined as an unpleasant feeling associated with the urge to vomit. Vomiting is defined as successful or unsuccessful (retching) excretion of gastric contents [28]. The risk factors and predictors for postoperative nausea and vomiting are generally considered to be almost identical [29]. Consequently, nausea or vomiting is not considered as a separate outcome in our research [30]. We focused our study on 6 h and 24 h after surgery.
In this study, the primary endpoint was the overall incidence of PONV within 24 h after surgery, with secondary outcomes being the severity of PONV, the type and use of rescue antiemetics, and the time for the first rescue antiemetic and analgesics. Based on the total VAS scores at 6 h and 24 h after operation and the use of rescue antiemetics, two groups were divided (PONV: total VAS score greater than 2 or use of rescue antiemetics; No PONV: total VAS score less than or equal to 2 and no use of rescue antiemetics). Depending on the total postoperative pain VAS (P-VAS) scores at 6 h and 24 h after surgery and the application of rescue analgesics, the definition of postoperative pain was the sum of P-VAS, which was higher than 2 points or applying rescue analgesics. At the same time, for further study, we respectively divided the PONV group and the pain group into three groups: mild (3-6 scores), moderate (7-12 scores) and severe (13-20 scores) [26].
## Data collection
A professional researcher reviewed patients’ electronic medical records and extracted the following data which contained demographic data and perioperative factors. The demographic variables included age, BMI, obesity-related comorbidity [type 2 diabetes mellitus (T2DM), hyperlipidemia (HLP), hypertension], and smoking status. Operational details were collected, mainly including duration of surgery, the use of prophylactic antiemetics and anesthesia methods. We used the C13 breath test to detect HP infection.
In our department, the same team performed one standardized questionnaire to all patients. By this way, we acquired the information including PONV score, pain level, alcohol consumption, and smoking status. PONV severity was assessed using the total VAS scores at 6h and 24h after the operation. A higher score indicated more severe nausea and vomiting [31]. Pain status was scored with a VAS at 6h and 24h post-operation [32]. The alcohol consumption level was quantified before operation using the Alcohol Use Disorders Identification Test (AUDIT) recommended by the World Health Organization. The AUDIT score could be classified into four risk levels: 0 point as a non-drinker; 1-7 points as low risk, 8-15 points as a moderate risk; 16-19 points as high risk; 20 and above as alcohol dependence [33]. Smoking status was expressed by the Brinkman index (BI), which is the number of years of smoking multiplied by the number of cigarettes smoked per day. BI results could be divided into four sequential groups: non-smokers as 0; mild smokers as 1-200; moderate smokers as 200-400; and heavy smokers as > 400 [34].
## Statistical analysis
To help overcome the selection bias from the confounding variables, we performed a PSM analysis in each group. The propensity score was calculated by logistic regression analysis. We applied the nearest-neighbor method to match the patients in a 1:1 ratio. As a result, A patient in the HP-positive group was matched with one patient in the HP-negative group. The caliper size was set 0.02 and bad matches were excluded from analysis.
Continuous variables of normal distribution were presented as means ± standard deviations (SD) and were analyzed using an independent t-test. Variables with a skewed distribution were presented as median (interquartile range) and were compared using the Mann-Whitney U-test. Categorical data were presented as percentages and compared using the χ2 and Wilcoxon test. The risk factors of PONV post LSG were firstly analyzed by a univariate analysis. After screening the variables, the likelihood ratio stepwise forward method included the significantly related variables in the binary logistic regression analysis. The analysis indexes included the odds ratio (OR), $95\%$ confidence interval ($95\%$ CI), and significance test results (P value).
All data were analyzed using SPSS 26.0 software (SPSS Inc., Chicago, IL). All P values were two-sided, and $P \leq 0.05$ was considered statistically significant.
## Patient characteristics
The study reviewed 822 patients (205 males and 617 females) who underwent LSG surgery in our hospital between June 1, 2018, and May 31, 2022. In those patients, 82 were younger than 18 years old, 25 were not examined for HP before the operation, 12 cases were transferred to ICU after the operation, 8 patients received revision surgery, 16 cases were treated for HP before the procedure, 10 cases were treated with antibiotics within four weeks before the operation, and 13 cases had nausea and vomiting before anesthesia. Finally, 656 patients were eligible to enter the study prior to the PSM, and we had 193 matched patients over 1:1 PSM, effectively balancing the preoperative confounding factors of the two groups. The research flow chart was shown in Figure 1. Demographic data and perioperative factors of all patients before and after PSM were shown in Table 1.
**Figure 1:** *Flow diagram of this study.* TABLE_PLACEHOLDER:Table 1
## Occurrence and severity of PONV in HP-positive group and HP-negative group
Before PSM, there were 390 patients of PONV in 656 patients undergoing LSG, and the infection rate was $59.45\%$. There was no significant difference in the incidence of PONV between HP-positive and HP-negative patients ($$P \leq 0.641$$) (Table 2).
**Table 2**
| Unnamed: 0 | PONV group (n=390) | NoPONV group (n=266) | P value |
| --- | --- | --- | --- |
| HP-negative (n=457) | 269 (58.9%) | 188 (41.1%) | 0.641 |
| HP-positive (n=199) | 121 (60.8%) | 78 (39.2%) | |
## Comparison of covariates before and after PSM in groups
Before PSM, there were 199 cases in the HP-positive group and 457 cases in the HP-negative group, respectively. There were significant differences between the two groups in terms of age ($$P \leq 0.027$$) and hyperuricemia ($$P \leq 0.018$$); After PSM, the infection of HP was taken as the dependent variable, and the above covariates were taken as the independent variables. After 1:1 matching of the data between the two groups, there were 193 cases in each of the two groups. The distribution of the above covariates between the groups reached equilibrium (all $P \leq 0.05$) (Table 1).
## Comparison of occurrence and severity of PONV
Among the 193 patients in the HP-negative and HP-positive groups, 114 ($59.1\%$) and 118 ($61.1\%$) developed PONV within 24 h after the operation. Most PONV cases were mild. The incidence, severity ($$P \leq 0.851$$), frequency of rescue antiemetics ($$P \leq 0.615$$), and the earliest antiemetics use ($$P \leq 0.359$$) in the two groups were not statistically significant (Table 3).
**Table 3**
| Variables | HP-negative (n=193) | HP-positive (n=193) | Unnamed: 3 | P value |
| --- | --- | --- | --- | --- |
| Severity of PONV | Severity of PONV | Severity of PONV | Z=0.188 | 0.851 |
| NO | 79 (40.9%) | 75 (38.9%) | | |
| Mild | 74 (38.4%) | 87 (45.1%) | | |
| Moderate | 32 (16.6%) | 24 (12.4%) | | |
| Severe | 8 (4.1%) | 7 (3.6%) | | |
| Times of rescue antiemetics | Times of rescue antiemetics | Times of rescue antiemetics | Z=0.503 | 0.615 |
| NO | 121 (62.7%) | 113 (58.5%) | | |
| 1 time | 38 (19.7%) | 50 (25.9%) | | |
| 2 times | 22 (11.4%) | 20 (10.4%) | | |
| ≥ 3 times | 12 (6.2%) | 10 (5.2%) | | |
| Earliest of having antiemetics | Earliest of having antiemetics | Earliest of having antiemetics | Z=0.918 | 0.359 |
| No | 106 (54.9%) | 100 (51.8%) | | |
| 0-6 h after surgery | 21 (10.9%) | 18 (9.3%) | | |
| 6-12 h after surgery | 30 (15.5%) | 27 (14.0%) | | |
| 12-24 h after surgery | 22 (11.4%) | 34 (17.6%) | | |
| > 24 h | 14 (7.3%) | 14 (7.3%) | | |
## Univariate analysis
After PSM, 386 patients were finally included, including 100 males and 286 females. A total of 232 occurred PONV, with an incidence rate of $60.1\%$. According to PONV occurrence, those patients were divided into the PONV group and the no PONV group. The univariate analysis showed that females had a significantly higher risk of PONV than males after LSG ($$P \leq 0.008$$). The incidence rate of PONV in patients with diabetes ($$P \leq 0.003$$) and OSAS was lower than in those who had not those complications ($$P \leq 0.007$$). The incidence of PONV was significantly higher in patients with postoperative pain ($$P \leq 0.000$$) and use of postoperative opioid ($$P \leq 0.001$$) than in patients without pain (Table 4).
**Table 4**
| Variables | PONV Group (n=232) | NoPONV Group (n=154) | P value |
| --- | --- | --- | --- |
| Mean age (years) | 31.41 ± 8.10 | 31.62 ± 7.83 | 0.803 |
| Preoperative BMI (kg/m2) | 37.03 ± 5.76 | 38.18 ± 6.14 | 0.062 |
| postoperative hospital stay (day) | 4.16 ± 1.20 | 4.12 ± 0.97 | 0.727 |
| Operation time (min) | 128.67 ± 40.10 | 137.17 ± 56.02 | 0.105 |
| Blood loss (mL) | 11.68 ± 5.20 | 11.62 ± 5.27 | 0.916 |
| Distance from incisal margin to pylorus (cm) | 2.78 ± 0.42 | 2.77 ± 0.43 | 0.749 |
| Gender, n(%) | 183 (78.9%) | 103 (66.9%) | 0.008 |
| T2DM, n(%) | 38 (16.4%) | 45 (29.2%) | 0.003 |
| Hypertension, n(%) | 38 (16.4%) | 21 (13.6%) | 0.463 |
| Hyperlipidemia, n(%) | 105 (45.3%) | 71 (46.1%) | 0.870 |
| Hyperuricemia, n(%) | 117 (50.4%) | 86 (55.8%) | 0.297 |
| OSAS, n(%) | 128 (55.2%) | 106 (68.8%) | 0.007 |
| Esophagitis, n(%) | 34 (14.7%) | 29 (18.8%) | 0.277 |
| HP-positive, n(%) | 118 (50.9%) | 75 (48.7%) | 0.678 |
| Alcohol consumption, n(%) | | | 0.801 |
| Non-drinker | 166 (71.6%) | 110 (71.4%) | – |
| Low-risk | 26 (11.2%) | 12 (7.8%) | – |
| Moderate-risk | 13 (5.6%) | 10 (6.5%) | – |
| High-risk | 7 (3.0%) | 5 (3.3%) | – |
| Alcohol dependence | 20 (8.6%) | 17 (11.0%) | – |
| Smoking habit, n(%) | | | 0.255 |
| Non-smoker | 182 (78.4%) | 128 (83.1%) | – |
| Light smoker | 38 (16.4%) | 20 (13.0%) | – |
| Moderate smoker | 6 (2.6%) | 4 (2.6%) | – |
| Heavy smoker | 6 (2.6%) | 2 (1.3%) | – |
| Postoperative pain, n(%) | | | 0.000 |
| No | 105 (45.3%) | 98 (63.6%) | – |
| Mild | 95 (40.9%) | 46 (29.9%) | – |
| Moderate | 25 (10.8%) | 8 (5.2%) | – |
| Severe | 7 (3.0%) | 2 (1.3%) | – |
| Use of postoperative opioid, n(%) | | | 0.001 |
| NO | 105 (45.3%) | 98 (63.6%) | - |
| 1 time | 90 (38.7%) | 40 (26.0%) | - |
| 2 times | 25 (10.8%) | 14 (9.1%) | - |
| ≥ 3 times | 12 (5.2%) | 2 (1.3%) | - |
## Multivariate regression analysis
Significant and independent predictors of PONV incidence were determined by a multivariate logistic regression analysis. Variables that were statistically significant in the univariate analysis were included in the multivariate logistic regression analysis model. Results showed that female sex and postoperative pain were important independent predictors of the increase in the incidence rate of PONV. At the same time, type 2 diabetes (T2DM) and OSAS significantly and independently reduced the incidence rate of PONV after adjusting for confounding variables. The OR ($95\%$ CIs, P value) of PONV incidence after LSG was 1.644 (1.017-2.655, $$P \leq 0.042$$) in females and 2.203 (1.430-3.396, $$P \leq 0.001$$) in the pain group; The group of use of postoperative opioid was 2.229 (1.446-3.434, $$P \leq 0.000$$); T2DM group was 0.510 (0.306-0.848, $$P \leq 0.009$$) and OSAS group was 0.545 (0.349-0.853, $$P \leq 0.008$$) (Table 5).
**Table 5**
| Unnamed: 0 | OR (95%CI) | P value |
| --- | --- | --- |
| Female gender | 1.644 (1.017-2.655) | 0.042 |
| T2DM | 0.510 (0.306-0.848) | 0.009 |
| OSAS | 0.545 (0.349-0.853) | 0.008 |
| Postoperative pain | 2.203 (1.430-3.396) | 0.001 |
| Use of postoperative opioid | 2.229 (1.446-3.434) | 0.0 |
## Incidence and severity of PONV
Previous studies have shown that postoperative PONV was the most common adverse effect of weight loss surgery, and its overall incidence exceeded $80\%$ in some types of surgery [35]. PONV following LSG was thought to be secondary to the sharp reduction of gastric volume and increased intragastric pressure [36]. A retrospective chart review study showed that the incidence of PONV in the LSG group ($66.9\%$) was higher than that in the primary laparoscopic Roux-en-Y gastric bypass group ($33.1\%$) [37]. Another study pointed out that $65\%$ of patients experience PONV within the first 24 h following LSG [18]. Our study found that the incidence of PONV in Chinese patients at 0-24 h following LSG was $60.1\%$, lower than the above reported incidences of patients from other countries (38–40). This could be attributed to: in our center, tropisetron hydrochloride (a potent and selective 5-HT3 receptor antagonist) and metoclopramide (a dopamine antagonist) were routinely used during and right after the surgery to prevent PONV [41, 42].
## Biological sex and PONV
It was identified that the female sex predicted a higher incidence of PONV following surgery (43–45). Halliday et al. Found that when two or even three preventive drugs were used, the incidence of PONV in female patients was still as high as $78\%$ following weight loss surgery, which was three times than that of male patients during the same period [18]. Another retrospective study showed that preventive antiemetic therapy did not have an ideal effect on preventing and treating PONV after weight loss surgery. After drug intervention, the incidence of PONV in female patients was still nearly $\frac{1}{3}$ higher than that in male patients ($60.4\%$ vs. $42.9\%$), suggesting that the risk of PONV after bariatric surgery in female patients will not be significantly reduced with the use of preventive drugs [19]. The incidence rate of PONV varies with the different phases of the menstrual cycle [19, 46, 47]. However, this conclusion was contradicted by a randomized controlled trial study involving more than 5,000 patients in 2007, in which no association between the menstrual cycle stage or menopausal status and the incidence of PONV was identified [48]. The molecular mechanism responsible for the correlation between the female sex and the incidence rate of PONV is still largely unknown.
## Postoperative pain and PONV
Previous studies had shown that PONV was strongly associated with postoperative pain in LSG [26]. Our study also demonstrated that postoperative pain was a risk factor for PONV after LSG. The possible reasons could be [1]: in essence, high pain intensity was inclined to increase the risk of PONV, and [2] in our center, opioids, such as tramadol, were preferred for postoperative pain, which may increase the risk of PONV and constituted one of the major risk factors in the scoring system [49, 50]. However, further study was warranted to confirm the impact of postoperative pain on PONV after LSG.
## OSAS and PONV
A major finding of our research was that patients without OSAS had a higher risk of PONV than patients with OSAS. Obesity is considered the main factor leading to OSAS, of which severity could be measured by the sleep apnea-hypopnea index (AHI). With the increase in BMI, the AHI of both males and females increases, and this trend is tendentious in males [51]. Although OSAS did not affect the prognosis of bariatric surgery, it indeed affected the postoperative complication of cardiopulmonary function [52]. Continuous positive airway pressure (CPAP) is currently the most effective method for treating moderate to severe OSAS, which improves the respiratory function of patients with morbid obesity and accelerates the reconstruction of preoperative pulmonary function [53]. A previous study found that in subjects receiving Roux-en-Y gastric bypass, the no-CPAP group reported a higher incidence of oxygenation disturbance, but a slightly lower incidence, although not statistically significant, of PONV when compared with the CPAP group [54]. Thus, a possible reason for the lower incidence of PONV in patients with OSAS in this study is the routine use of CPAP in the perioperative period of LSG. More substantial evidence and molecular pathway for this conclusion warrant further investigations.
## Alcohol drinking, smoking, and PONV
A recent study reported decreased risks of PONV in alcoholics than non-drinkers and light-drinkers who underwent abdominal surgery [55]. In addition, since chronic alcoholics have higher basal activity of cytochrome P450 2E1 (CYP2E1), which also accelerates the metabolic rate of volatile anesthetics, the main reason of PONV within the first two hours after surgery moderate- or heavy-drinkers (including alcohol dependence patients) may expect a reduced incidence of PONV post-LSG [56, 57]. This is not consistent with what we demonstrated here. Previous studies have also built an association between the reduced incidence of PONV and cigarette smoking in Bariatric surgeries [58]. However, we did not observe such correlations in our study.
## HP and PONV
A previous study has demonstrated no association between HP infection and nausea after general anesthesia [59]. Notash et al. Also found no relationship between HP infection and PONV who underwent general and urological surgery [60]. The incidence of PONV in our research was similar in HP-positive and negative patients. Although HP may be related to severe pregnancy-related vomiting, it did not exacerbate LSG-related nausea. In bariatric surgery, to our best knowledge, this is the first report showing that HP infection did not affect the prevalence of PONV after LSG. However, since our research is a single center, more extensive cohort studies are needed for the validation of this conclusion.
## Strengths and limitations
There are some limitations in this retrospective study: [1] The confirmation of a PONV event was determined by using rescue antiemetics or notating its manifestation in the medical records. This approach raises the possibility that the PONV frequencies were underestimated as some patients might experience untreated PONV; [2] Other potential factors, such as PONV history, migraine, and duration of anesthesia, were not considered, which may bias the results; [3] Since we only observed the PONV incidence within 24 h post-operation, a long-term follow-up study is needed to confirm and expand the conclusions; [4] Mechanistic research is required to investigate the molecular pathways leading to PONV after LSG and other types of bariatric surgery. However, as far as we know, this is the largest reported sample size in the study of PONV in LSG. Those confounding factors could be counterpoised after PSM. The relationship between HP and PONV in populations undergoing LSG was also interpreted. Further basic research is required to investigate the molecular mechanism leading to PONV after LSG and other types of bariatric surgery.
## Conclusions
In conclusion, the incidence of PONV after LSG is relatively high. Female sex, postoperative pain and use of postoperative opioid predicted a higher incidence of PONV. Patients with T2DM and OSAS had less likelihood of a related PONV. There was no clear association between HP infection and PONV after LSG.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary materials. Further inquiries can be directed to the corresponding authors.
## Ethics statement
As all participants receiving LSG were informed that the clinical data which were acquired during the perioperative period may be retrospectively analyzed and published, and all data were collected as a standard part of surgical care, and none were designed to collect data specifically for the research, written informed consent was not required. This study protocol was approved by the Ethical Committee of the First Affiliated Hospital of Jinan University (no. KY-2021-070).
## Author contributions
YS, JZ, and WY designed the study. YS collected patients’ data. YS, JZ, and JX performed the analyses and wrote the paper. ZD and CW assisted with the study design and analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Efficacy of unblinded and blinded intermittently scanned continuous glucose
monitoring for glycemic control in adults with type 1 diabetes
authors:
- Lixin Guo
- Yuxiu Li
- Mei Zhang
- Xinhua Xiao
- Hongyu Kuang
- Tao Yang
- Xiaofan Jia
- Xianbo Zhang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992877
doi: 10.3389/fendo.2023.1110845
license: CC BY 4.0
---
# Efficacy of unblinded and blinded intermittently scanned continuous glucose monitoring for glycemic control in adults with type 1 diabetes
## Abstract
### Objective
Intermittently scanned continuous glucose monitoring (isCGM) is used for unblinded or blinded monitoring of interstitial glucose. We aimed to compare the efficacy of blinded and unblinded isCGM with the FreeStyle Libre system for glycemic control in adults with type 1 diabetes (T1D).
### Research design and methods
This randomized clinical trial conducted between October 2018 and September 2019 across four endocrinology practices in China included 273 adults aged ≥18 years with T1D, who were randomly divided in a 2:1 ratio into the unblinded ($$n = 199$$) or blinded isCGM group ($$n = 78$$). In the blinded group, the clinician used FreeStyle Libre Pro system for monitoring, but self-monitoring was also performed by the patients.
### Results
Two hundred sixteen ($78\%$) participants completed the study (152 [$75\%$] in the unblinded and 64 [$82\%$] in the blinded group). At 12 weeks, a significant increase in TIR (3.9-10.0 mmol/L) was only observed in the unblinded group, along with a significant decrease in hyperglycemia (>13.9 mmol/L), hypoglycemia (<3.0 mmol/L), glycemic variability. Further, the mean HbA1c reduction from baseline to 12 weeks was $0.5\%$ in the unblinded isCGM group and $0.4\%$ in the blinded isCGM group respectively ($P \leq 0.001$), but the significance did not remain after adjustment for between-group differences. Finally, $99.5\%$ of the blinded isCGM values and $93.8\%$ the of unblinded isCGM values were obtained at the final visit.
### Conclusions
The unblinded isCGM system was associated with benefits for glucose management, but nearly $100\%$ of the attempted profiles were obtained successfully with the blinded isCGM system. Thus, combining real-time and retrospective data with isCGM might be the most impactful way to utilize flash glycemic monitoring devices.
## Introduction
Monitoring of glucose levels is essential for effective management of type 1 diabetes (T1D). Self-monitoring of blood glucose (SMBG) with glucose meters remains the mainstay of glycemic monitoring in T1D. However, this method can only provide point-in-time measurements of current glucose levels and does not indicate the trend in glucose levels. Therefore, silent glucose excursions could be missed with the SMBG method. In contrast, methods for continuous glucose monitoring (CGM) have been shown to have significant benefits in improving glycemic control in patients with type 1 and type 2 diabetes (1–3). In particular, they can help reduce the risk of hypoglycemia and hyperglycemia in patients with T1D (4–7).
CGM is an important adjunctive data collection strategy that provides a comprehensive 24-h glycemic profile compared to the relatively sparse information available with SMBG. Currently, three types of CGM devices are used in clinical practice: retrospective systems, real-time systems, and flash or intermittently viewed systems [8]. Retrospective CGM systems are typically used in a blinded manner over a 3- 7 days wear period, and the data are reviewed retrospectively by clinicians. Real-time CGM devices are also used for short-term monitoring, but they are used in an unblinded manner. The data obtained enable patients and clinicians to respond to medication requirements in a timely way in order to prevent acute glycemic events, and the data are also useful in other areas of their daily diabetes self-management [9]. Intermittently scanned CGM (isCGM) was developed for continuous monitoring of interstitial glucose and has a longer sensor life of 14 days, and it is often referred to as flash glucose monitoring.
FreeStyle Libre Flash Glucose (Abbott Diabetes Care, Alameda, CA) is the only isCGM system that is currently commercially available. The device is factory calibrated and does not need calibration against SMBG data over the course of the 14-day wear time. The use of isCGM has been associated with an increase in the amount of time in range (TIR), lower glycemic variability in randomized controlled trials with T1D cohorts, and reductions in hypoglycemia. Unlike real-time CGM systems that automatically transmit data to the patient’s receiver, isCGM requires the patient to swipe the receiver close to the sensor to obtain current and historical glucose data every 8 h [8]. If there is a gap of more than 8 h between scans, only the data over the most recent 8 h will be retained and available for review. Overall, isCGM technology has made the collection, transmission, and monitoring of glucose data convenient.
The FreeStyle Libre Pro system for clinicians (blinded isCGM), which is available only in China, can automatically transmit data to the patient’s receiver; this method does not require the patient to scan the reading every 8 h and provides blinded retrospective data for up to 14 days [10]. However, blinded CGM has not been convincingly proven to improve glycemic control [11, 12]. Flash glycemic monitoring has been shown to improve glycemic control in adults with T1D, but no study so far has demonstrated the efficacy of blinded and unblinded isCGM in glycemic control. Person-reported outcomes (PROs) are usually assessed as secondary outcomes in glycemic technology studies. PROs show that the use of isCGM in adolescents can improve diabetes related distress with validated questionnaires. isCGM which allows greater benefits on psychological outcomes [13]. However, several studies showed contradictory findings improvements associated with the use of glycemic technologies [14]. In the current randomized study, for the first time, we have explored clinically meaningful data to determine the degree of agreement between the blinded and unblinded isCGM systems for T1D management in the real-world setting. Moreover, we used PRO to explore the benefits of technologies on psychological outcomes.
## Study design and participants
Adults with T1D were consecutively recruited for this 12-week, multi-center, prospective, 2:1 randomized controlled trial (Figure 1). The participants were recruited from four endocrinology practices in China, including Beijing Hospital, Peking Union Medical College Hospital, the First Affiliated Hospital of Nanjing Medicine University, and the First Affiliated Hospital of Harbin Medicine University.
**Figure 1:** *Study flow in a study of the efficacy of unblinded and blinded intermittently flash continuous glucose monitoring on glycemic control in adults with type 1 diabetes.*
The major eligibility criteria were clinical diagnosis of T1D, age ≥18 years, use of insulin therapy, and no use of CGM in the 3 months prior to enrollment. Willingness to participate in a 2-week screening period and use the blinded isCGM system were other inclusion criteria. In addition, the individual was required to perform SMBG at least four times a day (before every meal and before sleeping) with the blinded isCGM device.
The exclusion criteria were current or previous use of CGM or sensor-enhanced insulin pump therapy; known allergy to medical-grade adhesive; adverse events that endanger life or could cause death and serious systemic diseases; known severe diabetic retinopathy and/or macular edema; lactation, pregnancy, or intention to become pregnant during the study; presence of any condition that is likely to require MRI; use of medication containing acetaminophen or vitamin C; and unwillingness to use the study device.
During the study period, all the patients were free to use unblinded isCGM real-time glucose values or SMBG to adjust their diet, physical activity, and insulin therapy. All participating centers provided ethical approval for the study prior to its commencement, and all the participants provided their written informed consent.
## Procedures
This study was scheduled to include a total of six clinic visits—from the screening visit to the final visit (Figure 1). At the screening visit, the investigators obtained information about the medication history of the participants, preformed a physical examination, and completed patient-reported outcome assessments including AST, ALT, eGFR, urinary human chorionic gonadotropin, and electrocardiogram readings. A 2-h mixed meal tolerance test was performed, and blood samples were obtained for laboratory analysis of relevant parameters, including HbA1c, plasma glucose (glucose oxidase method, which was performed at each participating institute) and C-peptide (chemiluminescence analysis, which conducted at the central laboratory), at three time points (0 min, 60 min, and 120 min). Participants filled out the Chinese version of the scale for diabetes self-care activities (SDSCA), diabetes distress scale (DDS), hypoglycemia fear survey (HFS-II), and hypoglycemic confidence scale (HCS). At the second, fourth, and sixth visit, participants again underwent the physical examinations and completed the SDSCA, DDS, HFS-II, and HCS questionnaires. HbA1c was measured at the central laboratory in a randomized way and at 12 weeks, with the high-performance liquid chromatography method.
Over the 2-week measurement period, the eligible participants were randomly divided in a 2:1 ratio via a computer-generated sequence into the blinded isCGM group (clinician FreeStyle Libre Pro system), in which patients could use fingerstick blood glucose meter checks as needed, and the unblinded isCGM group (FreeStyle Libre system). The blinded isCGM group used the fingerstick blood glucose test data for management of glucose levels, while the unblinded isCGM group was required to scan the sensor at least three times a day. The participants, investigators, and staff were not blinded to the group allocation.
At each visit, participants in both study groups provided sensor glucose data, and the sensor was replaced. They also provided information about their daily diet, exercise, adverse events, and sensor insertion-site symptoms. Further, they received general diabetes management education and were provided with individualized treatment recommendations based on their glucose data (isCGM and SMBG data). Participants completed patient-reported outcome assessments prior to randomization and at 12 weeks.
## Outcomes
Outcomes were calculated at the follow-up visit based on data pooled over the 14-day measurement period after the screening visit and the 14 days prior to the final visit. The primary outcome was TIR or the percentage of time during which the glucose level was in the target range of ≥3.9-≤10 mmol/L from baseline to 12 weeks [15].
Secondary outcomes were changes in the percentage of time in which glucose level was in the range of >10.0- ≤13.9 mmol/L, > 13.9 mmol/L, in the range of ≥3.0-<3.9 mmol/L, and <3.0 mmol/L; coefficient of variation (CV); standard deviation (SD) and mean amplitude of glycemic excursions (MAGE); and HbA1c. The other secondary outcomes included patient-reported outcomes, namely, changes in daily dietary calories and proportions of carbohydrates and fat; changes in the number of daily steps; and changes in the SDSCA, DDS, HFS-II, and HCS scores.
The safety objective was to evaluate the safety of wearing the FreeStyle Libre Flash Glucose Monitoring System device in patients with T1D. Reportable adverse events included severe hypoglycemia (defined as an event that required assistance from another person due to altered consciousness), adverse events regardless of causality, and serious adverse events that require hospitalization, prolong hospitalization, cause disability, endanger life or result in death, or result in birth defects.
## Statistical analysis
A sample size of 216 participants was determined to detect a between-group difference in the target range (3.9–10 mmol/L), assuming a significant difference of an α-level of 0.05, power of $80\%$ (β = 0.2), and a SD of 14. This number was increased to 270 participants to account for $20\%$ with missing follow-up data.
All participants were analyzed according to their randomization group and included in the primary analysis. For the primary analysis, differences in the primary and secondary CGM outcomes between the final visit and screening visit in the two groups were assessed using paired t-tests. Missing data were managed with the direct likelihood method, which maximizes the likelihood function integrated over possible values of the missing data.
Analyses of prespecified secondary outcomes were conducted in parallel with the analysis of the primary outcome (CGM data were pooled across follow-up time points). Analysis of covariance was used to adjust for chance imbalances in baseline measurements between the treatment groups. Modification of the treatment effect by baseline variables was assessed by including an interaction term in the primary model. Secondary outcomes were analyzed by analysis of covariance of the differences between post-baseline and baseline values with study center, diabetes duration, baseline BMI, baseline SD, and baseline HbA1c as covariates in the two groups. Confidence intervals were calculated for the group least-square mean of each measure and the difference between group least-square means. Two-sided statistical tests were performed, and a significance of 0.05 was used in all tests.
The results were reported as the mean ± SD [minimum, maximum] or documented as the constituent ratio. Analyses were conducted with the SPSS 23.0 software.
## Clinical characteristics of the study participants
From October 2018 to September 2019, a total of 273 eligible participants were randomly assigned to the unblinded isCGM ($$n = 199$$) group or the blinded isCGM group ($$n = 78$$). The 12-week visit was completed by 152 participants ($75\%$) in the unblinded isCGM and 64 participants ($82\%$) in the blinded isCGM group (Figure 2).
**Figure 2:** *flow of participants in a study of the efficacy of unblinded and blinded intermittently flash continuous glucose monitoring on glycemic control in adults with type 1 diabetes.*
The included participants had comparable baseline characteristics (Table 1): There was no significant difference in age (mean = 40.8 years [range = 18–77] versus 42.6 years [range = 19–71]), duration of diabetes (mean = 10.0 years [range = 0–52.2] versus 10.2 years [range = 0.3–32.1]), proportion of females ($58.8\%$ versus $62.5\%$), use of multiple daily injections ($80.3\%$ versus $79.7\%$), HbA1c (mean ± SD = 8.0 ± $1.8\%$ versus 7.7 ± $1.7\%$), and C-peptide levels (mean ± SD = 0.2 ± 0.4 ng/mL versus 0.2 ± 0.4 ng/mL) between the unblinded isCGM group and the blinded isCGM group ($P \leq 0.05$ for all the variables). No episodes of severe hypoglycemia or diabetic ketoacidosis were reported.
**Table 1**
| Characteristic | Unblinded isCGM(N=152) | Blinded isCGM(N=64) | P values |
| --- | --- | --- | --- |
| Age,year, Mean(SD)[range] | 40.8 (14.4) [18-77] | 42.6 (14.4) [19-71] | 0.406 |
| Diabetes duration, year | Diabetes duration, year | Diabetes duration, year | Diabetes duration, year |
| Mean(SD)[range] | 10.0 (9.5) [0.0-52.2] | 10.2 (9.3) [0.28-32.14] | 0.896 |
| Sex | Sex | Sex | Sex |
| Female[n(%)] | 90 (58.8) | 40 (62.5) | 0.654 |
| Male[n(%)] | 63 (41.2) | 24 (37.5) | / |
| BMI, kg/m2, Mean(SD)[range] | 22.0 (2.5) [16.8-29.2] | 21.3 (2.6) [16.7-32.7] | 0.068 |
| therapy | therapy | therapy | therapy |
| multiple daily injection[n(%)] | 122 (80.3) | 51 (79.7) | 0.923 |
| Insulin pump use[n(%)] | 30 (19.6) | 13 (20.3) | / |
| HbA1c, %, Mean(SD)[range] | 8.0 (1.8) [5.0-15.2] | 7.7 (1.7) [5.3-14.1] | 0.256 |
| C-peptide, Mean(SD)[range] | 0.2 (0.4) [0-2.7] | 0.2 (0.4) [0-2.5] | 0.980 |
## Comparison of scanning frequency and intra-day patterns
With regard to data reporting, $99.5\%$ of the blinded isCGM values and $93.8\%$ of the unblinded isCGM values were obtained at the final visit (Figure 3). Scanning was performed four times more often during typical awake hours (6 AM to 12 AM) than during typical sleeping periods (12 AM to 6 AM). Scanning was most frequently performed between 8 and 10 PM, while the frequency was the lowest at 2–3 AM. The pattern of daily scanning is shown in Figure 4.
**Figure 3:** *Glucose monitoring system utilization in a study of the efficacy of unblinded and blinded intermittently flash continuous glucose monitoring on glycemic control in adults with type 1 diabetes.*P<0.05 between unblinded isCGM and blinded isCGM.* **Figure 4:** *Glucose monitoring frequency in a study of the efficacy of unblinded and blinded intermittently flash continuous glucose monitoring on glycemic control in adults with type 1 diabetes. Total number of scans by time of day in the unblinded isCGM.*
## Glycemic metrics
The mean TIR percentage between 3.9 and 10 mmol/L was $55.2\%$ at the baseline and $61.3\%$ at 12 weeks in the unblinded isCGM group, and $57.4\%$ at the baseline and $59.7\%$ at 12 weeks in the blinded isCGM group. The values were significantly higher in the unblinded isCGM group ($P \leq 0.001$), but were not significant in the blinded isCGM group (Table 2, Figure 5A).
The percentage of time in which hyperglycemia occurred (>13.9 mmol/L) was $12.8\%$ at the baseline and $8.5\%$ during follow-up in the blinded isCGM group, and $11.4\%$ at the baseline and $9.1\%$ at 12 weeks in the blinded isCGM group. The mean hyperglycemia time was significantly lower in the unblinded isCGM group ($P \leq 0.001$), but the difference between the baseline and 12-week values were not significantly different in the blinded isCGM group (Table 2, Figure 5B). The mean percentage of time in which the glucose levels were in the hypoglycemia range (10-13.9mmol/L) was not compare in the two groups (Table 2, Figures 5C, D). The mean percentage of time in the hypoglycemia range (<3.0 mmol/L) was $5.3\%$ at the baseline and $3.4\%$ at 12 weeks ($$P \leq 0.032$$) in the unblinded isCGM group, but the difference between the baseline and 12-week values were not significantly different in the blinded isCGM group (Table 2, Figure 5E).
The CV (-$2.4\%$), SD (-0.3 mmol/L), and MAGE (-0.7 mmol/L) were significantly lower at 12 weeks in the unblinded isCGM group ($P \leq 0.001$), but these values did not decrease significantly compared to the baseline in the blinded isCGM group (Figures 5F–H).
Mean HbA1c was $8.0\%$ at the baseline and $7.5\%$ at 12 weeks in the unblinded isCGM group, and it was $7.7\%$ at the baseline and $7.3\%$ at 12 weeks in the blinded isCGM group. HbA1c showed a significant reduction of $0.5\%$ in the unblinded isCGM group and $0.4\%$ in the blinded isCGM group ($P \leq 0.001$ for both groups) (Table 2, Figure 5I).
After adjusting for between-group differences, no significant difference remained in the effect of the study treatment between the unblinded isCGM and blinded isCGM groups with regard to 12-week TIR, hypoglycemia time, hyperglycemia time, CV, SD, MAGE, and HbA1c ($P \leq 0.05$) (Table 2).
## Psychological questionnaires
The mean diabetes distress percentage was $34.5\%$ at the baseline and $31.5\%$ at 12 weeks in the unblinded isCGM group, and was $33.6\%$ at the baseline and $29.4\%$ at 12 weeks in the blinded isCGM group. Diabetes distress was significantly reduced from the baseline to 12 weeks in both groups ($P \leq 0.05$). Hypoglycemia fear behavior increased significantly from $8.2\%$ at the baseline to $10.0\%$ at 12 weeks in the blinded isCGM group ($P \leq 0.05$), but there was no significant change in the unblinded isCGM group. However, hypoglycemic confidence decreased from $18.3\%$ at the baseline to $16.8\%$ at 12 weeks in the unblinded isCGM group ($P \leq 0.05$). After adjusting for between-group differences, no significant difference remained between the unblinded isCGM and blinded isCGM groups (Table 2).
## Self-management questionnaires, steps, and diet
The questionnaire scores for SDSCA did not significantly favor either monitoring system. The number of daily steps was significantly reduced in the unblinded isCGM group (9933.0 ± 4198.4 vs. 9143.5 ± 4200.1, $P \leq 0.05$), while there was no significant difference in the blinded isCGM group (9614.3 ± 4147.9 vs. 8920.4 ± 4679.3, $P \leq 0.05$). There was no significant change in the self-management questionnaire scores for calories, carbohydrates, protein, and fat in either group (Table 2).
## Discussion
This prospective, randomized study was conducted to compare the unblinded and blinded isCGM glucose profiles in adults with T1D, and the findings showed that over 12 weeks of isCGM use is beneficial in the management of T1D.
Clinical application of CGM has been generally indicated to result in a significant improvement in diabetes management [8]. However, some studies have shown that retrospective CGM systems do not improve glycemic control. A study on 102 patients with T1D in a 3-day blinded CGM trial with iPRO (Medtronic, Northridge, CA) did not find any significant improvement in HbA1c for up to 7 months after the CGM device was worn [16]. Another study did not find a significant difference in HbA1c levels in patients with T1D when those using SMBG were compared with those using a 72-h blinded CGM device [11]. However, retrospective CGM systems have been found to be valuable for collecting detailed glycemic excursion data [17].
Real-time CGM devices enable patients to respond immediately to mitigate or prevent acute glycemic events and allow patients to make better informed decisions about their medication requirements and other areas of their daily diabetes self-management. In the IMPACT study, the use of an intermittently viewed system was associated with a reduction in hypoglycemia as compared with a conventional SMBG device in adults with well-controlled T1D [1, 3]. This indicates that increasing the frequency of glucose monitoring is sufficient to reduce hypoglycemic risk, even in the absence of alarms. The isCGM system provides actual and unblinded interstitial glucose concentrations, but the earlier generation of isCGM devices required patients to perform a sensor scan every 8 h. If more than 8 h elapsed between scans, the device would only display a plot profile of the last 8 h. Missing data in the isCGM system cannot be recovered after the fact. Therefore, one of the challenges with this system is to determine whether CGM data collection has been successful in real time versus after the CGM process has been completed.
Unlike unblinded isCGM, blinded isCGM can automatically transmit data to the patient’s receiver and provides blinded retrospective data for up to 14 days [10, 18]. A key strength of our study was the use of the clinician isCGM systems. So far, no study has reported the efficacy of blinded and unblinded isCGM for glycemic control.
According to recent international consensus, individuals with T1D should strive to achieve $4\%$ of time below the target range (<3.9 mmol/L), >$70\%$ of time within the target range (3.9–10.0 mmol/L), and <$25\%$ above the range (>10.0 mmol/L), with a glycemic variability (%CV) of <$36\%$ [18, 19]. In our study, compared with the baseline phase, unblinded isCGM use was associated with a significantly greater TIR percentage, which increased from $55.2\%$ at the baseline to $61.3\%$ at the end of the study. We also found lower values for hyperglycemia time (>13.9 mmol/L), hypoglycemia time (<3.0 mmol/L), CV, SD, and MAGE in the unblinded isCGM users. Further, both isCGM systems resulted in a significant reduction in HbA1c. Taken together, these data indicate that while both blood glucose monitoring methods could improve blood glucose control, unblinded isCGM could increase the TIR while reducing time above range, time below range, and glycemic variability. Thus, the use of these variables for generating predictive alerts might result in even greater glycemic improvements. However, after adjustment for between-group differences, no significant difference was found in the effect of study treatment at 12 weeks between the unblinded isCGM and blinded isCGM groups in terms of 12-week HbA1c, TIR, hypoglycemia time, hyperglycemic time, CV, SD, MAGE, and HbA1c ($P \leq 0.05$). This emphasizes the greater challenges that are present in the management of T1D in the real world.
In the present study, we found that that $99.5\%$ of the blinded isCGM values were obtained, compared with only $93.8\%$ of the unblinded isCGM values at the final visit. The data showed that the majority of scanning was conducted during the awake hours spanning 6–18 h, while only a few scans were performed over the night-time hours spanning 0–6 h. The possible reasons for missing data in the unblinded isCGM group may be scanning frequency and the time of day for measurements according to the patient’s age, lifestyle, eating habits, level of physical activity, and understanding and motivation with regard to maintenance of glucose monitoring [20]. The use of safety features may contribute to avoiding missing abnormal glycemic data and further improving glycemic control.
Diet, physical exercise, and psychological reactions are important components in the management of T1D across a patient’s lifespan [21, 22]. Therefore, in this study, we also explored data on these self-managed key aspects. During the study period, both groups of patients were free to use unblinded isCGM real-time glucose values and SMBG to adjust their diet, physical activity, and insulin therapy. The results showed that the number of daily steps was reduced in the unblinded isCGM group, while there was no difference in the blinded isCGM group at the end of the study. However, there was no change in calorie, carbohydrate, protein, and fat consumption in both groups. The challenging management of diabetes could result in diabetes distress and risk for psychological disorders. However, the real-world study showed no significant association of CGM use and the level of diabetes distress [23]. Our study showed that the participants of both groups reported improved diabetics distress, especially unblinded isCGM users. Our findings suggest that technology use, at least in the short term, may reduce diabetes distress. However, our findings also indicated that technology couldn’t address every aspect of living with diabetes. Not only individuals with T1D but also healthcare professionals should be involved in the interpretation of data in order to maximize the technological potential of these devices and improve their efficiency.
A major limitation of our study is that the intervention period of 12 weeks is relatively short. An extended monitoring period may provide insight into longer-term use of CGM and reflect the real-world setting. Additionally, these results also need to be confirmed in a large study population.
In conclusion, the use of isCGM systems resulted in a decrease in HbA1c level over 12 weeks among the adults with T1D in this study. The unblinded isCGM system was associated with benefits for glucose management, but with the blinded system, nearly $100\%$ of the profiles were obtained successfully. It appears that the blinded isCGM systems can overcome both expected and unexpected data collection hurdles. Thus, combining both real-time and retrospective data gathered by isCGM might be the most appropriate and impactful way to utilize flash glycemic monitoring devices. However, further research is needed to understand the clinical importance of this finding and the applicability of these systems in the real world.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the ethics committees of Beijing Hospital, Peking Union Medical College Hospital, the First Affiliated Hospital of Nanjing Medicine University, and the First Affiliated Hospital of Harbin Medicine University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
LG, YL, XX, HK, and TY directed the study and were responsible for study design. LG, YL, MZ, XX, HK, TY, XJ, and XZ contributed to the project recruitment. MZ performed statistical analyses and drafted the initial manuscript. LG, YL, MZ, XX, HK, and TY contributed to the discussion and helped edit the manuscript and suggested revisions. LG, YL, MZ, XX, HK, TY, XJ, and XZ approved the final manuscript. LG is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'The impact of internet use on health status among older adults in China: The
mediating role of social support'
authors:
- Yiting E
- Jianke Yang
- Long Niu
- Chunli Lu
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9992883
doi: 10.3389/fpubh.2023.1108096
license: CC BY 4.0
---
# The impact of internet use on health status among older adults in China: The mediating role of social support
## Abstract
### Background
With the popularization of the Internet, the use of the *Internet is* becoming more and more important in the daily life of older adults. However, previous research mainly focuses on Internet use and health in general, and the mechanism of this effect remains to be studied. To bridge this gap, this study aims to explore the mediational effects of social support between Internet use and health among older adults in China.
### Methods
The data used in this article are from the 2021 Chinese General Social Survey (CGSS). Social support is divided into two aspects and four dimensions: informal social support (relatives support, friends support, neighbors support) and formal social support (social insurance). This article uses the nested multivariate OLS regression models to analyze the impact of Internet use on health. Furthermore, Finally, SPSS macro PROCESS is applied to test their mediation effects.
### Results
Informal social support positively influenced the health status among older adults, while formal social support did not. Among the three types of informal social support, relatives support and friends support significantly affected health status among Chinese older adults. Regarding social support differences between urban and rural areas, it was found that relatives support is a positively significant factor for rural older adults, while friends support is significant for urban older adults.
### Conclusions
Since Internet use has many ways of impacting health status, social support only plays a partial mediating role in this study. It recommends that the government should take compelling measures to encourage and promote the use of the Internet among older adults and obtain various social support to improve their health status.
## Introduction
China's population has been rapidly aging over the last few decades. By the end of 2020, China had 264 million people over the age of 60, accounting for $18.9\%$ of the total population; this figure is expected to rise to more than 300 million by the end of 2025 [1]. The World Health Organization (WHO) has defined active aging as “the process of optimizing the opportunities for health, participation, and safety to improve the quality of life as the person ages” [2]. Consequently, improving the health of older adults has emerged as a priority for health policymakers. On the other hand, the arrival of the *Internet era* has greatly changed the lives of older adults. According to the 45th Statistical Report on China's Internet Development, the number of Internet users in China had reached 904 million by 2020, with $6.7\%$ being over the age of 60 [3]. Especially during the COVID-19 pandemic, Internet use has become a trend. Compared to the pre-lockdown period, Internet usage among older adults has increased significantly [4]. By December 2021, the Internet penetration rate of the older adults reached $43.2\%$ [5]. Therefore, with China's aging population, widespread Internet use has emerged as a new factor that may have an impact on their health status.
Researchers have recently begun to focus on the relationship between Internet use and the health of older adults. Studies have shown that older adults can not only search for health information through the Internet, but also help them build new social networks and reduce social isolation [6, 7]. For example, Bretherton conducted a survey of 214 older Australians and found that higher levels of social support mean not using mental health services [8]. Researchers have also investigated the role of social support on the health of older adults, and their findings showed that social support has a direct or indirect impact on the health of older adults [9, 10]. For example, a prospective repeated measurement cohort study on participants aged 60 and over showed that the higher the social support, the lower the psychological distress [11].
While previous studies have demonstrated that Internet use is associated with health status, few studies have explored the impact mechanism and path of Internet use on health status among older adults. Social support may be a very important mediating factor in this process. Internet use may not only have a direct effect on the health of older adults, but also have an indirect effect on the health of older adults through the mediating variables of social support. However, many previous studies have focused on the direct effects of social support on the physical and mental health of older adults, little is known about their underlying mechanisms. Furthermore, the correlation between social support and personal health has been analyzed in existing studies [12], and Internet use may have effects on social support (13–15). Therefore, whether and how social support mediates the relationship between Internet use and older adults' health status remains to be further tested.
On this basis, this study aims to study the impact of Internet use on the health status of older adults in China, focusing on the mediating role of social support in this process. By using a nationally representative survey and multiple mediating models, this study empirically tests whether different types of social support are potential mediation mechanisms for the relationship between Internet use and health status. In addition, we try to discover whether such impact mechanisms differ significantly between rural and urban areas based on the Chinese context to enrich the existing studies.
## Internet use and health
At present, the *Internet is* now widely used around the world and has become a way of life for an increasing number of people. Scholars have conducted numerous empirical studies on the impact of Internet use on health outcomes. On the one hand, Internet use by older adults has an impact on their physical health. The Internet provides a platform for people to communicate and help them acquire more health knowledge, which is of great significance for improving their health (16–19). For example, Flynn Kathryn and his colleagues pointed out in the study that one-third of the respondents searched their health or medical care information on the Internet [20]. In terms of disease intervention, Internet interventions have been widely adopted to aid disease management in a variety of areas, such as HIV and AIDS, malaria, tuberculosis, diabetes, asthma, obesity, and smoking [21], thus reducing mortality [22]. Besides, some scholars' research showed that the use of smartphones can effectively improve the elderly's self-health evaluation [23]. On the other hand, it is also closely related to their mental health. As it is known to all, with the growth of age, the social interaction of the elderly gradually shrinks, and the sense of loneliness of the elderly increases significantly. It is precisely because the Internet has brought about an increase in social interaction, which can effectively reduce the loneliness of the elderly, have a positive impact on their emotions, enable older adults to obtain a better psychological state, and reduce their mental diseases, such as depression, anxiety, post-traumatic stress disorder (PTSD) and stress [24], so older adults can use the Internet to expand social interaction, reduce loneliness, and improve mental health [25, 26]. For example, Atsushi found that when compared with the elderly who do not use the Internet at all, the elderly using the Internet every day can keep close contact with society, meet friends more frequently, and effectively improve their subjective well-being [27]; Some researchers have also revealed the relationship between Internet use and subjective well-being of older adults through path analysis, and their findings suggest that the use of the Internet can enhance the ability of older adults to maintain close intergenerational relationships and thus contribute to their subjective well-being [28]. In short, although considerable studies have confirmed the effects of internet use on older adults' heath and explained them from the perspectives of social relationships and lifestyles, there are still some gaps in existing research. First, since most of the above content comes from western culture, it is necessary to identify this phenomenon in the Chinese context to understand the differences between these empirical studies and those reported in the previous literature. Second, the direct effect of internet use on older adults' health has been well confirmed, while the possible mediating mechanism is not clear.
## Social support and health
In the early 1970s, social support was formally put forward as an academic concept and a professional term. Scholars in various research fields put forward different definitions of social support, and there are two main perspectives. First, in the perspective of social interaction, social support is not only one-way care and help, but also a social exchange and a social interaction between people in most situations [29, 30]. Bernard believes that social support is a collection of families, friends, and social institutions that people rely on to meet their social, physical, and psychological needs [31]. Second, in the perspective of social resources, social support refers to a kind of behavior or information that individuals feel is concerned, respected, and valued by the members of their social network, and also comes from the help of social relations and the resource exchange of members in the social network [32]. House and Turner demonstrated that there were meaningful groups around individuals, such as family members, friends, colleagues, relatives, and neighbors, who had positive support and effects on individuals, including practical help, social emotional help, and information help [33, 34]. Cullen defines social support as a variety of material and spiritual information that can be received by individuals from communities and social networks [35]. *In* general, social support refers to the process by which individuals receive and use help from the government, social organizations, and others [36].
Social support can be divided into formal social support and informal social support. Formal social support is defined as the support provided by the government, institutions, communities, and other formal organizations for vulnerable groups, such as endowment insurance and the system of medical safety and security [37]. In recent decades, China's social security has made phenomenal progress, with the widespread establishment and dramatic expansion of the social insurance system. Since 2003, China has established a basic social insurance system that covers almost all rural and urban residents till now, which is divided into two parts: health insurance and endowment insurance. For health insurance, the three pillars of the system are the New Rural Cooperative Medical Scheme (NCMS), Urban Resident Basic Medical Insurance (URBMI), and Urban Employees' Basic Medical Insurance (UEBMI); For endowment insurance, the new rural endowment insurance and the urban endowment insurance were included in the basic endowment insurance for urban and rural residents [38]. In our study, we used Zhang and Shen's research framework, that is, social insurance as a key indicator for measuring formal social support [39, 40]. Unlike formal support from official organizations, in Berkman and Cantors' research, informal social support mainly refers to emotional, behavioral support, which is provided by family members, neighbors, friends, and colleagues [41, 42]. For older adults, social support is a known contributor to health in the general [43, 44]. As they withdraw from the main areas of social life, social interaction is greatly reduced, which will lead to their mental health problems such as loneliness and depression. At this time, social support and its network members can help the elderly actively seek help, and can also serve as internal support to promote their mental health [45]. For example, Adams found in his research that the elderly living alone may rely on friends and neighbors to establish similar relationships with their families [46]. Using a sample survey from Chengdu, Sichuan Province, China, Tang et al. found that informal social support had significant positive health effects, especially on those with high age and agricultural household registration [47]. However, studies have not examined the independent effects of the two types of social support on the health of older adults separately. In the following sections, we elaborate on these two lines of research.
## Internet use, social support, and health
Recently, with the widespread use of the Internet, scholars began to focus on the relationship between Internet use, social support, and health. On the one hand, there was a significant correlation between specific Internet use (including online chat, game, and entertainment use) and social support [48, 49]. For example, a study in Finland found that as a source of social support, the Internet can help people obtain emotional and informational support [50]. According to a study based on seven European countries, researchers believe that the use of the *Internet is* more closely related to social support and subjective health than the use of other media [51]. Meanwhile, other scholars further pointed out that the main purpose of people accessing the *Internet is* to maintain the existing social support network in real life, rather than to develop online virtual social networks [52]. Moreover, research on older adults in China also found that the main purpose of older adults use the *Internet is* to keep in touch with relatives and friends, which enables them to expand or maintain social contacts and increase access to information. A high level of Internet use means the elderly have more opportunities to interact and connect with relatives, friends, and society, thus promoting social support [53]. On the other hand, many scholars further pointed out the relationship between Internet use, informal social support, and health. Kang pointed out that online chat can improve social support and reduce depression [54]. Some scholars found in the survey that the use of the Internet can reduce the loneliness of elderly Polish males, increase social support, and have a better quality of life [55].
To conclude, although scholars recently have started to examine the relationship between Internet use, social support, and health, they have not used the same research framework, and have only considered the relationship between the two of them. Therefore, this study will investigate how different types of social support affect the relationship between Internet use and older adults' health status.
## Research purposes and hypotheses
The main purpose of this study is to explore the relationships among Internet use, social support, and health status among older adults in the Chinese context, and examine whether different forms of social support play mediating roles between Internet use and health. Therefore, the current study has the following three steps. First of all, the nationally representative CGSS survey data is used to comprehensively measure two types of social support (formal social support and informal social support) through four indicators: social insurance, relatives, friends and neighbors; Secondly, the multiple mediation analysis is applied to explore the path of Internet use influencing the health status of older adults in China through social support. Finally, on the basis of existing research, it is believed that social support has a positive impact on older adults' health status, which is helpful to provide policy references for improving older adults' health in China. The hypothesis proposed are as follows (Figure 1).
**Figure 1:** *The hypothesized model.*
## Data sources
We use data from the 2021 Chinese General Social Survey (CGSS). This survey project mainly focuses on the major theoretical and practical issues in the changing social structure of China. It comprehensively collects some basic information on residents' behavior patterns, thinking patterns, lifestyles, and social changes. A total of 8,148 valid samples were completed in the 2021 CGSS. Currently, these data are suitable for this study because they collect rich information about Internet use, health, social support, and sociodemographic characteristics. Finally, a total of 2,929 older adults aged between the ages of 60 and 99 in urban and rural areas were included in this study.
## Dependent variable
This paper analyzed the health status of older adults. According to the definition of health by WHO, health included “physical health, mental health, social adaptation, and moral health” [56], we used three questions to describe the health status. Consistent with the measurement of health in the study by Wang et al. [ 57], the question “What do you think of your current health?” was selected to measure the variable of self-rated health. Respondents' answers ranged from “very unhealthy” (=1), “relatively unhealthy” (=2), “average” (=3), “relatively healthy” (=4), and “very healthy” (=5). The question “In the past four weeks, how often has your work or other daily activities been affected because of the health issues?” was selected to measure the variable of physical health. The question “In the past four weeks, how often have your felt depressed?” For physical health and mental health, the rating regarding the frequency on a 5-point scale ranged from “always” (=1), “often” (=2), “sometimes” (=3), “rarely” (=4), and “never” (=5). We sum up the above questions and generate a new variable “health”. Considering the accuracy of the study, we use factor analysis to show the strength of the inter-correlation between the items and also test the reliability of the data through the Kaiser-Meyer-Olkin (KMO) index range from 0 to 1 and through the Cronbach's alpha. From Table 1, we can see that the KMO value is 0.67 and the Cronbach's alpha is 0.76, suggesting that the above measurements have high reliability.
**Table 1**
| Unnamed: 0 | Variable | Uniqueness | Kmo | Kmo-overall | Cronbach α |
| --- | --- | --- | --- | --- | --- |
| 1 | Subjective evaluation on their current physical health | 0.493 | 0.654 | 0.667 | 0.762 |
| 2 | The frequency of health problems affecting work or other daily activities over the past 4 weeks | 0.427 | 0.627 | | |
| 3 | The frequency of depression over the past 4 weeks | 0.651 | 0.76 | | |
## Independent variable
The independent variable is Internet use. According to a study by Han et al. in 2021 [58], We chose the question “In the past year, what was your use of the internet (including mobile internet)?” to measure the use of the Internet in CGSS. Based on the respondents' answers, the rating in terms of the frequency on a 5-point scale ranged from “never”(=1), “rarely”(=2), “sometimes”(=3), “often”(=4) and “very frequently”(=5).
## Mediating variables
The mediating variable in this paper is social support. It could be divided into two aspects: informal social support and formal social support. As for informal social support, we apply Berkman and Cantor's conceptual framework [41], which describes informal social support along three key dimensions: relatives support, friends support and neighbors support. In CGSS, we use these questions in the questionnaire: “In the past year, have you often gathered with relatives who do not live together in your spare time?”, “ How often do you socialize with your neighbors?”, “ How often do you socialize with other friends?”, “ In the past year, you gathered with friends in your spare time”. The rating in terms of frequency on a 5-point scale ranged from “never” (=1), “several times a year or less” (=2), “several times a month” (=3), “several times a week” (=4) and “every day” (=5). We sum the last two questions together and form a new variable “friends support”. All the respondents' answers to these questions reflect the informal social support of older adults.
Formal social support, as mentioned earlier, which divided into two parts: health insurance and endowment insurance in the Chinese context. In the CGSS questionnaire, respondents were asked to answer these two questions “Are you currently enrolled in any of the following social insurance programs? – Urban Employee Basic Medical Insurance (UEBMI), Urban Resident Basic Medical Insurance (URBMI), or New Rural Cooperative Medical Scheme (NCMS)” and we dichotomized their answers into “yes” = 1 and “no” = 0; “Are you currently enrolled in any of the following social insurance programs? – the rural endowment insurance or the urban endowment insurance” and we dichotomized their answers into “yes” = 1 and “no” = 0. We sum the above two together to form a new variable “social insurance” to measure formal social support.
## Control variables
Five variables are included: age, gender, education level, income, and residence. Gender is coded as 0 for females and 1 for males. Education is structured based on residents' educational attainment. It is categorized into primary education or below, primary school education, middle school education, high school education, and university education or above (education =0, 1, 2, 3, and 4). To correct for the positive skewness of income, we used the natural log transformation. The residence type is coded as 0 for rural areas and 1 for urban areas.
## Statistical analysis
Descriptive statistics and Pearson correlation analyses were performed using STATA17.0 software (two-sided test $p \leq 0.05$ was considered to be significantly correlated); We then performed an OLS regression controlling for gender, age, education, and income to examine the relationship between Internet use, social support, and health status. All control variables were assumed to have an impact on health. At last, in SPSS25.0, Model 4 in the process plugin compiled by Hayes [2017] was used for the multiple mediation effect analysis, and the bootstrap test was used to estimate direct effects and indirect effects according to repeated sampling from sample data, so as to establish confidence intervals for each effect. When the confidence interval does not contain 0, the corresponding effect is significant.
## Descriptive statistics and correlation analyses
Table 2 presents the descriptive statistics of key variables. The average age of the sample was 70.2 years old. About $51\%$ were female$.15.4\%$ of the elderly have a senior high school education level. More than half of the participants came from rural areas ($59.4\%$). After using the natural log transformation of income, the value range is 0 to 16.12. The average score of health was 10.4 (SD = 3.0). Notably, among the participants, more than half of the elderly have never used the Internet ($61.4\%$). The mean score of friends support was 4.55 (SD =2.07). More than half of the participants ($56.18\%$) showed that interactions with relatives were several times a year or less. However, $28.54\%$ of the participants had never interacted with their neighbors. The average score of formal social support obtained by the sample was 1.74.
**Table 2**
| Variable | Mean/percentage | SD | Min | Max | Details |
| --- | --- | --- | --- | --- | --- |
| Health | 10.44 | 3.01 | 3.0 | 15.0 | Continuous variables |
| Internet use | 2.08 | 1.53 | 1.0 | 5.0 | Multi-categorical variables |
| Never | 61.43% | | | | |
| Rarely | 7.52% | | | | |
| Sometimes | 5.98% | | | | |
| Often | 11.51% | | | | |
| Always | 13.56% | | | | |
| Relatives support | 1.91 | 0.78 | 1.0 | 5.0 | Multi-categorical variables |
| Never | 28.83% | | | | |
| Several times a year or less | 56.18% | | | | |
| Several times a month | 10.78% | | | | |
| Several times a week | 3.25% | | | | |
| Every day | 0.96% | | | | |
| Friends support | 4.55 | 2.07 | 2.0 | 10.0 | Continuous variables |
| Neighbors support | 2.93 | 1.50 | 1.0 | 5.0 | Multi-categorical variables |
| Never | 28.54% | | | | |
| Several times a year or less | 9.55% | | | | |
| Several times a month | 24.42% | | | | |
| Several times a week | 15.73% | | | | |
| Every day | 21.77% | | | | |
| Social insurance | 1.74 | 0.52 | 0.0 | 2.0 | Continuous variables |
| Income | 8.34 | 4.41 | 0.0 | 16.12 | Natural log transformation. |
| Gender | 0.49 | 0.50 | 0.0 | 1.0 | Binary variables |
| Female | 51.31% | | | | |
| Male | 48.69% | | | | |
| Education | 1.50 | 1.12 | 0.0 | 4.0 | Multi-categorical variables |
| Illiteracy | 21.00% | | | | |
| Primary school | 32.70% | | | | |
| Junior high school | 26.32% | | | | |
| senior high school | 15.37% | | | | |
| college or above | 4.60% | | | | |
| Residence | 0.41 | 0.49 | 0.0 | 1.0 | Binary variables |
| Rural | 59.37% | | | | |
| Urban | 40.63% | | | | |
| Age | 2,929 (100.00) | 70.23 (6.88) | 60.0 | 99.0 | Continuous variables |
Table 3 presents the correlation matrix of the core studied variables. *In* general, positive correlations were found between the health status of older adults in China and Internet use, informal social support, formal social support, gender, age, residence, income, and education level, while age was negatively correlated with it. Internet use was significantly positively related to interaction with relatives and friends, formal social support, and other sociodemographic characteristics but negatively associated with age. There is a positive correlation between the three different types of informal social support. For formal social support, residence, income, and education level were all positively correlated with it.
**Table 3**
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1. Health | 1 | | | | | | | | | | |
| 2. Internet use | 0.207*** | 1 | | | | | | | | | |
| 3. Relatives support | 0.140*** | 0.143*** | 1 | | | | | | | | |
| 4. Friends support | 0.117*** | 0.142*** | 0.350*** | 1 | | | | | | | |
| 5. Neighbors support | 0.0140 | −0.0260 | 0.161*** | 0.493*** | 1 | | | | | | |
| 6. Social insurance | 0.052*** | 0.101*** | 0.038** | 0.035* | −0.0100 | 1 | | | | | |
| 7. Gender | 0.140*** | 0.0210 | 0.0190 | 0.0080 | −0.083*** | 0.045** | 1 | | | | |
| 8. Age | −0.083*** | −0.255*** | −0.0140 | −0.0100 | 0.0210 | −0.055*** | 0.00500 | 1 | | | |
| 9. Residence | 0.211*** | 0.314*** | 0.161*** | 0.110*** | −0.092*** | 0.142*** | 0.0110 | 0.076*** | 1 | | |
| 10. Income | 0.134*** | 0.174*** | 0.072*** | 0.073*** | −0.033* | 0.097*** | 0.126*** | 0.00900 | 0.332*** | 1 | |
| 11. Education | 0.243*** | 0.446*** | 0.134*** | 0.081*** | −0.109*** | 0.144*** | 0.232*** | −0.174*** | 0.451*** | 0.259*** | 1.0 |
## Relationship between internet use and health
The main results showed that all nested regression models are statistically significant between models (Table 4). As the baseline model, Model 1 only examined the effects of control variables on health. We can obviously see that gender, age, education level, and income have a significant impact on the health of older adults. Model 2 analyzed the impact of Internet use on health. The results showed that the regression coefficient of Internet use to health is 0.2, and it is significant at the level of 0.001. This finding indicates that these two variables are highly correlated. That is, the more their use of the Internet, the higher level of their health. Model 3, Model 4, and Model 5 analyzed the three dimensions of informal social support, including the effects of relatives support, friends support, and neighbors support on health. The results showed that relatives support (β = 0.34, $p \leq 0.001$), and friends support (β = 0.09, $p \leq 0.01$) were positively associated with health, while neighbors support had no significant impact on health. Furthermore, the result of Model 6 showed that formal social support had no significant impact on health. In addition, regression analysis indicated that among the control variables, gender, age, education, residence, and income all passed the significance test in the six models, indicating that all five variables were significantly associated with health. For example, in Model 1, gender (β = 0.61, $p \leq 0.001$), education (β = 0.36, $p \leq 0.001$), residence (β = 0.87, $p \leq 0.001$), and income (β = 0.03, $p \leq 0.05$) were positively associated with health. In contrast, age was negatively associated with health (β = - 0.03, $p \leq 0.01$). Therefore, based on the regression coefficients for the three dimensions of informal support, relatives support had the largest impact on health, followed by support from friends. Neighbors support and formal social support had no significant correlation with health.
**Table 4**
| Unnamed: 0 | Model | Model.1 | Model.2 | Model.3 | Model.4 | Model.5 |
| --- | --- | --- | --- | --- | --- | --- |
| | 1 | 2 | 3 | 4 | 5 | 6 |
| Gender | 0.613*** | 0.649*** | 0.651*** | 0.669*** | 0.669*** | 0.667*** |
| | (0.112) | (0.112) | (0.112) | (0.112) | (0.113) | (0.114) |
| Age | −0.033*** | −0.024** | −0.024** | −0.025** | −0.025** | −0.024** |
| | (0.008) | (0.008) | (0.008) | (0.008) | (0.008) | (0.008) |
| Education | 0.358*** | 0.266*** | 0.258*** | 0.257*** | 0.260*** | 0.260*** |
| | (0.058) | (0.061) | (0.060) | (0.061) | (0.061) | (0.061) |
| Residence | 0.871*** | 0.770*** | 0.695*** | 0.685*** | 0.687*** | 0.682*** |
| | (0.130) | (0.131) | (0.131) | (0.132) | (0.133) | (0.134) |
| Income | 0.030* | 0.027* | 0.026* | 0.026* | 0.027* | 0.027* |
| | (0.013) | (0.013) | (0.013) | (0.013) | (0.013) | (0.013) |
| Internet use | | 0.200*** | 0.184*** | 0.175*** | 0.174*** | 0.167*** |
| | | (0.041) | (0.041) | (0.041) | (0.041) | (0.042) |
| Relatives support | | | 0.345*** | 0.262*** | 0.262*** | 0.260*** |
| | | | (0.070) | (0.075) | (0.075) | (0.075) |
| Friends support | | | | 0.088** | 0.082* | 0.082* |
| | | | | (0.028) | (0.032) | (0.032) |
| Neighbors support | | | | | 0.017 | 0.018 |
| | | | | | (0.042) | (0.043) |
| Social insurance | | | | | | 0.004 |
| | | | | | | (0.107) |
| _Cons | 11.307*** | 10.430*** | 9.877*** | 9.700*** | 9.686*** | 9.633*** |
| | (0.594) | (0.618) | (0.626) | (0.632) | (0.636) | (0.668) |
| N | 2845 | 2845 | 2838 | 2812 | 2802 | 2768 |
| R 2 | 0.089 | 0.097 | 0.104 | 0.107 | 0.107 | 0.105 |
## Mediation effect analysis
The results of the first and second parts in Table 5 showed that the coefficient of Internet use to social support were 1.634, 3.474,2.681, and 1.829, and a $95\%$ confidence interval (CI) did not include 0. Meanwhile, the coefficient of social support to health were 0.260, 0.083, 0.018, and 0.004, and the $95\%$ confidence interval (CI) did not include 0 among the two of them. Combining the results of Internet use on health, social support was considered to play a partial mediating role in the relationship between Internet use and health.
**Table 5**
| Variables | B | BC95%LL | BC95%UL | R 2 | F |
| --- | --- | --- | --- | --- | --- |
| Internet use vs. informal social support | Internet use vs. informal social support | Internet use vs. informal social support | Internet use vs. informal social support | Internet use vs. informal social support | Internet use vs. informal social support |
| Internet use → relatives support | 1.634 | 1.306 | 1.962 | 0.035 | 16.738 |
| Internet use → friends support | 3.474 | 2.596 | 4.352 | 0.027 | 12.619 |
| Internet use → neighbors support | 2.681 | 2.046 | 3.316 | 0.021 | 9.858 |
| Internet use vs. formal social support | Internet use vs. formal social support | Internet use vs. formal social support | Internet use vs. formal social support | Internet use vs. formal social support | Internet use vs. formal social support |
| Internet use → social insurance | 1.829 | 1.612 | 2.046 | 0.033 | 15.685 |
| Social support vs. health | Social support vs. health | Social support vs. health | Social support vs. health | Social support vs. health | Social support vs. health |
| Relatives support → health | 0.260 | 0.112 | 0.408 | | |
| Friends support → health | 0.083 | 0.019 | 0.146 | | |
| Neighbors support → health | 0.018 | −0.066 | 0.102 | | |
| Social insurance → health | 0.004 | −0.207 | 0.214 | | |
| Internet use vs. health | Internet use vs. health | Internet use vs. health | Internet use vs. health | Internet use vs. health | Internet use vs. health |
| Internet use → health | 0.167 | 0.085 | 0.248 | | |
Model 4 of PROCESS was used to test the multiple mediating effects of social support. From the total effects in Table 6, Internet use had a significant positive effect on health [Bootstrap $95\%$ CI:0.111, 0.274]. The direct effect results showed that Internet use had a significant positive effect on health [Bootstrap $95\%$ CI:0.085, 0.248]. Thus, H1 was fully supported. Regarding the effects of social support on health status, the indirect effect results showed that among the three types of informal social support, two of them (relatives support and friends support) had significant and positive effects on the health of older adults in China ([Bootstrap $95\%$ CI: 0.004, 0.022]; [Bootstrap $95\%$ CI:0.002, 0.026]), whereas the other support had no significant effects. These findings fully supported H2 and H3, but H4 was not. With regards to formal social support, social insurance had no significant effect on this mechanism [Bootstrap $95\%$ CI:−0.003, 0.003], and similarly, H5 was not verified. Among all indirect paths via social support, two mechanisms were significant: Internet use → relatives support → health; Internet use → friends support → health. Therefore, relatives support and friends support were mediators between Internet use and health. Moreover, the effect of formal social support on health did not have a mediation effect, but some informal support did. Thus, we can conclude that informal social support may play a more significant mediating role than formal social support.
**Table 6**
| Paths | Standardized coef. | Bootstrap 95%CI | Bootstrap 95%CI.1 |
| --- | --- | --- | --- |
| | | Lower | Upper |
| Total effect | Total effect | Total effect | Total effect |
| Internet use → health | 0.193 | 0.111 | 0.274 |
| Direct effects | Direct effects | Direct effects | Direct effects |
| Internet use → health | 0.167 | 0.085 | 0.248 |
| Indirect effects (total) | 0.026 | 0.013 | 0.041 |
| Internet use → relatives support → health | 0.012 | 0.004 | 0.022 |
| Internet use → friends support → health | 0.014 | 0.002 | 0.026 |
| Internet use → neighbors support → health | 0.001 | −0.004 | 0.006 |
| Internet use → social insurance → health | 0.001 | −0.003 | 0.003 |
| Indirect effects contrast | Indirect effects contrast | Indirect effects contrast | Indirect effects contrast |
| Relatives support vs. Friends support | −0.002 | −0.017 | 0.014 |
| Relatives support vs. Neighbors support | 0.011 | 0.002 | 0.022 |
| Relatives support vs. Social insurance | 0.012 | 0.004 | 0.023 |
| Friends support vs. Neighbors support | 0.013 | −0.001 | 0.027 |
| Friends support vs. Social insurance | 0.014 | 0.003 | 0.026 |
| Neighbors support vs. Social insurance | 0.001 | −0.004 | 0.006 |
## Difference between rural and urban areas in social support
We also used PROCESS to test for differences between rural areas and urban areas (Tables 7, 8). We found significant differences between rural areas and urban areas in social support. When social support was used as a mediating variable, relatives support was considered to play a partial mediating role in the relationship between Internet use and health status [Bootstrap $95\%$ CI: 0.005, 0.040] in rural areas. However, this path was not significant in the urban sample. In urban areas, the results showed that friends support partially mediates the effect of Internet use on health status [Bootstrap $95\%$ CI: 0.002, 0.034].
## Discussion
Using the 2021 wave of CGSS data, the present study examined the impact of Internet use on health status among Chinese older adults. Despite the large number of studies on the association between Internet use and health status, the potential mechanisms underlying this process remain to be explored. Therefore, we proposed a multiple mediation model to examine the role of social support. The results of this study show that Internet use was positively associated with health status by promoting social support. Several important conclusions can be drawn from this study.
The first finding is that Internet use has direct effects on health among Chinese older adults, which is in line with most prior work [59, 60]. The mechanism of the effect of Internet use on the health status of older adults is that Internet use is conducive to expanding the scale of personal social networks, including communication with family and friends, access to effective information, and participation in leisure activities [61]. On this basis, the Internet can provide individuals with more emotional comfort, alleviate negative emotions, provide them with more health information and medical resources, and improve their health [62], thus reducing their depression and loneliness and achieving higher life satisfaction [63]. Our results are a good extension of previous studies that have simply separated Internet use into “use or not” [64, 65] to draw the general conclusion that Internet use has a positive effect on health status.
Second, this study distinguished three types of informal social support and examined the effect of each type on health separately. Our results show that not all types of informal social support have significant effects on the relationship between Internet use and health status, which is in line with prior studies. When compared to other types of informal social support, only relatives support and friends support significantly affected health status among Chinese older adults, which is consistent with previous research. For example, informal social support has been found to have a significant effect on health outcomes in a large number of studies, people with a higher level of informal social support have lower social pressure and higher health status [66, 67]. For older adults, regular contact with relatives and friends can effectively promote health [68]. Krause found that informal social support established through voluntary activities has a positive impact on the health of the elderly [69]. Meanwhile, we found that relatives support and friends support significantly mediated the effect of Internet usage on health. This is consistent with previous research, suggesting that social support from relatives and friends partially mediated the relationship between Internet use and self-rated health. The reason may be that the Internet can increase the frequency of interaction between older adults and their relatives and friends, from which they can obtain social support [53]. Social support means getting emotional encouragement, information, and help from relatives and friends they get along with. When older adults perceive higher levels of social support, they are more inclined to show better adjustment, thus resulting in better health. Therefore, the mediating role of relatives and friends support suggests that older adults can not only use the Internet to promote health but also use more relatives and friends support resources through the Internet to promote their health.
We also found that neighbors support did not have a significant influence on health status, which was contrary to our expectations. Prior research has empirically supported that neighborhood support can not only provide instant information, but also provide help at the first time in case of emergency. Therefore, neighborhood support helps to improve the mental health of older adults [70, 71]. This inconsistent finding could be due to the difference in the Chinese situation. Unlike traditional society, rapid urbanization has brought about modern urban lifestyles and dramatic changes to traditional neighborhoods. In particular, the development of network technology has freed people from face-to-face and proximity forms of interaction, and people can reach the needs of social interaction without leaving their homes. Meanwhile, the diversification of modern life and leisure styles has greatly weakened the importance of neighborhood relations. Moreover, neighbors support did not significantly mediate the role between Internet use and self-rated health, this may be due to COVID lockdowns, people generally go out less and have less opportunity to contact their neighbors, which also affects the impact of neighbor support on health to some extent.
Third, our results show that the mediating pathway of Internet use on the health status of older adults through social insurance is not significant. That is, the underlying mechanism between Internet use and health status among older adults in China was informal social support only, which is contrary to our expectations. The critical role of social insurance in motivating health outcomes and promoting health equity has been empirically supported by a strand of previous research [72, 73]. This inconsistent finding could be due to the difference in the measurement of social insurance, as well as whether to control the use of the Internet. Previous studies have generally focused on social insurance alone, so there is a need to consider the factors of Internet use to conduct more empirical studies and compare their effects on health status together.
The last and also most important finding in the current study is that regarding social support differences between urban and rural areas. For the rural elderly, relatives support has a significant mediating effect between Internet use and health status, while for the urban elderly, friends support has a significant mediating effect between Internet use and health status. The analysis is based on the following reasons: on the one hand, Chinese society is an “acquaintance society” characterized by relationship orientation [74, 75], and older adults have a higher consistency of family networks in rural areas of China. In line with past research, informal social support is also the main source of social support for rural residents [76]. Compared with other social support, relatives of rural people are close. Relatives support can provide higher interpersonal trust and reciprocity, which will help to generate positive emotional experiences and improve the health status of older adults. On the other hand, with the reduction of urban family size and the diversity and convenience of urban social space, it is easier for older adults to establish ties with friends. When the elderly can no longer obtain continuous and sufficient family support, the support of friends' network, as the second source of social support in the social interaction of the elderly, has become more important for health [77, 78].
There are still some limitations in this article. First, due to the limitation of existing data, the Internet use of the elderly only measures the frequency of use, which cannot fully show the diversity of Internet use of the elderly, such as the time of use, psychological motivation, browsing items, etc. Second, in this paper, we analyze CGSS data only in 2021, without considering the survey data of other years, and cannot obtain the dynamic changes of the impact of social support on health status. Third, causal interpretation cannot only rely on path analysis, and future research should introduce longitudinal design or experimental research design. Last but not least, future studies should further explore the stories behind research findings through qualitative research methods such as interviews to enhance the richness of results.
## Conclusion
To sum up, this paper analyzed the influence mechanism of Internet use on the health status of older adults by focusing on the mediating role of social support. To be more specific, through the Internet, older adults in China can keep in touch with geographically dispersed relatives and friends, and establish new contacts with the outside world, which not only strengthens their connection with external social networks, but also increases communication with relatives, which is conducive to their health status. Overall, as there are many pathways for the Internet to affect health, social support only plays a partial mediating role in our study. That is, informal social support positively influenced the health status among older adults, while formal social support does not. These findings suggest that government should take effective steps to encourage and improve Internet use among older adults and obtain various social support to improve their health status.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
Conceptualization: YE and JY. Data curation, methodology, and writing—original draft: YE. Formal analysis: CL. Writing—review and editing: CL and LN. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Association between skipping breakfast and prediabetes among adolescence in
Japan: Results from A-CHILD study'
authors:
- Keitaro Miyamura
- Nobutoshi Nawa
- Aya Isumi
- Satomi Doi
- Manami Ochi
- Takeo Fujiwara
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992887
doi: 10.3389/fendo.2023.1051592
license: CC BY 4.0
---
# Association between skipping breakfast and prediabetes among adolescence in Japan: Results from A-CHILD study
## Abstract
### Objective
Adolescents with prediabetes are at high risk of developing type 2 diabetes in later life. It is necessary to identify risk factors for prediabetes in adolescents. This study aimed to examine the association between skipping breakfast and prediabetes among adolescents in Japan.
### Study design
We used the population-based cross-sectional data of eighth grade in junior high school students from the Adachi Child Health Impact of Living Difficulty (A-CHILD) study conducted in Adachi City, Tokyo, Japan, in 2016, 2018, and 2020. Skipping breakfast was assessed using self-reported questionnaires ($$n = 1510$$). Prediabetes was defined as hemoglobin A1c (HbA1c) levels of 5.6-$6.4\%$. The association between skipping breakfast and prediabetes was evaluated using multivariate logistic regression analysis. Stratified analysis was also performed using BMI, 1 SD or more, or less than 1SD, as overweight was defined as 1SD or more.
### Results
Students who skipped breakfast were $16.4\%$ ($$n = 248$$). The prevalence of prediabetes was $3.8\%$ ($$n = 58$$). Skipping breakfast exhibited a significant association with prediabetes (OR:1.95, $95\%$ CI: 1.03 to 3.69) after adjusting for sex, annual household income, family history of diabetes mellitus, BMI, and survey year. Stratified analysis showed stronger association among students with overweight (BMI ≥1SD) (OR=4.31, $95\%$ CI 1.06-17.58), while non-sigificant among students without overweight (BMI<1SD) (OR=1.62, $95\%$ CI 0.76-3.47).
### Conclusions
Skipping breakfast in Japanese adolescents, especially those with overweight, was associated with prediabetes. The promotion of avoiding skipping breakfast may help to prevent prediabetes.
## Introduction
Type 2 diabetes is an emerging and unsolved global health problem. Recent studies reported that the prevalence of type 2 diabetes in adults worldwide was about $8\%$, and the incidence of diabetes are plateauing (1–3). Type 2 diabetes can lead to blindness, dialysis, and cardiovascular disease, significantly impairing patients’ quality of life [1]. The prevalence of patients with young-onset type 2 diabetes is increasing worldwide [4, 5], and mortality and cardiovascular morbidity associated with type 2 diabetes differed significantly by age at diagnosis, with mortality and cardiovascular morbidity being highest among patients with early-diagnosed type 2 diabetes [6]. To prevent type 2 diabetes, there is a need to identify risk factors in early-stage, including adolescents with prediabetes [7].
To identify possible risk factors for prediabetes in adolescence, the risk factors for type 2 diabetes would be the most prominent. For example, sedentary lifestyle or lack of physical exercise [8], improper dietary intakes [9], obesity, and family history of diabetes are well documented as risk factors for type 2 diabetes [8]. Among them, we focus on skipping breakfast as a risk factor for prediabetes in adolescence because it is prevalent among adolescents, for example, $8.0\%$ in junior high school in Japan [10, 11]. Previous studies have suggested robust biological mechanisms in the association between skipping breakfast and prediabetes. Skipping breakfast could affect glucose metabolism by elevating free fatty acid level [12] and disrupting circadian rhythms [13]. Furthermore, skipping breakfast can be associated with increased appetite [12] and poor diet [14]. In addition, skipping breakfast may also decrease physical activity in the morning [15, 16].
A few cross-sectional studies showed that skipping breakfast was associated with elevated fasting glucose levels in childhood (aged 6-17 years old) [17, 18]. However, population-based studies of adolescents in Asian populations are lacking. Considering the biological mechanisms of the effects of skipping breakfast on glucose metabolism, racial differences in insulin sensitivity and insulin response [19] may result in racial differences in the risk of skipping breakfast. In a European population-based study, significant differences among breakfast consumption habits and fasting blood glucose were seen only in boys [18]. In a Brazilian study, a higher frequency of eating breakfast was negatively correlated with fasting blood glucose levels [17]. However, it may be difficult to generalize the results because researchers investigated only children with obesity, with the subjects recruited via television commercials and newspaper advertisements. Conversely, a study among primary school children in Taiwan reported no association between skipping breakfast and prediabetes using fasting glucose levels [20]. However, it may be too early to assess the associations because insulin resistance increases during adolescence [21].
The effect of skipping breakfast on glucose metabolism may be even higher in children with obesity because obesity increases insulin resistance and the risk of glucose intolerance [22]. In individuals without obesity, skipping breakfast may decrease total daily energy intake. In contrast, in individuals with obesity, skipping breakfast may increase energy intake in the second half of the day without decreasing total energy intake [15, 16, 23]. In other words, obesity may be an effect modifier in the association between skipping breakfast and diabetes risk. In addition, it has been reported that Asians are more likely to accumulate visceral fat even at the same BMI and to develop diabetes even with mild obesity compared to Whites [24]. Therefore, it is also essential to evaluate the possibility that children with overweight may be a high-risk group.
In this research, we used a set of population-based data of junior high school children (aged 13–14 years old) from the Adachi Child Health Impact of Living Difficulty (A-CHILD) study in Tokyo, Japan, collected in 2016, 2018, and 2020. This study aimed to examine the association between skipping breakfast and prediabetes during adolescence in Japan and whether overweight status modify the association.
## Study design and subjects
We used the cross-sectional data from the A-CHILD study conducted in Adachi City, Tokyo, Japan, in 2016, 2018, and 2020 (25–27). Details of this study protocol can be found somewhere [27]. This study was approved by the Ethics Committee at the National Center for Child Health and Development (Study ID: 1147) and Tokyo Medical and Dental University (Study ID: M2016-284). Self-reported questionnaires with unique anonymous ID were administered to children in representative junior high schools (13-14 years old) in October 2016, 2018, and 2020. Children and their parents answered questionnaires at home and returned the questionnaires to their schools. Children responded to questions about lifestyle, while parents responded to questions about the family environment and their medical history. In 2016, 588 questionnaires were collected ($77.9\%$ return rate), in 2018, 583 questionnaires were collected ($86.2\%$ return rate), and in 2020, 551 questionnaires were collected ($83.6\%$ return rate), for a total of 1722 questionnaires collected ($82.4\%$ return rate). Questionnaire responses were linked to school health checkup data for body mass index (BMI) and blood test data conducted in Adachi City including HbA1c levels. Student participation in the health checkups was voluntary. The overall participation rate for health checkups was $75.4\%$, with $66.5\%$ in 2016, $82.0\%$ in 2018, and $79.0\%$ in 2020.
Parents or children who did not respond, who left all answers blank, who did not agree to participate in the study, or whose children did not receive school checkups were excluded as invalid responses, and the remaining respondents were considered valid. Children who had missing data about the frequency of breakfast or HbA1c value were excluded. Children with anemia (defined as less than 12.0 g/dl of hemoglobin levels [28]) were also excluded because chronic anemia such as iron deficiency anemia elevates HbA1c level due to the effect of erythrocyte turnover although blood glucose does not elevate [29]. The analysis was carried out using the data of 1510 participants (Figure 1).
**Figure 1:** *Participant flow chart. Of the 2090 2nd year students in seven representative junior high schools in Adachi City in 2016, 2018 and 2020, we analyzed 1510 students, who provided data for the frequency of breakfast and the blood tests.*
## Skipping breakfast
Skipping breakfast was assessed using the following question “How often do you eat breakfast per week?” based on previous studies [18, 20]. The responses to this question were “every day,” “sometimes,” “rarely,” or “never.” To compare those who eat breakfast every day with those who do not eat breakfast every day, we collapsed four categories into two: “every day” or “sometimes/rarely/never”.
## Prediabetes
In this study, we evaluated HbA1c levels of 5.6-$6.4\%$ as prediabetes because the Japanese Diabetes Diagnostic Criteria Review Committee considers HbA1c levels of 5.6-$6.4\%$ as a group at high risk of developing diabetes in the future [30]. We took a venipuncture blood sample from the arm at the laboratory and measured HbA1c level using an enzymatic assay. The students were not required to fast prior to having the blood test.
## Covariates
Breakfast habits and risk of prediabetes are affected by demographic factors and socioeconomic status [8, 31]. We chose child sex, annual household income as socioeconomic status, family history of diabetes, BMI, and survey year, as covariates, based on previous studies [17, 18, 20]. Annual household income was categorized into four groups (<3.0 million yen, 3-6 million yen, 6-10 million yen, ≥10 million yen) based on the previous study [25]. Family history of diabetes was categorized as “yes” when mother or father of participants had diabetes, and “no” when both mother and father of participants did not have diabetes. Children’s BMI was calculated from their height and weight and assessed by z-scores based on the WHO Child Growth Standards according to age and sex, which can be applied to Japanese [32]. BMI was categorized into three groups (<-1SD, -1SD to1SD, ≥1SD).
Items related to lifestyle habits other than breakfast habits were also investigated, such as sleep and exercise habits. Wake-up time was categorized into three groups (< 1 time/week, 1-2 times/week, ≥3 times/week). Sleep duration was calculated from the difference between waking and sleeping times for each hourly category because we did not ask about sleep duration. For example, the “7:00 - 8:00 a.m.” wake-up time category was considered as 7:30, and the “after 24:00” bedtime category was considered as 24:30. If the person went to bed at 1:00, his/her sleep duration could have been overestimated. Sleep duration was categorized into four groups (≤ 6hours, 7hours, 8-10 hours, ≥11 hours) based on a consensus statement of the American Academy of Sleep Medicine [33]. The frequency of exercise was categorized into three groups (< 1 time/week, 1-2 times/week, ≥3 times/week). Missing data with all covariates, which was adjusted for regression analysis, was created as a new dummy variable.
## Statistical analysis
The association between skipping breakfast and prediabetes was evaluated using logistic regression analysis to calculate crude and adjusted odds ratio (OR) with $95\%$ confidence intervals (CI). Sex, socioeconomic status, family history of diabetes, BMI, and survey year were put in the adjusted model. The VIF for the wake-up time variable was about 1 to 2, suggesting that there was no multicollinearity [34] (Supplementary Table 1). Thus, we performed the logistic regression analyses further adjusted for wake-up time and frequency of exercise. Furthermore, previous studies have shown that it is not the wake-up time but sleep duration [35, 36] and sleep disturbances [35] that affects diabetes. We also performed an analysis adjusted for sleeping time instead of wake-up time.
We also evaluated the effect of the interaction of overweight/obesity (BMI ≥ 1 SD [37]) on the association of skipping breakfast with prediabetes. Considering that estimating interactions requires a larger sample size than estimating main effects [38], the interaction term indicated a weak but possible effect modification (p-value for interaction 0.21), even though the interaction p-value is slightly larger [39]. In other words, the effect of frequency of breakfast on prediabetes could vary depending on the presence or absence of overweight, we conducted stratified analysis by BMI ≥1 SD (i.e., students with overweight) and BMI <1 SD (i.e., students without overweight). Data analyses were carried out using STATA version 15 (Stata Corp LP, College Station, TX, USA).
## Results
The proportion of students who ate breakfast every day and sometimes/rarely/never were $83.6\%$ and $16.4\%$, respectively. The prevalence of prediabetes was $3.8\%$. There were no students whose HbA1c level was more than $6.5\%$, which is one of the diagnostic criteria of diabetes (American Diabetes Association 2010). There was no large change in the percentage of students who ate breakfast daily and in the prevalence of prediabetes between survey years. ( Supplementary Table 2). The proportion of boys and girls were similar. A total of $12.3\%$ had an annual household income of fewer than 3million yen. The percentage of girls who did not have breakfast every day ($19\%$) was greater than that of boys who did not have breakfast every day ($14\%$) (Table 1).
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Total | Frequency of breakfast | Frequency of breakfast.1 |
| --- | --- | --- | --- | --- |
| | | Total | Every day | Sometimes/rarely/never |
| | | (N=1510) | (N=1262; 83.6%) | (N=248; 16.4%) |
| | | N (%) | N (%) | N (%) |
| Prediabetes | HbA1c<5.6 | 1452 (96.2%) | 1218 (96.5%) | 234 (94.4%) |
| Prediabetes | HbA1c≧5.6 | 58 (3.8%) | 44 (3.5%) | 14 (5.6%) |
| Child Sex | Boy | 757 (50.1%) | 651 (51.6%) | 106 (42.7%) |
| Child Sex | Girl | 753 (49.9%) | 611 (48.4%) | 142 (57.3%) |
| Child Sex | Missing | 0 (0%) | 0 (0%) | 0 (0%) |
| Annual household income(million yen) | < 3 | 185 (12.3%) | 141 (11.2%) | 44 (17.7%) |
| Annual household income(million yen) | 3 - 6 | 471 (31.2%) | 400 (31.7%) | 71 (28.6%) |
| Annual household income(million yen) | 6 - 10 | 500 (33.1%) | 434 (34.4%) | 66 (26.6%) |
| Annual household income(million yen) | ≥ 10 | 137 (9.1%) | 122 (9.7%) | 15 (6.0%) |
| Annual household income(million yen) | Unknown/Missing | 217 (14.4%) | 165 (13.1%) | 52 (21.0%) |
| Family history of diabetes | No | 1440 (95.4%) | 1204 (95.4%) | 236 (95.2%) |
| Family history of diabetes | Yes | 70 (4.6%) | 58 (4.6%) | 12 (4.8%) |
| Family history of diabetes | Missing | 0 (0%) | 0 (0%) | 0 (0%) |
| BMI | <-1SD | 262 (17.4%) | 227 (18.0%) | 35 (14.1%) |
| BMI | -1SD to 1SD | 991 (65.6%) | 833 (66.0%) | 158 (63.7%) |
| BMI | ≥1SD | 228 (15.1%) | 178 (14.1%) | 50 (20.2%) |
| BMI | Missing | 29 (1.9%) | 24 (1.9%) | 5 (2.0%) |
| Survey year | 2016 | 483 (32.0%) | 405 (32.1%) | 78 (31.5%) |
| Survey year | 2018 | 525 (34.8%) | 435 (34.5%) | 90 (36.3%) |
| Survey year | 2020 | 502 (33.2%) | 422 (33.4%) | 80 (32.3%) |
Table 2 shows the odds ratio (OR) of skipping breakfast for prediabetes. Students who did not eat breakfast every day were 1.66 times more likely to have prediabetes than those who ate breakfast every day in the crude model (OR: 1.66, $95\%$ CI: 0.89 to 3.07). After adjusting for child sex, annual household income, family history of diabetes, skipping breakfast showed significant association with prediabetes (OR:1.95, $95\%$ CI: 1.03 to 3.69) (Adjusted model). The OR of skipping breakfast to annual household income determined using univariate logistic regression was a negative association (OR: 0.39, $95\%$ CI: 0.21 to 0.74 (“≥ 10 million yen” with reference to “<3million yen”)), whereas that of prediabetes to annual household income determined using univariate logistic regression was a positive association (OR: 1.36, $95\%$ CI: 0.33 to 5.5 (“≥ 10 million yen” with reference to “<3million yen”)). Thus, annual household income was a negative confounder [40], leading to an underestimation of its effect. For this reason, the OR increased after adjusting for annual household income in the adjusted model.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Crude | Crude.1 | Adjusted model | Adjusted model.1 |
| --- | --- | --- | --- | --- | --- |
| | | OR | 95%CI | OR | 95%CI |
| Frequency of breakfast | Everyday | Ref | | Ref | |
| Frequency of breakfast | Sometimes/rarely/never | 1.66 | (0.89, 3.07) | 1.95 | (1.03, 3.69) |
| Child sex | Boy | Ref | | Ref | |
| Child sex | Girl | 0.56 | (0.32, 0.96) | 0.54 | (0.31, 0.94) |
| Annual household income (million yen) | < 3 | Ref | | Ref | |
| Annual household income (million yen) | 3 - 6 | 1.39 | (0.45, 4.27) | 1.45 | (0.47, 4.48) |
| Annual household income (million yen) | 6 - 10 | 2.99 | (1.04, 8.59) | 3.10 | (1.07, 8.98) |
| Annual household income (million yen) | ≥ 10 | 1.36 | (0.33, 5.54) | 1.42 | (0.35, 5.83) |
| Annual household income (million yen) | Unknown/Missing | 1.07 | (0.28, 4.03) | 1.09 | (0.28, 4.14) |
| Family history of diabetes | No | Ref | | Ref | |
| Family history of diabetes | Yes | 1.56 | (0.55, 4.42) | 1.25 | (0.43, 3.67) |
| BMI | <-1SD | 0.87 | (0.42, 1.82) | 0.84 | (0.40, 1.76) |
| BMI | -1SD to 1SD | Ref | | Ref | |
| BMI | ≥1SD | 1.12 | (0.55, 2.28) | 1.00 | (0.48, 2.09) |
| Year | 2016 | Ref | | Ref | |
| Year | 2018 | 1.02 | (0.54, 1.92) | 1.02 | (0.54, 1.95) |
| Year | 2020 | 0.91 | (0.47, 1.75) | 0.84 | (0.43, 1.64) |
Table 3 shows the odds ratio (OR) of skipping breakfast for prediabetes stratified by BMI. Among students with overweight (BMI≥1SD), skipping breakfast showed stronger association with prediabetes in the adjusted model (OR: 4.31, $95\%$ CI: 1.06, 17.58). In contrast, among students without overweight (BMI<1SD), skipping breakfast was not statistically significantly associated with prediabetes in the adjusted model (OR: 1.62, $95\%$ CI: 0.76, 3.47).
**Table 3**
| Unnamed: 0 | Frequency of breakfast | Crude | Crude.1 | Adjusted model | Adjusted model.1 |
| --- | --- | --- | --- | --- | --- |
| | Frequency of breakfast | OR | 95%CI | OR | 95%CI |
| BMI <1SD | Everyday | Ref | | Ref | |
| BMI <1SD | Sometimes/rarely/never | 1.28 | (0.61, 2.69) | 1.62 | (0.76, 3.47) |
| BMI ≥1SD | Everyday | Ref | | Ref | |
| BMI ≥1SD | Sometimes/rarely/never | 3.84 | (1.07, 13.86) | 4.31 | (1.06, 17.58) |
The proportion of those who skipped breakfast was higher among those who woke up late, slept longer on weekdays, and infrequently exercised (Supplementary Table 3). In univariate analysis, breakfast skipping was significantly more frequent when waking up late, sleeping longer, and exercising less frequently (Supplementary Table 4). The logistic regression analysis with additional adjustments for wake-up time and exercise frequency, skipping breakfast remained significantly associated with prediabetes (OR: 2.01. $95\%$CI: 1.04, 3.89) (Supplementary Table 1). The logistic regression analysis adjusted for sleeping time instead of wake-up time showed similar results (OR: 1.98. $95\%$CI: 1.04, 3.79) (Supplementary Table 5).
## Discussion
This study investigated the association between breakfast habits and prediabetes using HbA1c levels in Japanese adolescents. We found that skipping breakfast was associated with prediabetes in adolescents, and this association was stronger among students with overweight.
To our knowledge, this is the first study to investigate the association between skipping breakfast and prediabetes in adolescents in the Asian population. A few cross-sectional studies showed the association between skipping breakfast and fasting glucose levels on a continuous scale in childhood [17, 18]. However, these studies did not examine the association with prediabetes using specified cutoff for blood glucose or HbA1c levels. Our results suggest that skipping breakfast is also associated with prediabetes as measured by HbA1c levels, in addition to being associated with elevated blood glucose levels. In addition, our results suggest that the effect of skipping breakfast on glucose metabolism was greater among students with overweight.
The various biological mechanisms explaining the association between skipping breakfast and prediabetes can be speculated. As fasting conditions prolonged, energy sources are supplied by not only gluconeogenesis and degradation of glycogen but also lipolysis, leading to elevated levels of free fatty acid (FFA) [41]. For example, FFA levels before lunch in those who skip breakfast is higher than in those who consume breakfast [12]. Since the elevated FFA levels affect glucose metabolism by disrupting insulin receptor signaling in skeletal muscle and liver, elevated FFA levels by skipping breakfast may play an important role in developing insulin resistance [42].
Another potential biological mechanism is the disruption of the circadian clock, which normally controls the activity of enzymes and hormones associated with glucose metabolism. The central circadian clock, which is located in the suprachiasmatic nucleus of the hypothalamus, mainly responds to the external light-dark cycle [43], and the peripheral clocks located in peripheral tissues such as β-cells, muscles, adipose tissues, and the liver mainly respond to meal timing and content [15, 44]. Asynchrony of the central and the peripheral circadian clocks was associated with reducing insulin and glucagon-like peptide 1 (GLP-1) secretion [45], insulin resistance, β-cell proliferation, and β-cell apoptosis [44]. Randomized controlled trials reported that skipping breakfast affects clock and clock-controlled gene expression [13], and those who skip breakfast exhibit greater glucose of area under the curve and glucose variability after lunch than healthy, lean adults who consume breakfast [13, 15]. In addition, lower transcript levels of clock genes such as Bmal1, PER1, and PER3 were inversely correlated with HbA1c levels [46]. In other words, the disruption of the circadian clock due to skipping breakfast may affect insulin secretion and other factors, causing an increase in postprandial blood glucose, leading to an increase in HbA1c, i.e., the risk of prediabetes.
Obesity persistently increases plasma FFA levels both in the basal state and after glucose loading, and is a major contributor to insulin resistance [47]. Insulin resistance cause hyperinsulinemia to maintain normoglycemia. Hyperinsulinemia can maintain normal blood glucose levels to some degree; however, chronic progressive insulin resistance and compensatory insulin hypersecretion can be beta cell stress and eventually to beta-cell failure, leading to prediabetes and then to type 2 diabetes [48]. When individuals with overweight skip breakfast, insulin resistance can be further increased. Blood glucose levels after lunch in individuals with overweight may be even higher than in individuals without overweight due to inadequate compensatory insulin secretion for the elevation of insulin resistance. Since the lower the HbA1c level, the higher the contribution of postprandial blood glucose to the HbA1c level than fasting blood glucose [49], prediabetes assessed by HbA1c levels may capture the effect of skipping breakfast on the postprandial glucose level.
Based on current findings, skipping breakfast can be a risk factor for impaired glucose metabolism, leading to prediabetes. Therefore, breakfast consumption might be effective in modulating insulin sensitivity and secretion and reducing the risk of prediabetes. Breakfast consumption may be recommended, especially for people with obesity. Breakfast intake could not affect weight gain [23]. However, it is necessary to pay attention to eating habits other than breakfast so that eating breakfast does not lead to excessive daily caloric intake. Moreover, it is important to intervene targeting to parents at an earlier age to establish the habit of consuming breakfast daily because dietary patterns could be established between 1 and 2 years old and continue into young adulthood [50].
Several limitations of this study should be acknowledged. First, a self-reported questionnaire on skipping breakfast could result in recall bias. Moreover, individuals may have had different understandings of the options for breakfast frequency, as we did not provide specific explanations. However, since the breakfast categories were divided into “every day” and “sometimes/rarely/never,” there would be unlikely misclassifications. Second, breakfast was not defined by period since wake-up or a time frame in the morning. However, breakfast time on weekdays among junior high school students would not be very different. Third, we were unable to include blood glucose levels to diagnose prediabetes. Fourth, we were unable to assess the pubertal stage like the tanner stage of each student, although the effects of glucose metabolism may vary by tanner stage [51]. Fifth, we were not able to exclude other specific types of diabetes, such as type 1 diabetes. However, there were no students whose HbA1c level was more than $6.5\%$, and the incidence of childhood-onset type 1 diabetes in *Japan is* low ($\frac{2.25}{100}$,000 persons) [52] compared with most European countries and the US. Sixth, given the somewhat large interaction p-value, studies with a larger sample size would be needed to confirm our findings. Finally, this study is a cross-sectional study and does not clarify the causation between skipping breakfast and prediabetes in adolescents. In the future, longer duration randomized controlled trials and longitudinal studies from preschool children to adolescents are needed. In addition, it is necessary to evaluate the impact of skipping breakfast on prediabetes in other races to generalize our findings because there are racial differences in insulin sensitivity and insulin response [19]. Analysis using indices of insulin sensitivity and insulin resistance calculated by fasting blood glucose and fasting insulin levels would also be helpful.
In summary, we found that skipping breakfast was associated with prediabetes after adjusting for the students’ demographic, lifestyle, and socioeconomic status, and this association was stronger among students with overweight. Our findings suggest that avoiding skipping breakfast may help to prevent prediabetes, especially for people with overweight.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee at the National Center for Child Health and Development (Study ID: 1147) and Tokyo Medical and Dental University (Study ID: M2016-284). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
TF conceived the study. TF, MO, AI and SD conducted the survey and collected data. KM was primarily responsible for data analysis and wrote the first draft of paper. NN and TF reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1051592/full#supplementary-material
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|
---
title: 'Individual and combined associations of body mass index and waist circumference
with components of metabolic syndrome among multiethnic middle-aged and older adults:
A cross-sectional study'
authors:
- Mei Yang
- Yan Zhang
- Wanyu Zhao
- Meiling Ge
- Xuelian Sun
- Gongchang Zhang
- Birong Dong
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9992890
doi: 10.3389/fendo.2023.1078331
license: CC BY 4.0
---
# Individual and combined associations of body mass index and waist circumference with components of metabolic syndrome among multiethnic middle-aged and older adults: A cross-sectional study
## Abstract
### Objectives
Body mass index (BMI) and waist circumference (WC) are closely associated with metabolic syndrome and its components. Hence, a combination of these two obesity markers may be more predictive. In this study, we aimed to investigate the individual and combined associations of BMI and WC with selected components of metabolic syndrome and explored whether age, sex and ethnicity affected the aforementioned associations.
### Methods
A total of 6,298 middle-aged and older adults were included. Based on BMI and WC, the participants were divided into 4 groups: comorbid obesity (BMI ≥ 28 kg/m2 and WC< $\frac{85}{90}$ cm for women/men), abdominal obesity alone (BMI< 28 kg/m2 and WC≥ $\frac{85}{90}$ cm for women/men), general obesity alone (BMI ≥ 28 kg/m2 and WC< $\frac{85}{90}$ cm for women/men) and nonobesity subgroups (BMI< 28 kg/m2 and WC< $\frac{85}{90}$ cm for women/men). Selected components of metabolic syndrome were evaluated using the criteria recommended by the Chinese Diabetes Society. Poisson regression models with robust variance were used to evaluate the associations of obesity groups with selected components of metabolic syndrome. An interaction test was conducted to explore whether age, sex and ethnicity affect the aforementioned associations.
### Results
Compared with participants in the reference group (comorbid obesity), participants in the other 3 groups showed a decreased prevalence of fasting hyperglycemia (PR=0.83, $95\%$ CI=0.73–0.94 for abdominal obesity alone, PR=0.60, $95\%$ CI=0.38–0.96 for general obesity alone and PR=0.46, $95\%$ CI=0.40–0.53 for nonobesity), hypertension (PR=0.86, $95\%$ CI=0.82–0.90 for abdominal obesity alone, PR=0.80, $95\%$ CI=0.65–0.97 for general obesity alone and PR=0.69, $95\%$ CI = 0.66–0.73 for nonobesity) and hypertriglyceridemia (PR=0.88, $95\%$ CI=0.82–0.95 for abdominal obesity alone, PR=0.62, $95\%$ CI=0.47–0.81 for general obesity alone and PR=0.53, $95\%$ CI=0.49–0.57 for nonobesity). However, participants in the abdominal obesity alone and nonobesity groups showed a decreased prevalence of low HDL-C levels while participants in the general obesity alone group did not (PR=0.65, $95\%$ CI=0.41–1.03, $p \leq 0.05$). In addition, the aforementioned associations were not affected by age, sex or ethnicity (all p for interactions>0.05).
### Conclusions
Comorbid obesity is superior to general and abdominal obesity in identifying individuals at high risk of developing metabolic syndrome in middle-aged and older adults. Great importance should be attached to the combined effect of BMI and WC on the prevention and management of metabolic syndrome.
## Introduction
Metabolic syndrome is a health-threatening public health issue characterized by a combination of metabolic abnormalities including abdominal obesity, fasting hyperglycemia, hypertension, hypertriglyceridemia and reduced high-density lipoprotein cholesterol (HDL-C) levels. It is highly prevalent in China. A meta-analysis of 35 observational studies with 226,653 participants aged 15 years and over reported that the prevalence of metabolic syndrome in mainland China was $24.5\%$ [1]. Metabolic syndrome and its components are significantly influenced by sex, age, and ethnicity. A previous study of 3,423 adults showed that individuals aged 40–59 years had a threefold increased prevalence of metabolic syndrome compared with those aged 20–39 years [2]. Furthermore, male participants aged 60 years and over had a fourfold increased prevalence of metabolic syndrome compared with female participants of the same age. Non-Hispanic white males, non-Hispanic black and Mexican-American females had a higher prevalence of metabolic syndrome than the other participants. Metabolic syndrome and its components have been well accepted as important risk factors for cardiovascular diseases [3], which are the leading causes of disability and death in China [4]. Therefore, early identification of modifiable risk factors for metabolic syndrome has substantial importance.
Obesity is a serious health problem worldwide. When obesity is classified on the basis of BMI ≥ 30, it affects approximately $13\%$ of adults worldwide [5]. Almost 85 million adults aged 18–69 years in China suffer from obesity in 2018 [6]. Individuals with obesity are at increased risk of developing hypertension [7], dyslipidemia [8] and glucose intolerance [9].
Currently, body mass index (BMI) or waist circumference (WC) are the most frequently used measurements for obesity [10]. Both BMI and WC have been associated with metabolic syndrome and its components. However, the priority of these two measurements in identifying individuals at high risk of developing metabolic syndrome remains controversial. A previous study of 14,924 adults from the USA reported that obesity-related health risks were attributed to WC rather than BMI [11]. However, Yang et al. reported that BMI is a better predictor than WC for type 2 diabetes mellitus [12] and hypertension [13] in older adults. These conflicting results of previous studies suggest the need for better markers. In addition, the body fat distribution is significantly changed with aging, which limits the usefulness of a single marker to identify individuals at high risk of metabolic syndrome across all age groups. As a result, evaluating obesity and its related comorbidities based on BMI or WC may underestimate health risks.
Although BMI and WC are highly correlated with each other [14], increased BMI may not always be accompanied by an increased WC and the vice versa [15]. Considering the possibility that there exist unique characteristics of BMI and WC, a combination of BMI and WC might enhance the predictive value for metabolic syndrome and its components, which could expedite and simplify the screening process for individuals at high risks.
Although previous studies have reported that individuals with both increased BMI and increased WC are more prone to incident hypertension [16], stroke [17], cardiovascular diseases [18] and cognitive impairment [19], there exists substantial variation between obesity markers and disease development due to difference in age, sex and ethnicity [20].
The aim of our study was to evaluate the individual and combined associations of BMI and WC with selected components of metabolic syndrome in middle-aged and older adults and to explore whether age, sex and ethnicity affected the aforementioned associations.
## Study design and participants
This cross-sectional study analyzed the data from 6,298 participants of the West China Health and Aging Trend (WCHAT) study. The details of the WCHAT study have been published previously [21]. The WCHAT study recruited 7,536 community-dwelling individuals aged 50 years and over from Sichuan, Yunnan, Guizhou, and Xinjiang provinces in 2018. The demographic information of the participants was collected by trained interviewers via in-person interviews. Additionally, the participants underwent physical examination, anthropometric measurements, and blood tests.
The current study included participants with available data for BMI, WC, and one of the following: blood pressure, fasting plasma glucose level, triglyceride level, and HDL-C level. This study was approved by the Ethics Committee of West China Hospital, Sichuan University (reference no.: 2017-445). Informed consent was obtained from all participants.
## Data collection
We performed in-person interviews to collect information regarding sociodemographic characteristics including age, sex, educational background (illiterate, primary school, or secondary school or above), ethnicity (Han, Qiang, Tibetan, Yi, Uighur, or other ethnic minorities), marital status (married or single), smoking status (ever smokers [for > 6 months] or never smokers), alcohol consumption, hypertension, and diabetes.
## Anthropometric measurements and blood test
With the participants barefoot and wearing light clothes, stadiometer and weighing scale (Tsinghua tongfang, Beijing, China) were used to measure their height and weight, respectively. A soft tape was used to measure the waist at the level of the navel by trained volunteers [22]. The waist circumference was measured twice and the average value was recorded. BMI was calculated as weight divided by height in meters squared. Before blood pressure measurement, the participants were asked to rest in a seated position for 5–10 mins. The blood pressure was measured twice using an electronic sphygmomanometer (Yuwell, Jiangsu, China) and the average measurement was recorded. Blood samples were collected from each participant in the morning after at least 10 h of fasting. Fasting glucose, triglycerides and high-density lipoprotein cholesterol (HDL-C) were measured with an automatic biochemical analyzer (OLympus Au400, Japan).
## Obesity subgroups
Based on criteria validated for the Chinese population, obesity was defined as BMI ≥ 28 kg/m2 or WC≥ 85 cm for women and WC≥ 90 cm for men [23, 24]. In our study, participants were further divided into 4 groups: comorbid obesity (BMI ≥ 28 kg/m2 and WC< $\frac{85}{90}$ cm for women/men), abdominal obesity alone (BMI< 28 kg/m2 and WC ≥ $\frac{85}{90}$ cm for women/men), general obesity alone (BMI ≥ 28 kg/m2 and WC< $\frac{85}{90}$ cm for women/men) and nonobesity subgroups (BMI<28 kg/m2 and WC< $\frac{85}{90}$ cm for women/men).
## Assessment of selected components of metabolic syndrome
The criteria recommended by the Chinese Diabetes Society [25] were used to diagnose hypertriglyceridemia (triglyceride level≥1.70 mmol/L), reduced HDL-C (HDL-C level<1.04 mmol/L), hypertension (systolic blood pressure≥130 mmHg, diastolic blood pressure≥ 85 mmHg, or use of antihypertensive medications), and hyperglycemia (fasting plasma glucose level ≥6.1 mmol/L or use of antidiabetic medications).
## Statistical analysis
Stata software (version 14.1; Stata Corp, College Station, TX, USA) was used for data analysis. The normal distribution of the data was analyzed with the Shapiro-Wilk test. Continuous data were expressed as the means ± standard deviations or medians with interquartile range. Categorical data were presented as counts (percentages). The differences between groups were tested using analysis of variance (ANOVA) or the Kruskal-Wallis test for normally distributed or skewed continuous variables. The chi-square test was used to analyze the categorical variables. Poisson regression with robust variance was adopted to explore the associations of different obesity subgroups with selected components of metabolic syndrome. Prevalence ratios (PRs) and $95\%$ confidence intervals (CIs) were used to present the results. The confounding variables included age, sex, ethnicity, educational status, marital status, smoking, and alcohol consumption. Furthermore, to explore whether the aforementioned associations were affected by age, sex and ethnicity, participants were further divided according to sex (male/female), age (50–59 years/over 60 years) and ethnicity (Han/Qiang/Tibetan/Yi/Uighur) and an interaction test was conducted. $P \leq 0.05$ was considered statistically significant.
## Baseline characteristics of participants
The study included 6,298 individuals (mean age=62.3 ± 8.1 years; $63.02\%$ females). Participants with comorbid obesity were the youngest among the 4 groups and accounted for $21.9\%$ of the participants. The majority of participants was Han Chinese, followed by Qiang, Tibetan, Yi, Uighur, and other ethnic minorities. Significant differences were observed among the 4 groups in terms of age, sex, ethnicity, smoking status, BMI, and WC. Participants with comorbid obesity showed a higher prevalence of hyperglycemia, hypertriglyceridemia, hypertension, and reduced HDL-C levels than other groups. The baseline characteristics of the participants are presented in Table 1.
**Table 1**
| Unnamed: 0 | WC < 90 (men) or 85 (women) cm | WC < 90 (men) or 85 (women) cm.1 | WC ≥ 90 (men) or 85 (women) cm | WC ≥ 90 (men) or 85 (women) cm.1 | P value |
| --- | --- | --- | --- | --- | --- |
| | BMI < 28.0 kg/m2 | BMI ≥ 28.0 kg/m2 | BMI < 28.0 kg/m2 | BMI ≥ 28.0 kg/m2 | P value |
| | Nonobesity(n = 2,852) | General obesity alone(n = 96) | Abdominal obesity alone(n = 1,965) | Comorbid obesity(n = 1,385) | P value |
| Age, years † | 62.9 ± 8.5 | 62.1 ± 8.4 | 62.4 ± 8.0 | 61.0 ± 7.6 | < 0.001 |
| Female, n (%) | 1618 (56.7) | 58 (60.4) | 1352 (68.8) | 941 (67.9) | < 0.001 |
| Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) |
| Illiteracy | 808 (28.4) | 25 (26.0) | 530 (27.0) | 388 (28.1) | 0.160 |
| Primary school | 997 (35.1) | 29 (30.2) | 659 (33.6) | 440 (31.8) | |
| Secondary school and above | 1037 (36.5) | 42 (43.8) | 774 (39.4) | 554 (40.1) | |
| Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) |
| Han | 1185 (41.5) | 14 (14.6) | 723 (36.8) | 385 (27.8) | < 0.001 |
| Qiang | 459 (16.1) | 4 (4.2) | 485 (24.7) | 296 (21.4) | |
| Tibetan | 519 (18.2) | 22 (22.9) | 321 (16.3) | 361 (26.1) | |
| Yi | 314 (11.0) | 5 (5.2) | 134 (6.8) | 72 (5.2) | |
| Uighur | 68 (2.4) | 37 (38.5) | 197 (10.0) | 205 (14.8) | |
| Other ethnic minorities* | 307 (10.8) | 14 (14.6) | 105 (5.3) | 66 (4.8) | |
| Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) |
| Married | 2379 (83.7) | 75 (78.1) | 1653 (84.2) | 1143 (82.7) | 0.330 |
| Single | 463 (16.3) | 21 (21.9) | 310 (15.8) | 239 (17.3) | |
| History of smoking, n (%) | 666 (23.6) | 25 (26.0) | 299 (15.3) | 187 (13.6) | < 0.001 |
| History of alcohol consumption, n (%) | 762 (26.9) | 20 (20.8) | 503 (25.6) | 326 (23.6) | 0.095 |
| BMI, kg/m2 ‡ | 22.7 (21.1–24.3) | 29.7 (28.6–32.4) | 25.7 (24.3–26.8) | 30 (28.9–31.7) | < 0.001 |
| WC, cm ‡ | 80 (75–83.3) | 83 (80–84.8) | 91.7 (88.7–95.5) | 99 (94–104) | < 0.001 |
| Components of metabolic syndrome, n (%) | Components of metabolic syndrome, n (%) | Components of metabolic syndrome, n (%) | Components of metabolic syndrome, n (%) | Components of metabolic syndrome, n (%) | Components of metabolic syndrome, n (%) |
| Fasting hyperglycemia | 368 (12.9) | 15 (15.6) | 419 (21.3) | 338 (24.4) | < 0.001 |
| Hypertriglyceridemia | 831 (29.2) | 34 (35.4) | 923 (47.1) | 718 (52.0) | < 0.001 |
| Low HDL-C level | 435 (15.3) | 14 (14.6) | 406 (20.7) | 382 (27.6) | < 0.001 |
| Hypertension | 1234 (55.5) | 42 (60.9) | 1033 (65.9) | 821 (74.6) | < 0.001 |
## Associations of different obesity groups with selected components of metabolic syndrome
The results of Poisson regression analysis with robust variance were presented in Table 2. Comorbid obesity was selected as the reference. Compared with the reference group, participants in the other 3 groups had a lower prevalence of fasting hyperglycemia (PR=0.83, $95\%$ CI=0.73–0.94 for abdominal obesity alone, PR=0.60, $95\%$ CI=0.38–0.96 for general obesity alone and PR=0.46, $95\%$ CI=0.40–0.53 for nonobesity), hypertension (PR=0.86, $95\%$ CI=0.82–0.90 for abdominal obesity alone, PR=0.80, $95\%$ CI=0.65–0.97 for general obesity alone and PR=0.69, $95\%$ CI=0.66–0.73 for nonobesity), and hypertriglyceridemia (PR=0.88, $95\%$ CI=0.82–0.95 for abdominal obesity alone, PR=0.62, $95\%$ CI=0.47–0.81 for general obesity alone and PR=0.53, $95\%$ CI=0.49–0.57 for nonobesity) after adjustment for confounders. However, in the fully adjusted model, participants in the abdominal obesity alone and nonobesity groups showed a decreased prevalence of low HDL-C levels while participants in the general obesity alone group did not (PR=0.65, $95\%$ CI=0.41–1.03, $p \leq 0.05$)
**Table 2**
| Cases/Controls | Comorbid obesity | Abdominal obesity alone | General obesity alone | Nonobesity |
| --- | --- | --- | --- | --- |
| | | PR [95% CI] | PR [95% CI] | PR [95% CI] |
| Fasting hyperglycemia vs non fasting hyperglycemia(reference) | Fasting hyperglycemia vs non fasting hyperglycemia(reference) | Fasting hyperglycemia vs non fasting hyperglycemia(reference) | Fasting hyperglycemia vs non fasting hyperglycemia(reference) | Fasting hyperglycemia vs non fasting hyperglycemia(reference) |
| (1,140/5,158) | (1,140/5,158) | (1,140/5,158) | (1,140/5,158) | (1,140/5,158) |
| Crude | Ref. | 0.87 (0.77–0.99) * | 0.64 (0.39–1.02) | 0.52 (0.46–0.60) ** |
| Adjusted model | Ref. | 0.83 (0.73–0.94) * | 0.60 (0.38–0.96) * | 0.46 (0.40–0.53) ** |
| Hypertension vs nonhypertension (reference) | Hypertension vs nonhypertension (reference) | Hypertension vs nonhypertension (reference) | Hypertension vs nonhypertension (reference) | Hypertension vs nonhypertension (reference) |
| (3,130/1,829) | (3,130/1,829) | (3,130/1,829) | (3,130/1,829) | (3,130/1,829) |
| Crude | Ref. | 0.88 (0.84–0.92) ** | 0.81 (0.67–0.98) * | 0.74 (0.70–0.78) ** |
| Adjusted model | Ref. | 0.86 (0.82–0.90) ** | 0.80 (0.65–0.97) * | 0.69 (0.66–0.73) ** |
| Hypertriglyceridemia vs nonhypertriglyceridemia (reference) | Hypertriglyceridemia vs nonhypertriglyceridemia (reference) | Hypertriglyceridemia vs nonhypertriglyceridemia (reference) | Hypertriglyceridemia vs nonhypertriglyceridemia (reference) | Hypertriglyceridemia vs nonhypertriglyceridemia (reference) |
| (2,506/3,778) | (2,506/3,778) | (2,506/3,778) | (2,506/3,778) | (2,506/3,778) |
| Crude | Ref. | 0.9 (0.82–0.94) * | 0.68 (0.51–0.89) * | 0.56 (0.51–0.60) ** |
| Adjusted model | Ref. | 0.88 (0.82–0.95) ** | 0.62 (0.47–0.81) ** | 0.53 (0.49–0.57) ** |
| Low HDL-C level vs normal HDL levels (reference) | Low HDL-C level vs normal HDL levels (reference) | Low HDL-C level vs normal HDL levels (reference) | Low HDL-C level vs normal HDL levels (reference) | Low HDL-C level vs normal HDL levels (reference) |
| (1,237/5,051) | (1,237/5,051) | (1,237/5,051) | (1,237/5,051) | (1,237/5,051) |
| Crude | Ref. | 0.74 (0.66–0.84) ** | 0.52 (0.32–0.86) * | 0.55 (0.48.0.62) ** |
| Adjusted model | Ref. | 0.74 (0.66–0.83) ** | 0.65 (0.41–1.03) | 0.44 (0.39–0.50) ** |
Our findings indicated that individuals with comorbid obesity might be at higher risk of developing metabolic syndrome than other obesity groups. In addition, the aforementioned associations were not affected by sex, age or ethnicity in middle-aged and older adults of western China. ( all p for interactions>0.05) (Tables 3 – 6)
## Discussion
The main finding of our study was that comorbid obesity was superior to general or abdominal obesity alone in identifying individuals at high risk of selected components of metabolic syndrome in middle-aged and older adults. The results of our study revealed that compared with comorbid obesity, the prevalence of fasting hyperglycemia decreased by $17\%$ for abdominal obesity alone, $40\%$ for general obesity alone and $54\%$ for nonobesity. The prevalence of hypertension decreased by $14\%$ for abdominal obesity alone, $20\%$ for general obesity alone and $31\%$ for nonobesity. Moreover, the prevalence of hypertriglyceridemia decreased by $12\%$ for abdominal obesity alone, $38\%$ for general obesity alone and $47\%$ for nonobesity. In addition, the prevalence of low HDL-C levels decreased by $26\%$ for abdominal obesity alone, $35\%$ for general obesity alone and $54\%$ for nonobesity. The prevalence of selected components of metabolic syndrome decreased with a decrease in BMI and WC.
Our study indicated that comorbid obesity could augment the deleterious effects of general or abdominal obesity on the metabolic status of multiethnic middle-aged and older adults in western China. Our results were in line with the findings of previous studies on hypertension and diabetes. Momin et al. [ 16] found that men (odds ratio [OR]=3.10, $95\%$ CI=1.48–6.50) and women (OR=2.51, $95\%$ CI=1.43–4.40) with comorbid obesity had the highest prevalence of hypertension during the 2.3 years of follow-up. Furthermore, Kazuteru et al. [ 26] reported that a combination of overweight and abdominal obesity increased the risk of incident diabetes (OR=2.77, $95\%$ CI=1.55–5.15). Yet, Hyunsoo et al. found that higher risks of hypertriglyceridemia (OR=3.79, $95\%$ CI=1.75–8.22) and fasting hyperglycemia (OR=3.19, $95\%$ CI=1.47–6.89) were reported in individuals with abdominal obesity, whereas higher risks of low HDL-C level (OR=2.33, $95\%$ CI=1.59–3.43) and hypertension (OR=2.36, $95\%$ CI=1.54–3.62) were reported in individuals with composite obesity [27]. The discrepancy of these findings may derive from the relatively small sample size, younger age of participants, as well as the different diagnostic criteria.
As previously mentioned, there exists variation regarding associations of a single obesity marker (BMI or WC) with disease development due to difference in age, sex and ethnicity [20]. However, associations between comorbid obesity and individual components of metabolic syndrome were not affected by sex, age, and ethnicity, which supports that comorbid obesity is more reliable for identifying individuals at high risk of developing metabolic syndrome.
Although BMI and WC are closely related, there exists significant variability in the body fat distribution during our lifetime. An important concept is metabolically healthy obesity, which refers to individuals with obesity who are free of other metabolic diseases [28]. Presently, numerous studies have evaluated obesity and its related comorbidities by either BMI or WC. However, single obesity marker may underestimate the health risks. The combination of BMI and WC can better identify individuals at high risks of metabolic syndrome.
In addition to metabolic syndrome, individuals with both high BMI and enlarged WC have substantially increased risks of proteinuria [29], early menopause [30], cognitive impairment [19], several types of cancers [31], major cardiovascular events [32] as well as mortality [33]. Therefore, particular attention should be paid to the combination of BMI and WC in clinical practice.
The main strength of our study is that we have confirmed that comorbid obesity is superior to general or abdominal obesity in identifying individuals at high risk of developing metabolic syndrome based on a relatively large sample size. Nevertheless, this study had some limitations. First, the study participants were selected from community-dwelling residents in western China. Therefore, the results cannot be generalized to individuals residing in other regions. Second, the sample size in some subgroups was relatively small, which may have affected the power of the statistical analyses. Third, we only enrolled participants aged 50 years and over and future studies should also enroll younger participants. Last, we failed to include some confounders, such as drug use and other comorbidities.
## Conclusions
Comorbid obesity is superior to general and abdominal obesity in identifying individuals at high risk of developing metabolic syndrome and its components in middle-aged and older adults. The aforementioned associations were not affected by age, sex or ethnicity. Great importance should be attached to the combined effect of BMI and WC on the prevention and management of metabolic syndrome.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of West China Hospital, Sichuan University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MY and YZ contributed to the conception and design of this study. MY, YZ, GZ, and WZ contributed to the literature review. MY, XS, and WZ contributed to data analyses. WZ and MG contributed to data interpretation. MY drafted the article, while MG and BD critically appraised it and revised it. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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