Explaining the Variance in Cardiovascular Disease Risk Factors

Abstract

Background: Efforts to explain the burden of cardiovascular disease (CVD) often focus on genetic factors or social determinants of health. There is little evidence on the comparative predictive value of each, which could guide clinical and public health investments in measuring genetic versus social information. We compared the variance in CVDrelated outcomes explained by genetic versus socioeconomic predictors. Methods: Data were drawn from the Health and Retirement Study (N = 8,720). We examined self-reported diabetes, heart disease, depression, smoking, and body mass index, and objectively measured total and high-density lipoprotein cholesterol. For each outcome, we compared the variance explained by demographic characteristics, socioeconomic position (SEP), and genetic characteristics including a polygenic score for each outcome and principal components (PCs) for genetic ancestry. We used R-squared values derived from race-stratified multivariable linear regressions to evaluate the variance explained. Results:The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to the prediction of CVD-related outcomes.

Racial and Ethnic Disparities in Clinical Outcomes among Patients with Takotsubo Syndrome; A Nation-Wide Analysis

“…Conversely, for Black and Hispanic patients, socioeconomic status cannot be ignored as a crucial determinant of health outcomes, consistent with existing literature 1,16,17 . These observations underscore the complexity highlighted by recent cardiovascular research, which argues that both genetic factors and social determinants of health are integral to understanding disease outcomes 18 . This aligns with the emerging consensus that a multifaceted approach is essential for managing TTS, where neither genetic nor socioeconomic influences can be considered in isolation.…”

Explaining the Variance in Cardiovascular Health Indicators among Asian Americans: A Comparison of Demographic, Socioeconomic, and Ethnicity

“…Model 1 served as our baseline model, and examined the contribution of demographic factors (e.g., age and gender) on health. Our remaining models examined the contribution of our socioeconomic factors, health behaviors, and health conditions alongside the contribution of demographic factors, similar to previous work by 2022), 19 we determined the explained variance by calculating an adjusted R-squared. Although our outcomes are binary, thus necessitating the use of a logistic modeling framework, previous work has emphasized that using ordinary least squares regression is an appropriate method to calculate the adjusted R-squared for binary outcomes.…”

Risk factor contributions to socioeconomic inequality in cardiovascular risk in the Philippines: a cross-sectional study of nationally representative survey data

“…There has been a shift in primary prevention away from management of separate CVD risk factors, such as hypertension, dyslipidemia, high blood glucose, and smoking, to the assessment and control of CVD risk predicted from these factors [ 3 , 5 – 9 ]. And yet there is no evidence from LMICs, or even from high-income countries, [ 10 ] on socioeconomic inequality in predicted CVD risk broken down into the contributions of separate risk factors. This impedes effective implementation of a risk-based primary prevention strategy because information is lacking to plan the targeted distribution of treatments that meet the needs of high-risk groups.…”