Lipoprotein(a) Distribution Across Race/Ethnicity and Association with Mortality Outcomes in NHANES III (1988-1994) with Follow‑Up to 2019.

Lipoprotein(a) Lp(a) is a genetically determined and likely causal independent risk factor for cardiovascular outcomes and mortality with levels >50mg/dl considered risk enhancing. Over 90% of variation in levels is genetically determined with levels varying by race/ethnicity. Evidence on whether Lp(a) risk thresholds vary by race/ethnicity and remains inconsistent. This study examines whether the association between Lp(a) and mortality differs by race/ethnicity. We analysed survey-weighted data from a nationally representative muti-ethnic cohort of U.S. adults from NHANES III with mortality follow-up through 2019. Participants were stratified into non-Hispanic White, non-Hispanic Black or Mexican-American. Associations between Lp(a) and mortality outcomes were estimated using multivariable Cox and Fine-Gray competing risk models. Lp(a) were analysed as continuous variables, logarithmically transformed and divided into three groups (75 mg/dL). A total of 50,519,751 survey-weighted records were included. Mean follow-up was 22.6 years. Median Lp(a) concentrations were higher among non-Hispanic Black participants (36 mg/dL, IQR 22-66) than non-Hispanic White (12 mg/dL, IQR 3-30) and Mexican-American (8 mg/dL, IQR 2-22) participants. Mexican American participants with Lp(a) >75 mg/dL had a higher risk of cardiovascular mortality that persisted after multivariable adjustment (sHR 2.93, 95% CI 1.01-8.56, p-value 0.049). Among non-Hispanic Black participants, higher Lp(a) was linked to all-cause and cardiovascular mortality in unadjusted models but not after adjustment. No significant association was detected in non-Hispanic White participants. In conclusion, Lp(a) distributions and their relationship with clinical outcomes vary by race/ethnicity. Our findings suggest that prognostic thresholds for Lp(a) may differ, supporting the need to define and validate race/ethnicity-specific cut-offs that best predict cardiovascular outcomes and improve risk stratification.