Large‐Scale Plasma Proteomics Improves Prediction of Major Adverse Liver Outcomes in Individuals With <scp>MASLD</scp>

AIMS: Cirrhosis and hepatocellular carcinoma are severe major adverse liver outcomes (MALOs) of metabolic dysfunction-associated steatotic liver disease (MASLD), yet the relationship between proteomics and MALOs remains unclear. This study aimed to identify plasma proteomic features associated with MALOs and evaluate the potential of proteomics to enhance the prediction of MALOs risks in individuals with MASLD. MATERIALS AND METHODS: This prospective cohort study included 14 166 participants with MASLD from the UK Biobank. Associations between 2920 plasma proteins and incident MALOs were assessed using multivariable Cox proportional hazards regression models. Proteomic predictors were selected using elastic net-penalised Cox regression with five-fold cross-validation and a protein risk score (ProRS) was derived. Predictive performance was evaluated in validation sets using Harrell's C-index, category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Associations between the ProRS and liver disease activity were further examined using multivariable linear regression with MRI-derived iron-corrected T1 (cT1) values. RESULTS: Over a median follow-up of 13.4 years, 151 participants (1.1%) developed MALOs. We identified 834 proteins (803 positively and 31 inversely) associated with MALOs in multivariable analysis (false discovery rate-adjusted p-value < 0.01), primarily enriched in pathways related to cell adhesion, extracellular region, cytokine activity and cytokine-cytokine receptor interactions. The top five proteins positively associated with increased risks of MALOs were CDHR2, CEACAM1, FCAMR, LTBP2 and GGT1, while TTR, APOM, KITLG, C1orf56 and PROC showed inverse associations. Incorporation of the ProRS derived from 13 elastic net-selected proteins significantly improved the prediction of MALOs, achieving a C-index of 0.880 (95% CI: 0.847, 0.910) with an increment of 0.072 (95% CI: 0.041, 0.102). Furthermore, each standard deviation increase in ProRS was associated with higher cT1 values (β = 7.50; 95% CI: 1.91, 13.10). CONCLUSIONS: This study identified plasma proteins associated with MALOs in individuals with MASLD. Incorporation of a proteomics-derived risk score into the clinical model significantly improved predictive performance and was associated with greater liver disease activity, suggesting the potential to enable more precise risk stratification and personalised management.

Authors

Institutions

Publication Details

Journal
Diabetes Obesity and Metabolism
Published
2026-05-10
DOI
https://doi.org/10.1111/dom.70824
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Large‐Scale Plasma Proteomics Improves Prediction of Major Adverse Liver Outcomes in Individuals With MASLD

Yunfei Liao, Jijuan Zhang, Pan A, Yi-Xiang Wang et al.
Diabetes Obesity and Metabolism
Liver Disease Diagnosis and Treatment
article

Large‐Scale Plasma Proteomics Improves Prediction of Major Adverse Liver Outcomes in Individuals With MASLD

Yunfei Liao, Jijuan Zhang, Pan A, Yi-Xiang Wang, L Yang, Hancheng Yu, Gang Liu, Tingting Geng, Yumei Huang
article en

Abstract

AIMS: Cirrhosis and hepatocellular carcinoma are severe major adverse liver outcomes (MALOs) of metabolic dysfunction-associated steatotic liver disease (MASLD), yet the relationship between proteomics and MALOs remains unclear. This study aimed to identify plasma proteomic features associated with MALOs and evaluate the potential of proteomics to enhance the prediction of MALOs risks in individuals with MASLD. MATERIALS AND METHODS: This prospective cohort study included 14 166 participants with MASLD from the UK Biobank. Associations between 2920 plasma proteins and incident MALOs were assessed using multivariable Cox proportional hazards regression models. Proteomic predictors were selected using elastic net-penalised Cox regression with five-fold cross-validation and a protein risk score (ProRS) was derived. Predictive performance was evaluated in validation sets using Harrell's C-index, category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Associations between the ProRS and liver disease activity were further examined using multivariable linear regression with MRI-derived iron-corrected T1 (cT1) values. RESULTS: Over a median follow-up of 13.4 years, 151 participants (1.1%) developed MALOs. We identified 834 proteins (803 positively and 31 inversely) associated with MALOs in multivariable analysis (false discovery rate-adjusted p-value < 0.01), primarily enriched in pathways related to cell adhesion, extracellular region, cytokine activity and cytokine-cytokine receptor interactions. The top five proteins positively associated with increased risks of MALOs were CDHR2, CEACAM1, FCAMR, LTBP2 and GGT1, while TTR, APOM, KITLG, C1orf56 and PROC showed inverse associations. Incorporation of the ProRS derived from 13 elastic net-selected proteins significantly improved the prediction of MALOs, achieving a C-index of 0.880 (95% CI: 0.847, 0.910) with an increment of 0.072 (95% CI: 0.041, 0.102). Furthermore, each standard deviation increase in ProRS was associated with higher cT1 values (β = 7.50; 95% CI: 1.91, 13.10). CONCLUSIONS: This study identified plasma proteins associated with MALOs in individuals with MASLD. Incorporation of a proteomics-derived risk score into the clinical model significantly improved predictive performance and was associated with greater liver disease activity, suggesting the potential to enable more precise risk stratification and personalised management.

Diabetes Obesity and Metabolism
Union Hospital (HK), Wuhan Union Hospital (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Reduced inequalities
Openalex Percentile: Top 5%
Liver Disease Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.