Yonghai Lu1, Yeli Wang1, Li Zou1, Xu Liang2, Choon Nam Ong1,2, Subramaniam Tavintharan3, Jian-Min Yuan4,5, Woon-Puay Koh1,6, An Pan7,8. 1. Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Republic of Singapore. 2. NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Republic of Singapore. 3. Department of General Medicine, Diabetes Centre, Khoo Teck Puat Hospital, Singapore, Republic of Singapore. 4. Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania. 5. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania. 6. Duke-NUS Medical School Singapore, Singapore, Republic of Singapore. 7. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China. 8. Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Abstract
Context: We previously reported an association between lysophosphatidylinositol (LPI) (16:1) and risk for type 2 diabetes in a Chinese population using an untargeted analysis. Objective: To examine the overall associations of LPIs and their related metabolites, such as nonesterified fatty acids (NEFAs) and acylcarnitines, with incident and prevalent type 2 diabetes using a targeted approach. Design and Setting: A case-control study was nested within the Singapore Chinese Health Study. Cases and controls were individually matched by age, sex, and date of blood collection. We used both liquid and gas chromatography tandem mass spectrometry to measure serum metabolite levels at baseline, including 8 LPIs, 19 NEFAs, and 34 acylcarnitines. Conditional logistic regression models were used to estimate the associations between metabolites and diabetes risk. Participants: Participants included 160 incident and 144 prevalent cases with type 2 diabetes and 304 controls. Main Outcome Measure: Incident and prevalent type 2 diabetes. Results: On the basis of a false discovery rate <0.1, we identified 37 metabolites associated with prevalent type 2 diabetes, including 7 LPIs, 18 NEFAs, and 12 acylcarnitines, and 11 metabolites associated with incident type 2 diabetes, including 2 LPIs and 9 NEFAs. Two metabolites, LPI (16:1) and dihomo-γ-linolenic acid, showed independent associations with incident type 2 diabetes and significantly enhanced the risk prediction. Conclusions: We found several LPIs and NEFAs that were associated with risk for type 2 diabetes and may improve our understanding of the pathogenesis. The findings suggest that lipid profiles could aid in diabetes risk assessment in Chinese populations.
Context: We previously reported an association between lysophosphatidylinositol (LPI) (16:1) and risk for type 2 diabetes in a Chinese population using an untargeted analysis. Objective: To examine the overall associations of LPIs and their related metabolites, such as nonesterified fatty acids (NEFAs) and acylcarnitines, with incident and prevalent type 2 diabetes using a targeted approach. Design and Setting: A case-control study was nested within the Singapore Chinese Health Study. Cases and controls were individually matched by age, sex, and date of blood collection. We used both liquid and gas chromatography tandem mass spectrometry to measure serum metabolite levels at baseline, including 8 LPIs, 19 NEFAs, and 34 acylcarnitines. Conditional logistic regression models were used to estimate the associations between metabolites and diabetes risk. Participants: Participants included 160 incident and 144 prevalent cases with type 2 diabetes and 304 controls. Main Outcome Measure: Incident and prevalent type 2 diabetes. Results: On the basis of a false discovery rate <0.1, we identified 37 metabolites associated with prevalent type 2 diabetes, including 7 LPIs, 18 NEFAs, and 12 acylcarnitines, and 11 metabolites associated with incident type 2 diabetes, including 2 LPIs and 9 NEFAs. Two metabolites, LPI (16:1) and dihomo-γ-linolenic acid, showed independent associations with incident type 2 diabetes and significantly enhanced the risk prediction. Conclusions: We found several LPIs and NEFAs that were associated with risk for type 2 diabetes and may improve our understanding of the pathogenesis. The findings suggest that lipid profiles could aid in diabetes risk assessment in Chinese populations.
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