Background There is ongoing debate about whether education or socioeconomic status (SES) should be inputs into cardiovascular disease (CVD) prediction algorithms and clinical risk adjustment models. It is also unclear whether intervening on education will affect CVD, in part because there is controversy regarding whether education is a determinant of CVD or merely correlated due to confounding or reverse causation. We took advantage of a natural experiment to estimate the population-level effects of educational attainment on CVD and related risk factors. Methods and findings We took advantage of variation in United States state-level compulsory schooling laws (CSLs), a natural experiment that was associated with geographic and temporal differences in the minimum number of years that children were required to attend school. We linked census data on educational attainment (N = approximately 5.4 million) during childhood with outcomes in adulthood, using cohort data from the 1992–2012 waves of the Health and Retirement Study (HRS; N = 30,853) and serial cross-sectional data from 1971–2012 waves of the National Health and Nutrition Examination Survey (NHANES; N = 44,732). We examined self-reported CVD outcomes and related risk factors, as well as relevant serum biomarkers. Using instrumental variables (IV) analysis, we found that increased educational attainment was associated with reduced smoking (HRS β ?0.036, 95%CI: ?0.06, ?0.02, p 0.01; NHANES β ?0.032, 95%CI: ?0.05, ?0.02, p 0.01), depression (HRS β ?0.049, 95%CI: ?0.07, ?0.03, p 0.01), triglycerides (NHANES β ?0.039, 95%CI: ?0.06, ?0.01, p 0.01), and heart disease (HRS β ?0.025, 95%CI: ?0.04, ?0.002, p = 0.01), and improvements in high-density lipoprotein (HDL) cholesterol (HRS β 1.50, 95%CI: 0.34, 2.49, p 0.01; NHANES β 0.86, 95%CI: 0.32, 1.48, p 0.01), but increased BMI (HRS β 0.20, 95%CI: 0.002, 0.40, p = 0.05; NHANES β 0.13, 95%CI: 0.01, 0.32, p = 0.05) and total cholesterol (HRS β 2.73, 95%CI: 0.09, 4.97, p = 0.03). While most findings were cross-validated across both data sets, they were not robust to the inclusion of state fixed effects. Limitations included residual confounding, use of self-reported outcomes for some analyses, and possibly limited generalizability to more recent cohorts. Conclusions This study provides rigorous population-level estimates of the association of educational attainment with CVD. These findings may guide future implementation of interventions to address the social determinants of CVD and strengthen the argument for including educational attainment in prediction algorithms and primary prevention guidelines for CVD.
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