TitleAdiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study
AuthorsPang, 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
AffiliationPeking Univ, Sch Publ Hlth, Dept Epidemiol & Biostat, Beijing, Peoples R China
Univ Oxford, Nuffield Dept Populat Hlth, Clin Trial Serv Unit, Oxford, England
Univ Oxford, Nuffield Dept Populat Hlth, Epidemiol Studies Unit CTSU, Oxford, England
Univ Oxford, Populat Hlth Res Unit, Nuffield Dept Populat Hlth, Med Res Council,MRC PHRU, Oxford, England
Peking Univ, Peking Univ Ctr Publ Hlth & Epidem Preparedness &, Beijing, Peoples R China
Chinese Acad Med Sci, Beijing, Peoples R China
Jiangsu Ctr Dis Control & Prevent, Nanjing, Peoples R China
Natl Ctr Food Safety Risk Assessment, Beijing, Peoples R China
Natl Inst Hlth Res Oxford Biomed Res Ctr, Oxford Univ Hosp, Oxford, England
Issue Date4-Mar-2022
AbstractBackground: 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 arc associated with NAFLD risk and these biomarkers may lie on the pathway between adiposity and NAFLD.
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