Adiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study

Author:

Pang Yuanjie1ORCID,Kartsonaki Christiana23,Lv Jun14,Millwood Iona Y23,Fairhurst-Hunter Zammy2,Turnbull Iain2,Bragg Fiona23,Hill Michael R2,Yu Canqing14ORCID,Guo Yu5,Chen Yiping23,Yang Ling23,Clarke Robert2ORCID,Walters Robin G23,Wu Ming6,Chen Junshi7,Li Liming14,Chen Zhengming23,Holmes Michael V238

Affiliation:

1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China

2. Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

3. Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

4. Peking University Center for Public Health and Epidemic Preparedness and Response (PKU-PHEPR), Peking University, Beijing, China

5. Chinese Academy of Medical Sciences, Beijing, China

6. Jiangsu Center for Disease Control and Prevention, Nanjing, China

7. National Center for Food Safety Risk Assessment, Beijing, China

8. National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, United Kingdom

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/m2; 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.

Funder

Kadoorie Charitable Foundation

Wellcome Trust

National Natural Science Foundation of China

Chinese Ministry of Science and Technology

National Key Research and Development Program of China

British Heart Foundation

NIHR Oxford Biomedical Research Centre

China Postdoctoral Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Nutrition and Dietetics,Medicine (miscellaneous)

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