Liang Sun1, Liming Liang2, Xianfu Gao3, Huiping Zhang3, Pang Yao1, Yao Hu1, Yiwei Ma1, Feijie Wang1, Qianlu Jin1, Huaixing Li1, Rongxia Li3, Yong Liu1, Frank B Hu4, Rong Zeng5, Xu Lin6, Jiarui Wu7. 1. Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, China. 2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA. 3. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. 4. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. 5. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China Department of Life Sciences and Technology, ShanghaiTech University, Shanghai, China zr@sibs.ac.cn. 6. Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, China xlin@sibs.ac.cn. 7. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China Department of Life Sciences and Technology, ShanghaiTech University, Shanghai, China Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China wujr@sibs.ac.cn.
Abstract
OBJECTIVE: Acylcarnitines were suggested as early biomarkers even prior to insulin resistance in animal studies, but their roles in predicting type 2 diabetes were unknown. Therefore, we aimed to determine whether acylcarnitines could independently predict type 2 diabetes by using a targeted metabolic profiling approach. RESEARCH DESIGN AND METHODS: A population-based prospective study was conducted among 2,103 community-living Chinese individuals aged 50-70 years from Beijing and Shanghai with a mean follow-up duration of 6 years. Fasting glucose, glycohemoglobin, and insulin were determined at baseline and in a follow-up survey. Baseline plasma acylcarnitines were profiled by liquid chromatography-tandem mass spectrometry. RESULTS: Over the 6-year period, 507 participants developed diabetes. A panel of acylcanitines, especially with long chain, was significantly associated with increased risk of type 2 diabetes. The relative risks of type 2 diabetes per SD increase of the predictive model score were 2.48 (95% CI 2.20-2.78) for the conventional and 9.41 (95% CI 7.62-11.62) for the full model including acylcarnitines, respectively. Moreover, adding selected acylcarnitines substantially improved predictive ability for incident diabetes, as area under the receiver operator characteristic curve improved to 0.89 in the full model compared with 0.73 in the conventional model. Similar associations were obtained when the predictive models were established separately among Beijing or Shanghai residents. CONCLUSIONS: A panel of acylcarnitines, mainly involving mitochondrial lipid dysregulation, significantly improved predictive ability for type 2 diabetes beyond conventional risk factors. These findings need to be replicated in other populations, and the underlying mechanisms should be elucidated.
OBJECTIVE:Acylcarnitines were suggested as early biomarkers even prior to insulin resistance in animal studies, but their roles in predicting type 2 diabetes were unknown. Therefore, we aimed to determine whether acylcarnitines could independently predict type 2 diabetes by using a targeted metabolic profiling approach. RESEARCH DESIGN AND METHODS: A population-based prospective study was conducted among 2,103 community-living Chinese individuals aged 50-70 years from Beijing and Shanghai with a mean follow-up duration of 6 years. Fasting glucose, glycohemoglobin, and insulin were determined at baseline and in a follow-up survey. Baseline plasma acylcarnitines were profiled by liquid chromatography-tandem mass spectrometry. RESULTS: Over the 6-year period, 507 participants developed diabetes. A panel of acylcanitines, especially with long chain, was significantly associated with increased risk of type 2 diabetes. The relative risks of type 2 diabetes per SD increase of the predictive model score were 2.48 (95% CI 2.20-2.78) for the conventional and 9.41 (95% CI 7.62-11.62) for the full model including acylcarnitines, respectively. Moreover, adding selected acylcarnitines substantially improved predictive ability for incident diabetes, as area under the receiver operator characteristic curve improved to 0.89 in the full model compared with 0.73 in the conventional model. Similar associations were obtained when the predictive models were established separately among Beijing or Shanghai residents. CONCLUSIONS: A panel of acylcarnitines, mainly involving mitochondrial lipid dysregulation, significantly improved predictive ability for type 2 diabetes beyond conventional risk factors. These findings need to be replicated in other populations, and the underlying mechanisms should be elucidated.
Authors: Jordi Merino; Aaron Leong; Ching-Ti Liu; Bianca Porneala; Geoffrey A Walford; Marcin von Grotthuss; Thomas J Wang; Jason Flannick; Josée Dupuis; Daniel Levy; Robert E Gerszten; Jose C Florez; James B Meigs Journal: Diabetologia Date: 2018-04-06 Impact factor: 10.122
Authors: Marta Guasch-Ferré; Miguel Ruiz-Canela; Jun Li; Yan Zheng; Mònica Bulló; Dong D Wang; Estefanía Toledo; Clary Clish; Dolores Corella; Ramon Estruch; Emilio Ros; Montserrat Fitó; Fernando Arós; Miquel Fiol; José Lapetra; Lluís Serra-Majem; Liming Liang; Christopher Papandreou; Courtney Dennis; Miguel A Martínez-González; Frank B Hu; Jordi Salas-Salvadó Journal: J Clin Endocrinol Metab Date: 2019-05-01 Impact factor: 5.958
Authors: Yiwei Ma; Liang Sun; Jun Li; Yao Hu; Zhenji Gan; Geng Zong; He Zheng; Qianlu Jin; Huaixing Li; Frank B Hu; Rong Zeng; Qi Sun; Xu Lin Journal: J Lipid Res Date: 2018-12-14 Impact factor: 5.922
Authors: Michael Bergman; Muhammad Abdul-Ghani; Ralph A DeFronzo; Melania Manco; Giorgio Sesti; Teresa Vanessa Fiorentino; Antonio Ceriello; Mary Rhee; Lawrence S Phillips; Stephanie Chung; Celeste Cravalho; Ram Jagannathan; Louis Monnier; Claude Colette; David Owens; Cristina Bianchi; Stefano Del Prato; Mariana P Monteiro; João Sérgio Neves; Jose Luiz Medina; Maria Paula Macedo; Rogério Tavares Ribeiro; João Filipe Raposo; Brenda Dorcely; Nouran Ibrahim; Martin Buysschaert Journal: Diabetes Res Clin Pract Date: 2020-06-01 Impact factor: 5.602