Tiffany M. Powell-Wiley, Yvonne Baumer, Foster Osei Baah, Andrew S. Baez, Nicole Farmer, Christa T. Mahlobo, Mario A. Pita, Kameswari A. Potharaju, Kosuke Tamura, Gwenyth R. Wallen
4 min
This review addresses the critical need to better understand how social determinants of health (SDoH) contribute to cardiovascular disease (CVD). While SDoH—encompassing economic, social, environmental, and psychosocial factors—are known to influence health outcomes, the specific pathways linking these structural drivers to cardiovascular health remain understudied. The authors propose a new, equity-focused framework that centers on the lived experiences of marginalized populations to better inform research and clinical interventions.
The authors argue that existing SDoH models often fail to account for the structural processes of marginalization, such as systemic racism, discrimination, and social exclusion. Their revised framework categorizes these determinants into two domains: structural (sociopolitical and economic contexts, including laws and policies) and intermediary (social and community contexts, such as food environments and housing stability). By focusing on these constructs, the framework aims to help researchers move beyond using race and ethnicity as mere proxies for social risk, instead identifying the specific structural drivers that create health disparities.
A central contribution of this paper is the exploration of the biological mechanisms connecting SDoH to CVD, termed the biology of adversity. Chronic exposure to social stressors—such as neighborhood violence, discrimination, and financial strain—activates the sympathetic nervous system and the hypothalamic-pituitary-adrenal axis. This chronic activation leads to dysregulated stress hormone signaling, which promotes persistent systemic inflammation, alters immune cell function, and accelerates cellular aging (e.g., through telomere shortening). These biological sequelae directly contribute to the development of CVD risk factors like hypertension, obesity, and atherosclerosis.
The authors provide a roadmap for integrating SDoH into clinical care and research. They advocate for multilevel, community-engaged interventions that address both individual social needs and upstream structural factors. The paper emphasizes that future research must employ mixed-methods approaches—combining rigorous quantitative data with qualitative insights—to fully capture the impact of lived experiences on cardiovascular health. By standardizing SDoH measures in electronic health records and clinical trials, the medical community can better tailor care to the needs of the most vulnerable populations.
Social determinants of health (SDoH), which encompass the economic, social, environmental, and psychosocial factors that influence health, play a significant role in the development of cardiovascular disease (CVD) risk factors as well as CVD morbidity and mortality. The COVID-19 pandemic and the current social justice movement sparked by the death of George Floyd have laid bare long-existing health inequities in our society driven by SDoH. Despite a recent focus on these structural drivers of health disparities, the impact of SDoH on cardiovascular health and CVD outcomes remains understudied and incompletely understood. To further investigate the mechanisms connecting SDoH and CVD, and ultimately design targeted and effective interventions, it is important to foster interdisciplinary efforts that incorporate translational, epidemiological, and clinical research in examining SDoH-CVD relationships. This review aims to facilitate research coordination and intervention development by providing an evidence-based framework for SDoH rooted in the lived experiences of marginalized populations. Our framework highlights critical structural/socioeconomic, environmental, and psychosocial factors most strongly associated with CVD and explores several of the underlying biologic mechanisms connecting SDoH to CVD pathogenesis, including excess stress hormones, inflammation, immune cell function, and cellular aging. We present landmark studies and recent findings about SDoH in our framework, with careful consideration of the constructs and measures utilized. Finally, we provide a roadmap for future SDoH research focused on individual, clinical, and policy approaches directed towards developing multilevel community-engaged interventions to promote cardiovascular health.
Alex: [careful] Right, the position is that we've refined the pharmacology of lipids while an upstream signal stays active. That reads as an interpretation of the mechanistic model, not a trial result showing guideline failure. And the review notes that social factors are rarely operationalized in clinical trials, so the comparison hasn't really been run.
Sam: [reflecting] That's where allostatic load comes in, then. It's the proposed construct for the biological embodiment of environment. But if the goal is to move beyond race as a proxy, what do they actually propose measuring? [[RP_SECTION:roadmap-for-future-research|Roadmap for future research]]
Alex: [deliberate] They offer a roadmap rather than a validated panel. It centers on multilevel interventions and on integrating standardized social determinant measures into electronic health records, so exposure is captured consistently enough to be linked to biology later. I'd stress that this is infrastructure for testing the model, not evidence that the model holds.
Sam: [slower] So the order of operations is measure the structural exposures properly, collect longitudinal data, then see whether they map onto inflammatory pathways.
Alex: [concluding, quiet] Yes. If that mapping holds, it could give personalized cardiovascular prevention a biological target beyond isolated risk factors. For now the review's contribution is a coherent mechanism and a clear statement of what data would be needed to test it.
Sam: [grounded] That's a useful distinction. A well-specified hypothesis with an identified evidentiary gap is worth more than a confident claim built on the same gap.
Alex: If you want the figures and the method choices we skipped, you can generate a deep dive of this paper. The paper has the rest either way.
Sam: Thanks for listening.