Proteomic signature of metabolic dysfunction-associated steatotic liver disease and risk of atherosclerotic cardiovascular disease

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with increased atherosclerotic cardiovascular disease (ASCVD) risk. However, evidence on proteomic mechanisms linking MASLD to ASCVD is limited. This study aims to identify proteomic signatures of MASLD and ASCVD subtypes (ischemic heart disease [IHD], peripheral artery disease [PAD], and stroke), evaluate mediating effects of proteins, and develop a proteomic-based ASCVD risk prediction model in MASLD patients. Among 40,913 UK Biobank participants (median follow-up 13.42 years [interquartile range, 12.52-14.22]), 14,425 (35.26%) had MASLD at baseline, and 6,014 (14.70%) developed ASCVD during follow-up (4,420 IHD, 866 PAD, and 1,767 stroke events; subtypes not mutually exclusive). We constructed a binary variable representing proteomics-inferred MASLD (cProMASLD) from MASLD-associated proteins. Two-step Mendelian randomization was applied to assess the mediating effects of proteins associated with MASLD and ASCVD subtypes. Furthermore, we integrated the all shared proteins associated with both MASLD and ASCVD subtypes into the conventional SCORE2 model to develop a prediction model specifically for ASCVD subtypes in the MASLD population, named Pro-SCORE2. Both MASLD and cProMASLD were significantly associated with an increased risk of ASCVD subtypes, with stronger associations observed for cProMASLD (IHD: HR 1.50 [95% CI 1.41-1.60] vs. 1.58 [1.48-1.68]; PAD: 1.25 [1.09-1.44] vs. 1.43 [1.24-1.64]; stroke: 1.19 [1.08-1.31] vs. 1.21[1.10-1.34]). After adjusting for MASLD, cProMASLD remained positively associated with ASCVD risk. This suggests that cProMASLD may capture MASLD-related physiological heterogeneity beyond clinical MASLD classification. We found 15, 3, and 3 proteins mediating the associations of MASLD with IHD, PAD, and stroke, respectively, including FABP4 (MASLD-IHD, mediation proportion: 15.12%), IL7R (MASLD-PAD, 7.45%), and EDA2R (MASLD-stroke, 9.24%). The Pro-SCORE2 significantly improved ASCVD risk prediction in the MASLD population, with a c-index increase of 7.5-9.6% and a 10-year AUC increase of 5.8-9.2% compared to SCORE2. These findings may offer new insights for risk stratification and potential therapeutic targets for ASCVD in MASLD patients.

Keywords: ASCVD; MASLD; Mediation analysis; Mendelian randomization; Prediction model; Proteomic.

Fig. 1

illustrates the overall analytical framework of this study, with detailed descriptions of each step provided below

Fig. 2

Independent and joint associations of MASLD, ProMASLD, and FLI with ASCVD subtypes in overall and stratified populations. All models were adjusted for age, sex, race, Townsend Deprivation Index, smoking status, physical activity, diet score, chronic kidney disease, family history of cardiometabolic diseases, and aspirin use

Fig. 3

Mediation analysis of the association between MASLD and ASCVD subtypes through proteins using a two-step MR mediation method. Panel (a) displays the total effect of MASLD on ASCVD, while Panel (b) shows the MASLD-protein association, the protein-ASCVD association, and the mediation proportion

Fig. 4

Proteins associated with MASLD and ASCVD: interaction network analysis, KEGG and GO enriched pathways, and potential drug predictions. GO enrichment results show the top five pathways in each category, and drug predictions list the top 10 potential drugs for proteins with positive mediation effects, ranked by interaction score

Fig. 5

Performance of ASCVD risk prediction models in the MASLD population. Time-dependent area under the curve (AUC) and Harrell’s concordance index (C-index) were used to evaluate and compare the performance of SCORE2, SCORE2-PRO, and PRO models. The top 15 variables contributing most to prediction in SCORE2-PRO are visualized using SHAP