Methods We conducted a Mendelian randomization analysis to assess the causal effects of 2,925 molecular biomarkers (from proteomics data) and 35 clinical biomarkers on MASLD. Mediation analysis was performed to determine whether clinical biomarkers mediated the effects of molecular biomarkers. The association between key clinical biomarkers and MASLD was externally validated in a hospital-based cohort (n = 415). A machine learning–based diagnostic model for MASLD was developed and validated using the identified molecular biomarkers. Prognostic significance was evaluated for both molecular and clinical biomarkers.
Results Six molecular biomarkers—including canopy FGF signaling regulator 4 (CNPY4), ectonucleoside triphosphate diphosphohydrolase 6 (ENTPD6), and major histocompatibility complex, class I, A (HLA-A)—and eight clinical biomarkers (e.g., serum total protein (STP)) were identified as causally related to MASLD. STP partially mediated the effect of HLA-A on MASLD (23.61%) and was associated with MASLD in the external cohort (odds ratio = 1.080, 95% confidence interval: 1.011–1.155). A random forest model demonstrated high diagnostic performance (AUC = 0.941 in training; 0.875 in validation). High expression levels of CNPY4 and ENTPD6 were associated with the development of and poorer survival from hepatocellular carcinoma. Low STP (<60 g/L) predicted all-cause mortality (HR = 2.50, 95% confidence interval: 1.22–5.09).
Conclusions This study identifies six causal molecular biomarkers (e.g., CNPY4, ENTPD6, HLA-A) and eight clinical biomarkers for MASLD. Notably, STP mediates the effect of HLA-A on MASLD and is associated with all-cause mortality.
Background and Aims The causal biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD) and their clinical value remain unclear. In this study, we aimed to identify biomarkers for MASLD and evaluate their diagnostic and prognostic significance.
Methods We conducted a Mendelian randomization analysis to assess the causal effects of 2,925 molecular biomarkers (from proteomics data) and 35 clinical biomarkers on MASLD. Mediation analysis was performed to determine whether clinical biomarkers mediated the effects of molecular biomarkers. The association between key clinical biomarkers and MASLD was externally validated in a hospital-based cohort (n = 415). A machine learning–based diagnostic model for MASLD was developed and validated using the identified molecular biomarkers. Prognostic significance was evaluated for both molecular and clinical biomarkers.
Results Six molecular biomarkers—including canopy FGF signaling regulator 4 (CNPY4), ectonucleoside triphosphate diphosphohydrolase 6 (ENTPD6), and major histocompatibility complex, class I, A (HLA-A)—and eight clinical biomarkers (e.g., serum total protein (STP)) were identified as causally related to MASLD. STP partially mediated the effect of HLA-A on MASLD (23.61%) and was associated with MASLD in the external cohort (odds ratio = 1.080, 95% confidence interval: 1.011–1.155). A random forest model demonstrated high diagnostic performance (AUC = 0.941 in training; 0.875 in validation). High expression levels of CNPY4 and ENTPD6 were associated with the development of and poorer survival from hepatocellular carcinoma. Low STP (<60 g/L) predicted all-cause mortality (HR = 2.50, 95% confidence interval: 1.22–5.09).
Conclusions We identify six causal molecular biomarkers (e.g., CNPY4, ENTPD6, HLA-A) and eight clinical biomarkers (e.g., serum total protein) for MASLD across various independent cohorts. Serum total protein levels partially mediated the effect of HLA-A on MASLD, highlighting a novel immune-metabolic pathway. Based on these findings, we develop a random forest model that demonstrates high accuracy in identifying MASLD. Additionally, CNPY4 and ENTPD6 are associated with poor survival in HCC, while low serum total protein levels predicted higher all-cause mortality. These findings support a multi-omics framework for biomarker-driven diagnosis and risk prediction in MASLD.We identify six causal molecular biomarkers (e.g., CNPY4, ENTPD6, HLA-A) and eight clinical biomarkers (e.g., serum total protein) for MASLD across various independent cohorts. Serum total protein levels partially mediated the effect of HLA-A on MASLD, highlighting a novel immune-metabolic pathway. Based on these findings, we develop a random forest model that demonstrates high accuracy in identifying MASLD. Additionally, CNPY4 and ENTPD6 are associated with poor survival in HCC, while low serum total protein levels predicted higher all-cause mortality. These findings support a multi-omics framework for biomarker-driven diagnosis and risk prediction in MASLD.
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The study was recently published in the Journal of Clinical and Translational Hepatology.
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