Abstract Body: Introduction: In 2022, the American Heart Association introduced the Life's Essential 8 (LE8) enhanced metric of cardiovascular health (CVH), a strong predictor of all-cause and cardiovascular disease (CVD) mortality in US adults, with evidence of a dose-response relationship. We aimed to identify the LE8 factors most strongly associated with lower all-cause and CVD mortality to inform prevention strategies.

Methods: Data from 55,879 U.S. adults aged ≥20 years (mean age 47.5 years, 51.7% women) from NHANES 1999–2018 with mortality linkage through December 31, 2019 (mean follow-up 9.8 years) were used. LE8 lifestyle and biological factors and covariate data (age, sex, race/ethnicity, education, marital status, and poverty-income ratio) were imputed using multiple imputation (m=5 and m=10, predictive mean matching). Adaptive Least Absolute Shrinkage and Selection Operator (Lasso) variable selection was performed separately on each imputed dataset to identify the dominant LE8 predictors for all-cause and CVD mortality by removing less important variables. Factors selected in ≥60% of imputations were included in Cox proportional hazards adjusted models. Hazard ratios and 95% confidence intervals were pooled across imputations using Rubin's rules. Discrimination was assessed with Harrell's C-index.

Results: During follow-up, 8,254 all-cause deaths (14.8%) and 2,112 CVD deaths (3.8%) occurred. Adaptive lasso consistently selected seven LE8 predictors of all-cause mortality (Table 1) and six predictors of CVD mortality (Table 2). Physical Activity was not selected (0%). Nicotine exposure was selected only in 40% of CVD models. Each 10-point increases in blood lipids were associated with small increments in all-cause and CVD mortality (HR for all-cause mortality 1.003, 95% CI 1.002-1.004, and for CVD mortality 1.002,1.0, 1.004). Each 10-point increases in nicotine exposure showed the largest reduction in all-cause mortality (HR 0.994, 95% CI 0.993–0.994). Meanwhile, each 10-point reduction in blood glucose yielded the greatest drop in CVD mortality (0.994, 0.993–0.996). The model C-index was 0.742 (95% CI 0.716–0.768) for all-cause mortality and 0.781 (0.751–0.811) for CVD mortality, both indicating good discrimination.

Conclusion: Machine learning models identified six key CVH predictors most strongly associated with CVD mortality. Findings suggest that improving blood glucose and sleep health may offer the greatest mortality risk reduction.