Socioeconomic inequalities in physiological risk biomarkers and the role of lifestyles among Russians aged 35-69 years.

Sergi Trias-Llimós ORCID logo; Sarah Cook ORCID logo; Anne Elise Eggen; Alexander V Kudryavtsev; Sofia Malyutina; Vladimir M Shkolnikov; David A Leon ORCID logo; (2022) Socioeconomic inequalities in physiological risk biomarkers and the role of lifestyles among Russians aged 35-69 years. INTERNATIONAL JOURNAL FOR EQUITY IN HEALTH, 21 (1). 51-. ISSN 1475-9276 DOI: 10.1186/s12939-022-01650-3
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BACKGROUND: Socioeconomic inequalities in cardiovascular (CVD) health outcomes are well documented. While Russia has one of the highest levels of CVD mortality in the world, the literature on contemporary socio-economic inequalities in biomarker CVD risk factors is sparse. This paper aims to assess the extent and the direction of SEP inequalities in established physiological CVD risk biomarkers, and to explore the role of lifestyle factors in explaining SEP inequalities in physiological CVD risk biomarkers. METHODS: We used cross-sectional data from a general population-based survey of Russians aged 35-69 years living in two cities (n = 4540, Know Your Heart study 2015-18). Logistic models were used to assess the associations between raised physiological risk biomarkers levels (blood pressure levels, cholesterol levels, triglycerides, HbA1C, and C-reactive protein) and socioeconomic position (SEP) (education and household financial constraints) adjusting for age, obesity, smoking, alcohol and health-care seeking behavior. RESULTS: High education was negatively associated with a raised risk of blood pressure (systolic and diastolic) and C-reactive protein for both men and women. High education was positively associated with total cholesterol, with higher HDL levels among women, and with low triglycerides and HbA1c levels among men. For the remaining risk biomarkers, we found little statistical support for SEP inequalities. Adjustment for lifestyle factors, and particularly BMI and waist-hip ratio, led to a reduction in the observed SEP inequalities in raised biomarkers risk levels, especially among women. High financial constraints were weakly associated with high risk biomarkers levels, except for strong evidence for an association with C-reactive protein (men). CONCLUSIONS: Notable differences in risk biomarkers inequalities were observed according to the SEP measure employed. Clear educational inequalities in raised physiological risk biomarkers levels, particularly in blood pressure and C-reactive protein were seen in Russia and are partly explained by lifestyle factors, particularly obesity among women. These findings provide evidence-based information on the need for tackling health inequalities in the Russian population, which may help to further contribute to CVD mortality decline.


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