To what extent do biomarkers account for the large social disparities in health in Moscow?

Abstract

The Russian population continues to face political and economic challenges, has experienced poor general health and high mortality for decades, and has exhibited widening health disparities. The physiological factors underlying links between health and socioeconomic position in the Russian population are therefore an important topic to investigate. We used data from a population-based survey of Moscow residents aged 55 and older (n=1495), fielded between December 2006 and June 2009, to address two questions. First, are social disparities evident across different clusters of biomarkers? Second, does biological risk mediate the link between socioeconomic status and health? Health outcomes included subscales for general health, physical function, and bodily pain. Socioeconomic status was represented by education and an index of material resources. Biological risk was measured by 20 biomarkers including cardiovascular, inflammatory, and neuroendocrine markers as well as heart rate parameters from 24-hour ECG monitoring. For both sexes, the age-adjusted educational disparity in standard cardiovascular risk factors was substantial (men: standardized β= −0.16, 95% CI = −0.23 to −0.09; women: β= −0.25, CI = −0.32 to −0.18). Education differences in inflammation were also evident in both men ( β= −0.17, CI = −0.25 to −0.09) and women (β= −0.09, CI = −0.17 to −0.01). Heart rate parameters differed by education only in men (β= −0.10, CI = −0.18 to −0.02). The associations between material resources and biological risk scores were generally weaker than those for education. Social disparities in neuroendocrine markers were negligible for men and women. In terms of mediating effects, biological risk accounted for more of the education gap in general health and physical function (19–36%) than in bodily pain (12–18%). Inclusion of inflammatory markers and heart rate parameters—which were important predictors of health outcomes—may explain how we accounted for more of the social disparities than previous studies.

Role of allostatic load and health behaviours in explaining socioeconomic disparities in mortality: a structural equation modelling approach

“…For example, in the analysis of 1189 initially high functioning men and women aged 70–79 years from the MacArthur study of Successful Aging, Seeman et al 14 have demonstrated that approximately one-third of the educational differences in mortality was mediated by the summary index of AL. Another study found that biological risk accounted for a substantial portion (between 19% and 36%) of social disparities in health among participants of the Survey on Stress, Aging, and Health in Russia 15. A Taiwanese study showed that higher AL was independent determinant of self-rated health but no explanatory factor for socioeconomic differentials in health 33.…”

“…Using data from the MacArthur study, Seeman et al have identified that lower SEP is associated with increased AL, and that AL mediates the relationship between SEP and mortality, explaining around 35% of the difference in mortality attributable to educational differences 14. Similarly, another study found that biological risk factors explained 19% of the educational differences in general health 15. In contrast to these findings, an analysis based on the Taiwanese social environment and biomarkers of ageing study failed to find support for the mediating role of AL biomarkers in explaining the social gradient in health 12.…”

Recalibration of the SCORE risk chart for the Russian population

“…Thus, the results for males showing underestimation of CVD mortality risk using conventional SCORE-High and recalibrated SCORE-MoSP equations can be explained by specific CVD risk factors in Russia [27–31]. The findings also warn that conventional SCORE-High equations cannot adequately predict the elevated CVD mortality risk in Russia even if national or sub-national mortality data are used.…”

“…The Russian LRC studies found that differently from other developed countries showing increasing CHD risk with increasing levels of cholesterol, this relationship among adult males in Russia was non-linear but rather U or J shape [21]. Finally, there is some evidence that psychosocial factors such as stress, effort-reward imbalance, and a lack of social support, which were unaccounted for in the original and recalibrated SCORE algorithms, may also explain a substantial part of elevated risk of cardiovascular diseases in Russia [30, 31]. …”

Sex Differences in Biological Markers of Health in the Study of Stress, Aging and Health In russia

“…Anthropometry included body height measured with a wall-mounted stadiometer, body weight measured with calibrated scales, waist and hip circumferences measured with a calibrated tape in the standing position [43]. The standard cutoffs were used to define low versus high-risk levels of total cholesterol, high density lipoproteins (HDL), triglycerides [48], obesity [49], waist circumference [50], Glycosylated hemoglobin (Hb) [51] and C-reactive protein (CRP) [52] that had been previously used with the SAHR data ( Table 1) [53]. For a few other biomarkers, the high-risk group was defined as the highest quintile of unweighted sex-specific distribution for interleukin-6 (IL-6) and fibrinogen vs. all others (Table 1).…”