Adiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study. Issue 3 (13th December 2021)

• Record Type: Journal Article
• Authors: Pang, Yuanjie; Kartsonaki, Christiana; Lv, Jun; Millwood, Iona Y; Fairhurst-Hunter, Zammy; Turnbull, Iain; Bragg, Fiona; Hill, Michael R; Yu, Canqing; Guo, Yu; Chen, Yiping; Yang, Ling; Clarke, Robert; Walters, Robin G; Wu, Ming; Chen, Junshi; Li, Liming; Chen, Zhengming; Holmes, Michael V
• Is Part Of: American journal of clinical nutrition. Volume 115:Issue 3(2022)
• Page Start: 799
• Page End: 810
• Publication Date: 2021-12-13
• Subjects: adiposity -- nonalcoholic fatty liver disease -- metabolomics -- Mendelian randomization -- Chinese; Diet therapy -- Periodicals; Nutrition -- Periodicals; Dietetics -- Periodicals; 613.205
• DOI: 10.1093/ajcn/nqab392
• Languages: English
• ISSNs: 0002-9165

Abstract

ABSTRACT: Background: Globally, the burden of obesity and associated nonalcoholic fatty liver disease (NAFLD) are rising, but little is known about the role that circulating metabolomic biomarkers play in mediating their association. Objectives: We aimed to examine the observational and genetic associations of adiposity with metabolomic biomarkers and the observational associations of metabolomic biomarkers with incident NAFLD. Methods: A case-subcohort study within the prospective China Kadoorie Biobank included 176 NAFLD cases and 180 subcohort individuals and measured 1208 metabolites in stored baseline plasma using a Metabolon assay. In the subcohort the observational and genetic associations of BMI with biomarkers were assessed using linear regression, with adjustment for multiple testing. Cox regression was used to estimate adjusted HRs for NAFLD associated with biomarkers. Results: In observational analyses, BMI (kg/m 2 ; mean: 23.9 in the subcohort) was associated with 199 metabolites at a 5% false discovery rate. The effects of genetically elevated BMI with specific metabolites were directionally consistent with the observational associations. Overall, 35 metabolites were associated with NAFLD risk, of which 15 were also associated with BMI, including glutamate (HR per 1-SD higher metabolite: 1.95; 95% CI: 1.48, 2.56), cysteine-glutathione disulfide (0.44; 0.31, 0.62), diaclyglycerol (C32:1) (1.71; 1.24, 2.35), behenoyl dihydrosphingomyelin (C40:0) (1.92; 1.42, 2.59), butyrylcarnitine (C4) (1.91; 1.38, 2.35), 2-hydroxybehenate (1.81; 1.34, 2.45), and 4-cholesten-3-one (1.79; 1.27, 2.54). The discriminatory performance of known risk factors was increased when 28 metabolites were also considered simultaneously in the model (weighted C-statistic: 0.84 to 0.90; P < 0.001). Conclusions: Among relatively lean Chinese adults, a range of metabolomic biomarkers are associated with NAFLD risk and these biomarkers may lie on the pathway between adiposity and NAFLD.