Liver biomarkers improve ASCVD risk prediction in US and Chinese cohorts

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medRxiv Published April 24, 2026 Medically reviewed April 29, 2026 Study authors: Peng, T.; Liu, C. l. DOI ↗ By Dr. Amelia Tan, PhD · Internal Medicine & Chronic Disease

Key Takeaway

Consider adding liver biomarkers to ASCVD risk models to refine stratification, but interpret associations cautiously given observational design.

This cohort study pooled data from the US National Health and Nutrition Examination Survey (NHANES 2005-2018) and the China Health and Retirement Longitudinal Study (CHARLS) to assess liver function biomarkers (LFBs) for 10-year hard ASCVD risk prediction. The analysis focused on gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), and globulin, comparing their added value to traditional risk factors using ACC/AHA Pooled Cohort Equations.

In NHANES (n=5,731), GGT demonstrated an independent linear association with 10-year hard ASCVD risk (P-trend = 0.003). The association was partly mediated by systolic blood pressure (44.8%), HbA1c (19.0%), and high density lipoprotein cholesterol (13.4%). A Naive Bayes model incorporating LFBs improved predictive accuracy, achieving an AUC of 0.751 in NHANES validation. External validation was performed in CHARLS.

Adverse events, serious adverse events, discontinuations, and tolerability were not reported. The study design is observational, and while external validation was performed, causality cannot be inferred. The authors note that incorporating LFBs into risk prediction models, particularly with machine learning, may enhance risk stratification and facilitate early identification of high-risk individuals. However, the improvement in hard ASCVD risk prediction should not be overstated.

Study Details

Study typeCohort

EvidenceLevel 3

PublishedApr 2026

View Original Abstract ↓

Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. Liver function biomarkers (LFBs), particularly gamma - glutamyl transferase (GGT), have been linked to cardiovascular outcomes, yet their contribution to hard ASCVD risk prediction is not well defined. Methods: This study analyzed data from the National Health and Nutrition Examination Survey (NHANES, 2005 - 2018) to assess cross - sectional associations between LFBs and 10 - year hard ASCVD risk estimated by the ACC/AHA Pooled Cohort Equations. Multivariable regression, restricted cubic splines, and mediation analyses were applied to examine independent and dose - response relationships. External validation was performed in the China Health and Retirement Longitudinal Study (CHARLS) and NHANES using machine learning models (CoxBoost, Naive Bayes and Random Forest). Results: Among 5,731 NHANES participants, GGT showed an independent linear association with hard ASCVD risk (P - trend = 0.003), partly mediated by systolic blood pressure (44.8%), HbA1c (19.0%), and high density lipoprotein cholesterol (13.4%). Machine learning (ML) models incorporating GGT, alkaline phosphatase (ALP), and globulin alongside traditional risk factors improved predictive accuracy, with Naive Bayes achieving an AUC of 0.751 in NHANES validation. Conclusions: GGT is an independent and biologically plausible biomarker of hard ASCVD risk, acting through cardiometabolic pathways. Incorporating LFBs into risk prediction models, particularly with machine learning, enhances risk stratification and may facilitate early identification of high - risk individuals.