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--- title: Independent and interactive effect of type 2 diabetes and hypertension on memory functions in middle aged adults authors: - Kinga Kálcza Jánosi - Andrea Lukács journal: BMC Endocrine Disorders year: 2023 pmcid: PMC9999571 doi: 10.1186/s12902-023-01308-3 license: CC BY 4.0 --- # Independent and interactive effect of type 2 diabetes and hypertension on memory functions in middle aged adults ## Abstract ### Background The study distinguishes the effect of type 2 diabetes and hypertension on cognitive functions when the two diseases are alone or when they occur together, compared to healthy individuals. ### Methods A total of 143 middle-aged adults were screened using the Wechsler Memory Scale – Revised psychometric test (verbal memory, visual memory, attention/concentration and delayed memory). Participants were divided into four groups based on their diseases: patients with type 2 diabetes [36], patients with hypertension [30], patients having both diseases [33], and healthy controls [44]. ### Results This study found no differences among investigated groups in verbal and visual memory, however, hypertension and both-disease group performed unfavorably compared to patients with diabetes and to healthy individuals in attention/concentration and delayed memory. ### Conclusions The findings of this study suggest that there is a relationship between hypertension and cognitive dysfunction, whereas type 2 diabetes without consequences was not proved to have an association with cognitive decline in middle-aged people. ## Background Diabetes and hypertension are a worldwide epidemic, resulting in millions of deaths each year. These two diseases often occur together and exacerbate each other’s symptoms, and may share common pathways such as insulin resistance, inflammation, obesity and oxidative stress [1]. The coexistence of type 2 diabetes (T2D) and hypertension is increasing in the general population. Hypertension is disproportionately higher in patients with diabetes. Having one of the two conditions increases the risk of developing the other disease by 1.5–2.0 times [2]. The brain is one of the main target organs affected by high glucose level and hypertension, and both conditions might be associated with a wide variety of cognitive impairments. Especially type 2 diabetes and hypertension frequently coexist, and their combination may provide additive increases in the risk impairment of cognitive processes [3]. Cognitive disorders play an important role in diabetes and hypertension for two reasons: first, cognitive disorders are complications associated with the diseases and may indicate the patient has inadequate glucose metabolism and/or high blood pressure. Secondly, the cognitive dysfunctions have a major impact on the self-management of the disease and can influence the quality and progression of the diseases in the future. Awad’s et al. [ 4] proposed cognitive functions are more affected in patients with T2D, who have various associated complications such as hypertension. Other comorbid illnesses such as clusters of comorbidities that combine with hypertension may have a greater cognitive impact. Some studies suggest hypertension and diabetes, when combined, increase the risk of cognitive impairment [5, 6]. The main question, which has not yet been fully clarified, is whether having T2D combined with hypertension increases the risk of cognitive decline. Previous studies mainly evaluated older individuals with T2D [7], and knowledge regarding middle-aged people is scarce. To fill this gap, this study aims to answer the question of whether middle-aged individuals with independent T2D (without hypertension), independent hypertension (without T2D), or comorbid T2D and hypertension have some dysfunction in memory components (verbal memory, visual memory, attention and concentration, delayed memory) specific to the selected risk groups, compared to healthy individuals. ## Study design, ethics, and participants A cross-sectional quantitative survey was performed in Harghita County (Transylvania, Romania). The patients were recruited from three randomly chosen settlements (Joseni, Ciumani and Lăzarea) with the help of general practitioners of these villages. All of the patients were informed about the purpose of the study and the voluntary nature of the participation. Participants gave written informed consent before taking part in the study. The protocol of the study was approved by the Ethics Committee of the University of Babeș-Bolyai (RO) and by the Code of Deontology for the professions of psychologists, elaborated by the Romanian College of Psychologists. Cognitive functions were assessed independently for each participant by accredited clinical psychologists. Inclusion criteria were: 1) ages between 35 and 65 years [8, 9], 2) diagnosed with T2D or hypertension or both (comorbid T2D and hypertension) according to the standards of the American Diabetes Association [2020] [10] and according to the standards of the American College of Cardiology and American Heart Association guidelines [11], respectively, and 3) diabetes and/or hypertension duration at least 5 years. From this study, patients were excluded with 1) any medical illnesses other than T2D, dyslipidemia, hypertension and obesity, 2) a history of hypoglycemic coma or complications of diabetes, 3) primary neurological condition as history of transient ischemic attacks, cerebrovascular stroke or epilepsy or psychiatric disease, 4) previous serious head injury, 5) any sensory or motor disorder that would preclude psychological testing (including blindness), 6) regular treatment with any medications known to have psychoactive effect, and 7) drug or alcohol abuse. The control group was made up of people who applied at the invitation of the municipality's management and did not have a chronic illness. Their health status was checked at the health clinic of the municipality. The patients were divided into three groups: a group with T2D, a group with hypertension and a group with both diseases. The control group was specifically recruited from the settlement where the patients were registered. ## Demographics Patients provided data about their gender, age, and education duration in years, as well as the duration of the disease. ## Clinical parameters Blood pressure was measured on the left arm in sitting posture during the visit by the physician using an aneroid sphygmomanometer with a stethoscope. Glycemic control was explained by HbA1c (the gold standard measurement of glycemic control) [10]. The last three-month HbA1c value was extracted from the medical records of patients with diabetes, or was measured from a vein during the routine visit. ## Cognitive measures Cognitive functions were assessed using the Wechsler Memory Scale – Revised (WMS-R) [12]. The test was administered during a routine visit to the general practitioner and it required around 1 h to complete. The WMS-R is a neuropsychological test designed to measure different memory functions such as verbal, visual memory, attention/concentration and delayed memory. The psychometric characteristics of the WMS-R are evaluated and related to its clinical utility in the Romanian population [13, 14]. The weighted scores (weighted raw score summaries) were calculated according to the scoring system from in the WMS-R administrative and scoring manual, with a higher score indicating better functioning. ## Data analysis For data analysis SPSS (Statistical Package for the Social Sciences) version 25.0 was used (SPSS, Version 25, IBM Corporation, Armonk, NY). All p-values were two-tailed at the significance level of 0.05. In the first stage, the Z-score between -3 and 3 method of outlier detection was performed and removed from the database. Violations of the normality assumption were checked using Shapiro–Wilk’s test. As a result, the memory variables were transformed by square-root transformation (moderately positively and negatively skewed data). The differences between investigated groups on demographic and clinical information were tested with analysis of variance (one-way ANOVA) for continuous outcomes and Pearson’s chi-square test for dichotomous outcomes. Comparison of memory scores between groups was measured using multiple analysis of covariance (MANCOVA) with follow-up univariate ANOVAs and the Bonferroni post-hoc test. Age, sex and education duration were controlled, age and education as covariates and also gender as a factor. We checked the assumptions that are required for MANCOVA, assumption of independence, assumption of normality, assumption of homogeneity of variance and assumption of absence of multicollinearity. ## Power and sample size A post hoc power analysis was conducted using the software package GPower. The recommended effect sizes used for this assessment were as follows: small (f 2 = 0.02), medium (f 2 = 0.15), and large (f 2 = 0.35) [15]. The alpha level used for this analysis was p ≤ 0.05. The power of the study was high to detect the main outcome of interest. The post hoc analysis revealed that the statistical power for this study was $99\%$ (0.99) for detecting medium effect (0.12) for statistical comparisons (MANCOVA, $$n = 143$$, 4 groups, 4 response variables). ## Patients and clinical variables The initial group included 157 individuals but identifying outliers eventually resulted in 143 eligible persons. The control group consisted of 44 healthy adults. Participants were homogeneous by age, sex and education duration: there were no significant differences among the four investigated groups in age, gender or education duration, nor in clinical characteristics such as diabetes duration and glycemic control between groups with T2D and presenting both diseases. No significant differences were observed in duration of hypertension or systolic/diastolic blood pressure between the groups with hypertension and with both diseases. Characteristics of the participants stratified by disease are displayed in Table 1.Table 1Characteristics of the participants stratified by disease ($$n = 143$$)Mean, Standard Deviation (SD)Healthy group($$n = 44$$)Group with T2D ($$n = 36$$)Group with HTN ($$n = 30$$)Group with both diseases ($$n = 33$$)F(df)X2(df)pAge (years)46.40 (4.09)45.91 (5.16)45.83 (4.83)46.81 (5.48)0.295[3,139]-0.829Male sex, n (%)23 ($52.3\%$)17 ($47.2\%$)14 ($46.7\%$)17 ($51.1\%$)-.359[3]0.949Education (years)12.72 (1.70)12.02 (1.69)12.66 (1.98)12.36 (2.26)1.05[3,139]-0.371HbA1c-7.46 (0.54)-7.61 (0.55)1.35[1,67]-0.248Diabetes duration (years)-7.66 (2.44)-8.54 (2.57)2.11[1,67]-0.151Treatment regime (%) (frequency)-----Insulin11 [4]17 [6]OGLM57 [21]61 [20]Diet32 [11]22 [7]Antihypertensive med (%) (frequency)--34 [10]48 [16]---Hypertension duration (years)--8.96 (2.41)9.81 (2.93)1.56[1,61]-0.134SBP 24 h (mmHg)--144.47 (8.11)147.12 (7.41)1.84[1,61]-0.180DBP 24 h (mmHg)--90.73 (5.08)91.39 (4.19).319[1,61]-0.574T2D Type 2 diabetes, HTN Hypertension, HbA1c Glycosylated hemoglobin, OGLM Oral glucose-lowering medication, SBP Systolic blood pressure, DBP Diastolic blood pressure ## Neuropsychological functions (verbal memory, visual memory, attention and concentration, delayed memory) There were statistically significant differences in memory functions based on disease, F[12, 344] = 4.240, p ≤ 0.01; Wilk's Λ = 0.694, partial η2 = 0.114, approximately $11.4\%$ of multivariate variance of the dependent variables is associated with the group factor (MANCOVA) after controlling for age (F[4, 130] = 1.220, $p \leq 0.05$), education duration (F[4, 130] = 5.914, p ≤ 0.01) and sex (F[4, 130] = 0.052, $p \leq 0.05$). Follow-up univariate ANOVAs indicate significant differences between the groups in attention and concentration (F[3, 133] = 4.026, p ≤ 0.01; partial η2 = 0.083) and delayed memory (F[3, 133] = 5.665, p ≤ 0.01; partial η2 = 0.113), meaning that the disease has a statistically significant effect on these cognitive functions. Mean scores of attention and concentration were significantly different between the control group ($M = 8.79$, SD = 0.27) and group with hypertension ($M = 8.62$, SD = 0.19; p ≤ 0.05), and the status of the control group was better than that of the other two groups. For delayed memory, the control group ($M = 9.01$, SD = 0.39) showed a significantly better status than the group with hypertension ($M = 8.75$, SD = 0.30; p ≤ 0.05), while the group with T2D ($M = 9.02$, SD = 0.37) had a better status than the group with hypertension ($M = 8.75$, SD = 0.30; p ≤ 0.05) and the group with both diseases ($M = 8.79$, SD = 0.38; p ≤ 0.05). ( Table 2) Raw scores of the neuropsychological functions with mean and SD are displayed in Fig. 1.Table 2Neuropsychological functions with participants stratified by disease ($$n = 143$$)Mean (SD)memoryHealthy group($$n = 44$$)Group with T2D ($$n = 36$$)Group with HTN ($$n = 30$$)Group with both diseases ($$n = 33$$)F(df)ppartial η2Verbal9.13 (.40)8.89 (.47)8.97 (.44)8.86 (.47)2.515(3.133)0.0610.054Visual7.68 (.23)7.62 (.25)7.63 (.23)7.64 (.30).331(3.133)0.8030.007Attention and concentration8.79 (.27)8.75 (.28)8.62 (.19)8.63 (.28)4.026(3.133)0.0090.083Delayed9.01 (.39)9.02 (.37)8.75 (.30)8.79 (.38)5.665(3.133)0.0010.113T2D Type 2 diabetes, HTN HypertensionFig. 1Raw scores with mean and SD of the neuropsychological functions ($$n = 143$$). T2D—type 2 diabetes, HTN—hypertension, p ≤ 0.05 ## Discussion The present study investigated whether there are any differences in memory functioning among adults with independent T2D (without hypertension), independent hypertension (without T2D), with comorbid hypertension and T2D, and healthy individuals. The literature is not clear on whether diabetes affects premature cognitive decline [16–19]. In our study, we found no differences between groups in the capacity to remember what the individual had previously learned (read or heard) and recall when needed (verbal memory). Similarly, no significant differences were found in visual memory between the investigated groups. Individuals had similar capacity to remember or recall information that had been previously viewed. The findings of our study cannot confirm that cognitive functions such as verbal memory, visual memory, attention and concentration, as well as delayed memory have a relationship with the presence of T2D, however, for the latter two, differences were found among groups. Groups with hypertension and with both diseases displayed unfavorable results compared to the group with diabetes and the healthy group in the functions of attention and concentration and in delayed memory. Moran et al. [ 20] also failed to prove a direct effect of T2D on cognitive function. Demakakos et al. [ 21] examined the combined effect of diabetes and elevated depressive symptoms and found they significantly accelerate cognitive decline over time, especially in people aged 50–64, but do not accelerate cognitive decline separately. The results of our study suggest hypertension has a negative impact on cognitive functions, and co-occurrence with diabetes does not exacerbate this. Muela et al. [ 22] evaluated patients with hypertension compared to healthy controls and found poorer cognitive performance in almost all cognitive tests. Only a few studies have explicitly studied the cognitive impacts of comorbid hypertension and T2D, reporting that the two conditions may interact to increase the risk of cognitive impairment [23, 24] or may not [25, 26]. We found no significant differences in cognitive components between the people with diabetes and the healthy adults, which indicate that cognitive deficits appear more pronounced in individuals who have hypertension, with or without T2D. It should be mentioned that we cannot unequivocally determine the direction of the association between cognitive function and hypertension and T2D. The relationship between cognitive changes and health outcomes is bidirectional. Medical aspects of hypertension and T2D can negatively affect cognitive functioning, while deterioration of cognitive functioning can negatively influence the self-management of disease. Those who are cognitively impaired have poorer command of the skills necessary to manage their chronic diseases. It is worth noting that our middle-aged patients with T2D had no vascular complications that might have resulted in cognitive impairment. Both T2D and hypertension are chronic psychosomatic diseases and the resulting complications can affect the entire body, requiring specific attention to both the cognitive and the somatic components of the diseases [27, 28]. Limitations of the study include a relatively small proportion of participants from one county which precludes generalizability. The cross-sectional design does not allow us to infer causality. Smoking status has not been investigated, which could help to understand its weight in the genesis of the change in memory. Dyslipidaemia was not assessed, and could play a possible role in the impaired memory pattern. ## Conclusion Our results indicate that there is an association between hypertension and cognitive impairments in middle-aged patients with T2D, whereas this association is not proved between complication-free T2D and cognitive function. Regarding verbal and visual memory, no significant differences were found among groups, so it seems likely that these memory dimensions are not influenced by the investigated diseases. Regarding attention, concentration and delayed memory, the cognitive deficits are pronounced when hypertension is present. There is no evidence that co-occurrence of T2D amplifies the effect of hypertension on cognitive decline. ## References 1. Cheung BM, Li C. Diabetes and hypertension: is there a common metabolic pathway?. Curr Atheroscler Rep* (2012.0) 14 160-166. DOI: 10.1007/s11883-012-0227-2 2. Colussi G, Da Porto A, Cavarape A. 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--- title: The influence of socioeconomic aspects and hospital case volume on survival in colorectal cancer in Saxony, Germany authors: - Andreas Bogner - Jürgen Weitz - Daniela Piontek journal: BMC Cancer year: 2023 pmcid: PMC9999591 doi: 10.1186/s12885-023-10672-1 license: CC BY 4.0 --- # The influence of socioeconomic aspects and hospital case volume on survival in colorectal cancer in Saxony, Germany ## Abstract ### Background Colorectal cancer (CRC) is one of the most common types of cancer in Western civilization and responsible for a high number of yearly deaths. Long-term outcome is influenced by many factors, potentially including socioeconomic aspects like income, education, and employment. Furthermore, annual surgical case volume plays a major role in achieving good oncological results. In our retrospective study, we evaluated the effect of socioeconomic deprivation and hospital volume on overall survival (OS) in the federal state of Saxony, Germany. ### Methods All patients with CRC who underwent surgery in Saxony, Germany between 2010 and 2020 and were living in Saxony at the time of diagnosis were included in our retrospective analysis. Uni- and multivariate analyses were conducted considering age, sex, tumor localization, UICC tumor stage, surgical approach (open/laparoscopic), number of resected lymph nodes, adjuvant chemotherapy, year of surgery, and hospital case volume. In addition, our model was adjusted for social disparity using the German Index of Socioeconomic Deprivation (GISD). ### Results A total of 24,085 patients were analyzed (15,883 with colon cancer and 8,202 with rectal cancer). Age, sex, UICC tumor stage and tumor localization were distributed as expected for CRC. Median overall survival time was 87.9 months for colon cancer and 110.0 months for rectal cancer. Univariate analysis revealed laparoscopic surgery (colon and rectum $P \leq 0.001$), high case volume (rectum: $$P \leq 0.002$$) and low levels of socioeconomic deprivation (colon and rectum $P \leq 0.001$) to be significantly associated with better survival. In multivariate analyses, the associations of laparoscopic surgery (colon: HR = 0.76, $P \leq 0.001$; rectum: HR = 0.87, $P \leq 0.01$), and mid-low to mid-high socioeconomic deprivation (colon: HR = 1.18–1.22, $P \leq 0.001$; rectum: HR = 1.18–1.36, $P \leq 0.001$–0.01) remained statistically significant. Higher hospital case volume was associated with better survival only in rectal cancer (HR = 0.89; $P \leq 0.01$). ### Conclusion In Saxony, Germany, better long-term survival after CRC surgery was associated with low socioeconomic deprivation, laparoscopic surgery and partly with high hospital case volume. Thus, there is a need to reduce social differences in access to high-quality treatment and prevention and increase hospital patient volume. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12885-023-10672-1. ## Background Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths every year [1]. In Germany, CRC accounts for approximately 60,000 newly diagnosed tumors and around 25,000 tumor-related deaths annually. Due to the implementation of health programs offering colonoscopy as a widely available examination for early detection and screening, the incidence and mortality of CRC is only slowly declining [2]. Many diseases, including cancer, follow a socioeconomic gradient with higher rates in patients with lower socioeconomic status [3, 4]. In Germany, an aging society is being confronted with the risk of increasing social disparities. In view of the structure of the German healthcare system, which grants free access to healthcare for everyone, as well as several campaigns in the media, cancer therapy and outcome should theoretically be equally distributed. Since individual-level socioeconomic data are difficult to obtain, especially in large cohorts, the German Index of Socioeconomic Deprivation (GISD) can be used as an external indicator. It considers the dimensions of education, employment and income, with each contributing one-third to the overall score [5, 6]. Jansen et al. showed a worse relative survival of patients living in the most deprived districts compared to the survival of patients living in more privileged areas throughout Germany, regarding the 25 most common cancer sites in ten population-based registries (covering 32 million inhabitants).[7]. However, socioeconomic disparity is not the only factor influencing survival in CRC. High individual surgeon and institutional case volume is a well-documented factor influencing outcomes in oncological surgery. [ 8]. The centralization of care to “centers of excellence” in Europe has improved oncological outcomes over the last decades [9]. It has also been shown that treating patients in high-volume hospitals by high-volume surgeons is associated with better in-hospital mortality and oncological outcome [10, 11]. Even though centralization of cancer care is expected to yield superior results, the national strategy in Germany is still based on a voluntary certification process. Oncological colorectal surgery can still be performed in almost every hospital by every surgeon without restrictions [12, 13]. Nevertheless, only about one-third of CRC patients in Germany are treated in high-volume centers. In our retrospective analysis, we wanted to examine socioeconomic disparities using the GISD, and the influence of annual hospital case volume for colorectal disease and their influence on survival in the federal state of Saxony, Germany. ## Study population For the present retrospective analyses, we used data from the four clinical cancer registries in Saxony, Germany. This study does not include data of a clinical trial. In Germany, all inpatient and outpatient physicians as well as pathologists are obliged to report information on diagnosis, histological results, treatment and outcomes to the clinical cancer registries. Patients need to be informed about this process. Consent is not required but, patients have a right of objection, which is hardly used (fewer than five cases/year in Saxony). After documentation and validation of the data, analyses are conducted with an anonymized data set that does not allow identification of individual patients. Completeness of the registries has been estimated to be $98\%$ across all tumors since 2007 [14]. All cases with ICD-10 diagnoses C18 (excluding C18.1), C19 and C20, histologically verified adenocarcinoma and tumor-specific surgery in Saxony in the years 2010 to 2020 were included ($$n = 25$$,812). Patients had to be at least 18 years old and have their main residence in Saxony at the time of diagnosis ($$n = 24$$,306). We excluded 221 cases with missing information on UICC tumor stage. Thus, the final analytical sample comprised 24,085 cases (Fig. 1). Fig. 1Flowchart of data collection, inclusion criteria and patient selectionAbbreviations: UICC, Union for International Cancer Control. ## Included variables and statistical analyses Tumor localization was documented according to the International Classification of Diseases for Oncology (ICD-O; C18.0, C18.2 to C18.9, C19.9, C20.9). For multivariate analyses, colon cancer was further classified into right-sided (C18.0, C18.2–C18.4), left-sided (C18.5–C18.7) and others (C18.8, C18.9). We considered tumor stages defined by the Union for International Cancer Control (UICC; stages I, II, III, IV). Post-surgical histopathological classification of the UICC was used [15]. Regarding surgical treatment, only tumor-specific resections were considered. In the case of more than one resection, only the first procedure was analysed. The surgical approach was classified into open surgery, laparoscopic surgery, conversion and others/not specified. The urgency of surgery was documented as elective surgery vs. emergency surgery vs. unknown/missing. The documented number of resected lymph nodes was dichotomized into less than 12 and at least 12 lymph nodes. We further dichotomized patients based on the annual case volume of the hospital which was divided into low vs. high using a cut-off of 20 and 30 surgeries per year for rectal cancer (C19/C20) and colon cancer (C18), respectively. Adjuvant chemotherapy was documented if it was explicitly stated as a treatment option or based on the interval between surgery and start of chemotherapy (maximum time difference: 20 weeks). As an additional external indicator, a measure of area-based socioeconomic deprivation at the level of municipalities was used. The German Index of Socioeconomic Deprivation (GISD) considers the dimensions of education, employment and income, with each contributing one-third to the overall score [6]. Higher values indicate higher deprivation, i.e., lower socioeconomic status. For each tumor case, the GISD score of the patient’s place of residence at the time of diagnosis was used. The municipalities were then grouped into GISD quintiles based on our final analytical sample (1 – low level of deprivation, 2 – mid-low level of deprivation, 3 – medium level of deprivation, 4 – mid-high level of deprivation, 5 – high level of deprivation; see Fig. 2). Fig. 2Distribution of German Index of Socioeconomic Deprivation (GISD) quintiles in Saxony, Germany For descriptive purposes, absolute numbers, and percentages (categorical variables) as well as medians (continuous variables) are presented. Absolute overall survival (OS) was calculated based on Kaplan–Meier estimation. Median survival time as well as 5-year survival rates with corresponding $95\%$ confidence intervals are reported. Univariate survival analyses were conducted for hospital case volume, surgical approach, and socioeconomic deprivation. For descriptive comparisons, the survival time of different groups of these categorical variables was plotted as Kaplan–Meier curves. Log-rank tests were used to test whether survival time differed between the included groups. Multivariate analyses were performed using weighted Cox regression, which provides unbiased average hazard ratio estimates even in the case of non-proportional hazards [16]. The resulting hazard ratios (HR) can be interpreted as relative risks. Survival analyses included only primary tumors and cases with a minimum follow-up time of one month ($$n = 19$$,845). Statistical analyses and graphical illustration were carried out using the R software package (version 3.6.0, https://www.R-project.org, The R Foundation). ## Patient characteristics A total of 24,085 patients were included in the analysis (15,882 with colon cancer and 8,202 with rectal cancer). The description of the included colon and rectum cancer cases is shown in Table 1. The sample was predominantly male, also in terms of the proportion of cases of rectal cancer compared to colon cancer ($64.8\%$ vs. $54.6\%$). The median age was 70 and 74 years, respectively. The majority of colon cancer cases was localized on the right-side ($58.9\%$). Metastatic tumor stage (UICC IV) was present in $17.3\%$ (colon) and $13.9\%$ (rectum) of all cases. In most surgeries, at least 12 lymph nodes were resected (colon: $92.5\%$, rectum: $87.6\%$). The proportion of surgeries performed using a laparoscopic approach was higher for rectal cancer than for colon cancer ($24.8\%$ vs. $14.8\%$). Emergency surgery was more prevalent in colon cancer than in rectal cancer ($7.5\%$ vs. $1.8\%$). High hospital case volume was found in $56.1\%$ of colon cancer resections and $45.3\%$ of rectal cancer resections. Socioeconomic deprivation was evenly distributed due to the use of quintiles. The distribution of patient characteristics by case volume is shown in Supplementary Tables 1 and 2. Colon resections were conducted in a total of 64 hospitals. There were 43 low-volume hospitals with a median number of 16.7 surgeries per year (minimum 0.1, maximum 29.1) and 21 high-volume hospitals with a median number of 42.7 surgeries per year (minimum 30.0, maximum 65.7). With regard to rectal cancer, the 64 hospitals were divided into 50 low-volume hospitals conducting a median of 9.5 resections per year (minimum 0.1, maximum 19.7) and 14 high-volume hospitals with a median of 25.4 resections per year (minimum 20.4, maximum 57.0). Table 1Patient characteristics (2010–2020)Colon cancer (C18)n (%)Rectal cancer (C19/C20)n (%)Totaln (%)Sex Male8.675 (54.6)5.311 (64.8)13.986 (58.1) Female7.208 (45.4)2.891 (35.2)10.099 (41.9) Age (Median) 747073 Localization 1 Colon right-sided9.352 (58.9)-9.352 (38.8) Colon left-sided6.423 (40.4)-6.423 (26.6) Colon others108 (0.7)-108 (0.4) Rectosigmoid junction-67 (0.8)67 (0.3) Rectum-8.135 (99.2)8.135 (34.0) UICC tumor stage I3.373 (21.2)1.542 (18.8)4.915 (20.4) II5.611 (35.3)1.904 (23.2)7.515 (31.2) III4.157 (26.2)3.612 (44.0)7.769 (32.3) IV2.742 (17.3)1.144 (13.9)3.886 (16.1) Surgical approach Open10.194 (64.2)4.147 (50.6)14.341 (59.6) Laparoscopic2.357 (14.8)2.033 (24.8)4.390 (18.2) Conversion259 (1.6)589 (7.2)848 (3.5) Others/n. a.3.073 (19.3)1.429 (17.4)4.502 (18.7) Urgency of surgery Elective surgery10.063 (63.4)5.723 (69.8)15.786 (65.5) Emergency surgery1.198 (7.5)148 (1.8)1.346 (5.6) Unknown/missing4.622 (29.1)2.331 (28.4)6.953 (28.9) Number of resected lymph nodes < 121.167 (7.5)1.006 (12.4)2.173 (9.2) 12+14.451 (92.5)7.085 (87.6)21.536 (90.8) Adjuvant chemotherapy no11.358 (71.5)4.963 (60.5)16.321 (67.8) yes4.525 (28.5)3.239 (39.5)7.764 (32.2) Hospital case volume 2 Low6.965 (43.9)4.484 (54.7)11.449 (47.5) High8.918 (56.1)3.718 (45.3)12.636 (52.5) Year of surgery 2010–20135.374 (33.8)3.104 (37.8)8.478 (35.2) 2014–20175.819 (36.6)2.974 (36.3)8.793 (36.5) 2018–20204.690 (29.5)2.124 (25.9)6.814 (28.3) GISD, Socio-economic deprivation 1 - low3.282 (20.7)1.584 (19.3)4.866 (20.2) 2 - mid-low3.104 (19.5)1.694 (20.7)4.798 (19.9) 3 - medium3.106 (19.6)1.696 (20.7)4.802 (19.9) 4 - mid-high3.624 (22.8)1.795 (21.9)5.419 (22.5) 5 - high2.767 (17.4)1.433 (17.5)4.200 (17.4) Total number of cases 15,883 8,202 24,085 1 Colon right-sided: C18.0, C18.2-C18.4, Colon left-sided: C18.5-C18.7, Colon others: C18.8-C18.9.2 C18: low < 30 surgeries/year, high ≥ 30 surgeries/year. C$\frac{19}{20}$: low < 20 surgeries/year, high ≥ 20 surgeries/year. Abbreviations: GISD, German Index of Socioeconomic Deprivation; UICC, Union for International Cancer Control. ## Univariate analysis Median survival time was 87.9 months for colon cancer and 110.0 months for rectal cancer (Table 2). Five years after diagnosis, a total of $58.5\%$ (C18) and $65.6\%$ (C19/C20) of patients were still alive. Males and females had comparable 5-year survival rates (colon: $57.7\%$ vs. $59.5\%$, $$P \leq 0.1$$; rectum: $65.6\%$ vs. $65.6\%$, $$P \leq 0.2$$), whereas patients aged 70 years and younger had better 5-year survival than older patients (colon: $68.8\%$ vs. $51.6\%$, $P \leq 0.001$; rectum: $73.7\%$ vs. $55.4\%$, $P \leq 0.001$). Survival probability significantly decreased with higher UICC tumor stage (colon: stage I $80.0\%$ vs. stage IV $15.2\%$; rectum: stage I $80.3\%$ vs. stage IV $26.9\%$). Further univariate analyses revealed a significant association with surgical approach, hospital case volume (rectum only) and socioeconomic deprivation (Table 2; Fig. 3A–C). Laparoscopic surgery was associated with a better 5-year survival for both colon cancer ($71.2\%$ vs. $55.9\%$, $P \leq 0.001$) and rectal cancer ($73.2\%$ vs. $63.8\%$, $P \leq 0.001$). In addition, patients with rectal cancer had better 5-year survival when tumor resection was carried out in a high-volume hospital ($68.2\%$ vs. $63.5\%$, $$P \leq 0.002$$). Regarding socioeconomic deprivation, 5-year survival was worse for mid-low, mid and mid-high levels of deprivation compared to a low level of deprivation (overall $P \leq 0.001$). No difference could be observed for the highest level of deprivation compared to the lowest level of deprivation. Table 2Results of overall and univariate survival analyses (Kaplan-Meier estimation)Colon cancer (C18)Rectal cancer (C19/C20)Median survival(Months, $95\%$ CI)5-year survival(%, $95\%$ CI)Median survival(Months, $95\%$ CI)5-year survival(%, $95\%$ CI) Overall 87.9 (84.5; 91.6)58.5 (57.6; 59.5)110.0 (105.0; 115.0)65.6 (64.4; 66.8) Sex $$p \leq 0.1$$ $$p \leq 0.2$$ Male84.1 (79.7; 88.9)57.7 (56.4; 59.0)105.0 (98.4; 112.0)65.6 (64.1; 67.2) Female92.2 (87.2; 101.1)59.5 (58.2; 61.0)121.0 (110.4; n. a.)65.6 (63.5; 67.7) Age $p \leq 0.001$ $p \leq 0.001$ ≤ 70 yearsn. a. (134.3.; n. a.)68.8 (67.4; 70.2)n. a. (135.9; n. a.)73.7 (72.2; 75.2) > 70 years63.7 (60.9; 67.3)51.6 (50.4; 52.9)71.7 (67.2; 76.3)55.4 (53.5; 57.3) UICC tumor stage $p \leq 0.001$ $p \leq 0.001$ I134.3 (132.5; n. a.)80.0 (78.2; 81.8)n. a. (130.0; n. a.)80.3 (77.8; 82.8) II121.2 (113.1; 131.4)69.9 (68.4; 71.4)116.3 (107.6; 128.7)70.5 (68.1; 73.0) III83.2 (76.8; 91.1)57.9 (56.1; 59.8)127.7 (115.2; 135.9)69.6 (67.9; 71.4) IV19.3 (17.8; 20.5)15.2 (13.6; 16.9)32.5 (30.3; 34.9)26.9 (24.0; 30.2) Surgical approach $p \leq 0.001$ $p \leq 0.001$ Open77.4 (73.3; 82.7)55.9 (54.7; 57.1)102.1 (94.5; 110.0)63.8 (62.1; 65.5) Laparoscopic138.5 (121.5; n. a.)71.2 (68.6; 73.9)129.9 (123.2; n. a.)73.2 (70.7; 75.8) Conversion122.7 (61.8; n. a.)61.4 (53.3; 70.8)90.8 (78.8; n. a.)62.8 (57.7; 68.3) Others/n. a.91.4 (85.4; 103.1)58.4 (56.3; 60.6)100.5 (84.9; 119.0)62.1 (59.3; 65.1) Hospital case volume 1 $$p \leq 0.8$$ $$p \leq 0.002$$ Low87.4 (82.2; 94.4)57.8 (56.3; 59.2)100.0 (95.2; 112.0)63.5 (61.8; 65.2) High88.0 (84.3; 93.4)59.2 (57.9; 60.4)115.0 (109.7; 128.0)68.2 (66.4; 70.0) GISD $p \leq 0.001$ $p \leq 0.001$ 1 – low104.7 (95.3; 118.3)62.1 (60.1; 64.2)129.3 (125.0; n. a.)71.7 (69.1; 74.4) 2 – mid-low87.4 (79.7; 93.6)58.6 (56.5; 60.8)98.8 (87.8; 114.0)64.0 (61.3; 66.8) 3 – mid83.2 (72.4; 91.1)56.3 (54.1; 58.5)96.3 (86.9; 105.0)62.3 (59.6; 65.1) 4 – mid-high79.9 (74.2; 86.7)57.0 (55.0; 59.0)108.6 (99.2; 123.0)64.4 (61.8; 67.1) 5 - high90.6 (83.3; 105.3)58.7 (56.4; 61.1)120.3 (110.0; n. a.)66.2 (63.3; 69.2)1 C18: low < 30 surgeries/year, high ≥ 30 surgeries/year. C$\frac{19}{20}$: low < 20 surgeries/year, high ≥ 20 surgeries/year. Abbreviations: CI, Confidence interval; GISD, German Index of Socioeconomic Deprivation; n. a., survival probability did not reach $50\%$; UICC Union for International Cancer Control. Fig. 3Univariate survival analyses (Kaplan-Meier curves) and results of log-rank testFigure 3A: Univariate survival analysis for surgery in low-volume vs. high-volume hospitalsFigure 3B: Univariate survival analysis for surgical approachFigure 3C: Univariate survival analysis for socioeconomic deprivation (GISD-quintiles)Abbreviations: GISD, German Index of Socioeconomic Deprivation. ## Multivariate analysis The results of multivariate analyses, including all relevant variables, are presented in Table 3. Females had better survival rates than males (colon: HR = 0.89, $95\%$ CI = 0.84–0.94, $P \leq 0.001$; rectum: HR = 0.85, $95\%$ CI = 0.78–0.92, $P \leq 0.001$) and older age was associated with worse survival (colon: HR = 1.91, $95\%$ CI = 1.79–2.03, $P \leq 0.001$; rectum: HR = 2.12, $95\%$ CI = 1.95–2.30, $P \leq 0.001$). Compared to early-stage cancer, increasing UICC was associated with worse survival (colon: HR = 10.81, $95\%$ CI = 9.65–12.11, $P \leq 0.001$ for stage IV; rectum: HR 7.30, $95\%$ CI = 6.24–8.53, $P \leq 0.001$). Laparoscopic surgery was associated with better survival compared to open surgery in colon cancer patients (HR = 0.76, $95\%$ CI = 0.68–0.83, $P \leq 0.001$) as well as rectal cancer patients (HR = 0.87, $95\%$ CI = 0.78–0.98, $P \leq 0.01$). Survival was worse in case of emergency surgery compared to elective surgery (colon: HR = 1.78, $95\%$ CI = 1.62–1.95, $P \leq 0.001$; rectum: HR = 2.14, $95\%$ CI = 1.68–2.74, $P \leq 0.001$). A higher number of resected lymph nodes was associated with better survival only in colon cancer patients (HR = 0.78, $95\%$ CI = 0.70–0.87, $P \leq 0.001$). Better survival was observed for patients receiving adjuvant chemotherapy (colon: HR = 0.49, $95\%$ CI = 0.46–0.53, $P \leq 0.001$; rectum: HR = 0.64, $95\%$ CI = 0.58–0.70, $P \leq 0.001$). Patients operated in a high-volume hospital showed better survival for rectal cancer (HR = 0.89, $95\%$ CI = 0.82–0.97, $P \leq 0.01$), whereas no significant effect was observed for colon cancer patients. Compared to patients living in a low deprivation municipality, those living in areas of mid-low to mid-high deprivation had worse survival outcomes (mid-low: HR = 1.18, $95\%$ CI = 1.08–1.30, $P \leq 0.001$; mid: HR = 1.18, $95\%$ CI = 1.07–1.29, $P \leq 0.001$; mid-high: HR = 1.22, $95\%$ CI = 1.12–1.33, $P \leq 0.001$). Rectal cancer patients showed almost similar results (Table 3). Table 3Results of multivariate weighted Cox regressionColon cancer (C18)Rectal cancer (C19/C20)Hazard Ratio($95\%$ CI)pHazard Ratio($95\%$ CI)p Sex MaleReferenceReference Female0.89 (0.84; 0.94)< 0.0010.85 (0.78; 0.92)< 0.001 Age ≤ 70 yearsReferenceReference > 70 years1.91 (1.79; 2.03)< 0.0012.12 (1.95; 2.30)< 0.001 Localization 1 Colon right-sidedReference- Colon left-sided0.92 (0.86; 0.97)< 0.001- Colon others1.13 (0.84; 1.53)0.410- Rectosigmoid junction-Reference Rectum-1.01 (0.61; 1.68)0.969 UICC tumor stage IReferenceReference II1.51 (1.37; 1.67)< 0.0011.60 (1.39; 1.85)< 0.001 III2.98 (2.68; 3.31)< 0.0011.89 (1.64; 2.16)< 0.001 IV10.81 (9.65; 12.11)< 0.0017.30 (6.24; 8.53)< 0.001 Surgical approach OpenReferenceReference Laparoscopic0.76 (0.68; 0.83)< 0.0010.87 (0.78; 0.98)< 0.01 Conversion0.94 (0.73; 1.21)0.6321.09 (0.92; 1.28)0.318 Others/n. a.0.96 (0.89; 1.04)0.2881.05 (0.94; 1.17)0.383 Urgency of surgery Elective surgeryReferenceReference Emergency surgery1.78 (1.62; 1.95)< 0.0012.14 (1.68; 2.74)< 0.001 Unknown/missing1.06 (0.99; 1.14)0.8870.96 (0.88; 1.06)0.424 Resected lymph nodes < 12ReferenceReference 12+0.78 (0.70; 0.87)< 0.0011.05 (0.93; 1.19)0.412 Adjuvant chemotherapy noReferenceReference yes0.49 (0.46; 0.53)< 0.0010.64 (0.58; 0.70)< 0.001 Hospital case volume 2 LowReferenceReference High0.97 (0.91; 1.03)0.3030.89 (0.82; 0.97)< 0.01 Year of surgery 2010–2013ReferenceReference 2014–20170.97 (0.90; 1.03)0.3340.89 (0.81; 0.98)< 0.01 2018–20201.06 (0.97; 1.16)0.2330.83 (0.72; 0.95)< 0.001 GISD 1 – lowReferenceReference 2 – mid-low1.18 (1.08; 1.30)< 0.0011.36 (1.19; 1.55)< 0.001 3 – mid1.18 (1.07; 1.29)< 0.0011.34 (1.18; 1.53)< 0.001 4- mid-high1.22 (1.12; 1.33)< 0.0011.18 (1.03; 1.34)< 0.01 5 - high1.06 (0.96; 1.18)0.2401.04 (0.90; 1.21)0.5731 Colon right-sided: C18.0, C18.2-C18.4, Colon left-sided: C18.5-C18.7, Colon others: C18.8-C18.9.2 C18: low < 30 surgeries/year, high ≥ 30 surgeries/year. C$\frac{19}{20}$: low < 20 surgeries/year, high ≥ 20 surgeries/year. Abbreviations: CI, Confidence interval; GISD, German Index of Socioeconomic Deprivation; UICC Union for International Cancer Control. ## Conclusion In accordance with other studies, we confirmed that characteristics like male sex, higher age and increasing UICC tumor stage are risk factors for worse survival [17, 18]. In addition, we found that a laparoscopic approach was associated with better survival. A mid-low, medium and mid-high GISD score was also an independent risk factor for worse survival. Only in rectal cancer patients, treatment in high volume hospitals was associated with better survival. According to the literature, both the laparoscopic and open techniques seem to be equivalent in terms of tumor-specific survival [19, 20]. However, results from recent trials indicate that an open surgical approach was associated with a higher risk in terms of long-term mortality and OS [21, 22].Our findings also confirmed these data. Nevertheless, the reasons for this remain unclear and speculative. In our data, high-volume hospitals had almost double the percentage of laparoscopic surgeries compared to low-volume hospitals regarding rectal cancer. A more oncological adequate (better mesocolonic/mesorectal excision) resection may have been carried out. Compared with laparoscopic surgery, the open approach can result in postoperative morbidity (e.g., higher rates of incisional hernia, wound infection and other septic complications leading to reduced mortality and OS), which may contribute to the better outcomes following laparoscopic treatment [23]. The presence of a selection bias may also explain the superiority of the laparoscopic technique for overall survival. For elderly patients with higher risk profiles and secondary diseases, some hospitals are more likely to perform open procedures in order to avoid longer anesthesia and operating times. Unfortunately, we could adjust our analyses only for age, not for preexisting diseases or ASA (American Society of Anesthesiologists) risk classification or similar scores, due to missing data. We are aware that our study has several weaknesses on account of its retrospective character. Due to lack of data (not reported to the clinical cancer registries), no statement about pre-existing illnesses or medication can be made. In Germany, older patients are more often operated in emergency and smaller hospitals,, which could result in a selection bias[24]. In addition, it is not possible to differentiate between high and low rectal cancer due to missing tumor height. No information on robotic surgery or tumor-specific survival is due to the current state of data collection. Use of the GISD is a necessary step because there is no valid better way to assess an individual socioeconomic score by income and educational level. Its limitations are set by the nature of GISD representing the patients’ socioeconomic status using habitation-based data with high individual variability. The fact that the lowest GISD strata did not show worse results is quite interesting. The explanation is speculative, but might occur due to the nature of GISD, which uses population-based and not individual data until 2012, whereas survival and effects of healthcare programs might result in the later episode. Also, you might state, that the quality of medical treatment in areas with highest GISD is quite good. Only rectal cancer patients profited from treatment in high-volume hospitals achieving better survival. The highest level of evidence provided by the meta-analysis of Huo et al. indicates that the best outcomes occur in high-volume hospitals with high-volume surgeons, followed by low-volume hospitals with high-volume surgeons [9]. Thus, the individual surgeon is the main factor in achieving superior survival and oncological outcome. Unfortunately, the cancer registry data does not provide information on the specific surgeon. Rectal cancer surgery is more complex (narrow space in the pelvis) and less frequent (about one third of all colorectal operations) compared to colon cancer surgery. Training and experience of the surgeon is extremely important to achieve high quality of total mesorectal excision, which is directly associated with better patients’ survival and higher rates of sphincter preserving surgeries [25, 26]. Also, patients profit enormously from a laparoscopic or robotic surgical approach, which needs lots of practice and is more often carried out in high volume hospitals[27–29]. In addition, the fact that only one hospital specializing in rectal surgery had higher numbers than 50 operations per year is quite remarkable, and not competitive to other countries. For a better understanding of these problems in Germany, you need to look at the hospital allocation. In recent years, many hospitals in Germany, including Saxony, introduced evidence-based quality standards and improved their surgical and oncological outcomes [30]. However, since many hospitals are allowed to treat cancer patients, only few hospitals reach high patient volumes. In addition, in each hospital, there is not only one surgeon operating CRC patients, meaning the volume is shared by more surgeons, leading to low individual surgeon case load per year. Consequently, CRC care in Germany remains decentralized with high in-hospital morbidity and mortality rates compared to other western countries like USA, France, and UK [31]. While caseload can serve as a surrogate for treatment quality assessment in CRC surgery, our data do not suggest better survival for high-volume hospitals regarding colon cancer. [ 30]. Implementing independent controls and an auditing system surely helped maintaining standards [32]. Higher case numbers to achieve an obligatory certification as a pre-condition to treat CRC patients, and financial retribution could lead to more economical impact concerning the treatment of CRC patients [33]. There is a political and structural need for centralization and specialization in order to improve outcomes in the treatment of CRC patients in Saxony, Germany. Our study also provides further evidence of social disparities in the treatment of CRC. Socioeconomic deprivation in Saxony, Germany is* inversely associated with survival in CRC treatment. This association persists after demographic and cancer-related factors are considered. Relatively few data and studies exist on this topic in Germany. For the US, on the other hand, the existence of social and racial discrimination has been clearly demonstrated [34]. These major regional socioeconomic inequalities indicate a high potential for improving cancer care and survival worldwide. As many cancer entities have a long period of latency, changes in lifestyle, nutrition and physical activity, led to changes in incidence over the last decades. As obesity, one of the best-known risk factors for CRC, became epidemic and the nutrition worsened, colorectal cancer incidence increased, especially in rural areas with low income. In comparison to the 1950s, CRC changed from a prosperity disease to a disease of higher prevalence in low-income social stratum [35, 36]. This fact was clearly confirmed by our data indicating rural areas to have higher GISD and thus worse survival. An increased travelling distance to inpatient and outpatient care might be another reason for the high urban- rural disparities. Also, higher education and income, overrepresented in urban areas (lower GISD strata) showed better survival. This could be due to better access to prevention coloscopy and healthier lifestyle. Unfortunately, exact data on this topic is missing. Although, excessive food intake and insufficient physical activity are individual decision, political, economic, and social disparities play a major role in fighting adiposity and CRC[37]. We live in an environment where obesity is stimulated. This might be a key to further decrease CRC incidence and mortality. An efficient allocation of health resources therefore is indispensable.[38]. Our data analysis from Saxony could be extended to the other federal states and Germany as a whole. The data transfer process should also be expanded to create nationwide databases. Therefore, data from health insurance companies could also be added. Since individual data on income, employment status and educational level are often missing, future studies should address individual-level patient data with access to treatment information. This would allow more detailed examination of the reasons for these socioeconomic inequalities in cancer survival and could help establish improvements in access to health protection, diagnostic and therapy[7]. The social disparities leading to an elevated risk for cancer development and worse survival require targeted public health action and policy in order to address the complexity of these relationships [5]. ## Summary In the analyzed population in Saxony, Germany, we showed that laparoscopic surgical technique and lower socioeconomic deprivation were associated with better survival in the treatment of CRC. There was also some indication that high hospital case volume improves patient outcome in rectal cancer. Our study adds evidence that might help in changing national health policies with the aim of achieving a better outcome for patients with CRC. Only a change at the national level can help improve therapy and elevate the quality of colorectal cancer care delivery. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. 1.Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Pineros M, Znaor A et al. Cancer statistics for the year 2020: An overview.International journal of cancer Journal international du cancer. 2021. 2. 2.2017/2018 Krebs in Deutschland. Robert Koch-Institut, Herausgeber und die Gesellschaft der epidemiologischen Krebsregister in Deutschland e.V, Herausgeber. Berlin., 2018. 12. Ausgabe. 3. 3.Rohleder S, Stock C, Bozorgmehr K. 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--- title: Adherence to antidiabetic drug therapy and reduction of fatal events in elderly frail patients authors: - Federico Rea - Laura Savaré - Valeria Valsassina - Stefano Ciardullo - Gianluca Perseghin - Giovanni Corrao - Giuseppe Mancia journal: Cardiovascular Diabetology year: 2023 pmcid: PMC9999593 doi: 10.1186/s12933-023-01786-8 license: CC BY 4.0 --- # Adherence to antidiabetic drug therapy and reduction of fatal events in elderly frail patients ## Abstract ### Background To evaluate the protective effect of oral antidiabetic drugs in a large cohort of elderly patients with type 2 diabetes differing for age, clinical status, and life expectancy, including patients with multiple comorbidities and short survival. ### Methods A nested case–control study was carried out by including the cohort of 188,983 patients from Lombardy (Italy), aged ≥ 65 years, who received ≥ 3 consecutive prescriptions of antidiabetic agents (mostly metformin and other older conventional agents) during 2012. Cases were the 49,201 patients who died for any cause during follow-up (up to 2018). A control was randomly selected for each case. Adherence to drug therapy was measured by considering the proportion of days of the follow-up covered by the drug prescriptions. Conditional logistic regression was used to model the risk of outcome associated with adherence to antidiabetic drugs. The analysis was stratified according to four categories of the clinical status (good, intermediate, poor, and very poor) differing for life expectancy. ### Results There was a steep increase in comorbidities and a marked reduction of the 6-year survival from the very good to the very poor (or frail) clinical category. Progressive increase in adherence to treatment was associated with a progressive decrease in the risk of all-cause mortality in all clinical categories and at all ages (65–74, 75–84 and ≥ 85 years) except for the frail patient subgroup aged ≥ 85 years. The mortality reduction from lowest to highest adherence level showed a tendency to be lower in frail patients compared to the other categories. Similar although less consistent results were obtained for cardiovascular mortality. ### Conclusions In elderly diabetic patients, increased adherence to antidiabetic drugs is associated with a reduction in the risk of mortality regardless of the patients’ clinical status and age, with the exception of very old patients (age ≥ 85 years) in the very poor or frail clinical category. However, in the frail patient category the benefit of treatment appears to be less than in patients in good clinical conditions. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12933-023-01786-8. ## Background Randomized controlled trials have shown that, in patients with type 2 diabetes, old conventional antidiabetic drug therapy is associated with a reduction in the risk of microvascular complications [1] while meta-analyses of old conventional drug-based trials have shown that when data from several trials are pooled [2–6], the benefit of glucose-lowering treatment extends to macrovascular outcomes [7, 8]. Protection against macrovascular outcomes has been found to be even greater with use of newer antidiabetic agents such as sodium-glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP1-RA), particularly for heart failure [9–12]. However, although randomized trial evidence on the protective effect of antidiabetic treatment has been extended to patients aged 65 years and beyond [13], knowledge is scanty for people aged 80 years or more [14] and especially limited for old patients with several comorbidities and a short life expectancy, i.e. those often referred to as “frail” patients [15], in part because frail patients tend to be excluded from recruitment in trials with a several year duration. The objective of this observational study was to evaluate the protective effect of oral antidiabetic drugs in frail older adults (≥ 65 years) with type 2 diabetes. Frail individuals were identified via a multisource comorbidity score that accurately predicts the risk of mortality [16]. Analysis was extended to older adults in better clinical conditions. ## Setting The data used for the present study were retrieved from the healthcare utilization databases of Lombardy, a region of Italy that accounts for about $16\%$ (almost 10 million people) of its population. All Italian citizens have equal access to healthcare services (e.g., hospitalizations, outpatient visits, instrumental examinations, laboratory tests, bioimaging, and drugs for chronic diseases) as part of the National Health Service (NHS). In Lombardy, these data are included in an automated system of databases that provides information on individual demographics, drug prescriptions (according to the Anatomical Therapeutic Chemical—ATC—system), medical or surgical interventions, and hospitalizations, according to diagnoses and procedures coded as in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) system. Because a unique identification code was used for all databases, their linkage provided information on the complete care pathway supplied to NHS beneficiaries for several years. To preserve privacy, in the analyses of the Lombardy databases each individual identification code is automatically deidentified, the inverse process being allowed only to the Regional Health Authority upon request from judicial authorities. A detailed description of the healthcare utilization databases of the Lombardy region in the field of cardiovascular and metabolic diseases is available in previous studies [17, 18]. The ICD-9-CM and ATC codes used for the current study are reported in Additional file 1: Table S1. ## Cohort selection The target population consisted of the Lombardy residents aged ≥ 65 years who were NHS beneficiaries. Of these, those who received ≥ 3 consecutive prescriptions of oral antidiabetic agents during 2012 were identified and the date of the third prescription was defined as the index date. We considered that three consecutive prescriptions within a year are indicative of regular prescription and use. Because insulin might require frequent changes in dose requirements over time, patients prescribed only insulin were not included in the study. Two additional categories of patients were excluded from the analysis, i.e., those who had not been NHS beneficiaries for at least 5 years before the index date and those who did not reach at least 6 months of follow-up. The remaining patients were included into the final cohort whose members accumulated person-years of follow-up from the index date until the earliest date among death, emigration or June 30th, 2018. ## Selection of cases and controls When the effect of time-dependent exposure needs to be investigated in the context of large databases, the nested case–control design is a valid alternative to the cohort design [19]. The case–control study consists of four steps: (i) cohort selection, (ii) case definition and selection, (iii) for each case, identification of all possible controls, and (iv) random selection of m controls for each case [20]. In the present study, the cohort involved the oral antidiabetic drug users as described above. Death from any cause was the primary outcome of interest, and cases were thus the cohort members who died during follow-up. For each case patient, all cohort members who survived when the matched case died were identified (i.e. the incidence density sampling method was adopted). For each case patient, one control was randomly selected from the cohort members to be matched for sex, age at index date, clinical status (see below) and date of index prescription. A secondary outcome was cardiovascular mortality, i.e., death for ischemic heart disease, cerebrovascular disease, or heart failure, which was addressed by another nested case–control study in which patients who died for cardiovascular causes were the cases, and patients matched for age, sex and clinical status and index date were the controls, as described above. ## Assessing the clinical category For each cohort member, the clinical status was assessed by the Multisource Comorbidity Score (MCS), i.e., a prognostic score based on 34 morbidities identified by the ICD-9-CM and ATC codes, which has been shown to predict mortality better than the Charlson, Elixahauser and Chronic Disease Scores in the Italian population [16, 21]. We assigned to each morbidity a weight proportional to its strength in predicting mortality, and calculated the sum of the morbidity' weights suffered by a patient. Because all cohort members suffered from diabetes, the contribution of diabetes to the MCS was not considered. Further details on the calculation of MCS are available in the original manuscript [16]. The score was used to separate patients according to 4 categories of clinical status: good (MCS = 0), intermediate (1 ≤ MCS ≤ 4), poor (5 ≤ MCS ≤ 14), and very poor (MCS ≥ 15). ## Adherence to oral antidiabetic drug treatment For each patient, all antidiabetic drugs prescribed during the follow-up were identified. The period covered by an individual prescription was calculated by dividing the total amount of the drug prescribed for the defined daily dose. For overlapping prescriptions, the patient was assumed to have taken all drugs contained in the first prescription before starting the second one. Adherence was measured by the cumulative number of days in which the drug was available divided by the days of the follow-up, i.e. by the proportion of days covered (PDC) by treatment [22]. We classified patients prescribed more than one antidiabetic drug class as “adherent” if they were covered by at least one drug prescription. Because information on drug therapies dispensed during hospitalization was not available, the exposure to antidiabetic treatment before hospital admission was assumed to be continued for the entire span of the-hospital stay [23]. Four categories of adherence with antidiabetic drug therapy were considered, i.e. very low (≤ $25\%$), low ($26\%$-$50\%$), intermediate ($51\%$-$75\%$) and high (> $75\%$) PDC values. These cut-off values were used because in previous studies on the Lombardy database these adherence levels showed a clear association with mortality among elderly patients in treatment with antihypertensive and lipid-lowering drugs [24, 25]. ## Covariates Additional information included (i) the class of antidiabetic drugs, (ii) the use of insulin, (iii) comedications, e.g., use of antihypertensive, lipid-lowering, antiarrhythmic and other cardiovascular agents, and (iv) comorbidities, i.e., cardiovascular, kidney, respiratory disease, mental disorder, and cancer. Comedications and comorbidities were identified from out-of-hospital prescriptions and in-hospital diagnoses within the 5 years prior to the index date. ## Data analysis Survival curves were built by means of the Kaplan–Meier method according with categories of clinical status, and compared through the log-rank test. Linear regression and chi square for the trend were used to test trend in covariates along the categories of clinical status. In addition, standardized mean differences were used to test differences between cases and controls. Standardized mean differences < 0.10 were considered negligible [26]. Conditional logistic regression models were fitted to estimate the odds ratio, and its $95\%$ confidence interval (CI), of all-cause and cardiovascular mortality in relation to the PDC categories, using the lowest category (≤ $25\%$) as reference. Adjustments were made for the above-reported covariates. Odds ratio trends were tested according to the statistical significance of the regression coefficient of the recoded variable obtained by scoring the corresponding categories. All estimates were obtained by stratifying the cohort members according to the categories of clinical status. The impact of adherence on the outcomes according to categories of both clinical status and age (65–74 years, 75–84 years, and ≥ 85 years) was also measured. ## Sensitivity analysis To verify the robustness of the main findings, five sensitivity analyses were performed. First, a different categorization of adherence was adopted: low (< $80\%$) and high (≥ $80\%$), as commonly used in the medical literature. Second, because the prescribed daily doses (not included in our database) might not be closely correspond to the defined daily doses [27], analyses were repeated by calculating the period covered by prescriptions from the number of tablets in the dispensed canisters, assuming a treatment schedule of one tablet per day. Third, the potential bias associated with unmeasured confounders was investigated by the rule-out approach described by Schneeweiss [28], which detects the extent of the unmeasured confounding required to fully account for the observed exposure–outcome association. We set the unmeasured confounder to exert a potentially marked effect on the results: (i) to have a $30\%$ prevalence in the study population; (ii) to increase the risk of death up to tenfold in patients exposed to the unmeasured confounder than in those unexposed; and (iii) to be up to tenfold less common in high than in low adherent patients. Fourth, we investigated the possible presence of “healthy user bias”, i.e. the possibility that more adherent patients were more likely to follow healthy lifestyle advices or seek other preventive services than less adherent patients. The number of outpatient services (e.g. outpatient visits, laboratory examinations, instrument-based examinations) provided by the NHS in the previous 2 years was considered as a proxy for the patient’s behaviour to search for preventive non-pharmacological services. We proceeded with the following two-stage procedure: (i) ordinal logistic regression was fitted to estimate the odds ratio of adherence to antidiabetic treatment in relation to the number of outpatient services, and (ii) the association between adherence to antidiabetic treatment and survival was further investigated by stratifying for the number of outpatient services. Fifth, because the study cohort included prevalent users, i.e. patients already taking antidiabetic drug therapy before cohort entry, results might be affected by selection bias [29]. The analyses were then repeated by restricting the cohort to new users, i.e. patients who did not receive a prescription of antidiabetic drugs in the 5 years before the index date [30]. The Statistical Analysis System Software (version 9.4; SAS Institute, Cary, NC, USA) was used for the analyses. For all hypotheses tested, p-values less than 0.05 were considered to be significant. ## Data and resource availability The data that support the findings of this study are available from Lombardy Region, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the Lombardy Region upon reasonable request. ## Patients The distribution of exclusion criteria is reported in Additional file 1: Fig S1. Among the 276,336 patients on treatment with antidiabetic agents during 2012, 188,983 met the inclusion criteria. The cohort members accumulated 1,072,151 person-years of observation (on average, 5.7 years) and generated 49,219 deaths, with a mortality rate of 45.9 per 1000 person-years. As shown in Fig. 1 (upper panel), in the group as a whole the 6-year survival decreased from 85 to $52\%$ from the group of patients with good to the group of patients with a very poor clinical status. Death incidence increased progressively as age increased and in each age group it was progressively greater as the clinical category changed from good to very poor or frail. More than $80\%$ of patients aged ≥ 85 years with a very poor clinical status died during the study follow-up (Fig. 1, lower panel).Fig. 1Kaplan–Meier survival curves for all-cause death according to the clinical category as determined by Multisource Comorbidity Score and 6-year mortality probabilities according to the clinical category and age strata The baseline characteristics of cohort members are reported in Additional file 1: Table S2, according to the clinical category. Age and men prevalence increased as the clinical category deteriorated, this being the case also for use of cardiovascular drugs, non-cardiovascular drugs and previous hospitalization for a variety of diseases. The increase was particularly large for patients in the very poor clinical category. Among antidiabetic drugs, use of metformin and sulfonylurea were by far the most widely used drugs. Use of metformin, sulfonylurea, and pioglitazone decreased from the good to the very poor clinical category. The opposite trend was observed for the other antidiabetic drugs, including insulin and the much less frequently used newer oral antidiabetic agents. Overall, 49,219 patients of the selected cohort died during follow-up (cases), of whom 49,201 were matched with alive patients from the cohort (controls). As expected, cases and controls had superimposable age, sex representation and clinical status (Table 1). This was the case also for the index date (see Methods). There were only small differences in virtually all clinical and therapeutic characteristics between the two groups. Compared to controls, cases showed an overall lower adherence to antidiabetic drug therapy. Table 1Comparison of demographic, clinical and therapeutic characteristics of the cohort members who died (cases) or survived (controls)Cases ($$n = 49$$,201)Controls ($$n = 49$$,201)SMDBaseline Men26,035 ($52.9\%$)26,035 ($52.9\%$)MV Age (years): mean (SD)79.7 (7.2)79.6 (7.2)MV Clinical categoryaMV Good6285 ($12.8\%$)6285 ($12.8\%$) Intermediate11,378 ($23.1\%$)11,378 ($23.1\%$) Poor22,717 ($46.2\%$)22,717 ($46.2\%$) Very poor8821 ($17.9\%$)8821 ($17.9\%$)Antidiabetic agents at cohort entry Metformin33,078 ($67.2\%$)34,031 ($69.2\%$)0.042 DPP-4 inhibitor1869 ($3.8\%$)2348 ($4.8\%$)0.048 Sulfonylurea25,767 (52.4)25,127 ($51.1\%$)0.026 Pioglitazone1991 ($4.0\%$)2316 ($4.7\%$)0.032 GLP-1 RA293 ($0.6\%$)331 ($0.7\%$)0.010 Meglitinide6917 ($14.1\%$)6064 ($12.3\%$)0.051 Alpha glucosidase inhibitors1664 ($3.4\%$)1534 ($3.1\%$)0.015 Insulin3675 ($7.5\%$)2919 ($5.9\%$)0.061Other drugs Antihypertensive agents45,601 ($92.7\%$)45,320 ($92.1\%$)0.022 Lipid-lowering agents28,169 ($57.3\%$)30,063 ($61.1\%$)0.078 Antiarrhythmic agents4835 ($9.8\%$)4312 ($8.8\%$)0.037 Antiplatelet drugs33,169 ($67.4\%$)32,637 ($66.3\%$)0.023 Oral anticoagulant agents7638 ($15.5\%$)6153 ($12.5\%$)0.087 Digitalis4486 ($9.1\%$)3312 ($6.7\%$)0.088 Nitrates11,453 ($23.3\%$)10,988 ($22.3\%$)0.023 Anti-gout drugs10,466 ($21.3\%$)9674 ($19.7\%$)0.040 Antidepressant agents12,294 ($25.0\%$)10,732 ($21.8\%$)0.075 Drugs for respiratory disease17,010 ($34.6\%$)17,297 ($35.2\%$)0.012Previous hospitalizations Cardiovascular disease21,955 ($44.6\%$)20,014 ($40.7\%$)0.080 Kidney disease2928 ($6.0\%$)2143 ($4.4\%$)0.072 Metal disorders2159 ($4.4\%$)1658 ($3.4\%$)0.053 Respiratory disease7347 ($14.9\%$)5240 ($10.7\%$)0.128 Cancer7359 ($15.0\%$)7342 ($14.9\%$)0.001During follow-up Adherence with antidiabetic drugs b0.213 Very low4201 ($8.5\%$)3149 ($6.4\%$) Low10,272 ($20.9\%$)8400 ($17.1\%$) Intermediate13,437 ($27.3\%$)12,054 ($24.5\%$) High21,291 ($43.3\%$)25,598 ($52.0\%$)MV matching variable, SD standard deviation, SMD standardized mean differences, DDP-4 Dipeptidyl peptidase-4, GLP-1 RA glucagon-like peptide 1 receptor agonistsa Clinical frailty was assessed by the Multisource Comorbidity Score (MCS) and four categories were considered: good (MCS = 0), intermediate (1 ≤ MCS ≤ 4), poor (5 ≥ MCS ≤ 14) and very poor (MCS ≥ 15)b Adherence was measured by the ratio between the days with available antidiabetic drug prescriptions and all days of follow up. Adherence categories are: very low: ≤ $25\%$; low: 26 to $50\%$; intermediate: 51 to $75\%$; and high: > $75\%$ ## Antidiabetic drug therapy and mortality The association between adherence to drug treatment and all-cause mortality is shown in Fig. 2, top panel. A progressive increase of adherence to treatment was associated with a progressive decrease in the risk of all-cause mortality in all clinical categories. The reduction of all-cause mortality from the lowest to the highest adherence level was lowest in the very poor clinical category compared to the other clinical categories, i.e. $26\%$ ($95\%$ CI 17–$34\%$) vs $36\%$ (25–$46\%$), $50\%$ (44–$56\%$) and $38\%$ (33–$42\%$) for the good, intermediate and poor clinical category, respectively. Similar trends were observed for cardiovascular mortality, i.e. the reduction from the lowest to the highest adherence level were $26\%$ (-15–$52\%$), $48\%$ (28–$62\%$), $44\%$ (33–$53\%$), and $37\%$ (16–$52\%$) for the good, intermediate and poor clinical category, respectively (Fig. 2, lower panels).Fig. 2Effect of adherence with antidiabetic drugs on the odds ratio of all-cause and cardiovascular death according to the clinical category as measured by Multisource Comorbidity Score The results of the stratified analysis for clinical category and age are reported in Fig. 3. In all age strata, there was an association between adherence to treatment and all-cause mortality, i.e. a decrease in the risk of fatal events from any cause as adherence increased. An exception was the group of patients aged ≥ 85 years with a very poor clinical status (or frailty) in whom changes in adherence did not modify the total mortality risk (p-trend = 0.722). The results for adherence and cardiovascular mortality were similar but less consistent (Additional file 1: Table S3). In the group of frail patients aged ≥ 85 years, an increase of adherence was associated with a $27\%$ reduction of cardiovascular mortality, which did not achieve statistical significance (p-trend = 0.069). Furthermore, in the groups in good clinical conditions aged 65–74 years and aged ≥ 85 years, an increase of adherence had a paradoxical effect, i.e. an increase of cardiovascular mortality, albeit not significant (p-trend = 0.308 and 0.286, respectively). In these groups, however, the number of lethal cardiovascular events was extremely small (Additional file 1: Table S4).Fig. 3Effect of adherence with oral antidiabetics on the risk of all-cause mortality according to clinical category and age ## Sensitivity analyses The results on all-cause mortality did not change either by varying the criteria for categorization of adherence with drug treatment (Additional file 1: Table S5) or by using a different way to estimate the duration of each prescription (Additional file 1: Table S6). As shown by the rule-out approach analysis reported in Additional file 1: Fig S2, assuming that highly adherent patients had a three-fold lower odds of exposure to an unmeasured confounder than patients with a very low adherence, the confounder should have increased the outcome risk of all-cause mortality by three-fold for nullifying the observed protective effect of drug adherence in patients with a very poor clinical category. The required nullifying confounder–outcome associations had to be even greater in the other clinical categories. Additional file 1: Table S7 shows that the number of outpatient services used by the patients in the 2 years before the beginning of the study observations was associated with adherence to antidiabetic treatment. However, the results did not substantially change after accounting for this variable (Additional file 1: Table S8). Finally, 61,722 cohort members were new users, i.e. they did not receive a prescription of antidiabetic drugs in the previous 5 years. Among these, 16,915 patients died during follow-up, of whom 16,897 were matched with alive new-user patients. The results did not change by restricting the cohort to new users (Additional file 1: Table S9). ## Discussion Our study provides the following main findings. First, adherence to antidiabetic drug therapy reduced all-cause mortality in patients aged ≥ 65 years with type 2 diabetes. Second, this was the case regardless of the patients’ clinical status, i.e. antidiabetic treatment showed a protective effect not only in patients exhibiting relatively good clinical conditions but also in those with a progressive increase in the number of comorbidities, co-treatments, previous hospitalizations for a variety of diseases and a progressively marked reduction in the chance of survival, justifying their definition as “frail” individuals. Third, the reduction of all-cause mortality associated with better adherence to treatment was less pronounced in frail patients than in the other clinical categories. Furthermore, in frail patients there was no effect of antidiabetic treatment on survival from 85 years of age and beyond. These findings extend to the real-life setting the results of randomized clinical trials on the protective effect of antidiabetic drug treatment in patients aged ≥ 65 years [13]. They further suggest that protection extends to a very advanced age and that includes patients with a wide range of background clinical conditions and life expectancy, including those characterized by a high number of comorbidities, hospitalizations and risk of lethal events, which justifies their definition as frail patients. They also suggest, however, not only that in frail patients the protective effect of treatment on survival may be less than that seen in patients with better clinical conditions but fail to extend at or beyond 85 years of age. Several other aspects of our study deserve to be mentioned. One, it is important to mention that “frail” patients aged 85 years or more were only 2644 out of 188,983 patients, i.e., about $1\%$ of our cohort. Thus, only a very small fraction of old patients with diabetes may eventually fail to benefit from antidiabetic treatment and be candidates to a deprescribing decision [31]. Two, the results of our study that an increase of adherence to antidiabetic drug treatment was associated with a reduction of all-cause mortality in octogenarians patients expands available information which is scarce in people with diabetes in this age range. Three, the results obtained by the analysis of adherence to treatment and cardiovascular mortality exhibited trends that were in general similar to the trends exhibited by all-cause mortality, with, however, a lower level of consistency and some between clinical and adherence group differences that were not seen for all-cause mortality. We can speculate that a factor involved was the low number of cardiovascular lethal events, and thus the insufficient statistical power, in some groups, e.g. 4 and 22 lethal events in patients with very low adherence and an age of 65–74 and ≥ 85 years, respectively. Assuming that the proportion of highly adherent patients among controls is $89\%$ (i.e. what was observed) and accepting a type 1 error of 0.05 with a statistical power of $80\%$, our study needed 1770 outcomes to detect a $25\%$ significant reduction of mortality risk in people with high adherence to drug therapy. This number was not available in all subgroups and more frequently lower for cardiovascular than for all-cause mortality, making the latter a safer basis for conclusions. Four, the reduction of outcomes associated with higher levels of adherence to treatment may be originated by factors different from the increase of adherence to treatment, for example from the fact that patients adherent to drug treatment may also be also more prone to follow healthier lifestyles and control their health conditions via more frequent medical visits and laboratory or instrumental examinations. However, although the number of outpatient medical services utilized by more adherent patients was greater than that utilized by non-adherent ones, our findings provide evidence that the difference in the risk of mortality between low and high adherence to antidiabetic drugs did not disappear after accounting for this proxy measure of health seeking behaviour. This supports the conclusion that an increased adherence to antidiabetic drugs was responsible for the associated protective effect. Finally, albeit restricting cohort members to new users is considered one of the best approaches to reduce confounding in observational studies assessing the effectiveness of drug therapies [30], this reduces the generalizability of study results, especially among elderly frail patients. Indeed, only $33\%$ of our cohort members did not have any prescription of antidiabetic drugs in the preceding years. Because the main results did not change by applying the new-user study design [30], this reinforces the robustness of the results. Our study has several elements of strength as well as limitations. The strengths are that the study was based on a large and unselected population, which was made possible by the extension of the Italian healthcare system to virtually all citizens [32]. Furthermore, the drug prescription database we used provides accurate data because pharmacists are required to report prescriptions in detail in order to obtain reimbursement, and incorrect reports have legal consequences [33]. Finally, adoption of the “user-only” design (i.e. comparison between patients with the same indication at baseline, but with a different level of exposure to the drug of interest) reduces the potential for confounding [34]. Also, the choice of all-cause mortality as the primary outcome avoided any uncertainty about diagnostic accuracy in hospital records or causes of death reported in our database [35]. The limitations are that adherence to treatment was derived from drug prescriptions, a widely employed method to assess drug use in large populations which requires the assumption that the days covered by a prescription correspond to the days of drug use [32]. Because this is obviously not the case in all patients, our data on adherence to treatment are overestimated true adherence. Second, in absence of recorded daily doses of antidiabetic agents (not provided by our database [32]), we adopted the defined daily doses based on the reports of the World Health Organization (https://www.whocc.no/atc_ddd_index/) to estimate the time coverage of each prescription. However, the defined daily doses may overestimate the prescribed daily doses [27], making our adherence values lower than real adherence. However, it is unlikely that this had a substantial impact on the results because the main findings of the study were confirmed by a sensitivity analysis in which the drug coverage was estimated by the number of tablets in the dispensed canisters. Third, the inclusion and follow-up periods (from 2012 to 2018) did not allow us to suitably investigate the impact of the newer antidiabetic agents on mortality in different clinical categories and ages [9–12]. Between 2012 and 2018 treatment was still largely based on conventional antidiabetic drugs and only 3157 and 6771 cohort members started treatment with SGLT inhibitors and GLP1-RA, respectively. Finally, and most importantly, because several clinical data (e.g. blood glucose glycated haemoglobin, lipid profile, blood pressure, body mass index, smoking, diet, duration of diabetes) are not included in the Lombardy database, we cannot exclude that a clinical imbalance between the adherence groups affected the results. However, our data were adjusted for several potential confounders. In addition, the “rule-out” sensitivity analysis showed that only a highly prevalent confounder closely associated with survival and extremely unbalanced between adherence groups could nullify the observed protection provided by greater adherence to antidiabetic drug therapy. ## Conclusions In conclusion, adherence to antidiabetic drug therapy reduced the risk of death in elderly patients with diabetes, regardless of their background clinical conditions. The protective effect of treatment included patients definable as frail because of their high level of comorbidities, previous hospitalizations and short survival in whom in these patients the protective effect of antidiabetic treatment was less than that of patients in better clinical conditions but appreciable at least up to 85 years of age. ## Supplementary Information Additional file 1: Table S1. Diagnostic and therapeutic codes used in the current study. Table S2. Comparison of demographic, clinical and therapeutic characteristics of the cohort members according to the clinical category. Table S3. Effect of adherence with oral antidiabetics on the risk of cardiovascular mortality according to categories of clinical frailty and age. Table S4. Number of cardiovascular deaths according to categories of clinical frailty and age. Table S5. Effect of adherence with antidiabetic drug therapy on the odds ratio (OR) of all-cause and cardiovascular death according to categorization of drug adherence by a different criterion than that used in the main analysis. Table S6. Effect of adherence with antidiabetic drug therapy on the odds ratio (OR) of all-cause death by assessing the coverage of each prescription from the number of tablets in the dispensed canister. Table S7. Effect of the number of outpatient services provided by the National Health Service on the odds ratio (OR) of adherence with antidiabetic drug therapy. Table S8. Effect of adherence with oral antidiabetics on the risk of all-cause mortality according to clinical categories of clinical frailty and number of outpatient services provided by the NHS in the previous two years. Table S9. Effect of adherence with oral antidiabetics on the risk of all-cause mortality by restricting the cohort to new users (i.e. patients with no antidiabetic drug prescriptions in the five years before the cohort entry). Figure S1. Flow-chart of inclusion and exclusion criteria for patients considered for data analysis. ## References 1. Zoungas S, Arima H, Gerstein HC, Holman RR, Woodward M, Reaven P, Hayward RA, Craven T, Coleman RL, Chalmers J. Effects of intensive glucose control on microvascular outcomes in patients with type 2 diabetes: a meta-analysis of individual participant data from randomised controlled trials. Lancet Diabetes Endocrinol (2017) 5 431-437. DOI: 10.1016/S2213-8587(17)30104-3 2. 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--- title: Moderation effects of serotype on dengue severity across pregnancy status in Mexico authors: - Esther Annan - Uyen-Sa D. T. Nguyen - Jesús Treviño - Wan Fairos Wan Yaacob - Sherry Mangla - Ashok Kumar Pathak - Rajesh Nandy - Ubydul Haque journal: BMC Infectious Diseases year: 2023 pmcid: PMC9999597 doi: 10.1186/s12879-023-08051-z license: CC BY 4.0 --- # Moderation effects of serotype on dengue severity across pregnancy status in Mexico ## Abstract ### Background Pregnancy increases a woman’s risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. ### Method Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. ### Results Pregnant women were found to have higher odds of severe dengue [1.50 ($95\%$ CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, ($95\%$ CI 1.21, 1.74)], DENV-2 [1.33, ($95\%$ CI 1.18, 1.53)] and DENV-4 [3.78, ($95\%$ CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. ### Conclusion The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12879-023-08051-z. ## Introduction In Mexico, it is estimated that 139,000 symptomatic dengue fever (DF) cases occur yearly on average, with an estimated yearly cost of $170 million and an average annual disease burden of 65 disability-adjusted life years (DALYs) per million population [1]. The overlap between DF symptoms and the physiological alterations seen among women during pregnancy may make the identification of warning signs difficult [2]. However, for identified cases, pregnancy increases the risk of hospitalization and the development of severe dengue [2]. Maternal mortality rates (MMR) vary across and within regions, in Mexico [3]. MMR is associated with factors like pregnancy-related hypertension, obstetric hemorrhage, quality of health care [4], and infections like DF [5]. Severe DF is associated with a high rate of fetal distress, intrauterine death, obstetric hemorrhage, preeclampsia and eclampsia, caesarian section deliveries, and death due to multiple organ failure days after delivery [5, 6]. Prior studies have also implicated DF in vertical transmission during late pregnancy, and implicated serotypes have been serotypes 1 and 2 (DENV-1 and DENV-2) [7]. DF-specific serotypes have been linked with severe outcomes of DF. Severer complications have mostly been associated with the DENV-2 serotype [8]. There is, however, a gap in literature portraying how the severity of DF in pregnancy is modified by the DF serotype in Mexico. Other factors related to severe outcomes of DF are comorbidities. Adults with self-reported hypertension have 1.6 times the odds of developing dengue hemorrhagic fever (DHF) compared to non-hypertensives [9], while diabetes is associated with 2.75 times the odds of DHF [10]. Diabetes presents with a far worse prognosis in Mexico compared to high-income countries [11]. Approximately $20\%$ of preventable deaths in Mexico are attributable to diabetes [12] and account for one-third of all mortality between the ages of 35 and 74 years [11]. Prior studies assert that the risk of dying among individuals hospitalized for dengue increases 11-fold when there are underlying comorbidities like diabetes [13]. A recent study done in Mexico, Brazil, and Colombia explored mortality associated with DF and found that comorbidities increase case fatality rates 3–17-fold [14]. This study aimed to explore (i) the moderation effects of DF serotype on pregnancy in causing severe DF. ( ii) The spatial distribution of severe and non-severe dengue across pregnancy status in Mexico. Findings from this will inform policies regarding the management of DF, particularly among pregnant women. ## Data collection The dataset used in the analysis was retrieved from Mexico’s Ministry of Health and contains non-identifiable health information collected from notifying health units across 2469 Mexican municipalities from 2012 to 2020. The total sample size was 94,832 women. ## Definition of variables Figure 1 shows a directed acyclic graph (DAG) of factors associated with pregnancy and dengue severity. Analysis was restricted to women of their reproductive age and defined by an age range of 15 to 49 years [15]. At each municipality clinic, women presenting with febrile illness characteristic of dengue fever were further tested for dengue antigens. An individual was defined as having dengue if she had a clinical diagnosis of DF and there was laboratory-confirmed evidence of non-structural protein (NS1) of DENV or a positive immunoglobulin M (IgM). DENV serotypes were determined based on polymerase chain reaction RT-qPCR results [16]. An individual with confirmed dengue had either DENV-1, DENV-2, DENV-3, or DENV-4 serotype. An individual with dengue was reported as having non-severe dengue, severe dengue, dengue without warning signs, dengue with warning signs, or ‘other’. Individuals with no dengue classification or ‘other’ classification were excluded from the analysis. The World Health Organization’s revised 2009 classification of DF emphasizes the inclusion of warning signs as a diagnostic criterion for probable and potentially severe dengue [17]. However, this classification requires laboratory-confirmed results to prevent inflation of the number of severe dengue cases. Because our dataset contained both clinical and laboratory-confirmed diagnoses, severe dengue was defined as individuals with severe dengue or having dengue with warning signs, while non-severe dengue was defined as individuals ‘having non-severe dengue or dengue without warning signs’. A woman was identified as pregnant or not pregnant based on pregnancy status classification retrieved from the dataset. Region was categorized as Center, Center West, Northeast, Northwest, and Southeast. Classification of the region has been defined elsewhere [18]. Hypertension and Diabetes were binary variables with ‘1’ indicating the presence of disease and ‘0’ indicating the absence of disease. Fig. 1A directed acyclic graph of factors associated with pregnancy and dengue severity ## Analysis i)Model selection exploring the moderation effect of DENV serotype on dengue severity across pregnancy status A multicollinearity test was performed and tolerance for hypertension (0.93) and diabetes (0.93) showed no multicollinearity between them. Similarly, there was no multicollinearity observed among the other covariates. Year and region were considered as potential random effects and were evaluated in a two-level hierarchical model. The Wald tests for random effects for the region ($$p \leq 0.049$$) and year ($$p \leq 0.032$$) were both significant. A comparison between random and fixed effects model using the Bayesian information criteria (BIC) revealed that the random-effects model performed better. Hence, both region and year were retained in the model as random effects. Based on the hierarchical model [1], a multiple logistic regression was performed with severe or non-severe DF as the outcome variable and pregnancy as the exposure variable.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y=X\beta +Zu+{\varvec{\varepsilon}}$$\end{document}y=X∗β+Z∗u+ε where X a matrix (N p) with p predictor variables and Z is a matrix (Nq) for q random effects [19]. Two-way interactions were explored, and multiple interactions were found to be statistically significant and retained. Other covariates in the model included dengue serotype, diabetes, hypertension, and age. The final model was chosen by comparing AIC and BIC between models. The AUC for the predicted probabilities of the final model was 0.7156 (Additional file 1: Fig. S1). ## Sensitivity analysis The dataset shows the results of individuals who were tested for pregnancy, diabetes, and hypertension. However, individuals without a test could potentially have been misclassified as ‘negative’. To quantitatively assess for this kind of systematic error, a sensitivity analysis is recommended [20]. Using a misclassification spreadsheet [21], results from the regression analysis were explored for exposure bias. The misclassification spreadsheet provides adjusted bias data based on observed data on pregnant women stratified by dengue severity. As suggested for best practices [22], pairs of sensitivity and specificity were explored to study exposure misclassification.ii)Spatial analysis The sums of severe and non-severe dengue cases were calculated by pregnancy status. The spatial distribution of severe and non-severe dengue was visualized and compared across pregnancy status using ArcGIS. To measure spatial autocorrelation, Moran’s Index (I) was calculated for both pregnant and non-pregnant women with severe dengue. Moran’s I for the attributes pregnant (0.010, $$p \leq 0.13$$) and non-pregnant women (0.003, $$p \leq 0.54$$) were both not statistically significant, and the spatial distributions of these two attributes were random. ## Coding and environment Data preprocessing, analysis, and generation of figures were done using SAS (version 9.4, SAS Institute Inc., Cary, NC, USA), R (version 4.1.2, The R Foundation, Vienna, Austria), and STATA/SE (Stata Corp LLC, College Station, TX, USA). All codes can be found in the Additional file. ## Sample size determination A study conducted in Brazil found that 4 out of 707 (0.006) pregnant women and 19 out of 15,576 (0.001) non-pregnant had severe dengue [2]. To determine such an association with a power of 0.80, at an alpha of 0.05, a total sample size of 4547 was needed. The sample size was determined using the GPower 3.1.9.4 software. Our study was comprised of 94,832 women of their reproductive age, of which 4943 were pregnant ($5.21\%$) and 25,018 ($26.38\%$) of them had severe dengue. ## Ethical approval This study was reviewed and approved by the ethics and research committee of the Universidad Autónoma de Nuevo León and “North Texas Regional Institutional Review Board” as an exempt category (reference # 2021-035). All methods were performed in accordance with the DECLARATION OF HELSINKI guidelines for reporting observational studies. The need for informed consent was waived by the North Texas Regional IRB because the data analyzed was aggregated, de-identified and delinked, and therefore, obtaining informed consent was not applicable. ## Results Table 1 shows the distribution of the sample of women from 2012 to 2020. The average age was 29 years old, with most women living in the Southeast region ($50.12\%$) of Mexico. Across regions, DENV-2 was the commonest serotype found among individuals with severe DF (Additional file 2: Fig. S2). Compared to the other four regions, pregnant women in the Northeast region had the highest proportion of DENV-2 serotype (Additional file 3: Fig. S3). The chi-square statistic for the differences observed across various regions for dengue severity was statistically significant (χ2 = 2782.29, $p \leq 0.0001$). Among pregnant women, $33.57\%$ had severe dengue, compared to $25.23\%$ of non-pregnant women (Additional file 6: Table S1). The difference observed between pregnancy status for dengue classification was statistically significant (χ2 = 187.12, $p \leq 0.0001$).Table 1Demographic characteristics by dengue classificationParameterDengue classification, n (%)Severe dengueNon-severe dengueAllRegionCenter1968 ($7.87\%$)6490 ($9.30\%$)8458 ($8.92\%$)Center-West4805 ($19.21\%$)18580 ($26.61\%$)23385 ($24.66\%$)North-East2084 ($8.33\%$)9856 ($14.12\%$)11940 ($12.59\%$)North-West1817 ($7.26\%$)8059 ($11.54\%$)9876 ($10.41\%$)South-East14344 ($57.33\%$)26829 ($38.43\%$)41173 ($43.42\%$)Pregnancy statusPregnant1707 ($6.82\%$)3236 ($4.64\%$)4943 ($5.21\%$)Not Pregnant23311 ($93.18\%$)66578 ($95.36\%$)89889 ($94.79\%$)Age in years (SD)29.46 (± 9.87)29.61 (± 9.75)29.57 (± 9.78)Year20126031 ($24.11\%$)11560 ($16.56\%$)17591 ($18.55\%$)20136251 ($24.99\%$)15547 ($22.27\%$)21798 ($22.99\%$)20142873 ($11.48\%$)8667 ($12.41\%$)11540 ($12.17\%$)20151930 ($7.71\%$)8591 ($12.31\%$)10521 ($11.09\%$)20169 ($0.04\%$)20 ($0.03\%$)29 ($0.03\%$)2017825 ($3.30\%$)4580 ($6.56\%$)5405 ($5.70\%$)20181085 ($4.34\%$)3162 ($4.53\%$)4247 ($4.48\%$)20194471 ($17.87\%$)10325 ($14.79\%$)14796 ($15.60\%$)20201543 ($6.17\%$)7362 ($10.55\%$)8905 ($9.39\%$)Total (n)25018 ($26.38\%$)69814 ($73.62\%$)94832 ($100.00\%$)Chi-square or t-test performed for group differences had $p \leq 0.001$ Dengue severity has had both downward and upward trends from 2012 to 2020 (Additional file 4: Fig. S4). Among women with severe dengue, DENV-2 was the commonest variant, while DENV-1 was the commonest variant among those with non-severe dengue (Table 2). There was a similar distribution by pregnancy category; while most pregnant women had DENV-2, non-pregnant women mostly had the DENV-1 variant (Additional file 6: Table S1). DENV-2 was the commonest serotype in the Southeast region, while DENV-1 was the commonest serotype among women in the other regions of Mexico (Additional file 6: Table S2). The Southeast region had the highest proportion of severe dengue cases compared to other regions and this difference was statistically significant ($p \leq 0.0001$) (Additional file 6: Table S2).Table 2Proportions and p-value for different Chi-squared test results with $95\%$ significanceParameterDengue classification, n (%)Chi-square p-valueNon-severe dengueSevere dengueSerotypeDENV-19825 ($53.65\%$)1887 ($31.16\%$)973 (<.0001)DENV-28145 ($44.48\%$)4081 ($67.40\%$)DENV-3167 ($0.91\%$)63 ($1.04\%$)DENV-4175($0.96\%$)24 ($0.40\%$)HypertensionHypertension249 ($0.36\%$)465 ($1.86\%$)556 (<.0001)No Hypertension69565 ($99.64\%$)24553 ($98.14\%$)DiabetesDiabetes342 ($0.49\%$)566 ($2.26\%$)610 (<.0001)No Diabetes69472 ($99.51\%$)24452 ($97.74\%$)IgGPositive12655 ($83.45\%$)2754 ($93.17\%$)183 (<.0001)Negative2509 ($16.55\%$)202 ($6.83\%$)IgMPositive18388 ($80.49\%$)11594 ($95.42\%$)1441 (<.0001)Negative4457 ($19.51\%$)556 ($4.58\%$) Additional file 5: Fig. S5 shows variations in severe dengue prevalence from 2012 to 2020 across Mexican states and pregnancy status. Both pregnancy strata showed a similar pattern of spread of severe dengue, although the number of cases in non-pregnant women was higher. A look at the proportions between severe and non-severe dengue for each pregnancy strata shows variations across states (Fig. 2). While most states recorded higher counts of non-severe dengue compared with severe dengue, Chiapas in the Southeast region, and Nayarit in the Center west region had a higher prevalence of severe dengue for pregnant women. This was contrasted with non-pregnant women who had similar proportions across severity strata in both Chiapas and Nayarit. Fig. 2Dengue distribution among pregnant and non-pregnant women from 2012 to 2020 Unadjusted odds of a pregnant woman experiencing severe dengue were 1.5 times the odds of that of non-pregnant women. When adjusted for in a multiple logistic regression model, serotype moderated the effect of pregnancy. Respectively, among individuals with DENV-1, DENV-2, and DENV-4, pregnant women had 1.45, 1.35, and 3.78 times the odds, of severe dengue, compared to non-pregnant women after adjusting for other variables. Compared to those living in the Southeast region, individuals living in the Center, Center West, Northeast, and Northwest regions, had 0.38, 0.31, 0.37, and 0.66 times the odds of severe dengue (Table 3). Based on the random effects from yearly variations, on average, severe DF cases were higher in 2014 and lower in 2017 and 2020, compared to non-severe dengue (Additional file 6: Table S3). Similarly, severe DF cases were significantly higher in the Southeast and Center west regions. A one-unit increase in age was associated with 0.992 times lower odds of severe dengue after adjusting for other variables. All else equal, individuals diagnosed with diabetes had 2.6 times the odds, while those with hypertension had about 3.0 times the odds of severe dengue compared to those without diabetes and hypertension respectively. The variance seen among individuals with DENV-4 between pregnant and non-pregnant women is higher compared to the other serotypes. At a specificity of $99\%$, and sensitivity of $90\%$ the adjusted OR for pregnancy was 1.63 (CI 1.52, 1.75) (Additional file 6: Table S4) compared to the unadjusted OR of 1.49 (CI 1.41, 1.59).Table 3Univariate and multivariate logistic regression showing predictors of the odds of dengue severityIndependent VariablesUnivariateMultivariateOR$95\%$ CIp-valueAOR$95\%$ CIp-valuePregnant Yes, vs No1.4971.413, 1.5870.00011.420.95, 2.11<.0001Age0.9970.996, 0.9990.00030.990.99, 0.99<.0001Year0.9420.937, 0.9470.0001–––Serotype DENV-2 vs DENV-12.5702.419, 2.7310.00012.952.63, 3.30<.0001 DENV-3 vs DENV-11.7931.368, 2.3500.00010.940.56, 1.560.0128 DENV-4 vs DENV-10.6770.441,1.0380.07370.590.32, 1.08<.0001Diabetes Yes, vs No4.5654.014, 5.1930.00012.581.95, 3.40<.0001Hypertension Yes, vs No5.3654.620, 6.2290.00012.972.14, 4.10<.0001Region Center vs Southeast0.5650.535, 0.5970.0001––– Center West vs Southeast0.4820.464, 0.5000.0001––– Northeast vs Southeast0.3960.376, 0.4170.0001––– Northwest vs Southeast0.4220.400, 0.4460.0001–––Pregnant vs non-pregnant DENV-1–––1.451.2, 1.7<.0001 DENV-2–––1.331.2, 1.5<.0001 DENV-3–––0.550.2, 1.50.2438 DENV-4–––3.781.1, 12.60.0305p-values are adjusted, using false discovery rate (FDR) ## Discussion Our study found that the association between pregnancy and severe DF is moderated by the DENV serotype. The effect of DENV-4 in pregnant women may indicate effects of genetic diversification and the emergence of new serotype-specific genotypes [23, 24] and this warrants further investigation. Further, compared to other regions, the Southeast region had higher odds of severe DF. Dengue control programs and policies need to be expanded, using a multidisciplinary approach across Mexico. Although a previous study found individuals with DENV-2 to have a lower risk of dengue hemorrhagic fever [25], the association between DENV-2 and higher risk of severe dengue, particularly when compared to DENV-1 [26], is consistent with most literature [26–29]. Similarly, DENV-2 and DENV-3 are more commonly associated with severe dengue compared to DENV-4 [26]. A recent study in Brazil, found that pregnant women had 1.92 times the odds of having DENV-4 serotype compared to non-pregnant women [2]. However, the authors concluded that the persistence of DENV-4 in a region and a higher number of cases in particular years may have explained the results found [2]. Our study’s finding of similar associations in Mexico may point to other potential mechanisms inherent in serotype-specific variations and/or their interaction with the pregnancy status. Another potential mechanism could involve the presence of DENV-4 as a heterologous infecting serotype. A high predominance of DENV-4 has previously been implicated in an outbreak in Jember, Indonesia, an area that did not frequently report DENV-4 but previously had outbreaks due to DENV-1, DENV-2, and DENV-3 serotypes [30]. Pregnant women are generally more at risk and predisposed to certain clinal conditions [31]. The risk of hospitalization as well as DF severity tends to be higher among pregnant women compared to non-pregnant women [2]. A higher proportion of IgG-positive serology compared to IgM in pregnant women might point to a higher risk of severe DF among those with secondary infection. While a host’s genetic background and immune status may influence disease presentation, it is suggested that certain viral structures may aid in replication in human target cells [32]. Differences among DENV serotypes may be attributed to genotype-specific (within serotypes) variances [25, 32]. One potential explanation is genetic diversity from clade replacements [33] which may be independent of pregnancy status. A clade replacement of a DENV serotype may be associated with a decrease or an increase in the prevalence of a heterologous DENV serotype [34]. Although the evidence does not support the transmission of antigenically aberrant strains, prior research suggests the displacement of DENV genotypes of less epidemiological significance by more virulent genotypes [32]. Genetic diversification and an emergence of a DENV-4 genotype-I have been found in a molecular analysis in Brazil and parts of tropical and subtropical America [23]. This may explain the moderation effect of DENV-4 serotype specifically among pregnant women compared to non-pregnant women. This is contrary to what is expected in the general population, where DENV-2 has been mostly associated with severer outcomes and DENV-4 has generally been associated with causing clinically mild diseases [25, 28]. It is also worth noting that although the effect of DENV-4 was statistically significant, the large confidence interval and smaller sample size may have influenced this finding. Hence, future prospective studies which may involve phylogenetic analysis or gene sequencing may further explore DENV-4 specific effects among pregnant women and the moderation effect of dengue serotypes in pregnancy. The changing trend of increasing and decreasing cases of severe dengue may indicate a change in programs/policies associated with dengue fever eradication. The local health system mainly spearheads the charge toward dengue prevention programs, with minimal effort from other sectors like water and sanitation [35]. When comparing regions, women in the Southeast regions had higher odds of severe dengue compared to those in other regions. Central regions on average had lower proportions of severe dengue. This may be explained by the fact that cities like Mexico City in the Center region are free of endemic mosquito-borne viral diseases [36]. With a subtropical climate and high elevation, there is a lower occurrence of Aedes spp. in Central Mexico [36]. On the contrary, Pacific and Coastal regions tend to be at a higher risk of dengue [37]. For instance, states like Oaxaca in the pacific and southeastern region is one among the most affected states in Mexico, and the persistence of high dengue cases has been attributed to the presence of all four serotypes, favorable climate, and socioeconomic level of the population [38]. Our study had several limitations. Firstly, only a confirmed diagnosis of pregnancy status was reported in the dataset. Hence, to address potential misclassification bias, a sensitivity analysis was performed to assess the misclassification of pregnancy status. The analysis showed that at a sensitivity and specificity higher than $80\%$ and $97\%$ respectively, findings from our study were conservative. Another limitation was missing data for all variables. Particularly, prevalence estimates for pregnant women in Zacatecas and Chihuahua were missing across the period of study. However, our sample size of 94,832 women, satisfied the requirement for this study. Also, the cross-sectional nature of the study limits the inferential interpretation of results from the logistic regression model. The unavailability of information about vaccination status and behavioral factors that affect mosquito control also limits the study’s ability to control for these confounders. However, the spatial trend analysis provides additional reasons for further exploration in future studies. Furthermore, since pregnant women are more likely than other women to visit the clinic for antenatal care and be hospitalized, the likelihood of being diagnosed with dengue might be higher than non-pregnant women. However, this assumes a high antenatal care uptake and secondly, it also assumes that serological tests are performed routinely for pregnant women. Serological tests are performed upon clinical diagnosis of dengue. Since most dengue cases are asymptomatic and might go unnoticed, an acute presentation with a febrile illness among women is likely to present to the clinic regardless of pregnancy status. Lastly, restriction of the data to only women with serotype data means the likely exclusion of more people with non-severe dengue. However, the proportions of missingness among those with severe ($76\%$) and non-severe ($74\%$) dengue were similar. I higher proportion of missingness among non-pregnant women ($75\%$) compared to pregnant women ($60\%$), however, this supports the theory that more frequent access to healthcare may influence more diagnoses among pregnant women. Future studies may prospectively collect serotype data to ensure further limitation of potential bias.’ has been added to the limitations. ## Conclusion Pregnancy increases a woman’s risk of severe dengue. However, this may be modified by the DENV-specific serotype. Of note is the DENV-4 serotype, which is otherwise the least severe serotype in the general population. Across Mexican regions, the southeast region had the highest number of severe dengue cases. Particularly, perinatal care in states like Chiapas and Nayarit may warrant further surveillance. This may especially be important for individuals with comorbid conditions like hypertension and diabetes and under the age of 24 years. An intersectoral approach is still needed across Mexico, particularly in the Southeast region to address the risk of DF severity. Further research is needed to fully understand the moderation effect of dengue serotype in pregnancy. ## Supplementary Information Additional file 1: Figure S1. ROC Curve for Hierarchical logistic regression model. Additional file 2: Figure S2. Distribution of dengue serotype by Dengue Severity and region. Additional file 3: Figure S3. Distribution of dengue serotype by Pregnancy status* and region. Additional file 4: Figure S4. 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--- title: 'A cross-sectional study: a breathomics based pulmonary tuberculosis detection method' authors: - Liang Fu - Lei Wang - Haibo Wang - Min Yang - Qianting Yang - Yi Lin - Shanyi Guan - Yongcong Deng - Lei Liu - Qingyun Li - Mengqi He - Peize Zhang - Haibin Chen - Guofang Deng journal: BMC Infectious Diseases year: 2023 pmcid: PMC9999612 doi: 10.1186/s12879-023-08112-3 license: CC BY 4.0 --- # A cross-sectional study: a breathomics based pulmonary tuberculosis detection method ## Abstract ### Background Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection. ### Method Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients. ### Results The breathomics-based PTB detection model achieved an accuracy of $92.6\%$, a sensitivity of $91.7\%$, a specificity of $93.0\%$, and an AUC of 0.975 in the blinded test set ($$n = 430$$). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases ($$n = 182$$), the VOC modes also achieve good performance with an accuracy of $91.2\%$, a sensitivity of $91.7\%$, a specificity of $88.0\%$, and an AUC of 0.961. ### Conclusions The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis. ## Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies. ## Introduction Tuberculosis (TB) continues to be a major global health threat, with an estimated 10 million incident cases and 1.4 million deaths per year globally. In 2019, only $57\%$ of pulmonary TB cases were confirmed by bacteriological examination. There is still a large gap, 2.9 million cases in 2019, between reported and estimated cases [1]. The absence of available technology for the timely and accurate detection of TB has been one of the major impediments to preventing and ending TB. Undiagnosed TB is associated with substantial morbidity and mortality and leads to ongoing TB transmission in the community, which makes improving the performance and delivery of diagnostic testing services a leading priority [2]. Sputum-based TB diagnostics are usually either inaccurate, expensive, or complicated in their usage [3]. Sputum specimens are difficult to collect, process, and transport, and only one-third of suspected TB patients can give adequate high-quality sputum samples [4], while it is even harder in children, HIV-infected patients, and those with extrapulmonary TB. Acid-fast bacilli staining of sputum has a high false-negative rate (up to $50\%$) [5]. The culture of sputum alone has a poor sensitivity of approximately $30\%$ [6, 7]. GeneXpert MTB/RIF (Xpert) achieved good performances in TB detection and drug resistance testing in the clinic and has been recommended by the WHO. However, it still requires good infrastructure and sputum samples [8, 9]. WHO has identified four high-priority test types for diagnostic development and created target product profiles (TPPs) for each, among which some non-sputum tests should be offered [10]. Thus, there is a greater need than ever for fast, accurate, and non-sputum TB detection technologies. Breathomics, a branch of metabolomics, is a promising tool because of its significant advantages: good accessibility, noninvasiveness, and specificness [11, 12]. A breath test could diagnose TB by detecting volatile organic compounds (VOCs) produced by mycobacterium tuberculosis (M.tb) and the infected host, which has been approved by many studies [13]. The most commonly used breath detection methods for TB diagnosis include gas chromatography–mass spectrometry (GC–MS) [14, 15] and electric or chemical sensors [16]. For GC–MS based studies, Phillips et al. used GC–MS to detect the VOCs in the exhalation of pulmonary TB (PTB) patients with positive culture results and healthy controls (HC), and the headspace air of M.tb culture flask. They found that patients' expiratory VOCs were similar to culture VOCs in naphthalene, 1-methyl- and cyclohexane, 1,4-dimethyl-. Based on the small sample modeling on 12 identified VOCs, the author obtained a sensitivity of $82.6\%$ and specificity of $100\%$, which verified the feasibility of the breath test for PTB detection [17]. They further validated the VOCs-based PTB detection method within a larger transcontinental and ethnic group of 226 symptomatic high-risk patients in United States, Philippines, and United Kingdom, which achieves an overall accuracy of approximately $85\%$ [18]. Beccaria et al. also used GC–MS to analyze the VOCs of exhaled breath of patients with active PTB and health controls in South Africa, achieving a sensitivity of $100\%$ and specificity of $60\%$ via the random forest method [19]. In addition, they performed another validation study using two-dimensional GC–MS for breath analysis on PTB and PTB-free patients in Haiti and found that a random forest model based on 22 characteristics VOCs can distinguish well between PTB and PTB-free patients, in which 2-butyl-1-octanol was the most expressed in the breath of TB positive population and was detected in $85\%$ of this group ($\frac{12}{14}$), while only in $50\%$ in the control group ($\frac{10}{20}$) [20]. 2-butyl-1-octanol was also identified by fuzzy logic analysis as the best discriminator between patients whose sputum cultures were positive or negative for Mycobacteria in Phillips’s study [17]. Bobak et al. conducted an exploratory study on the exhaled diagnosis of PTB in 31 children in South Africa and found that PTB could be identified with $90\%$ accuracy from other respiratory infections based on four VOCs, including decane and 4-methyloctane[21]. Furthermore, the sensor based breath test method also achieved good performance on TB/PTB detection. For example, Marcel et al. constructed and evaluated a DiagNose (C-it BV) based TB diagnosis method on 194 participants, and achieved a sensitivity of $93.5\%$ and a specificity of $85.3\%$ in discriminating TB patients and HC, and got a sensitivity of $76.5\%$ and specificity of $87.2\%$ when identifying TB patient within the entire test-population [22]. Morad et al. evaluated a nano-sensor based TB detection method on 60 blinded validation datasets, and achieved a specificity, positive predictive value (PPV), and negative predictive value (NPV) of $88\%$, $76\%$, and $94\%$, respectively [23]. In 2017, Mohamed et al. distinguished TB patients [260] from HC participants [240] for multiple biological samples (blood, breath, sputum, and urine) with sensitive and specificity > $95\%$ via e-Nose analyses [24]. The above studies proved the feasibility of breath VOCs based PTB detection. GC–MS has advantages in the qualitative and quantitative detection of substances. However, the selection of chromatography columns and the complex procedures limited the detection scope of GC–MS. Besides, the consistency of reported VOCs from different studies is poor, since GC–MS analysis requires complex procedures and specialized skills [13]. The sensor based solution usually uses a single or a series of sensor to identify the response pattern to breath without considering the specific compositions. It is fast but easily affected by other interference factors such as the environment [13]. Thus, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for VOC detection. The online mass spectrometry platform could meet such requirements. Recently, different online mass spectrometry technologies have been developed to analyze exhaled breath, such as proton transfer reaction MS (PTR-MS) [25], secondary electrospray ionization MS (SESI-MS) [26, 27], and high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOF-MS) [28]. The HPPI-TOFMS platform is designed and developed by our team and has been used for lung cancer and esophageal cancer detection [29–31] and achieved good performances with sensitivity and specificity > $90\%$. In this study, we aimed to develop a breathomics based PTB detection and investigate its performance on the clinical data set in this study. ## Study design and participants We conducted a cross-sectional study from 1 March 2020 to 31 March 2021 at The Third People's Hospital of Shenzhen. The study was approved by the Ethics Committee of The Third People's Hospital of Shenzhen (number: 2020-012). Written informed consent was obtained from all participants. The total participants consisted of a case group and a control group. For the case group, confirmed PTB patients were prospectively and consecutively recruited based on the following criteria: [1] aged 18–70 years old; [2] diagnosed by Xpert and/or culture, with suggestive clinical and radiological findings; [3] anti-TB treatment not initiated or started less than 2 weeks. The control group consists of two parts: healthy controls with no pulmonary diseases (HC) and patients with pulmonary diseases (unhealthy controls, UHC) which could be noninfectious diseases or infectious diseases other than PTB. HCs were simultaneously recruited and underwent a physical examination with the following criteria: [1] aged 18–70 years old; [2] no respiratory symptoms (e.g., cough, sputum, hemoptysis, shortness of breath, dyspnea, or chest pain); [3] no pulmonary lesions by chest imaging (chest X-ray or computed tomography). For UHC, they should: [1] aged 18–70 years old; [2] have pathogenic confirmed infectious diseases or treatment response suggestive of pulmonary infectious diseases, or have chronic noninfectious diseases, without evidence of infection. Both the case group and the control group would be excluded if the airbag leaked or were unable to take enough breath volume. The participant enrollment flow is illustrated in Fig. 1a. A total of 518 PTB patients and 887 controls with 77 UHC and 810 HC were enrolled in this study. Fig. 1The flow of participants enrollment and PTB detection model construction and test The physicians were responsible for making a clinical diagnosis and for the collection of the breath samples. The other researchers performed the VOCs detection and ML modeling and were blinded to clinical data and other test results. Additionally, the physicians were also blinded to the breath test results. The demographic and clinical characteristics of all participants were collected and summarized in Table 1, including age, sex, and antituberculosis therapy. Table 1Demographic characteristics of participantsDiscovery data setTest data setPTB ($$n = 361$$)Control ($$n = 614$$)p-valuePTB ($$n = 157$$)Control ($$n = 273$$)p-valueAge Median (min.–max.)36 (18–70)28 (18–69) < 0.00132 (18–70)28 (18–70) < 0.001 < 30 (%)115 (31.9)345 (56.2)0.00864 (40.8)169 (61.9)0.258 ≥ 30 (%)246 (68.1)269 (43.8) < 0.00193 (59.2)104 (38.1)0.009Sex Male (%)223 (61.8)325 (52.9)0.009101 (64.3)142 (52.0)0.004 Female (%)138 (38.2)289 (47.1)–56 (35.7)131 (48.0)–Bold p-value shows that there are significant differences between PTB and controls ## Sampling procedures All breath samples were collected using a predefined protocol and tested within twenty-four hours. The sampling apparatus was composed of a disposable gas nipple and a sampling bag made of polyether-ether-ketone (PEEK). In this study, we set standard sampling demands and protocols to minimize the influence of the daily diet. Firstly, we conducted sampling at a second visit if he/she was an inpatient and informed the participants to prepare for sampling in advance: no smoking, alcohol, or diets within an hour before sampling. Secondly, participants were required to rinse their mouths with purified water instantly before sampling to minimize the influence of diet, smoking, etc. Thirdly, all samples are required to be collected in the same environment, which could minimize the effects of environmental facts. With a deep nasal inhalation, participants completely exhaled the air into the sampling bag with over 1.2 L volume. ## Breath sample detection HPPI-TOFMS, which consisted of a vacuum ultraviolet (VUV) lamp-based HPPI ion source and an orthogonal acceleration time-of-flight (TOF) mass analyzer, was used to detect and analyze the breath samples. A commercial VUV-Kr lamp with a photon energy of 10.6 eV was adopted in this platform. Most VOCs with an ionization potential lower than 10.6 eV were ionized in the ionization region directly [32]. Breath samples were directly introduced through a 250 μm i.d. 0.60 m long stainless-steel capillary. The HPPI ion source works in soft HPPI ionization mode, which will produce mostly radical cations (M+) by ionization reaction as M + hγ → M+ + e. Then, the ion transmission system effectively transferred these ions from the ion source into the orthogonal acceleration, reflection TOFMS mass analyzer. The TOFMS signals were recorded by a 400 ps time-to-digital conversion rate at 25 kHz, and all the mass spectra were accumulated for 60 s. Thus, it takes 1 min for one sample to go through a detection. A spectrogram with 31,666 data pairs was extracted from each exhaled breath sample. Based on the flight time and m/z calibration on the standard gas with nine compounds at a concentration of 1 ppmv, the timeline of flight can be transferred as m/z, which is in the range of [0, 350]. The TOFMS signals were positively correlated with the concentration of the VOC ions. The detection limit is down to 0.015 ppbv (parts per billion by volume) for aliphatic and aromatic hydrocarbons [28]. The gas-phase breath sample was directly inhaled into the ionization region through a 250 μm i.d. 0.60 m long capillary from the sampling bag. The TOF signals were recorded by a time-to-digital converter, and all the mass spectra were accumulated for 60 s. Mass spectrum peaks with m/z < 350 were detected by HPPI-TOFMS for each exhaled breath sample. The noise-reducing and base-line correction were implemented via anti-symmetric wavelet transformation, which was achieved by Python package pywavelets [33]. To transfer the discrete signal of mass spectra data to standard breathomics data, we calculate the area of the strongest peak in the range of [x − 0.1, x + 0.1) as the feature of VOC with m/z close to x. In this study, 1500 breathomics data were detected for machine learning (ML) model construction in the ions m/z range of [20, 320) with an interval of 0.2. A statistical analysis based feature selection was executed to avoid model over-fitting, in which the features without significant difference ($p \leq 0.05$) were excluded before model training. ## PTB detection model construction As illustrated in Fig. 1b, all the enrolled participants were randomly split into two groups: $70\%$ of them for model construction and the remaining $30\%$ of them for model blinded testing. Thus, 361 PTB patients and 614 controls were randomly selected as the discovery data set. Through 100 times of 7:3 randomization, the discovery data set was further divided into a training subset and an internal validation subset. On the training subset, several popular ML models including Random Forest (RF) [34], Support Vector Machine (SVM) [35], Logistic Regression (LR) [36], eXtreme Gradient Boosting (XGB) [37], and Decision Tree (DT) [38] were employed as the classifier to distinguish PTB patients and controls. The descriptions and main parameter settings of these ML models are illustrated in Table 2. Then, the optimal classifier for distinguishing PTB patients and controls is selected according to the model performance in the internal validation subset, which is named as “BreaTB”. Table 2The descriptions and main parameter settings of the employed ML modelsML modelsDescriptionsMain parameter settingsaRFA meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fittingn_estimators = 100, max_features = 0.5, min_samples_split = 4, min_samples_leaf = 10, criterion = "entropy"SVMSolves the separation hyperplane which can divide the training data set correctly and has the maximum geometric intervalpenalty = "l2", loss = "squared_hinge", tol = 1e−5, $C = 5.0$, max_iter = 1e + 5LREstimates the probability of an event occurring based on a given dataset of independent variablestol = 1e−5, $C = 5.0$, max_iter = 1e + 4XGBA boosting algorithm based on gradient boosted decision trees algorithmbooster: "gbtree", max_depth: 8, n_estimators: 100, min_child_weight: 3, gamma: 0.15, lambda: 2DTEmploys a divide and conquer strategy by conducting a greedy search to identify the optimal split points within a treecriterion = "gini", splitter = "best", min_samples_split = 2, min_samples_leaf = 1aThese algorithms were achieved based on python packages: xgboost (https://xgboost.readthedocs.io/en/stable/python/python_intro.html) and sklearn (https://scikit-learn.org/stable/user_guide.html) ## Performance evaluation and statistical analysis As BreaTB is constructed, the most important features can be confirmed based on the feature importance or coefficient in model training. Feature differences analysis was also implemented on the relative density of VOCs among different patient groups. BreaTB was applied and evaluated on the blinded testing data set, which consisted of 157 PTB patients, 248 HC, and 25 UHC. The model detection results were compared with the clinically confirmed diagnosis results. Furthermore, we also assessed the performance of BreaTB stratified by clinical characteristics. We calculated the sensitivity, specificity, PPV, NPV, accuracy, AUC (the area under the receiver operating characteristic curve (ROC)), and the relative $95\%$ confidence interval (CI) were calculated to evaluate the performance of BreaTB. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and Origin software (version 2018). Descriptive statistics were reported as frequencies (percentages) for categorical variables or median (minima to maxima) for continuous variables. We compared the demographic characteristics among different patient groups using the Mann–Whitney U test for continuous variables and the chi-square test for categorical variables. A p-value < 0.05 was considered statistically significant in all analyses. All the tests were two-tailed. ## Results For different ML models, the mean performance metrics of 100 models on randomly selected training sets were illustrated in Table 3. Since the scale of the dataset enrolled is relatively large in this study, these basic classifiers such as SVM, LR, and DT all perform well in the PTB detection task. As the meta and boosting classifiers of DT, the RF and XGB based PTB detection models have superior performances. Based on the validation results, the best-performing RF and XGB based PTB detection models were selected for further testing. The results in Table 3 showed the XGB model has better performance than the RF model in the validation data set. However, the RF model performs superior to the XGB model in the blinded test data set with an accuracy of $92.6\%$ ($95\%$ CI 90.1–$95.0\%$), a sensitivity of $91.7\%$ ($95\%$ CI 88.5–$95.0\%$), and a specificity of $93.0\%$ ($95\%$ CI 88.9–$97.2\%$). It implies that the RF model is more robust than XGB. Thus, we only further analyze the RF-based PTB detection model (termed as BreaTB). Figure 2 illustrated the prediction scores of BreaTB on all tested samples, which represent the probability of PTB infection. The cut-off line(threshold = 0.5) divides the PTB patients from controls well with fewer false positives and false negatives. Table 3Performance metrics (mean ± STD) of difference ML models for PTB detection in internal validation and blinded test datasetData setsModelsSensitivity (%)Specificity (%)PPV (%)NPV (%)Accuracy (%)AUCValidation ($$n = 295$$)RF90.6 ± 3.190.6 ± 2.485.1 ± 3.294.3 ± 1.790.6 ± 1.70.960 ± 0.011SVM67.7 ± 20.083.4 ± 12.674.3 ± 10.483.1 ± 7.677.6 ± 4.80.755 ± 0.061LR78.6 ± 4.482.0 ± 4.172.2 ± 4.786.8 ± 2.580.8 ± 3.10.856 ± 0.030XGB88.1 ± 3.093.6 ± 2.189.0 ± 3.293.1 ± 1.691.5 ± 1.60.969 ± 0.010DT76.1 ± 5.090.5 ± 2.882.6 ± 4.286.7 ± 2.485.2 ± 2.50.833 ± 0.028Test ($$n = 430$$)RF90.7 ± 1.592.1 ± 1.586.9 ± 2.194.5 ± 0.891.6 ± 1.00.970 ± 0.005SVM69.4 ± 20.483.6 ± 12.774.5 ± 9.784.3 ± 7.878.4 ± 5.20.765 ± 0.066LR82.5 ± 3.383.2 ± 4.074.1 ± 4.589.2 ± 1.882.9 ± 2.80.877 ± 0.021XGB88.1 ± 1.794.6 ± 1.290.5 ± 2.093.2 ± 0.992.2 ± 0.90.970 ± 0.004DT75.3 ± 4.189.4 ± 1.980.5 ± 3.086.3 ± 2.084.3 ± 1.90.824 ± 0.023Bold values represent the best performance metrics achieved among differences mahcine learning methodsFig. 2Predictive score of BreaTB on the test data set As shown in Table 1, in the training data set, the median age of PTB patients was significantly higher than that of controls (36 (18–70) vs. 28 (18–69) years old), and there were more males in PTB patients than in controls ($61.8\%$ vs. $52.9\%$). The distribution of age ≥ 30 and gender in the test data set is as same as that in the training data set, except for that in age < 30. Thus, it is necessary to evaluate the influence of these clinic characteristics on model performance. As illustrated in Fig. 3 and Table 4, the ROC curve showed that BreaTB achieved an AUC of 0.975 ($95\%$ CI, 0.961–0.998) in the overall test data set. The diagnostic performance of BreaTB was fairly consistent across different subgroups based on demographic and clinical baseline characteristics, such as age, gender, and anti-tuberculosis therapy. The results demonstrated that age, sex, and anti-tuberculosis therapy have no evident influence on BreaTB. In detail, BreaTB has superior performance on participants with age < 30 than those with age ≥ 30. For different genders, BreaTB also performs slightly differently with superior sensitivity and inferior specificity in females than in males. After the anti-TB therapy, the PTB patients are more difficult to be distinguished from the controls for BreaTB. Except for the general characteristics, we also analyzed the PTB distinguish performance against HC and UHC. BreaTB had a sensitivity of $91.7\%$ ($95\%$ CI 87.4–$96.0\%$), and a specificity of $93.5\%$ ($95\%$ CI 90.5–$96.6\%$) for the identification of confirmed PTB from HC, which is a quasi-screening scenario. In contrast, inferior specificity of $88.0\%$ ($95\%$ CI 75.3–$100\%$) was achieved by BreaTB in distinguishing TB from UHC, which is a quasi-diagnosis scenario. Fig. 3Performance of the BreaTB on different tuberculosis subgroupsTable 4Performance metrics ($95\%$ CI) of BreaTB on the test data set and on different subgroupsGroupsSensitivity (%)Specificity (%)PPV (%)NPV (%)Accuracy (%)AUCOverall Test ($$n = 430$$)91.7 (87.4–96.0)93.0 (90.0–96.1)88.3 (84.5–92.2)95.1 (91.8–98.4)92.6 (90.1–95.0)0.975 (0.961–0.990)Age, year < 30 ($$n = 233$$)90.6 (83.5–97.8)91.7 (87.6–95.9)80.6 (74.4–86.7)96.3 (91.9–100.0)91.4 (87.8–95.0)0.972 (0.951–0.993) ≥ 30 ($$n = 197$$)92.5 (87.1–97.8)95.2 (91.1–99.3)94.5 (90.2–98.8)93.4 (88.3–98.5)93.9 (90.6–97.2)0.978 (0.958–0.999)Sex Male ($$n = 243$$)90.1 (84.3–95.9)95.1 (91.5–98.6)92.9 (88.7–97.0)93.1 (88.1–98.1)93.0 (89.8–96.2)0.975 (0.955–0.994) Female ($$n = 187$$)94.6 (88.7–100)90.8 (85.9–95.8)81.5 (74.7–88.4)97.5 (93.8–100)92.0 (88.1–95.9)0.981 (0.962–1.000)Anti-TB Untreated ($$n = 385$$)92.9 (88.1–97.6)93.0 (90.0–96.1)84.6 (80.2–88.9)96.9 (93.9–100)93.0 (90.4–95.5)0.978 (0.964–0.993) Treated ($$n = 318$$)88.9 (79.7–98.1)93.0 (90.0–96.1)67.8 (62.1–73.5)98.1 (94.6–100)92.5 (89.5–95.4)0.968 (0.949–0.987)Controls HC ($$n = 405$$)91.7 (87.4–96.0)93.5 (90.5–96.6)90.0 (86.2–93.8)94.7 (91.2–98.2)92.8 (90.3–95.4)0.977 (0.962–0.991) UHC ($$n = 182$$)91.7 (87.4–96.0)88.0 (75.3–100.0)98.0 (93.3–100)62.9 (55.0–70.7)91.2 (87.1–95.3)0.961 (0.933–0.989) In this study, over 30 VOC ions were selected via statistical analysis for the BreaTB model training in each iteration. To analyze the importance of different VOC ions for PTB detection, we selected the best VOC ion combinations through RF model based feature selection for 100 iterations. Then, all selected VOC ions were ordered by the selection frequency in RF modeling. As shown in Fig. 4a, there are five VOC ions with m/z of 72, 68, 65, 67, and 65.2 selected at each iteration. There are eleven VOC ions selected in over 90 iterations. Thus, we analyzed the most important eleven VOC ions between confirmed PTB patients and controls. Figure 4b shows the mass spectrum examples of a PTB patient and control individual. It demonstrates that there are some differences in the top eleven VOC ions, which are shown in color bars. To further explore these VOC ions, we analyzed the group differences between PTB and controls and evaluated the performance of each VOC ion in discriminating the PTB and controls. As demonstrated in Fig. 4c, d, all these eleven VOC ions are significantly different between the PTB group and controls with a p-value < 0.05 (the blue line in Fig. 4c). The discernibility (AUC in discriminating PTB group and controls) of VOC ions is related to the scale and significance of group differences. The ROC curve in Fig. 4d shows the discrimination of a single VOC ion is limited (AUC < 0.75). However, the combination of all eleven VOC ions performs well on the test data set with an AUC of 0.905($95\%$ CI: 0.878–0.933). It implies that the panel of VOC ions is the basis for breathomics based PTB detection. The heat map in the PTB group, UHC, and HC illustrated the patterns of these eleven VOC ions are visually different. Fig. 4Investigations of breath VOC ions and PTB. a The volcano plot shows the group changes and differences in breath VOC ion intensity between PTB and controls. b The performances of the top eleven VOC ions in distinguishing PTB patients and controls. c The heatmap of the top eleven VOC ions in PTB, UHC, and HC, shows the pattern differences of VOC ions Since the qualitative ability of the TOF mass spectrometer is limited, we can just infer the possible chemicals of these PTB related VOC ions based on their m/z (72.0, 68.0, 65.0, 67.0, 65.2, 69.0, 66.0, 59.0, 61.0, 53.0, 58.0), correlation-ship (Fig. 4e), intensity distribution (Fig. 5), other published potential biomarkers, and the human breathomics database [39]. Considering the ions intensity distribution similarity and the relationship of m/z values, the VOC ions with m/z of 68 and 69 could be isoprene and its protonated cation. The VOC ions with m/z of 58 and 59 could be acetone and its protonated cation. Isoprene and acetone are common metabolites in human breath [40]. Isoprene is proven to be related to oxidative stress responses [41, 42]. Acetone is related to diabetes [43], and tuberculosis patients have a high incidence of diabetes [44]. The VOC ion with m/z of 72 could be 2-butanone, which is also found as the top eleven biomarkers for PTB in Machel Phillips’s study [17]. The VOC ion with m/z of 61 could be the protonated ions of acetic acid, which was proven related to tuberculosis in skin samples [45]. The VOC ion with m/z of 65, 65.2, and 66 could be the fragment ion of 4-nitrophenol and the corrspounding protonated cation, respectively. The VOC ion with m/z of 67 could be Pyrrole or 3-Butenenitrile. The low peak intensity VOC ions with m/z of 53 could be the fragment ion of other unknown VOC with low concentration. These VOCs would be potential biomarkers of TB.Fig. 5Intensity comparison of VOC ions between PTB group and controls ## Discussion In this study, for the first time, we explore the diagnostic value of breathomics data detection on HPPI-TOF–MS for PTB in a large cohort. The results demonstrated that the developed BreaTB model performs well in distinguishing PTB individuals and control with high sensitivity and specificity of $91.7\%$ and $93.0\%$. It implies that the proposed breathomics method via online HPPI-TOF–MS could be a potentially feasible diagnostic or screening tool in the clinical setting. In the past decades, no breathomics-based method has been translated into clinical practice for the diagnosis of TB, which is primarily due to the complexity and the high cost of existing spectrometers and the limitations of sensor technologies [13]. Compared with past research on PTB detection, there are several advantages in our study. Firstly, the diagnostic accuracy of our VOCs-based PTB detection method was high, with sensitivity and specificity of $91.7\%$ and $93.0\%$. Furthermore, our study was tested on a large-size patient cohort. As participants were stratified based on their demographic and clinical characteristics: age, sex, and anti-tuberculosis therapy, the diagnostic performance was fairly consistent. Thirdly, TB diagnostic methods using non-sputum samples are strongly advocated by the WHO [46]. Breath sampling has excellent clinical accessibility, especially for certain categories of patients whose sputum is difficult to collect. Fourthly, the breath sample detection on HPPI-TOF-MS only takes about one minute. Thus, the total time cost from breath sampling to getting PTB detection results is about five minutes. However, there are several limitations in our study. Firstly, the qualitative and metabolic pathways of ions have not been defined. Thus, the logical and mechanistic evidence of the breathomics-based PTB detection method is not enough to make it clinically convincing, although it performs well in clinical data. Further chemical composition analysis via GC–MS is the focus of our future works. Fortunately, many studies have demonstrated the VOCs similarities and differences between the breath of PTB patients and M.tb culture-released gases. For example, Phillips et al. found the common compounds: 1-methyl- and cyclohexane, 1,4-dimethyl- in the breath of PTB patients and headspace air of culture [17]. Using computational approaches, Purva et al. proposed putative biosynthetic pathways in M.tb for three VOCs(methyl nicotinate, methyl phenylacetate, and methyl p-anisate), and methyl nicotinate was also found in the exhaled breath of patients with tuberculosis [47]. Kuntzel et al. detected and analyzed the headspace VOCs of 17 different mycobacteria and control strains. Their result demonstrated the feasibility of identifying M.tb from other pathogens based on their metabolism of VOC [48]. Our team is also working on finding the links between the VOCs in the breath samples of PTB patients and the VOCs in the headspace air of M.tb culture. Secondly, the control group contained only a small sample of patients with pulmonary diseases other than PTB. Thus, the performance needs to be further evaluated in detecting PTB from other pulmonary diseases. Thirdly, our enrollment was restricted to adults with possible PTB. Similar independent validation studies are needed for children whose diagnostic tools are even more urgently needed [21], as well as patients living with diabetes or HIV and patients suspected of EPTB. At last, this is a single-center study conducted in a TB specialist hospital, which may limit the universality of the research results. ## Conclusion In conclusion, we developed a breathomics model: BreaTB for PTB detection, which achieved high diagnostic accuracy on clinical data set with a sensitivity and a specificity of $91.7\%$ and $93.0\%$, respectively. Due to its simplicity and low cost, the breathomics-based PTB detection model on online breath analysis platforms such as HPPI-TOF-MS has the potential to meet the ongoing demand for TB diagnosis that would not require sputum and may work in active case finding in large populations, especially in resource-limited settings where it is urgently needed [12]. However, more clinical and basic researches are needed to evaluate this method in patients with more complex health conditions and with various lung diseases. 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--- title: 'Metabolomic profiles in night shift workers: A cross-sectional study on hospital female nurses' authors: - Elisa Borroni - Gianfranco Frigerio - Elisa Polledri - Rosa Mercadante - Cristina Maggioni - Luca Fedrizzi - Angela Cecilia Pesatori - Silvia Fustinoni - Michele Carugno journal: Frontiers in Public Health year: 2023 pmcid: PMC9999616 doi: 10.3389/fpubh.2023.1082074 license: CC BY 4.0 --- # Metabolomic profiles in night shift workers: A cross-sectional study on hospital female nurses ## Abstract ### Background and aim Shift work, especially including night shifts, has been found associated with several diseases, including obesity, diabetes, cancers, and cardiovascular, mental, gastrointestinal and sleep disorders. Metabolomics (an omics-based methodology) may shed light on early biological alterations underlying these associations. We thus aimed to evaluate the effect of night shift work (NSW) on serum metabolites in a sample of hospital female nurses. ### Methods We recruited 46 nurses currently working in NSW in Milan (Italy), matched to 51 colleagues not employed in night shifts. Participants filled in a questionnaire on demographics, lifestyle habits, personal and family health history and work, and donated a blood sample. The metabolome was evaluated through a validated targeted approach measuring 188 metabolites. Only metabolites with at least $50\%$ observations above the detection limit were considered, after standardization and log-transformation. Associations between each metabolite and NSW were assessed applying Tobit regression models and Random Forest, a machine-learning algorithm. ### Results When comparing current vs. never night shifters, we observed lower levels of 21 glycerophospholipids and 6 sphingolipids, and higher levels of serotonin (+$171.0\%$, $95\%$CI: 49.1–392.7), aspartic acid (+$155.8\%$, $95\%$CI: 40.8–364.7), and taurine (+$182.1\%$, $95\%$CI: 67.6–374.9). The latter was higher in former vs. never night shifters too (+$208.8\%$, $95\%$CI: 69.2–463.3). Tobit regression comparing ever (i.e., current + former) and never night shifters returned similar results. Years worked in night shifts did not seem to affect metabolite levels. The Random-Forest algorithm confirmed taurine and aspartic acid among the most important variables in discriminating current vs. never night shifters. ### Conclusions This study, although based on a small sample size, shows altered levels of some metabolites in night shift workers. If confirmed, our results may shed light on early biological alterations that might be related to adverse health effects of NSW. ## 1. Introduction Shift work (SW) refers to any organization of work hours that differ from the traditional diurnal work period (from 7:00 a.m. to 6:00 p.m.) [1], including evening, night, and early morning shifts, as well as fixed or rotating schedules [2, 3]. In particular, night shift work (NSW) refers to any kind of work that covers at least 3 h of work between 11:00 p.m. and 6:00 a.m. [4, 5]. In industrialized countries, SW and NSW are common work schedules [6]. Indeed, according to the U.S. Bureau of Labor Statistics, ~$16\%$ of employees surveyed in 2017–2018 followed SW schedules, including $6\%$ of evening shifts workers and $4\%$ of night shifts workers [7]. SW, especially if including night shifts, has been found to be associated with several diseases, e.g., cardiovascular diseases [8], cancers [9], metabolic disorders such as obesity [10, 11] and type 2 diabetes [12, 13], sleep disturbances [14], gastrointestinal disorders [15], and impaired mental health [16]. However, the underlying mechanisms are not fully understood. Some might be mediated by psychosocial stress deriving from interference with social rhythms, but there are indications also suggesting that disruption of normal sleep-wake cycle (circadian rhythm) following SW leads to neuroendocrine and cardiometabolic stress, curtailed and disturbed sleep, and, as a consequence, altered immune functioning and cellular stress [17, 18]. In recent years, omics-based approaches have shown great potential to shed light on mechanisms underlying diseases and their possible association with exposure to relevant risk factors through the identification of biomarkers. Metabolomics, one of the omics-based methodologies, refers to the techniques used to quantify the metabolites present within a cell, tissue or organism [19]. These techniques are mainly divided into two strategies: i.e., untargeted and targeted metabolomics. Targeted metabolomics is the measurement of defined groups of chemically characterized and biochemically annotated metabolites [20]. To our knowledge, only a few studies investigated the effects of shift work on the human metabolome. One measured plasma metabolites in 49 male workers at the beginning and end of a rotating shift schedule including nights and observed an association between NSW and alterations in several metabolites [21]. Two were laboratory studies aimed at evaluating the impact of simulated 3- [22] and 4-day [23] night shift schedules on the metabolic profile of healthy volunteers, with a particular focus on sleep/wake and feeding/fasting cycles. Other two studies compared shifters and non-shifters. The first one analyzed urinary metabolites, and found altered long-chain acylcarnitines, three amino acids, and one sphingomyelin [24] in night shift workers as compared to day workers, based on both crude and adjusted models. The second study evaluated serum metabolites and identified 76 of them in shift workers (including L- tryptophan, acylcarnitines, and several fatty acids) which may represent important biomarkers of impaired lipid metabolism, leading to weight gain and central obesity [25]. As such, evidence on this topic is still limited and further studies are needed. The aim of the present study is thus to evaluate the effect of NSW on serum metabolites in a sample of female nurses, using a targeted metabolomics approach. ## 2.1. Study population, personal data, and biological samples Procedures for recruitment of the study population and collection of personal data and biological samples have been described elsewhere [26]. Briefly, 46 female nurses working in night shifts at the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico in Milan, Italy, were recruited on a voluntary basis and matched by age and length of services to 51 colleagues not working in night shifts. Inclusion criteria were Caucasian ethnicity, age 30–45 years and length of service ≥1 year. After signing informed consent, all participants filled in a questionnaire on demographics, lifestyle habits, personal and family health history, and work history (with a particular focus on SW) and donated a 12 mL blood sample. The sample was drawn in the morning, at the end of the night shift for night shifters (7:15–7:45 a.m.) and at the beginning of the working day for day shifters (8:30–9:00 a.m.), to try to maximize potential differences between the two groups. The metabolomics profile (see below) could not be assessed for six subjects, and we thus performed our analyses on a total of 91 nurses. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the Policlinico Hospital (approval number 702_2015). ## 2.2. Metabolomic analysis The metabolomic profile was assessed with a validated targeted metabolomics approach, implementing liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and using the AbsoluteIDQ p180 kit (Biocrates Life Sciences AG AbsoluteIDQ® p180 Kit, Innsbruck, Austria), which benefits of an established good interlaboratory reproducibility [27]. Briefly, the serum samples were placed on a 96-well plate pre-loaded with the isotopic labeled internal standards, along with a phosphate buffer solution as blank sample, a calibration curve (7 levels), and three levels of quality control samples. Two different plates were implemented for this study. The sample preparation consisted in the derivatization of amino acids and biogenic amines with phenyl isothiocyanate, evaporation, extraction with 5 mM ammonium acetate in methanol, centrifugation, and dilution. Amino acids and biogenic amines were separated and analyzed through an analytical column before the mass spectrometry (LC-MS/MS), while lipids and the hexose were analyzed with a simple flow injection analysis (FIA-MS/MS). A total of 188 metabolites were measured, including 21 amino acids, 21 biogenic amines, the sum of hexoses, 40 acylcarnitine, 15 sphingolipids (SM), and 90 glycerophospholipids among which 14 lysophosphatidylcholines (LysoPC), 38 diacylphosphatidylcholine (PC aa), and 38 acylalkylphosphatidylcholine (PC ae). Further instrumental and analytical details have been previously reported [28]. ## 2.3. Statistical analysis Differences in the distribution of the main adjustment variables across categories of NSW were assessed through the analysis of variance (ANOVA) for age and BMI (continuous) and chi-squared test for smoking habit (categorical). Metabolomic data (from both LC-MS/MS and FIA-MS/MS) were batch-normalized through the MetIDQ software (Biocrates) using, for each metabolite, the median values of three repetitions of a quality control (reference sample) analyzed on the same plate, according to the manufacturer's instructions [29]. Only metabolites with at least $50\%$ of the observations above the limit of detection (LOD) were considered for the statistical analyses. Among these, each remaining value below the LOD was replaced with a value equal to the minimum LOD (specific for each metabolite). Metabolite concentrations were then log-transformed (base e) and standardized (each value subtracted by the mean and divided by the standard deviation). To visualize how metabolites correlate with each other, we performed network analyses where metabolites were considered as nodes, and correlation coefficients obtained from each pair of metabolites as edges; the Fruchterman-Reingold force-directed layout algorithm was used, and the values of r were set as edge weights; only statistically significant correlations with r > 0.4 were considered and metabolites with no connection were not considered. To assess the association between each metabolite and NSW, we applied Tobit censored linear regression models, which are useful to estimate linear relationships when considering dependent variables with left- or right-censoring [30, 31]: in the present work, we considered metabolite concentrations lower than LOD as left-censored. We built a Tobit model for each metabolite, with the metabolite concentration as dependent variable, and NSW as the main independent variable. NSW was modeled both as current or former vs. never NSW and as ever (i.e., current + former) vs. never NSW. As a sensitivity analysis, we stratified current night shifters according to their shift schedule (see below). We also considered “number of years worked in night shifts” as a variable of interest (equal to 0 in never night shifters). Adjustment variables considered a priori as potential confounders were body mass index (BMI) (kg/m2), age, plate (plate 1 or 2: i.e., which of the two 96-well plates the serum sample was loaded on during sample preparation for metabolomic analyses), and smoking habit (current vs. former/never smokers). The models assessing the association between metabolites and number of years in night shifts also included the variable “never vs. ever night shift.” Before implementing all models, we imputed the few values missing from our database (one for age, three for BMI, three for smoking habit) using the k-nearest neighbors algorithm (k-NN) [32] with a k-value = 9 [32]. From each model, we estimated the standardized beta coefficients and calculated the percent variation (Δ%) using the following formula: (exp(β)−1) x 100, where β is the regression coefficient representing the variation in the metabolite level for a unit increase in the independent variable. The p-values were adjusted for multiple testing by controlling the false discovery rate (FDR) according to the method of Benjamini and Hochberg [33] and a FDR p-value lower than 0.1 was considered statistically significant. To have a visual representation of the Tobit models, Volcano plots were created, assigning a dot to each molecule and plotting the Δ% vs. the negative logarithm of the FDR p-value. A confirmatory analysis was also conducted applying a supervised machine-learning algorithm called Random Forest (RF). A RF consists of many unpruned individual decision trees that operate as an ensemble. Individual trees are grown by bootstrapping a random sample of the original data set and by selecting at random, at each node, a small group of input variables to split on. Results from different decision trees are, subsequently, averaged to make final predictions [34]. We used RF to classify subjects into current or former vs. never night shifters and into ever (i.e., current + former) vs. never night shifters, considering correlations among metabolites. K-fold cross-validation, a statistical method for evaluating a machine-learning model and testing its performance, was applied to assess RF performances and to tune parameters in order to obtain optimal predictions. In the present study, based on 5-fold cross-validation results, we implemented RF algorithms setting the number of trees at 10,000, and the number of variables from which to choose at each node at 11 (i.e., the approximate square root of the total number of metabolites included in the analysis). Variables importance scores were then calculated. They are RF-derived measures that facilitate results interpretation by ranking the importance of each feature (i.e., metabolite), and can be computed mainly through two methods: [1] Mean Decrease Accuracy, indicating how much the accuracy (i.e., the number of data points out of all data points which are correctly predicted) decreases when the interested variable is excluded; and [2] Mean Decrease Gini, indicating how much the Gini score (which calculates the probability of a specific feature to be classified incorrectly when selected at random) decreases when a variable is chosen to split a node. The larger the scores, the greater the importance of a variable [34]. We evaluated variable importance, using both above-cited methods, in order to produce more accurate results. All statistical analyses were performed using R (R version 4.1.2, R Foundation, Vienna, Austria) [35] with the Rstudio interface (Version 1.4.1717, RStudio Inc., Boston, MA, USA) and the packages “tidyverse” [36], “VIM,” “AER” [37], “tidygraph,” “ggraph” [38, 39], and “randomForest” [40]. ## 3. Results Mean age of our study subjects was similar across categories of night shift work, ranging from 35.1 years in current shift workers to 36.8 years in former shift workers. The majority of current night shifters ($67\%$) followed a counterclockwise, very rapidly rotating schedule (A), in details: day 1: morning (6:00 a.m.−2:00 p.m. or 7:00 a.m.−2:00 p.m.); day 2: either morning or afternoon (2:00 p.m.−10:00 p.m. or 2:00 p.m.−9:00 PM); day 3: both morning and night (10:00 p.m.−6:00 a.m. or 9:00 p.m.−7:00 a.m.), followed by three rest days (72 h). Eight nurses followed a clockwise, rapidly rotating schedule (B), in details: day 1: morning (7:00 a.m.−2:00 p.m.); day 2: afternoon (2:00 p.m.−9:00 p.m.); day 3: night (9:00 p.m.−7:00 a.m.), followed by two or three rest days (48–72 h). Only five nurses worked on a 12 h schedule (C): day – day – night – (night) – rest – rest. For one subject the information was not available. Never night shift workers had a lower BMI compared to former and current shift workers ($$p \leq 0.057$$). Percent of current smokers increased from $19\%$ among never night shifters to about $33\%$ in current night shifters (Table 1). Descriptive statistics of metabolite concentrations are reported in Supplementary Table S1. Table 1 \| Characteristic \| Never night shift workers \| Former night shift workers \| Current night shift workers \| P * \| \| --- \| --- \| --- \| --- \| --- \| \| N \| 26 \| 22 \| 43 \| \| \| Type of night shift work \| Type of night shift work \| Type of night shift work \| Type of night shift work \| Type of night shift work \| \| Schedule A—N (%) \| - \| - \| 29 (67) \| \| \| Schedule B—N (%) \| - \| - \| 8 (19) \| \| \| Schedule C—N (%) \| - \| - \| 5 (12) \| \| \| Missing—N (%) \| - \| - \| 1 (2) \| \| \| Age \| Age \| Age \| Age \| Age \| \| Mean ± SD \| 36.6 ± 5.4 \| 36.8 ± 5.4 \| 35.1 ± 5.4 \| 0.382 \| \| Missing \| 0 \| 0 \| 1 \| \| \| BMI \| \| \| \| \| \| Mean ± SD \| 21.4 ± 2.4 \| 23.2 ± 3.9 \| 23.1 ± 3.0 \| 0.057 \| \| Missing \| 0 \| 0 \| 2 \| \| \| Smoking habit \| Smoking habit \| Smoking habit \| Smoking habit \| Smoking habit \| \| Former/never smokers—N (%) \| 21 (81) \| 14 (64) \| 27 (63) \| \| \| Current smokers—N (%) \| 5 (19) \| 7 (32) \| 14 (33) \| 0.389 \| \| Missing—N (%) \| 0 (-) \| 1 (4) \| 2 (4) \| \| Network analysis (Supplementary Figure S1) mainly showed that (i) metabolites belonging to the same category are highly correlated, (ii) serotonin is correlated with taurine, and (iii) taurine is also correlated with aspartic acid. When comparing current vs. never night shift workers (Figure 1A), 6 SM and several glycerophospholipids, among which 12 PC aa and 9 PC ae, were significantly decreased; while taurine, serotonin, and aspartic acids were significantly increased (with a percent variation of +182.1, +171.0, and +$155.8\%$, respectively). When comparing former vs. never night shift workers (Figure 1B), only taurine emerged as a significantly different metabolite, with a percent variation of +$208.8\%$. The Tobit regression comparing ever (i.e., current + former) vs. never night shift workers returned similar results (Supplementary Figure S2). Comparable findings were also observed when stratifying current night shifters by shift schedule and focusing on nurses following schedule A (Supplementary Figure S3A). Only four metabolites were found to be significantly altered in night shift workers following schedule B (Supplementary Figure S3B) while no alteration was observed when inspecting shift schedule C (Supplementary Figure S3C). No metabolite was found to be associated with increasing number of years worked in night shifts (Supplementary Figure S4). Complete results from Tobit regression models are reported in Supplementary Table S2. Figure 1:** (A, B) Volcano plots showing the results of the Tobit linear regression models considering the metabolites (dependent variables) in relation to night shift work: current vs. never night shift workers (A) and former vs. never night shift workers (B). The models are adjusted for BMI, age, plate, and smoking habit. Each dot represents a metabolite and is displayed based on the percentage variation of its concentration (x-axis) vs. the negative logarithm (base 10) of the FDR p-value (y-axis). The dashed line represents a FDR p-value equal to 0.1. Figure 2A shows variable importance scores from the RF algorithm for current vs. never night shift workers. Taurine and aspartic acid were the most important variables discriminating subjects in the two groups, according to both Mean Decrease Accuracy and Mean Decrease Gini. Several PC aa and some PC ae were found to be among the 30 most important metabolites. Figure 2B reports variable importance scores for former vs. never night shift workers: C12.1, aspartic acid and taurine were found to be the three most important metabolites, according to both indexes. ADMA, and several PC aa, PC ae, and sphingolipids were also observed among the 30 most important metabolites. Again, when pooling current and former night shifters, we obtained similar results (Supplementary Figure S5). Figure 2: (A, B) Variable importance scores plots of the 30 most important metabolites in predicting current vs. never night shift workers (A) and former vs. never night shift workers (B), according to both Mean Decrease Accuracy and Mean Decrease Gini. In Supplementary Table S3, 5-fold cross-validation results are reported. RF performance was high in the analysis of current vs. never night shift workers as both sensitivity and specificity were above 0.80, while it was medium-low for former vs. never shifters. When analyzing ever vs. never night shifters, sensitivity was 0.80 while specificity was 0.65. ## 4. Discussion In the present study, we evaluated the association between exposure to night shift work and metabolites levels, using a targeted metabolomic approach, in a sample of 91 nurses. In particular, when compared to never night shifters, current night shift workers had higher levels of taurine, serotonin and aspartic acid, while lower levels of several glycerophospholipids and sphingolipids. Similar results were observed in ever night shift workers, while the observed associations disappeared when comparing former shift workers to never night shifters, except for taurine levels. Findings across type of night shift schedule, although mostly confirming the overall results, were hampered by the very small number of subjects following schedules B and C. Number of years worked in night shifts did not impact the levels of metabolites. Serum metabolites in shift workers were previously investigated on 60 subjects from China [25]. Shift workers had altered levels of several lipids, including some glycerophospholipids and sphingolipids, as observed in our study. However, authors also found altered levels of some amines and androgens, and they did not observe any change in serotonin and taurine levels. Differences might relate to the fact that, in the Chinese study, shifters worked two shifts not including the night (i.e., 7:00 a.m.−3:00 p.m.; 3:00 p.m.−11:00 p.m.): as such, their occupational exposure cannot be directly compared to the one in our study. In addition, the applied statistical techniques were different: Huang and colleagues used linear regression models in combination with an Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA), while we used Tobit linear regression models in combination with Random Forests. A second study [21] investigating male workers, which rotated through 3 weeks of night shifts (10:00 p.m.−6:00 a.m.), followed by 3 weeks of evening shifts (2:00 p.m.−10:00 p.m.) and 3 weeks of early morning shifts (6:00 a.m.−2:00 p.m.), did observe alterations in some lipids (e.g., glycerophospholipids and lysophospholipids) associated with night shifts (i.e., somehow similarly to our results). Nonetheless, the shift scheme is hardly comparable to that experienced by our study population which, in addition, consists of female workers only. Two other studies conducted in experimental settings simulated night shifts protocols on a small number of healthy volunteers and collected blood samples at repeated time points [22, 23]. Both concluded that the observed rhythmicity of several metabolites was driven mainly by behaviors imposed by the simulated shift schedule rather than by the central circadian clock. Notwithstanding the peculiar differences characterizing the experimental settings of these investigations, their findings do confirm the relevant role of night shift work in influencing health, in particular for what concerns metabolic imbalance. We observed higher levels of taurine in both current and former night shifters when compared to never night shifters. To exclude this finding could be related to energy drink use, we inspected the distribution of the available variable “drinks other than coffee” and found no differences across categories of night shift (chi-squared p-value = 0.78). Previous investigations documented higher levels of taurine in both humans and rats during periods of sleep deprivation (41–43), a condition typically related to night shift work [44]. In fact, it seems that increased levels of taurine activate the extrasynaptic GABA (A) receptors in the mouse ventrobasal thalamus [45], an area involved in the regulation of the transitions between sleep and wakefulness [46]. Current night shifters showed also higher levels of serotonin. This neurotransmitter is another important factor involved in sleep/wake regulation, functioning primarily to promote wakefulness [42]. In addition, altered levels of both serotonin and taurine have been found to be involved in depression onset (47–49). This is particularly interesting in light of a recent meta-analysis, which estimated a $33\%$ increased risk of depressive symptoms associated with shift work, that rose to more than $70\%$ when restricting the analyses to female workers [16]. In our study, we found serotonin to be positively associated with night shift work in Tobit regression models only and not in the Random Forests analysis: this inconsistency may be explained by the high correlation existing between serotonin and taurine. Current night shift workers had lower levels of several glycerophospholipids. Decreased levels of such lipids were also found in the plasma sample of breast cancer patients [50], reflecting a higher activity of phospholipase A2 (PLA2), an important pro-inflammatory mediator [51]. On the other hand, higher concentrations of several glycerophospholipids were associated with decreased risk of prostate cancer subtypes, especially those in advanced stage [52]. Positive associations between night shift work and both these cancer types have been mentioned by IARC in supporting the evaluation of NSW as probably carcinogenic to humans (group 2A carcinogen) [5]. Higher levels of phospholipids were also documented by several investigations to be negatively associated with metabolic diseases, as altered concentrations of such lipids were found in subjects with dyslipidemia, hypertension, obesity, insulin resistance or type 2 diabetes (53–58). In particular, it was found that elevated levels of phosphatidylcholines showed a possibly anti-inflammatory role under different conditions (e.g., oxidative stress and ulcerative colitis) (59–61). Indeed, phosphatidylcholines inhibit the upregulation of the inflammatory cytokines tumor necrosis factor alpha and interleukin-6 as well as the actin-assembly in phagosomes and macrophages [59, 61]. In this multifaceted scenario, night shift work emerges as a potentially relevant player in the development of metabolic disorders and cancers. Another class of lipids we found to be decreased in night shift workers are the sphingolipids. Lower levels of sphingolipids were found in patients with a diagnosis of major depressive disorder: Demirkan and colleagues identified significant negative associations between the sphingomyelin (SM) ratio 23:1 to SM 16:0 and a psychometric depression measure (Center for Epidemiological Studies-Depression Scale: CES-D) [62]. However, subsequent analysis of an independent replication dataset did not confirm previous results. Another study from Liu and colleagues found that several differential lipid species were significantly correlated with depression severity measured by the Hamilton Depression Scale (HAMD) [63]. Moreover, rats exposed to chronic stress had reduced sphingomyelin and dihydrosphingomyelin levels in the prefrontal cortex (PFC) [64]. This region vulnerability fits with previous studies showing that PFC is the brain region displaying major lipid alterations after the use of maprotiline, an antidepressant. In our study population, only four subjects (ever shifters) declared a prolonged use of psychotropic drugs (not better specified). Decreased levels of sphingolipids were also found in subjects with dyslipidemia [56] and with diabetes mellitus [53], even if some other publications showed opposite results [54], indicating that there is no clear pattern between sphingolipids and metabolic disorders. Levels of aspartic acid were found to be elevated in current shift workers. This is a relative new result. Indeed, few publications on elevated levels of aspartate were published. A study from Guevara-Cruz conducted in Mexico found that levels of aspartate were elevated in a 20-years-old population affected by obesity and insulin resistance [65]. Similar results were also found in a study conducted by Yamada and colleagues, in a Japanese non-diabetic population [66]. Moreover, higher levels of aspartic acid were also found in subjects with epilepsy, as compared to disease-free controls [67]. However, the available research is still too limited and further studies are needed to draw robust conclusions. The present study has some strengths. This is one of the few studies investigating the effects of night shift work on human serum metabolome. Blood samples were collected within a relatively narrow time window (7:15–9:00 a.m.) to minimize the 24-h variations of metabolites levels [68]. Moreover, we evaluated the investigated associations using two different statistical methodologies (i.e., Tobit linear regression models and Random Forests) to make more robust conclusions. The first ones allow to adjust for individual confounders but are not able to consider correlations among the different metabolites. On the contrary, Random Forests are able to take into proper consideration inter-metabolite correlations but not to adjust for individual confounders. As such, the use of both methods provides a more comprehensive picture of our findings. In addition, we considered observations with non-determinable metabolite levels as left-censored, and applied Tobit linear regression models which are particularly adequate when dealing with dependent variables with censored values [30]. This study has also some limitations. First, it is a cross-sectional study, thus preventing to assess causality. Second, sample size is relatively small, not allowing to obtain optimal prediction results in the application of machine-learning algorithms and to thoroughly investigate the potential role of night shift schedule in influencing our findings. Third, our sample was entirely composed of women, preventing the possibility to detect sex-related differences which have been previously observed with metabolomics data, even if in experimental sleep-deprivation settings [42, 69]. Fourth, all variables including the exposure of interest (i.e., shift work status) as well as all the confounders were self-reported, although the absence of a pathologic outcome should allow to avoid major distortions. Last, given the limited set of available information, we were not able to fully disentangle whether the observed metabolic alterations were related directly to NSW (i.e., by modification of the endogenous circadian clock) or rather due to changes in behavioral (e.g., sleep/wake or feeding/fasting) cycles. In conclusion, our study, although based on a small sample size, shows an alteration of metabolites levels in night shift workers when compared to never night shifters. In particular, serum concentrations of taurine, serotonin, and aspartic acid were higher, while those of several glycerophospholipids and sphingolipids were lower, independently from number of years worked in night shifts. These findings may shed light on early biological alterations that might be related to adverse health effects of NSW, such as metabolic disorders, cancers, and mental diseases. However, further studies including a larger sample size and male workers are needed to confirm our results. ## 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 Review Board of the Policlinico Hospital (approval number 702_2015). The patients/participants provided their written informed consent to participate in this study. ## Author contributions Conceptualization: MC, GF, CM, ACP, and SF. Methodology: EB, GF, and MC. Software and formal analysis: EB, GF, and LF. Validation: LF and MC. Investigation and writing—original draft preparation: EB and GF. Resources and data curation: GF, EP, and RM. Writing—review and editing: MC, EP, RM, CM, LF, ACP, and SF. Visualization: MC and GF. Supervision: ACP, SF, and MC. Project administration: CM. Funding acquisition: MC and CM. 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. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1082074/full#supplementary-material ## References 1. Costa G. Factors influencing health of workers and tolerance to shift work. Theor Issues Ergon Sci. (2003.0) 4 4-263. DOI: 10.1080/14639220210158880 2. Vyas MV, Garg AX, Iansavichus AV, Costella J, Donner A. Shift work and vascular events: systematic review and meta-analysis. BMJ (2012.0). DOI: 10.1136/bmj.e4800 3. Wang XS, Armstrong MEG, Cairns BJ, Key TJ, Travis RC. Shift work and chronic disease: the epidemiological evidence. Occup Med (Chic Ill). (2011.0) 61 78-89. DOI: 10.1093/occmed/kqr001 4. Härmä M, Ropponen A, Hakola T, Koskinen A, Vanttola P, Puttonen S. 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--- title: 'Acceptability of patient-centered, multi-disciplinary medication therapy management recommendations: results from the INCREASE randomized study' authors: - Noah I. Smith - Ashley I. Martinez - Mark Huffmyer - Lynne Eckmann - Rosmy George - Erin L. Abner - Gregory A. Jicha - Daniela C. Moga journal: BMC Geriatrics year: 2023 pmcid: PMC9999619 doi: 10.1186/s12877-023-03876-4 license: CC BY 4.0 --- # Acceptability of patient-centered, multi-disciplinary medication therapy management recommendations: results from the INCREASE randomized study ## Abstract ### Background Polypharmacy and inappropriate medications may be a modifiable risk factor for Alzheimer’s Disease and Related Dementias (ADRD). Medication therapy management (MTM) interventions may mitigate medication-induced cognitive dysfunction and delay onset of symptomatic impairment. The objective of the current study is to describe an MTM protocol for a patient-centered team intervention (pharmacist and non-pharmacist clinician) in a randomized controlled trial (RCT) directed at delaying the symptomatic onset of ADRD. ### Methods Community dwelling adults 65 + years, non-demented, using ≥ 1 potentially inappropriate medications (PIM) were enrolled in an RCT to evaluate the effect of an MTM intervention on improving medication appropriateness and cognition (NCT02849639). The MTM intervention involved a three-step process: [1] pharmacist identified potential medication-related problems (MRPs) and made initial recommendations for prescribed and over-the-counter medications, vitamins, and supplements; [2] study team reviewed all initial recommendations together with the participants, allowing for revisions prior to the finalized recommendations; [3] participant responses to final recommendations were recorded. Here, we describe initial recommendations, changes during team engagement, and participant responses to final recommendations. ### Results Among the 90 participants, a mean 6.7 ± 3.6 MRPs per participant were reported. Of the 259 initial MTM recommendations made for the treatment group participants ($$n = 46$$), $40\%$ percent underwent revisions in the second step. Participants reported willingness to adopt $46\%$ of final recommendations and expressed need for additional primary care input in response to $38\%$ of final recommendations. Willingness to adopt final recommendations was highest when therapeutic switches were offered and/or with anticholinergic medications. ### Conclusion The evaluation of modifications to MTM recommendations demonstrated that pharmacists’ initial MTM recommendations often changed following the participation in the multidisciplinary decision-making process that incorporated patient preferences. The team was encouraged to see a correlation between engaging patients and a positive overall response towards participant acceptance of final MTM recommendations. ### Trial registration Study registration number: clinicaltrial.gov NCT02849639 registered on $\frac{29}{07}$/2016. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12877-023-03876-4. ## Background Many prior studies have provided evidence that medication therapy management (MTM) can lead to improved health and economic outcomes [1–4]. MTM involves five core components: availability of a personal medication record, medication therapy review, development of a medication-related action plan, intervention and/or referral, and documentation and follow-up of medication changes or lack thereof [5–7]. Though most MTM services share these five basic elements, there is heterogeneity in how these services are operationalized. Specifically, there is variability in how potentially inappropriate medications (PIMs) are identified, whether certain medications are targeted specifically, the types of recommendations made, and patient’s acceptance of the proposed changes from an MTM intervention. Additionally, patient and pharmacist engagement with prescribing clinicians varies, [5–7] though evidence shows that pharmacist-prescriber-clinician teams engaging together in MTM activities results in better medication optimization outcomes [2, 8–10]. It is important to characterize MTM-related services in collaborative practices in order to estimate their impact on patient health outcomes, especially for MTM services targeting vulnerable populations such as older adults receiving PIMs. We recently completed the INtervention for Cognitive Reserve Enhancement in delaying the onset of Alzheimer's Symptomatic Expression (INCREASE) study, a randomized controlled trial where we tested an MTM intervention that actively involved the patient, a board-certified geriatric pharmacy specialist (BCGP), and a non-pharmacist clinician [11, 12]. INCREASE was designed to evaluate the effect of the MTM intervention on changes in medication appropriateness and cognitive function; study data included comprehensive information on health history, medication use and experience with medication taking, as well as the process of implementing the MTM intervention. We previously reported on the successful implementation of the MTM intervention that translated into an improved medication appropriateness at the one-year follow-up [12]. The current study characterizes the stepwise process of delivering the MTM intervention in the INCREASE trial with the goal of helping to fill a qualitative gap in the literature surrounding MTM interventions, specifically focused on patient-centered, multidisciplinary approaches. The specific approach described, including details of the process, provides a model for future evidence based, multidisciplinary MTM interventions that may be implemented rationally in practice. The objectives of the current manuscript are twofold: [1] describe the recommendations made by the study BCGPs using participant-reported medical and medication histories for all INCREASE participants, prior to randomization to either the MTM intervention (specific medication recommendations plus provision of educational materials on inappropriate medication use) or usual care (i.e., only provision of educational materials on inappropriate medication use), and [2] describe final recommendations for patients randomized to the MTM intervention. The second objective describes (a) revisions to the preliminary baseline MTM recommendations over the course of the intervention, and (b) participant response to the MTM recommendations following the intervention. ## INCREASE study overview The INCREASE study was a randomized controlled trial enrolling community-dwelling adults 65 years and older who did not have dementia and were using at least one PIM as defined in the 2015 Beers Criteria (the most recent version at the time of the study) [13]. Complete details of the INCREASE protocol and results are available elsewhere and briefly described below [11, 12]. After 1:1 randomization that was stratified based on baseline amyloid burden, participants randomized to the control group received usual care with educational pamphlets on medication appropriateness for older adults and risks associated with polypharmacy. In addition to educational materials, participants randomized to the MTM intervention met with the BCGP and a non-pharmacist study clinician (e.g., nurse practitioner, neurologist) to discuss the baseline recommendations. This meeting allowed for 1) participant education on risks, benefits, and alternatives to optimize medication use; and 2) the collection of additional relevant information, including participant beliefs, preferences, and treatment goals. During the MTM team meeting, final recommendations were formalized, and the details of any relevant revisions to the baseline recommendation were noted in the pre-specified data collection forms. The INCREASE study was approved by the University of Kentucky Institutional Review Board (IRB #43239) and all the study participants provided informed consent. The protocol for the study was registered on clinicaltrials.gov (NCT02849639) on $\frac{29}{07}$/2016, in accordance with the relevant guidelines and regulations or in accordance with the Declaration of Helsinki. Study data were collected and managed using the Research Electronic Data Capture (REDCap), a secure, web-based software platform designed to support data capture for research studies [14, 15]. ## Baseline recommendations (all INCREASE study participants) Before randomization, comprehensive medication reviews were conducted by BCGPs for all participants using participant-reported medical conditions and information on dose, frequency, indication, duration of treatment, tolerability, and adverse drug reactions for all prescription medications, vitamins, and supplements. The BCGP medication review process involved 1) assessing the clinical appropriateness of each medication using the Beers Criteria [13] and Medication Appropriateness Index (MAI); [16] 2) evaluating potential drug-drug and drug-disease interactions in accord with the above and also taking into account prescription label information; and 3) assessing whether medication regimens followed relevant disease-specific evidence-based guidelines [13, 17, 18]. Of note, blood laboratory work results, electronic medical records, and previous therapies (e.g., medication failures) were not available to BCGPs when devising baseline recommendations, but were available to the clinician member of the MTM team. Following randomization, the MTM recommendations were only shared with those participants randomized to the intervention group ($$n = 46$$). Recommendations for the control group were recorded in the study database but not shared with those participants. During the INCREASE study period, the pharmacy team of two BCGPs utilized drug and health information resources (e.g., Lexicomp and UpToDate [Wolters Kluwer Health Inc. Riverwoods, IL]), Beers Criteria [13], relevant guidelines (e.g., Diabetes Standards of Care [17] and Clinical Practice Guidelines for Hypertension [18]), and clinical judgement to justify their recommendations. Each recommendation was reviewed by both BCGPs and a consensus pharmacy recommendation was decided via discussion. Detailed information for each recommendation was then entered into a series of pre-specified study protocol data collection forms, allowing for systematic categorization of recommendations as either: 1) medication discontinuation with or without tapering; 2) switch to a different medication; 3) dose adjustment (e.g., decrease dose, adjust dose for organ function/tolerability, or increase dose); 4) new medication initiation; 5) drug or disease monitoring recommendation (e.g., vital signs, falls risk, sedation); or 6) a non-pharmacologic recommendation (e.g., sleep hygiene, avoiding gastroesophageal reflux triggers, referral for diagnostic workup). Baseline recommendations were also categorized by pharmacologic class and over the counter (OTC) or supplement status of the medication prompting a baseline MTM recommendation. A full schematic for medication categorization is available in the supplementary material (see Supplementary Table S1). ## Final recommendations (MTM intervention group only) After 1:1 stratified randomization, study pharmacists met with the participant and study clinician to deliver the MTM intervention. During the intervention, the team gathered further information from the patient and discussed baseline recommendations together, in-person, with additional context provided by the participant on their health status, needs, and preferences. Because health status and medication use in participants may have changed in the time between the baseline assessment and the initial MTM recommendation, comparison of baseline to final recommendations was limited to those baseline MTM recommendations that proposed medication changes at the time of the initial MTM study visit. The non-medication related recommendations (see supplementary material for additional information) were discussed during the team MTM intervention, but they were not included in the present analysis. Participant responses to each final MTM recommendation for participants randomized to the MTM intervention were collected at the conclusion of the initial MTM intervention visit using a standardized form where the participant selected his or her response to the recommendation as 1) willing to change, 2) refusing to change, 3) needing to confer with a primary care provider or other specialist (e.g., cardiologist), or 4) not applicable (e.g., the participant had already discontinued the medication, dose adjustment was no longer warranted per clinical judgement). In this manuscript we are describing in detail the immediate participant response as recorded following the baseline intervention. The impact of the intervention on medication appropriateness is described in detail elsewhere [12]. ## Baseline characteristics Of the 104 participants screened, 90 were eligible and randomized in the INCREASE study. Of these, 46 participants were randomized to the MTM intervention group. The mean (SD) age at enrollment was 73.9 years (6.0). The majority of the participants reported female gender ($64\%$) and white race ($89\%$), with an average of 16.5 (2.8) years of education. The mean Charlson Comorbidity Index score was 1.9 (1.9), with participants reporting an average of 12.8 (4.8) total medications 2.4 (1.4) medications per participant were identified as PIMs per 2015 Beers Criteria. Supplementary Table S2 provides additional information on baseline characteristics for all the participants in the INCREASE study as well as for those randomized to the MTM intervention. ## Baseline recommendations (all INCREASE study participants) A total of 602 pre-randomization recommendations were made across the 90 INCREASE participants, averaging 6.7 ± 3.3 MTM recommendations per participant and ranging from 1 to 17 baseline recommendations per participant (median [IQR] of 7 [4, 8.9]). Table 1 shows the distribution of medication categories associated with baseline recommendations and the types of recommendations provided. Table 1Baseline MTM recommendations by medication category† and recommendation type among all INCREASE trial participants ($$n = 90$$)RecommendationsMTM recommendations (total: $$n = 602$$)N%Medication category†Cardiometabolic$13822.9\%$Gastrointestinal$10216.9\%$Pain management$8714.5\%$Anticholinergics$7712.8\%$Vitamins and supplements$7612.6\%$Neuropsychiatric$6711.1\%$Other$559.1\%$Recommendation typeDose adjustment$17028.2\%$Switch to preferred agent$16627.6\%$Drug and disease monitoring$10116.8\%$Non-pharmacologic therapy$7712.8\%$Discontinuation$457.5\%$Initiation of new medication$437.1\%$†See supplementary Table S1 for full listing of medication categories The most common class of medications with recommendations were cardiometabolic agents ($$n = 138$$, $23\%$), followed by medications for gastrointestinal conditions ($$n = 102$$, $17\%$), pain management ($$n = 87$$, $15\%$), anticholinergics ($$n = 77$$, $13\%$), vitamins and supplements ($$n = 76$$, $13\%$), neuropsychiatric agents ($$n = 67$$, $11\%$), and other medications ($$n = 55$$, $9\%$). Across all baseline recommendations, one-third ($$n = 201$$, $33\%$) were prompted by use of PIMs available on the US market as over-the counter (OTC) products without a prescription. The most frequent OTC medications included proton pump inhibitors, vitamins/supplements, antihistamines, OTC non-steroidal anti-inflammatories, aspirin, and H2 receptor antagonists. The most common type of baseline recommendation was continuation of therapy with dose adjustment (e.g., decrease pain medication dose, intensify antihypertensive medication dose) ($$n = 170$$, $28\%$). Second most common were therapeutic switches to a less risky pharmacotherapeutic alternative ($$n = 166$$, $28\%$; e.g., de-escalate from a proton pump inhibitor to a H2 receptor antagonist ± calcium-based antacid; switch from a first-generation to non-sedating second-generation antihistamine). Monitoring ($$n = 101$$, $17\%$) and non-pharmacologic recommendations ($$n = 76$$, $13\%$) accounted for about one-third of all baseline MTM recommendations. The most frequent monitoring recommendations involved recommending objective testing (e.g., blood pressure, blood chemistry/organ function tests) and recording self-reported measures (e.g., dizziness, pain). Non-pharmacologic recommendations most frequently involved counseling for fall prevention strategies with and without physical therapy referral, dietary and lifestyle changes for gastrointestinal conditions, non-pharmacologic pain management, and sleep hygiene. Although recommendations to discontinue medications were relatively less frequent ($$n = 46$$, $8\%$), those medications most commonly associated with a baseline MTM recommendation to discontinue included vitamins/supplements and medications with therapeutic duplication (e.g., participant was taking two separate antihistamines for seasonal allergies). All recommendations for initiation of a new medication ($$n = 43$$, $7\%$) involved treating an unmet clinical need and/or initiating a preventative medication, most often a guideline-recommended statin or aspirin in the setting of cardiovascular risk factors. ## Final recommendations (Intervention Group only) Following randomization, INCREASE participants who were assigned to MTM ($$n = 46$$) met with the BCGP and a non-pharmacist clinician. There were 296 baseline recommendations across the MTM arm’s participants. Of these, 37 recommendations ($12.5\%$) proposed at baseline did not relate directly to a medication change and were therefore excluded from the final recommendation analysis included in this manuscript. An account of these 37 excluded recommendations is provided in supplementary table S3. Finalized, unblinded MTM recommendations that were directly related to a medication change, comprised 259 of the original 602 blinded baseline recommendations, averaging 5.6 (SD 2.3) MTM recommendations per participant. The distribution of final recommendations by medication category was as follows: cardiometabolic ($$n = 58$$, $22\%$), pain management ($$n = 42$$, $16\%$), vitamins and supplements ($$n = 38$$, $15\%$), anticholinergics ($$n = 32$$, $12\%$), gastrointestinal ($$n = 32$$, $12\%$), neuropsychiatric ($$n = 31$$, $12\%$), and other ($$n = 26$$, $10\%$). The distribution of by recommendation type was as follows: dose adjustment ($$n = 98$$, $38\%$), switch to preferred agent ($$n = 92$$, $36\%$), drug and disease monitoring ($$n = 30$$, $12\%$), discontinuation ($$n = 26$$, $10\%$), and initiation of a new medication ($$n = 13$$, $5\%$). Table 2 shows the results of the patient-pharmacist-clinician team MTM interventions after randomization. Less than half of the baseline recommendations were revised through the team discussion and deliberation process ($$n = 104$$, $40\%$). Baseline recommendations were least likely to be revised for vitamins/supplements and cardiometabolic medications, or with a recommended dose adjustment or new initiation. Conversely, baseline recommendations were the most likely to be revised when involving GI therapy and pain management medications, or for recommended medication monitoring or discontinuation. The most frequent reasons for revisions were due to missing information relevant to the participant’s medical history (e.g., a missing diagnosis for Barrett’s esophagus warranting proton pump inhibitor use) and/or missing medication information (e.g., previous failure or intolerability of a guideline-preferred pharmacotherapeutic agent).Table 2Revision status of MTM recommendations through intervention delivery, and participant responses to final MTM recommendations by medication category and recommendation type ($$n = 259$$ final recommendations) among INCREASE intervention group participants ($$n = 46$$ participants) aRecommendationsRecommendation was revised during the MTM interventionParticipant response to final MTM recommendation after revision, if applicableWilling to change ($$n = 118$$)Must first confer with another provider* ($$n = 99$$)Refusal to change ($$n = 15$$)Not applicable** ($$n = 27$$)N (%)N (%)N (%)N (%)N (%)Medication categoryCardiometabolic ($$n = 58$$)19 ($33\%$)19 ($33\%$)27 ($47\%$)1 ($2\%$)11 ($19\%$)Pain management ($$n = 42$$)23 ($55\%$)20 ($48\%$)15 ($36\%$)4 ($10\%$)3 ($7\%$)Vitamins and supplements ($$n = 38$$)11 ($29\%$)20 ($53\%$)12 ($32\%$)4 ($11\%$)2 ($5\%$)Anticholinergics ($$n = 32$$)13 ($41\%$)19 ($59\%$)10 ($31\%$)1 ($3\%$)2 ($6\%$)Gastrointestinal ($$n = 32$$)19 ($59\%$)14 ($44\%$)15 ($47\%$)1 ($3\%$)2 ($6\%$)Neuropsychiatric ($$n = 31$$)13 ($42\%$)12 ($39\%$)16 ($52\%$)1 ($3\%$)2 ($6\%$)Other ($$n = 26$$)6 ($23\%$)14 ($54\%$)4 ($15\%$)3 ($12\%$)5 ($19\%$)Recommendation typeDose adjustment ($$n = 98$$)36 ($37\%$)38 ($39\%$)44 ($45\%$)4 ($4\%$)12 ($12\%$)Switch to preferred agent ($$n = 92$$)45 ($49\%$)48 ($52\%$)34 ($37\%$)3 ($3\%$)7 ($8\%$)Drug and disease monitoring ($$n = 30$$)9 ($30\%$)14 ($47\%$)8 ($27\%$)2 ($7\%$)6 ($20\%$)Discontinuation ($$n = 26$$)9 ($35\%$)13 ($50\%$)8 ($31\%$)5 ($19\%$)0 ($0\%$)Initiation ($$n = 13$$)5 ($38\%$)5 ($38\%$)5 ($38\%$)1 ($8\%$)2 ($15\%$)aCell percentages are displayed as a percent of the row totalAnother provider could be a primary care physician, specialist such as a cardiologist, or other non-study prescribing clinicianReasons for lack of applicability included medication use having been appropriately modified since baseline medication use information was collected, or the proposed medication change was no longer clinically relevant given additional information from the participant and/or MTM team discussion Upon receiving the finalized MTM recommendations, participants responded about half the time that they were willing to make the changes proposed ($$n = 118$$, $46\%$), and often needed to confer with a primary care provider or other clinical specialist ($$n = 99$$, $38\%$) before making a decision, but rarely refused to make the proposed changes ($$n = 15$$, $6\%$). In some cases ($$n = 27$$, $10\%$), the recommendation was no longer clinically relevant and participant responses were recorded as not applicable. Lack of applicability arose from medication use having been appropriately modified since baseline medication use information was collected ($$n = 11$$), or from the proposed medication change no longer being clinically relevant given additional information from the participant and/or MTM team discussion ($$n = 16$$). A full account of these 27 recommendations in provided in supplementary table S4. Participant willingness to adopt recommended MTM changes was highest for vitamins/supplements and anticholinergic agents, and for recommendations involving a pharmacotherapeutic switch. Participants most often responded that they needed to confer with a primary care provider or other specialist when the MTM recommendations included psychiatric, GI, and cardiometabolic medications, or for dose adjustments or medication switches. Participant refusal to adopt final recommended changes ($$n = 15$$, $6\%$) was low across all medication categories and recommendation types in the INCREASE trial MTM intervention. Refusal was highest among recommendations involving vitamins and supplements ($$n = 4$$) or pain management ($$n = 4$$), as well as for recommendations involving medication discontinuation ($$n = 5$$). ## Discussion This study describes MTM recommendations for participants enrolled in the INCREASE trial. The most common medication categories flagged at baseline included 1) cardiometabolic medications, 2) gastrointestinal medications, 3) pain management medications, 4) anticholinergics, and 5) vitamins/supplements. The most common types of recommendations made at baseline were 1) dose adjustments and 2) switches to more appropriate therapeutic alternatives. Notably, BCGP recommendations were not strictly medication related. In this study, many MTM recommendations did not directly involve a medication change, but rather addressed other potential medical problems (e.g., disease monitoring, referral for diagnostic workup or physical therapy, addition of non-pharmacologic therapies). Each of the top five medication categories identified in the analysis for baseline recommendations included at least some OTC medication options, and one-third of baseline MTM recommendations involved a medication available OTC. OTC products are available without a prescription and were identified frequently as PIMs ($13\%$ of all baseline recommendations and $15\%$ of final recommendations). Thus, our study points to the importance of educating patients on the risk–benefit profile of OTCs and the role of pharmacists in OTC stewardship. Among the final MTM recommendations analyzed, $40\%$ underwent revision compared to the baseline MTM recommendation provided. This reflects the potential for several factors to influence recommendations as more information is gathered in a multidisciplinary MTM intervention. Notably, input from the patient on previous therapies, medication tolerability, feasibility/adherence, and condition severity may help inform the MTM team’s final decision-making process. Our results demonstrate that engaging the patient in a team-based intervention may result in patient-motivated revisions to baseline recommendations. This comparison of pre-intervention recommendations to final recommendations after team deliberation has not been discussed in previous literature. Participant responses indicated willingness to make recommended changes about half of the time and a need to confer with a primary care provider or other clinical specialist about one third of the time. This was interpreted as generally positive, since participants were most often willing to either accept the final recommendation as specific, or to further engage with another healthcare provider to seek additional medical advice. While participant refusal to change was generally low, our findings suggest that patients may be less willing to adopt MTM recommendations for certain categories of medications or for certain recommendation types. Previous literature has addressed acceptability of MTM recommendations [19–23]; however, the recommendation type and medication category have not been described in relation to participant willingness to make changes. Further research is needed to determine if willingness to adhere long-term to recommendations is impacted by the type of recommendation and medication in question. Though extensive medication and medical histories were collected from participants, the baseline recommendations were limited to self-reported information before randomization, and complete clinically relevant information was not always available to the BCGP at baseline (e.g., renal function from an electronic medical record). This finding indicates that pharmacists engaged in MTM processes need access to relevant clinical information and an opportunity for direct engagement with prescribers and the patient who have first-hand knowledge of such clinical variables. In addition, chart review may not capture all information necessary to make a patient-centered MTM recommendations, which has not been reviewed in previous literature [19–23]. As health status, medication use and tolerability change over time, there is a need to routinely review previous recommendations and adjust them as needed to reflect the patient’s current needs. There are several limitations to this study. The 2015 Beers Criteria [13]. was used in the study, which was the latest version at the time of the study. During the INCREASE trial, updated Beers Criteria were published by the American Geriatrics Society in 2019 [24]. As an example, the 2015 Beers Criteria recommended caution in aspirin use for primary cardiovascular event prevention among adults aged ≥ 80 years. In contrast, the 2019 Beers Criteria expanded the recommendation to caution in aspirin use for both primary cardiovascular event and colorectal cancer prevention for adults aged ≥ 70 years. This may limit generalizability as medical treatments, guidelines, and prescribing patterns evolve over time in response to scientific evidence. Another limitation of this descriptive study was that the INCREASE participant experiences may not be generalizable to populations that have different distributions of demographic and health characteristics. Similarly, local prescribing practices and the use of PIMs observed in the INCREASE trial may not be representative of the entire US population today. Additionally, the number of study pharmacists and prescribing clinicians was small. Ability for multiple pharmacists to independently review and adjudicate the categorization of MTM recommendations would strengthen future studies. Though this study adds to the body of descriptive literature on baseline MTM trial recommendations, further studies in diverse populations are needed to identify culturally appropriate MTM strategies, as well as to allow a more detailed examination of prescribing inequities that might influence MTM outcomes over time. When teams gather to critically evaluate an individual’s medication use process (i.e., diagnostician/prescriber, dispenser, and medication user), open dialogue may facilitate transparency in strategic medication use decisions. Negotiation of an evidence-based approach to medication use should be to the individual’s unique combination of diseases, medications, clinical status, and very importantly – personal preference that may have its roots in cultural/social/racial/ethnic diversity. It is important to note that not all MTM interventions are equivalent. The INCREASE trial modeled its intervention on a foundation of multidisciplinary team interaction with active participant engagement. This is often beyond the scope of traditional, community pharmacy-based MTM models in practice today. The present results suggest that medical advice from a patient-centered team with multiple healthcare perspectives is both appealing to patients and may elicit a stronger patient acceptance of MTM recommendations. Further studies characterizing patient responses to different MTM models are needed to determine whether the qualities such as mode of delivery and multidisciplinary involvement impact long-term recommendation adherence, and/or influences patient outcomes. ## Conclusion Multidisciplinary interventions such as the pharmacist-clinician-patient MTM team used in the INCREASE study may hold promise for improving health-related outcomes among community dwelling persons. Thorough characterization of MTM interventions is needed to specifically describe the nuances of MTM approaches for making recommendations. It is also critical for guiding future endeavors in the area of MTM science. The present data demonstrate that the recommendations suggested by patient-centered multidisciplinary healthcare teams can be dynamic and complex, and that participant responses may vary depending on the medication targeted and the type of recommendation proposed. ## Supplementary Information Additional file 1: Supplementary Table S1. Medication categorization schematic for medications prompting baseline medication recommendations in the INCREASE trial. Supplementary Table S2. Baseline characteristics of all INCREASE trial participants, and those randomized to the MTM intervention. Supplementary Table S3. Full account of baseline recommendations for the MTM intervention arm that were excluded from final recommendation analysis ($$n = 37$$). Supplementary Table S4. Full account of final recommendations designated as not applicable ($$n = 27$$) ## References 1. Barnett M, Frank J, Wehring H. 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--- title: Influence of health literacy on health outcomes of different social strata—— an empirical study based on the data of China's health literacy investigation authors: - Huifang Yu - Siwen Sun - Jie Ling - Haixiao Chen - Guilin Liu journal: International Journal for Equity in Health year: 2023 pmcid: PMC9999621 doi: 10.1186/s12939-023-01858-x license: CC BY 4.0 --- # Influence of health literacy on health outcomes of different social strata—— an empirical study based on the data of China's health literacy investigation ## Abstract ### Background Health literacy has always been considered as an important factor to promote people's health, but does it have a significant effect on health across all social strata and especially lower social strata? This study aims to analyze the influences of health literacy on health outcomes of different social strata, and then infer whether improving health literacy can reduce health disparities among different social strata. ### Methods Utilizing health literacy monitoring data from a city in Zhejiang Province in 2020, the samples are divided into three social strata according to the socioeconomic status score: low, middle and high social stratum, to compare whether there are significant differences in health outcomes between population with lower and higher health literacy among different social strata. In the strata with significant differences, control the confounding factors to further verify the influence of health literacy on health outcomes. ### Results In low and middle social strata, there are significant differences between population with lower and higher health literacy, when considering the two types of health outcomes (chronic diseases and self-rated health), but in high social stratum, this difference is not significant. After controlling the relevant variables, the influence of health literacy on the prevalence of chronic diseases is statistically significant only in low social stratum, and the health literacy is negatively correlated with the prevalence of chronic diseases(OR = 0.722, $$P \leq 0.022$$). In addition, there are statistical significances for positive impact of health literacy on self-rated health in both low and middle social strata (OR = 1.285, $$P \leq 0.047$$; OR = 1.401, $$P \leq 0.023$$). ### Conclusion Compared with high social stratum, the influence of health literacy on health outcomes of low social stratum (chronic diseases) or both middle and low social strata (self-rated health) is more significant, and both are to improve the health outcomes. This finding suggests that improving residents' health literacy may be an effective way to alleviate the health disparities among different social strata. ## Introduction Since the Black Report was published in 1980 [1], the health disparities among different social strata have gradually attracted the attention of scholars from all countries. Most studies have found that the health status of low social stratum is often worse [2, 3]. With the development of medical technology and the continuous promotion of public health measures, the life expectancy of people in all countries around the world has been increasing, but the health disparities among different social strata still exist and even tend to get worse. Ten years after the Black Report was published, Smith and other scholars conducted another survey in British society and found that the health disparities among different British social strata were still expanding [4], and similar findings were found in the studies conducted by Tetzlaff and Fors [5, 6]. In order to alleviate this phenomenon of health inequality, researchers began to look for the reasons why health disparities exist among different social strata. In the field of health and medicine, some researchers believe that one of the important reasons for the emergence and continuous expansion of such health disparities is the uneven distribution of benefits brought by the progress of medical technology and various health promotion policies and measures in the whole society [7, 8]. For example, Pavalko believed that the advantages and resources possessed by people with higher socioeconomic status will make it easier for them to access and utilize new health promotion mechanisms, which resulted in population with high socioeconomic status would benefit more, while the poorest and the least educated population will benefit least. In order to reduce the uneven distribution of benefits among different social strata, governments and academia in all countries have begun to taken measures to improve the health status of low social stratum. Most of the measures are committed to providing a healthy supportive environment for population with low socioeconomic status, so that they have "the opportunity" to make healthy choices, such as establishing medical insurance systems, basic public health service systems, medical resources sinking and other measures [9, 10]. However, besides a few mandatory measures, most of the health services need residents to actively participate and utilize, especially the cultivation of a healthy lifestyle needs long-term self-consciousness. Compared with the population with high socioeconomic status, the population with low socioeconomic status often lack the ability to acquire, distinguish and utilize health information and health services [11–13], which is just the manifestation of lack of health literacy. The existing researches also indicate that the level of health literacy1 of the population with low socioeconomic status is generally low [14]. Health literacy is defined as " The degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions "[15]. The basic differences between improving health literacy and other public health measures lie in that it is an internalized process, and its purpose is to enable people to make healthy choices sincerely, voluntarily and willingly. It can be believed that improving health literacy is the internal driving force for other public health measures to play their role. Therefore, analyzing influence of health literacy on health outcomes plays an irreplaceable role in finding ways to reduce the health disparities among different strata. The current researches on the relationship between health literacy and health outcomes can be roughly divided into the following aspects according to different health outcomes: [1] The influence on disease prevalence and prognosis. For example, the population with low health literacy have a higher prevalence of chronic diseases [16], and perform worse in disease control and complication prevalence [17]. [ 2] Influence on mortality. Lower health literacy is associated with higher mortality [18]. [ 3] Influence on self-rated health status. The self-rated health status of the population with low health literacy is worse [19, 20]. Most of these studies are conducted in the whole population or divided into different subgroups according to gender, age and other characteristics, respectively studying the influence of health literacy on health outcomes in each subgroup. However, few studies have considered social strata of samples according to their socioeconomic status to understand the relationship between health literacy and health outcomes in different social strata. Although health literacy plays a positive role in promoting health for most health outcomes in the study of the whole population, it is uncertain whether health literacy can also play a positive role in different social strata, especially in low social stratum. If we want to alleviate the health disparities among different social strata by improving health literacy, we must first understand the influence of health literacy on health outcomes in all social strata, and then further infer whether this method to improving health literacy can play desired role in reducing health disparities among different social strata. In this study, three classical measurement indicators, education level, income level and professional status [21], are used to measure socioeconomic status, the scores of health literacy questionnaire are used to measure whether the samples have health literacy, and the prevalence of chronic diseases and self-rated health status are used as indicators to measure health outcomes. According to the score of socioeconomic status, the samples are divided into three social strata: low, middle and high social stratum, and the influence of health literacy on health outcomes in different social strata is analyzed, so as to provide scientific evidences to find effective ways to reduce the health disparities among different social strata. ## Respondent This survey is part of a survey of residents' health literacy in Zhejiang Province conducted by the Zhejiang Center for Disease Control and Prevention. The respondents were found in seven counties of a city in Zhejiang Province. Residents aged 15–69 who had lived in the local area for more than 6 months totally from July 2019 to June 2020 are selected as the respondents, but do not include residents who collectively lived in hospitals, dormitories, nursing homes, etc. ## Sampling method The samples are selected by stratified multistage random sampling. In the first stage, four townships are randomly selected from each county, and a total of 28 townships surveyed places are selected. In the second stage, two communities are randomly selected from each township. In the third stage, 100 households are selected from each community, and one resident aged 15–69 is selected from each household as the respondent. It is enough to complete 85 questionnaires in each community, and a total of 4,760 samples are obtained (Fig. 1). In this study,we excluded respondents aged 15 to 17. Because we need to know the professions of the respondents to measure people's socioeconomic status, yet most respondents did not work before the age of 18. After initial screening according to age, there are 4,693 respondents aged between 18 and 69.Fig. 1Sampling flow chart ## Survey method In this survey, the method of questionnaire survey and household survey are both adopted. The questionnaire is completed by the respondents. If the respondents can not complete the questionnaire independently, it will be surveyed by face-to-face inquiry. Before the survey, investigators were trained standardly to ensure the consistency of survey method used. During the survey, the on-site coordinators will supervise and verify whether the investigators comply with the survey technical specifications. After the survey, the disease control department will conduct quality control by checking the answer time in the system background, extracting sound recordings and on-site review, exclude the unqualified questionnaires, and select new respondents again, so as to obtain all the qualified data finally. The questionnaire includes three parts: General information survey, Health literacy survey and Health status survey. The general information survey mainly collects the age, gender, profession, education level, income level and other information of the respondents. Health literacy was assessed by the Chinese Citizen Health Literacy Questionnaire, which was designed by Delphi method [22]. Experts in the fields of public health, health education and promotion, and clinical medicine jointly designed this questionnaire. And the respondents of this questionnaire are permanent urban and rural residents aged 15–69 in China. The overall Cronbach’s alpha of the questionnaire was 0.95 and Spearman-Brown coefficient was 0.94 [23]. This questionnaire is not only used in the annual China Health Literacy Survey (CHLS) [24], but also in many studies on health literacy in China [25–27]. The health status survey part is used to investigate the health outcomes of recent chronic diseases and self-rated health status. ## Statistical method SPSS 22.0 is used for statistical analysis. Because the variables in this study are categorical variables, the categorical variables are expressed as constituent ratio (%) in statistical description, and the chi-square test is adopted for the inter-group difference test. Logistic regression model is used to further determined the influence of health literacy on health outcomes, and the significance level is set at α = 0.05. ## Assignment standard Use profession status, education level and income level to measure the socioeconomic status of the respondents. As for which of the three variables of education, income and occupation is more important, the opinions of various researchers are not consistent [28, 29], so this paper still adds these three variables with equal weight [30]. The health literacy questionnaire included three types of questions: true/false (correct response received 1 points), single-answer (correct response received 1 points), and multiple-answer (correct responses received 2 points). The total score of the health literacy questionnaire is 66, and those who reach $80\%$ or more of the total score are judged to have basic health literacy [31]. Chronic disease and self-rated health are selected as indicators to measure health status. See Table 1 for specific assignment standards of each variable. Table 1Variable definition and assignmentVariableDefinition and assignmentEducation level1 = Illiterate/Primary school, 2 = Junior high school, 3 = Senior high school/Vocational high school/Technical secondary school, 4 = Junior college/University, 5 = Postgraduate and higherIncome levelAnnual per capita household income = Total annual household income/Household size. 1 = Less than 10,000 yuan; 2 = 10,000–29,999 yuan; 3 = 30,000–49,999 yuan; 4 = 50,000–69,999 yuan; 5 = 70,000 yuan and higherProfessional status1 = The unemployed/Retiree; 2 = Farmer/Worker; 3 = Enterprise employee/Personnel of other public institutions/Businessman/College student; 4 = Teacher/Medical staff; 5 = Civil servantSocioeconomic statusThe individual's comprehensive socioeconomic status is measured by adding the scores of education level, income level and professional status. The higher the score is, the higher the status is. The actual lowest score in all samples is 4 and the highest score is 14. 4–7 of socioeconomic status score = Population with low socioeconomic status, 8–10 = Population with middle socioeconomic status, 11–14 = Population with high socioeconomic statusChronic disease1 = Suffering from any one or more chronic diseases, such as hypertension, diabetes, cerebrovascular disease, etc.; Otherwise = 0Self-rated health1 = Self-rated health is "good" or "better"; Otherwise = 0 ## General information of respondents Four thousand six hundred ninety-three samples are screened by logical test and outlier cleaning, and 4011 valid questionnaires are obtained, with an effective rate of $85.47\%$. Descriptive statistical analysis is made on 4011 valid samples after screening, and the general information is as follows: 1,981 males, accounting for $49.4\%$, and 2,030 females, accounting for $50.6\%$; The age distribution is dominated by middle-aged people aged 40–59, accounting for $47.8\%$, young people aged 18–39, accounting for $30.0\%$, and elderly people aged 60–69, accounting for $22.1\%$; The marital status is mainly married, accounting for $84.0\%$; Census register is dominated by local census register, accounting for $88\%$; After the sample is stratified according to the socioeconomic status score, the low, middle and high levels account for $61.6\%$, $28.5\%$ and $9.9\%$ respectively; The population with higher health literacy accounts for $31.2\%$ of the total sample. See Table 2 for details. Table 2General information of respondents ($$n = 4011$$)VariableFrequencyPercentage%Gender Male198149.4 Female203050.6Age 18 ~ 39 years120430.0 40 ~ 59 years191947.8 60 ~ 69 years88822.1Rural/Urban Rural241660.2 Urban159539.8Marital Status Single40610.1 Married336984.0 Separated210.5 Divorced1203.0 Widowed952.4Census register Local353088.0 Other places48112.0Socioeconomic status Low247161.6 Middle114228.5 High3989.9Health literacy Lower276168.8 Higher125031.2 ## Differences in health outcomes and health literacy among different social strata The statistical results in Table 3 show that there are significant differences between the two types of health outcomes and health literacy among different social strata. The prevalence of chronic diseases is $34.4\%$, $16.3\%$ and $7.5\%$ in low, middle and high social stratum respectively. The proportion of self-rated health as good and better in low social stratum is $65.5\%$, while that in middle and high social strata is $75.5\%$ and $80.7\%$ respectively. There is a significant stratum gradient in the prevalence of chronic diseases and self-rated health status of different social strata. There are also stratum differences in health literacy. The health literacy level of low social stratum is $15.3\%$, which is significantly lower than that in middle and high social strata ($49.1\%$ and $78.1\%$).Table 3Differences in health outcomes and health literacy among different social strataSocioeconomic statusHealth outcomesHealth literacy levelChronic diseasesGood self-rated health status%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ$2\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ$2\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ2Low34.4211.69865.561.64615.3871.128Middle16.375.549.1High7.580.778.1$p \leq 0.01$ ## Differences in health outcomes between population with lower and higher health literacy in stratified samples Based on the significant differences in health literacy and health outcomes among different social strata, it is speculated that the influence of health literacy on health outcomes may be different among different social strata. To test this hypothesis, in this study, whether there are differences between the two types of health outcomes in population with lower and higher health literacy in different social strata are compared at first (Table 4). The results show that there are significant differences in the prevalence of chronic diseases and self-rated health status between population with lower and higher health literacy in low and middle social strata. In high social stratum, although the prevalence of chronic diseases in population with higher health literacy is slightly lower than that in population with lower health literacy and the self-rated health status is better, the difference is not statistically significant. Therefore, it can't be considered that there is a difference in the two types of health outcomes between population with lower and higher health literacy in high social stratum. Table 4Differences in health outcomes between population with lower and higher health literacy in stratified samplesSocioeconomic statusHealth literacyChronic diseasesGood self-rated health status%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ$2\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ2LowLower36.628.23864.38.285Higher22.572MiddleLower22.431.09871.311.409Higher10.279.9HighLower9.20.439770.946Higher7.181.7$p \leq 0.01$ ## Influences of health literacy on health outcomes in stratified samples In order to further verify the relationship between health literacy and health outcomes found in low and middle social strata, multivariate logistic regression analyses of health literacy and chronic diseases or self-rated health in low and middle social strata are conducted respectively. Before regression analysis, difference test is performed to determine confounders that might affect health outcomes (Table 5).Table 5Difference test for health outcomes in populations with different characteristicsVariableLow socioeconomic statusMiddle socioeconomic statusChronic diseasesGood or better self-rated health statusChronic diseasesGood or better self-rated health status%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ$2\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ$2\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ$2\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ2Gender Male38.113.57267.23.00920.215.09676.91.384 Female3163.911.873.9Age 18 ~ 39 years4.0320.98679.132.3563.3226.30880.120.011 40 ~ 59 years28.365.225.672.1 60 ~ 69 years57.160.757.661.6Rural/Urban Rural33.80.91364.61.49915.21.57174.31.14 Urban35.767.018.077.1Marital Status Single24.49.28962.21.4193.056.57185.220.037 Married34.565.719.573.4 Separated40.070.014.385.7 Divorced24.265.715.961.4 Widowed4560.061.576.9Census register Local38.1106.33163.529.39217.913.99974.91.689 Other places8.079.45.280.0$p \leq 0.01$ The difference test results show that, in low social stratum, there are significant differences in the prevalence of chronic diseases among different genders, ages and census registers, and in self-rated health status in different ages and census registers. In middle social stratum, there are significant differences in the prevalence of chronic diseases among different genders, ages, marital statuses and census registers, and in self-rated health status among different ages and marital statuses. These variables, which may affect the prevalence of chronic diseases and self-rated health status, are included as control variables in the regression model with health literacy as independent variable and chronic diseases or self-rated health are included as dependent variable (Table 6).Table 6Analysis of the relationship between health literacy and health outcomes in different social strataVariableOR (Chronic disease)OR (Self-rated health)Socioeconomic statusLowMiddleLowMiddleConstant0.0820.0442.7324.759*Health literacy(ref. Lower) Higher0.7220.7901.2851.401Gender (ref. Male) Female0.7600.583Age (ref. 18 ~ 39 years) 40 ~ 59 years6.3867.8550.6260.850 60 ~ 69 years19.23826.9670.5500.521Marital Status(ref. Single) Married1.4220.581 Live apart0.8471.545 Divorced1.2040.327 Widowed2.9511.102Census register(ref. Local) Other places0.3050.4331.760* Likelihood ratio testP < 0.001P < 0.001P < 0.001P < 0.001 Hosmer–Lemeshow testP = 0.221P = 0.353P = 0.886P = 0.536$p \leq 0.05$$p \leq 0.01$ The validity test results of models of health literacy and two kinds of health outcomes are shown in Table 6. All models have passed the Likelihood ratio test ($P \leq 0.05$) and Hosmer–Lemeshow test ($P \leq 0.05$). The fitting results of each regression model are good. After stratification, it can be observed that the risk of chronic diseases in population with higher health literacy is lower than that in population with lower health literacy. It can be considered that, for low social stratum, having higher health literacy can reduce the risk of chronic diseases(OR = 0.722, $$P \leq 0.022$$). However, in the middle socioeconomic stratum, after controlling other related variables, the influence of health literacy on chronic diseases is no longer statistically significant. The positive influence of health literacy on self-rated health status is statistically significant in low and middle socioeconomic strata (OR = 1.285, $$P \leq 0.047$$; OR = 1.401, $$P \leq 0.023$$). For low and middle social strata, having higher health literacy is helpful to improve self-rated health status. ## Influence of health literacy on health outcomes of low social stratum (chronic diseases) or low and middle social strata (self-rated health) is more significant than that of high social stratum. The difference of chronic disease prevalence and self-rated health status between population with lower and higher health literacy is only significant in low and middle social strata. After controlling the related variables, the influence of health literacy on chronic diseases is still statistically significant in population with low socioeconomic status, but such significant influence is not found in population with middle and high socioeconomic status. The influence of health literacy on self-rated health is statistically significant in population with low and middle socioeconomic status, but the correlation between health literacy and self-rated health is not found in population with high socioeconomic status. Based on these results, it can be preliminarily inferred that the influences of health literacy on the health outcomes in low social stratum (chronic diseases) or low and middle social strata (self-rated health) is more significant than that in high social stratum, which is similar to the results of research conducted by Gibney in Ireland [32]. Gibney found that the influence of health literacy on health outcomes, such as chronic diseases and hospital attendance rate, was significant in low or middle and low social strata, but not in high social stratum. However, he did not explain detailly for this finding. In this paper, we will attempt to explain this phenomenon from the following perspectives: Some researchers have found that the population that people come into contact with in work and life are mostly people in similar social stratum [33]. Because people have social needs, they are often imperceptibly influenced by the values and behavioral norms of surrounding people [34]. Population with high socioeconomic status have a high level of health literacy ($78.1\%$). Even if population with high social stratum have not health literacy, they will still be influenced and restrained by the surrounding people and environment, which will encourage them to maintain a healthy lifestyle. In addition, most of the population with high socioeconomic status have a good living and working environment, and their chances to be exposed to the risk factors affecting their health are less [35, 36],which also weakens the role of health literacy to some extent. However, in low social stratum, the proportion of people with health literacy is very low ($15.3\%$), and they are more likely to be exposed to health risk factors than those with high socioeconomic status. Therefore, health literacy has a greater influence on the health outcomes of population in low social stratum. ## Having higher health literacy can improve health outcomes (chronic diseases, self-rated health) Among the significant influences of health literacy on health outcomes found in low and middle social strata, health literacy will all play a role to improve health outcomes. Those with higher health literacy had lower rates of chronic disease and better self-rated health status than those with lower health literacy. It is consistent with previous findings [16, 25]. People with higher health literacy are more willing and able to acquire and understand health knowledge, and utilize it to improve their lifestyle. However, one of the important reasons for chronic diseases and many other health damage is the long-term accumulation of health damage caused by unhealthy lifestyles [37]. In addition, Parikh believed that people with lower health literacy were easy to feel ashamed and embarrassed about their ignorance, which would hinder them from seeking health help including medical care services and acquisition of health knowledge, thus affecting their health status [38]. These findings can explain the improvement of health literacy on health outcomes to some extent. ## There are significant differences in health outcomes among different social strata There is a significant stratum gradient in the prevalence of chronic diseases and self-rated health status among low, middle and high social strata. The prevalence of chronic diseases in population with low socioeconomic status ($34.4\%$) is significantly higher than that in population with middle and high socioeconomic status ($16.3\%$,$7.5\%$), and their proportion of self-rated health as good or better ($65.5\%$) is significantly lower than that in middle and high social strata ($75.5\%$,$80.7\%$). This is consistent with many study conclusions. For example, Roberto found that in almost all of the 22 European countries he surveyed, socioeconomic status had a significant negative correlation with the mortality rate and self-rated health [2]; Wolff believed that low subjective social status was significantly related to poor/common health status [39]. The same findings are found in the researches conducted in China [40, 41]. Generally speaking, the lower the socioeconomic status is, the worse the health status is. There are many reasons leading to health disparities among different social strata, including poor living and working environment [42], unhealthy lifestyle [37] and unequal access to medical resources [43] of population with low socioeconomic status, and the lack of health drive force caused by the insufficient health literacy discussed in this paper. Combining conclusion 1 and 2, it can be found that health literacy has a more significant influence on health outcomes of population in low social stratum than that in high social stratum, and health literacy is a protective factor for health outcomes. Therefore, it can be considered that improving residents' health literacy is an effective measure to alleviate the health gap among social strata. While the government is committed to creating a healthy supportive environment for population with low socioeconomic status and improving the fairness of medical resources, it should also pay attention to the improvement of residents' health literacy, so that population of low social stratum not only have the opportunity but also have the ability to make healthy choices. ## Conclusions and shortcomings In this paper, the influences of health literacy on health outcomes in all population with different socioeconomic status are discussed, according to the monitoring data of health literacy from a city of Zhejiang Province in 2020. The main conclusions are as follows: [1] The influences of health literacy on health outcomes of population in low social stratum (chronic diseases) or low and middle social strata (self-rated health) is more significant than that in high social stratum, which suggests that improving residents' health literacy may be an effective way to alleviate the health gap among different social strata; [2] Health literacy will play an role to improve health outcomes (chronic diseases and self-rated health); [3] There are significant differences in health outcomes among different social strata. This study also has the following limitations. Firstly, this study is a cross-sectional survey, which can only provide some clues for causal inference between health literacy and health outcomes, but can not verify the causal relationship. Further research is needed to verify the causal relationship. Secondly, the types of health outcomes selected in this study are limited, and it is unknown whether health literacy has the same influence on other health outcomes with social stratum differences. Finally, this paper measures socioeconomic status by simply adding education level, income level and professional status. However, the influences of education level, income level and professional status on socioeconomic status are probably different, so we should further look for a more accurate way to measure socioeconomic status. ## References 1. 1.Black D, Morris J, Smith C, Townsend P. Inequalities in health: a report of a research working group. 1980. 2. De Vogli R, Gimeno D, Kivimaki M. Socioeconomic inequalities in health in 22 European countries. N Engl J Med* (2008.0) 358 2468-2481. DOI: 10.1056/NEJMsa0707519 3. Nwosu CO, Oyenubi A. Income-related health inequalities associated with the coronavirus pandemic in South Africa: A decomposition analysis. 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--- title: Different associations of general and abdominal obesity with upper and lower extremity artery disease among a community population in China authors: - Yong Wang - Xiaoyan Guo - Yi Zhang - Ruiyan Zhang - Jue Li journal: Nutrition & Metabolism year: 2023 pmcid: PMC9999629 doi: 10.1186/s12986-023-00736-1 license: CC BY 4.0 --- # Different associations of general and abdominal obesity with upper and lower extremity artery disease among a community population in China ## Abstract ### Background The associations between obesity and abnormalities of upper and lower extremity arteries remain to be elucidated. This study is aimed to investigate whether general obesity and abdominal obesity are associated with upper and lower extremity artery diseases in a Chinese community population. ### Methods This cross-sectional study included 13,144 participants in a Chinese community population. The associations between obesity parameters and abnormalities of upper and lower extremity arteries were evaluated. Multiple logistic regression analysis was used to assess the independence of associations between obesity indicators and abnormalities of peripheral arteries. Nonlinear relationship between body mass index (BMI) and risk of ankle-brachial index (ABI) ≤ 0.9 was evaluated using a restricted cubic spline model. ### Results The prevalence of ABI ≤ 0.9 and interarm blood pressure difference (IABPD) ≥ 15 mmHg in the subjects was $1.9\%$ and $1.4\%$ respectively. Waist circumference (WC) was independently associated with ABI ≤ 0.9 (OR 1.014, $95\%$ CI 1.002–1.026, $$P \leq 0.017$$). Nevertheless, BMI was not independently associated with ABI ≤ 0.9 using linear statistical models. Meanwhile, BMI and WC were independently associated with IABPD ≥ 15 mmHg respectively (OR 1.139, $95\%$ CI 1.100–1.181, $P \leq 0.001$, and OR 1.058, $95\%$ CI 1.044–1.072, $P \leq 0.001$). Furthermore, prevalence of ABI ≤ 0.9 was displayed with a U-shaped pattern according to different BMI (< 20, 20 to < 25, 25 to < 30, and ≥ 30). Compared with BMI 20 to < 25, risk of ABI ≤ 0.9 was significantly increased when BMI < 20 or ≥ 30 respectively (OR 2.595, $95\%$ CI 1.745–3.858, $P \leq 0.001$, or OR 1.618, $95\%$ CI 1.087–2.410, $$P \leq 0.018$$). Restricted cubic spline analysis indicated a significant U-shaped relationship between BMI and risk of ABI ≤ 0.9 (P for non-linearity < 0.001). However, prevalence of IABPD ≥ 15 mmHg was significantly increased with incremental BMI (P for trend < 0.001). Compared with BMI 20 to < 25, the risk of IABPD ≥ 15 mmHg was significantly increased when BMI ≥ 30 (OR 3.218, $95\%$ CI 2.133–4.855, $P \leq 0.001$). ### Conclusions Abdominal obesity is an independent risk factor for upper and lower extremity artery diseases. Meanwhile, general obesity is also independently associated with upper extremity artery disease. However, the association between general obesity and lower extremity artery disease is displayed with a U-shaped pattern. ## Introduction Atherosclerotic cardiovascular diseases (ASCVDs) which may involve coronary artery disease (CAD), atherosclerotic cerebral infarction, peripheral arterial disease (PAD), and atherosclerotic changes in other arteries, are the main causes of mortality worldwide [1–4]. PAD may include arterial disease of lower extremities, upper extremities, renal artery, carotid artery, or other peripheral arteries, and is one of the manifestation of systemic atherosclerosis [5]. PAD is an important component of the ASCVD, but is often underestimated by cardiologists. In fact, PAD was associated with higher risk of all-cause and cardiovascular disease (CVD) mortality in Chinese patients with high cardiovascular risk in our previous studies [6, 7]. Ankle-brachial index (ABI) ≤ 0.90 can be considered as a criterion for the diagnosis of lower extremity PAD [8]. At the same time, increased interarm systolic blood pressure difference (IABPD) often signifies the potential abnormalities of upper extremity arteries mainly including subclavian artery, brachiocephalic trunk, and axillary artery [9, 10]. Previous studies revealed that lower ABI and higher IABPD were associated with increased mortalities respectively in Chinese [6, 11]. Obesity is associated with a much higher prevalence of comorbidities such as diabetes, hypertension, and metabolic syndrome, which then increase the risk of ASCVD. In addition, obesity may also be an independent risk factor for the development of ASCVD [12]. With the improvement of living standards and change of lifestyle, the prevalence of obesity has been significantly elevated in China in recent years. Thus, more attention should be paid to obesity related metabolic and cardiovascular disorders in China. Obesity can be classified as general obesity and abdominal obesity. However, the associations between various kinds of obesity and abnormalities of upper and lower extremity arteries remain to be elucidated to date. It is worth noting that the data on the relationship between body mass index (BMI) and abnormalities of lower extremity arteries are controversial. A previous study found that the risk of lower extremity PAD was increased with incremental BMI [13]. But another study indicated that BMI did not increase the risk of developing lower extremity PAD [14]. Meanwhile, the association between obesity and abnormalities of upper extremity arteries was rarely investigated in previous studies. Thus, this study is aimed to investigate whether general obesity and abdominal obesity are associated with the prevalence of upper and lower extremity artery disease in a community population in China. ## Study subjects The study subjects ($$n = 13$$,750) were enrolled through cluster multistage and random sampling to community population from several districts of Shanghai in China in this cross-sectional study. The participants aged more than 18 years old were investigated in each center from May to September in 2016. Exclusion criteria included history of aortic dissection, history of amputation surgery, atrial fibrillation, mental disorder or lack of compliance. After the subjects with incomplete data or exclusion criteria were removed, there were totally 13,144 participants left (Fig. 1).Fig. 1Flow chart of subjects enrollment The study complied with the Declaration of Helsinki. It was also approved by the ethics committee of Shanghai Jiao Tong University and informed consent was obtained from all the participants prior to enrollment. ## Four-limb blood pressure and ABI measurement Four-limb blood pressure and ABI measurement was performed by trained technicians using a non-invasive vascular profiling system (Omron VP-1000 vascular profiling system, Japan) [3]. This system ensured accurate and reliable ABI measurement using advanced oscillometric technology. Simultaneous blood pressure measurement at all four limbs was included, using a dual chamber cuff system and a proprietary algorithm. Measurement was performed after a 10-min rest in the supine position with the upper body as flat as possible. The device simultaneously and automatically measured the blood pressures twice, and then we calculated the means to get final blood pressure values. Bilateral ankle and brachial artery pressures, and bilateral ABI were supplied after measurement. ACC/AHA guidelines recommend ABI ≤ 0.90 as the criterion for the diagnosis of lower extremity PAD [8]. Meanwhile, IABPD ≥ 15 mmHg was considered as the potential abnormalities of upper extremity arteries according to literatures in this study [9, 10]. ## Clinical data collection A case report form was developed to record general characteristics, clinical diagnosis, and biochemical examination. Waist circumference (WC) was measured at the middle point between the costal margin and iliac crest. BMI was calculated as body weight in kilograms divided by body height in meters squared (kg/m2). Smoking habit was categorized as current smoking, ever smoking, or no smoking. Current smoking was determined when subjects were smoking currently and more than one cigarette daily in at least one year continuously. Ever smoking was determined when subjects smoked more than one cigarette daily, but had quitted smoking at least one year before. Drinking habit was categorized as current drinking, ever drinking, or no drinking. Current drinking was determined when subjects were drinking liquor, beer or wine currently in at least one year. Ever drinking was determined when subjects drank previously, but had quitted drinking at least one year before. History of lipid disorders included that plasma total cholesterol was ≥ 5.7 mmol/l, or low-density lipoprotein cholesterol (LDL-C) was ≥ 3.6 mmol/l, or high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/l, triglyceride was ≥ 1.7 mmol/l, or treatment with antihyperlipidemic agents due to hyperlipidemia. Hypertension was diagnosed by systolic blood pressure (SBP) ≥ 140 mmHg, or diastolic blood pressure (DBP) ≥ 90 mmHg, or being actively treated with anti-hypertension drugs. Diabetes mellitus was diagnosed by a fasting plasma glucose ≥ 7.0 mmol/l, or by a random plasma glucose ≥ 11.1 mmol/l, or when they were actively receiving therapy using insulin or oral medications for diabetes. Chronic kidney disease was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2. ## Statistical analysis Data entry and management were performed using Epidata software, version 3.1 (Epidata Association, Odense, Denmark). All statistical analyses were conducted with SPSS 22.0 (IBM, Armonk, NY, USA) and R language software (version 4.1.1). Continuous variables were expressed as the mean ± standard deviation, and categorical variables as frequencies (percentages). The chi-square test was used to compare categorical variables. The linear tendency was evaluated among several groups using trend test. The independent-sample t-test and one-way analysis of variance (ANOVA) were used to compare continuous variables among two or more groups. Multiple logistic regression analysis was used to assess the independence of the associations between obesity indicators and various abnormalities of peripheral arteries, and the odds ratio (OR) and $95\%$ confidence interval ($95\%$ CI) was calculated. We also explored the nonlinear relationship between BMI and the risk of ABI ≤ 0.9 using a restricted cubic spline model by multivariable adjustment with three knots (at the 10th, 50th, and 90th percentiles). $P \leq 0.05$, which is two-sided, was considered significant. ## Study participants characteristics General characteristics of the 13,144 participants by gender were shown in Table 1. The mean age was 52.2 ± 13.1 years old. 7181 subjects of them ($54.6\%$) were man. The average BMI of all participants was 25.2 ± 3.81 kg/m2, and the average WC was 88.5 ± 11.7 cm respectively. The average ABI was 1.08 ± 0.09, and the average IABPD was 3.55 ± 3.79 mmHg respectively. Furthermore, the prevalence of ABI ≤ 0.9 and IABPD ≥ 15 mmHg in this study population was $1.9\%$ and $1.4\%$ respectively. Table 1Clinical characteristics of study participants according to genderVariablesAll ($$n = 13$$,144)Man ($$n = 7181$$)Woman ($$n = 5963$$)P valueAge (years)52.2 ± 13.151.7 ± 13.352.8 ± 13.0 < 0.001BMI (kg/m2)25.2 ± 3.8125.5 ± 3.5724.8 ± 4.05 < 0.001WC (cm)88.5 ± 11.791.0 ± 10.685.5 ± 12.2 < 0.001Smoking––– < 0.001 Current smoking (n, %)3667 ($27.9\%$)3529 ($49.1\%$)138 ($2.3\%$)– Ever smoking (n, %)654 ($40.1\%$)619 ($8.6\%$)35 ($0.6\%$)– No smoking (n, %)8823 ($40.1\%$)3033 ($42.2\%$)5790 ($97.1\%$)–Drinking––– < 0.001 Current drinking (n, %)2612 ($19.9\%$)2496 ($34.8\%$)116 ($1.9\%$)– Ever drinking (n, %)485 ($3.7\%$)463 ($6.4\%$)22 ($0.4\%$)– No drinking (n, %)10,047 ($76.4\%$)4222 ($58.8\%$)5825 ($97.7\%$)–Diabetes mellitus (n, %)1399 ($10.6\%$)805 ($11.2\%$)594 ($10.0\%$)0.021Hypertension (n, %)5720 ($43.5\%$)3040 ($42.3\%$)2680 ($44.9\%$)0.003Lipid disorders (n, %)6212 ($47.3\%$)4133 ($57.6\%$)2079 ($34.9\%$) < 0.001Chronic kidney disease (n, %)335 ($2.5\%$)143 ($2.0\%$)192 ($3.2\%$) < 0.001Total cholesterol (mmol/l)4.83 ± 1.044.82 ± 1.044.85 ± 1.040.289Total triglyceride (mmol/l)2.01 ± 1.822.22 ± 2.041.63 ± 1.25 < 0.001LDL-C (mmol/l)2.74 ± 0.902.74 ± 0.922.74 ± 0.860.737HDL-C (mmol/l)1.20 ± 0.321.13 ± 0.301.33 ± 0.31 < 0.001Fasting plasma glucose (mmol/l)5.36 ± 1.715.39 ± 1.745.31 ± 1.650.046Serum creatinine (umol/l)81.0 ± 38.086.8 ± 37.872.7 ± 36.6 < 0.001eGFR (ml/min/1.73m2)95.1 ± 22.397.0 ± 21.592.3 ± 23.2 < 0.001ABI1.08 ± 0.091.10 ± 0.091.07 ± 0.08 < 0.001Systolic BP in left arm (mmHg)130 ± 20.1130 ± 18.0130 ± 22.30.438Diastolic BP in left arm (mmHg)78.8 ± 12.280.3 ± 11.677.1 ± 12.6 < 0.001Systolic BP in right arm (mmHg)131 ± 20.0131 ± 18.1131 ± 22.10.706Diastolic BP in right arm (mmHg)79.4 ± 12.280.8 ± 11.777.6 ± 12.6 < 0.001Systolic BP in left ankle (mmHg)146 ± 26.3147 ± 24.6144 ± 28.0 < 0.001Diastolic BP in left ankle (mmHg)76.6 ± 12.277.9 ± 11.974.9 ± 12.4 < 0.001Systolic BP in right ankle (mmHg)148 ± 26.7149 ± 25.1146 ± 28.3 < 0.001Diastolic BP in right ankle (mmHg)76.3 ± 12.277.8 ± 12.074.6 ± 12.3 < 0.001IABPD (mmHg)3.55 ± 3.793.50 ± 3.973.61 ± 3.550.092ABI ≤ 0.9256 ($1.9\%$)120 ($1.7\%$)136 ($2.3\%$)0.012IABPD ≥ 15 mmHg180 ($1.4\%$)82 ($1.1\%$)98 ($1.6\%$)0.014BMI body mass index, WC waist circumference, BP blood pressure, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, eGFR estimated glomerular filtration rate, ABI ankle-brachial index, IABPD interarm systolic blood pressure differenceValues are means ± SD, or numbers with percentage in parenthesis ## BMI and WC values according to different ABI and IABPD categories The BMI and WC according to different ABI and IABPD categories were calculated and compared. WC was significantly higher in subjects with ABI ≤ 0.9 than that in subjects with ABI > 0.9 ($P \leq 0.001$, Table 2). However, BMI was not significantly different in subjects with ABI ≤ 0.9 and with ABI > 0.9 ($$P \leq 0.844$$, Table 2). At the same time, the WC and BMI were significantly higher in subjects with IABPD ≥ 15 mmHg than those in subjects with IABPD < 15 mmHg respectively (both $P \leq 0.001$, Table 2).Table 2BMI and WC values according to different ABI and IABPD categoriesVariablesABI > 0.9 ($$n = 12$$,888)ABI ≤ 0.9 ($$n = 256$$)P valueIABPD < 15 mmHg ($$n = 12$$,964)IABPD ≥ 15 mmHg ($$n = 180$$)P valueBMI (kg/m2)25.2 ± 3.7925.3 ± 4.750.84425.2 ± 3.7927.8 ± 4.66 < 0.001WC (cm)88.4 ± 11.691.0 ± 13.30.00188.4 ± 11.697.9 ± 12.2 < 0.001BMI body mass index, WC waist circumference, ABI ankle-brachial index, IABPD interarm systolic blood pressure differenceValues are means ± SD ## Independence of BMI and WC associated with different ABI and IABPD categories In order to evaluate the independence of BMI and WC associated with different ABI and IABPD categories, multiple logistic regression analysis was used to calculate the OR and $95\%$ CI of BMI and WC associated with ABI ≤ 0.9 and IABPD ≥ 15 mmHg respectively with adjustment for other potential confounders including age, men, smoking, drinking, hypertension, diabetes mellitus, lipid disorders, and chronic kidney disease. These indicators of obesity entered regression equation as continuous variables respectively. We found that WC was independently associated with ABI ≤ 0.9 (OR 1.014, $95\%$ CI 1.002–1.026, $$P \leq 0.017$$, Table 3). Nevertheless, BMI was not independently associated with ABI ≤ 0.9 using this multiple logistic regression analysis. At the same time, the data showed that BMI and WC were independently associated with IABPD ≥ 15 mmHg respectively (OR 1.139, $95\%$ CI 1.100–1.181, $P \leq 0.001$, and OR 1.058, $95\%$ CI 1.044–1.072, $P \leq 0.001$, Table 3).Table 3Independence of BMI and WC associated with ABI ≤ 0.9 and IABPD ≥ 15 mmHgVariablesOR$95\%$ CIP valueABI ≤ 0.9––– BMI (kg/m2)0.9990.965–1.0340.954 WC (cm)1.0141.002–1.0260.017IABPD ≥ 15 mmHg––– BMI (kg/m2)1.1391.100–1.181 < 0.001 WC (cm)1.0581.044–1.072 < 0.001Multiple logistic regression analysis was used to calculate the odds ratio (OR) and $95\%$ CI of body mass index (BMI), and waist circumference (WC) (independent variables) associated with ankle-brachial index (ABI) ≤ 0.9, or interarm systolic blood pressure difference (IABPD) ≥ 15 mmHg respectively with adjustment for other potential confounders including age, men, smoking, drinking, hypertension, diabetes mellitus, lipid disorders, and chronic kidney disease. BMI and WC entered regression equation as continuous variables respectively ## Prevalence of ABI ≤ 0.9 and IABPD ≥ 15 mmHg with different categories of BMI As we mentioned in the above section, though we cannot discover a linear relationship between BMI and ABI statistically, we still try to explore the prevalence of ABI ≤ 0.9 in study subjects when they were categorized as four groups according to BMI (< 20, 20 to < 25, 25 to < 30, and ≥ 30). As a result, we found that prevalence of ABI ≤ 0.9 was displayed with a U-shaped pattern according to different BMI categories (Fig. 2). Prevalence of ABI ≤ 0.9 in subjects with BMI < 20 and BMI ≥ 30 was significantly higher compared with that in subjects with BMI 20 to < 25 respectively (both $P \leq 0.001$).Fig. 2Prevalence of ABI ≤ 0.9 and IABPD ≥ 15 mmHg in different categories of BMI. ABI: ankle-brachial index; BMI: body mass index; IABPD: interarm systolic blood pressure difference. $$n = 925$$ for BMI < 20, 5643 for BMI ≥ 20 to < 25, 5203 for BMI ≥ 25 to < 30, and 1373 BMI ≥ 30. Prevalence of ABI ≤ 0.9 in subjects with BMI < 20 and BMI ≥ 30 was significantly higher compared with that in subjects with BMI ≥ 20 to < 25 respectively (both $P \leq 0.001$). Prevalence of IABPD ≥ 15 mmHg was significantly increased with incremental BMI (P for trend < 0.001) At the same time, we also tried to observe the prevalence of IABPD ≥ 15 mmHg when study subjects were categorized as four groups according to BMI. A different trend was discovered that prevalence of IABPD ≥ 15 mmHg was significantly increased with incremental BMI (P for trend < 0.001, Fig. 2). ## Relationship between BMI and abnormalities of peripheral arteries The above data showed that, unlike WC, relationship between BMI and abnormalities of peripheral arteries appeared to be different. Thus, we further carefully evaluated whether various BMI categories (< 20, 20 to < 25, 25 to < 30, and ≥ 30) were associated with ABI ≤ 0.9 and IABPD ≥ 15 mmHg using multiple logistic regression analysis. The data displayed that, compared with BMI 20 to < 25, the risk of ABI ≤ 0.9 was significantly increased when BMI < 20 or ≥ 30 respectively (OR 2.595, $95\%$ CI 1.745–3.858, $P \leq 0.001$, and OR 1.618, $95\%$ CI 1.087–2.410, $$P \leq 0.018$$, Table 4). However, the risk of IABPD ≥ 15 mmHg tended to be increased when participants had bigger BMI. Compared with BMI 20 to < 25, the risk of IABPD ≥ 15 mmHg was significantly increased when BMI ≥ 30 (OR 3.218, $95\%$ CI 2.133–4.855, $P \leq 0.001$, Table 4).Table 4Various categories of BMI associated with ABI ≤ 0.9 and IABPD ≥ 15 mmHgVariablesOR$95\%$ CIP valueABI ≤ 0.9–– < 0.001 < 202.5951.745–3.858 < 0.001 20 to < 25ReferenceReferenceReference 25 to < 300.9950.736–1.3450.973 ≥ 301.6181.087–2.4100.018IABPD ≥ 15 mmHg–– < 0.001 < 200.2680.065–1.1060.069 20 to < 25ReferenceReferenceReference 25 to < 301.3570.944–1.9500.099 ≥ 303.2182.133–4.855 < 0.001Multiple logistic regression analysis was used to calculate the odds ratio (OR) and $95\%$ CI of body mass index (BMI) categories (< 20, $$n = 925$$; 20 to < 25, $$n = 5643$$; 25 to < 30, $$n = 5203$$; and ≥ 30, $$n = 1373$$), associated with ankle-brachial index (ABI) ≤ 0.9, or interarm systolic blood pressure difference (IABPD) ≥ 15 mmHg respectively with adjustment for other potential confounders including age, men, smoking, drinking, hypertension, diabetes mellitus, lipid disorders, and chronic kidney disease Furthermore, we also explored the nonlinear relationship between BMI and the risk of ABI ≤ 0.9 using a restricted cubic spline model by multivariable adjustment. Restricted cubic spline analysis (Fig. 3) indicated a significant U-shaped relationship between BMI and the risk of ABI ≤ 0.9 (P for non-linearity < 0.001).Fig. 3Nonlinear relationship between BMI and the risk of ABI ≤ 0.9. Y-axis stands for the odds ratio (OR) and $95\%$ CI of body mass index (BMI) (independent variable) associated with ankle-brachial index (ABI) ≤ 0.9 using restricted cubic spline analysis, with adjustment for other potential confounders including age, men, smoking, drinking, hypertension, diabetes mellitus, lipid disorders, and chronic kidney disease. A significant U-shaped relationship between BMI and the risk of ABI ≤ 0.9 was exhibited (P for non-linearity < 0.001) ## Discussion The association between obesity and abnormalities of upper extremity arteries was rarely investigated in previous studies. The data in our study showed that both general and abdominal obesity parameters were independently associated with IABPD ≥ 15 mmHg respectively. A previous data demonstrated that BMI was in connection with IABPD ≥ 10 mmHg. However, WC was only statistically associated with inter-arm differences in diastolic blood pressure ≥ 10 mmHg [15]. The cut-off value of inter-arm differences was different from that in our study, and the sample size of the previous study was relatively small. However, only abdominal obesity parameter was significantly higher in subjects with ABI ≤ 0.9. Meanwhile, BMI was not independently associated with ABI ≤ 0.9 using linear statistical models. In fact, previous studies also manifested that more patients with ABI ≤ 0.9 had abdominal obesity than those without lower extremity PAD [16]. Lots of data demonstrated that abdominal obesity was an independent risk factor for the development of ASCVD including PAD [12, 16]. These were possibly because the people with abdominal obesity tended to have more atherosclerotic plaques in arteries. Nevertheless, previous study provided controversial data on association between BMI and ASCVD including lower extremity PAD [17]. More abundant data on both abdominal obesity and general obesity associated with lower extremity PAD were supplied in our study, and we think the data in this study can be helpful to explore the relationship between obesity and lower extremity PAD. In order to explore the relationship between BMI and lower extremity PAD further, subjects in this study were categorized as four groups according to BMI. As a result, we found that compared with BMI 20 to < 25, the risk of ABI ≤ 0.9 was significantly increased by more than 2.5-fold and 1.6-fold when BMI < 20 or ≥ 30 respectively. Additionally, a significant U-shaped relationship was observed between BMI and the risk of ABI ≤ 0.9 using restricted cubic spline analysis, which indicated that the risk of ABI ≤ 0.9 increased when BMI exceeded or less than the median value (i.e., 24.97). These data manifested that it was not a linear relationship, but a U-shaped pattern between BMI and ABI ≤ 0.9 in this Chinese population of our study. We speculated that when subjects had bigger BMI, they would possibly have more atherosclerotic plaques in lower extremity arteries. However, this study showed that underweight subjects also had increased prevalence of ABI ≤ 0.9. Similar results were found in a previous study [18], but the reason is not very clear yet. Some researchers considered that the underweight patients possibly had higher levels of inflammation which might promote the development of atherosclerosis [19]. In fact, a phenomenon called obesity paradox showed that a low body weight was also associated with cardiovascular disease and mortalities [20]. A previous study manifested that obesity was associated with lower in-hospital mortality in PAD patients relative to those with normal-weight/over-weight. This obesity survival paradox was independent of age, gender and comorbidities and observed for all obesity classes [21]. However, the precise mechanism is still not clear. We think that the obesity paradox between BMI and ABI needs to be further studied. We not only studied the relationship between general obesity and lower extremity PAD, but also studied the relationship between general obesity and upper extremity PAD in this study. Univariate analysis and multiple logistic regression analysis indicated that prevalence of IABPD ≥ 15 mmHg was significantly increased with incremental BMI. This data was quite different according to the above analysis on the relationship between general obesity and ABI ≤ 0.9. However, the causes for this discrepancy were unknown. We speculated the possible causes as follows. First, ABI ≤ 0.90 can be considered as the presence of lower extremity PAD. However, IABPD ≥ 15 mmHg possibly signifies abnormalities in upper extremity arteries mainly including subclavian artery, brachiocephalic trunk, and axillary artery [10]. Risk factors for abnormalities of arteries at different anatomical locations might be different. Second, though atherosclerosis is the main cause of the upper or lower extremity PAD, there are also other divergent causes. Lower extremity PAD might be caused by atherosclerosis, takayasu arteritis, and so on. Meanwhile, more causes of the upper extremity PAD were found such as atherosclerosis, thoracic outlet syndrome, giant cell arteritis, takayasu arteritis, radiation artery fibrosis, fibromuscular dysplasia, and so on [10]. In fact, atherosclerosis in lower extremity PAD is possibly more frequently to be found compared with that in upper extremity PAD [10, 11]. The associations of these divergent pathogenic risk factors with BMI appear to be more complex. 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--- title: Diagnostic value of combining preoperative inflammatory markers ratios with CA199 for patients with early-stage pancreatic cancer authors: - Yuanlong Gu - Qianjin Hua - Zhipeng Li - Xingli Zhang - Changjie Lou - Yangfen Zhang - Wei Wang - Peiyuan Cai - Juan Zhao journal: BMC Cancer year: 2023 pmcid: PMC9999638 doi: 10.1186/s12885-023-10653-4 license: CC BY 4.0 --- # Diagnostic value of combining preoperative inflammatory markers ratios with CA199 for patients with early-stage pancreatic cancer ## Abstract ### Background An early diagnosis of pancreatic cancer (PC) is extremely difficult because of the lack of sensitive liquid biopsy methods and effective biomarkers. We attempted to evaluate whether circulating inflammatory marker could complement CA199 for the detection of early-stage PC. ### Methods We enrolled 430 patients with early-stage PC, 287 patients with other pancreatic tumors (OPT), and 401 healthy controls (HC). The patients and HC were randomly divided into a training set ($$n = 872$$) and two testing sets (n1 = 218, n2 = 28). The receiver operating characteristic (ROC) curves were investigated to evaluate the diagnostic performance of circulating inflammatory markers ratios, CA199, and combinations of the markers ratios in the training set, which would then be validated in the two testing sets. ### Results Circulating fibrinogen, neutrophils, and monocytes in patients with PC were significantly higher while circulating albumin, prealbumin, lymphocytes, and platelets of patients with PC were significantly lower compared to those of HC and OPT (all $P \leq 0.05$). The fibrinogen-to-albumin (FAR), fibrinogen-to-prealbumin (FPR), neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), monocyte-to-lymphocyte (MLR), and fibrinogen-to-lymphocyte (FLR) ratios were significantly higher while the prognostic nutrition index values (PNI) were lower in patients with PC than in HC and OPT (all $P \leq 0.05$). Combining the FAR, FPR, and FLR with CA199 exhibited the best diagnostic value for distinguishing patients with early-stage PC from HC with an area under the curve (AUC) of 0.964, and for distinguishing patients with early-stage PC from OPT with an AUC of 0.924 in the training sets. In the testing set, compared with HC, the combination markers had powerful efficiency for PC with an AUC 0.947 and AUC 0.942 when comparing PC with OPT. The AUC was 0.915 for the combination of CA199, FAR, FPR, and FLR for differentiating between patients with pancreatic head cancer (PHC) and other pancreatic head tumors (OPHT), and 0.894 for differentiating between patients with pancreatic body and tail cancer (PBTC) and other pancreatic body and tail tumors (OPBTT). ### Conclusion A combination of FAR, FPR, FLR, and CA199 may serve as a potential non-invasive biomarker for differentiating early-stage PC from HC and OPT, especially early-stage PHC. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12885-023-10653-4. ## Introduction Pancreatic cancer (PC) is the seventh leading cause of cancer-related deaths in both men and women with nearly equal rates of annual incidence and mortality [1]; it has been projected that by 2030, PC will be the second leading cause of cancer-related deaths, surpassing breast cancer, prostate, and colorectal cancers [2]. Surgical resection remains the primary form of treatment for patients with PC [3]. Currently, the diagnosis of PC is mainly based on clinical signs and symptoms, imaging techniques, serum CA199, and pathological features. However, most patients with PC are already at an advanced stage when they first visit the hospital, losing the opportunity for surgery, with a five-year survival rate of < $5\%$ [4]. Thus, more reliable diagnostic biomarkers are urgently needed to improve early diagnosis of PC. In recent years, liquid biopsies to isolate circulating tumor DNA (ctDNA) [5], circulating tumor cells (CTCs) [6], circulating exosomal miRNA [7], and exosomal GPC1 [8] for the early detection of PC have re ceived much attention. However, these methods are complex, time-consuming, expensive, and difficult to perform. Tumor-promoting inflammation is the seventh most important feature of cancer cells [9]. Circulating inflammatory markers such as C-reactive protein (CRP) [10], neutrophils [11], lymphocytes, platelets, monocytes [12], and fibrinogen [13] play an essential role in the oncogenesis and development of cancer. Some studies have found that inflammation markers ratios could predict the prognoses of patients with PC. For example, CRP-to-albumin score, the Glasgow Prognostic Score (GPS) each have an independent prognostic value in patients with PC [14]. A high neutrophil-to-lymphocyte ratio (NLR) is associated with an adverse overall survival (OS) in pancreatic cancer [15]. A low fibrinogen-to-albumin ratio (FAR) was positively correlated with a good OS in locally advanced or metastatic PC [16]. Notably, inflammation is evident at the earliest stages of tumor progression and could promote the development of incipient tumors into full-blown cancers [17]. Therefore, we hypothesized that these circulating inflammatory markers change within the early stages of cancer and could act as reliable indicators for early diagnoses of PC. In this study, we assessed inflammation indicator values including FAR, fibrinogen-to-prealbumin ratio (FPR), NLR, platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and prognostic nutritional index (albumin + 5 × lymphocyte count; PNI) in early-stage PC, healthy controls (HC), and other pancreatic tumors (OPT), with the aim of exploring whether inflammation indicators could be used as markers for the diagnosis of early-stage PC. ## Patients collection This study included 422 patients with PC, 119 patients with benign pancreatic tumors (BPT; 39 chronic pancreatitis, 56 pancreatic serous cystadenomas, and 24 pancreatic mucinous cystadenomas), 98 patients with solid pseudo-papilloma of the pancreas (SPT), 59 patients with pancreatic neuroendocrine tumors (PNET), and 392 healthy controls (HC) from January 2015 to December 2021 at the Harbin Medical University Cancer Hospital. Eight patients with PC, 11 with other pancreatic diseases (OPT; two CP, two SPT, and seven pancreatic serous or mucinous cystadenoma), and nine HC from January 2017 to December 2021 in the Municipal Hospital Affiliated to Taizhou University were also enrolled in this study. The inclusion and exclusion criteria were as follows:1) age ≥ 18 years; 2) pathologically confirmed diagnoses of PC(adenocarcinoma, pancreatic ductal adenocarcinoma, and mucinous adenocarcinoma), neuroendocrine tumor (G1, G2, and G3), solid pseudopapillary neoplasm, chronic pancreatitis, pancreatic serous cystadenoma, and pancreatic mucinous cystadenoma; 3) R0 resection (radical surgical resection); 4) PC pathology at TNM stage I—II; 5) available clinical baseline information; 6) no antitumor therapy performed before surgery; 7) no second primary cancer; 8) no history of autoimmune disorders, hepatitis, nephropathy, coagulation disorders, or HIV infection; and 9) no acute inflammation before surgery. Each disease group and HC from Harbin Medical University Cancer Hospital were randomly divided into training and testing sets 1 at a ratio of 4:1. The patients and HC from Municipal Hospital Affiliated to Taizhou University were used as testing set 2. Ethical approval for this study was granted by the Harbin Medical University Cancer Hospital and Municipal Hospital Affiliated to Taizhou University Ethics Committee, and all participants provided signed informed consent forms. ## Data collection Detailed baseline and clinicopathological information, including sex, age, tumor location, tumor size, pathological type, differentiation, lymph node metastasis, and TNM stage of the patients with pancreatic diseases and HC, were obtained from the medical records of the inpatients or outpatients. The preoperative hematological parameters and liver function tests included neutrophils (× 109/L), lymphocytes (× 109/L), monocytes (× 109/L), platelets (× 109/L), plasma fibrinogens (g/L), serum albumins (g/L), prealbumin (mg/L), and CA199 (U/L) within seven days before surgery (average 2—7 days) were gathered from the medical records. TNM staging was performed using the 8th edition of the AJCC Cancer Staging Manual for Pancreatic Cancer. ## Inflammation markers ratios definitions FAR, FPR, NLR, PLR, MLR, and FLR were defined as the plasma fibrinogen value divided by the serum albumin value, plasma fibrinogen value divided by the serum prealbumin value, neutrophil count divided by the lymphocyte count, platelet count divided by the lymphocyte count, monocyte count divided by the lymphocyte count, and plasma fibrinogen value divided by the lymphocyte count, respectively. PNI was defined as serum albumin value + 5 × lymphocyte count. ## Statistical analysis Data were presented as mean ± standard deviation (SD). The differences in inflammatory markers and inflammatory markers ratios in different groups were examined using the Student’s t-test. A two-sided $p \leq 0.05$ was considered statistically significant. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to evaluate the diagnostic accuracy of the inflammation indicator and CA199 for early-stage PC and the discrimination ability between early-stage PC and PNET, SPT, and BPT. ROC curve analysis was also used to determine the best cut-off values for FAR, FPR, NLR, PLR, MLR, PNI, FLR, and CA199 based on the maximum Youden index. AUC values < 0.7, 0.7—0.9, and > 0.9 were considered as low, medium, and high diagnostic power, respectively. All statistical analyses were conducted using SPSS (version23.0, IBM Corp., Armonk, NY, USA) and GraphPad Prism (version 5.0, La Jolla, CA, USA). ## Clinical characteristics and circulating inflammatory markers of patients with pancreatic diseases and HC A total of 338 patients with early-stage PC, 96 with BPT, 78 with SPT, 47 with PNET, and 313 HC were assigned to the training set. Among the patients with PC, 187 ($55.3\%$) were male, and the average age was 57.5 ± 8.3 years, whereas among 78 patients with SPT, 66 ($84.6\%$) were female, and the average age was 35.5 ± 14.1 years. Most patients with SPT were young women. Most patients with PC had tumors located in the pancreatic head ($76.9\%$), whereas most patients with BPT, SPT, and PNET had tumors located in the pancreatic body and tail cysts (76, 67.9, and $72.3\%$, respectively). Most patients with PC had invasive ductal carcinomas ($91.1\%$). The clinical and pathological characteristics of the patients in the training and testing sets were similar. Detailed information on the patients and HC in the training and testing sets are listed in Table 1.Table 1Clinical characteristics of patients with pancreatic diseases and healthy controls in training and testing setsTraining setGroupsPC [338]HC [313]BPT [96]SPT [78]PNET [47]Gender Male187(55.3)164(52.3)25(26.0)12(15.4)27(57.4) Female151(44.7)149(47.6)71(74.0)66(84.6)20(42.6)Age ≤ 60215(63.6)205(65.4)70(72.9)70(89.7)20(42.6) > 60123(36.4)103(34.5)26(27.1)8(10.3)27(57.4)CA199 ≥ 37240(71.0)5(1.6)13(13.5)4(5.1)2(4.3) < 3798(29.0)308(98.4)83(86.5)74(94.9)45(95.7)Location: Head260(76.9)23(24.0)25(32.1)13(27.7) Body or Tail78(23.1)73(76.0)53(67.9)34(72.3)Tumor size > 4 cm81(24.0)40(41.6)40(51.3)16(34.0) ≤ 4 cm257(76.0)56(58.4)38(48.7)31(66.0)Pathological type Ductal adenocarcinoma308(91.1)31(32.3; chronic pancreatitis) others30(8.9)65(67.7; adenoma)Differentiation High and Moderate218(64.5)40(85.1; G1-G2) Poor120(35.5)7(14.94; G3)Lymph nodes + 107(31.7) -231(68.3)TNM stage I166(49.1) II172(50.9)Fibrinogen(g/L)3.44 ± 0.952.87 ± 0.562.76 ± 0.762.46 ± 0.692.54 ± 0.58albumin(g/L)38.52 ± 4.0443.75 ± 2.3839.62 ± 3.4540.79 ± 3.7240.69 ± 3.11prealbumin(mg/L)215.17 ± 69.86322.74 ± 58.73255.85 ± 61.72239.98 ± 56.07271.97 ± 65.09neutrophil(× 109/L)3.95 ± 1.643.45 ± 1.053.41 ± 1.953.70 ± 1.473.45 ± 1.24lymphocyte(× 109/L)1.62 ± 0.621.94 ± 0.562.03 ± 0.632.09 ± 0.541.98 ± 0.59platelet(× 109/L)224.43 ± 69.06238.48 ± 52.91227.42 ± 67.02248.58 ± 75.31212.57 ± 51.78monocyte(× 109/L)0.51 ± 0.190.37 ± 0.110.46 ± 0.190.52 ± 0.200.43 ± 0.16AFR (Mean ± SD)0.091 ± 0.030.066 ± 0.0130.71 ± 0.020.061 ± 0.0170.063 ± 0.01APR (Mean ± SD)0.019 ± 0.0120.009 ± 0.0030.011 ± 0.0050.011 ± 0.0060.009 ± 0.003NLR (Mean ± SD)2.78 ± 1.671.89 ± 0.741.81 ± 1.261.84 ± 0.771.87 ± 0.83PLR (Mean ± SD)156.32 ± 75.68131.15 ± 45.19119.15 ± 40.90123.36 ± 42.09115.21 ± 40.50MLR (Mean ± SD)0.36 ± 0.210.20 ± 0.060.24 ± 0.110.26 ± 0.100.23 ± 0.09PNI (Mean ± SD)46.62 ± 5.3353.47 ± 3.8149.79 ± 4.5051.23 ± 4.9250.59 ± 4.64FLR (Mean ± SD)2.46 ± 1.261.57 ± 0.541.47 ± 0.561.26 ± 0.511.38 ± 0.44Testing set 1GroupsPC [84]HC [79]BPT [23]SPT [20]PNET [12]Gender Male46(54.8)46(58.2)9(39.1)4(20.0)8(66.7) Female38(45.2)33(41.8)14(60.9)16(80.0)4(33.3)Age ≤ 6044(52.4)56(70.9)13(56.6)17(85.0)7(58.3) > 6040(47.6)23(29.1)10(43.4)3(15.0)5(41.7)CA199 ≥ 3763(75.0)2(2.6)9(39.1)1(5.0)2(16.7) < 3721(35.0)77(97.4)14(60.8)19(95.0)10(83.3)Location: Head58(69.0)4(17.4)5(25.0)5(41.7) Body or Tail34(31.0)19(82.6)15(75.0)7(58.3)Tumor size > 4 cm21(44.7)11(47.8)11(55.0)4(33.3) ≤ 4 cm63(55.3)12(52.2)9(45.0)8(66.7)Pathological type Ductal adenocarcinoma75(89.3)8(34.7; chronic pancreatitis) others9(10.7)15(65.2; adenoma)Differentiation High and Moderate43(51.2)10(83.3; G1-G2) Poor41(48.8)2(16.7; G3)Lymph nodes + 17(20.2) -67(79.8)TNM stage I50(59.5) II34(40.5)Fibrinogen(g/L)3.56 ± 0.942.82 ± 0.492.59 ± 0.582.42 ± 0.602.59 ± 0.69albumin(g/L)37.98 ± 3.0743.78 ± 2.0938.68 ± 3.2340.54 ± 3.2540.5 ± 3.28prealbumin(mg/L)212.73 ± 45.03312.30 ± 57.40269.65 ± 63.85231.74 ± 55.87280.17 ± 52.37neutrophil(× 109/L)3.88 ± 1.263.46 ± 0.953.37 ± 1.243.24 ± 1.123.17 ± 1.15lymphocyte(× 109/L)1.59 ± 0.452.01 ± 0.661.89 ± 0.442.05 ± 0.761.84 ± 0.42platelet(× 109/L)234.04 ± 77.48250.29 ± 61.98234.26 ± 78.76241.37 ± 49.04198.08 ± 46.20monocyte(× 109/L)0.49 ± 0.140.37 ± 0.100.42 ± 0.140.42 ± 0.130.40 ± 0.14AFR (Mean ± SD)0.095 ± 0.0280.065 ± 0.010.067 ± 0.0150.060 ± 0.0150.064 ± 0.015APR (Mean ± SD)0.018 ± 0.0090.009 ± 0.0030.010 ± 0.0030.011 ± 0.0050.009 ± 0.002NLR (Mean ± SD)2.57 ± 0.851.85 ± 0.651.83 ± 0.721.73 ± 0.751.74 ± 0.59PLR (Mean ± SD)157.09 ± 60.35133.85 ± 45.32128.29 ± 49.03135.00 ± 57.96112.36 ± 34.29MLR (Mean ± SD)0.33 ± 0.130.20 ± 0.060.22 ± 0.060.24 ± 0.150.22 ± 0.07PNI (Mean ± SD)45.91 ± 3.8853.83 ± 3.9548.16 ± 4.1150.78 ± 5.1349.70 ± 4.67FLR (Mean ± SD)2.45 ± 0.971.55 ± 0.551.45 ± 0.501.34 ± 0.591.46 ± 0.47Testing set 2GroupsPC [8]HC [9]BPT [11]Gender Male5(62.5)7(77.8)6(54.5) Female3(37.5)2(22.2)5(45.4)Age ≤ 605(62.5)6(66.7)6(54.5) > 603(37.5)3(33.3)5(45.4)CA199 ≥ 376(75.0)1(11.1)1(9.1) < 372(25.0)8(88.9)10(90.9)Location: Head6(75.0)4(36.4) Body or Tail2(25.0)7(63.6)Tumor size > 4 cm2(25.0)5(45.5) ≤ 4 cm6(75.0)6(54.4)Pathological type Ductal adenocarcinoma7(87.5)2(18.2; chronic pancreatitis)7 (63.6; adenoma)2(18.2, Solid pseudo papilloma) others1(12.5)Differentiation High and Moderate5(62.5) Poor3(37.5)Lymph nodes + 3(37.5) -5(62.5)TNM stage I4(50.0) II4(50.0)Fibrinogen(g/L)3.16 ± 0.552.55 ± 0.542.81 ± 0.52albumin(g/L)38.20 ± 3.0840.83 ± 4.4943.66 ± 3.62prealbumin(mg/L)205.25 ± 22.32287.78 ± 15.70278.72 ± 10.51neutrophil(× 109/L)3.69 ± 0.862.97 ± 0.712.75 ± 0.71lymphocyte(× 109/L)1.46 ± 0.341.95 ± 0.431.86 ± 0.54platelet(× 109/L)192.5 ± 54.5230.67 ± 48.95237.24 ± 97.47monocyte(× 109/L)0.48 ± 0.160.40 ± 0.160.36 ± 0.13AFR (Mean ± SD)0.082 ± 0.110.063 ± 0.0150.65 ± 0.13APR (Mean ± SD)0.015 ± 0.0020.009 ± 0.0020.010 ± 0.002NLR (Mean ± SD)2.60 ± 0.731.58 ± 0.431.54 ± 0.44PLR (Mean ± SD)135.35 ± 40.17121.40 ± 28.15133.67 ± 54.68MLR (Mean ± SD)0.33 ± 0.810.20 ± 0.060.20 ± 0.07PNI (Mean ± SD)45.51 ± 3.5850.57 ± 4.5952.90 ± 4.44FLR (Mean ± SD)2.24 ± 0.561.35 ± 0.341.56 ± 0.32PC pancreatic cancer, HC Healthy controls, BPT Benign pancreas tumors, SPT Solid pseudo papilloma of the pancreas, PNET Pancreatic neuroendocrine tumors, FAR Fibrinogen-to-albumin ratio, FPR Fibrinogen-to-prealbumin ratio, NLR Neutrophil-to-lymphocyte ratio, PLR Platelets-to-lymphocyte ratio, MLR Monocytes-to-lymphocyte ratio, PNI Albumin + 5 × the lymphocyte count, FLR Fibrinogen-to- lymphocyte ratio We compared the hematological and biochemical parameters of patients with PC, BPT, SPT, PNET, and HC. As shown in Fig. 1, in the training set, the average fibrinogen, neutrophil, platelet, and monocyte levels in patients with PC were 3.44 ± 0.95 g/L, 3.95 ± 1.64 × 109/L, 224.43 ± 69.06 × 109/L, and 0.51 ± 0.19 × 109/L, respectively; these were significantly higher compared to those of the HC and OPT groups, with P values < 0.05. In contrast, the average albumin, prealbumin, lymphocytes, and platelets of patients with PC were 38.52 ± 4.04 g/L, 215.17 ± 69.86 mg/L, 1.62 ± 0.62 × 109/L, 224.43 ± 69.06 × 109/L, respectively, which were significantly lower than those in the HC and OPT groups, with P values < 0.05. The results obtained from the testing set were consistent with those obtained from the training set (Supplementary Fig. 1). These results suggest that circulating inflammatory markers had already changed in the early stages of PC.Fig. 1The circulating inflammation markers in PC, HC, BPT, SPT, and PNET in training sets. The plasma fibrinogens (A), serum albumins (B), prealbumin (C), neutrophils (D), lymphocytes (E), platelets (F), and monocytes (G) in PC, HC, BPT, SPT, and PNET. Abbreviations: PC, pancreatic cancer; BPT, benign pancreas tumors; SPT, solid pseudo papilloma of the pancreas; PNET, patients with pancreatic neuroendocrine tumors; HC, healthy controls ## Inflammation markers ratios values in pancreatic diseases and HC As shown in Table 1, in the training set, FAR, FPR, NLR, PLR, MLR, PNI, and FLR values of patients with PC were 0.091 ± 0.03, 0.019 ± 0.012, 2.78 ± 1.67, 156.32 ± 75.68, 0.36 ± 0.21, 46.62 ± 5.33, and 2.46 ± 1.26, respectively. FAR values were significantly higher in patients with PC than those in the HC, BPT, SPT, and PNET groups (Fig. 2A, $P \leq 0.0001$, $P \leq 0.0001$, $P \leq 0.0001$, and $P \leq 0.0001$, respectively). FPR values were significantly higher in patients with PC than those of HC, BPT, SPT, and PNET (Fig. 2B, $P \leq 0.0001$, $P \leq 0.0001$, $P \leq 0.0001$, and $P \leq 0.0001$, respectively). NLR values were significantly higher in patients with PC than those in the HC, BPT, SPT, and PNET groups (Fig. 2C, $P \leq 0.0001$, $P \leq 0.0001$, $P \leq 0.0001$, and $$P \leq 0.0003$$, respectively). PLR values were higher in patients with PC than those in the HC, BPT, SPT, and PNET groups (Fig. 2D, $P \leq 0.0001$, $P \leq 0.0001$, $$P \leq 0.0002$$, and $$P \leq 0.0003$$, respectively). MLR values were higher in patients with PC than those in the HC, BPT, SPT, and PNET groups (Fig. 2E, $P \leq 0.0001$, $P \leq 0.0001$, $P \leq 0.0001$, and $P \leq 0.0001$, respectively). FLR values were higher in patients with PC than those in the HC, BPT, SPT, and PNET groups (Fig. 2F, $P \leq 0.0001$, $P \leq 0.0001$, $P \leq 0.0001$, and $$P \leq 0.0008$$, respectively). In contrast, PNI values were lower in patients with PC than those in the HC, BPT, SPT, and PNET groups (Fig. 2G, $P \leq 0.0001$, $P \leq 0.0001$, $P \leq 0.0001$, and $P \leq 0.0001$, respectively). The results from the testing sets were consistent with those from the training set; the detailed data in the testing sets are shown in supplementary Fig. 2A—G. These results indicated that the inflammation markers ratios were significantly altered in patients with early-stage PC.Fig. 2The inflammation markers ratios in PC, HC, BPT, SPT, and PNET in training sets. The FAR (A), FPR (B), NLR (C), PLR (D), MLR (E), FLR (F), and PNI (G) in PC, HC, BPT, SPT, and PNET. Abbreviations: PC, pancreatic cancer; BPT, benign pancreas tumors; SPT, solid pseudo papilloma of the pancreas; PNET, patients with pancreatic neuroendocrine tumors; HC, healthy controls; FAR, fibrinogen-to-albumin ratio; FPR, fibrinogen-to-prealbumin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelets-to-lymphocyte ratio; MLR monocytes-to-lymphocyte ratio; PNI, albumin + 5 × the lymphocyte count, FLR, fibrinogen-to- lymphocyte ratio ## Diagnostic and differential diagnosis values of inflammation markers ratios values in PC In the training sets, the ROC curve was used to evaluate the capabilities of CA199, FAR, FPR, NLR, PLR, MLR, and PNI in discriminating between early stage PC and HC. The AUC values were 0.868 for CA199 ($P \leq 0.0001$, cutoff 24.540, sensitivity 0.939, specificity 0.817), 0.776 for FAR ($P \leq 0.0001$, cutoff 0.080, sensitivity 0.885, specificity 0.556), 0.869 for FPR ($P \leq 0.0001$, cutoff 0.012, sensitivity 0.837, specificity 0.775), 0.686 for NLR ($P \leq 0.0001$, cutoff 2.252, sensitivity 0.780, specificity 0.527), 0.584 for PLR ($$P \leq 0.0002$$, cutoff 177.218, sensitivity 0.879, specificity 0.299), 0.818 for MLR ($P \leq 0.0001$, cutoff 0.249, sensitivity 0.830, specificity 0.678), 0.748 for FLR ($P \leq 0.0001$, cutoff 1.864, sensitivity 0.773, specificity 0.639), and 0.860 for PNI ($P \leq 0.0001$, cutoff 49.025, sensitivity 0.907, specificity 0.707) (Fig. 3A, Table 2). The AUC was 0.942 for a combination of CA199 and FAR, 0.964 for CA199 and FPR, 0.940 for CA199 + MLR, 0.955 for CA199 + PNI, 0.964 for CA199 + FAR + FPR, 0.964 for CA199 + FAR + FPR + FLR, and 0.976 for CA199 + FAR + FPR + MLR + PNI (Fig. 3B, Table 2). To determine whether inflammation indicator values could differentiate PC from other pancreatic diseases (OPT), we generated ROC curves. As shown in Fig. 3C-D and Table 3, the AUC was 0.846 for CA199 ($P \leq 0.0001$, cut-off 32.205, sensitivity 0.887, specificity 0.772), 0.778 for FAR ($P \leq 0.0001$, cut-off 0.070,sensitivity 0.701, specificity 0.734), 0.779 for FPR ($P \leq 0.0001$, cut-off 0.013, sensitivity 0.778, specificity 0.666), 0.716 for NLR ($P \leq 0.0001$, cut-off 1.961, sensitivity 0.674, specificity 0.642), 0.648 for PLR ($P \leq 0.0001$, cut-off 128.575, sensitivity 0.679, specificity 0.565), 0.697 for MLR ($P \leq 0.0001$, cut-off 0.271, sensitivity 0.733, specificity 0.607), 0.714 for PNI ($P \leq 0.0001$, cut-off 47.225, sensitivity 0.774, specificity 0.595), and 0.813 for FLR ($P \leq 0.0001$, cut-off 1.631, sensitivity 0.747, specificity 0.743). The AUC was 0.914 for a combination of CA199 + FAR, 0.915 for CA199 + FPR, 0.917 for CA199 + FAR + FPR, and 0.924 for CA199 + FAR + FPR + FLR. We calculated the ROC curves and AUC for the testing set 1 and testing set 2 using the best cut-off value from the ROC curve in the training set. In testing set 1, the AUC was 0.941 for a combination of CA199 + FAR + FPR, 0.947 for CA199 + FAR + FPR + FLR, 0.975 for CA199 + FAR + FPR + MLR + PNI to distinguish patients with PC from HC; 0.925 for CA199 + FAR + FPR, and 0.942 for CA199 + FAR + FPR + FLR to differentiate patients with PC from those with OPT. The results revealed that combinations of CA199 and inflammation indicator values had a strong capability for differentiating patients with PC from the HC and OPT groups, especially the combination of CA199 + FAR + FPR + FLR (Fig. 3E-H, and supplementary Tables 1 and 2). In testing set 2, the AUC was 0.993 for combination of CA199 + FAR + FPR + FLR to distinguish patients with PC from HC, and 0.994 for combination of CA199 + FAR + FPR + FLR to differentiate patients with PC from those with OPT (supplementary Fig. 3).Fig. 3Diagnostic value of single and combined inflammation markers ratios in early-stage PC. A The ROC curve analysis of FAR, FPR, NLR, PLR, MLR, FLR, PNI, and CA199 between PC and HC in the training set. B The ROC curve analysis of combined inflammation markers ratios and CA199 in PC and HC in the training set. C The ROC curve analysis of FAR, FPR, NLR, PLR, MLR, FLR, PNI, and CA199 between PC and OPT in the training set. D The ROC curve analysis of combined inflammation indicator and CA199 between PC and OPT in the training set. E The ROC curve analysis of FAR, FPR, MLR, FLR, PNI, and CA199 between PC and HC in testing set 1. F The ROC curve analysis of combined inflammation markers ratios and CA199 between PC and HC in testing set 1. G The ROC curve analysis of FAR, FPR, FLR, and CA199 between PC and OPT in testing set 1. H The ROC curve analysis of combined inflammation markers ratios and CA199 between PC and OPT in testing set 1. Abbreviations: PC, pancreatic cancer; OPT, other pancreas tumors; HC, healthy controls; FAR, fibrinogen-to-albumin ratio; FPR, fibrinogen-to-prealbumin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelets-to-lymphocyte ratio; MLR monocytes-to-lymphocyte ratio; PNI, albumin + 5 × the lymphocyte count; FLR, fibrinogen-to- lymphocyte ratio; ROC, receiver operating characteristicTable 2ROC curve results based on FAR, FPR, NLR, PLR, LMR, PNI, FLR, and CA199 for distinguishing PC patients from HC in training setMarkerAUC ($95\%$CI)P—valuecut-offSensitivitySpecificityFAR0.776(0.740–0.811)< 0.00010.0800.8850.556FPR0.869(0.842–0.896)< 0.00010.0120.8370.775NLR0.686(0.645–0.726)< 0.00012.2520.7800.527PLR0.584(0.540–0.628)0.0002177.2180.8790.299MLR0.818(0.786–0.850)< 0.00010.2490.8310.678PNI0.860(0.831–0.888)< 0.000149.0250.9070.707FLR0.748(0.711–0.785)< 0.00011.8640.7730.639CA1990.868(0.836–0.901)< 0.000124.5400.9390.817CA199 + FAR0.942(0.924–0.960)< 0.0001-0.4590.9710.814CA199 + FPR0.964(0.951–0.977)< 0.0001-0.4700.9780.840CA199 + MLR0.940(0.921–0.960)< 0.00010.3710.9460.870CA199 + PNI0.955(0.939–0.971)< 0.00010.0020.9490.870CA199 + FLR0.917(0.893–0.940)< 0.0001-0.0350.9650.793CA199 + FAR + FPR0.964(0.951–0.978)< 0.0001-0.2780.9650.858CA199 + FAR + FPR + FLR0.964(0.951–0.978)< 0.0001-0.4660.9740.849CA199 + FAR + FPR + MLR + PNI0.976(0.965–0.988)< 0.0001-0.2940.9740.891Abbreviations: PC Pancreatic cancer, HC Heathy controls, ROC Receiver operating characteristic, AUC Area under the receiver operating characteristic curve, CI Confidence interval, FPR Fibrinogen-to-prealbumin ratio, FAR Fibrinogen-to-albumin ratio, NLR Neutrophil-to-lymphocyte ratio, PLR Platelets-to-lymphocyte ratio, MLR Monocytes-to-lymphocyte ratio, PNI Albumin + 5 × the lymphocyte count, FLR Fibrinogen-to-lymphocyte ratioTable 3ROC curve results based on FAR, FPR, NLR, PLR, LMR, PNI, FLR, and CA199 for distinguish PC patients from OPT in testing set 1MarkerAUC ($95\%$CI)P—valuecut-offSensitivitySpecificityFAR0.778(0.740–0.817)< 0.00010.0700.7010.734FPR0.779(0.740–0.817)< 0.00010.0130.7780.666NLR0.716(0.674–0.759)< 0.00011.9610.6740.642PLR0.648(0.603–0.694)< 0.0001128.5750.6790.565MLR0.697(0.654–0.741)< 0.00010.2710.7330.607PNI0.714(0.671–0.757)< 0.000147.2250.7740.595FLR0.813(0.778–0.848)< 0.00011.6310.7470.743CA1990.846(0.812–0.880)< 0.000132.2050.8870.772CA199 + FAR0.914(0.891–0.937)< 0.0001-0.1820.8960.799CA199 + FPR0.915(0.891–0.938)< 0.00010.1010.8600.831CA199 + FLR0.915(0.829–0.938)< 0.0001-0.5230.9460.778CA199 + FAR + FPR0.917(0.895–0.940)< 0.0001-0.1250.8870.799CA199 + FAR + FPR + FLR0.924(0.903–0.946)< 0.0001-0.5060.9410.799Abbreviations: PC Pancreatic cancer, OPT Other pancreas tumors, ROC Receiver operating characteristic, AUC Area under the receiver operating characteristic curve, CI Confidence interval, FPR Fibrinogen-to-prealbumin ratio, FAR Fibrinogen-to-albumin ratio, NLR Neutrophil-to-lymphocyte ratio, PLR Platelets-to-lymphocyte ratio, MLR Monocytes-to-lymphocyte ratio, PNI Albumin + 5 × the lymphocyte count, FLR Fibrinogen-to- lymphocyte ratio ## Relationship between inflammation markers ratios values and clinical characteristics of patients with PC The relationship between inflammation markers ratios and the clinical characteristics of patients with PC was analyzed. In the training set, patients with pancreatic head cancer had higher FAR, FPR, NLR, PLR, MLR, FLR, and lower PNI values than patients with pancreatic body or tail cancers (Fig. 4A-G; $P \leq 0.001$, $P \leq 0.001$, $P \leq 0.001$, $P \leq 0.001$, $P \leq 0.001$, and $P \leq 0.001$, respectively). Patients aged > 60 years had higher FAR values than those aged ≤ 60 years (Fig. 4A; $$P \leq 0.037$$). Male patients with PC had higher MLR values than female patients with PC (Fig. 4E, $$P \leq 0.011$$). In the testing set, the same trend was observed for the FAR, FPR, NLR, FLR, and PNI values (Supplementary Fig. 4A-C, F-G; $P \leq 0.001$, $$P \leq 0.007$$, $$P \leq 0.04$$, $P \leq 0.05$, and $$P \leq 0.004$$, respectively). Similarly, patients who were > 60 years of age had higher FAR, FPR, MLR, FLR, and lower PNI values than those aged ≤ 60 years (Supplementary Fig. 4A-B, E-F; $$P \leq 0.017$$, $$P \leq 0.007$$, $$P \leq 0.013$$, $P \leq 0.05$, and $$P \leq 0.02$$, respectively). In the two groups, the inflammation markers ratios values were independent of tumor size, differentiation, lymph nodes, TNM stage and sex ($P \leq 0.05$ in all inflammation markers ratios values).Fig. 4Comparison of inflammation markers ratios in different clinical characteristics early-stage PC. The FAR(A), FPR (B), NLR (C), PLR (D), MLR (E), FLR (F), and PNI (G), and in different tumor location, tumor size, differentiation, lymph nodes, stage, sex, and age in the training set. Abbreviations: PC, pancreatic cancer; FAR, fibrinogen-to-albumin ratio; FPR, fibrinogen-to-prealbumin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelets-to-lymphocyte ratio; MLR monocytes-to-lymphocyte ratio; PNI, albumin + 5 × the lymphocyte count; FLR, fibrinogen-to- lymphocyte ratio; Blue column: location, pancreatic head/tumor size ≤ 4 cm/ differentiation well / lymph node metastasis no /stage I/sex male/age ≤ 60; Red column: location, pancreatic body and tail/tumor size > 4 cm/ differentiation poor / lymph node metastasis yes /stage II/sex male/age > 60 ## Differential diagnosis power of inflammation markers ratios values in different tumors location of PC Patients with PC and OPT were classified into four subgroups according to the locations of the pancreatic lesions: pancreatic head cancer (PHC), pancreatic body and tail cancer (PBTC), other pancreatic head tumors (OPHT), and other pancreatic body and tail tumors (OPBTT). The AUC was 0.855 for CA199, 0.750 for FAR, 0.751 for FPR, 0.824 for FLR, 0.767 for NLR, 0.686 for PLR, 0.766 for MLR, and 0.709 for PNI, to differentiate between patients with PHC and OPHT (Fig. 5A). The AUC was 0.834 for FAR + FPR + FLR and 0.915 for CA199 + FAR + FPR + FLR, to differentiate between patients with PHC and those with OPHT (Fig. 5B). The AUC was 0.838 for CA199, 0.706 for FAR, 0.693 for FPR, 0.660 for FLR, 0.585 for NLR, 0.576 for PLR, 0.529 for MLR, and 0.601 for PNI, to differentiate between patients with PHC and OPHT (Fig. 5C). The AUC was 0.714 for FAR + FPR + FLR and 0.894 for CA199 + FAR + FPR + FLR, to differentiate between patients with PBTC and OPBTT (Fig. 5D). These results showed that a combination of CA199 + FAR + FPR + FLR could better help identify PHC and OPHT.Fig. 5Diagnostic value of single and combined inflammation markers ratios in different tumor location PC. A The ROC curve analysis of FAR, FPR, NLR, PLR, MLR, PNI, FLR, and CA199 between PHC and OPHT in the training sets. B The ROC curve analysis of combined inflammation markers ratios and CA199 between PHC and OPHT in the training sets. C The ROC curve analysis of FAR, FPR, NLR, PLR, MLR, PNI, FLR, and CA199 between PBTC and OPBTT in the training sets. D The ROC curve analysis of combined inflammation markers ratios and CA199 between PBTC and OPBTT in the training sets. Abbreviations: PHC, pancreatic head cancer; OPHT, other pancreas head tumors; PBTC, pancreatic body, and tail cancer; OPBTT, other pancreas body, and tail tumors; FAR, fibrinogen-to-albumin ratio; FPR, fibrinogen-to-prealbumin ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelets-to-lymphocyte ratio; MLR monocytes-to-lymphocyte ratio; PNI, albumin + 5 × the lymphocyte count; FLR, fibrinogen-to- lymphocyte ratio; ROC, receiver operating characteristic ## Discussion Cancer-associated inflammation has been reported as the seventh hallmark of cancer [9]. Nearly all human cancers harbor inflammatory reactions, which play an important role in tumor development, progression, and metastasis [18]. Systemic inflammation can play a supporting role in the evolution of PC. For example, chronic pancreatitis is a known risk factor for the development of PC [19]. Obesity, another risk factor for pancreatic cancer, can induce inflammation by promoting the release of IL-6, CCL2, and CCL5, and the infiltration of macrophages and immunosuppressive cells [20]. Smoking is an established risk factor for PC and can induce inflammation and immune activation [21]. In addition, cancer cells can promote systemic inflammation that can, in turn, support tumor growth and lead to a poor prognosis in PC [18]. Inflammatory cells and chemokines shape the inflammatory microenvironment leading to cancer [22]. For example, IL-6, IL-1β, and TNF-α were increased at cancer early stage and associated with disease severity [23]. In Pancreatic ductal adenocarcinoma (PDAC) mouse models, adipocyte-secreted IL-1β could promote obesity-induced pancreatic carcinogenesis and drug resistance through recruitment of tumor-associated neutrophils [24]. High intra-tumoral and serum IL-1β levels in PC patients correlate with poor overall survival and increased chemoresistance [25]. IL-6, a pro-inflammatory cytokine that shows diverse functions of cell multiplication, injury, infection, and inflammation, affects tumor cells to develop PC by controlling vascular endothelial growth factor (VEGF) secretion [26]. IL-8 which derived from macrophages, platelets, and epithelial cells could promote the angiogenesis of PC. Serum levels of IL-6, IL-8, IL-10, and IL-1RA were significantly increased in pancreatic cancer patients and were associated with worse survival rates, poor performance status. A panel of IP-10, IL-6, PDGF plus CA19-9 could discriminate PDAC patients from patients with pancreatic benign disease [27]. TNF-α is associated with acute and chronic inflammation and inflammation related to cancers [28]. In addition, increased expression of tumor-related inflammatory mediators and cytokines, such as TNF-α, IL-1, and IL-6 may stimulate the bone marrow to release neutrophils, resulting in an increase in the circulating neutrophil count and decrease in the circulating lymphocytopenia [29]. Neutrophils could promote growth and metastasis of tumors through secreting a variety of cytokines, including matrix metalloproteinase-9, chemokines and vascular endothelial growth factor (VEGF). It was reported that neutrophils could promote adhesion between circulating tumor cells and distant target organs through acting as an adhesive adapter, finally increasing the chance of distant metastasis. Moreover, neutrophil could also inhibit the antitumor immune function of natural killer cells and cytotoxic T cells [12]. Presently, it is believed that lymphocytes in the peripheral blood can cause synergistic cytotoxicity and play an anti-cancer role. Several subtypes of tumor infiltrating lymphocyte such as CD8 + T cells and memory T cells were associated with better outcomes of a variety of tumors, while regulatory T cells and Th17 cells were associated with progression and unfavorable prognosis of tumors [30]. Although different subset of T cells was associated with adverse prognosis of tumors, high level of absolute lymphocyte count was demonstrated to be associated with favorable prognosis of gastric cancer patients [31]. A study by Dominic et al. showed that inflammatory monocytes were lower in the bone marrow and higher in the blood of patients with resectable PC, and an increased blood-to-bone marrow monocyte ratio was a novel poor prognostic factor for PC [32]. Platelets are also involved in tumor development [33]. Meanwhile, patient’s nutritional status is associated with metabolic changes and immune status impairment. Circulating albumin and prealbumin are markers for evaluating nutritional status and immune status. Albumin can inhibit tumor progression by stabilizing DNA replication and enhancing the immune response [34]. The inflammatory factors may influence nutritional status through inhibition of appetite, alteration of gastrointestinal function, alteration of the carbohydrate metabolism and insulin resistance. Serum levels of IL-6 and IL-8 were inversely correlated to serum albumin and prealbumin. Serum IL-6 and IL-8 were highly expressed in patients with nutritional risk [35]. Genetic and pharmacological studies have revealed the key role of fibrinogen in determining the degree of local or systemic inflammation [36]. Fibrinogen is an important coagulation factor that can be recognized by a variety of integrin and non-integrin receptors on tumor, stromal, and inflammatory cells. These fibrinogen-mediated receptors are thought to control cell proliferation, apoptosis, cell migration, and the expression of inflammatory mediators [37]. In cancer, cytokines mediate signalling between cancer cells, and the cells of the TME, including PSCs, CAFs, endothelial cells, and a range of immune cells including macrophages, mast cells, neutrophils, and regulatory T-cells [38]. For example, glioblastoma (GBM) cells reduced lymphocyte infiltration by secreting immunosuppressive cytokines such as IL-10, IL-2, and TGF-β, and recruited and induced macrophages to become M2 phenotypes by secreting IL-10, IL-4, IL-6, macrophage–colony stimulating factor (M-CSF), TGF-β, and prostaglandin E2 (PGE2) [39]. Higher serum IL-8 and IL-6 levels were positively correlated with high NLR, modified glasgow prognostic score (mGPS), CRP-albumin ratio (CAR), and PLR [40, 41]. Fibrinogen induced the production of IL-6, IL-8, monocyte chemoattractant protein-1, vascular endothelial growth factor, angiopoietin-1 and type I collagen in pancreatic stellate cells [42]. CAR, NLR, and PNI were positively associated with IL-10, IL-23, and IL-1β [43]. Park et al. found moderate-to-strong correlations within circulating cytokines (TNF-α, IL-1β, IL-6, IL-8, IL-9, IL-10, and VEGF) as well as within systemic inflammatory markers (mGPS, NLR, and PNI) [44]. Higher mGPS was involved in increased plasma levels of IL-4, IL-6, IL-8 [45]. Patients with a low PNI exhibited high levels of TNF-αin advanced pancreatic cancer [46]. To sum up, there was a close relationship between systemic inflammatory markers and plasma cytokines. Currently, routine measurement of serum inflammatory cytokines is not common in daily clinical practice. Many studies used inflammatory cell in the peripheral blood to reflect the systemic immune conditions of patients. In this study, we included HCs and patients with chronic pancreatitis, pancreatic serous/mucinous cystadenoma, solid pseudo-papilloma, and pancreatic neuroendocrine tumors. The results showed that serum albumin, prealbumin, and lymphocytes were significantly decreased, while fibrinogen, neutrophils, and monocytes were significantly increased in early-stage PC compared with HC and OPT. Our results provide supporting evidence that inflammation is emerging as a hallmark of early—stage cancer. Since neutrophil, monocyte, and lymphocyte counts are influenced by many factors, researchers are more inclined to use the ratio values between the two inflammation markers to explore the relationship between the ratio values and malignant tumor prognosis. To date, many studies have shown that FAR, FPR, NLR, PLR, MLR, and PNI are predictive of outcomes in various types of cancer. For example, Michael et al. [ 47] found that an increased lymphocyte-to-monocyte ratio (LMR) was an independent prognostic factor for better cancer-specific survival in patients with PC (HR 0.70; $P \leq 0.001$). Qi et al. [ 36] showed that NLR, PLR, and LMR were independent predictors of survival in patients with advanced PC. Yi et al. [ 46] showed that a low PNI was associated with a systemic inflammatory response and was an independent poor prognostic factor for advanced PC. Fang et al. [ 16] reported that a high FAR was associated with poor OS in patients with locally advanced or metastatic PC. Xie et al. [ 48] found that high FPR was an independent poor prognostic factor for patients with stage I-III colorectal cancer (CRC). In addition, inflammatory indicators have important implications in cancer diagnosis. The combination of NLR, PLR, and CEA had a high diagnostic efficacy (AUC = 0.831, $95\%$ CI = 0.807–0.852) for early-stage CRC. Zheng et al. [ 39] found that NLR + LMR and the derived neutrophil-to-lymphocyte ratio (dNLR) + LMR had good diagnostic performance in patients with glioma (AUC = 0.777 and 0.778, respectively). Wu et al. [ 49] showed that a combination of PLR and CEA had a better AUC of 0.780 than CEA alone for diagnosing gastric cancer. Lu et al. [ 50] found that the combination of CA199 and AFR distinguished PC from HC with an ROC of 0.932. Liu et al. [ 51] showed that combined circulating dNLR and Alb was an effective diagnostic biomarker for early stage PC (AUC = 0.931), and that dNLR could distinguish early-stage PC from HC (AUC 0.895) and from additional cancers (AUC 0.794). Similar to the above results, this study found that FAR, FPR, NLR, PLR, MLR, and FLR were higher in early-stage PC than in HC and OPT, whereas PNI was lower in patients with early-stage PC. These results indicate that inflammatory indicators could act as early diagnostic markers for PC. Moreover, ROC analysis indicated that the FAR, FPR, PLR, MLR, and PNI were promising diagnostic indicators. Among these inflammation markers, a combination of FAR, FPR, FLR, and CA199 could be used to differentiate early-stage PC from HC and OPT with a better AUC (0.964 and 0.924 in training sets). The results obtained in the training set were confirmed for two independent testing sets. The inflammation indicators were similar over differences in sex, age, tumor size, differentiation, lymph nodes, and TNM stage, but varied greatly for different tumor locations. PHC always obstructs bile ducts, which leads to the levels of albumin (38.2 g/L vs 39.6 g/L, $$P \leq 0.007$$) and prealbumin (207.7 g/L vs 240.1 g/L, $$P \leq 0.0003$$) that are lower than PBTC. We further explored the discriminating ability of inflammation indicators for different tumor locations in early-stage PC. The combination of CA199, FAR, FPR, and FLR could better distinguish PHC from OPHT (AUC = 0.915) than PBTC from OPBTT (AUC = 0.894). Hence, for patients with pancreatic head tumors at the first medical visit, a combination of FAR, FPR, FLR, and CA199 would significantly guide the initial clinical diagnosis and aid in a more accurate final diagnosis. Our study had some limitations. First, it was a retrospective analysis of data from a clinical trial and lacked prospective data. Second, although all patients were from two single-centers, we enrolled only a small number of patients from one center. Third, the participants in our study had no measurements of serum inflammatory cytokines such as IL-2, IL-6 and so on, we have no way to compared correlations between cytokine levels and inflammation markers ratios. However, despite several limitations, this study confirmed that FAR, FPR, FLR, and CA199 have a potential as diagnostic markers for early-stage PC. These results need to be confirmed in a multicenter, large-scale, prospective study. ## Conclusion This study established that circulating inflammation markers ratios, especially FAR, FPR, and FLR, could be used as cost-effective diagnostic biomarkers for early-stage PC that improve the diagnostic accuracy over CA199. The combination of FAR, FPR, FLR, and CA199 was a potentially effective biomarker for distinguishing early -stage PC patients from HC and in differentiating early -stage PC patients from patients with OPT. The combination of FAR, FPR, FLR, and CA199 may be useful as a differential diagnostic marker for patients with pancreatic head cancer. ## Supplementary Information Additional file 1: Supplementary Figure 1. The circulating inflammation markers in PC, HC, BPT, SPT, and PNET in testing set 1.Additional file 2: Supplementary Figure 2. The inflammation markers ratios in PC, HC, BPT, SPT, and PNET in testing set 1.Additional file 3: Supplementary Figure 3. Diagnostic value of single and combined inflammation markers ratios in early-stage PC.Additional file 4: Supplementary Figure 4. Comparison of inflammation markers ratios in different clinical characteristics early-stage PC.Additional file 5: Supplementary Table 1. ROC curve results based on FAR, FPR, MLR, PNI, FLR and CA199 for distinguishing PC patients from HC in testing set 1.Additional file 6: Supplementary Table 2. ROC curve results based on FAR, FPR, FLR, and CA199 for distinguish PC patients from OPT in Testing set 1.Additional file 7: Supplementary Table 3. ROC curve results based onFAR, FPR, NLR, PLR, LMR, PNI, FLR and CA199 for distinguishing PC from OPT in training sets 1. ## References 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin (2018.0) 68 394-424. DOI: 10.3322/caac.21492 2. Ferlay J, Partensky C, Bray F. 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--- title: Correlation and agreement between arterial and venous blood gas analysis in patients with hypotension—an emergency department-based cross-sectional study authors: - Hari Prasad - Nagasubramanyam Vempalli - Naman Agrawal - U. N. Ajun - Ajmal Salam - Soumya Subhra Datta - Ashutosh Singhal - Nishant Ranjan - P. P. Shabeeba Sherin - G. Sundareshan journal: International Journal of Emergency Medicine year: 2023 pmcid: PMC9999648 doi: 10.1186/s12245-023-00486-0 license: CC BY 4.0 --- # Correlation and agreement between arterial and venous blood gas analysis in patients with hypotension—an emergency department-based cross-sectional study ## Abstract ### Background Blood gas analysis is integral to assessing emergency department (ED) patients with acute respiratory or metabolic disease. Arterial blood gas (ABG) is the gold standard for oxygenation, ventilation, and acid–base status but is painful to obtain. Peripheral venous blood gas (VBG) is a valuable alternative as it is less painful and easy to collect. The comparability of ABG and VBG was studied in various conditions. But in hypotension, previous findings were inconsistent. So, we studied the correlation and agreement between ABG and VBG in hypotensive patients. ### Methodology The study was conducted at the emergency department of a tertiary healthcare center in Northern India. Patients with hypotension above 18 years who satisfied the inclusion criteria were clinically evaluated. Patients who require ABG as a part of routine care were sampled. ABG was collected from the radial artery. VBG was obtained from the cubital or dorsal hand veins. Both samples were collected within 10 min and were analyzed. All ABG and VBG variables were entered in premade proforma. The patient was then treated and disposed of according to institutional protocol. ### Results A total of 250 patients were enrolled. The mean age was 53.25 ± 15.71 years. $56.8\%$ were male. The study included $45.6\%$ septic, $34.4\%$ hypovolemic, $18\%$ cardiogenic, and $2\%$ obstructive shock patients. The study found a strong correlation and agreement for ABG and VBG pH, pCO2, HCO3, lactate, sodium, potassium, chloride, ionized calcium, blood urea nitrogen, base excess, and arterial/alveolar oxygen ratio. Hence, regression equations were made for the aforementioned. There was no correlation observed between ABG and VBG pO2 and SpO2. Our study concluded that VBG could be a reasonable alternative for ABG in hypotensive patients. We can also mathematically predict values of ABG from VBG using regression equations derived. ### Conclusions ABG sampling causes most unpleasant experiences to patients and is associated with complications like arterial injury, thrombosis, air or clotted-blood embolism, arterial occlusion, hematoma, aneurysm formation, and reflex sympathetic dystrophy. The study has shown strong correlations and agreements for most ABG and VBG parameters and can predict ABG mathematically using regression formulas formulated from VBG. This will decrease needle stick injury, consume less time, and make blood gas evaluation easy in hypotensive settings. ## Background The first examination of gas content in blood dates back to the 1670 s when Magnus, Hooke, and Boyle obtained gas from the blood by employing a vacuum pump [1]. Hasselbalch adapted Henderson’s laws to the logarithmic form in 1917, creating the Henderson-Hasselbalch equation (pH = pK + log [HCO3 −]/[CO2]), which forms the foundation of clinical acid–base analysis [2, 3]. The clinical use of blood gas analysis originated from the poliomyelitis epidemic in the early 1950s, which showed unprecedented mortality rates in Denmark [4]. In emergency departments (ED), blood gas analysis is used for three primary purposes: establishing acid–base state (mainly pH but also to lesser extent bicarbonate and pCO2), assessing ventilation function (mainly pCO2 but also pH and to a lesser extent pO2), and obtaining lactic acid levels in sepsis. These parameters are measured by blood gas analysis. Historically, analyses were performed on arterial blood [5]. Since the original description in the nineteenth century, the techniques for assessing gas tensions in blood have undergone refinements resulting in accurate point-of-care machines. The physiological description of respiratory failure and metabolic status was initially defined in arterial gas tensions and pH measurements. As a result, various clinical guidelines for treating respiratory failure describe using ABG for evaluation and response to treatment [6]. The parameters commonly measured by modern blood gas analyzers include pH; PCO2; partial pressure of O2 (PO2); concentration of hemoglobin (Hb); dyshemoglobin—COHb (carboxyhemoglobin) and MetHb (methemoglobin); lactate; glucose; and electrolytes (sodium, potassium, and chloride). At the same time, HCO3 and base excess are calculated from measured values [7]. Arterial blood gas analysis is the gold standard to obtain information about oxygenation, ventilation, and acid–base status. Parameters like pH, pO2, pCO2, HCO3, lactate, and base excess obtained from ABG are considered the gold standard. Peripheral venous blood gas (VBG) sampling is a valuable alternative to arterial blood gas (ABG) sampling in the emergency department evaluating metabolic and acid–base disorders. It is easier to obtain venous blood, so obtaining venous blood gas (VBG) is less painful, and samples may be drawn along with sampling for other laboratory tests. Venous blood sampling reduces the risk of arterial hematoma, dissection, and thrombosis. So, it is increasingly performed these days in the emergency department [1, 2]. Peripheral, mixed, and central venous blood can also be sampled. Venous blood gas (VBG) measurements obtained from peripheral, mixed, or central venous blood can be used interchangeably with ABGs to assess acid–base status in hemodynamically stable critically ill patients [8]. Adrogue and Weil concluded in their study that in the presence of severe circulatory failure, there is a worse agreement between arterial and central or mixed venous values, with central or mixed venous blood having a higher CO2 concentration and lower pH than arterial blood because of impaired removal of generated CO2 from the tissues. This increase in the venous–arterial PCO2 difference occurs in states of decreased flow irrespective of the reason for the circulatory failure and has an inverse relationship with cardiac output [9]. The main complications of arterial blood gas measurements include arterial injury, thrombosis, air or clotted-blood embolism, arterial occlusion, hematoma, aneurysm formation, and reflex sympathetic dystrophy [10]. Turner et al. evaluated recall of patients’ collective experience in their ICU stay. He found that ABG sampling was rated by $48\%$ of the patients as the most unpleasant experience during admission, followed by tracheal suction in $44\%$ of the patients [11]. Till 2006, only 22 cases of radial artery aneurysms were reported in the literature [12]. Less severe adverse events such as hematoma after radial artery puncture occur in up to $59\%$ of the patients [13]. Many studies have found a strong correlation between arterial pH, partial pressure of CO2 (PCO2), and calculated bicarbonate and corresponding venous values in different clinical conditions [14]. In patients with diabetic ketoacidosis, pH and PCO2 levels obtained from venous blood gas reasonably correlate with ABG values [15]. Elborn et al. conducted a study on COPD patients and found no significant difference between the arterial and venous CO2 tensions, and the two were closely correlated [16]. In a study by Rees et al. on 40 patients with chronic lung disease, they supported the use of peripheral VBG to estimate PaO2 in a vast majority of patients. They also showed that peripheral venous carbon dioxide tension and pH correlate well with arterial values [17]. In another study, Malinoski et al. concluded that VBG and arterial PCO2, pH, and base excess values had a good correlation [18]. Hypotension is one condition where findings of ABG and VBG comparability are inconsistent. Few studies tried to study the correlation between arterial and venous samples in patients with hypotension, but their study findings are not uniform and vary widely. The various studies done on comparability between ABG and VBG are shown in Table 1.Table 1Various studies done on comparability between ABG and VBGStudy nameStudy settingParameters that showed correlationParameters that did not show correlationYear of publicationYildizdas et al. [ 19]ICUpH, pCO2, HCO3, base excesspO22004Malinoski et al. [ 18]ICUpH, pCO2, and base excesspO22005Kelly et al. [ 20]HCO3pCO22010Shirani et al. [ 21]EDpH, HCO3, base excesspCO22011Kim et al. [ 22]ICUpH, pCO2, HCO3-2013Byrne et al. [ 23]pHpCO22014Hynes et al. [ 24]ICUpH, HCO3, lactate, base excess-2015Zeserson et al. [ 25]ED, ICUpH, pCO2-2018White et al. [ 26]ICUpHpCO2, HCO32018Rudkin et al. [ 27]ED-pH, pCO22020Shin et al. [ 28]EDpH, HCO3, Ca2 +, Na +, K +, Cl −, lactate, glucosepCO22020Nanjayya et al. [ 29]ICUpHpCO22020Boon et al. [ 30]EDLactatepH, base deficit2021 ABG sampling is technically challenging and time-consuming, is necessitating sampling skill, and is occasionally associated by the risk of staff needle stick exposure. Because venous blood sample requires fewer punctures, the danger of needle stick injury in medical staff is reduced [21]. Therefore, we intend to do correlation and agreement between ABG and VBG in hypotensive patients. Also, there were no studies done correlating values of ABG and VBG in hypotensive patients in academic emergency medicine settings in India to our knowledge. Our cross-sectional study aimed to determine the relationship and agreement between arterial and venous blood gas analysis for the parameters pH, pCO2, HCO3, lactate, and base excess in hypotensive patients in the emergency department and also to develop prediction models in measuring parameters like pH, pCO2, HCO3, lactate, and base excess in arterial and venous samples. ## Study design and settings This cross-sectional study was conducted in the Emergency Department of All India Institute of Medical Sciences, Rishikesh, Uttarakhand, between January 2021 and June 2022 (patient recruitment period from March 2021 to March 2022). ## Inclusion criteria Patients with systolic blood pressure less than 90 mmHg or MAP less than 65 mmHg in the emergency department who require ABG as a part of routine carePatients more than 18 years of age ## Exclusion criteria Those who did not give consentExistence of contraindications for arterial blood sampling, including impalpable or negative Allen’s test in the upper extremities, infection or fistula at the desired site of puncture, or having severe coagulation disordersInterval of more than 10 min between arterial and venous sampling and inappropriate sample transfer to the laboratoryPostcardiac arrest patients ## Sample size Patients with hypotension above 18 years of age in the emergency department who satisfied the inclusion criteria were clinically evaluated. The minimum sample size was found to be 14 [22]. Because of the very small sample size, we included as much as participants using the consecutive sampling method during the study course (a total of 250 patients were included). ## Operational definition of hypotension Patients with systolic blood pressure less than 90 mmHg or mean arterial blood pressure less than 65 mmHg. ## Clinical evaluation Clinical evaluations were performed on emergency room patients with hypotension who were older than 18 years and met the inclusion criteria. After explaining the study to the patient or relative, written consent was obtained. The patient’s demographic details, vitals, clinical details, and diagnosis were entered in the data collection proforma. Patients who required ABG as a part of routine care per treating physician were sampled. An arterial sample (0.5–1 mL) was collected using a heparinized syringe from the radial artery at the wrist level. The venous blood sample was obtained from the cubital or dorsal hand veins. Both samples were collected with minimum delay (less than 10 min). Both samples were analyzed as soon as possible using a blood gas analyzer Nova Biomedicals Stat profile pHOX ultra. ## Data collection All variables of ABG and VBG were entered in premade data collection proforma. The patient was then treated and disposed of according to institutional protocol. The study flowchart is attached (Fig. 1).Fig. 1Flow chart ## Statistical analysis All data were entered into an Excel sheet and analyzed using SPSS 23.0 version. Descriptive statistics for numerical variables were calculated as the mean and standard deviation for normal distribution and median (IQR) for non-normal distribution, whereas percentages for qualitative variables. Agreement between arterial and venous blood gas analysis parameters was done using the Bland–Altman plot. Pearson correlation coefficients were applied to estimate the correlation between arterial and venous blood gas analysis parameters for Gaussian distribution and the Spearman correlation coefficient for non-Gaussian distribution. Arterial parameters were predicted from venous samples using linear regression. ## Results Clinical evaluations were performed on emergency room patients with hypotension who were older than 18 and met the aforementioned inclusion criteria. We obtained their consent and enrolled them in our research. Of all the patients, 250 patients were finally analyzed in the study. ## Baseline characteristics of the study population The study population’s main baseline characteristics and comorbidities are shown in Table 2.Table 2Baseline characteristics of the study population Characteristics Values Age in years, mean (standard deviation)53.25 ± 15.71 Men, number (%)142 ($56.8\%$) Women, number (%)108 ($43.2\%$) Final diagnosis Number (%) Infectious diseases27 ($10.8\%$) Cerebrovascular accident11 ($4.4\%$) Acute gastroenteritis9 ($3.6\%$) Acute decompensated heart failure3 ($1.2\%$) Poisoning7 ($2.8\%$) Malignancy24 ($9.6\%$) Pneumonia29 ($11.6\%$) Chronic liver disease22 ($8.8\%$) Intestinal obstruction3 ($1.2\%$) Pulmonary thromboembolism2 ($0.8\%$) Cardiac tamponade2 ($0.8\%$) Acute on chronic kidney disease5 ($2.0\%$) Acute pancreatitis2 ($0.8\%$) Obstructive airway disease19 ($7.6\%$) Acute coronary syndrome17 ($6.8\%$) Diabetic ketoacidosis3 ($1.2\%$) Dilated cardiomyopathy7 ($2.8\%$) Trauma3 ($1.2\%$) Urosepsis8 ($3.2\%$) Others47 ($18.8\%$) Type of shock Number (%) Septic114 ($45.6\%$) Hypovolemic86 ($34.4\%$) Cardiogenic45 ($18.0\%$) Obstructive5 ($2.0\%$) ## Examination findings The main examination findings are shown in Table 3.Table 3Examination findings Examination Values Pulse rate in beats/minute, mean (standard deviation)103.78 ± 16.52 Respiratory rate in breaths/minute, mean (standard deviation)21.67 ± 4.31 Systolic BP in mmHg, mean (standard deviation)81.40 ± 6.01 Diastolic BP in mmHg, mean (standard deviation)49.31 ± 4.42 MAP in mmHg, mean (standard deviation)59.98 ± 4.16 SpO2 to maintain target saturation of $94\%$ or more except 88–$92\%$ for obstructive airway disease Number (%) Room air174 ($69.6\%$) Oxygen with nasal prongs41 ($16.4\%$) Oxygen with face mask18 ($7.2\%$) Oxygen with non-rebreather mask1 ($0.4\%$) NIV4 ($1.6\%$) Ventilator12 ($4.8\%$) Temperature in F, mean (standard deviation)98.68 ± 0.44 GCS, mean (standard deviation)13.75 ± 3.11 POCUS findings Values Lung profile Number (%) A profile123 ($49.2\%$) B profile127 ($50.8\%$) Lung sliding (yes)249 ($99.6\%$) Effusion (yes)11 ($4.4\%$) Other findings None238 ($95.2\%$) Mild pleural effusion7 ($2.8\%$) Moderate pleural effusion3 ($1.2\%$) Gross pleural effusion1 ($0.4\%$) Mass lesion1 ($0.4\%$) Contractility Fair210 ($84.0\%$) Moderately reduced27 ($10.8\%$) Severely reduced13 ($5.2\%$) Ejection fraction, mean (standard deviation)51.52 ± 8.73 IVC size (cm), mean (standard deviation)1.53 ± 0.34 ## Assessment of parameters The assessment of various parameters of ABG and VBG with mean, standard difference, and p-value is shown in Table 4.Table 4Statistically significant correlation between ABG and VBGParametersABG (mean ± SD)VBG (mean ± SD)Absolute difference (mean ± SD) P-valuepH7.37 (0.11)7.34 (0.12) − 0.03 (0.03) < 0.001pO2 (mmHg)87.76 (20.78)37.60 (12.80) − 50.17 (21.81) < 0.001pCO2 (mmHg)32.76 (15.25)36.17 (16.02)3.41 (5.19) < 0.001HCO3 (mmol/L)18.57 (6.11)19.09 (6.27)0.52 (1.53) < 0.001Lactate (mmol/L)2.84 (2.70)2.97 (2.83)0.13 (0.82) < 0.001Sodium (mmol/L)139.09 (8.52)138.97 (8.77) − 0.12 (3.40)0.365Potassium (mmol/L)3.91 (0.71)3.92 (0.71)0.02 (0.31)0.114Chloride (mmol/L)108.79 (7.93)108.70 (7.88) − 0.10 (2.81)0.590Ionized calcium (mmol/L)0.93 (0.20)0.91 (0.19) − 0.01 (0.15)0.350BUN (mg/dL)35.89 (22.15)35.73 (21.90) − 0.16 (4.89)0.432Base excess (mmol/L) − 5.41 (5.78) − 5.12 (5.90)0.29 (1.45)0.001Arterial/alveolar oxygen ratio (a/A)0.87 (0.29)0.37 (0.41) − 0.50 (0.26) < 0.001Oxygen saturation (%)95.93 (2.68)59.90 (19.11) − 36.03 (19.16) < 0.001 ## Assessment of correlation, agreement, and regression of various parameters Correlation and agreement of various parameters studied are shown in Table 5. Scatterplots showing the correlation of various parameters are shown in Figs. 2, 3, and 4. Bland–Altman plots showing agreement of various parameters are shown in Figs. 5 and 6. Regression analysis done for parameters shown strong correlation and agreement are as follows:Table 5Correlation and agreement of various parametersParametersInterclass correlation coefficientLimits of agreementpH0.96 ± 0.07pO2 (mmHg)0.20 ± 42.74pCO2 (mmHg)0.95 ± 10.16HCO3 (mmol/L)0.97 ± 3.00Lactate (mmol/L)0.96 ± 1.61Sodium (mmol/L)0.92 ± 6.67Potassium (mmol/L)0.91 ± 0.60Chloride (mmol/L)0.94 ± 5.51Ionized calcium (mmol/L)0.70 ± 0.30BUN (mg/dL)0.98 ± 9.59Base excess (mmol/L)0.97 ± 2.84Arterial/alveolar oxygen ratio (a/A)0.72 ± 0.52Oxygen saturation (%)0.01 ± 37.55Fig. 2Scatterplot for correlation between ABG and VBG for various parameters: A pH, B pO2, C pCO2, and D HCO3Fig. 3Scatterplot for correlation between ABG and VBG for various parameters: A lactate, B sodium, C potassium, and D chlorideFig. 4Scatterplot for correlation between ABG and VBG for various parameters: A ionized calcium, B BUN, C base excess, D a/A, and E SO2Fig. 5Bland–Altman plot for agreement between ABG and VBG for various parameters: A pH, B pO2, C pCO2, D HCO3, E lactate, and F sodiumFig. 6Bland–Altman plot for agreement between ABG and VBG for various parameters: A potassium, B chloride, C ionized calcium, D BUN, E base excess, F a/A, and G SO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A\mathrm{BG}:\mathrm{pH}=0.42+0.95\times\mathrm{VBG}:\mathrm{pH}(R^2=0.91)$$\end{document}ABG:pH=0.42+0.95×VBG:pH(R2=0.91)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{pCO}2 (\mathrm{mmHg}) = 0.19 + 0.9 \times \mathrm{ VBG}:\mathrm{pCO}2 (\mathrm{mmHg}) ({R}^{2}=0.90)$$\end{document}ABG:pCO2(mmHg)=0.19+0.9×VBG:pCO2(mmHg)(R2=0.90)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A\mathrm{BG}:\mathrm{HCO}3 (\mathrm{mmol}/\mathrm{L}) = 0.53 + 0.95 \times \mathrm{ VBG}:\mathrm{HCO}3 (\mathrm{mmol}/\mathrm{L}) ({R}^{2}=0.94)$$\end{document}ABG:HCO3(mmol/L)=0.53+0.95×VBG:HCO3(mmol/L)(R2=0.94)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{lactate }(\mathrm{mmol}/\mathrm{L}) = 0.12 + 0.92 \times \mathrm{ VBG}:\mathrm{lactate }(\mathrm{mmol}/\mathrm{L}) ({R}^{2}=0.92)$$\end{document}ABG:lactate(mmol/L)=0.12+0.92×VBG:lactate(mmol/L)(R2=0.92)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{S}.\mathrm{ sodium }(\mathrm{mmol}/\mathrm{L}) = 14.44 + 0.9 \times \mathrm{ VBG}:\mathrm{S}.\mathrm{ sodium }(\mathrm{mmol}/\mathrm{L}) ({R}^{2}=0.85)$$\end{document}ABG:S.sodium(mmol/L)=14.44+0.9×VBG:S.sodium(mmol/L)(R2=0.85)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{S}.\mathrm{ potassium }(\mathrm{mmol}/\mathrm{L}) = 0.34 + 0.91 \times \mathrm{ VBG}:\mathrm{S}.\mathrm{ potassium }(\mathrm{mmol}/\mathrm{L}) ({R}^{2}= 0.82)$$\end{document}ABG:S.potassium(mmol/L)=0.34+0.91×VBG:S.potassium(mmol/L)(R2=0.82)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{S}.\mathrm{ chloride }(\mathrm{mmol}/\mathrm{L}) = 6.3 + 0.94 \times \mathrm{ VBG}:\mathrm{S}.\mathrm{ chloride }(\mathrm{mmol}/\mathrm{L}) ({R}^{2}=0.88)$$\end{document}ABG:S.chloride(mmol/L)=6.3+0.94×VBG:S.chloride(mmol/L)(R2=0.88)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{ionized Ca}2+ (\mathrm{mmol}/\mathrm{L}) = 0.29 + 0.7 \times \mathrm{ VBG}:\mathrm{ionized Ca}2+ (\mathrm{mmol}/\mathrm{L}) ({R}^{2}=0.49)$$\end{document}ABG:ionizedCa2+(mmol/L)=0.29+0.7×VBG:ionizedCa2+(mmol/L)(R2=0.49)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{BUN }(\mathrm{mg}/\mathrm{dL}) = 0.65 + 0.99 \times \mathrm{ VBG}:\mathrm{BUN }(\mathrm{mg}/\mathrm{dL}) ({R}^{2}=0.95)$$\end{document}ABG:BUN(mg/dL)=0.65+0.99×VBG:BUN(mg/dL)(R2=0.95)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm{ Base E}\times \mathrm{cess }(\mathrm{mmol}/\mathrm{L}) = -0.55 + 0.95 \times \mathrm{ VBG}:\mathrm{base excess }(\mathrm{mmol}/\mathrm{L}) ({R}^{2}=0.94)$$\end{document}ABG:BaseE×cess(mmol/L)=-0.55+0.95×VBG:baseexcess(mmol/L)(R2=0.94)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ABG}:\mathrm a/\mathrm $A = 0.67$+0.55\times\mathrm{VBG}:\mathrm a/\mathrm A(R^{\mathit2}=0.58)$$\end{document}ABG:a/$A = 0.67$+0.55×VBG:a/A(R2=0.58) ## Discussion Our study enrolled 250 patients with hypotension and required ABG as a part of routine care and satisfied inclusion criteria. ## Demographics Our study population comprised 250 patients with a mean (standard deviation) years of 53.25 (± 15.71). The study included $56.8\%$ [142] males and $43.2\%$ [108] females. A study by Kim et al. done on 34 patients had a mean age of 65.57 ± 12.4 years, and males were $58.8\%$ [20] and females $41.2\%$ [14] [22]. In a study done by Shirani et al., 192 patients had a mean age of 51.6 ± 23.6 years, and there were males $67.7\%$ and females $32.3\%$ [21], whereas in the study by Shin et al., 231 hypotensive patients had a mean age (SD) of 68.2 (16.6) years and males $64.5\%$ and females $35.5\%$ [28]. $50.8\%$ were residents of Uttarakhand, $46.8\%$ were residents of Uttar Pradesh, and $2.4\%$ were from other states. $45.6\%$ of patients were septic, $34.4\%$ hypovolemic, $18\%$ cardiogenic, and $2\%$ obstructive shock. ## Size and site of needle used For ABG, 24G needles were used in all 250 patients, whereas for VBG, 18G was used for sampling in $59.6\%$ of cases, and in $40.4\%$ of cases, a 20G needle was used. The radial artery obtained all ABGs in our study, while VBG was obtained from forearm veins. In the study by White et al., ABG was collected by either needle puncture or withdrawal from an arterial catheter. The VBG was collected from the upper extremity, and a tourniquet could be left in place for no more than 1 min [26]. ## pH There was a very strong correlation between ABG:pH and VBG:pH, and this correlation was statistically significant (interclass correlation coefficient = 0.96, p ≤ 0.001) in our study, with $94.0\%$ of the observations having a difference which was within the limits of agreement (± 0.07). The r-value was 0.91 and the formula for ABG pH was devised as ABG:pH = 0.42 + 0.95 × VBG:pH. In the study by Yildizdas et al., pH showed a good interclass correlation of 0.907 with a p-value < 0.001. The r-value for pH for that study was 0.994 [19]. In the study by White et al., pH had an interclass correlation of 0.90 with a p-value < 0.001. Bias ± SD of ABG-VBG was 0.03 ± 0.04 [26]. In Zeserson et al. study, the mean difference for pH between VBG and ABG was 0.03 ($95\%$ confidence interval: 0.03–0.04) with a Pearson correlation of 0.94 [25]. In the study by Nanjayya et al., the mean bias for pH was + 0.036 with $95\%$ LOA ranging from − 0.005 to + 0.078 [29]. In a meta-analysis by Byrne et al., there was little difference between the pH obtained from the PVBG and the ABG, with the arterial pH typically 0.03 higher than the venous pH ($95\%$ confidence interval 0.029–0.038) [23]. Kim et al. found a good correlation between ABG and VBG pH with a Pearson correlation coefficient of 0.783 with a p-value of 0.0001. Regression equations were derived to predict ABG pH from peripheral VBG pH as follows: arterial pH = 0.763 × venous pH + 1.786 (R 2 = 0.544). The multivariate regression equations were found as follows: arterial pH = − 1.108 + 1.145 × venous pH + 0.008 × PCO2 – 0.012 × venous HCO3 + 0.002 × venous total CO2 ($r = 0.655$) [22]. In a study by Shirani et al., the average VBG-ABG amount of difference ($95\%$ limits of agreement) in the hypotension versus normotensive group were − 0.030 (− 0.09 to 0.03) vs. − 0.016 (− 0.1 to 0.068) for pH ($$p \leq 0.01$$) [21]. In studies by Boon et al. and Rudkin et al., pH was not correlating between ABG and VBG [27, 30]. ## pO2 There was a weak correlation between ABG:pO2 (mmHg) and VBG:pO2 (mmHg), and this correlation was statistically significant (interclass correlation coefficient = 0.20, p ≤ 0.001 in our study). No correlation was found between ABG:pO2 and VBG:pO2 in previous studies. ## pCO2 There was a very strong correlation between ABG:pCO2 (mmHg) and VBG:pCO2 (mmHg), and this correlation was statistically significant (interclass correlation coefficient = 0.95, p = < 0.001) in our study. In the study by Yildizdas et al., there was a good correlation between ABG and VBG with an interclass correlation of 0.978 with a p-value < 0.001. The r-value found was 0.957 [19]. In the study by Malinoski et al., pCO2 values have a good correlation and agreement between ABG and VBG values with $R = 0.88$, $p \leq 0.001$, and $95\%$ LOAs of − 2.2 to 10.9 [18]. Kim et al., in their study, found a good Pearson correlation coefficient of 0.705 with a p-value of 0.0001 for pCO2. Regression equation derived for pCO2-arterial PCO2 = 0.611 × venous PCO2 + 9.521 (R 2 = 0.497). Multivariate regression equation derived was arterial PCO2 = 88.6 − 10.888 × venous pH + 0.150 × PCO2 + 0.812 × venous HCO3 + 0.124 × venous total CO2 ($r = 0.609$) [22], whereas in the study by Zeserson et al., the mean difference for pCO2 between VBG and ABG was 4.8 mm Hg ($95\%$ confidence interval: 3.7–6.0 mm Hg) with a Pearson correlation of 0.93 [25]. In our study, $95.6\%$ of the observations had a difference within the limits of agreement (± 10.16). R 2 = 0.90 for pCO2 (mmHg) and the formula was devised as ABG:pCO2 (mmHg) = 0.19 + 0.9 × VBG:pCO2 (mmHg). ## HCO3 There was a very strong correlation between ABG:HCO3 (mmol/L) and VBG:HCO3 (mmol/L), and this correlation was statistically significant (interclass correlation coefficient = 0.97, p ≤ 0.001) in our study. In the study by Shin et al., there was a good correlation and agreement between ABG and VBG HCO3 with a mean (SD) of 19.27 (5.39) with $95\%$ CI as − 1.927 to − 1.461 and LOA ± 3.53 [28]. Yildizdas et al. found that HCO3 has a good Pearson correlation coefficient of 0.976 with a p-value < 0.001 and $r = 1.676$ [19]. Venous–arterial difference of HCO3 − is − 0.37mmmol/L with good agreement in the study by Hynes et al. [ 24]. Pearson correlation coefficient of HCO3 in a study by Kim et al. was 0.846 with a p-value of 0.0001. The regression equation derived for HCO3 was arterial HCO3 = 0.822 × venous HCO3 + 2.815 (R 2 = 0.716). Multivariate regression equation derived was arterial HCO3 = − 89.266 + 12.677 × venous pH + 0.042 × PCO2 + 0.675 × venous HCO3 + 0.185 × venous total CO2 ($r = 0.782$) [22]. In a meta-analysis by Kelly et al., the weighted mean difference between arterial and venous values for bicarbonate was –1.41 mmol/L ($$n = 905$$), with $95\%$ limits of agreement of the order of ± 5 mmol/L [20]. Shirani et al. found the average VBG-ABG amount of difference ($95\%$ limits of agreement) in the hypotension versus normotensive group was 1.79 (− 1.91 to 5.49) vs. 1.32 (− 1.94 to 4.58) mEq/L for HCO3 ($$p \leq 0.032$$) [21]. In our study, $93.6\%$ of the observations had a difference within the limits of agreement (± 3.00). R 2 = 0.94 for HCO3 (mmol/L) and the formula was devised as ABG:HCO3 (mmol/L) = 0.53 + 0.95 × VBG:HCO3 (mmol/L). ## Lactate There was a very strong correlation between ABG:lactate (mmol/L) and VBG:lactate (mmol/L), which was statistically significant (interclass correlation coefficient = 0.96, p = < 0.001) in our study. $97.6\%$ of the observations had a difference within the limits of agreement (± 1.61). R 2 = 0.92 for lactate (mmol/L) and the formula was devised as ABG:lactate (mmol/L) = 0.12 + 0.92 × VBG:lactate (mmol/L). In a study by Hynes et al., the venous–arterial difference for lactate was found to be 0.16 mmol/L [24]. In a study by Boon et al., venous lactate was clinically equivalent based on the pre-determined threshold limits of − 1.5 to 1.5 mmol/L, where $96.0\%$ of the values were within this acceptable range [30]. Shin et al., in their study, found the mean (SD) for lactate as 3.27 (3.23) with $95\%$ CI (− 0.406, − 0.241) and LOA ± 1.25 [28]. ## Sodium In the study by Shin et al., the mean (SD) for sodium was 136.21 (6.60) with $95\%$ CI (− 3.104, − 2.532) and LOA ± 4.33 [28]. There was a very strong correlation between ABG and VBG S. Sodium (mmol/L), and this correlation was statistically significant (interclass correlation coefficient = 0.92, p ≤ 0.001) in our study. $95.6\%$ of the observations had a difference within the limits of agreement (± 6.67). R 2 = 0.85 for sodium (mmol/L) and the formula was devised as ABG:S. sodium (mmol/L) = 14.44 + 0.9 × VBG:S. sodium (mmol/L). ## Potassium There was a very strong correlation between ABG and VBG potassium (mmol/L), and this correlation was statistically significant (interclass correlation coefficient = 0.91, p ≤ 0.001) in our study. $95.6\%$ of the observations had a difference within the limits of agreement (± 0.60). The regression equation for potassium (mmol/L) was devised as ABG:S. potassium (mmol/L) = 0.34 + 0.91 × VBG:S. potassium (mmol/L) [R 2 = 0.82], whereas in the study by Shin et al., mean (SD) for potassium was 4.42 (1.18) with $95\%$ CI (− 0.365, − 0.257) and LOA ± 0.82 [28]. ## Chloride The study by Shin et al. found the mean (SD) for chloride as 102.94 (6.31) with $95\%$ CI (− 4.062, − 2.934) and LOA as ± 8.53 [28], whereas in our study, there was a very strong correlation between ABG and VBG chloride (mmol/L), and this correlation was statistically significant (interclass correlation coefficient = 0.94, p ≤ 0.001). $94.0\%$ of the observations had a difference within the limits of agreement (± 5.51). The regression equation for chloride was done using R2 = 0.88 formula was devised as ABG:S. chloride (mmol/L) = 6.3 + 0.94 × VBG:S. chloride (mmol/L). No other studies have done a correlation between ABG and VBG for chloride. ## Ionized calcium Ionized calcium was found to have a strong correlation between ABG and VBG with an interclass correlation coefficient = 0.70 with p-value ≤ 0.001. $94.0\%$ of the observations had a difference which was within the limits of agreement (± 0.30). The regression equation devised for ionized calcium (mmol/L) was ABG:ionized Ca2 + (mmol/L) = 0.29 + 0.7 × VBG:ionized Ca2 + (mmol/L) [R 2 = 0.49] in our study, whereas in the study by Shin et al., the mean (SD) for ionized calcium was 1.21 (0.08) with $95\%$ CI (0.064, 0.089) and LOA as ± 0.18 [28]. ## Blood urea nitrogen Blood urea nitrogen was found to have a very strong correlation between ABG and VBG with an interclass correlation coefficient = 0.98 and p-value ≤ 0.001. $96.0\%$ of the observations had a difference which was within the limits of agreement (± 9.59). The regression equation derived for BUN (mg/dL) was ABG:BUN (mg/dL) = 0.65 + 0.99 × VBG:BUN (mg/dL) [R 2 = 0.95] in our study. We found no other study which correlates ABG:BUN and VBG:BUN. ## Base excess There was a very strong correlation between ABG:base excess (mmol/L) and VBG:base excess (mmol/L) with an interclass correlation coefficient = 0.97 and p ≤ 0.001 in our study. $94.8\%$ of the observations had a difference within the limits of agreement (± 2.84). The regression equation derived for base excess (mmol/L) was ABG:base excess (mmol/L) = − 0.55 + 0.95 × VBG:base excess (mmol/L) [R 2 = 0.94]. The study by Yildizdas et al. found a good correlation with a Pearson correlation coefficient of 0.972 with a p-value < 0.001. R 2 was found to have 0.945 for base excess [19]. Malinoski et al. compared ABG and VBg values and found that base excess has $R = 0.96$, $p \leq 0.001$, and $95\%$ LOAs of − 2.2 to 1.8 in their study [18]. The average VBG-ABG amount of difference ($95\%$ limits of agreement) in the hypotension versus normotensive group found in a study by Shirani et al. was d 0.25 (− 3.73 to 4.23) vs. 0.79 (− 2.51 to 4.09) for BE ($$p \leq 0.036$$) [21]. In a study by Hynes et al., the venous–arterial difference for base excess was found to be 0.08 mEq/L [24]. ## Arterial/alveolar oxygen ratio Our study found a strong correlation between ABG:a/A and VBG:a/A, and this correlation was statistically significant (interclass correlation coefficient = 0.72, p ≤ 0.001). $98.4\%$ of the observations had a difference within the limits of agreement (± 0.52). The regression equation was derived with R 2 = 0.58 for a/A as ABG:a/$A = 0.67$ + 0.55 × VBG:a/A. Our study is the first to evaluate and find a correlation for arterial/alveolar oxygen ratio between ABG and VBG. ## Oxygen saturation Our study found a weak correlation between ABG:SO2 (%) and VBG:SO2 (%), and this correlation was not statistically significant (interclass correlation coefficient = 0.01, $$p \leq 0.412$$). All other studies comparing ABG and VBG found weak or no correlation for oxygen saturation. ## Limitations In our study, patients were recruited using convenience sampling as study investigators will not be available in all shifts. The study involved collecting single pair of arterial and peripheral venous samples from each patient. So, the homogeneity of ABG and peripheral VBG was not studied as multiple samples at different intervals were not done. We also had many cases where one patient had multiple diagnoses making it challenging to characterize into various subgroups. Subgroups of shock were defined at the time of “working diagnosis” when enrolling the patient and not the final diagnosis. The study design and setting did not allow for a follow-up on the mortality of the patients studied. Patients differed according to many demographic factors, which were not all the same. ## Conclusions ABG and VBG assessments are essential tests for assessing ventilation, acid–base disturbances, and other metabolic parameters of patients. Obtaining ABG or VBG in hypotension is challenging as many previous studies have shown conflicting results. Our study has shown either strong or very strong correlations and agreements for most ABG and VBG parameters except pO2 and SO2. We can also predict an ABG mathematically using regression formulas devised from a VBG sample. This will decrease needle stick injury, consume less time, and make blood gas evaluation easy in hypotensive settings. 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--- title: Extracellular vesicle distribution and localization in skeletal muscle at rest and following disuse atrophy authors: - Ahmed Ismaeel - Douglas W. Van Pelt - Zachary R. Hettinger - Xu Fu - Christopher I. Richards - Timothy A. Butterfield - Jonathan J. Petrocelli - Ivan J. Vechetti - Amy L. Confides - Micah J. Drummond - Esther E. Dupont-Versteegden journal: Skeletal Muscle year: 2023 pmcid: PMC9999658 doi: 10.1186/s13395-023-00315-1 license: CC BY 4.0 --- # Extracellular vesicle distribution and localization in skeletal muscle at rest and following disuse atrophy ## Abstract ### Background Skeletal muscle (SkM) is a large, secretory organ that produces and releases myokines that can have autocrine, paracrine, and endocrine effects. Whether extracellular vesicles (EVs) also play a role in the SkM adaptive response and ability to communicate with other tissues is not well understood. The purpose of this study was to investigate EV biogenesis factors, marker expression, and localization across cell types in the skeletal muscle. We also aimed to investigate whether EV concentrations are altered by disuse atrophy. ### Methods To identify the potential markers of SkM-derived EVs, EVs were isolated from rat serum using density gradient ultracentrifugation, followed by fluorescence correlation spectroscopy measurements or qPCR. Single-cell RNA sequencing (scRNA-seq) data from rat SkM were analyzed to assess the EV biogenesis factor expression, and cellular localization of tetraspanins was investigated by immunohistochemistry. Finally, to assess the effects of mechanical unloading on EV expression in vivo, EV concentrations were measured in the serum by nanoparticle tracking analysis in both a rat and human model of disuse. ### Results In this study, we show that the widely used markers of SkM-derived EVs, α-sarcoglycan and miR-1, are undetectable in serum EVs. We also found that EV biogenesis factors, including the tetraspanins CD63, CD9, and CD81, are expressed by a variety of cell types in SkM. SkM sections showed very low detection of CD63, CD9, and CD81 in myofibers and instead accumulation within the interstitial space. Furthermore, although there were no differences in serum EV concentrations following hindlimb suspension in rats, serum EV concentrations were elevated in human subjects after bed rest. ### Conclusions Our findings provide insight into the distribution and localization of EVs in SkM and demonstrate the importance of methodological guidelines in SkM EV research. ## Background Extracellular vesicles (EVs) are thought to play a role in the adaptive response of skeletal muscle and the ability to communicate with other tissues and organs [1]. EVs are endosomal or plasma membrane-derived vesicles released from cells [2] and are primarily classified into subtypes based upon their physical characteristics like size (50–1000 nm), density (low, middle, high), and/or biochemical composition (i.e., surface markers) [3]. EVs differ by their content, which can include mRNAs, microRNAs (miRNAs), proteins, lipids, and metabolites [4]. While it was initially thought that EVs were a mechanism to rid cells of unwanted material, it is now understood that the cargo carried by EVs can be delivered to local and distant cells and have biological and physiological effects on recipient cells and tissues [5, 6]. Skeletal muscle is not only the largest organ in the human body, playing a central role in whole-body energy metabolism, but also acts as a secretory organ, producing and releasing hundreds of products, including myokines, which can have autocrine, paracrine, and endocrine effects [7, 8]. Recently, mechanistic studies on EV-mediated cell-cell communication have shown the importance of EVs in organ crosstalk in normal physiology and diseased states [9], and this includes those derived from the skeletal muscle. However, EV research is complicated by the fact that EVs are produced nearly ubiquitously in all cells and tissues. While accumulating evidence supports the presence of vascular cell-derived EVs in circulation, the degree to which nonvascular cells release EVs across the vascular endothelium and into the bloodstream is not well-understood [10]. The lack of tissue-specific EV markers makes it even more difficult to track EVs from their “parent” cells or tissues. Skeletal muscle is a complex heterogeneous tissue comprising not only multinucleated muscle fibers, but also several mononuclear cell populations, including immune cells, endothelial cells, fibro-adipogenic progenitors (FAPs), and satellite cells, which also play a role in EV production and release from the muscle [11]. Due to the heightened skeletal muscle energy metabolism during exercise and increases in skeletal muscle contractions, studies have used different exercise modalities to assess the changes in circulating EVs, which may be skeletal muscle-derived. Serum or plasma EVs are increased following acute bouts of exercise in both animals and human participants [5, 12–17], and detailed EV phenotypic analyses demonstrated that a majority of EVs released during exercise originate from immune cells, platelets, and endothelial cells [18]. It remains unclear whether the skeletal muscle contributes to these exercise-induced increases in circulating EVs. By contrast, mechanical unloading leads to skeletal muscle atrophy with important clinical ramifications such as decreased muscle force production and functional independence, and the etiology of disuse atrophy is not well understood [19]. Emerging evidence suggests that miRNAs can modulate muscle size in response to different conditions and may play a role in associated systemic consequences [20]. In vitro, dexamethasone treatment-induced atrophy led to a reduction in miR-23a levels in C2C12 myotubes via increased release into EVs [21]. Our laboratory has also previously shown that miR-203a-3p expression in circulating EVs was associated with skeletal muscle protein turnover and atrophy [22]. However, the source of miR-203a-3p was not determined due to the aforementioned issues with tracking circulating EVs in vivo. Thus, the purpose of this study was to investigate EV biogenesis factors, marker expression, and localization across cell types in the skeletal muscle; release of EVs from the atrophied skeletal muscle; and whether EV concentrations are altered by disuse atrophy in rats and human participants. ## Cell culture Monolayer cultures of C2C12 and L6 myoblasts were grown in Dulbecco’s modified Eagle’s medium (DMEM) (Thermo Fisher, Waltham, MA) supplemented with $10\%$ (L6) or $20\%$ (C2C12) fetal bovine serum (FBS) (HyClone Laboratories, Logan, UT) and 1X Penicillin/Streptomycin (Thermo Fisher) at 37 °C in a humidified $10\%$ CO2-$90\%$ air atmosphere incubator, as previously described [23, 24]. ## Animals and experimental procedures All procedures were approved by the University of Kentucky’s Institutional Animal Care and Use Committee. Male Brown Norway/F344 rats at 10 months of age (National Institute on Aging, Bethesda, MD) were used in this study. Rats were randomly assigned into one of four groups: weight-bearing control conditions (WB), hindlimb suspension (HS) for 4 h (4h HS), HS for 24 h (24h HS), and HS for 7 days (7d HS). Rats were allowed free access to food and water at all times and were housed on a 12:12-h light-dark cycle. Hindlimb suspension was performed as previously described [22]. Briefly, a tail device containing a hook was attached with gauze and cyanoacrylate glue while the animals were anesthetized with isoflurane ($2\%$ by inhalation). The tail device was connected via a thin cable to a pulley sliding on a vertically adjustable stainless steel bar running longitudinally above a high-sided cage. The system was designed in such a way that the rats could not rest their hindlimbs against any side of the cage but could move around the cage on their front limbs and could reach water and food easily. Cages were randomly placed in the room, and the room temperature was 27 °C. ## Blood and tissue collection At the end of the experimental period, rats were anesthetized with pentobarbital sodium, and blood was immediately collected through cardiac puncture. Rats were euthanized, and the soleus muscles were excised, weighed, and used for ex vivo collection of muscle EVs (right) or dissected, weighed, frozen in liquid nitrogen, and stored at − 80 °C for later biochemical analyses (left). For immunohistochemistry (IHC) analyses, the soleus muscles were covered in Tissue-Tek optimal cutting temperature compound (Sakura Finetek, Torrance, CA, USA), frozen in liquid nitrogen-cooled isopentane, and stored at − 80 °C. The soleus muscles were used in these experiments because in rats, the soleus, which is almost exclusively type I, is especially susceptible to hindlimb suspension-induced muscle atrophy [25, 26]. Furthermore, the smaller size of the soleus limits issues of oxygen diffusion during ex vivo muscle assessments [27]. The gastrocnemius muscles were used for single-cell RNA sequencing (scRNA-seq) as described in [28]. The serum was isolated by allowing blood to clot at room temperature for 30 min before centrifugation for 10 min at 2000g at 4 °C. The serum supernatant was collected and stored at − 80 °C until analysis. The number of animals used in each experiment is listed in the figure legends. ## Human participants Serum and muscle biopsies obtained from the vastus lateralis from a previously published bed rest study (young group, age: 23 ± 3 years) were used for EV and RNA isolation, respectively [29]. Human participants were recruited at the University of Utah under an approved Institutional Review Board protocol, and the study conformed to the Declaration of Helsinki. Bed rest (5 days; Monday–Friday) took place according to the protocol and safety guidelines described in detail in the original publication [29]. For total RNA isolation from the muscle, samples were homogenized in TRIzol Reagent (Invitrogen, Waltham, MA), and 1-bromo-3-chloropropane was added for phase separation. Finally, 2-propanol was used to precipitate the RNA, and RNA was pelleted by centrifugation (12,000g for 10 min). The Bio-Rad iScript Reverse Transcription Supermix (1708841, Bio-Rad Laboratories, Hercules, CA) was used for cDNA synthesis from 1 μg of total RNA. Real-time PCR was used to determine the relative mRNA expression of the tetraspanins, CD63, CD9, and CD81. PCR reactions used primer sets and Applied Biosystems PowerUp SYBR Green Master Mix (A25742, Applied Biosystems, Waltham, MA). ## Ex vivo collection of muscle EVs For ex vivo experiments, the soleus muscle was excised intact, rinsed with Krebs-Henseleit buffer (KHB) (118.5mM NaCl, 1.2mM MgSO4, 4.7mM KCl, 1.2mM KH2PO4, 25mM NaHCO3, 2.5mM CaCl2; pH 7.4), and then suspended prior to incubation in continuously gassed ($95\%$ O2/$5\%$ CO2) KHB supplemented with 5 mM glucose at 37 °C for 1 h. EV abundance in the KHB was measured using nanoparticle tracking analysis (NTA) (Zetaview®, Particle Metrix, Meerbusch, Germany) immediately after the 1-h incubation period. The Zetaview® instrument uses a laser scattering video microscope to track individual nanoparticle movement under Brownian motion, measuring the size and concentration [30]. ## Serum EV isolation EVs isolated from rat and human serum for miRNA analysis were isolated from 500 μL of serum with ExoQuick Exosome Precipitation Solution (System Biosciences (SBI), Palo Alto, CA). The serum was first centrifuged at 3000g for 15 min to remove debris, and the supernatant was collected and filtered through a 0.22-μm low-binding PVDF filter (Millex-GV; Millipore, Tullagreen, Ireland). Approximately 240 μL of ExoQuick was added to the sample and incubated at 4 °C overnight. The ExoQuick-serum mixture was centrifuged at 1500g for 30 min to pellet the EVs. The supernatant was removed, and the EV pellet was reconstituted in 300 μL of PBS. To isolate a purer sample of EVs and better assess the contribution of EVs to the overall particle population in rat serum, we used a slightly modified density gradient ultracentrifugation (DGUC) protocol from Onodi et al. [ 31]. First, rat serum was centrifuged 2500g for 15 min at 4 °C to remove the debris, and the supernatant was collected. The supernatant was filtered through a 0.22-μm low-binding PVDF filter (Millex-GV; Millipore, Tullagreen, Ireland). The sample was layered on top of an iodixanol (OptiPrep™, BioVision Inc., Milpitas, CA) density gradient. The iodixanol was diluted to 50, 30, and $10\%$ in 0.25M sucrose/10mM Tris buffer, and a discontinuous gradient was formed by layering 3.66 mL of the 50, 30, and $10\%$ iodixanol solutions in a 13-mL ultracentrifuge tube (Beckman Coulter, Pasadena, CA). The volume of the filtered serum sample was brought up to 1 mL with PBS if necessary and then layered onto the top of the discontinuous gradient. The samples were centrifuged in a SW41 Ti rotor for 24 h at 120,000g at 4 °C. Twelve 1-mL fractions of the density gradient layers were collected (F1–F12). ## EV miRNA isolation and expression Total RNA was isolated from EVs as previously described [22] using the commercial miRCURY RNA Isolation Kit (Exiqon, Woburn, MA). miRNA concentrations were quantified with a small RNA kit on an Agilent Bioanalyzer (Agilent, Santa Clara, CA), followed by reverse transcription of miRNA performed with 10 ng of total RNA using the miRCURY LNA RT kit (Qiagen, Hilden, Germany). RT-qPCR reactions used the miRCURY LNA SYBR Green PCR kit (Qiagen) and the appropriate miRCURY LNA primer sets for the miRNAs of interest (Qiagen). miRNA expression was normalized to the expression of UniSp6, an exogenous spike-in that resembles miRNAs, using the −ΔCT method [32]. ## EV protein isolation and protein expression Total protein was isolated from EVs using Pierce RIPA lysis buffer with Halt protease and phosphatase inhibitor cocktail (Thermo Fisher, Waltham, MA), and protein concentration was determined using the Pierce BCA protein assay kit (Thermo Fisher). For Western blotting, samples were prepared in Laemmli buffer, boiled at 95 °C for 5 min, and 5 μg protein was loaded. Proteins were separated by SDS-PAGE using 4–$15\%$ TGX Gels (Criterion, Bio-Rad, Hercules, CA) by running at 200 V at room temperature. Proteins were transferred for 60 min at 100 V on ice onto a nitrocellulose membrane in $20\%$ methanol Tris-glycine buffer. The Revert Total Protein Stain Kit (Li-Cor Biosciences, Lincoln, NE) or Ponceau S solution (Thermo Fisher) was used to stain total protein, and the membranes were imaged to verify transfer efficiency and loading. The membranes were subsequently blocked in $5\%$ nonfat dry milk in Tris-buffered saline-Tween (TBS-T, $0.1\%$ Tween-20) for 1 h at room temperature, then incubated overnight at 4 °C in primary antibody (anti-CD63, EXOAB-CD63A-1; System Biosciences, Palo Alto, CA and anti-Apolipoprotein A1 (ApoA1, 701239; Thermo Fisher) at a 1:1000 dilution in $5\%$ nonfat dry milk in TBS-T. The membranes were then washed before incubation in goat anti-rabbit secondary antibodies (EXOAB-CD63A-1; System Biosciences) (1:10,000 dilution) for 1 h at room temperature. Blots were developed with enhanced chemiluminescence (Clarity Western ECL Substrate, Bio-Rad), imaged, and quantified with ImageJ (National Institutes of Health). ## Fluorescence correlation spectroscopy (FCS) of EVs To assess α-sarcoglycan protein levels, Western blotting was performed as described above for CD63, and the membranes were incubated with anti-α-sarcoglycan antibody (Santa Cruz, SC-271321) (1:1000 dilution). To further determine the number of EVs that are positive for α-sarcoglycan, we used fluorescence correlation spectroscopy (FCS). FCS is a powerful technique that can quantitatively evaluate picomolar concentrations, with sensitivity that can be up to a single-molecule level [33, 34]. Specifically, an anti-α-sarcoglycan antibody was used (Santa Cruz, SC-271321). The antibody was first labeled with CF488 dye using antibody labeling kits (Mix-n-Stain, Biotium) following the manufacturer’s antibody labeling protocol. Fifty ng/mL CF488 labeled antibody was added to each EV sample and allowed to incubate for 60 min at room temperature. The vesicles were purified from free dye using a 5000-molecular weight cutoff size exclusion column (PD Minitrap G25, GE Healthcare) as described previously [35]. Briefly, the binding of fluorescently labeled anti-α-sarcoglycan antibody to EVs was confirmed via FCS based on their diffusion times. All FCS measurements were done as reported previously by Fu et al. [ 36]. Briefly, 40 μL of fluorescently labeled EVs were placed onto a coverslip mounted on an Olympus IX83 microscope equipped with a PicoQuant PicoHarp 300 time-correlated single photon counting (TCSPC) system. We employed a 488-nm laser (50 μW) to excite the fluorescent labels, and a 60× water immersion objective was used to focus this laser beam into the sample solution. Two avalanche photodiodes (APDs) were used for photon detection, and the signal was directed to a PicoHarp 300 TCSPC module controller. All measurements were performed 30 μm above the glass surface in the sample solution. For the unconjugated fluorophore, the fitted autocorrelation functions (ACF) yield a diffusion time (τD) of 0.21 ± 0.02 ms. A longer diffusion time of 2.5 ± 0.2 ms was observed for the CF-488-labeled anti-α-sarcoglycan antibody. The immunolabeled (anti-α-sarcoglycan-CF488 antibody) EVs exhibited a diffusion time of 32 ± 5 ms. In order to calculate the average number of immunolabeled (anti-α-sarcoglycan-CF488 antibody) EVs within the focal volume, the FCS focal volume was first calibrated using commercially available 0.1-μm tetra speck beads with a known diffusion constant and concentration. The number of vesicles per mL of solution was determined using NTA, and the number of labeled vesicles per mL was determined using FCS and the calibrated size of the focal volume. ## Mononuclear cell isolation and scRNA-seq Cell isolations were performed as previously described in mice [37] and modified slightly for rats [28]. Briefly, the gastrocnemius muscles from WB and HS male rats were excised and placed in muscle dissociation media (MDM) (Hams F-10 (Gibco, USA), $10\%$ Horse Serum (Thermo Fisher), $1\%$ penicillin/streptomycin (Gibco), 800 U/ml Collagenase II (Gibco)), and minced using sterilized surgical equipment. The muscle homogenate was then incubated in MDM for 1 h at 37 °C with gentle agitation. Following incubation, samples underwent further incubation in 1000 U/ml Collagenase II (Gibco) and 11 U/ml dispase (Gibco) for 30 min at 37 °C. The single-cell suspension was passed through an 18-gauge needle approximately 10 times prior to 0.2-μm filtration. Single cells were incubated in propidium iodide to identify dying/dead cells for removal via fluorescence-activated cell sorting (Sony Biotechnology, USA). Single-cell suspensions from each group were added to a Chromium Controller (10X Genomics, USA) using the Single Cell 3’ Reagent Kit per manufacturer’s instructions and sequenced on an Illumina HiSeq platform (Novogene, USA), yielding 200 million reads/sample. ## Data processing and cell population annotation scRNA-seq data were processed using the Partek Genomics Suite (Partek, USA) as previously described [28]. Briefly, following data quality control, samples were aligned to the rn6 genome and low-quality cells and/or reads were excluded based on the following criteria: mitochondrial reads exceeding $20\%$, an indication of doublets via read counts/cell, lowly expressed genes in only $0.01\%$ of total cells, and high expression of myofiber-related RNA resulting from muscle mincing. Following dimensionality reduction, graph-based clustering was used in combination with known muscle mononuclear cell-related gene markers for population annotation [11, 28, 38]. ## Mononuclear cell EV-related gene expression Bubble plots were generated using the Extracellular Vesicle Biogenesis GO term (http://www.informatics.jax.org/vocab/gene_ontology/GO:0140112) in combination with the identified mononuclear cell populations. Following the filtration of genes represented by the selected GO term (GO: 0140112), a bubble plot was made with the average expression of the gene of interest represented by heatmap, and the percent of cells expressing each gene represented by the size of the bubble. Cell populations are grouped by sample for population-specific comparison. ## Immunohistochemistry (IHC) The muscles were cut on a cryostat at − 23 °C (7 μm), air-dried, and stored at − 20 °C. Slides were air-dried, rehydrated, and fixed in $4\%$ paraformaldehyde (PFA) for 20 min at the time of staining. For CD63/DAPI/laminin staining, sections were incubated with mouse anti-CD63 IgG1 antibody (1:100 dilution, ab108950, Abcam, Cambridge, UK) and rabbit anti-laminin IgG antibody (1:100 dilution, L9393, Sigma-Aldrich, St. Louis, MO) overnight at 4 °C. Slides were washed in PBS, then incubated with Alexa Fluor 488 goat anti-mouse IgG1 (1:250 dilution, A11001, Invitrogen, Waltham, MA) and Alexa Fluor 594 goat anti-rabbit IgG (1:250 dilution, A11012, Invitrogen) secondary antibodies for 1 h at room temperature. Slides were washed in PBS and mounted with VectaShield fluorescent mounting media with DAPI (H-1200-10, Vector Laboratories, Newark, CA). For CD9/DAPI/dystrophin staining, sections were incubated with rabbit anti-CD9 IgG (1:100 dilution, SA35-08, Invitrogen) and mouse anti-dystrophin IgG2b (1:250 dilution, 08168, Sigma-Aldrich) overnight, followed by incubation with Alexa Fluor 594 goat anti-rabbit IgG (1:250 dilution, A11012, Invitrogen) and Alexa Fluor 647 goat anti-mouse IgG2b (1:250 dilution, A32728, Invitrogen) for 1 h at room temperature. For CD81/DAPI/dystrophin staining, sections were incubated with rabbit anti-CD81(1:100 dilution, SN206-01, Novus Biologicals, Centennial, CO) and mouse anti-dystrophin IgG2b (1:250 dilution, 08168, Sigma-Aldrich) overnight, followed by incubation with Alexa Fluor 594 goat anti-rabbit IgG (1:250 dilution, A11012, Invitrogen) and Alexa Fluor 647 goat anti-mouse IgG2b (1:250 dilution, A32728, Invitrogen) for 1 h at room temperature. For Pax7/CD9/DAPI/WGA staining, sections were subjected to epitope retrieval using sodium citrate (10 mM, pH 6.5) at 92 °C, followed by blocking of endogenous peroxidase activity with $3\%$ hydrogen peroxide in PBS. Sections were incubated overnight in mouse anti-Pax7 IgG1 (1:100 dilution, Developmental Studies Hybridoma Bank, Iowa City, IA) and rabbit anti-CD9 IgG (1:100 dilution, SA35-08, Invitrogen), followed by incubation in goat anti-mouse biotin-conjugated secondary antibody (dilution 1:1,000, 115-065-205; Jackson ImmunoResearch, West Grove, PA) and Alexa Fluor 647 goat anti-rabbit IgG (1:250 dilution, A32733, Invitrogen) for 1 h at room temperature. Next, sections were incubated with streptavidin-HRP (1:500 dilution, S-911, Invitrogen) and Texas Red-conjugated Wheat Germ Agglutinin (WGA) (1:50 dilution, W21405, Invitrogen) at room temperature for 1 h, before incubation in Tyramide Signal Amplification (TSA) Alexa Fluor 488 (1:500 dilution, B40953, Invitrogen). Sections were mounted with VectaShield fluorescent mounting media with DAPI (H-1200-10, Vector Laboratories). Images were captured with a Zeiss upright microscope (AxioImager M1, Oberkochen, Germany). To quantify the percentage of nuclei (DAPI+) expressing CD63, MyoVision software was used for automated analysis of nuclear density in cross-sections [39], and nuclei-expressing CD63 (identified as DAPI+/CD63+ events) were counted manually in a blinded manner by the same assessor for all sections using the Zen Blue software. ## Statistical analysis Differences between the two groups (HS vs WB) were analyzed by unpaired Student’s t-tests. When comparing 4 groups, a one-way ANOVA was used, with Tukey’s multiple comparisons test for post hoc analysis. A two-way ANOVA was used to assess the differences in particle concentrations between WB and HS across fractions of the density gradient. Paired t-tests were used to examine the changes in measures from pre- to post-immobilization in human samples. All statistical analyses were performed in GraphPad Prism (v7.00, GraphPad Software, La Jolla, CA), and statistical significance was set at an α < 0.05. ## Skeletal muscle-specific EV markers Using Western blotting, α-sarcoglycan was detected in the heart and skeletal muscle, but not in other organs from the rat (Fig. 1A) and was absent from rat serum EVs (Fig. 1B). We were also unable to detect α-sarcoglycan in serum EVs from WB or HS rats or from EVs derived from ex vivo skeletal muscle experiments using FCS methods (Table 1). The only samples in which we detected EVs positive for α-sarcoglycan using FCS were EVs collected from conditioned media from L6 and C2C12 myotubes ($13.8\%$ and $28.6\%$, respectively) (Fig. 1C and Table 1).Fig. 1α-Sarcoglycan is expressed specifically in the muscle tissues and is not detectable in the serum or muscle-derived EVs. A Representative western blot image showing protein expression of α-sarcoglycan in rat tissues. B Western blot of α-sarcoglycan in rat serum EVs and rat skeletal muscle (positive control). Transfer efficiency verified by Ponceau S staining. C The normalized autocorrelations for the fluorescently labeled anti-α-sarcoglycan antibody and the immunofluorescently labeled EVs pooled from L6 myotubes ($$n = 3$$ biological replicates) and C2C12 myotubes ($$n = 3$$ biological replicates)Table 1Proportion of α-sarcoglycan positive EVs using FCSEV sampleParticles (EVs)/ml% of EVs positive for α-sarcoglycanWB rat serum2.80E+10No bindingWB rat serum3.00E+10No bindingHS rat serum4.30E+10No bindingHS rat serum4.10E+10No bindingL6 myotubes2.50E+$1113.8\%$C2C12 myotubes1.40E+$1128.6\%$Ex vivo muscle1.10E+10No bindingWB weight bearing, HS hindlimb suspension ## Skeletal muscle-specific miRNAs As expected, miR-1 was detected at high abundance in the skeletal muscle, but not in the liver or kidney of rats (Fig. 2A). miR-1 was also detected at a higher abundance in the skeletal muscle than miR-23a-3p, miR-26a-5p, miR-27a-3p, and miR-29a-3p (Fig. 2A). miR-1 was not detected in serum EVs from rats or from humans under basal conditions in contrast to other miRs, such as miR23-3p and miR-29a (Fig. 2B, C).Fig. 2“Muscle-specific” miR-1 is not detectable in serum EVs under basal conditions in rats or humans. A miRNA abundance of selected miRNAs across the skeletal muscle, liver, and kidney from rats ($$n = 5$$). B miRNA abundance of selected miRNAs in serum EVs from rat serum ($$n = 12$$). C miRNA abundance of the selected miRNAs in serum EVs from human serum ($$n = 6$$) ## Single-cell analysis of EV biogenesis factors Most EV biogenesis factors extracted from the Gene Ontology annotation “extracellular vesicle biogenesis” (GO: 0140112) had very low expression across all cell types in gastrocnemius muscle (Fig. 3). The tetraspanin CD63 was most highly expressed in most cell types, while CD9 was highly expressed only in some cell types, such as FAPS and tenocytes. CD81, a tetraspanin often used as a specific marker for EVs, however, was not highly expressed in cells present in skeletal muscle. It is also noted that differences between WB and HS exist in the expression of the abundant tetraspanins in some cell types, but not others. For example, CD9 is higher in HS than in WB in neutrophils, while CD63 is not different, but this is not observed in pericytes (Fig. 3).Fig. 3Gene expression of EV biogenesis factors across cell types in the skeletal muscle. Bubble plot of genes selected for supervised-based classification corresponding to each identified cell population from weight bearing (WB) and hindlimb suspended (HS) muscle. The average gene expression is denoted by heat map and the non-zero percent of cells expressing the gene is denoted by bubble size. FAPs, fibro/adipogenic progenitors; MCs, mast cells; TCs, T cells; SCs, satellite cells; APCs, antigen-presenting cells; NKs, natural killer cells; ECs, endothelial cells; SMCs, smooth muscle cells ## Cellular localization of tetraspanins in the skeletal muscle We further investigated the CD63 expression at the protein level via Western blotting and found that it was undetectable in rat soleus muscle (Fig. 4A) despite its readily detectable levels at the mRNA level (Fig. 3). However, using IHC, we showed that CD63 protein is not detected in myofibers of the soleus but is highly abundant in a subset of cells residing in the muscle interstitial space (Fig. 4B–D, white arrows). The CD63+ cells reside outside the laminin border and are therefore not satellite cells. We found that, on average, CD63+/DAPI+ nuclei made up 2.30 ± $0.48\%$ of the total nuclei. Fig. 4CD63 protein is not detected in myofibers but is expressed in a small subset of cells residing in the muscle interstitial space. A Representative western blot of total protein and CD63 in the skeletal muscle. B Cross-section of the soleus muscle from a control F344/BN rat showing laminin and CD63+ cells. White arrows indicate CD63+ cells. C Image of the small subset of CD63+ cells in the interstitial space of the skeletal muscle. D Close-up image of the CD63+ cells in the interstitial space of the skeletal muscle Likewise, CD9 was not detected in the myofibers but was detected primarily in mononuclear cells residing in the extracellular space, albeit, at a much higher frequency than CD63 (Fig. 5A, white arrows). There was also a high amount of CD9 staining surrounding the blood vessels, nerves, and muscle spindles (Fig. 5B). We also observed a large amount of CD9 within the extracellular compartments around necrotic muscle fibers (Fig. 5C). Co-staining with Pax7 showed only a small subset of CD9+ satellite cells (Fig. 5D, white arrowheads). Most satellite cells did not express CD9 (Fig. 5D, white arrows). Notably, these results are consistent with the scRNA-seq analyses demonstrating low expression of CD9 by satellite cells (Fig. 3). Finally, CD81 exhibited a strikingly different distribution in rat skeletal muscle compared with CD63 and CD9. CD81 protein was abundant in the extracellular space (gray arrows) and residing mononuclear cells (white arrows) as well as in high abundance surrounding some of the myonuclei (white arrowheads) (Fig. 6A, D). There was also a significant overlap of CD81 with the dystrophin borders of myofibers suggesting the presence of CD81 protein in myofiber membranes (Fig. 6A–C).Fig. 5CD9 protein is not detected in myofibers but is abundantly expressed in the blood vessels, nerves, and cells residing in the muscle interstitial space. A Cross-section of the soleus muscle from a control F344/BN rat showing dystrophin and CD9+ cells. The magnified image shows CD9+ cells in the interstitial space (white arrows). B Image of the CD9 expression surrounding the blood vessels, nerves, and muscle spindles. C Image of the CD9 expression around the necrotic muscle fibers. D Staining for WGA as well as CD9+ and Pax7+ cells. Pax7+/CD9+ cells indicated by the white arrowhead, Pax7+/CD9− cells identified by white arrows, and Pax7−/CD9+ cells indicated by gray arrowsFig. 6CD81 protein is primarily detected in the interstitial space with some expression near the myonuclei and on myofiber membranes. A Cross-section of the soleus muscle from a control F344/BN rat showing dystrophin and CD81+ cells. The magnified image shows the CD81 protein in the extracellular space (gray arrows), in the mononuclear cells (white arrows), and in the surrounding myonuclei (white arrowheads). B Image of the CD81 expression in the myofiber membrane. C The same image as B, with dystrophin border shown. D Close-up image demonstrating the expression of CD81 ## Serum EV concentrations after hindlimb suspension in rats Separation of CD63-positive EVs from other serum components using an iodixanol density gradient revealed that CD63-positive EVs are present in only a few fractions (F5–F8, Fig. 7A), and these particular fractions only make up a very small percentage of the total vesicle number (Fig. 7B). Indeed, the largest percentage of detected particles is positive for ApoA1 (F1–F3, Fig. 7A, B). A high particle number was observed in fraction 1 of the iodixanol density gradient from WB serum, but there were no differences in the concentration of non-EV particles (ApoA1-rich fractions 1–4 of gradient) or CD63-positive EVs (fractions 5–7 of gradient) when comparing HS serum with WB serum (Fig. 7C–E).Fig. 7EVs are a small portion of particles in the serum, and the concentration is not changed with muscle atrophy in rats. A Western blot showing the separation of lipoprotein particles and CD63+ EVs in the serum using an iodixanol density gradient. B NTA quantification of the particle concentration from each fraction collected from the iodixanol density gradient shown in A ($$n = 3$$ WB and $$n = 3$$ HS). C Magnification of the NTA concentration data shown in B ($$n = 3$$ WB and $$n = 3$$ HS). D Summed particle concentration of fractions 1 through 4 of the density gradient. E Summed particle concentration of fractions 5 through 7 of the density gradient ## Ex vivo release of EVs from rat soleus muscle Rat soleus muscle incubated in KHB released particles into the buffer in the expected size range of small EVs containing a mix of exosomes and other microvesicles with a mode in size of approximately 120 nm (Fig. 8A). In contrast to the in vivo findings (Fig. 7D, E), the number of EVs in KHB from the soleus muscles that had undergone hindlimb suspension for 24 h or 7 days was significantly higher than WB (Fig. 8B) ($$p \leq 0.01$$ for both).Fig. 8The muscle releases EVs ex vivo, and the release of EVs is elevated with disuse atrophy. A Size distribution of EVs collected in KHB measured by NTA. B Concentration of EVs in the KHB after incubation of a rat soleus muscle excised from WB ($$n = 7$$), HS 4 h ($$n = 4$$), HS 24 h ($$n = 4$$), and HS 7 days ($$n = 5$$) rats. * $p \leq 0.05$ compared with WB ## Serum and muscle EV concentrations after bed rest in human participants Serum EV concentration was elevated by $31\%$ in human participants following 5 days of bed rest (Fig. 9A). There was a significant decrease in CD63 ($$p \leq 0.04$$) and CD9 ($$p \leq 0.001$$) mRNA abundance and no change in CD81 in the vastus lateralis muscle in response to bed rest (Fig. 9B).Fig. 9Bed rest for 5 days in human subjects elevates serum EV concentration but lowers mRNA abundance of vastus lateralis CD63 and CD9. A Serum EV concentration before and after 5 days of bed rest in human subjects ($$n = 13$$). B mRNA abundance of CD63, CD9, and CD81 in human vastus lateralis muscle ($$n = 9$$) before and after 5 days of bed rest. * $p \leq 0.05$ compared to pre, *$p \leq 0.001$ compared to pre ## Muscle-specific EV markers The primary goal of this study was to explore the expression and localization of markers related to EV assembly and secretion across cell types in the skeletal muscle. The lack of tissue-specific EV markers has made it impossible to determine whether skeletal muscle-derived EVs actually reach circulation and have systemic actions [40]. Guescini et al. first reported that EVs positive for α-sarcoglycan, an integral membrane protein localized to the sarcolemma of skeletal muscle, could be detected in 1–$5\%$ of EVs found in the plasma [16]. Although a number of other studies also reported the detection of α-sarcoglycan in circulation in vivo [15, 41, 42], studies using gold-standard, iodixanol-based density gradients for EV isolation fail to detect any α-sarcoglycan in plasma EVs, even after acute bouts of exercise [18, 43, 44]. Similarly, in the present study, we were unable to detect α-sarcoglycan in serum EVs by Western blot. Moreover, α-sarcoglycan was also undetected in serum EVs using FCS, which employs maximum sensitivity and can detect molecules at picomolar concentrations. Indeed, α-sarcoglycan was only detected in EVs in cell culture media from L6 and C2C12 myotubes and even then in only a small percentage. Overall, there is not enough evidence to suggest that α-sarcoglycan can be used as a skeletal muscle-specific EV marker in vivo. Beyond the lack of detection in pure serum or plasma-derived EV preparations, it is critical to note that α-sarcoglycan is not exclusively expressed in skeletal muscle, but is also expressed in cardiac muscle and in the lung, further questioning the designation of α-sarcoglycan-positive EVs as skeletal muscle EVs [45]. Similarly, APT2A1, β-enolase, and desmin have been suggested as marker proteins to identify skeletal muscle-derived EVs in vivo [46]; however, these proteins can be even more abundant in cardiac muscle tissue than skeletal muscle [47–49]. To address issues of tissue specificity, Estrada et al. recently developed a skeletal muscle myofiber-specific fluorescent reporter mouse [50]. Using this model, the researchers identified that myofiber-derived EVs do in fact reach circulation in vivo, and up to $5\%$ of circulating EVs may be derived from skeletal muscle myofibers [50]. However, in addition to tissue-specific markers, the lack of cell type-specific EV markers further limits the ability to distinguish EVs from the distinct subsets of cells residing in skeletal muscle. For example, although some groups have used markers such as platelet-derived growth factor receptor A (PDGFRα) to isolate fibro-adipogenic progenitor cells (FAPs) in the muscle [51], other cell types, including fibroblasts and smooth muscle cells, also express this protein [52]. Thus, the poor characterization of tissue- or cell-specific EV markers suggests that caution should be exercised in the interpretation of results when these nonspecific markers are used. Another concept for tracking circulating EVs that may be skeletal muscle-derived is an analysis of myomiRs, muscle-enriched miRNAs, such as miR-1, which are expressed in much greater abundance in the muscle in comparison with other tissues [53, 54]. In this study, we were unable to detect miR-1 in EVs from either rat or human serum under resting, basal conditions. In contrast, other studies which used different EV RNA isolation methods and reverse transcription reactions for miR-1 have reported the detection of serum/plasma EV miR-1 in humans and mice [55, 56]. Notably, miR-1 abundance in circulating EVs has been reported to be low at rest and dramatically increased after mechanical overload in mice, as well as following an acute bout of high-intensity resistance exercise in humans [56]. This is in line with several studies which have demonstrated higher levels of circulating miR-1 following different exercise modalities (reviewed in [57–59]). Thus, miR-1 may be a useful circulating miRNA signature for response to acute or chronic exercise, but its utility may be less relevant for tracking EVs in sedentary or atrophic conditions. However, it is important to note that similar to α-sarcoglycan, miR-1 expression is not limited to skeletal muscle and is also expressed in other organs including cardiac tissue. In fact, increases in circulating miR-1 following exercise have been suggested to possibly be due to active heart remodeling rather than skeletal muscle secretion [60]. ## Skeletal muscle expression of EV biogenesis factors The tetraspanins CD63, CD9, and CD81 are most commonly used as ubiquitous markers of EVs to demonstrate isolation or enrichment [61]. Despite early research suggesting universal enrichment of the three tetraspanins in EVs across cell types, recent data suggest that there may be heterogeneous tetraspanin expression across EVs [62–64]. Our scRNA-seq data show large variations in tetraspanin and other EV biogenesis marker mRNA expression across different cell types in skeletal muscle. Variation in the expression of tetraspanins is thought to reflect distinct EV subpopulations, which may have functional differences. The expression of EV biogenesis-related genes was not significantly altered by HS, and the only notable changes induced by HS were small increases in CD9 expression of neutrophils and CD63 expression of macrophages, which may be related to stress and immune dysregulation that can accompany exposure to hindlimb unloading [65, 66]. Interestingly, of the three tetraspanins assessed, CD63 showed the highest mRNA expression across all cell types, including satellite cells. Despite this high expression, we were unable to detect CD63 protein in skeletal muscle homogenates. This finding prompted immunohistochemical staining of the tetraspanins in the skeletal muscle cross-sections, which demonstrated hardly any detection within myofibers and instead accumulation within the interstitium. This is consistent with the recent findings of Watanabe et al., showing EVs concentrated in the muscle interstitium, attached to extracellular matrix (ECM)-like structures by transmission electron microscopy [46]. Within muscle, satellite cell-derived EVs have been previously shown to influence early phases of myofiber growth in response to overload by regulating extracellular matrix (ECM)-related factors in both the myofiber and FAPs [67, 68]. These findings suggest that there is an interaction between EVs and ECM in the muscle, and in vitro and ex vivo analyses of EV secretion that fail to recapitulate this muscle microenvironment may not reflect in vivo conditions. ## Effects of mechanical unloading on skeletal muscle EVs In the present study, we found no differences in serum EV concentrations following HS in rats. Although there tended to be a difference in fractions 1–4 of the iodixanol gradient between WB and HS rats, Western blot analysis of ApoA1 showed contamination of these fractions with lipoprotein particles. This finding suggests that future research should include ApoA1 analysis to indicate contamination of lipoproteins in EV-enriched fractions. In contrast to the in vivo findings, however, isolated muscles from rats that underwent HS for 24 h or 7 days secreted a greater amount of EVs into the media compared to WB muscles. Thus, ex vivo measurements of EV secretion may not reflect in vivo changes in the serum. One possibility for this difference is that EVs represent only a small portion of particles in the serum, and those that may be muscle-derived represent an even smaller portion. Therefore, changes due to muscle mechanical unloading may be harder to detect from a large pool of circulating particles. Alternatively, EVs released from the muscle ex vivo into the medium of an isolated muscle may not make it into the circulation in vivo. This is further supported by our finding that muscle-specific miRNAs, such as miR-1, are not detected in serum EVs in rats or humans. Interestingly, 5 days of bed rest in human participants led to elevated serum EV concentrations in vivo. Notably, bed rest better represents physical inactivity compared with HS and results in whole-body physiological responses that affect different organ systems, including the cardiovascular, pulmonary, hepatic, and gastrointestinal systems [69]. The increase in EVs in human serum following bed rest may be due to the release from other tissues besides the skeletal muscle. In addition to the changes in circulating EV levels, we also found a reduction in the mRNA abundance of CD63 and CD9 in human muscles following bed rest. While these changes suggest that mechanical unloading may modulate skeletal muscle EV concentrations and composition, the clinical relevance of these changes remains unclear. There has been a growing interest in studying the effects of different stimuli on EV release. For example, acute exercise has been shown to elevate the muscle mRNA content of CD9, CD63, and CD81 [18, 70]. Although tetraspanins have mostly been applied as markers of EVs, increasing evidence suggests that these proteins may also influence cellular communication and regulate aspects including cellular metabolism [71]. Importantly, tetraspanins have been shown to interact with immune receptors, which can lead to immune cell signaling and modulation of immune cell adhesion and proliferation [72]. In rat skeletal muscle, signaling via the CXCR4 receptor has been shown to improve skeletal muscle regeneration by upregulating CD9 expression and increasing stem cell mobilization to injured muscles [73]. Similarly, increases in CD9 and CD81 mRNA expression have also been shown to accompany muscle regeneration in rats [74], and mice lacking either CD9 or CD81 show abnormal muscle regeneration due to altered myogenic cell fusion [75]. However, it is not known whether tetraspanin expression by specific cell types mediates their function, and our scRNA-seq and immunohistochemistry data suggest that tetraspanins are, in fact, expressed by various cell types within the skeletal muscle. Further work should be done to determine the mechanisms and effects of CD63 and CD9 reductions in muscle disuse atrophy. Future studies are also required to unravel the effects of muscle disuse atrophy on EV concentrations and composition, as well as their role in tissue crosstalk and mode of action in target cells. Identification of EV signaling mechanisms associated with disuse atrophy may translate into future therapeutic applications. ## Conclusions In conclusion, we provide evidence that traditional markers used to demarcate muscle-specific EVs, including α-sarcoglycan and miR-1, are not reliable in vivo. Furthermore, our scRNA-seq and IHC data demonstrate that EV biogenesis factors including the tetraspanins CD63, CD9, and CD81 are expressed by a variety of cell types in skeletal muscle and that the tetraspanins accumulate within the muscle interstitial space. 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--- title: 'Factors associated with severity and anatomical distribution of diabetic foot ulcer in Uganda: a multicenter cross-sectional study' authors: - Bienfait Mumbere Vahwere - Robinson Ssebuufu - Alice Namatovu - Patrick Kyamanywa - Ibrahim Ntulume - Isaac Mugwano - Theophilus Pius - Franck Katembo Sikakulya - Okedi Francis Xaviour - Yusuf Mulumba - Soria Jorge - Gidio Agaba - George William Nasinyama journal: BMC Public Health year: 2023 pmcid: PMC9999659 doi: 10.1186/s12889-023-15383-7 license: CC BY 4.0 --- # Factors associated with severity and anatomical distribution of diabetic foot ulcer in Uganda: a multicenter cross-sectional study ## Abstract ### Background Diabetic foot ulcer (DFU) is a devastating complication of diabetes mellitus (DM) that is associated with increased mortality, morbidity, amputation rate and economic burden. This study aimed at identifying the anatomical distribution and factors associated with severity of DFU in Uganda. ### Methodology This was a multicenter cross-sectional study conducted in seven selected referral hospitals in Uganda. A total of 117 patients with DFU were enrolled in this study between November 2021 and January 2022. Descriptive analysis and modified Poisson regression analysis were performed at $95\%$ confidence interval; factors with p-value < 0.2 at bivariate analysis were considered for multivariate analysis. ### Results The right foot was affected in $47.9\%$ ($$n = 56$$) of patients, $44.4\%$ ($$n = 52$$) had the DFU on the plantar region of the foot and $47.9\%$ ($$n = 56$$) had an ulcer of > 5 cm in diameter. The majority ($50.4\%$, $$n = 59$$) of patients had one ulcer. $59.8\%$ ($$n = 69$$) had severe DFU, $61.5\%$ ($$n = 72$$) were female and $76.9\%$ had uncontrolled blood sugar. The mean age in years was 57.5 (standard deviation 15.2 years). Primary ($$p \leq 0.011$$) and secondary ($p \leq 0.001$) school educational levels, moderate ($$p \leq 0.003$$) and severe visual loss ($$p \leq 0.011$$), 2 ulcers on one foot ($$p \leq 0.011$$), and eating vegetables regularly were protective against developing severe DFU ($$p \leq 0.03$$). Severity of DFU was 3.4 and 2.7 times more prevalent in patients with mild and moderate neuropathies ($p \leq 0.01$), respectively. Also, severity was 1.5 and 2.5 higher in patients with DFU of 5–10 cm ($$p \leq 0.047$$) and in those with > 10 cm diameter ($$p \leq 0.002$$), respectively. ### Conclusion Most DFU were located on the right foot and on the plantar region of the foot. The anatomical location was not associated with DFU severity. Neuropathies and ulcers of > 5 cm diameter were associated with severe DFU but primary and secondary school education level and eating vegetables were protective. Early management of the precipitating factors is important to reduce the burden of DFU. ## Background Diabetic Foot Ulcer (DFU) is an advanced consequence of diabetes mellitus (DM). There is a $15\%$ lifetime chance of developing the ulcer among diabetic patients and when it occurs, it is associated with high mortality [1]. The 5-year survival rate among patients with DFU varies between $25\%$ and $45\%$ worldwide [2–4]. Globally, DFU has become one of the leading causes of lower limb amputation with over 1 million patients amputated annually, an average of a limb amputation every 20 s [5]. Following amputation for DFU, $85\%$ of patients will still develop chronic infection and other forms of gangrene which lead to poor quality of life and financial stress [6, 7]. One third of the management costs of diabetes is estimated to be linked to foot ulcers as compared to patients without DFU and the cost of care is estimated to be 5.4 and 2.6 times higher in the year of first episode and second episode, respectively [8]. The cost of treatment also increases with severity of the DFU with the highest grade DFU ulcers costing eight times higher than the lowest grade [7]. In addition to high management costs, the severity of DFU ultimately leads to high mortality [9]. The prevalence of diabetic foot has been reported to be higher among people with type 2 DM compared to those with type 1 diabetes worldwide [10]. Globally, the prevalence of DFU averages at $6.4\%$ with a higher predilection in men compared to women [11]. This prevalence varies between $3\%$ in Oceania to $13\%$ in North America, with a prevalence of $7.2\%$ across Africa; in Uganda it varies from 1 to $4\%$ [10]. In Ethiopia, a prevalence of $13.6\%$ of DFU was reported among type 2 diabetes mellitus patients and it was associated with rural residence, poor foot self-care practice, obesity, and neuropathy [12]. Studies have shown that factors usually associated with occurrence of DFU include older age, longer duration of DM, hypertension, diabetic retinopathy and smoking history [13]. Peripheral neuropathy, peripheral vascular disease and foot trauma were also reported risk factors in the pathophysiology of foot ulcer [14]. Other factors include low educational status, high body mass index (BMI)and inadequate foot self-care practice [15–17], however these factors may differ based on the patient’s socio-economic status, demographic characteristics, and the evolution of DFU within the facility [12]. The factors precipitating progression to severe DFU in Ugandan patients have not been well studied. The management of DFU requires the participation of the patient [18] and the outcome is related to patient awareness and self-foot care behavior [19–21]. Self-management of DM has a significant impact on the outcome of blood sugar level control and complications such as DFU [18]. Although Wagner grade 3 and 4, metatarsal is the most common location of DFU and associated factors have been largely studied in developed countries [19–23], there is still a paucity of data regarding the anatomical distribution and factors associated with severe DFU among patients in LMICs such as Uganda. An evidence-based understanding of the factors associated with increasing severity of DFU is necessary to establish effective control measures to reduce its burden. The purpose of this study, therefore, was to determine the anatomical distribution and factors associated with increasing severity of DFU in patients with DM in Uganda. ## Study design and setting This multicenter cross-sectional study was conducted in 7 hospitals in Uganda (Kampala International Teaching Hospital, Kitagata General Hospital, Mbarara Regional Referral Hospital, Fort Portal Regional Referral Hospital, Hoima Regional Referral Hospital, Jinja Regional Referral Hospital and Kiruddu Specialized Hospital) (Fig. 1). The hospitals were deliberately chosen based on the high prevalence of DM in Central, Western and Eastern Uganda where the hospitals are located [24]. Fig. 1Distribution of study sites according to district. RRH: Regional referral hospital; NRH: National referral hospital ## Patients and recruitment All patients aged 18 years and above with DM type 1 and type 2, having a wound located below the ankle and attending the surgical department and/or DM clinics of selected hospitals in Uganda between November 1, 2021 and January 31, 2022 were recruited. Purposive. consecutive sampling method was used until the desired sample size was reached. Patients provided written informed consent to participate in the study. Patients without mental capacity and those without an adult to consent for them were excluded from the study. DFU patients with communication difficulty, such as those with severe cognitive impairment or those who could not consent, were excluded from the study. The required sample size for the study patients with DFU was calculated using the Kish Leslie formula as cited by Singh [25]. Data about the prevalence of DFU in Uganda is still scanty, therefore we used the prevalence estimate of DFU in a cross-sectional study done in Egypt ($8.7\%$ among adult patients aged 18 years and above attending Alexandria University Teaching Hospital Diabetic clinic) to determine the sample size [26]. Using the prevalence estimate from Egypt, which is similar to the one in Kenya [27], resulted in a calculated sample size of 122 patients. ## Study procedure We developed and piloted a data collection tool informed by relevant literature. The tool was translated by a language expert into the local languages spoken in each of the study areas, namely Luganda/Lusoga, Runyakore/Rukiga and Runyoro/Rutoro. A medical doctor, an ophthalmologist/ophthalmology clinical officer, a nurse and a surgical resident were recruited and trained as research assistants in each of the selected hospitals. Data collection was supervised by the principal investigator (BMV) and other authors (RS, GA, IM, and FKS). Physical examination to assess the Wagner classification, anatomical distribution of DFU, neuropathy, and blood pressure was performed by a doctor. The ankle-brachial index (ABI) was calculated using the ratio of the blood pressure taken at the ankle of the affected foot and the blood pressure taken at the one third distal of the arm. The ABI was not measured for patients with DFU affecting the ankle and the variable was considered as “not applicable”. The key variables included socio-demographic variables such as age, sex, occupation, monthly income, tribe and residency, general characteristics of the patients such as type of DM, duration of DM, history of trauma, duration of the diabetic foot ulcer, type of therapy and behavior-related factors such as history of smoking, alcohol intake, type of diet, frequency of consulting the diabetic clinic, foot care and lifestyle. Patients were asked about their regular weekly diet and the food was categorized depending on the main constituent: carbohydrates (matoke/plantains, cassava, posho, rice, sweet potatoes, Irish potatoes, pumpkins, yams), proteins (meat, eggs, fish, beans, milk), lipids (ground nuts, sim sim paste, ghee, cooking oil) and fruits and vegetables. Frequency of consumption was categorized as: daily (if they ate the mentioned food in a category every day), usually (if they ate any of the foods in the category at least 4 to 6 times in a week), occasionally (if at least 2 to 3 times in a week) and rarely (if once a week or less frequent). Regarding alcohol intake, the patient was regarded as an alcoholic if they drink alcohol regularly irrespective of the type of alcohol and non-alcoholic if the patient does not take alcohol at all, if the patient drinks alcohol once in a while or if a patient had completely stopped drinking alcohol for two years. For the smoking variable, a smoker was a patient who chewed or smoked tobacco irrespective of the type and number of times per day whilst a non-smoker status referred to a patient who has never smoked or one that has ceased smoking for at least two years. Diabetic clinic consultation was assessed as the frequency of consultation and as categorized as monthly, every 6 months, once in a year or not at all. Foot care of the patients specifically considered what the patient uses to cut the nails (nail cutter, knife, or razor blade),who cuts the nails for the patient (self or assisted by another person) and the type of shoes regularly worn by the patient (closed, open or fitted) while the foot score consisted of: washing feet - score 1; drying in between the toes - score 2; and using moisturizing products - score 3. Based on this scoring, a good score meant a patient practices all three, a medium score if patient practices any two and a poor score if the patient practices only one of the three. Blood samples for HbA1c and fasting blood sugar (FBS) testing were collected from one of the main superficial veins of the cubital fossa (cephalic, basilic, median cubital, and median ante brachial).The blood sample was collected in an ethylenediaminetetraacetic acid (EDTA) grey top vacutainer for blood sugar tests and glycosylated hemoglobin (HbA1c) [22, 28] and transported in a cooler box (AL medical cool box, England UK) at 2 to 6 Celsius degree to the laboratory of Kampala International University-Teaching Hospital (KIU-TH) [29]. Four milliliters (ml) of blood were withdrawn from the anterior cubital fossa of each subject using a sterile disposable syringe and needle after cleaning the site with a swab soaked in $70\%$ alcohol. HbA1c was analyzed using aIchroma II Machine [2017] and results were interpreted as follows: <$6.5\%$ HbA1c was considered as controlled DM and an HbA1c of $6.5\%$ and above were uncontrolled DM [30]. Each study patient received a printed copy of their results. Patients’ weight in kilograms and height in meters were determined using a calibrated analogue weighing scale and wall mounted station meter manufactured by Southern Early Child Association (SECA). The body mass index was calculated using the formula: BMI = kg/m2 where kg was a patient’s weight in kilograms and m2 was their height in meters squared. Patients were categorized as normal (BMI of 18.5 to 24.9), overweight (BMI of 25.0 to 29.9) or obese (BMI of 30 and above) [31]. Blood pressure was taken using a digital automated sphygmomanometer (bosomedicus vital D-72,417 JUNGINGEN/ German) with an appropriate cuff size for the arm. High blood pressure was defined as systolic blood pressure ≥ l40 mmHg or diastolic pressure ≥ 90 mmHg (European Society of Cardiology/European Society of Hypertension, 2018) [32]. Pressure sensation was assessed using $\frac{5.07}{10}$ g monofilament (Semmes Weinstein monofilament test, made in China) at 4 of the 10 standard sites of the sole of the feet (plantar base of the big toe, 2nd and 5th toes and at the heel), avoiding areas with callosity [33]. Vibration sense was elicited using a 128 Hz tuning fork at the hallux [33, 34]. Diabetic neuropathy was classified as absent, minor, moderate or severe using the new classification by Picon and collaborators [35]. The Neuropathy Disability Score (NDS) was used to assess the grade of diabetic peripheral neuropathy (DPN) for each patient. The NDS system is a neuropathy scoring tool ranging from 0 to 10 which can also be used for assessment of severity of peripheral neuropathy by considering four parameters: vibration sense by using a 128 Hz tuning fork (0 = present,1 = reduced/absent for each foot), temperature sensation by using a cold tuning fork (0 = present,1 = reduced/absent for each foot), pin-prick sensation by a monofilament test (0 = present,1 = reduced/absent for each foot and ankle reflex), Achilles tendon reflex by using a patellar hammer (0 = normal, 1 = present with reinforcement, 2 = absent per side) [36]. Absence of neuropathy (normal) was considered when the score was 0 to 2. The classification of DPN disability was graded as follows: mild (score 3–5), moderate (score 6–8), and severe (score 9–10) as described by Dyck [37]. Patients’ feet were examined to determine the characteristics of the foot ulcer, number of ulcers, size of ulcers and location. The ulcer was classified using the Wagner classification [23]. The patients were classified into two [2] categories based on severity of DFU, as early DFU (less severe) (grade 1 and grade 2 foot ulcer) and severe or late DFU (grade 3 and above) using the Wagner classification [9]. Visual loss was assessed by either a trained ophthalmology clinical officer or ophthalmologist using a Snellen chart to assess the smallest letter a patient can read or orientation of letters at 20 feet or 6 m with one eye closed. Visual impairment was categorized according to the WHO classification where low vision is classified into three categories: mild, moderate, and severe. Mild visual impairment is visual acuity of ≥ $\frac{6}{18}$, moderate visual impairment is less than $\frac{6}{18}$ but equal or better than $\frac{6}{60}$, whilst severe visual impairment is < $\frac{6}{60}$ [38]. ## Data processing and analysis The raw data was entered into MS Excel spreadsheet software, cleaned and later exported to Stata version 15 (Stata Corp®) for analysis. Categorical variables were analyzed using the proportions whilst the difference in proportions was assessed using the Chi-square test and data was summarized and presented in form of frequencies and percentages. Continuous variables were presented as means and standard errors. Bivariate and multivariable analyses were performed using modified Poisson regression to assess the association between severity of the DFU and the factors studied, with significance determined at $p \leq 0.05.$ Variables with a p-value < 0.2 in bivariate analysis were considered for multivariable analysis. Poisson regression was chosen to obtain prevalence risk ratios (PRR) over logistic regression to avoid overestimation of the prevalence ratio and to allow for appropriate control of confounding variables, because the latter poorly estimates the standard errors of the estimated risk ratios especially when dealing with severe DFU which was a common outcome of interest among patients with DFU [39]. ## Ethical considerations Ethical approval was granted by the Research Ethics Committee of Kampala International University (KIU), reference KIU-REC-2021-57 and permission to access the patients was obtained from management of the selected hospitals before data collection. ## Results Among 122 targeted study patients, 117 ($96\%$) patients with DFU had complete data and this was subsequently analyzed. Five ($4\%$) patients that had incomplete data were excluded from further analysis. Among the 117 patients with DFU who formed the definitive sample, the majority ($$n = 70$$; $59.8\%$) had severe DFU and 47 ($40.2\%$) less severe DFU. The majority of the patients ($61.5\%$, $$n = 72$$) were female. The mean age of study patients was 57.1 years (49.5 for patients with less severe DFU and 67.5 for patients with severe DFU). The majority of the patients ($76.9\%$; $$n = 90$$) had poor blood sugar control. Most patients ($44\%$; $$n = 52$$) had a grade 3 diabetic foot ulcer followed by grade 2 ($28\%$, $$n = 33$$), grade 1 ($12\%$, $$n = 14$$), grade 4 ($8\%$, $$n = 9$$) and grade 5($8\%$, $$n = 9$$) according to the Wagner classification. ## Severity of the diabetic foot ulcer As shown in Figs. 2 and 70 ($59.8\%$) of the study patients had severe DFU ($95\%$CI 50.4–68.8) and 47 ($40.2\%$) had less severe DFU ($95\%$CI 31.22–49.6). The proportions were significantly different ($p \leq 0.05$). Fig. 2Proportion of patients with severe and less severe diabetic foot ulcers in selected referral hospitals in Uganda, November 2021 to January 2022 ($$n = 117$$) ## Socio demographic characteristics of DFU patients Most ($33.3\%$) of the patients were in the age-group between 50 and 59 years; and the majority ($74.4\%$) were from rural areas (Table 1). The age group of 70–95 years was associated with severe DFU ($$p \leq 0.02$$). Table 1Sociodemographic characteristics of patients with DFU in selected referral hospitals in Uganda, November 2021 to January 2022 ($$n = 117$$)VariablesLess severe DFU (G1&G2): n(%)Severe DFU (G3, G4, &G5): n(%)Totaln (%)P value Age group in years 0.02618–3910 (21.3)4 (5.7)14 (12.0)40–497 (14.9)8 (11.4)15 (12.8)50–5913 (27.7)26 (37.1)39 (33.3)60–6912 (25.5)13 (18.6)25 (21.4)70–955 (10.6)19 (27.1)24 (20.5) Sex 0.720Female28 (38.9)44 (61.1)72 (61.5)Male19 (40.4)26 (37.1)45 (38.5) Residence 0.682Urban13 (27.7)17 (24.3)30 (25.6)Rural34 (72.3)53 (75.7)87 (74.4) Region 0.555Western33 (70.2)51 (72.9)84 (71.8)Central6 (12.8)12 (17.1)18 (15.4)Eastern7 (14.9)5 (7.1)12 (10.3)Non-Ugandan1 (2.1)2 (2.9)3 (2.6) Occupation 0.502Peasant farmer28 (59.6)42 (60.0)70 (59.8)Business/self employed11 (23.4)13 (18.6)24 (20.5)Non-employed7 (14.9)9 (12.9)16 (13.7)Formerly employed1 (2.1)6 (8.6)7 (6.0) Average monthly income in UgShs 0.144< 10,00015 (31.9)21 (30.0)36 (30.8)10,000-100,00016 (34.0)14 (20.0)30 (25.6)100,001-500,00014 (29.8)22 (31.4)36 (30.8)500,001-1million1 (2.1)9 (12.9)10 (8.5)Above 1million1 (2.1)4 (5.7)5 (4.3)Ug. Shs: Ugandan Shillings; 1 United States (US) dollar = 3800Ug. Shs (exchange rate as of 1st September 2022) ## Medical characteristics of DFU patients Table 2 shows that the majority of patients had DM type 2 ($94.9\%$, $$n = 111$$) and 65 ($55.6\%$) had suffered with DM for a duration of 9 years and above. In $55\%$ the DFU had lasted for a duration of 1–6 months, $25.6\%$ had a prior history of DFU and $18.8\%$ had a history of amputation. The majority of patients ($65\%$) were on insulin. Table 2Medical characteristics of patients in selected referral hospitals in Uganda, November 2021 to January 2022 ($$n = 117$$)VariablesLess severe (G1&G2): n(%)Severe (G3, G4, &G5): n(%)Totaln (%)p-value Type of DM 0.174Type 14 (8.5)2 (2.9)6 (5.1)Type 243 (91.5)68 (97.1)111 (94.9) Duration of DM in years, mean (SD) 12.2 (11.0)10.0 (8.8)10.9 (9.7)0.236 Duration of DM in years 0.7440–312 (25.5)19 (27.1)31 (26.5)4–810 (21.3)11 (15.7)21 (17.9)9 and above25 (53.2)40 (57.1)65 (55.6)Method of treatment Insulin 0.834No17 (36.2)24 (34.3)41 (35.0)Yes30 (63.8)46 (65.7)76 (65.0) Herbal medicine 0.330No26 (55.3)45 (64.3)71 (60.7)Yes21 (44.7)25 (35.7)46 (39.3) Oral hypoglycemic agents (OHA) 0.783No20 (42.6)28 (40.0)48 (41.0)Yes27 (57.4)42 (60.0)69 (59.0) On treatment 0.683No45 (95.7)68 (97.1)113 (96.6)Yes2 (4.3)2 (2.9)4 (3.4) Number of types of medication received 0.17302 (4.3)2 (2.9)4 (3.4)120 (42.6)39 (55.7)59 (50.4)217 (36.2)13 (18.6)30 (25.6)38 (17.0)16 (22.9)24 (20.5) Duration of DFU in months, mean (SD) 4.9 (10.1)4.2 (7.3)4.5 (8.5)0.670 Duration of DFU in months 0.527Less than 117 (38.6)19 (28.4)36 (32.4)1–622 (50.0)39 (58.2)61 (55.0)Over 65 (11.4)9 (13.4)14 (12.6) History of trauma to an affected foot 0.604No40 (85.1)57 (81.4)97 (82.9)Yes7 (14.9)13 (18.6)20 (17.1) History of previous DFU 0.535No36 (76.6)50 (71.4)86 (73.5)Yes11 (23.4)20 (28.6)31 (26.5) History of amputation due to DFU 0.375No40 (85.1)55 (78.6)95 (81.2)Yes7 (14.9)15 (21.4)22 (18.8)SE: standard error ## Behavioral characteristics of patients with DFU As shown in Table 3, only 20 ($17.1\%$, $$n = 117$$) of patients attended a diabetes clinic twice a month and only 22 ($18.8\%$) patients reported to be doing regular physical exercise. The prevalence of other factors associated with severity on DFU such as foot care, history of smoking, not doing physical exercise and eating a mixture of foods are also shown. Table 3Behavioral characteristics of DFU patients in selected referral hospitals in Uganda, November 2021 to January 2022 ($$n = 117$$)VariablesEarly (G1&G2):n(%)Late (G3, G4, &G5): n (%)Totaln (%)p-value Taught About complications of DM 0.274No20 (42.6)37 (52.9)57 (48.7)Yes27 (57.4)33 (47.1)60 (51.3) Counselled about risk of DFU when you have DM 0.411No14 (29.8)26 (37.1)40 (34.2)Yes33 (70.2)44 (62.9)77 (65.8) Counselled by a friend 0.707No31 (66.0)41 (58.6)72 (61.5)Yes16 (4.3)29 (51.4)45 (38.5) Counselled by a healthcare worker 0.480No30 (63.8)37 (52.9)67 (57.3)Yes17(6.4)33 (47.1)50 (42.7) Counselled by social media 0.707No31 (66.0)41 (58.6)72 (61.5)Yes16 (34.1)29 (41.4)45 (39.0) Frequency of DM clinic attendance 0.577Never13 (27.7)25 (35.7)38 (32.5)Once a month22 (46.8)28 (40.0)50 (42.7)Every 2 months7 (14.9)13 (18.6)20 (17.1)Once yearly5 (10.6)4 (5.7)9 (7.7) Smoking status 0.040Non smoker44 (93.6)56 (80.0)100 (85.5)Ever smoked3 (6.4)14 (20.0)17 (14.5) Alcohol consumer 0.512No38 (80.9)53 (75.7)91 (77.8)Yes9 (19.1)17 (24.3)26 (22.2) Foot care score 0.464112 (25.5)25 (35.7)37 (31.6)25 (10.6)5 (7.1)10 (8.5)330 (63.8)40 (57.1)70 (59.8) Type of shoes worn 0.908Open shoes30 (63.8)47 (67.1)77 (65.8)Any type of shoes12 (25.5)17 (24.3)29 (24.8)Don’t wear shoes5 (10.6)6 (8.6)11 (9.4) How often nails are cut since DM diagnosed 0.791Always14 (29.8)22 (31.4)36 (30.8)Occasionally32 (68.1)45 (64.3)77 (65.8)Don’t1 (2.1)3 (4.3)4 (3.4) Individual who cuts the toenails 0.762Self30 (63.8)46 (65.7)76 (65.0)Other people17 (36.1)24 (34.3)41 (35.0)What do you use to cut your nails (multiple answers) Nail cutter 0.329No33 (70.2)43 (61.4)76 (65.0)Yes14 (29.8)27 (38.6)41 (35.0) Razor blade 0.152No14 (29.8)30 (42.9)44 (37.6)Yes33 (70.2)40 (57.1)73 (62.4) Knife 0.807No46 (97.9)68 (97.1)114 (97.4)Yes1 (2.1)2 (2.9)3 (2.6) Frequency of exercise 0.529Always8 (17.0)14 (20.0)22 (18.8)Occasionally27 (57.4)39 (55.7)66 (56.4)Never12 (25.5)17 (24.3)29 (24.8) Work in the garden 0.921No27 (57.4)37 (52.9)64 (54.7)Yes20 (42.6)33 (47.1)53 (45.3) Walk at least 30 min a day 0.625No25 (53.2)47 (67.1)72 (61.5)Yes22 (46.8)23 (32.9)45 (38.5) Housework 0.128No38 (80.9)56 (80.0)94 (80.3)Yes9 (19.1)14 (20.0)23 (19.7) Jogging 0.910No40 (85.1)67 (95.7)107 (91.5)Yes7 (14.9)3 (4.3)10 (8.5) No exercise 0.044No36 (76.6)57 (81.4)93 (79.5)Yes11 (23.4)13 (18.6)24 (20.5) Diet (how often food eaten:) Vegetables 0.103Daily26(0.2)32(0.3)58(0.5)Usually4(0.03)1(0.01)5(0.04)Occasionally2(0.02)8(0.1)10(0.1)Rarely15(0.1)29(0.2)44(0.4) Fruits 0.924Daily21(0.2)30(0.3)51(0.4)Usually1(0.01)1(0.01)2(0.02)Occasionally6(0.1)7(0.1)13(0.1)Rarely19(0.2)32(0.3)51(0.4) Fatty food 0.492Daily26(0.2)39(0.3)65(0.6)Usually1(0.01)6(0.1)7(0.1)Occasionally9(0.1)10(0.1)19(0.2)Rarely11(0.1)15(0.1)26(0.2) Proteins 0.215Daily30(0.3)47(0.4)77(0.7)Usually1(0.01)6(0.1)7(0.1)Occasionally8(0.1)5(0.04)13(0.1)Rarely8(0.1)12(0.1)20(0.2) Carbohydrates 0.209Daily33(0.3)56(0.5)89(0.8)Usually4(0.03)3(0.03)7(0.1)Occasionally4(0.03)1(0.01)5(0.04)Rarely6(0.1)10(0.1)16(0.1) ## Comorbidities of patients with DFU The study showed that most patients ($47.9\%$) had a normal BMI whilst 38 ($32.5\%$) had pre-obesity. Hypertension was found in 69 ($59\%$) patients, moderate kidney failure in 23 ($19.7\%$), moderate neuropathy in 44 ($37.6\%$) and severe neuropathy in 38 ($32.5\%$) of patients with severe DFU (p-value 0.01). Hba1C levels above $6.5\%$ were found in 90 ($76.9\%$) of patients, compensated heart disease in 22 ($18.8\%$) and decompensated heart disease in 5 ($4.3\%$). Furthermore, claudication was the most common peripheral vascular disease, reported by 29 ($24.8\%$) patients, followed by gangrene ($6.0\%$) and deep venous thrombosis (DVT) ($4.3\%$) (Table 4). Table 4Co-morbidities of patients with DFU in selected referral hospitals in Uganda, November 2021 to January 2022 ($$n = 117$$)VariablesEarly (G1&G2) n(%)Late (G3, G4, &G5)n(%)Totaln (%)p-value Neuropathy 0.013Absent16 (34.0)8 (11.4)24 (20.5)Mild3 (6.4)8 (11.4)11 (9.4)Moderate18 (38.3)26 (37.1)44 (37.6)Severe10 (21.3)28 (40.0)38 (32.5) Peripheral Vascular Disease 0.103No disease35 (74.5)41 (58.6)76 (65.0)Claudication10 (21.3)24 (27.1)34 (29.1)Gangrene0 (0.0)7 (10.0)7 (6.0)DVT2 (4.3)3 (4.3)5 (4.3) Hba1C, mean (SD) 8.3 (2.6)8.8 (2.6)8.6 (2.6)0.158 Hba1C levels Less than $6.5\%$14 (29.8)13 (18.6)27 (23.1)$6.5\%$ and above33 (70.2)57 (81.4)90 (76.9)0.158 BP class Normal19 (40.4)28 (40.0)47 (40.2)Hypertension28 (59.6)41 (58.6)69 (59.0)Hypotension0 (0.0)1 (1.4)1 (0.9) BMI, mean (SD) 25.9 (6.7)25.0 (5.2)25.3 (5.9)0.393 BMI group 0.614Underweight < 18.53 (6.4)5 (7.1)8 (6.8)Normal 18.5–24.921 (44.7)35 (50.0)56 (47.9)Pre-obesity 25.0–29.916 (34.0)22 (31.4)38 (32.5)Obesity Class I 30.0–34.93 (6.4)6 (8.6)9 (7.7)Obesity Class II 35.0–39.92 (4.3)0 (0.0)2 (1.7)Obesity Class III 40 and above2 (4.3)2 (2.9)4 (3.4) ABI group 0.192Normal (1.0 to 1.4)9(0.1)19(0.2)28(0.2)Acceptable (0.9 to less 1.0)4(0.03)3(0.03)7(0.1)Some Arterial Disease (0.8 to less 0.9)1(0.01)0(0.0)1(0.01)Moderate (0.5 to 0.7)0(0.0)2(0.02)2(0.02)Severe (Less than 0.5)0(0.0)2(0.02)2(0.02)Not assessed31(0.3)46(0.4)77(0.7)ABI: ankle brachial index; BMI: body mass index, Hba1C: glycosylated hemoglobin, DVT: deep venous thrombosis, BP: blood pressure, SD = standard deviation ## Anatomical distribution of DFU Most patients ($47.9\%$, $$n = 56$$) had DFU of the right foot, with the majority ($54.7\%$) found on the dorsum of the foot. Location of the ulcer on the plantar region ($$p \leq 0.01$$), having more than 4 ulcers ($$p \leq 0.01$$) and a size of ulcer of > 10 cm of diameter ($$p \leq 0.00$$) were associated with higher severity of the DFU (Table 5). Table 5Anatomical distribution of DFU among patients in selected referral hospitals in Uganda, November 2021 to January 2022 ($$n = 117$$)VariablesLess severe (G1&G2):n (%)Severe (G3, G4, &G5): n (%)Totaln(%)p-value Foot affected by DFU 0.633Right23 (48.9)33 (47.1)56 (47.9)Left20 (42.6)27 (38.6)47 (40.2)Both4 (8.5)10 (14.3)14 (12.0) Location of DFU Heel 0.409No38 (80.9)52 (74.3)90 (76.9)Yes9 (19.1)18 (25.7)27 (23.1) Dorsum 0.074No26 (55.3)27 (38.6)53 (45.3)Yes21 (44.7)43 (61.4)64 (54.7) Plantar 0.009No33 (70.2)32 (45.7)65 (55.6)Yes14 (29.8)38 (54.3)52 (44.4) Toes 0.788No22 (46.8)31 (44.3)53 (45.3)Yes25 (53.2)39 (55.7)64 (54.7) Number of ulcers 0.010129 (61.7)30 (42.9)59 (50.4)214 (29.8)15 (21.4)29 (24.8)34 (8.5)22 (31.4)26 (22.2)40 (0.0)3 (4.3)3 (2.6) Size of DFU in cm < 0.0011 to 534 (72.3)22 (31.4)56 (47.9)5 to 1013 (27.7)29 (41.4)42 (35.9)Over 100 (0.0)19 (27.1)19 (16.2)cm: centimeter ## Factors influencing severity of DFU The patients with mild neuropathy (aPRR = 3.4; $95\%$ CI = 1.51–7.63; p-value = 0.003) and moderate neuropathy (aPRR = 2.65; $95\%$ CI = 1.34–5.23; p-value = 0.005) were more at risk of developing severe DFU as compared to those with normal foot sensation, when other factors were held constant. Although at bivariate analysis patients with severe neuropathy were twice more likely ($95\%$ CI = 1.21–4.03, $$p \leq 0.009$$) to develop severe DFU compared to those with normal foot sensations, this turned out not to be a significant risk factor at multivariable analysis. The age group of 70–95 years was significantly associated with increased risk of severe DFU (APR = 3.02; $95\%$ CI = 1.28–7.17; $$p \leq 0.02$$); the size of the ulcer of more than 5 cm (APR = 1.51; $95\%$CI= (1.01–2.27); $p \leq 0.05$) and size > 10 cm (APR = 2.46;$95\%$=1.38–4.39; $$p \leq 0.002$$). Regular eating of vegetables (APRR = 0.062; $95\%$ CI = 0.004–0.863; p-value = 0.038), education level: primary (APRR = 0.58; $95\%$ CI = 0.38–0.89; $$p \leq 0.011$$), secondary (APR = 0.53; $95\%$ CI = 0.29–0.97; $$p \leq 0.039$$); moderate (APRR = 0.48; $95\%$ CI =; $95\%$ CI = 0.29–0.78; $$p \leq 0.003$$) and severe visual loss (APR = 0.31; $95\%$=0.17–0.54; $$p \leq 0.011$$), for 2 ulcers on one foot (APR = 0.52; $95\%$CI = 0.32–0.86; $$p \leq 0.011$$) were significantly less likely associated with severe DFU (Table 6). Table 6Bivariate and multivariable analysis for factors associated with severity of DFU in selected referral hospitals in Uganda, November 2021 to January 2022 ($$n = 117$$)VariableDFUBivariate analysisMultivariable analysisLess severen (%)Severen (%)c.PRR ($95\%$CI)p-valuea. PRR ($95\%$CI)p-value Age 47 (40.2)70 (59.8)1.01(1.01–1.02)0.0031.02(1.01–1.03)0.001 Age group in years 18–3910 (21.3)4 (5.7)1140–497 (14.9)8 (11.4)1.87(0.72–4.87)0.2022.5(0.91–6.91)0.07650–5913 (27.7)26 (37.1)2.33 (0.99–5.52)0.0541.43(0.63–3.26)0.39760–6912 (25.5)13 (18.6)1.82 (0.73–4.54)0.1991.67 (0.72–3.91)0.23470–955 (10.6)19 (27.1)2.77 (1.18–6.53)0.0203.023 (1.28–7.17)0.012 Sex Female28 (59.6)44 (62.9)11Male19 (40.4)26 (37.1)0.95 (0.69–1.29)0.724-- Residence Rural34 (72.3)53 (75.7)11Urban13 (27.7)17 (24.3)0.95 (0.69–1.29)0.724-- Profession Peasant farmer28 (59.6)42 (60.0)11Business/self employed11 (23.4)13 (18.6)0.9 (0.6–1.37)0.6300.84 (0.43–1.66)0.621Non employed7 (14.9)9 (12.9)0.94 (0.58–1.51)0.7901.02 (0.61–1.71)0.939Formerly employed1 (2.1)6 (8.6)1.43 (1–2.05)0.0521.13 (0.5–2.53)0.773 Level of education No formal3 (6.4)12 (17.1)11Primary29 (61.7)40 (57.1)0.73 (0.52–1)0.0520.58 (0.38–0.89)0.011Secondary14 (29.8)14 (20.0)0.63 (0.4–0.98)0.0410.53 (0.29–0.97)0.039Tertiary1 (2.1)4 (5.7)1 (0.6–1.66)1.0000.45 (0.18–1.17)0.102 Religion Muslim4 (8.5)9 (12.9)11Christian43 (91.5)61 (87.1)0.8 (0.52–1.38)0.5060.9 (0.54–1.45)0.629 Average monthly income in UgShs < 10,00015 (31.9)21 (30.0)0.73 (0.43–1.23)0.2340.42 (0.13–1.33)0.14210,000-100,00016 (34.0)14 (20.0)0.58 (0.33–1.05)0.0710.39 (0.12–1.31)0.128100,001-500,00014 (29.8)22 (31.4)0.76 (0.46–1.28)0.3030.47 (0.16–1.42)0.181500,001–1,000,0001 (2.1)9 (12.9)1.13 (0.69–1.83)0.6350.8 (0.3–2.14)0.650Above 1 M1 (2.1)4 (5.7)1.1. Smoking status Non smoker44 (93.6)56 (80.0)1.1.Ever smoked3 (6.4)14 (20.0)1.47 (1.11–1.95)0.0071.08 (0.68–1.71)0.751 Vegetables Rarely15(0.1)29(0.2)1.1.Daily26(0.2)32(0.3)0.69 (0.20–2.37)0.5584.50 (0.68–1.68)0.118Usually4(0.03)1(0.01)0.13 (0.01–1.26)0.0780.06 (0.00-0.86)0.038Occasionally2(0.02)8(0.1)0.85 (0.38–1.88)0.6850.69 (0.28–3.06)0.430 Carbohydrates Rarely6(0.1)10(0.1)1.1.Daily33(0.3)56(0.5)0.15 (0.01–1.68)0.1230.18 (0.01–2.40)0.193Usually4(0.03)3(0.03)0.45 (0.07–2.74)0.3861.08 (0.11–10.30)0.946Occasionally4(0.03)1(0.01)1.02 (0.34–3.06)0.9741.43 (0.40–5.09)0.581 Dorsum No26 (55.3)27 (38.6)1.1.Yes21 (44.7)43 (61.4)1.32 (0.96–1.81)0.0861.32 (0.88–1.98)0.176 Plantar No33 (70.2)32 (45.7)1.1.Yes14 (29.8)38 (54.3)1.48 (1.1–2)0.0091.46 (0.89–2.38)0.135 Number of locations for one Ulcer 129 (61.7)30 (42.9)1.1.214 (29.8)15 (21.4)1.02 (0.66–1.57)0.9380.52 (0.32–0.86)0.01134 (8.5)22 (31.4)1.66 (1.23–2.25)0.0010.57 (0.27–1.21)0.14540 (0.0)3 (4.3)1.97 (1.53–2.53)< 0.0010.72 (0.33–1.6)0.421 Visual loss Normal8 (17.0)18 (25.7)1.1.Mild7 (14.9)14 (20.0)0.96 (0.65–1.43)0.8530.55 (0.28–1.08)0.084Moderate15 (31.9)23 (32.9)0.87 (0.61–1.26)0.4700.48 (0.29–0.78)0.003Severe17 (36.2)15 (21.4)0.68 (0.43–1.06)0.0900.31 (0.17–0.54)< 0.001 Neuropathy Normal16 (34.0)8 (11.4)1.1.Mild3 (6.4)8 (11.4)2.18 (1.11–4.28)0.0233.4 (1.51–7.63)0.003Moderate18 (38.3)26 (37.1)1.77 (0.95–3.29)0.0702.65 (1.34–5.23)0.005Severe10 (21.3)28 (40.0)2.21 (1.21–4.03)0.0092.06 (0.96–4.43)0.063 Peripheral Vascular Disease No disease35 (74.5)41 (58.6)1.1.Claudication10 (21.3)19 (27.1)1.21 (0.87–1.7)0.2591.39 (0.88–2.2)0.162Gangrene0 (0.0)7 (10.0)1.85 (1.51–2.28)< 0.0011.26 (0.71–2.24)0.424DVT2 (4.3)3 (4.3)1.11 (0.53–2.35)0.7811.74 (0.44–6.89)0.433Hba1C $6.5\%$, n (%)Less than $6.5\%$14 (29.8)13 (18.6)1.6.5 and above33 (70.2)57 (81.4)1.32 (0.86–2.01)0.205-- Blood Pressure class Normal19 (40.4)28 (40.0)1.1.Hypertension28 (59.6)41 (58.6)1 (0.73–1.36)0.9871.05 (0.64–1.73)0.851Hypotension0 (0.0)1 (1.4)1.68 (1.33–2.13)< 0.0011.93 (0.56–6.64)0.296 Size of DFU in cm 1 to 534 (72.3)22 (31.4)1.1.5 to 1013 (27.7)29 (41.4)1.76 (1.2–2.58)0.0041.51 (1.01–2.27)0.047Over 100 (0.0)19 (27.1)2.55 (1.84–3.53)< 0.0012.46 (1.38–4.39)0.002UgShs: Ugandan shillings; 1US dollar ($) = 3800ugShs (01, September, 2022); M: million; DVT: Deep venous thrombosis, CPPR: crude prevalence risk ratio, APPR: adjusted prevalence risk ratio ## Discussion This study assessed the anatomical distribution and factors associated with severity of DFU among patients with DM in Uganda. The majority of patients in our study had poor blood sugar control. The most common location of DFU was the plantar region of the foot unlike other studies which reported metatarsal as the common region [22, 23]. Factors associated with severity of DFU were size of the ulcer, neuropathy and age. The burden of DM and DFU is increasing worldwide, especially in developing countries such as Uganda [8]. Adult patients have a 10–$15\%$ risk of developing DFU during their diabetic lifetime [1, 40]. Our study showed the mean age in years was 57.5 (SD15.2), and older age was significantly associated with higher severity of DFU, especially the 70–95 age group. This result is similar to what was reported in a systematic review where age was associated with severity of DFU [9]. Although our study had a female majority, sex was not significantly associated with severity of DFU. A similar result was reported by Agwu [39] and other authors in Uganda [39, 41]. However, a male majority and preponderance have been reported in other studies [2, 42]. Jupiter [40] in a worldwide systematic and meta-analysis review in found that male patients were significantly more affected by severe DFU than female patients. In addition, level of education, profession, level of income, duration of DM/DFU are significantly associated with the severity of the DFU although these factors have been reported to contribute to the genesis of the DFU [17]. History of smoking was not associated with severity of the DFU in this study although it has been reported to be associated with development of DFU. However, other studies have found history of smoking to be associated with severity of DFU [9, 22]. This finding can be explained by the relatively low number of smokers found in this study. High BMI has been reported to be associated with occurrence of DFU and with severe DFU [9]; however, our results did not support this association. This could be explained by the finding that most patients had normal BMI. The majority of DFU patients in this study had poor blood sugar control, although this did not appear to be a risk factor associated with severe DFU. This result is different from what has reported in a recent systematic review and by Bekele in Ethiopia where uncontrolled glycemia was significantly associated with severe DFU [9, 42]. Although poor blood sugar control was not associated with severity of DFU in this study, there is still a need to stabilize the sugar levels of all DFU patients. The majority of DFU patients presented with severe DFU, a finding similar to a report by Smith-Strøm in Norway [43]. However, contrary findings have been reported by Jalilian and colleagues in a systematic review where a higher proportion of patients had less severe DFU [9]. Development of clear guidelines on prevention and cure of DFU with evidence-based specifics for our setting could help reduce the burden of complications due to DFU. Most patients had Wagner classification Grade 3 DFU which is in agreement with results of a study in Sri Lanka [22]. However, studies from Ethiopia and India, have reported Wagner classification Grade 2 DFU to be the most common among diabetic patients [44–46]. This calls for emphasis on education of patients living with DM to seek medical consultation early in case of any foot wound to reduce progression to severe DFU and its complications in our setting. Living in a rural area, having a long duration of DM of type 2 or having a history of DFU for more than 6 months was not associated with severity of DFU in this study, although these factors have been reported elsewhere to be associated with development of severe DFU [9, 17, 22]. Our results showed that DFU among patients was mostly on the right foot which concurs with the results of Smith-Strøm H in Norway [43]. Most patients in this study had DFU measuring less than 10cm2 which was similar to a study in India where DFU measuring < 10cm2was the most common [47]. Many factors have been reported to be associated with the severity of DFU. In this study, a number of factors were identified to be associated with severe DFU and these included neuropathies and the size of the ulcer. This result is similar to a study in Ethiopia where neuropathies were 4 times more likely in patients with severe DFU [47]. This study showed that the larger the ulcer, the more likely it was to be severe. Therefore, DFU should be treated early and effectively to avoid progression towards severity. Ambageda in Sri Lanka did not find any significant association between any factor and severity of DFU [22]. Several studies have reported factors associated with severity of DFU among DM patients [22, 28, 41]. A study in Norway found the duration of the ulcer to be associated with the severity whilst a systematic review by Campbell reported that high HbA1c b was associated with increased severity of DFU [22, 28, 41] and in a systematic review by Jalalian, the location of DFU on the plantar region was associated with increased severity of DFU [9]. Since factors affecting the severity of DFU seem to vary from region to region, location-specific research is recommended in order to contextualize the mitigation of complications of DFU. ## Limitations and strength of this study The study was hospital-based and this limits the generalization of the results to the entire Ugandan population. Patients interviewed may withhold some information but, in this study, they were assured of confidentiality to avoid this bias. The other limitation is the nature of being a cross-sectional study, where the causal relationship between the contributing factors and the severity of DFU could not be established. Assessment of the vascular insufficiency should have been done using a Doppler scan or CT angiogram which are the recommended tests, however we used the ABI, which was not performed on all patients because of extensive lesions around the ankle joint in some of these patients. The study did not capture data from northern Ugandan and from all major hospitals of Eastern Uganda due to limitation of funds. The strength of this study lies in its multi-center approach and as data was collected concurrently, there is a possibility of generalizing to the catchment area of the study. However, a nationwide prospective study on severity of the DFU and outcomes in the region is proposed. More attention should be focused upon treating aggressively the neuropathy to prevent extension of the wound. ## Conclusion Most patients were from western Uganda and have poor health-seeking behavior leading to late consultation in diabetic clinics when the condition is rather severe. The majority of patients had severe DFU and grade 3 was the most common. The dorsum of the right foot was most affected with ulcers and the majority of patients had poor blood sugar control. Primary and secondary educational level, moderate and severe vision loss and eating frequently vegetables were less likely to be associated with severe DFU. There was an association between age of 70–95 years, neuropathies, ulcer size of more than 5 cm and severity of the DFU. 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--- title: 'Cognitive Frailty and Functional Disability Among Community-Dwelling Older Adults: A Systematic Review' authors: - Kar Foong Tang - Pei-Lee Teh - Shaun Wen Huey Lee journal: Innovation in Aging year: 2023 pmcid: PMC9999676 doi: 10.1093/geroni/igad005 license: CC BY 4.0 --- # Cognitive Frailty and Functional Disability Among Community-Dwelling Older Adults: A Systematic Review ## Abstract ### Background and Objectives This review aimed to summarize the association between cognitive frailty (presence of frailty and cognitive impairment) and the risk of disabilities in activities of daily living (ADL), instrumental ADL (IADL), mobility, or other functional disabilities among older adults. ### Research Design and Methods PubMed, Embase, CINAHL Plus, and PsycINFO were searched from January 2001 to May 14, 2022, for observational studies that reported cognitive frailty among community-dwelling individuals aged 60 years and above. Results were narratively synthesized. ### Results Eleven studies encompassing 44 798 participants were included, with a prevalence of cognitive frailty ranging from $1.4\%$ to $39.3\%$. Individuals with cognitive frailty were more likely to develop disabilities in ADL and IADL compared to robust (absence of frailty and cognitive impairment) individuals. Significant disability burden and elevated risk of combined ADL/IADL disability or physical limitation among participants with cognitive frailty were reported. There was limited evidence on the association between cognitive frailty and mobility disability. ### Discussion and Implications Individuals with cognitive frailty were likely at higher risk of developing functional disability and incurring higher disability burden than robust individuals, but evidence remains limited for those with prefrailty with cognitive impairment. Further research on this gap and standardization of cognitive frailty assessments would facilitate comparisons across populations. ### PROSPERO Registration CRD42021232222 ## Background and Objectives Frailty is a state of increased vulnerability due to the decline in the function of various physiologic systems [1,2]. This increases an older adult’s risk of developing adverse health outcomes (3–5) and represents a public health concern in light of the global aging population. However, to date, frailty remains a dynamic yet heterogeneous concept due to a lack of consensus on its definition (6–8). The most commonly used construct is the frailty phenotype proposed by Fried and colleagues, which is characterized by shrinking (unintentional weight loss), weakness, and poor endurance as indicated by exhaustion, slowness, and low physical activity level [1]. Individuals without these characteristics are considered robust, those with 1 or 2 characteristics are considered prefrail, and those with 3 or more characteristics are considered frail. The construct primarily focuses on the physical domain of frailty, but some studies suggest that frailty represents a multidimensional concept and that the cognitive domain should be included in the definition of frailty [9,10], as both domains are thought to be interlinked [7,8,11]. As such, the concept of cognitive frailty was introduced to encompass a broader definition of frailty, where an older adult exhibits the simultaneous presence of physical frailty and cognitive impairment in the absence of dementia [10]. The International Academy on Nutrition and Aging and the International Association of Gerontology and Geriatrics were the first international consensus group to provide this definition of cognitive frailty in older adults in 2013 [10] to conceptualize cognitive impairment due to physical frailty and not the presence of neurological condition. The definition of cognitive impairment is heterogeneous, ranging from impairment in 1 or more cognitive domains, cognitive changes reported by the patient, caregiver or physician, or impairment based on clinical dementia rating (CDR). The consensus group suggested using a comprehensive cognitive and physical frailty assessment to identify cognitive frailty (physical frailty and cognitive impairment). Physical frailty has been widely reported to be predictive of adverse health outcomes, ranging from mortality, hospitalization, falls, fractures, disability, cognitive decline, and institutionalization (12–14). Studies on the association between physical frailty and disability have indicated that older adults with physical frailty had higher odds of developing disability than robust older adults, although this association was not found in some studies [14,15]. In comparison, evidence on the association between cognitive frailty and adverse health outcomes other than mortality, such as functional disability, is still relatively underexplored [16]. In a previous systematic review on adverse outcomes of cognitive frailty [16], 2 studies identified in the review reported on the relationship between cognitive frailty and activities of daily living (ADL) or instrumental ADL (IADL) dependency, which showed a two- to fivefold increased risk of dependence. Another review [17] reported 6 other studies which evaluated the association between cognitive frailty and disability. Of the additional 6 studies, only 1 reported on mobility disability, but the association could not be determined because all 4 participants with cognitive frailty developed mobility disability during the study follow-up period [18]. In these 2 reviews, the authors found only a limited number of studies reporting disability as an adverse outcome of cognitive frailty whereas 2 other reviews [19,20] did not specify the types of disability measured. These reflect large gaps in cognitive frailty studies, which have mostly focused on well-known adverse outcomes and less on disability due to challenges in measuring and comparing disability, especially mobility disability, which represents an important health indicator for older adults. In line with a rapidly aging population globally, an increasing proportion of older adults will have declining functional status and an increased risk of frailty and cognitive impairment, among other age-related conditions. There remains a gap in understanding how frailty and cognitive impairment affect functional ability, such as ADL or IADL, among older adults because frailty and cognitive impairment have usually been studied separately. Physical frailty [14] and cognitive impairment [21] are known independent risk factors of ADL or IADL disability, but it is unclear if the concurrent presence of both frailty and cognitive impairment will accentuate the risk of developing functional disability. Frailty [22,23], cognitive impairment, and functional disability [24] are expected to contribute to increased healthcare burden and cost due to the increased need for health and social care services, whereas the increase in cognitive frailty prevalence over time [25] is also likely to increase adverse outcomes among older adults. Given the implications of frailty on health outcomes and healthcare utilization [8,22], there is a need for a focused review to understand the occurrence of functional disabilities in the presence of physical frailty and cognitive impairment (cognitive frailty). This can help guide frailty prevention strategies and interventions to maintain functional ability in older adults. This review aims to summarize the association between cognitive frailty and the risk of developing functional disability among community-dwelling older adults. In this review, we included functional disabilities, namely ADL, IADL, and mobility disabilities; limitations in physical function; and other functional disabilities to provide readers with an overview of the association between cognitive frailty and risk of functional disability. ## Research Design and Methods The systematic review protocol was registered in PROSPERO (CRD42021232222). Reporting was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [26]. ## Search Strategy A systematic search was conducted in four databases: PubMed, EMBASE, CINAHL Plus, and PsycINFO from 2001 (corresponding to the year when the Fried frailty phenotype construct was published) until May 14, 2022. The search included a combination of keywords and Medical Subject Headings terms related to frailty, older adults, ADL, disability, mobility, physical function, and adverse outcome. The list of databases and search terms are available in Supplementary Material. Reference lists of included articles and previous systematic reviews on adverse outcomes of cognitive frailty were screened to identify additional studies. ## Selection Criteria Observational studies (cross-sectional or longitudinal cohort studies) were included if they reported on community-dwelling older adults aged 60 years and above. This age cutoff point was selected because studies on frailty typically included participants aged 60 years and above [27]. Cross-sectional, prospective cohort studies were included due to the small number of longitudinal studies that reported on the association between cognitive frailty and disability. Cognitive frailty was defined by the presence of frailty or prefrailty, and concurrent cognitive impairment was identified using validated physical frailty and cognitive assessments. A preliminary literature search has identified that most studies on cognitive frailty have slightly modified the definition of cognitive frailty by the consensus group, defining this condition with the presence of mild cognitive impairment instead of a CDR of 0.5 with the exclusion of concurrent Alzheimer’s disease or other dementias, and physical frailty using the modified Fried frailty phenotype [28]. Thus, the utilization of CDR was not compulsory for study inclusion in this review if a validated cognitive assessment tool was reported. Studies must report the association between cognitive frailty and functional disability (ADL or IADL, mobility, physical function). Studies were excluded if they included hospitalized or institutionalized older adults or those with neurological disorders or dementia. Conference abstracts, reviews, randomized controlled trials, protocols, and studies published in other languages besides English were excluded. Study titles and abstracts were screened based on the inclusion/exclusion criteria, and full texts of relevant studies were screened for eligibility. Data were extracted using a piloted data extraction form, including study and participant characteristics, frailty assessment and classification, and corresponding disability outcomes and measurement. Data extraction was conducted by 1 reviewer (K.F.T.) and checked by the second reviewer (S.W.H.L.), with discrepancies resolved by consensus. ## Quality Assessment The methodological quality of included studies was evaluated using the Joanna Briggs Institute’s Critical Appraisal Checklist for cohort and cross-sectional studies. Two reviewers independently conducted the assessment, with disagreements resolved by consensus. The methodological quality assessment of studies is shown in Supplementary Table 3. All studies were judged to be representative based on sampling methods but may be biased due to recruitment procedures (e.g., recruitment at community health centers). All studies reported covariate adjustments due to confounding such as age and gender. Two studies were assessed to have an inadequate follow-up of cohorts due to attrition [33,35]. The highest risk of bias was present in outcome assessment, given that disability was based on self-reporting by participants during interviews, and there would be a possible risk of recall bias, especially among participants with cognitive impairment. All studies have excluded participants with functional disability at baseline, but 7 studies did not have exclusion criteria for participants with Parkinson’s disease, which is known to increase the risk of functional disability. ## Data Analysis The primary outcomes were ADL, IADL, and mobility disabilities. Other outcomes include the combination of disabilities and physical function limitations. Data were synthesized narratively and summarized using a Harvest plot [29,30] due to the small number of studies, heterogeneous outcome measurement, and cognitive frailty definition. Findings were reported based on fully adjusted statistical measures with a $95\%$ confidence interval (CI). In the reporting of results, participants were categorized into 4 groups defined previously: (a) robust (absence of frailty and cognitive impairment), (b) prefrailty with cognitive impairment, (c) cognitive frailty (physical frailty and cognitive impairment), and (d) combined cognitive frailty (only for studies which grouped both frail and prefrail participants with cognitive impairment). Comparison between groups was made by using the robust group as the reference. ## Study Characteristics A total of 18 184 records were screened, 170 studies were selected for full-text screening, and 11 records were included in this review (Supplementary Figure 1). The 11 studies included 44 798 participants, with mean age ranging from 67.7 to 75.2 years. These include 9 cohort studies with a follow-up duration of between 1 and 11 years (3,18,31–37) and 2 cross-sectional studies(Supplementary Table 1) [38,39]. Nine studies were conducted in high-income countries, and 2 were in upper–middle-income countries. No studies were identified from low- and lower–middle-income countries. ## Definition of Frailty and Cognitive Frailty by Studies Physical frailty was evaluated using the Fried frailty phenotype [1] or its modified version in all studies except for one that used walking and grip strength measurements [39]. Older adults were classified as physically frail in the presence of 3 or more phenotype criteria, prefrail in the presence of 1 or 2 phenotype criteria, and robust in the absence of phenotype criteria. Cognitive function was most commonly assessed using the Mini-Mental State Examination (31,33,36–38). Several studies used other measures, such as the National Center for Geriatrics and Gerontology–Functional Assessment Tool (Supplementary Table 1). The definition of cognition function/impairment varied across studies as classifications were based on cognitive test score distributions or cutoff points set for the population. The prevalence of cognitive frailty reported in 11 studies varied widely, ranging from $1.4\%$ to $39.3\%$ due to differences in cognitive frailty classification. ADL disability was reported in 5 studies [3,18,31,32,37], IADL disability in 4 studies [18,31,37,39], and mobility disability in 3 studies [18,31,37]. Participants were classified as having a disability if they were free from disability at baseline but could not perform at least 1 activity without assistance during follow-up assessment. Other functional disabilities and physical function limitations were reported in 4 studies (33–36) and are summarized in Supplementary Table 2. ## ADL disability Five longitudinal and prospective cohort studies [3,18,31,32, 37] involving 20 778 participants described the relationship between cognitive frailty and ADL disability (Table 1). These studies reported that older adults with cognitive frailty had a higher risk of incident ADL disability than robust older adults (Figure 1). In a national cohort study in the United States [3], participants with cognitive frailty had a higher risk of incident ADL dependency (sub-hazard ratio [sHR]: 2.00; $95\%$ CI: 1.60–2.60) compared with robust participants with normal cognition after 8 years of follow-up. Similar findings were reported in a 4-year longitudinal cohort study in China [18], where cognitively frail participants had a twofold higher risk of developing ADL disability compared to robust participants (odds ratio [OR]: 2.22; $95\%$ CI: 0.97–5.08). Older adults with cognitive frailty were reported to incur a higher burden of ADL disability over 11 years (adjusted rate ratio [aRR]: 20.60; $95\%$ CI: 15.70–26.90) compared to the group without cognitive frailty [37]. ## IADL disability The association between cognitive frailty and IADL disability was reported in 3 longitudinal/prospective cohort studies and 1 cross-sectional study [18,31,37,39] encompassing 20 697 older adults (Table 1). Similar to ADL, older adults with cognitive frailty had a higher incidence and burden of IADL disability (Figure 1). Two longitudinal cohort studies in France and China showed that cognitively frail individuals were at increased risk of IADL disability compared to robust individuals (OR: 3.17; $95\%$ CI: 1.47–6.83 in [31] and OR: 2.80; $95\%$ CI: 1.00–7.87 in [18], respectively). Similarly, older adults with prefrailty and cognitive impairment had a higher IADL disability risk, with this association being significant albeit weaker than cognitive frailty [31]. In a cross-sectional study involving 8 864 Japanese older adults from the National Center for Geriatrics and Gerontology–Study of Geriatric Syndromes cohort [39], cognitive frailty was associated with IADL limitation (OR: 2.63; $95\%$ CI: 1.74–3.97). ## Mobility disability There was very limited evidence of the association between cognitive frailty and mobility disability (Table 1). Nevertheless, the burden of mobility disability was reported to be substantially higher in participants with cognitive frailty than those without (aRR: 3.10; $95\%$ CI: 2.60–3.60) [37]. ## Other Outcomes A longitudinal cohort study in China [36] showed that the odds of incident physical function limitation were increased in individuals with prefrailty and cognitive impairment or prefrailty alone compared with robust individuals after 4 years of follow-up, but this was higher in the prefrailty and cognitive impairment group (OR: 1.78; $95\%$ CI: 1.26–2.51). In 2 studies that combined cognitive frailty and prefrailty and reported disability outcomes using different measures [33,35], the risk of disability was at least 4 or 5 times higher in the combined cognitive frailty group compared with the robust group. The risk of incident disability based on the need for long-term care certification (LTCI) in Japan was higher among participants with cognitive frailty compared with robust participants (hazard ratio [HR]: 3.86; $95\%$ CI: 2.95–5.05) [34]. The mandatory social LTCI system in Japan categorizes older adults according to levels of need based on monthly follow-up for 24 months, and the onset of long-term care/support needs in movement, ADL, IADL, and other functions denotes incident disability. A cross-sectional study in Italy revealed that cognitively frail individuals were more likely to develop ADL–IADL disability than robust individuals, but no differences in the association with disability were observed between prefrailty with cognitive impairment and robust groups [38]. ## Discussion and Implications In this review, we found 11 studies that examined ADL, IADL, or mobility disability among older adults with cognitive frailty. We observed that the measurement of cognitive frailty and prefrailty varied, resulting in the mixed prevalence of cognitive frailty in the community setting. Across all studies, community-dwelling participants with cognitive frailty were reported to have an increased risk of functional disability, particularly in ADL and IADL, compared with robust participants with normal cognition. Findings are in line with previous reviews on cognitive frailty [16,17], where cognitive frailty was predictive for adverse outcomes, including functional disability. However, due to the limited number of cohort studies that reported on the use of similar statistical effects or outcome measurements, evidence remains limited for robust comparisons for functional disability outcomes in cognitive frailty. Our review identified 4 additional studies that reported the association between cognitive frailty and functional disability, but similar limitations hindered a pooled analysis of findings. There were another 4 studies that reported the association between cognitive frailty and functional disability but did not meet the inclusion criteria (age [4], population characteristics [28,40], and outcome combined with mortality [41]). Although there is an agreement that cognitive frailty is associated with an increased risk of ADL or IADL disability in these studies, the use of standardized cognitive frailty or prefrailty definition and measurements would better facilitate comparisons of frailty subtypes and the risk of disability. Previous studies have similarly reported differences in predictive values on adverse outcomes depending on the operationalization of frailty/phenotype criteria or cognitive function assessments [14,16,30,42]. The association between cognitive frailty and functional disability was stronger than physical frailty in 7 of 9 longitudinal cohort studies included in this review. Nevertheless, studies to date do not allow for a comparison that could show the higher importance of assessing cognitive frailty over physical frailty or cognitive impairment alone for this adverse outcome. Previous studies have suggested that the higher risk of adverse outcomes in cognitive frailty is attributed to the cumulative risk of adverse outcomes conferred when physical frailty and cognitive impairment coexisted [16,18]. This led to the suggestion for the inclusion of cognitive function in frailty assessment to improve the identification of adverse outcomes among frail older adults [3,4,16,36]. Despite this, the synergistic interaction of frailty/prefrailty and cognitive impairment on adverse health outcomes is still not well understood [17], necessitating further research in this area. This may be important in evaluating functional disability as an adverse outcome, given that frailty or cognitive impairment alone has been associated with an increased risk of functional disability (15,21,43–45). Among prefrail older adults with cognitive impairment, results indicated an increased odds of developing ADL or IADL disability compared with robust older adults, but associations with the risk of disability were much weaker compared to cognitive frailty in studies that reported on both frailty and prefrailty. The presence of cognitive impairment did not appear to be a significant predictor of disability among those with prefrailty. As only a few studies reported on prefrailty with cognitive impairment, and its association with primary and other outcomes (31–33), we urge caution in the interpretation of review findings for this group. As such, more research is needed to understand the association between prefrailty with cognitive impairment and adverse outcomes, given that early prefrailty or cognitive impairment may be reversible with targeted interventions, thus allowing the prevention of transition to frailty and development of functional disability [9,36]. Only a few included studies reported on mobility disability or physical function limitation among older adults with cognitive frailty. Within the literature, studies have focused on mobility decline or disability as a risk factor for frailty (46–49), which may explain the lack of reporting of mobility disability as an adverse outcome of cognitive frailty. Further research will be needed to ascertain the association between cognitive frailty and mobility disability. The assessments for mobility disability or functional disabilities other than ADL or IADL disability were also varied, which limits comparisons across populations. This study has several limitations. Studies included in the review were heterogeneous due to differences in inclusion and exclusion criteria, participant characteristics, frailty (modified phenotype criteria), and cognitive function assessments. We included observational studies, where a causal relationship between cognitive frailty and outcomes cannot be ascertained. Hospitalized and institutionalized participants are more vulnerable to adverse health outcomes irrespective of frailty status [50] and, thus, were not included in this review, but this might limit the generalizability of the findings. We did not include non-English language articles and, therefore, might have missed related studies. Differences in cognitive frailty prevalence were observed due to differences in the study population and cognitive frailty classifications. Future research should focus on these gaps to estimate cognitive frailty burden and inform health and social care policy, especially for lower–middle- and low-income countries. With an increased risk of functional disability, early prevention of cognitive frailty or its progression through screening of frailty and cognitive impairment is vital. The effort to incorporate this into routine health care of older adults is needed [10], given that screening is often hindered by impracticability and not widely practiced in primary care. Above all, more longitudinal cohort studies and consensus on assessing and classifying cognitive frailty/prefrailty with cognitive impairment [17] would allow better comparisons across populations. The present review found that older adults with cognitive frailty were likely to have an increased risk of developing disabilities than robust older adults. Further research is needed to ascertain the associations between prefrailty with cognitive impairment and the risk of disability. With functional disability and frailty being emerging public health issues, further research on cognitive frailty, especially in rapidly aging populations, is needed to advance our understanding of the burden of this condition, prevention of its adverse outcomes, and maintenance of functional ability among older adults. ## Funding The AGELESS research program was funded by the Ministry of Higher Education Long Term Research Grant Scheme (LRGS/$\frac{1}{2019}$/UM/$\frac{01}{1}$/1, LR005-2019, GOV-000047). The financial sponsor had no role in the study design, analysis and interpretation of the data, writing, or decision to submit the paper for publication. ## Conflict of Interest None declared. ## Author Contributions K.F.T.: conceptualization; methodology; investigation; data curation; formal analysis; validation; visualization; writing—original draft. 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--- title: 'Causes of Granulomatous Inflammation in Native and Allograft Kidneys: Case Series from A Single Center and A Review of the Literature' authors: - Cihan Heybeli - Berna Demir Yuksel - Mehtat Unlu - Mehmet Ası Oktan - Hayri Ustun Arda - Ozcan Uzun - Filiz Yıldırım - Serkan Yıldız - Caner Cavdar - Aykut Sifil - Ali Celik - Sulen Sarıoglu journal: Turkish Journal of Pathology year: 2022 pmcid: PMC9999700 doi: 10.5146/tjpath.2021.01561 license: CC BY 4.0 --- # Causes of Granulomatous Inflammation in Native and Allograft Kidneys: Case Series from A Single Center and A Review of the Literature ## Abstract Objective: Granulomatous interstitial nephritis is a rare finding, and etiology differs by geography. We aimed to investigate the distribution of causes of granuloma/granulomata in the kidney and renal survival of these patients in a tertiary care hospital in Western Turkey. Material and Method: Medical records of adults who underwent a kidney biopsy procedure in our institution between January 2000 and June 2019 were reviewed. Pathology reports were searched for biopsies where a granuloma was identified. Results: Nineteen of 1121 ($1.7\%$) kidney biopsies included granuloma, 17 in native kidneys, and 2 in transplants. The majority of indications for native kidney biopsy was a rise in serum creatinine. Etiologies of granuloma included the following: pauci-immune vasculitis ($$n = 11$$, $64.7\%$), tuberculosis ($$n = 2$$, $11.8\%$), drug-induced ($$n = 2$$, $11.8\%$), tubulointerstitial nephritis/uveitis (TINU) syndrome ($$n = 1$$, $5.9\%$), and systemic-lupus erythematosus ($$n = 1$$, $5.9\%$). Despite treatment, 6 of 11 ($54.5\%$) patients with vasculitis developed end-stage kidney disease (ESKD) during the median follow-up of 16 months. Both of the patients with tuberculosis, and the patient with TINU syndrome developed ESKD months after the kidney biopsy, despite appropriate therapies. The only case with drug-induced granuloma and both cases with allograft kidney granuloma responded well to glucocorticoids, achieving a complete renal recovery. Conclusion: The majority of our series had granuloma in the kidney secondary to vasculitis and renal outcomes appear considerably unfavorable despite treatment, probably related to the primary diagnosis. Multicenter studies are needed to better determine the etiology and outcome of each granuloma etiology at different geographic locations. ## INTRODUCTION Granulomatous interstitial nephritis (GIN) is a histological diagnosis which comprises less than $1\%$ of all kidney biopsies [1]. The primary etiology is wide an includes various systemic inflammatory disorders such as granulomatous polyangitis (GPA), sarcoidosis (2–5), Crohn’s disease [6], Sjögren syndrome [7], malignancies such as chronic lymphocytic lymphoma [8,9], fungal infections [10,11], and mycobacterial infections [12,13]. Numerous drugs may also cause GIN and these include diuretics [14], proton-pump inhibitors [15,16], non-steroidal anti-inflammatory drugs [17,18], zoledronic acid [19], captopril [20], ciprofloxacin [21], vancomycin [22], anti-TNF agents [23,24], tramadol [25], atazanavir [26,27], and immunotherapy with ipilimumab and nivolumab [28]. It is not straightforward to differentiate between these causes using renal histology alone, and the diagnosis is usually made based on the clinical presentation and extrarenal findings [29,30]. The most common cause differs between geographical regions. Drug-induced GIN and renal involvement of sarcoidosis are quite common along with idiopathic GIN in Western countries [31,32], whereas tuberculosis is the most common cause of GIN in endemic areas such as India [12,13]. The number of studies on granuloma/granulomata formation in kidney is quite limited. Despite the endemicity of tuberculosis, there is no data on the prevalence of GIN in Turkey. We therefore aimed to investigate the causes of granuloma formation in the kidney and analyze long-term outcomes of these patients. ## MATERIALS and METHODS Medical records of adults (≥18 years of age) who underwent a kidney biopsy procedure at Dokuz Eylul University Hospital between January 2000 through June 2019 were reviewed. Kidney biopsies of adult patients (≥18 years of age) with tissue sufficient to make the diagnosis were included in the study. Patients who had granuloma/granulomata formation in kidney biopsy specimens were determined. Given the numerous etiological factors for granuloma/granulomata formation, all subjects underwent a detailed evaluation in order to determine the cause. The following data were recorded: demographic details, comorbid diseases, drug exposures, clinical findings at the time of presentation, urinalysis, full blood count, serum biochemistry (creatinine, calcium, albumin, liver function tests), autoantibodies (antinuclear antibody [ANA], anti-neutrophil cytoplasmic antibody [ANCA]), complement C3 and C4, viral serology tests (hepatitis B, hepatitis C, human immunodeficiency virus), ultrasonography of the abdomen, and chest x-ray. The following work-up was carried out patients with no apparent cause for granuloma formation (excluding drug exposures): angiotensin-converting enzyme (ACE) levels, cytomegalovirus and Epstein-Barr virus serologies, acid fast bacilli in the urine, polymerase chain reaction and culture of mycobacterium tuberculosis on bronchoalveolar lavage specimens, and computed tomography of the chest and abdomen. The date of kidney biopsy was recorded as the baseline for laboratory records. Acute kidney injury was defined according to consensus criteria [33]. Rapidly progressive glomerulonephritis was defined as the loss of kidney function within days to weeks along with remarkable findings in urinalysis [34]. Microscopic hematuria was accepted if >3 red blood cells per high power field were seen in urine microscopy [35]. End-stage kidney disease (ESKD) was defined according to consensus criteria [36]. Given that the majority of cases presented with a rise in serum creatinine, the definition of response to therapy was made as follows. Complete response was a return of serum creatinine to <0.35 mg/dL above the baseline value and partial response was a return of serum creatinine to >.0.35 mg/dL but less than twice the baseline value [37]. ## Renal Histopathology Histological data was retrieved from pathology reports. Renal biopsy specimens were evaluated using hematoxylin–eosin, Masson’s trichrome, periodic acid schiff, and methenamine silver stained sections by light microscopy. Immunofluorescent analysis was made after staining for antibodies against immunoglobulins G-A-M, complement components C3 and C1q, and kappa and lambda light-chains for immunofluorescence. Electron microscopic evaluation was not routinely performed. Previous studies used the term GIN if at least 1 granuloma in kidney sections was found [32]. For this study, we have used the term ‘’granuloma/granulomata formation’’ since there is no consensus for the definition of GIN. Given that tuberculosis is endemic in our country, Ziehl-Neelsen staining was performed on kidney sections of patients with a history of pulmonary tuberculosis, on allograft kidney with granulomatous inflammation, and on patients with no identified cause of granuloma formation. ## Statistical Analysis Quantitative variables were expressed as median with the range (minimum-maximum). Qualitative variables were expressed as proportions. Overall renal survival was estimated using the Kaplan-Meier method. Statistical analysis was performed using SPSS 22.0 version (IBM SPSS, Chicago, IL). ## RESULTS Granuloma was identified in 19 of 1121 ($1.7\%$) kidney biopsies performed in our hospital between 2007-2019. The median age at the time of kidney biopsy was 60 (range, 20-84), and 12 ($63.2\%$) were male. Two of the biopsies were allograft kidney biopsies and seventeen were native kidney biopsies. ## Native Kidney Granuloma/Granulomata Of the 17 native kidney granulomata, 11 ($64.7\%$) were male and the median age was 60 (range, 21-84). At baseline, hypertension and diabetes mellitus constituted $52.9\%$ (9 patients) and $29.4\%$ (5 patients) of the cohort. Indications for kidney biopsy included acute kidney injury (AKI) in 12 ($70.6\%$), rapidly progressive glomerulonephritis (RPGN) in 4 ($23.5\%$), and asymptomatic urinary abnormalities in 1 ($5.9\%$) case. The following etiologies were captured after the detailed evaluation: pauci-immune vasculitis ($$n = 11$$, $64.7\%$), tuberculosis ($$n = 2$$, $11.8\%$), drug-induced ($$n = 2$$, $11.8\%$), tubulointerstitial nephritis/uveitis (TINU) syndrome ($$n = 1$$, $5.9\%$), and systemic-lupus erythematosus ($$n = 1$$, $5.9\%$). Detailed description of each native kidney granuloma is given in Table 1. Table 1 \| Age/Gender \| Clinical presentation \| Histology \| Crescent \| Diagnosis \| Extrarenal involvement(s) \| Treatment \| Outcome \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 84/M \| RPGN, hemoptysis, hematuria, SCr:9.1 mg/dl, MPO-ANCA+ \| Necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| Lung \| Corticosteroids, cyclophosphamide \| ESKD \| \| 72/F \| AKI, weight loss, proteinuria, SCr:2.8mg/dl \| Non-necrotizing granulomatous inflammation \| No \| Drug-induced GIN (penicillin) \| \| Corticosteroids \| CR \| \| 74/M \| AKI, weight loss, hematuria, hepatosplenomegaly, SCr:2,3mg/dL. ANCA-neg \| Severe necrotizing granulomatous inflammation. Ziehl-Neelsen (+) \| Yes \| Miliary tuberculosis \| Bone marrow \| Ethambutol-Pyrazinamide-Isoniazid-Rifampin \| ESKD \| \| 69/M \| AKI, weight loss, fever, SCr:2,6 mg/dl, MPO-ANCA+ \| Severe neutrophilic necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| Liver \| Corticosteroids, cyclophosphamide, AZA \| PR \| \| 21/M \| AKI, hair loss, myalgia, uveitis, oral aphthae, proteinuria, SCr:2.65 mg/dl \| Severe non-necrotizing granulomatous inflammation \| Yes \| TINU syndrome \| Eye, skin \| Corticosteroids, cyclophosphamide, RTX, MMF, IVIG \| ESKD \| \| 62/M \| AKI, weight loss, fatigue, SCr:3.74 mg/dL. ANCA-neg \| Severe necrotizing granulomatous inflammation \| Yes \| Drug-induced (non-steroid anti-inflammatory drug) \| \| Corticosteroids, cyclophosphamide \| ESKD \| \| 67/F \| AKI, hemoptysis, SCr:1.69 mg/dl, PR3-ANCA+ \| Necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| Lung \| Corticosteroids, cyclophosphamide, AZA \| CR \| \| 64/M \| RPGN, fever, hematuria, proteinuria, SCr:8.11 mg/dl, PR3-ANCA+ \| Non-necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| Lung \| Corticosteroids, PLEX, cyclophosphamide \| ESKD \| \| 60/M \| AKI, dyspnea, hematuria, SCr:9.87mg/dl, MPO-ANCA+ \| Non-necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| \| Corticosteroids, cyclophosphamide, PLEX, AZA \| ESKD \| \| 59/F \| RPGN, fatigue, fever, hematuria, SCr:6.46mg/dl, MPO-ANCA+ \| Necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| \| Corticosteroids, cyclophosphamide, PLEX, AZA \| ESKD \| \| 80/F \| RPGN, fatigue, hematuria, SCr:4.61mg/dl, MPO-ANCA+ \| Necrotizing granulomatous interstitial inflammation \| Yes \| ANCA-associated GN \| \| Corticosteroids, cyclophosphamide \| ESKD \| \| 49/F \| RPGN, weight loss, fatigue, proteinuria, SCr:4.3 mg/dl, MPO-ANCA+ \| Necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| \| Corticosteroids, cyclophosphamide, PLEX, MMF \| ESKD \| \| 50/F \| AKI, fatigue, weight loss, proteinuria, SCr:2.61mg/dl. ANCA-neg. ANA and Anti-dsDNA positive. \| Severe necrotizing granulomatous inflammation \| Yes \| Systemic lupus erythematosus \| Joints, skin \| Corticosteroids, cyclophosphamide, PLEX \| PR \| \| 52/M \| AKI, arthralgia, hematuria, SCr :1.05 mg/dl, PR3-ANCA+ \| Non-necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| \| Corticosteroids, cyclophosphamide, AZA \| CR \| \| 28/M \| Microscopic hematuria, proteinuria, PR3-ANCA+ \| Necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| \| ACE-inhibitor \| Stable \| \| 50/M \| AKI, proteinuria, SCr :3.32mg/dl \| Necrotizing granulomatous inflammation \| No \| Tuberculosis \| Lung \| Ethambutol-Pyrazinamide-Isoniazid-Rifampin \| ESKD \| \| 60/M \| RPGN, SCr 3.87 mg/dl, PR3-ANCA+ \| Necrotizing granulomatous inflammation \| Yes \| ANCA-associated GN \| Lung \| Corticosteroids, PLEX, cyclophosphamide \| PR \| ## Pauci-Immune Vasculitis The median age of these patients was 60 (range, 28-84), and 7 ($63.6\%$) were male. Clinical presentations were as follows: AKI (6 patients, $54.5\%$), RPGN (4 patients, $30.8\%$), and asymptomatic urinary abnormalities (1 patient, $7.7\%$). The median serum creatinine at baseline was 4.46 (range, 1.69-9.87) mg/dL. Six patients required hemodialysis at the time of diagnosis. Seven of these had positive antibodies against myeloperoxidase (MPO-ANCA), and 4 had positive antibodies against proteinase-3 (PR3-ANCA). All of these patients were pauci-immune based on immunofluorescence microscopy findings. Lung involvement was evident in 5 ($38.5\%$) of patients. Histology was remarkable for severe necrotizing granulomatous inflammation with crescent formation in the majority (Figure 1). All subjects received glucocorticoids while some were also treated with a mixture of cyclophosphamide, plasma exchange, azathioprine, and/or mycophenolate mofetil. Among the 10 patients who presented with AKI or RPGN, 6 had no response, 2 had partial response, and 2 achieved a complete response. The patient who had asymptomatic urinary abnormalities did not receive immunosuppressive therapy and findings in urine persisted following therapy with angiotensin-converting enzyme inhibitor. During the median follow-up of 16 (range, 1-84) months, 6 ($54.5\%$) patients developed ESKD and 2 ($18.2\%$) of them died. Figure 1: Necrotizing granulomatous interstitial inflammation in a case with granulomatous microscopic polyangiitis (H&E, x40). ## Tuberculosis Two patients had tuberculosis of the kidney. Both subjects already had a diagnosis of tuberculosis of the lung by polymerase chain reaction and sputum culture prior to kidney biopsy. The first patient was a 74-year-old-male with long-standing hypertension. The clinical presentation was AKI, with a serum creatinine level of 2.3 mg/dL. Kidney functions deteriorated and hemodialysis was initiated. There was a history of lung tuberculosis and Ziehl-Neelsen staining was positive in the kidney. In addition to severe necrotizing granulomatous inflammation, there was also crescent formation in the histology. A subsequent bone marrow biopsy also showed severe granulomatous inflammation. The patient received a combination of rifampin, isoniazide, ethambutol, and pyrazinamide, but could not come off dialysis. Unfortunately, the patient died 3 months after the kidney biopsy. Indication for kidney biopsy in the second patient, a 50-year-old male, was AKI, with a serum creatinine of 3.32 mg/dL. Nephrotic syndrome was also evident, with 7.9 grams/24 hours of urinary protein excretion. Kidney biopsy showed severe necrotizing granulomatous intersitial nephritis. The same treatment protocol was given for tuberculosis. Unfortunately, the patient developed ESKD within 3 months after the kidney biopsy. Although both cases had a rise in serum creatinine after hospitalization, baseline serum creatinine levels were not known. They were both HIV-negative. Kidney histology revealed moderate to severe interstitial fibrosis/tubular atrophy, suggesting a preceding chronic damage. ## Tubulointerstitial Nephritis/Uveitis (TINU) Syndrome The patient with TINU syndrome was a 21-year-old-male, who was admitted to the hospital for allopecia totalis, red eye, and malaise. Examination of the eye was compatible with anterior uveitis. Serum creatinine was 3.3 mg/dL and proteinuria was subnephrotic. Urine sediment showed pyuria but no microhematuria. Serological work-up was unremarkable. Kidney histology showed severe tubulointerstitial granulomatous inflammation with mild interstitial fibrosis and crescent formation. There was non-necrotizing granulomatous inflammation (Figure 2). Following treatment with cyclophosphamide and glucocorticoids, the alopecia and uveitis completely resolved with a mild improvement in kidney functions. However, kidney functions deteriorated during the following year requiring permanent dialysis, despite therapy with mycophenolate mofetil, rituximab, and intravenous immunoglobulin. Figure 2: Non-necrotizing granulomatous interstitial inflamma-tion in a case with tubulointerstitial nephritis/uveitis (TINU) syndrome (H&E, x40). ## Drug-Induced GIN The first patient with drug-induced GIN, a 72-year-old woman, presented with AKI. Abnormal test results included a serum creatinine of 2.8 mg/dl with mild (<1 g/day) proteinuria and pyuria, but no microhematuria. Penicillin was given for upper respiratory tract infection a week before the AKI event. Kidney biopsy showed granulomatous inflammation and mild interstitial fibrosis and tubular atrophy (IFTA). Work-up for possible other etiologies such as tuberculosis, sarcoidosis, and inflammatory rheumatic diseases were unremarkable. Glucocorticoids provided a complete remission with no further relapse during the following 63 months of follow-up. The second patient was a 62-year-old male with long-standing hypertension and diabetes. The presentation was AKI, with a serum creatinine of 3.74 mg/dL, which climbed from a baseline of 1.5 mg/dL. Urine sediment was active and the kidney biopsy showed severe necrotizing granulomatous interstitial nephritis with multiple multinucleated giant cells, and crescent formation. A detailed work-up including ANA and ANCA tests, serum calcium, hepatitis serology, ACE levels, and chest CT were unremarkable. The patient stated that he received daily non-steroid anti-inflammatory drugs within the last 2 weeks for headache. Due to crescents in histology, the patient received a combination of corticosteroids and cyclophosphamide. Unfortunately, he developed ESKD with no response to therapy during the follow-up of 6 months. ## Systemic Lupus Erythematosus This was a 50-year-old woman with a history of chronic polyarthritis in small joints of the hand. Her presentation was with AKI, with a serum creatinine of 2.61 mg/dL. Baseline serum creatinine was told to be normal. There was approximately 3 g/24 hours of urinary protein excretion, but no hematuria. A kidney biopsy showed severe necrotizing granulomatous interstitial nephritis with crescent formation. Immunofluorescence was negative for immunoglobulins and complement. ANA and Anti-double stranded DNA (Anti-dsDNA) results were positive. Induction immunosuppression included corticosteroid and cyclophosphamide. The patient had partial response and she was maintained with azathioprine. After partial response, kidney functions remained stable with no further relapse during the follow-up of 52 months. Among all patients with native kidney granuloma formation, the median follow-up was 24 months. Ten ($58.8\%$) patients developed ESKD, and 4 ($23.4\%$) died. Overall, the median estimated renal survival of 17 patients with granuloma/granulomata in the native kidney was 12 months. ## Granuloma in the Allograft Kidney The first patient with granuloma in the allograft kidney, a 49-year-old female, had allograft dysfunction 7 years after the kidney transplantation. The primary etiology of ESKD was chronic pyelonephritis. Serum creatinine during allograft biopsy was 1.1 mg/dL, which increased from a baseline of 0.7 mg/dL. Urine tests showed a subnephrotic proteinuria and microhematuria. Biopsy showed granulomatous inflammation with crescents. There was no lung or upper respiratory-tract involvement. Detailed work-up including ANCA tests and other autoantibodies were all negative. Following glucocorticoid therapy (1 mg/kg), she achieved a complete remission with the serum creatinine returning to baseline levels around 0.7 mg/dl. Renal function remained stable for the following 68 months. The second patient with granuloma in the allograft kidney was a 20-year-old male. He presented with a rising serum creatinine, from 1.2 mg/dL to 2.67 mg/dL. This was 11 years after the kidney transplantation and the primary etiology of the ESKD was chronic glomerulonephritis. Native kidney biopsy was compatible with immune-complex glomerulonephritis (including C1q positivity on immunofluorescence microscopy) but no granuloma, ANA and ANCA tests were negative. After a detailed evaluation, the cause of granuloma formation could not be found, and the etiology was deemed to be idiopathic. With the introduction of 1 mg/kg of glucocorticoids and continuation of mycophenolate mofetil with calcineurin-inhibitor, he achieved a complete response with a serum creatinine returning close to the baseline levels of 1.4 mg/dL. ## DISCUSSION With this case series, we have observed that the ANCA-associated vasculitis was the most common cause of granuloma formation in the kidney. The number of patients with tuberculosis of the kidney is probably overlooked, since kidney biopsy is rarely performed in patients with tuberculosis of the lung. There were only a few patients who had acute onset disease with GIN, while the majority had more chronic onset diseases, such as chronic rheumatic conditions, vasculitis, and infections. Previous reports included several cases with sarcoidosis of the kidney [38]; yet we have not observed any, despite detailed diagnostic tests such as angiotensin-converting enzyme levels, computed tomography, and bronchoscopy were performed. Excluding 2 cases of drug-induced GIN and 2 allograft biopsies, more than half of our patients developed ESKD. This is probably be due to the high frequency of glomerulonephritis with crescents in our cohort and low number of cases with drug-induced GIN and absence of sarcoidosis, rather than granuloma formation itself. Thus, diseases causing acute granulomatous inflammation in the kidney, which may respond better to treatment were less frequently observed in our cohort. Indeed, Zajjari et al. stated that the outcome was good in patients with drug-induced GIN or sarcoidosis [39]. In contrast to the outcomes of our patients, Joss et al. reported quite acceptable renal response to therapies [32]. However, the authors excluded cases with crescents from their study, as these were accepted as secondary GIN. We have not excluded these subjects, since GIN frequently occurs secondary to a systemic disease such as autoimmune disorders or particular infections. Crescent formation was also evident in one of our patients with tuberculosis and the one with TINU syndrome, which would be another argument to support the inclusion of patients with crescents. Crescent formation with granulomatous inflammation is characteristic of granulomatous polianjitis (GPA), yet granulomatous inflammation is frequently seen in lung biopsies rather than kidney specimens [40]. It is not known whether renal survival is worse among subjects with GPA who have granulomatous inflammation in the kidney versus GPA without renal granulomatosis. This issue requires further study. In a multicenter study, kidney biopsies of patients with pauci-immune crescentic glomerulonephritides were classified according to the extent of the lesions in the Bowman space, and the authors used the term ‘’full moon’’ for those who had circumferential crescents [41]. The main message of the paper was that patients with full moon crescents had more unfavorable renal survival. Interestingly, granuloma formation was more common in patients with full moon crescents. Among the pauci-immune glomerulonephritides, GPA and eosinophilic GPA are known to cause granulomatous inflammation. Although ANCA against proteinase (c-ANCA) is usually the positive antibody found in GPA, p-ANCA positivity was more common in this cohort. Another typical chronic disease that may cause granulomatous inflammation in the kidney is tuberculosis. Renal tuberculosis is easily overlooked, and the diagnosis sometimes made post-mortem [42]. The unfavorable outcomes of our 2 cases with renal tuberculosis may be due to the delayed diagnosis. Moreover, despite a high index of clinical suspicion, the diagnosis of GIN secondary to tuberculosis may be difficult and require PCR-based techniques [43]. Microorganisms may not be detected in histological examination of the kidney. Ziehl-Neelsen staining helped only in 1 of 9 cases in the study by Agrawal and co-workers [43]. The Ziehl-Neelsen stain result was positive in 1 of 2 our cases with tuberculosis. Some authors recommend combining the auramine O stain in order to increase sensitivity and specificity for the detection of tuberculosis [44]. Timely diagnosis and early treatment of tuberculosis is associated with more favorable outcomes [42]. GIN due to tuberculosis is even more common among subjects infected with HIV, and is associated with increased mortality in that case [45]. None of our cases were HIV-positive. Similar to our paper, tuberculosis was not the predominant etiology in some of the previous reports from endemic locations [46]. One chronic systemic inflammatory disease causing GIN is the TINU syndrome. The presentation as GIN is quite rare for this syndrome [30]. The presence of a crescent in our patient with TINU is also unusual. Another unexpected thing in our case is the unfavorable outcome, despite the prescription of a mixture of immunosuppressive drugs including glucocorticoids, cyclophosphamide, and rituximab. The patient uneventfully developed ESKD and was maintained on hemodialysis. Our case is the exception rather than the rule, and more studies are needed to delineate the prognostic impact of TINU syndrome on kidney outcomes. Similar to native kidney GIN, the etiological factors are numerous in allograft kidney GIN, including several acute and chronic disorders. Infections are the most common cause of GIN in allograft kidneys [47]. For transplant patients, it is important to determine if the etiology of ESKD recurs after kidney transplantation. Data for transplant kidney GIN are more lacking and come from case reports (Table 2). It is not clear if GIN recurs after kidney transplantation. However, recurrences of sarcoidosis [48], idiopathic GIN [49], TINU [50,51], and crescentic GN [47] were reported. The potential to recur probably depends on the etiology but the majority of case reports and the data from our study indicate a favorable survival in most patients, although some developed graft loss. Concurrent rejection episodes may occur and contribute to graft failure in some patients with GIN [52]. Table 2 \| Study \| n \| Time after KTx (mean) \| Etiology \| Recurrence \| Outcome \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Alsaad et al.(53) \| 1 \| 12 years \| Adenovirus \| No \| Recovered \| \| al Sulaiman et al. (54) \| 2 \| ~2 years \| Tbc \| No \| Graft loss \| \| Asim et al.(55) \| 1 \| 31 days \| Adenovirus \| No \| Recovered \| \| Aouizerate et al. (56) \| 5 \| Median 12 months \| Sarcoidosis \| Yes \| 1 death, 1 stable, 1 improved \| \| Baden et al.(57) \| 1 \| 9 years \| Coccidioidomycosis \| No \| Dead \| \| Bagnasco et al. (58) \| 1 \| 12 days \| Candida \| Donor-transmitted \| Recovered \| \| Barraclough et al. (59) \| 1 \| 14 days \| Adenovirus \| No \| Improvement \| \| Bijol et al. (31) \| 3 \| 3 weeks \| Bactrim (1), Unknown (2) \| \| \| \| Brown et al.(60) \| 1 \| 1 year \| Sarcoidosis \| Yes \| Recovered \| \| Farris et al.(47) \| 22 \| Mean 552 (range, 10-5898) days \| Viral (5), bacterial (5), drugs (5), Idiopathic (4), fungal (2), GPA (1) \| 1 (GPA) \| 22.2% graft loss due to infections, others improved/recovered \| \| Gaspert et al.(61) \| 1 \| 2 months \| Adenovirus \| No \| Improvement \| \| Gonçalves et al. (62) \| 3 \| \| Tbc \| \| \| \| Hatlen et al.(63) \| 1 \| 6 weeks \| Adenovirus \| Donor-transmitted \| Recovered \| \| Hotta et al.(64) \| 3 \| 6 (3-15) months \| Idiopathic (2), Drug (1) \| No \| All recovered \| \| Josephson et al. (65) \| 2 \| 24 months \| Drug \| No \| Graft-loss due to rejection \| \| Khaira et al.(52) \| 3 \| Range, 3-13 years \| Tbc \| ?* \| Graft loss (1), stable/improved (2) \| \| Kukura et al.(66) \| 1 \| 3 years \| Sarcoidosis \| Yes \| Stable \| \| Lachiewicz et al. (67) \| 1 \| 20 months \| Adenovirus \| No \| No improvement \| \| Lapasia et al.(68) \| 3 \| Median 4 (range, 1-6) weeks \| Infection (1), Drug (1), Idiopathic (1) \| No \| Improved (Infection, Drug) Graft loss (Idiopathic) \| \| Lorimer et al.(69) \| 4 \| Range, 12-26 months \| Tbc \| No \| Graft loss (2) Improved (2) \| \| Meehan et al.(70) \| 3 \| Median 7 (range, 1-24) months \| Tbc (1), Candida (1) E. coli /antibiotics (1) \| No \| 1 death (Candida), 1 stable (Tbc), 1 transplant nephrectomy (E.coli) \| \| Ozdemir et al. (71) \| 3 \| 7.3±4.6 months \| Tbc (2), Candida albicans (1) \| No \| Graft loss \| \| Parasuraman et al. (72) \| 1 \| 24 months \| Adenovirus \| No \| Recovered \| \| Park et al. (73) \| 1 \| 10 months \| Adenovirus \| No \| Improved \| \| Shen et al.(74) \| 1 \| 6 years \| Sarcoidosis \| Yes \| Recovered \| \| Storsley and Gibson (75) \| 1 \| 6 weeks \| Adenovirus \| No \| Improved \| \| Sujeet et al.(76) \| 1 \| 3 years \| Adenovirus \| No \| Recovered \| \| Teranishi et al. (49) \| 1 \| 8 months \| Idiopathic \| Yes \| Recovered \| \| Tse et al. (77) \| 1 \| 9 months \| Rhodococcus \| No \| Stabled \| \| Varma et al.(78) \| 1 \| 14 days \| Adenovirus \| No \| Recovered \| \| Vargas et al.(48) (pediatric) \| 1 \| 2 years \| Sarcoidosis \| Yes \| Died of disseminated histoplasmosis \| \| Veer et al. (79) \| 1 \| 1 year \| Adenovirus \| No \| Recovered \| \| Zhang et al.(80) \| 3 \| 6 (range, 4-12) months \| BKV-associated GIN (3) \| No \| Recovered \| Our study contains a number of limitations. This was a descriptive study with a small sample size, and etiologies of renal granuloma were determined retrospectively. In the majority of our cases, GIN was probably caused by chronic kidney damage, and it may not be reasonable to compare outcomes of acute causes of GIN versus those with subacute/chronic damage. Despite these significant limitations however, there are only a few studies on GIN and our data shows the considerably unfavorable renal survival of patients, unlike previous case series (Table 3). This is particularly the case for patients with chronic causes GIN, such as GPA. All of our cases had granuloma/granulomata formation but it is not clear if all of them should be regarded as GIN. There is no consensus for the definition of GIN, and this issue should be studied. Effects of granuloma formation on kidney outcomes may not be the same for each etiology, and it may not be straightforward to use a common definition for all. Apparently, acute and chronic causes of GIN should be separately evaluated. Table 3 \| Study \| n \| Era \| Male gender \| Age, mean \| SCr, mean \| Etiology \| Renal recovery \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Agrawal et al. (43) \| 17 \| 2004-2014 \| 64.7% \| 35 \| 6 ±2.3 (tbc) 4.8±1.5 (sarcoid) 2.2±1.3 (idiopathic) \| Tbc (52.9%), Idiopathic (23.5%), Sarcoidosis (17.6%), Fungal (5.9%) \| The majority responded to therapy. Two dialysis-dependent (Tbc and sarcoidosis), 1 mortality (tbc) \| \| Bijol et al.(31) \| 35 (0.5% of all biopsies) \| 1987-2004 \| 50% \| 52 (range, 21-84) \| 4.1 (range, 1.5-12.8) \| Sarcoidosis (28.9%), Drug (44.7%) GPA (15.9%) \| \| \| Gupta et al. (12) \| 16 (1.08% of all biopsies) \| 2009-2013 \| 62.5% \| 34 (range, 12-68) \| 6.25 ± 3.53 \| Tbc (56.3%), Cresc. GN (12.5%) Idiopathic (12.5%), Drugs (12.5%) Infection (6.2%) \| The majority responded to therapy. Two dialysis-dependent (Tbc), 1 underwent transplantation (Tbc) \| \| Javaud et al. (81) \| 40 (1.37% of all biopsies) \| 1991-2004 \| 62.5% \| 53 \| median GFR 26 mL/min (range, 5-80) \| Sarcoidosis (50%), Drug (17.5%) Tbc (7.5%), GPA (5%), Leprosy (2.5%), M. avium (2.5%), Crohn (2.5%) \| 3 dialysis-dependent (GPA, Crohn, drug-induced), 1 transplant (sarcoidosis), 2 mortality (drug-induced GIN, Tbc) \| \| Joss et al.(32) \| 18 \| 1990-2004 \| 61% \| 55 \| 4.21 (1.15 to 15.41) \| Idiopathic (50%), Sarcoidosis (28%), Drug (11%), TINU (11%) \| None required long-term renal replacement therapy \| \| Karmakar et al. (46) \| 6 \| \| 33.3% \| range, 14-65 \| range, 0.9 - 7.13 \| SLE (33.3%), Cresc. GN (33.3%) Idiopathic (33.3%) \| \| \| Mignon et al. (82) \| 32 (0.9% of all biopsies) \| \| \| 20-76 \| \| Drug (31.2%), GPA (25%), Idiopathic (25%), Sarcoidosis (9.3%), Tbc (9.3%) \| Most recovered or stable, 5 died, 1 required long-term dialysis. \| \| Naidu et al. (83) \| 14 (0.5% of all biopsies) \| 2000 to 2012 \| 57.1% \| 35 (range, 20-70) \| 6.7±3.8 (2.3-14.7) \| Tbc (64.3%), Drug (14.4%), SLE (7.1%), GPA (7.1%), IgAN (7.1%) \| 5 dialysis, 1 transplant, 8 recovered/improved \| \| Oliveira et al. (29) \| 21 \| 2000-2012 \| 57% \| 53 (range, 19-73) \| GFR, range, 11-113 ml/min \| Sarcoidosis (62%), Tbc (24%), Idiopathic (10%), Drugs (5%) \| 1 death (idiopathic), 1 dialysis (tbc) \| \| Viero and Cavallo (84) \| 12 (5.9% of all biopsies) \| 1974-1994 \| 33.3% \| 46 (range, 24-78) \| 5.1 (range, 1.9-8.7) \| Drugs (25%), Sarcoidosis (25%), Infections (25%), Oxalosis (8%), GPA (8%), Idiopathic (8%) \| The majority were lost to follow-up. One mortality (infection). Three developed chronic renal failure. \| \| Zajjari et al. (39) \| 11 (2.7% of all biopsies) \| \| 36.4% \| 44.2 \| 3.91 ± 2.07 \| Sarcoidosis (45.4%), Drugs (27.2%) \| Patients with drug-induced GIN and sarcoidosis recovered, but no renal recovery in other etiologies \| In conclusion, ANCA-associated vasculitis appeared to be the most common cause of granuloma formation in the kidney in our study. Renal survival is significantly shortened and multicenter studies are needed in order to delineate the nature of different etiologies of granuloma formation in the kidney, and determine the best treatment option for each category. ## Conflict of Interest The authors declare no conflict of interest. ## Ethics approval This study was approved by the Ethics Committee of Dokuz Eylül University School of Medicine (IRB code: $\frac{2019}{17}$-230307). ## Informed consent Informed consent was waived due to the retrospective design, confidentiality of patient identity, and absence of any invasive procedures. ## References 1. 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--- title: 'A cost-effectiveness analysis of risk-based intervention for prevention of cardiovascular diseases in IraPEN program: A modeling study' authors: - Amirparviz Jamshidi - Rajabali Daroudi - Eline Aas - Davood Khalili journal: Frontiers in Public Health year: 2023 pmcid: PMC9999709 doi: 10.3389/fpubh.2023.1075277 license: CC BY 4.0 --- # A cost-effectiveness analysis of risk-based intervention for prevention of cardiovascular diseases in IraPEN program: A modeling study ## Abstract ### Background IraPEN, a program developed in Iran based on the World Health Organization (WHO) package of essential noncommunicable (PEN) disease interventions for primary healthcare, was launched in 2015. Preventive interventions for cardiovascular diseases (CVDs) are based on the level of risk calculated using the WHO CVD risk chart. ### Objective The main objective of this study was to measure the potential cost-effectiveness (CE) of IraPEN preventive actions for CVD in comparison with the status quo. ### Methods A CE analysis from a healthcare perspective was conducted. Markov models were employed for individuals with and without diabetes separately. Based on the WHO CVD risk chart, four index cohorts were constructed as low (<$10\%$), moderate ($10\%$−$19\%$), high ($20\%$−$29\%$), and very high risk (≥$30\%$). Life years (LY) gained and quality-adjusted life years (QALY) were used as the outcome measures. ### Results The intervention yields an incremental cost-effectiveness ratio (ICER) of $804, $551, and –$44 per QALY for moderate, high, and very high CVD risk in groups without diabetes, respectively. These groups gained 0.69, 0.96, and 1.45 LY, respectively, from the intervention. The results demonstrated an ICER of $711, $630, –$42, and –$71 for low, moderate, high, and very high-risk groups with diabetes, respectively, while they gained 0.46, 1.2, 2.04, and 2.29 years from the intervention. ### Conclusion The IraPEN program was highly cost-effective for all CVD risk groups in the individuals without diabetes except the low-risk group. The intervention was cost-effective for all patients with diabetes regardless of their CVD risk. The results demonstrated that the IraPEN program can likely provide substantial health benefits to Iranian individuals and cost savings to the national healthcare provider. ## Background Cardiovascular diseases (CVDs) are the leading cause of death worldwide and people die from CVDs more than any other causes. CVDs are considered a development issue as almost $75\%$ of global CVD deaths occur in low- and middle-income countries. However, the majority of CVDs can be prevented by reducing the burden of risk factors [1]. Iran with an 80 million population in 2016, as an upper-middle-income developing country, has acquired many achievements in the public health sector during the past three decades. A well-balanced referral system within a broad PHC network, even in far stretches of villages, could provide access to healthcare for $95\%$ of the community and control communicable diseases efficiently. This accomplishment resulted in a life expectancy of more than 75 years for men and more than 77 years for women. At present, the transition to chronic and noncommunicable diseases (NCDs) including CVDs, cancer, and mental disorders is the main problem in the health system. Based on the last report by the World Health Organization (WHO), NCDs are estimated to account for almost $80\%$ of total deaths in Iran, while almost half of them ($43\%$) are caused by CVDs. The comparison of Iranians' CVD mortality rate with other countries shows that not only it is substantially higher than high-income countries but also it is much higher than the countries in the region [2]. During the last two decades, many positive actions (e.g., public education, opportunistic finding of diabetic and hypertensive cases in network system, etc.) have been carried out to control NCDs in the country. Despite major achievements, NCDs and their subsequent burden have increased in the country [3]. In 2010, the WHO launched the package of essential noncommunicable (PEN) disease intervention for primary care in low-resource settings to deliver an adequate quality of care and, consequently, reduce the burden of these diseases in developing countries. WHO PEN has effective tools to facilitate early diagnosis and management of CVD, chronic respiratory diseases, diabetes, and cancer to prevent their upcoming morbidities and premature mortalities (e.g., stroke, myocardial infarction, renal failure, blindness, amputations, etc.) [ 4]. In 2015, IraPEN, an adaptation of WHO PEN, was launched as a part of the national Healthcare Reform Plan in Iran. Providing universal healthcare coverage and access to NCD prevention and treatment for all were the main goals of this reform. The first phase of the IraPEN program had been piloted in four cities, and the results were promising. In 2018, the program was expanded to all provinces of Iran. It was expected that at this phase, the program would cover up to four million people, and then based on the results, it would be expanded nationwide [4]. Due to the IraPEN project size and its impact on the national healthcare budget, it is essential to make a detailed evaluation of these pilot enforcements to pave the way for IraPEN national implementation. Therefore, there is a need for an economic analysis that can estimate costs and effects as well as an incremental cost-effectiveness ratio (ICER) of the IraPEN program in comparison with the status quo (no prevention). Therefore, the main objective of this analysis is to measure the cost per life-year (LY) gained as well as the cost per quality-adjusted life years (QALY) gained by the IraPEN program. ## Methods This study evaluates the potential cost-effectiveness (CE) of the IraPEN program in comparison to the status quo through a health economic evaluation and the outcomes expressed in terms of QALY and LY gained for each CVD risk group. The target group of this analysis is all Iranian people aged older than 40 years and the evaluated intervention is the same as the recommended intervention of WHO PEN which is included screening, monitoring, and medications. ## Model structure Two separate Markov decision models were developed to compare the long-term costs and health benefits of the IraPEN program (primary CVD prevention) with the status quo (no prevention) in two distinct scenarios. In the base case scenario, individuals without diabetes were included, while patients with diabetes were included in the alternative scenario. Each Markov model has four health states with transitions between the states according to age, sex, and the CVD risk characteristics of participants (Figure 1). In contrast to the usual Markov models, which are structured based on cohorts with average profiles, we decided to categorize the individuals based on their CVD risks. As the intervention (treatment) varied according to CVD risk level, it is logical to model them separately. In this way, we can take into account their specific characteristics. Therefore, based on WHO/ISH CVD risk prediction charts for EMR B, four index cohorts were constructed [5]. These hypothetical cohorts were used as a representative for individuals with low, moderate, high, and very high CVD risk profiles. The CVD risk state represents the starting point for all people who are 40 years old. It was assumed that people in this state may either remain in the same health state, move to the stroke state, or CHD (coronary heart disease) state, or die. As long as they are event-free, these individuals can stay in a healthy state, but after the first event, they move to the CHD or stroke state and stay there until their death. Figure 1: The structure of the Markov model used for the IraPEN analysis. CVD risk index cohort state, healthy individuals with different CVD risk; Stroke state, alive individuals after the first stroke event; CHD state, alive individuals after the first CHD event; Death state, dead individuals. In WHO/ISH CVD risk prediction charts, the CVD risk is calculated based on individuals' age and risk factors such as blood pressure, lipid profile, diabetes, and smoking status and categorized into the following five groups: below $10\%$ (low-risk group), between 10 and $19\%$ (moderate-risk group), between 20 and $29\%$ (high-risk group), between 30 and $39\%$, and above $40\%$ (very high-risk group). As the individuals in the two latter groups are treated the same, in the IraPEN program, whoever has a CVD risk above $30\%$ is categorized as the very high-risk group. Therefore, considering what was mentioned earlier, all the Iranians aged older than 40 years who did not have CHD or stroke events before were eligible for this program. According to the recent census [2016], $31.16\%$ of Iranians were older than 40 years [6]. By adding individuals aged older than 30 years with the aforementioned risk factors, we can conclude that this program is going to screen at least 25 million people yearly. The healthcare perspective and a 40-year time horizon were adopted for this analysis. As the analysis is a comparison between IraPEN (intervention) and status quo (no intervention) which both have the same Markov structure and transition probabilities, it is not expected that half cycle correction (HCC) approach makes any difference in ICER results; therefore, HCC was not applied to this analysis [7]. The hypothetical cohorts were used as a representative for individuals with low, moderate, high, and very high CVD risk profiles (Table 1). Progressively, a proportion of the cohort can go to the CHD state, who are the survivors of the first CHD event, or to the stroke state who are the survivors of the first stroke event. Those CHD and stroke events that were fatal moved to the death state. In general, the people in these two states are at a higher risk of dying from CHD or stroke, but they may die from any other causes like the normal population. Table 2 summarizes the assumptions of this analysis. ## Data input This analysis tried to use the Iranian data wherever available. In case of a lack of local data, the inputs were derived from the global literature. Therefore, all transition probabilities of the models were extracted from available Iranian data, while medications' effects and states' utilities were driven from the literature of Western countries (refer to Supplementary material). No individual data have been used for this analysis. ## Transition probabilities The annual incidence rate for CHD [8] and stroke was calculated from the Framingham study equations [9]. As four-index cohorts had been defined with specific characteristics, there was a need to calculate the risk based on those profiles. Based on the literature, one out of four CHD events are fatal in the first year [10], while $60\%$ of them are pre-hospital deaths [11]. Therefore, it was assumed that of those who have a CHD event in the model, $25\%$ die in the first year. Approximately $60\%$ of these deaths were costless as they are pre-hospital deaths. Regarding first-year stroke mortality, the range varies in different resources and is reported from 22 to $34\%$ [12]. For this analysis, the rate was applied from the largest available cohort [13]. Almost $25\%$ of stroke events are fatal in the first year, while half of them occur during the first 28 days. Therefore, it is assumed that although at the end of the cycle, they move to the death state, $40\%$ of the cycle cost should be considered for them. The fatality rate for stroke and CHD survivors was derived from a study that had been done on the Iranian population [14]. The background mortality rate from all causes other than stroke and CHD is calculated by excluding the total death attributed to these two diseases from the Iranian life table1. The total mortality rate of these two events had been calculated in Tehran Lipid and Glucose Study (TLGS). At first, the annual rates were derived from the life table and then the CHD- and stroke-attributed deaths were excluded. ## Intervention effect The IraPEN's preventive actions are expected to reduce cardiovascular events. The relative risks (RRs) of these preventive actions and the medications that are used in the program were obtained from meta-analyses or randomized clinical trials (RCTs). By multiplying or adding up the RRs of different medications, there is a risk of effect overestimation, and a correction was made by using the formula below wherever multiple interventions were involved: This equation has been developed based on a study that compared the effect of controlling the risk factors separately vs. controlling all of them simultaneously [15]. Based on the field interviews, it was clear which medications are used for each index cohort. Almost in all cases, angiotensin-converting enzyme (ACE) inhibitors are the first choice for hypertension treatment. Enalapril is the most prescribed one as monotherapy. Thiazides (diuretics) are the second choice followed by beta-blockers. In case the hypertension is not controlled by monotherapy instead of increasing the dose, the second drug is added. As recommended by guidelines, small doses of various classes of antihypertensive medications are more useful than a high dose of one [16]. In general, the combination of ACE inhibitors and thiazide is the most common one. This pattern is aligned with Joint National Committee (JNC8) guidelines. Statins are prescribed for hyperlipidemia treatment. Among statins, Atorvastatin is the choice as it is one of the most potent ones. For diabetes, Metformin is started and increased to the maximum dose (2 g) and then the second medication that is Glibenclamide is added. Due to its potential harm and insufficient evidence of its efficacy, Aspirin was not recommended for primary prevention by PEN protocols. Therefore, Aspirin is not used in IraPEN as well. Here are the list of medications and their daily dosages which are used in IraPEN: The unit price of each of these medications was derived from the Iranian Annual Pharma Statistics file. For the calculation of the intervention's effects, it is assumed that the adherence of individuals to the treatment is $100\%$. Table 3 lists the RRs of different interventions (medications) for CHD and stroke. Table 3 \| Unnamed: 0 \| Unnamed: 1 \| RR \| (95% CI) \| \| --- \| --- \| --- \| --- \| \| Angiotensin-converting enzyme inhibitor (17) \| Angiotensin-converting enzyme inhibitor (17) \| Angiotensin-converting enzyme inhibitor (17) \| Angiotensin-converting enzyme inhibitor (17) \| \| RR \| CHD \| 0.81 \| (0.70–0.94) \| \| \| Stroke \| 0.65 \| (0.52–0.82) \| \| Thiazide diuretics (18) \| Thiazide diuretics (18) \| Thiazide diuretics (18) \| Thiazide diuretics (18) \| \| RR \| CHD \| 0.84 \| (0.75–0.95) \| \| \| Stroke \| 0.63 \| (0.57–0.71) \| \| Beta-blockers (19) \| Beta-blockers (19) \| Beta-blockers (19) \| Beta-blockers (19) \| \| RR \| CHD \| 0.90 \| (0.78–1.03) \| \| \| Stroke \| 0.83 \| (0.72–0.97) \| \| Statin (20) \| Statin (20) \| Statin (20) \| Statin (20) \| \| RR \| CHD \| 0.86 \| (0.82–0.90) \| \| \| Stroke \| 0.90 \| (0.85–0.95) \| \| Metformin (21) \| Metformin (21) \| Metformin (21) \| Metformin (21) \| \| RR \| CHD \| 0.67 \| (0.51–0.89) \| \| \| Stroke \| 0.80 \| (0.50–1.27) \| \| Sulfonylureas \| Sulfonylureas \| Sulfonylureas \| Sulfonylureas \| \| RR \| CHD \| 0.85 \| (0.74–0.97) \| \| \| Stroke \| 0.91 \| (0.73–1.13) \| \| Lifestyle counseling (22) \| Lifestyle counseling (22) \| Lifestyle counseling (22) \| Lifestyle counseling (22) \| \| RR \| CHD \| 0.86 \| (0.81–0.91) \| \| \| Stroke \| 0.86 \| (0.81–0.91) \| ## Costs A healthcare perspective was adopted; therefore, we only included costs associated with healthcare such as direct medical costs (Table 4). The costs considered in the model are the cost of IraPEN screening, the cost of IraPEN monitoring, the cost of CHD survivors, and the cost of stroke survivors. It is assumed that the cost of individuals who are event-free in the status quo is zero as long as undiagnosed or untreated. These two facts were considered for the status quo costs. Furthermore, it was assumed that the cost of dying was equal to zero. According to PEN protocols, the needed resources for each index cohort were identified. Then, the items were quantified based on discussions with the physicians and supervisors of the visited centers. The cost of index cohorts consists of two different types. First, variable costs are different for each group based on the characteristics of each. Second, fixed cost is the same for all and consists of staff training, administration, IT, promotional stuff, and leaflets. The unit price of each item was derived from the last report of the Ministry of health [23]. The report estimated all the costs related to IraPEN implementation except the medications. In addition, the reported costs were adjusted by the 2018 inflation rate and the cost of each cohort was calculated. Table 4 \| Unnamed: 0 \| Low-risk \| Moderate-risk \| High-risk \| Very high-risk \| \| --- \| --- \| --- \| --- \| --- \| \| Behvarz's visit (Screening) \| 108173 \| 324519 \| 432692 \| 432692 \| \| Physician visit \| – \| 216346 \| 288462 \| 432692 \| \| Lab data (included in screening) \| \| – \| – \| – \| \| Nutrition consultation \| 38400 \| 38400 \| 38400 \| 38400 \| \| Psychiatrist consultation \| – \| – \| 38400 \| 38400 \| \| Anti-hypertensive medication \| – \| One agent \| Two agents \| Three agents \| \| Statin Therapy \| – \| + \| + \| + \| \| Fixed costs \| 19231 \| 19231 \| 19231 \| 19231 \| \| Cost of each group in 2017 (IRR) \| 165804 \| 598496 \| 817185 \| 961416 \| \| The inflation rate was applied to the costs (IRR) \| 216540 \| 781636 \| 1067243 \| 1255609 \| \| Cost of each index cohort without medication \| $5.16 \| $18.61 \| $25.41 \| $29.90 \| \| Cost of medication of each index cohort \| $16 \| $38.85 \| $48.27 \| $55.39 \| \| Cost of each index cohort for the model \| $21.63 \| $57.46 \| $73.68 \| $85.29 \| The cost of CHD state and stroke state was derived from an Iranian CE that had estimated the cost of these two states [24]. These two costs contain all the related medical costs such as hospital admissions and procedures, monitoring, follow-ups, medications, and secondary prevention (Table 5). Based on experts' opinions, it is assumed that the cost of CHD after the first year would be a third and the cost of stroke state after the first year would be a quarter. In addition, it is assumed that the standard error of costs for the consecutive year is $10\%$ of the mean. Table 5 \| Unnamed: 0 \| Mean \| SE \| \| --- \| --- \| --- \| \| CHD cost for the first year \| $519 \| $51 \| \| CHD cost for consecutive years \| $173 \| $17 \| \| Stroke cost for the first year \| $5,691 \| $569 \| \| Stroke cost for consecutive years \| $1,422 \| $142 \| ## Utilities As all people who enter the model are healthy individuals, the utility for the first health state is considered 1. For the death health state utility, it was adopted the standard approach by setting the utility to zero. CHD and stroke state utilities were derived from the published literature. In the models after the first event, patients move to these states and stay there until they die. Although they remain in the same states (CHD or stroke), their utilities are different over time. From a medical view, acute post-event utilities are (much)lower than chronic post-event utilities. Therefore, it was essential to use the data from the study which assessed the acute and chronic utilities with the same participants at an appropriate time. For this purpose, the utilities were derived from the study which assessed the utilities within the first year and consecutive years [13]. Table 6 shows the utilities used for the model. All the costs and effects were discounted at the rate of $3.5\%$. Table 6 \| Unnamed: 0 \| Utility mean \| SE \| \| --- \| --- \| --- \| \| Utility of CHD survivors—first year \| 0.67 \| 0.024 \| \| Utility of stroke survivors—first year \| 0.33 \| 0.033 \| \| Utility of CHD survivors—second year onwards \| 0.82 \| 0.012 \| \| Utility of stoke survivors—second year onwards \| 0.52 \| 0.027 \| ## Sensitivity analysis To quantify the level of confidence in the models' results, a deterministic sensitivity analysis (DSA) and a probabilistic sensitivity analysis (PSA) were performed. In the DSA, the input parameters were varied to the maximum and minimum possible values. This range is usually defined by the confidence interval of parameters. Therefore, for the examined parameters, a range of $95\%$ confidence interval was specified and then based on this range (maximum and minimum input values), one value in the model was varied manually each time. For the patients' adherence, the range of $50\%$−$100\%$ was considered. The results (new ICERs) were collected and expressed with tornado diagrams. The tornado diagrams depict the impact on the ICER whenever one single parameter changed. For the PSA, as the main assumption, it was considered the deterministic input values in the parameter sheet as the mean values. As the standard errors of the cost items were not available, it was considered to mean value times by 0.1. Based on logical constraints, the probabilistic distribution for each of the different sources of uncertainty was defined. A gamma distribution for all cost items and a beta distribution for the utilities were defined. The PSA was conducted by drawing a random number for each of the input distributions and each time, the ICER was calculated by Excel. By running a macro, its action is repeated 1,000 times. ## Results For the interpretation of the results, 1 GDP per capita was assumed as the CE threshold, which is equal to $4,091 [25]. In Table 7, the results of the “CVD risk model without diabetes” are reported. The IraPEN intervention for individuals having moderate CVD risk yields an ICER of $804 per QALY, while for high-risk groups, this intervention provides an ICER of $551 per QALY. The results showed that this intervention would be cost-saving and improve health if the IraPEN program targets only individuals with a CVD risk higher than $30\%$. The model yields the ICER of –$44 per QALY for this group. Moreover, the model results showed that individuals with higher CVD risks gained higher LY out of the intervention. The moderate, high, and very high CVD risk groups gained 0.69, 0.96, and 1.45 LY, respectively, from the intervention. Table 8 shows the model results for the “CVD risk model with diabetes”. Here, the results demonstrate that the intervention would be cost-saving while improving health if target individuals with CVD risk comprise higher than $20\%$. The intervention yields an ICER of –$42 per QALY for the high-risk group and an ICER of –$71 per QALY for the very high-risk group. Similar to the previous model, individuals with higher risks gain more LY from the intervention. The one-way sensitivity analysis reveals that the patients' adherence, the treatment effectiveness, and the total cost of the IraPEN program have the most impact on the ICER. The influence of patients' adherence is more noticeable in the higher CVD risk groups, while the results are more sensitive to treatment effectiveness in the lower risk groups (Figures 2, 3). Figure 2: One-way sensitivity analysis of modeled cost–effectiveness of CVD risk-based prevention for very high-risk without diabetes group. Figure 3: One-way sensitivity analysis of modeled cost–effectiveness of CVD risk-based prevention for low risk with diabetes group. In the scenario analysis of $50\%$ adherence, an ICER of $1,451, $1,141, and $329 was achieved for moderate, high, and very high CVD risk in groups without diabetes, respectively. In this scenario, the intervention yields an ICER of $1,029, $1,022, $236, and $199 for low, moderate, high, and very high CVD risk groups with diabetes, respectively. It was found that the ICERs are lower than the threshold at both the upper and lower limits of all examined parameters. The result of 1,000 PSAs illustrates that the intervention for all groups, except the low CVD risk group without diabetes, is cost-effective while being cost-saving for at least half of high-risk and very high-risk patients (Figures 4, 5). By adopting 1 GDP per capita of Iran as the willingness to pay per quality-adjusted life-year (WTP/QALY) gained, $100\%$ of all the runs were cost-effective in these groups. Figure 4: Probabilistic sensitivity analysis of the Markov model without diabetes. Yellow color: represents the moderate-risk group (individuals with CVD risk between 10 and 19%). Orange color: represents the high-risk group (individuals with CVD risk between 20 and 29%). Red color: represents the very high-risk group (individuals with CVD risk more than 30%). Figure 5: Probabilistic sensitivity analysis of the Markov model with diabetes. Green color: represents the low-risk group (individuals with CVD risk <10%). Yellow color: represents the moderate-risk group (individuals with CVD risk between 10 and 19%). Orange color: represents the high-risk group (individuals with CVD risk between 20 and 29%). Red color: represents the very high-risk group (individuals with CVD risk more than 30%). Both models captured that men would be benefited more than women in terms of LY gained. In addition, the intervention generates a lower ICER for men than women in individuals with identical characteristics. For example, in the low CVD risk group with diabetes, the interventions yield an ICER of $239 per QALY for men, while it is $711 per QALY for women. Regarding the LY in this group, in equal circumstances, men saved 0.58 of a year and women saved 0.46 of a year. In higher risk groups, this difference is more prominent. For example, for very high-risk groups, regardless of their diabetes status, the intervention for men is cost-saving, while for women it is not (Tables 9, 10). ## Discussion This analysis aimed to measure the potential CE of IraPEN preventive actions for CVD in comparison with the status quo. Our results illustrated that this intervention is not cost-effective for the low CVD risk group, whereas the other groups under study proved to be highly cost-effective. The reason why this group was not cost-effective could be justified by the fact that the low CVD risk group has lower CVD risk factors, which is why such individuals are just screened without being intervened. In this study, we adopted the CE threshold, i.e., 1–3 GDP per capita, as proposed by WHO [26]. The study of the literature shows that by using this threshold, almost all interventions seem to be cost-effective [27]. It means that by adopting this CE threshold, there is a risk that the budgets are spent on interventions that should not and vice versa. The threshold which is recommended by WHO has received some criticism as it is believed that it does not reflect the true “opportunity cost.” This is more critical in low- to middle-income countries, because while they have a higher demand for health, in comparison with high-income countries, fewer resources are available to them. In 2016, Woods et al. [ 28] calculated the CE threshold based on the empirical estimates of opportunity cost, the relationship between a country's GDP per capita, and the value of statistical life. For that reason, they estimated the threshold for different countries with different levels of income. Based on their estimation, Woods et al. suggested the CE threshold to be about $50\%$ of GDP per capita. As appointing the precise CE threshold level is beyond the scope of our analysis, we interpret and discuss the results with the lowest recommended ICER and leave the decision to policymakers to choose an intervention that best fits their budget. The latest reported GDP per capita of Iran is $4,091. By comparing the results with this threshold, it is shown that all of them, except the low CVD risk group, are highly cost-effective, whereas if the program only targets the people in higher risk groups, it is both cost-saving and improves their health. Based on the results, the intervention for the low-risk group was not cost-effective as the ICER was undefined. This could be explained by the fact that in this group, just screening is done without offering any intervention. So here, there is a cost for screening without any tangible effect assigned. Since this group does not receive any treatment annually, it is, by all means, sensible that no effects are observed. While it seems to be justifiable, we have every reason to believe that this is the only way to find the groups with a higher CVD risk. Another point that should be mentioned is that the individuals enter and are screened in our model at the age of 40 years. At this age, the proportion of individuals with low CVD risk is significantly higher than that of those with higher CVD risk. The older a person becomes, the more the probability of being in the higher-risk groups will be. However, the fact that the intervention for the aforementioned group is not cost-effective needs to be approached more comprehensively and conservatively. Screening of this group is the first step and essential for all individuals in the other groups that cannot be disregarded. This means that the other groups can benefit from this, a fact that has not been considered in the analysis of the CE of this group in this study. From another perspective, a closer observation reveals that screening has a wide range of benefits. For instance, according to the latest national data [29], the prevalence of people with diabetes in Iran is $11.4\%$, a quarter of whom are undiagnosed [30]. This means that at the moment, there are almost 1.5 Iranian people with undiagnosed diabetes. These undiagnosed individuals are discovered only when their complications have started to appear in them. Such complications as retinopathy, nephropathy, and neuropathy are very costly and can impose burdensome pressures on the healthcare system of the country. Other typical examples of this kind are blood hypertension and hyperlipidemia. It is predicted that huge monetary resources should be allocated to the overall screening of all the individuals, which may not be conveniently supplied. Therefore, appropriate measures could be taken by the authorities to have the costs tailored. This can help manage the financial resources and distribute them as optimally as possible. If the available financial resources do not allow us to screen everyone, it is possible to screen all high-risk people. Although this may not sound optimal, still it has a lot of benefits to offer. In other words, when considered at a higher scale, it can be realized that since the proportion of high CVD risk group individuals outweighs those with lower risks, this may lead to much more favorable results. It is applicable to have a paper pre-screening. The idea is that alternatively, paper questionnaires can be distributed among both households and health center visitors. These questionnaires aim to detect individuals with higher CVD risks. Typical examples might be those who are obese or have a positive history of CVDs in their intimidate relatives. Once identified, such participants can be invited to health centers to get screened. In this way, we can narrow the target population and screen those who are at higher risk levels. The study of the literature suggested some good examples of this kind of practice. For example, Chamnan et al. [ 31], who conducted a modeling study using the data from a prospective cohort study (EPIC-Norfolk), concluded that adopting a stepwise screening approach can prevent the same number of CVD events annually. All the participants of the EPIC-Norfolk study had completed the questionnaire about their lifestyle and drug use and family history of diseases between 1993 and 1997. This population had been observed and followed for 10 years and all CVD events had been recorded. By adopting the Cambridge risk score (a British risk scoring tool) and based on the results of completed questionnaires, the Chamnan group ranked the population CVD risk. Then, they defined and modeled seven different stepwise screening strategies. By comparing the results of a 10-year follow-up of the population with their model, they found that inviting the individuals with a Cambridge risk score >60 might have had the same results as screening the population. According to their report, this strategy could have enabled them to have the same results about the whole population by screening only $60\%$ of the population. Similar results were observed by Móczár and Rurik [32] who concluded that performing screening for a selected target group is most likely to be more cost-effective than screening the whole population. It is essential to consider that although this approach is practical and likely more cost-effective in budget-strained situations, it has some serious ethical issues regarding equity because in this approach, a smoker would get screened and a non-smoker would not. The same is true for a person who has an unhealthy diet or lifestyle. Moreover, while it is conveniently applicable to rural areas, it is not easy to do in urban areas. Since our literature search revealed no similar studies either in the WHO East Mediterranean region (EMR) or in the Middle East region, inevitably, we compared the results of our analysis with the European studies. Our findings in this analysis, in terms of CE and the trend between the different CVD risks, are similar to the results of Schuetz et al. 's [33] study. The researchers of this study estimated the CE of several different preventive strategies compared with a control scenario in six European countries. By using country-specific data from France, Germany, Denmark, Italy, Poland, and the United Kingdom, they generated six simulated populations of people aged 40–75 years eligible for preventive actions in those countries. Their model showed that the cost per QALY of offering these preventive services to the people in the study cohort ranged from €14,903 for France to €115 for Germany, while it was cost-saving for Poland. Their results showed that the health checks for detecting and managing CVDs at the early stages not only are highly cost-effective but also cost-saving in some scenarios. For example, their analysis for the UK showed that during the 30-year follow-up, the cost per QALY would be €2,426. This ICER in comparison with the UK threshold, which is between 20 and 30 thousand pounds, is highly cost-effective. Moreover, their results demonstrated that the program would be cost-saving if it targets only the top quartile of CVD risk groups. Furthermore, offering prevention checks after the pre-screening of individuals based on some characteristics such as higher age or obesity would pull the results in the direction of more favorable ICER [33]. Finally, while this screening program can have substantial benefits for individuals with CVD risk, it is essential to consider the potential disutility of lifelong preventive treatments. The search of the literature indicates that the disutility of medications' adverse events [34] and the disutility arising from taking daily medication [35] can play a key role in the decision that leads to non-adherence. It is vital to take into account that some individuals need to be on preventive treatments from their 40s for around 30–40 years. Although the sensitivity analysis demonstrated that this program can likely be cost-effective even with $50\%$ of adherence, it is crucial to enhance treatment compliance through patient education and take effective strategies to increase the engagement of target groups. ## Strengths and limitations Based on our literature search, this analysis is the first CVD risk-based CE to be conducted in Iran, in the Middle East, and the WHO EMR so far. Furthermore, it is one of the few studies which model the individual with and without diabetes separately. Similar to every CE analysis, this study has several limitations which were mainly caused by a large number of input parameters used in the model. Although the model was designed for the Iranian population, some input parameters were derived from various studies performed in different countries other than Iran directly because of the unavailability or a lack of Iranian data. Furthermore, it was assumed that the intervention effect is equal for all subgroups regardless of their initial CVD risk. Such an assumption can probably generate an underestimation of the intervention effect for high-risk groups and produce an overestimation of the treatment effect for lower CVD risk groups. In this analysis, the second event of CVDs was not accounted for due to a lack of data. Based on the literature, almost $50\%$ of patients would experience the second or third event during their life once they have had the first event [36]. The second event may not necessarily be the same as the first one. For example, a patient who has had a CHD event could have the same event again or can even experience a stroke event. This cannot affect the result of our analysis unfavorably. As the IraPEN intervention causes the first CVD event to decrease or delay, it is logical to assume that the second event in the IraPEN group is lower than the status quo. Therefore, we could assume that adding the second CVD event to our model would be in favor of our ICER. It could be better if we could adopt a lifetime horizon for this analysis. However, as the Iranian statistical data were just available for people aged below 80 years, inevitably 40 years/cycles were employed. The results of Kim et al. 's [37] systematic review, which was done on more than 750 CE analyses, showed that the usage of a lifetime horizon captures all consequences and health benefits most of the time and yields more favorable ICER. Therefore, it is logical to assume that by increasing the time horizon from 40 years to a lifetime, the ICER would be more favorable. Finally, it should be expected that the effectiveness of the intervention would be lower in the real world than in the model. It could be explained by the fact that the intervention effects, which were used in this study, all had been derived/extracted from a controlled trial setting. ## Conclusion In Iran, CVDs are the leading cause of mortality. Therefore, planning and implementing preventive actions are highly demanded. Our analysis results demonstrated that the IraPEN program implementation is highly cost-effective for all the CVD risk groups, except the low risk without diabetes group, whereas if the program only targets the people in higher risk groups, it is both cost-saving and improves their health. ## 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/s. ## Author contributions AJ contributed to the design, carried out the analysis, and wrote the first draft. RD contributed to the analysis and reviewed the manuscript critically. EA contributed to the design and supervised the process of analysis reviewed the manuscript critically. DK designed the whole project and contributed to the analysis and drafting of the manuscript. All authors read the final manuscript and approved it. ## 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.2023.1075277/full#supplementary-material ## References 1. Chrysant S. A new paradigm in the treatment of the cardiovascular disease continuum: focus on prevention. Hippokratia. (2011.0) 15 7-11. PMID: 21607028 2. 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--- title: Liver fibrosis-4 score predicts outcome of patients with ischemic stroke undergoing intravenous thrombolysis authors: - Davide Norata - Simona Lattanzi - Serena Broggi - Chiara Rocchi - Marco Bartolini - Mauro Silvestrini journal: Frontiers in Neurology year: 2023 pmcid: PMC9999710 doi: 10.3389/fneur.2023.1103063 license: CC BY 4.0 --- # Liver fibrosis-4 score predicts outcome of patients with ischemic stroke undergoing intravenous thrombolysis ## Abstract Some evidence suggests a possible influence of liver disease on stroke prognosis. We investigated the association between fibrosis-4 (FIB-4) score, a marker of liver disease, and the 3-month outcome in patients with ischemic stroke undergoing intravenous thrombolysis. We also evaluated the rate of symptomatic intracranial hemorrhage after thrombolysis. In this prospective cohort study, we enrolled consecutive patients with ischemic stroke treated with thrombolysis who had a 3-month follow-up. The FIB-4 score was calculated and the validated cut-off values were used to indicate high/low risk of advanced liver fibrosis. The primary outcome was 3-month poor prognosis estimated as a modified Rankin scale score ≥3. Of the 264 included patients, 131 ($49.62\%$) had a 3-month mRS ≥3, with a significantly higher FIB-4 score, compared to those with a mRS <3 score (adjp <0.001). When adjusted for possible confounders by multivariate logistic regression, FIB-4 score remained a significant predictor of poor outcome (OR 1.894, $$p \leq 0.011$$), along with history of atrial fibrillation (OR 3.488, $$p \leq 0.017$$), admission NIHSS score (OR 1.305, $p \leq 0.001$), and low values of hemoglobin (OR 0.730, $p \leq 0.001$). Mechanical thrombectomy had a favorable effect on patients' outcome (OR 0.201, $$p \leq 0.005$$). The risk of poor 3-month outcome was significantly higher among the 32 patients ($12.1\%$) with high risk of severe fibrosis ($$p \leq 0.007$$). FIB-4 score values were also related to symptomatic intracranial hemorrhage ($$p \leq 0.004$$), specifically among patients with high probability of advanced hepatic fibrosis ($$p \leq 0.037$$). FIB-4 score can be considered as a promising independent predictor of poor prognosis in patients with acute ischemic stroke undergoing intravenous thrombolysis. ## 1.1. Background According to the World Health Organization, 15 million people worldwide suffer from stroke each year. With a mortality rate of approximately one-third, it is the second most common cause of death and a leading cause of disability [1]. Ischemic stroke is the most common type of stroke, accounting for approximately $80\%$ of all acute strokes [2]. Treatment approaches have been primarily directed at preserving neurons in the ischemic territory. The internationally approved treatments, recombinant tissue plasminogen activator (rt-Pa) and endovascular intervention, aim at rapid arterial recanalization to restore oxygen and nutrient supply to the affected area [3]. Early recanalization after stroke is associated with a greater likelihood of favorable outcome [4, 5]. Liver fibrosis, the histologic precursor of cirrhosis, is a chronic disease [6], often preceded and promoted by an inflammatory process in combination with the accumulation of extracellular matrix in the liver [7]. Several biomarkers have been proposed for the assessment of liver fibrosis. Among them, the fibrosis index (FIB)-4 has shown the best diagnostic accuracy for advanced hepatic fibrosis, as demonstrated by ultrasonographic studies in nonalcoholic fatty liver disease (NAFLD) [8, 9], the most common cause of liver dysfunction in Western countries [10]. In recent studies, liver disease has been shown to be a strong predictor of both in-hospital and long-term mortality in stroke patients [11, 12]. Moreover, it is independently associated with an increased risk of hemorrhagic complications [13], the most threated complication of intravenous thrombolysis, leading to poor outcome and increased risk of mortality [14]. It is not yet clear whether these findings can also be applied to subclinical liver disease, which may not be uncommon in patients with stroke [15]. In a recently published study, Fandler-Höfler et al. [ 16] showed that stroke patients with higher FIB-4 score values had worse clinical outcomes 3 months after mechanical thrombectomy but they didn't find any increased risk of postoperative parenchymal hematoma, hemorrhagic infarction and symptomatic intracerebral hemorrhage. ## 1.2. Objectives The aim of the present study was to investigate the association of FIB-4 score with 3-month neurological outcome and symptomatic intracranial hemorrhage in patients with acute ischemic stroke treated with IV rt-Pa. ## 2.1. Study design, setting, and participants We retrospectively identified consecutive patients admitted to the Stroke Unit of the University Hospital of Ancona, Italy, from January 2017 to April 2021 for acute ischemic stroke treated with IV thrombolysis. Each patient underwent routine blood sampling at admission (within 24 h of admission). Supplementary Table 1 provides an overview of the eligibility criteria. The study was approved by the ethics committee of the Marche Polytechnic University (ID $\frac{57}{2020}$) and conducted according to the Declaration of Helsinki. Informed consent was obtained from all subjects involved in the study or their representatives. ## 2.2. Variables Demographics, medical history, National Institutes of Health Stroke Scale (NIHSS) scores [17], and admission blood pressure were documented at baseline. Laboratory tests [including serum levels of creatinine, glucose levels, hemoglobin (Hb), platelet count (PLT), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), alanine aminotransferase (ALT), aspartate aminotransferase (AST) total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, γ-glutamyltransferase (γGT), and creatine phosphokinase (CPK)] were determined by admission blood tests. To quantify the extent of liver fibrosis, we used the noninvasive liver fibrosis score (FIB-4) for each patient at the time of admission. The FIB-4 score was computed for every patient as follows: As validated in previous clinical trials, prediction of advanced liver fibrosis was indicated using a cut-off value ≥2.67, whereas a score value <1.30 was used to exclude severe liver fibrosis with high probability [18, 19]. ## 2.3. Outcome measures The primary outcome measure was functional status at 3 months, evaluated in the hospital's outpatient setting. Because of its ease of use and interpretability, the modified Rankin Scale (mRS) is a widely applied clinical measure of global disability. In particular, it is used to assess recovery from stroke and as a primary end point in randomized clinical trials of stroke treatments. In our study, poor outcome was defined as the occurrence of death or major disability (mRS≥3) [20]. We also considered symptomatic intracranial hemorrhage (sICH) as a secondary outcome. We defined this hemorrhagic complication usually linked to rt-Pa, through the European Cooperative Acute Stroke Study (ECASS) III criteria, as follows [21]. [ 1] Clinical deterioration: an increase of ≥4 points in NIHSS score or that led to death. [ 2] Radiographic features: any intracranial hemorrhage on CT/MRI performed at 22–36 h after stroke onset. ## 2.4. Biases and study size We conducted this study on consecutive patients to avoid any selection bias. In order to address information bias, two aspects should be considered: the number of lost to follow-up was acceptable (Figure 1); the admission FIB-4 score was calculated only after the 3-month assessment, so the experimenter did not know the score value when assessing the 3-month mRS (primary outcome measure). Based on previous RCTs on Alteplase effectiveness [22], the minimum number of samples required to achieve a $95\%$ confidence level with a marginal error of 0.05 was 241. Figure 1: Patient selection flow diagram. EVI, endovascular treatment; IVT rt-PA, intravenous thrombolysis with recombinant tissue plasminogen activator; mRS; modified Rankin Scale. ## 2.5. Statistical methods We used standard statistical methods for descriptive statistics. Categorical variables were presented as frequencies and continuous variables as mean (standard deviation, SD) or median (interquartile range, IQR), when appropriate. Normality was assessed through the Shapiro–Wilk test. Depending on the normality of the distribution, comparisons were made by Student's t-test or Mann–Whitney test for continuous variables, and by Pearson χ2 test for categorical variables. The multivariate logistic regression was used to identify whether the FIB-4 score could be an independent predictor of poor 3-month outcome, and to establish the real prognostic value of demographic, clinical, and laboratory variables that reached statistical significance in the univariate analysis. To prevent biases, we did not include the variables already used for calculating the FIB-4 score in the logistic regression. An equivalent analysis was carried out for the secondary outcome, and is available in the Supplementary material. A two-tailed p-value of <0.05 was considered statistically significant for all tests. False discovery rate (FDR) correction was applied to deal with the multiple testing problem (results are expressed as adjusted p or adjp-values). Analysis was performed using JASP Team [2020]. JASP (version 0.14.1). ## 3.1. Participants and descriptive data Of the 306 patients who suffered ischemic stroke treated with IV rt-Pa, 42 were excluded (Figure 1). Of the 264 enrolled patients, 131 ($49.62\%$) had a modified Rankin Scale score of ≥3 after 3 months and 35 ($13.3\%$) experienced a symptomatic intracranial hemorrhage (sICH; Table 1). Table 1 \| Baseline characteristics \| Full cohort n = 264 \| mRS < 3 n = 133 \| mRS ≥3 n = 131 \| adjp-value \| \| --- \| --- \| --- \| --- \| --- \| \| Demographics \| Demographics \| Demographics \| Demographics \| Demographics \| \| Female sex \| 123 (46.6%) \| 53 \| 70 \| 0.074a \| \| Age, years \| 69.3 (13.8) \| 65.9 (14.0) \| 72.7 (12.7) \| < 0.001b* \| \| Clinical history \| Clinical history \| Clinical history \| Clinical history \| Clinical history \| \| Hypertension \| 157 (63.3%) \| 73 \| 84 \| 0.080a \| \| Diabetes mellitus \| 35 (14.6%) \| 18 \| 17 \| 0.963a \| \| Actual smoking \| 59 (24.9%) \| 34 \| 25 \| 0.575a \| \| Hypercholesterolemia \| 90 (36.6%) \| 51 \| 39 \| 0.337a \| \| Atrial fibrillation \| 45 (19.2%) \| 11 \| 34 \| < 0.001a* \| \| Ischemic heart disease \| 31 (13.3%) \| 16 \| 15 \| 0.986a \| \| Prior stroke \| 32 (13.5%) \| 18 \| 14 \| 0.116a \| \| Blood test variables \| Blood test variables \| Blood test variables \| Blood test variables \| Blood test variables \| \| FIB-4 score \| 1.284 (0.989) \| 1.112 (0.734) \| 1.436 (1.186) \| < 0.001c* \| \| FIB-4 score ≥2.67 \| 32 (12.1%) \| 9 \| 23 \| 0.021a* \| \| FIB-4 score 1.31–2.66 \| 95 (36.0%) \| 42 \| 53 \| 0.244a \| \| FIB-4 score < 1.30 \| 137 (51.9%) \| 82 \| 55 \| 0.004a* \| \| Total cholesterol, mg/dl \| 176.1 (40.34) \| 178.0 (39.63) \| 174.2 (41.14) \| 0.653b \| \| HDL cholesterol, mg/dl \| 51.76 (14.67) \| 53.21 (14.81) \| 50.24 (14.44) \| 0.233b \| \| LDL cholesterol, mg/dl \| 103.47 (32.64) \| 105.24 (34.04) \| 101.59 (31.13) \| 0.575b \| \| Triglycerides, mg/dl \| 96.00 (63.00) \| 96.00 (62.00) \| 95.00 (59.25) \| 0.760c \| \| Creatinine, mg/dl \| 0.860 (0.330) \| 0.850 (0.265) \| 0.880 (0.425) \| 0.575c \| \| Glucose, mg/dl \| 108.00 (45.00) \| 107.00 (43.25) \| 109.00 (44.00) \| 0.080c \| \| Platelets, × 109/L \| 207.00 (80.50) \| 207.00 (80.00) \| 207.00 (79.00) \| 0.768c \| \| Hemoglobin, g/dl \| 13.05 (2.425) \| 13.40 (2.10) \| 12.60 (2.65) \| < 0.001c* \| \| ANC, × 109/L \| 7.304 (3.338) \| 6.616 (3.228) \| 8.047 (3.312) \| 0.007b* \| \| ALC, × 109/L \| 1.670 (1.828) \| 1.722 (0.737) \| 1.614 (2.527) \| 0.768b \| \| AMC, × 109/L \| 0.697 (0.407) \| 0.642 (0.255) \| 0.757 (0.519) \| 0.090b \| \| AST, U/L \| 17.00 (11.00) \| 16.00 (8.00) \| 19.00 (13.50) \| 0.021c* \| \| ALT, U/L \| 22.00 (12.00) \| 22.00 (10.00) \| 22.00 (12.00) \| 0.768c \| \| γGT, U/L \| 26.00 (24.00) \| 26.00 (23.00) \| 27.00 (23.00) \| 0.714c \| \| CPK, U/L \| 99.00 (86.50) \| 94.00 (84.00) \| 108.50 (90.00) \| 0.389c \| \| In-hospital variables \| In-hospital variables \| In-hospital variables \| In-hospital variables \| In-hospital variables \| \| Systolic blood pressure \| 139.0 (20.93) \| 139.0 (21.20) \| 139.0 (20.74) \| 0.986b \| \| Diastolic blood pressure \| 76.4 (10.98) \| 76.4 (11.05) \| 76.4 (10.96) \| 0.986b \| \| Admission NIHSS score \| 12.49 (6.16) \| 9.61 (6.17) \| 15.54 (4.49) \| < 0.001b* \| \| EVT \| 146 (55.3%) \| 60 \| 86 \| < 0.001a* \| \| sICH \| 35 (13.3%) \| 28 \| 35 \| < 0.001a* \| The mean FIB-4 score was significantly higher among patients with a poor prognosis compared with the other group (1,436 vs. 1,112, Student t-test −3.303, adjp < 0.001). ## 3.2. Main results Demographic, clinical, and laboratory characteristics of patients are presented in Table 1. In univariate analyses, patients with poor prognosis more frequently had the following characteristics (Table 1): older age, history atrial fibrillation, high admission-NIHSS scores, high blood levels of ANC and AST, and low blood levels of Hb. As shown in Table 1, in the baseline study, adjuvant treatment with mechanical thrombectomy was associated with poor outcome. We performed a multivariate logistic regression to assess the true predictive value of variables that apparently had an influence on prognosis on univariate analysis. SICH rates (Tables 1, 2) were not included in the calculation because they were not obtainable at baseline assessment. Although age and AST values were statistically significant in univariate testing, they neither were included in the logistic regression since they were already factored into the FIB-4 score, in order to avoid a distortion of FIB-4 score effect on prognosis (i.e., a confusion bias). Table 2 \| Unnamed: 0 \| Full cohort n = 264 \| No sICH n = 229 \| sICH n = 35 \| p- value \| \| --- \| --- \| --- \| --- \| --- \| \| FIB-4 score \| 1.284 (0.989) \| 1.249 (0.796) \| 1.697 (1.348) \| 0.004c* \| \| FIB-4 score ≥2.67 \| 32 (12.12%) \| 24 \| 8 \| 0.037a* \| \| FIB-4 score < 1.30 \| 137 (51.89%) \| 125 \| 12 \| 0.025a* \| On multivariate analysis (Table 3), FIB-4 score (OR 1.894, $$p \leq 0.011$$), history of atrial fibrillation (OR 3.488, $$p \leq 0.017$$), high admission NIHSS score (OR 1.305, $p \leq 0.001$) and low blood values of Hb (OR of high Hb levels OR 0.730, $p \leq 0.001$) remained significant predictors of poor prognosis. In spite of what was hypothesized with the univariate analysis, the regression demonstrated a protective effect of thrombectomy (OR 0.201, $$p \leq 0.005$$). Other variables (female sex, and ANC) were not significant prognostic predictors. Table 3 \| Coefficients \| Odds ratio \| p -value \| 95% confidence interval \| 95% confidence interval.1 \| \| --- \| --- \| --- \| --- \| --- \| \| Coefficients \| Odds ratio \| p -value \| Lower bound \| Upper bound \| \| FIB-4 score \| 1.894 \| 0.011* \| 1.160 \| 3.094 \| \| Female sex \| 0.666 \| 0.285 \| 0.316 \| 1.404 \| \| Atrial fibrillation \| 3.488 \| 0.017* \| 1.253 \| 9.710 \| \| Admission NIHSS \| 1.305 \| < 0.001* \| 1.177 \| 1.448 \| \| EVT \| 0.201 \| 0.005* \| 0.066 \| 0.609 \| \| Hb, g/dl \| 0.730 \| < 0.001* \| 0.661 \| 0.807 \| \| ANC, × 109/L \| 1.000 \| 0.223 \| 1.000 \| 1.000 \| This statistical model showed good discriminatory power, with an area under the Receiver Operating Characteristic (ROC) curve of 0.877 (Supplementary Figure 1). It also produced a precision of $79.5\%$ and an accuracy of $79.3\%$. Considering the FIB-4 cut-off values, we observed that the 32 patients ($12.1\%$) with a high risk of advanced fibrosis (i.e., FIB-4 score ≥2.67) were more frequently associated with a poor 3-month outcome (adjp = 0.021), whereas the 137 patients ($51.9\%$) with a high probability of exclusion of significant liver fibrosis (i.e., FIB-4 score <1.30) more frequently had a favorable 3-month outcome (adjp = 0.004; Figure 2). Figure 2: Patient proportional distribution based on the FIB-4 score cut-off values and the 3-month modified Ranking Scale. As with the primary outcome, univariate analyses were used to investigate the influence of variables on sICH (Table 2 and Supplementary Table 2). Regarding the secondary outcome, our study showed a statistically significant relationship between rates of sICH and FIB-4 values ($$p \leq 0.004$$), admission NIHSS (adjp = 0.035), EVT (adjp < 0.001), and serum levels of glucose (adjp < 0.001) and ANC (adjp = 0.009). However, multivariate analysis only confirmed the effect of admission NIH score on the secondary outcome (OR 0.901, $$p \leq 0.035$$, Supplementary Table 3). Moreover, we divided the entire study population according to the cut-off values of the FIB-4 score, and we noted that patients with FIB-4 score <1.30 (exclusion of liver fibrosis) had lower probability of sICH (FIB-4 score <1.30, $$p \leq 0.025$$), whereas ischemic lesions from patients with high risk of advanced fibrosis (i.e., FIB-4 values ≥2.67) tended statistically to bleed more frequently ($$p \leq 0.037$$). ## 4. Discussion The extension of indications for intravenous rt-Pa in patients with stroke and, in particular, the lengthening of the time window, has stimulated the search for reliable predictors able to provide early information on the risk/benefit ratio of the treatment. The ability to predict the outcome shortly after hospitalization can play an important role in the decision-making process regarding the best therapeutic approach in stroke patients and to plan a proper overall therapeutic care. Recently, some of the main predictors of outcome in patients with ischemic stroke treated with rt-Pa have been described [23]. High NIHSS scores, elevated systolic blood pressure values on admission, history of atrial fibrillation, and coronary artery disease were associated with poor outcome after 3 months. Another recent study on stroke patients undergoing rt-Pa reported that glycosylated hemoglobin blood levels were related to a poor early outcome but not to a poor functional prognosis at 3 months [24]. ## 4.1. Interpretation of key results In our study, in addition to clinical data, we considered using a simple and rapidly available index such as the FIB-4 score, based on laboratory parameters, to obtain prognostic information. After adjustment for confounding factors by logistic regression analysis, we found that high values of FIB-4 score predicted outcome at 3 months in stroke patients treated with intravenous rt-Pa. Moreover, considering the validated cut-off values of this index, we were able to select a group of patients, characterized by a high risk of advanced liver fibrosis, who had a significantly higher probability of poor outcome than other patients. On the other hand, patients with exclusion of significant hepatic fibrosis had a higher probability of a favorable prognosis. The FIB-4 score, which integrates blood levels of ALT, AST, and PLT, is not only a simple measure of patients' liver function but also reflects the complex systemic role of the liver itself. As highlighted by a cross-sectional study [11], liver dysfunction can lead to brain damage by several mechanisms, including small vessel disease or coagulopathy [25]. In addition, NAFLD is associated with systemic inflammation [26, 27], vascular inflammation [28] and atherosclerosis (25, 29–33). Advanced liver disease is associated with mixed coagulopathy [34], which increases the risk of both thrombotic and hemorrhagic stroke. It's intuitive that worse outcomes may be the consequence of higher comorbidity in general, not a worse effect of thrombolysis. The selection of outcomes more specifically linked to this treatment should be considered in further dedicated works. For that very reason, we introduced the evaluation of the symptomatic intracerebral hemorrhage, a crucial mechanism involved in modulating the prognosis of patients with ischemic stroke undergoing fibrinolysis. Although the multivariate analysis would seem to exclude a role for the FIB-4 score in predicting bleeding complications, this hypothesis could not be entirely ruled out for two reasons: the limited number of patients with symptomatic cerebral hemorrhage, and the statistical model's inability to corroborate data from previous studies, which have also constantly indicated that admission hyperglycemia plays a significant role in predicting post-thrombolysis intracranial hemorrhagic events (35–37). Our findings from univariate analysis suggested that being affected by severe hepatic fibrosis may increase the risk of intracerebral hemorrhage. Based on these findings, the poor outcome at 3 months in patients with advanced hepatic fibrosis may be, at least in part, related to hemorrhagic complications. As demonstrated in previous studies, the intravenous use of rt-Pa significantly increases the risk of intracranial hemorrhage, which is otherwise uncommon in ischemic stroke [38]. Therefore, we hypothesize that for patients with severe hepatic fibrosis and ischemic stroke, the option for intravenous thrombolysis should be carefully evaluated considering the possible related risks. In the present study, other indicators able to predict outcome at 3 months were identified. The negative prognostic role of atrial fibrillation in our patients was not unexpected although its significance has not been fully elucidated [39]. In the Virtual International Stroke Trials Archive, no significant association was found between atrial fibrillation and overall stroke outcome [40]. However, some studies found that atrial fibrillation was associated with favorable outcomes after thrombolysis for severe stroke, probably because of the effect of the thrombolytic agent on embolic arterial occlusion [37]. In agreement with our findings, most studies suggest that atrial fibrillation may increase the risk of symptomatic intracranial hemorrhage and early death, and decrease the likelihood of favorable outcome after thrombolysis [41, 42]. Our finding of negative predictive effects of high NIHSS scores (23, 43–45) and low serum levels of hemoglobin (46–49) on outcome confirm previous findings in patients undergoing thrombolysis for stroke. The results of our multivariate analyses showing a favorable effect of endovascular therapy on stroke outcome are consistent with the results of a recent systematic review of 19 randomized clinical trials (RCTs) [50]. In this review, endovascular thrombectomy in patients with acute ischemic stroke due to occlusion of large arteries in the anterior circulation increased the chance of survival with good functional outcome (3-month mRS <3) with no negative effect on the risk of intracerebral hemorrhage or death. The predictive influence of anamnestic and laboratory variables on patients undergoing mechanical thrombectomy was recently investigated in a 2021 publication [51]. Toh et al. published an article at the beginning of 2023 addressing the same topic as the current study, with impactful results that confirm the significant influence of the FIB-4 score on the outcome of stroke patients undergoing thrombolysis in a highly representative sample of Asian population [52]. ## 4.2. Strengths and limitations Our study has some limitations. Because of its observational nature, this retrospective investigation does not reach the quality of evidence needed to draw definitive conclusions. Therefore, future prospective studies with established time points for blood sampling need to be conducted to assess the true cause-and-effect relationship between liver injury and stroke. In the event of a demonstration of a causal relationship, it will be critical to understand whether any improvement in liver condition can lower the risk of poor prognosis in stroke. Furthermore, it is not sufficiently clear whether the calculation of FIB-4 on admission can be considered reliable in expressing chronic liver damage, or is too influenced by stroke-related changes in blood levels of AST, ALT, and PLT. Further investigation is needed to obtain a clear answer with simultaneous assessment of the FIB-4 score and other markers of chronic liver disease. On the other hand, the large sample of patients included and the easy usability of the score in a clinical setting with ordinary and cost-effective laboratory tests are the most important strengths of the study. We also used the already validated cut-off values of the FIB-4 score, that are strong indicators for the presence/absence of advanced liver fibrosis, significantly simplifying the calculation of the risk of poor outcome. ## 5. Conclusion The results of the present study suggested that the FIB-4 score, a rapidly available and cost-effective parameter, can be considered as an independent predictor of poor prognosis, with high predictive accuracy, in patients with acute ischemic stroke undergoing intravenous thrombolysis. In the new perspective of patient-centered medicine, identification of simple factors that predict treatment response is crucial to guide physicians in providing therapeutic strategies tailored to each single patient. ## 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 approved by the Ethics Committee of the Marche Polytechnic University (ID $\frac{57}{2020}$) and conducted according to the Declaration of Helsinki. Informed consent was obtained from all subjects involved in the study or their representatives. ## Author contributions Conceptualization, software, formal analysis, writing—original draft preparation, project administration, and had full access to all the data in the study and takes responsibility for its integrity and the data analysis: DN. Methodology: SL. Investigation: DN, SB, and CR. Resources: SL and MB. Data curation: MB, DN, SB, and CR. Writing—review and editing: DN, SB, CR, and MS. Supervision: SL and MS. 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. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2023.1103063/full#supplementary-material ## References 1. 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