Plasma Circulating Metabolites Associated With Steatotic Liver Disease and Liver Enzymes: A Multiplatform Population-Based Study.

BACKGROUND AND AIMS

Steatotic liver disease (SLD) is the most common chronic liver disease strongly associated with metabolic dysfunction, but its pathogenesis remains incompletely understood. Exploring plasma circulating metabolites may help in elucidating underlying mechanisms and identifying new biomarkers for SLD.

METHODS

We examined cross-sectionally the association between plasma metabolites and SLD as well as liver enzymes using data from 4 population-based cohort studies (Rotterdam study, Avon Longitudinal Study of Parents and Children, The Insulin Resistance Atherosclerosis Family Study, and Study of Latinos). Metabolites were assessed in the Nightingale platform (n = 225 metabolites) by nuclear magnetic resonance spectroscopy and in the Metabolon platform (n = 991 metabolites) by ultra-high-performance liquid chromatography-mass spectrometry. Serum levels of liver enzymes (alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transpeptidase) were measured and SLD was diagnosed by ultrasound or computed tomography scan. Logistic and linear regression models were performed per cohort and meta-analyzed. A false discovery rate < 0.05 was considered as significant threshold.

RESULTS

Several metabolites were significantly associated with SLD and liver enzymes, of which 21 metabolites were associated with both traits. The most significant associations were observed with phenylalanine, triglycerides in (high-density lipoprotein, intermediate-density lipoprotein, and small low-density lipoprotein), fatty acid (FA) ratios of (18:2 linoleic acid-to-total FA, omega 6 FA-to-total FA, and polyunsaturated FA-to-total FA) from the Nightingale and glutamate and sphingomyelin from the Metabolon platform. Other associated metabolites were mainly involved in lipid, amino acid, carbohydrates, and peptide metabolism.

CONCLUSION

Our study indicates a landscape of circulating metabolites associated with SLD. The identified metabolites may contribute to a better understanding of the metabolic pathways underlying SLD and hold promising for potential biomarkers in early diagnosis and monitoring of the disease.