中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study

文献类型:期刊论文

作者Sun, Liang1,2; Liang, Liming3,4; Gao, Xianfu5; Zhang, Huiping5; Yao, Pang1,2; Hu, Yao1,2; Ma, Yiwei1,2; Wang, Feijie1,2; Jin, Qianlu1,2; Li, Huaixing1,2
刊名Diabetes care
出版日期2016-09-01
卷号39期号:9页码:1563-1570
ISSN号0149-5992
DOI10.2337/dc16-0232
通讯作者Zeng, rong(zr@sibs.ac.cn) ; Lin, xu(xlin@sibs.ac.cn) ; Wu, jiarui(wujr@sibs.ac.cn)
英文摘要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.
WOS关键词FATTY-ACID OXIDATION ; MUSCLE INSULIN-RESISTANCE ; METABOLIC SYNDROME ; L-CARNITINE ; OLDER CHINESE ; LAURIC ACID ; REGULARIZATION ; LIPOGENESIS ; BIOMARKERS ; CONTRIBUTE
WOS研究方向Endocrinology & Metabolism
WOS类目Endocrinology & Metabolism
语种英语
出版者AMER DIABETES ASSOC
WOS记录号WOS:000383663000025
URI标识http://www.irgrid.ac.cn/handle/1471x/2375994
专题中国科学院大学
通讯作者Zeng, Rong; Lin, Xu; Wu, Jiarui
作者单位1.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Nutr Sci, Key Lab Nutr & Metab, Shanghai, Peoples R China
2.Univ Chinese Acad Sci, Shanghai, Peoples R China
3.Harvard Univ, Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA
4.Harvard Univ, Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
5.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Biochem & Cell Biol, Key Lab Syst Biol, Shanghai, Peoples R China
6.Harvard Univ, Dept Nutr, Harvard TH Chan Sch Publ Hlth, Boston, MA 02115 USA
7.Brigham & Womens Hosp, Dept Med, Channing Div Network Med, 75 Francis St, Boston, MA 02115 USA
8.Harvard Med Sch, Boston, MA USA
9.ShanghaiTech Univ, Dept Life Sci & Technol, Shanghai, Peoples R China
10.Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai, Peoples R China
推荐引用方式
GB/T 7714
Sun, Liang,Liang, Liming,Gao, Xianfu,et al. Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study[J]. Diabetes care,2016,39(9):1563-1570.
APA Sun, Liang.,Liang, Liming.,Gao, Xianfu.,Zhang, Huiping.,Yao, Pang.,...&Wu, Jiarui.(2016).Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study.Diabetes care,39(9),1563-1570.
MLA Sun, Liang,et al."Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study".Diabetes care 39.9(2016):1563-1570.

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来源:中国科学院大学

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