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Inflammatory biomarkers and motoric cognitive risk syndrome: Multicohort survey

Highlights • Raised IL-6 and CRP levels are associated with increased odds of MCR.• IL-6 and CRP are involved in overlapping inflammatory, vascular, and neurological pathology.• Establishing these pathways can guide interventions to prevent progression of MCR to dementia.


Introduction
Motoric Cognitive Risk (MCR) syndrome is a pre-dementia syndrome characterized by cognitive complaints and slow gait; first proposed in 2013 [1]. The predictive validity of MCR exceeds its cognitive or gait components taken individually, and even after accounting for the presence of mild cognitive impairment (MCI) [1,2]. Despite its clinical utility, the pathogenesis of MCR remains unknown. MCR is associated with risk of developing both Vascular Dementia and Alzheimer Disease [1,3,4]. In line with these findings, risk factors for MCR include stroke, depressive symptoms, obesity, and low physical activity [1,5,6]. These risk factors share derangements in underlying inter-related biological pathways such as inflammation and oxidative stress, that co-occur in many diseases, including vascular disease [7]. Furthermore, vascular brain lesions such as white matter hyperintensities and subcortical infarcts are associated with both inflammation [8,9] and components of MCR (gait and cognition) [10,11]. These findings suggest that inflammation might also contribute to the pathogenesis of MCR [12][13][14][15][16][17].
MCR prevalence and associated risk factors may vary worldwide due to heterogeneity in population characteristics. Hence, we set out to perform a preliminary analysis of participants with biomarker data from five cohorts from three countries (USA, Japan, and Australia) in the MCR consortium. Our objective was to report the cross-sectional association between two inflammatory markers (interleukin-6 (IL-6) and C-reactive protein (CRP)) and MCR as a first step towards generating hypotheses, which could be tested in longitudinal studies. Given the established links between inflammation and vascular disease [18][19][20][21], we also examined the association between inflammatory markers and MCR in those with and without vascular disease. We hypothesized that higher IL-6 and CRP levels would be associated with MCR, and that the association would be stronger among those with vascular disease than those without vascular disease. Establishing an association between inflammation and MCR will help elucidate the pathogenesis of MCR as a prelude to developing treatments in individuals with, or at risk for, MCR. This, in turn, may reduce the incidence, downstream, of dementia.

Participants
This study includes data from five established cohorts within the MCR consortium. The goal of the MCR consortium funded by the National Institute on Aging is to determine the biological underpinnings of MCR. This study includes individual data from 1026 participants from the National Center for Geriatrics & Gerontology Study of Geriatric Syndromes (NCGG-SGS); 351 participants from the Central Control of Mobility in Aging Study (CCMA); 412 participants from the Tasmanian Study of Cognition and Gait (TASCOG); 433 participants from the Lon-Genity Study; and 879 participants from the Einstein Aging Study (EAS). Cohort details are summarized in Table 1. Eligibility criteria for each cohort are described elsewhere [1,[22][23][24][25]. For this analysis, we included participants with at least one inflammatory marker data available. The determination of MCR status and collection of demographic and health data were during the same wave as the biomarkers. Participants with a diagnosis of dementia or who were unable to ambulate were excluded from this study. See Table 1 for dementia and gait criteria. This study was approved by the institutional review board of the Albert Einstein College of Medicine. Each cohort site also obtained approval from their respective local ethics committees.

Dependent variable
The dependent variable was MCR. MCR is operationalized by four diagnostic criteria: (1) subjective cognitive complaints; (2) slow gait; (3) ability to ambulate; and (4) absence of dementia [1,2]. The presence of cognitive complaints was determined by interviewers in each cohort based on participant responses on standardized cognitive concern questionnaires; specific items are listed in Table 1. The GDS memory item was common to all cohorts, but additional cognitive complaint items that were available in individual cohorts were permitted to define subjective cognitive complaints to help identify a wider dementia at-risk pool. Gait speed measurement method and cut scores for slow gait are shown in Table 1. Cohort specific cut-offs for slow gait was selected because of the variability in gait speed across populations as well as by age and sex [26,27]. This approach is consistent with definition of slow gait used in our multi-country MCR prevalence study [2].

Independent variables
CRP and IL-6 were measured from stored frozen sera and plasma samples. IL-6 and CRP assay kits used in each cohort are listed in Table 1.

Other covariates
We selected demographic covariates (age, sex, years of education) to account for differences in cohorts and across cohorts. Additional variables included as covariates in analyses were body mass index (BMI), depressive symptoms measured by the Geriatric Depression Scale Short Form (GDS-S) and vascular disease. Vascular disease included a physician diagnosis of any one of diabetes, hypertension, stroke, and heart disease (myocardial infarction, angina or chronic heart failure). We include diabetes because it is associated with vascular manifestations such as retinopathy and atherosclerosis. We selected these clinical covariates based on previous studies showing that these clinical pathways might mediate the association between inflammation and MCR [8,[28][29][30][31][32][33][34]. Ethnicity was included as a covariate for the CCMA and EAS cohort analyses. Ethnicity in other cohorts was not included in cohort specific analyses due to homogeneity within each cohort of white Australian adults (TASCOG), Japanese adults (NCGG-SGS), and Ashkenazi Jewish adults (LonGenity).

Data analysis
Comparisons of demographic and health characteristics between participants with and without MCR were conducted in each cohort using chi-square tests or Fisher exact tests for categorical variables, and twosample T-tests for continuous variables. Comparisons of median levels of biomarkers (non-normal distribution) between participants with and without MCR were conducted in each cohort using Mann Whitney tests. A series of logistic regression models were performed to test whether each biomarker was associated with MCR while adjusting for potential confounders (see above) in each cohort. Biomarkers were examined as continuous variables and as tertiles derived within each sample, where the reference group was always the lowest (least inflammation) group. All logistic regression models were adjusted for demographic (age, sex, education), and clinical data (BMI, depressive symptoms, and presence of any vascular disease), and reported as odds ratios (aOR) with 95% confidence intervals (95% CI). We also conducted logistic regression models stratified by the presence or absence of vascular disease.
Random-effects meta-analysis used adjusted beta and standard error from each individual cohort level model to pool adjusted odds of MCR across all five cohorts for IL-6 as continuous variable and categorical variable (tertiles) and for CRP as continuous variable and categorical variable (tertiles). Thus, the meta-analysis was adjusted for the same major demographic variables as the analyses for each individual cohort. Heterogeneity between studies was tested using the x 2 test and quantified by the I 2 statistic.

Participant characteristics
Demographic characteristics of each cohort are presented in Table 2. Prevalence of MCR varied by cohort; 5.13% in CCMA, 7.02% in NCGG-SGS, 7.19% in TASCOG, 12.47% in LonGenity, and 11.04% in EAS. Across the cohorts, mean age ranged from 70.31 in NCGG-SGS to 79.54 in EAS. Participants with MCR were more likely to be older in the CCMA (mean age 81.43 vs 77.13, p<0.01) and EAS (mean age 81.57 vs 79.29, p<0.01) cohorts. Age did not differ by MCR status in the other cohorts. Participants in the TASCOG cohort had the lowest mean years of education (10.87 ± 3.66) and participants in the LonGenity cohort had the highest mean years of education (17.47 ± 2.81). Women comprised as few as 42.96% (TASCOG) and as much as 61.32% (EAS) of participants among the cohorts. Education and female sex did not differ between those with and without MCR within any cohort. Within CCMA, 77.49% of participants identified as non-Hispanic white, 17.66% identified as Non-Hispanic Black, and 4.84% identified as Other; there was no difference in MCR status by ethnicity in CCMA (not shown). Within EAS, 65.30% of participants were non-Hispanic White, 28.33% were non-Hispanic Black, and 6.37% were of another ethnicity. MCR was more prevalent among non-Hispanic Blacks (16.47%) than among non-Hispanic Whites (9.06%) or participants of other ethnicities (7.14%) in the EAS; p<0.01.

Vascular disease
Vascular disease within each cohort is shown in Table 2. In TASCOG and LonGenity, those with MCR were more likely than those without MCR to report diagnoses of diabetes and hypertension. In NCGG-SGS, TASCOG and EAS, those with MCR were more likely than those without MCR to report history of stroke. In TASCOG and in EAS, participants with MCR compared to those without MCR were more likely to have experienced any heart disease. Those with MCR were more likely than those without MCR to report having at least one vascular disease in TASCOG, LonGenity, and EAS.

Other health variables
BMI was higher among those with MCR than those without MCR in the NCGG-SGS, LonGenity, and EAS) cohorts. Depressive symptoms were higher among those with MCR than those without MCR in all cohorts.

IL-6
Median IL-6 concentration ranged from 0.31 pg/mL in CCMA to 2.98 pg/mL in EAS. Table 3 summarizes associations of IL-6 with MCR within each cohort The highest IL6 tertile was only associated with MCR in EAS (Table 3). IL6 as a continuous variable was not associated with MCR in any cohort.
Results of meta-analysis of IL-6 and MCR are shown in Fig. 1. In the meta-analysis, IL-6 as a continuous variable was not associated with MCR. However, compared to participants in the lowest tertile of IL-6, participants in the highest tertile (worse) of IL-6 did have increased odds of MCR in the pooled sample (aOR 1.57, 95% CI 1.01-2.44, p = 0.04).

CRP
Median CRP concentration ranged from 0.30 mg/L in NCGG-SGS to 2.00 mg/L in TASCOG. Table 4 summarizes associations of CRP with MCR within each cohort. CRP (continuous) was associated with increased odds of MCR in EAS among patients without any vascular disease (aOR 1.11, 95% CI 1.03-1.19). Significant associations of CRP tertiles with MCR were present in TASCOG and LonGenity, and EAS ( Table 4).
Results of meta-analysis of CRP and MCR are shown in Fig. 1. In meta-analysis, CRP as a continuous variable was not associated with MCR. However, compared to participants in the lowest tertile of CRP, participants in the highest tertile (worse) of CRP did have increased odds of MCR in the pooled sample (aOR 1.64, 95% CI 1.09-2.48, p = 0.02).

Discussion
We examined the association between two pro-inflammatory markers, IL-6 and CRP, and prevalent MCR in five community-based aging cohorts from three countries within the MCR Consortium. We found that higher concentrations of IL-6 were associated with increased odds of MCR only in the EAS cohort, both overall and among those with vascular disease. Higher concentrations of CRP were associated with increased odds of MCR in three out of the five cohorts, TASCOG, Lon-Genity, and EAS. Although associations between the higher (worse) CRP and IL-6 tertiles and MCR were only seen in three of the five cohorts, when a pooled meta-analysis was performed, a robust association was demonstrated. In meta-analysis of all five cohorts, we found that those with the highest concentrations of IL-6 and CRP had increased odds of MCR. Interestingly, in stratified analyses, higher concentrations of CRP were associated with increased odds of MCR among those with vascular disease in TASCOG and LonGenity, and among those without vascular disease in EAS.
To the best of our knowledge, there has been limited examination of inflammatory biomarkers in the context of MCR. One previous study based in 5642 older adults participating in the China Health and Retirement Longitudinal study reported that higher CRP levels were associated with increased odds of prevalent MCR [16]. The association with CRP was stronger in MCR cases with memory impairment [16]. On the other hand, several studies have associated CRP and IL-6 levels to presence of MCI and risk of conversion to dementia [35][36][37].
Derangements in pro-inflammatory cytokines (such as IL-6 and CRP) in older adults have been linked to presence and/or risk of vascular disease and vascular risk factors such as obesity as well as depressive symptoms [18][19][20][21]31,34]. All these health factors in turn have been reported to predict MCR [3,[38][39][40]. Hence, these clinical factors may mediate the relationship between inflammation and MCR [8,[28][29][30][31][32][33][34]. However, even after adjusting for BMI, depressive symptoms, and vascular disease, the association of the inflammatory biomarkers with MCR were attenuated but remained (unadjusted models not shown); indicating that other unmeasured clinical pathways may also mediate this relationship.
We noted that the association of inflammation with MCR varied in the presence of vascular disease. Elevated concentrations of IL-6 were associated with MCR among those with vascular disease only in the EAS. Elevated concentrations of CRP were associated with MCR among those with vascular disease in TASCOG and LonGenity. These results favor a vascular pathway that links inflammation to MCR. Inflammation has been linked to risk of ischemic strokes [41] and white matter disease on neuroimaging [42,43], which in turn has been linked to MCR [44]. The relationship of vascular risk factors and inflammation is bidirectional with higher vascular risk predicting progression of inflammation as well as white matter hyperintensities on neuroimaging [45]. However, Eastern and Western general populations have different anatomical distributions of small vessel disease [46]; suggesting population specific factors that may determine pathogenesis of MCR regionally. Alternatively, in EAS, elevated CRP was associated with increased odds of MCR only among those without vascular disease, though the confidence intervals were broad due to the relatively small non disease vascular sample. However, another possibility given that mean age in EAS was the highest among the five cohorts is that a different process maybe at play at advanced ages [4,17,[47][48][49]. The disassociation of IL-6 and CRP associations with MCR by presence of vascular disease also highlights that these two biomarkers may represent different inflammatory processes [50] or that underlying processes are incompletely captured in our study, which measured only two markers when many other inter-related inflammatory pathways and markers may be at play [51].
Limitations. Medical history was based on self-report. The number of co-morbidities included in our definition of vascular disease were limited based on availability across cohorts in the MCR consortium, and should not be viewed as exhaustive. We controlled for several potential clinical pathways that might mediate the association between inflammation and MCR, but other unmeasured confounders may be present. For example, data on physical activity, smoking or alcohol use was not available in all our cohorts, and should be examined in other populations. IL-6 and CRP have been extensively studied in the context of inflammation, vascular disease and cognitive decline, and were included in our analysis due to their availability in the MCR consortium. However, other inflammatory biomarkers and pathways may also play a role in the pathogenesis of MCR. As this is a cross-sectional study, we cannot establish causality or temporality.
Strengths include the availability of different populations in three countries in which the MCR definition was uniformly defined. The heterogeneity in populations is a strength that enabled us to build on previous single cohort and country studies that do not account for population differences in risk factors. The inflammatory marker ranges were specific to our cohorts, but the cut-scores and ranges provided should allow comparison with other cohorts. Fig. 1. Meta-analysis of associations of IL-6 (continuous and tertiles) and CRP (continuous and tertiles) with prevalent MCR in each cohort and pooled sample. Prevalence estimates (ES) for each study are graphically represented by dots and 95% confidence intervals by horizontal bars. gray boxes surrounding the dot graphically represent study weighting in the analysis, which is also shown in the last column. The diamond is the pooled prevalence estimate. Pooled effect estimate was similar for random-and fixed-effects model. The vertical dotted line represents the prevalence estimates of the pooled result.

Conclusion
Our preliminary analyses indicate that IL-6 and CRP are associated with increased odds of MCR among community-dwelling older adults, and the association varied by the presence of vascular disease. Our crosssectional findings should provide the foundation for longitudinal studies to establish the temporal associations of inflammation with MCR as well as provide insights into causality. A better understanding of the biological underpinnings of MCR will also help refine definitions of this syndrome by providing the possibility to reverse engineer definition of MCR based on emerging biological knowledge.

Funding
The research was supported by the National Institute On Aging (NIA) under award number 1R01AG057548-01A1. Funding agencies for the participating cohorts are as follows. The Einstein Aging Study is funded by the NIH/National Institute on Aging grant (PO1 AG03949). The LonGenity study is supported by grants from the NIH (R01AG044829, P01AG021654, R01 AG 046,949, K23AG051148, the Nathan Shock Center of Excellence for the Biology of Aging P30AG038072, Glenn Center for the Biology of Human Aging Paul Glenn Foundation Grant, American Federation for Aging Research. The TASCOG study is support by the National Health and Medical Research Council Project Grants (403,000). The CCMA study is supported by grants from the NIH/NIA (R01AG036921; RO1AGO44007-01A1). The NCGG-SGS was supported by grants from the Japan Agency for Medical Research and Development (15dk0107003h0003, 15dk020 7004h0203), JSPS KAKENHI Grant Number JP26242059 and Ministry of Health, Labour and Welfar (Research project on health and welfare promotion for the elderly).

Declaration of Competing Interest
The authors report no disclosures relevant to the manuscript.