Volunteering and Cardiovascular Biomarkers: A Machine-Learning Causal Approach in Health and Retirement Study
Seoyoun Kim, Koichiro Shiba, Cal Halvorsen

TL;DR
Volunteering may improve cardiovascular health by reducing biomarkers like blood glucose and inflammation, according to a study using causal analysis.
Contribution
This study uses machine learning and causal inference to estimate the impact of volunteering on cardiovascular biomarkers.
Findings
Sustained volunteering is linked to lower levels of unhealthy HbA1c and TC/HDL ratio.
Volunteering is associated with reduced chronic inflammation and improved kidney function biomarkers.
Long-term volunteering correlates with lower blood pressure but a slight increase in BMI.
Abstract
Prosocial engagement, such as volunteering, has been recognized for its protective effects against cardiovascular diseases (CVD), yet its causal impact on CVD-related biomarkers remains unclear. This study employs targeted maximum likelihood estimator (TMLE), a doubly robust causal inference method, to estimate the effect of the changes in volunteer activity (2006/2008 and 2010/2012) and seven key CVD-related biomarkers (in 2014/2016) using the Health and Retirement Study (N = 17,479). The biomarkers assessed include blood glucose (HbA1c), lipid regulation (TC/HDL ratio), chronic inflammation (CRP), kidney function (Cystatin C), blood pressure (systolic and diastolic), and BMI. Volunteering status was categorized into non-volunteers, initiators, stoppers, and sustained volunteers. TMLE results indicate that, had the entire population engaged in volunteering, we would expect improvements…
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Taxonomy
TopicsCardiovascular Health and Risk Factors · Health disparities and outcomes · Cardiac Health and Mental Health
