Prognostic value of abdominal obesity indicators for all-cause mortality in familial hypercholesterolemia

This study aimed to investigate the association of abdominal obesity indicators (Waist circumference (WC), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), weight-adjusted waist index (WWI), and visceral adiposity index (VAI)) with the risk of all-cause mortality in familial hypercholesterolemia (FH) and to compare their prognostic performance.

A cohort of 1,188 FH patients from the National Health and Nutrition Examination Survey (1999-2018) was analyzed. Mortality outcomes through 2019 were identified via linkage to the National Death Index. Multivariable Cox proportional hazards models estimated hazard ratios (HRs) and 95 % confidence intervals (CIs) for mortality. The incremental predictive value of each indicator beyond a base model was assessed using the C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

During a median follow-up of 8.4 years, 215 patients (18.1 %) died. After full multivariable adjustment, individuals in the highest tertile of ABSI exhibited a significantly elevated mortality risk compared to the lowest tertile (HR = 1.97, 95 % CI: 1.31-2.95). Similarly, the highest WWI tertile was associated with increased mortality (HR = 1.49, 95 % CI: 1.01-2.22). No significant associations were observed for WC, LAP, BRI, or VAI. Among all evaluated indicators, ABSI conferred the most substantial incremental predictive value when added to the base model, significantly improving discrimination (C-statistic: 0.809, 95 % CI: 0.781-0.838), integrated discrimination (IDI: 0.033, 95 % CI: 0.011-0.063), and risk reclassification (NRI: 0.256, 95 % CI: 0.138-0.347).

Elevated ABSI and WWI independently predicted increased all-cause mortality in familial hypercholesterolemia patients. Crucially, ABSI demonstrated superior prognostic performance, significantly enhancing mortality risk stratification beyond established factors, and its incorporation into clinical models may improve prognostication and guide tailored management.