Eur J Prev Cardiol. 2026 May 8:zwag257. doi: 10.1093/eurjpc/zwag257. Online ahead of print.
ABSTRACT
BACKGROUND AND AIMS: Lipid abnormalities-particularly low-density lipoprotein cholesterol (LDL-c) and lipoprotein(a) [Lp(a)]-have been implicated in aortic stenosis (AS), yet translation into clinically actionable risk assessment remains underdeveloped, especially regarding sex-specific evaluation. This study aims to quantify sex-specific associations between a comprehensive lipid profile and the risks of AS and aortic regurgitation (AR), and to identify the most informative markers and marker combinations for improved risk assessment.
METHODS: In 365,771 UK Biobank participants (mean age 56.1±8.07 years; 55.74% female) free of baseline cardiovascular disease. Routine lipid traits underwent hierarchical clustering and were related to incident AS and AR using sex-stratified Cox models. Discordance analyses and time-dependent concordance index were employed to compare risk assessment performance of different markers.
RESULTS: Hierarchical clustering revealed three clusters in both sexes-an apolipoprotein B (apoB)-containing cluster and an apolipoprotein A containing cluster-while Lp(a) occupied an independent branch. During a median follow-up of 13.8 years, there were 3118 incident AS and 1239 AR cases. Total cholesterol, apoB, LDL-c, non-high-density lipoprotein cholesterol in apoB-containing cluster and Lp(a) were each independently associated with higher AS risk in both sexes (all P < 0.01), with Lp(a) conferred additional sex-specific effect (P for interaction = 0.004). Discordance analyses showed that apoB outperformed LDL-c in association with AS. Addition of Lp(a) to ApoB or LDL-c improved AS risk prediction over either marker alone-especially in men. No lipid trait was associated with AR.
CONCLUSION: ApoB may substitute for LDL-c as the primary particle-burden marker, whereas Lp(a) should be incorporated as an independent sex-specific risk enhancer in AS risk assessment. These results support sex-specific, multi-biomarker assessment to optimize AS risk stratification and future preventive strategies.
PMID:42104638 | DOI:10.1093/eurjpc/zwag257