Literature DB >> 31818810

Plasma Lipidome and Prediction of Type 2 Diabetes in the Population-Based Malmö Diet and Cancer Cohort.

Céline Fernandez1, Michal A Surma2, Christian Klose3, Mathias J Gerl3, Filip Ottosson4, Ulrika Ericson4, Nikolay Oskolkov5, Marju Ohro-Melander4, Kai Simons3, Olle Melander4.   

Abstract

OBJECTIVE: Type 2 diabetes mellitus (T2DM) is associated with dyslipidemia, but the detailed alterations in lipid species preceding the disease are largely unknown. We aimed to identify plasma lipids associated with development of T2DM and investigate their associations with lifestyle. RESEARCH DESIGN AND METHODS: At baseline, 178 lipids were measured by mass spectrometry in 3,668 participants without diabetes from the Malmö Diet and Cancer Study. The population was randomly split into discovery (n = 1,868, including 257 incident cases) and replication (n = 1,800, including 249 incident cases) sets. We used orthogonal projections to latent structures discriminant analyses, extracted a predictive component for T2DM incidence (lipid-PCDM), and assessed its association with T2DM incidence using Cox regression and lifestyle factors using general linear models.
RESULTS: A T2DM-predictive lipid-PCDM derived from the discovery set was independently associated with T2DM incidence in the replication set, with hazard ratio (HR) among subjects in the fifth versus first quintile of lipid-PCDM of 3.7 (95% CI 2.2-6.5). In comparison, the HR of T2DM among obese versus normal weight subjects was 1.8 (95% CI 1.2-2.6). Clinical lipids did not improve T2DM risk prediction, but adding the lipid-PCDM to all conventional T2DM risk factors increased the area under the receiver operating characteristics curve by 3%. The lipid-PCDM was also associated with a dietary risk score for T2DM incidence and lower level of physical activity.
CONCLUSIONS: A lifestyle-related lipidomic profile strongly predicts T2DM development beyond current risk factors. Further studies are warranted to test if lifestyle interventions modifying this lipidomic profile can prevent T2DM.
© 2019 by the American Diabetes Association.

Entities:  

Year:  2019        PMID: 31818810     DOI: 10.2337/dc19-1199

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  11 in total

1.  Associations of plasma glycerophospholipid profile with modifiable lifestyles and incident diabetes in middle-aged and older Chinese.

Authors:  Shuangshuang Chen; Geng Zong; Qingqing Wu; Huan Yun; Zhenhua Niu; He Zheng; Rong Zeng; Liang Sun; Xu Lin
Journal:  Diabetologia       Date:  2021-11-20       Impact factor: 10.122

2.  Longitudinal Plasma Lipidome and Risk of Type 2 Diabetes in a Large Sample of American Indians With Normal Fasting Glucose: The Strong Heart Family Study.

Authors:  Guanhong Miao; Ying Zhang; Zhiguang Huo; Wenjie Zeng; Jianhui Zhu; Jason G Umans; Gert Wohlgemuth; Diego Pedrosa; Brian DeFelice; Shelley A Cole; Amanda M Fretts; Elisa T Lee; Barbara V Howard; Oliver Fiehn; Jinying Zhao
Journal:  Diabetes Care       Date:  2021-10-26       Impact factor: 19.112

3.  Serum Metabolomics of Incident Diabetes and Glycemic Changes in a Population With High Diabetes Burden: The Hispanic Community Health Study/Study of Latinos.

Authors:  Jin Choul Chai; Guo-Chong Chen; Bing Yu; Jiaqian Xing; Jun Li; Tasneem Khambaty; Krista M Perreira; Marisa J Perera; Denise C Vidot; Sheila F Castaneda; Elizabeth Selvin; Casey M Rebholz; Martha L Daviglus; Jianwen Cai; Linda Van Horn; Carmen R Isasi; Qi Sun; Meredith Hawkins; Xiaonan Xue; Eric Boerwinkle; Robert C Kaplan; Qibin Qi
Journal:  Diabetes       Date:  2022-06-01       Impact factor: 9.337

4.  Metabolome-Defined Obesity and the Risk of Future Type 2 Diabetes and Mortality.

Authors:  Filip Ottosson; Einar Smith; Ulrika Ericson; Louise Brunkwall; Marju Orho-Melander; Salvatore Di Somma; Paola Antonini; Peter M Nilsson; Céline Fernandez; Olle Melander
Journal:  Diabetes Care       Date:  2022-05-01       Impact factor: 17.152

5.  Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program.

Authors:  Tibor V Varga; Jinxi Liu; Ronald B Goldberg; Guannan Chen; Samuel Dagogo-Jack; Carlos Lorenzo; Kieren J Mather; Xavier Pi-Sunyer; Søren Brunak; Marinella Temprosa
Journal:  BMJ Open Diabetes Res Care       Date:  2021-03

6.  Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Authors:  Chris Lauber; Mathias J Gerl; Christian Klose; Filip Ottosson; Olle Melander; Kai Simons
Journal:  PLoS Biol       Date:  2022-03-03       Impact factor: 9.593

7.  Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies.

Authors:  Jakub Morze; Clemens Wittenbecher; Lukas Schwingshackl; Anna Danielewicz; Andrzej Rynkiewicz; Frank B Hu; Marta Guasch-Ferré
Journal:  Diabetes Care       Date:  2022-04-01       Impact factor: 17.152

8.  LipidSig: a web-based tool for lipidomic data analysis.

Authors:  Wen-Jen Lin; Pei-Chun Shen; Hsiu-Cheng Liu; Yi-Chun Cho; Min-Kung Hsu; I-Chen Lin; Fang-Hsin Chen; Juan-Cheng Yang; Wen-Lung Ma; Wei-Chung Cheng
Journal:  Nucleic Acids Res       Date:  2021-07-02       Impact factor: 16.971

9.  A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population.

Authors:  Barbara Thorand; Astrid Zierer; Mustafa Büyüközkan; Jan Krumsiek; Alina Bauer; Florian Schederecker; Julie Sudduth-Klinger; Christa Meisinger; Harald Grallert; Wolfgang Rathmann; Michael Roden; Annette Peters; Wolfgang Koenig; Christian Herder; Cornelia Huth
Journal:  J Clin Endocrinol Metab       Date:  2021-03-25       Impact factor: 5.958

10.  Mouse lipidomics reveals inherent flexibility of a mammalian lipidome.

Authors:  Michał A Surma; Mathias J Gerl; Ronny Herzog; Jussi Helppi; Kai Simons; Christian Klose
Journal:  Sci Rep       Date:  2021-09-29       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.