Arjana Begzati, Karla P. Godinez-Macias, Tao Long, Jeramie D. Watrous, Rafael Moranchel, Edward D. Kantz, Jaakko Tuomilehto, Aki S. Havulinna, Teemu J. Niiranen, Pekka Jousilahti, Veikko Salomaa, Bing Yu, Faye Norby, Casey M. Rebholz, Elizabeth Selvin, Elizabeth A. Winzeler, Susan Cheng, Mona Alotaibi, Ravi Goyal, Trey Ideker, Mohit Jain, Amit R. Majithia
{"title":"Plasma Lipid Metabolites, Clinical Glycemic Predictors, and Incident Type 2 Diabetes","authors":"Arjana Begzati, Karla P. Godinez-Macias, Tao Long, Jeramie D. Watrous, Rafael Moranchel, Edward D. Kantz, Jaakko Tuomilehto, Aki S. Havulinna, Teemu J. Niiranen, Pekka Jousilahti, Veikko Salomaa, Bing Yu, Faye Norby, Casey M. Rebholz, Elizabeth Selvin, Elizabeth A. Winzeler, Susan Cheng, Mona Alotaibi, Ravi Goyal, Trey Ideker, Mohit Jain, Amit R. Majithia","doi":"10.2337/dc24-2266","DOIUrl":null,"url":null,"abstract":"OBJECTIVE Plasma metabolite profiling has uncovered several nonglycemic markers of incident type 2 diabetes (T2D). We investigated whether such biomarkers provide information about specific aspects of T2D etiology, such as impaired fasting glucose and impaired glucose tolerance, and whether their association with T2D risk varies by race. RESEARCH DESIGN AND METHODS Untargeted plasma metabolite profiling was performed of participants in the FINRISK 2002 cohort (n = 7,564). Cox regression modeling was conducted to identify metabolites associated with incident T2D during 14 years of follow-up. Metabolites were clustered into pathways using Gaussian graphical modeling. Clusters enriched for T2D biomarkers were further examined for covariation with fasting plasma glucose (FPG), 2-h postchallenge plasma glucose (2hPG), HbA1c, or fasting insulin. Validation analyses and tests of interaction with race were performed in the Atherosclerosis Risk in Communities study. RESULTS Two clusters of metabolites, representing diacylglycerols (DAGs) and phosphatidylcholines (PCs), contained the largest number of metabolite associations with incident T2D. DAGs associated with increased T2D incidence (hazard ratio [HR] 1.22; 95% CI 1.14–1.30) independent of FPG, HbA1c, and fasting insulin, but not 2hPG. PCs were inversely associated with T2D risk (HR 0.78; 95% CI 0.71–0.85) independent of FPG, 2hPG, HbA1c, and fasting insulin. No significant interaction between DAGs or PCs and race was observed. CONCLUSIONS Fasting DAGs may capture information regarding T2D risk similar to that represented by 2hPG; PCs may capture aspects of T2D etiology that differ from those represented by conventional biomarkers. The direction of effect and strength of DAG and PC associations with incident T2D are similar across European and African Americans.","PeriodicalId":11140,"journal":{"name":"Diabetes Care","volume":"35 1","pages":""},"PeriodicalIF":14.8000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2337/dc24-2266","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
OBJECTIVE Plasma metabolite profiling has uncovered several nonglycemic markers of incident type 2 diabetes (T2D). We investigated whether such biomarkers provide information about specific aspects of T2D etiology, such as impaired fasting glucose and impaired glucose tolerance, and whether their association with T2D risk varies by race. RESEARCH DESIGN AND METHODS Untargeted plasma metabolite profiling was performed of participants in the FINRISK 2002 cohort (n = 7,564). Cox regression modeling was conducted to identify metabolites associated with incident T2D during 14 years of follow-up. Metabolites were clustered into pathways using Gaussian graphical modeling. Clusters enriched for T2D biomarkers were further examined for covariation with fasting plasma glucose (FPG), 2-h postchallenge plasma glucose (2hPG), HbA1c, or fasting insulin. Validation analyses and tests of interaction with race were performed in the Atherosclerosis Risk in Communities study. RESULTS Two clusters of metabolites, representing diacylglycerols (DAGs) and phosphatidylcholines (PCs), contained the largest number of metabolite associations with incident T2D. DAGs associated with increased T2D incidence (hazard ratio [HR] 1.22; 95% CI 1.14–1.30) independent of FPG, HbA1c, and fasting insulin, but not 2hPG. PCs were inversely associated with T2D risk (HR 0.78; 95% CI 0.71–0.85) independent of FPG, 2hPG, HbA1c, and fasting insulin. No significant interaction between DAGs or PCs and race was observed. CONCLUSIONS Fasting DAGs may capture information regarding T2D risk similar to that represented by 2hPG; PCs may capture aspects of T2D etiology that differ from those represented by conventional biomarkers. The direction of effect and strength of DAG and PC associations with incident T2D are similar across European and African Americans.
目的:血浆代谢物分析揭示了2型糖尿病(T2D)的几个非血糖标志物。我们调查了这些生物标志物是否提供了关于T2D病因的特定方面的信息,如空腹血糖受损和糖耐量受损,以及它们与T2D风险的关联是否因种族而异。研究设计和方法对FINRISK 2002队列(n = 7564)的参与者进行非靶向血浆代谢物分析。在14年的随访中,采用Cox回归模型确定与T2D事件相关的代谢物。使用高斯图形建模将代谢物聚类成通路。进一步检测富含T2D生物标志物的簇与空腹血糖(FPG)、攻药后2小时血糖(2hPG)、糖化血红蛋白(HbA1c)或空腹胰岛素的共变。在社区动脉粥样硬化风险研究中进行了验证分析和与种族相互作用的测试。结果两类代谢物,即二酰基甘油(dag)和磷脂酰胆碱(PCs),与T2D事件相关的代谢物最多。与T2D发病率增加相关的dag(风险比[HR] 1.22;95% CI 1.14-1.30)与FPG、HbA1c和空腹胰岛素无关,但与2hPG无关。pc与T2D风险呈负相关(HR 0.78;95% CI 0.71-0.85),与FPG、2hPG、HbA1c和空腹胰岛素无关。未观察到dag或pc与种族之间的显著相互作用。结论:空腹DAGs可能捕捉到类似于2hPG所代表的T2D风险信息;pc可以捕捉到与传统生物标志物不同的t2dm病因。在欧洲人和非裔美国人中,DAG和PC与T2D事件相关的影响方向和强度相似。
期刊介绍:
The journal's overarching mission can be captured by the simple word "Care," reflecting its commitment to enhancing patient well-being. Diabetes Care aims to support better patient care by addressing the comprehensive needs of healthcare professionals dedicated to managing diabetes.
Diabetes Care serves as a valuable resource for healthcare practitioners, aiming to advance knowledge, foster research, and improve diabetes management. The journal publishes original research across various categories, including Clinical Care, Education, Nutrition, Psychosocial Research, Epidemiology, Health Services Research, Emerging Treatments and Technologies, Pathophysiology, Complications, and Cardiovascular and Metabolic Risk. Additionally, Diabetes Care features ADA statements, consensus reports, review articles, letters to the editor, and health/medical news, appealing to a diverse audience of physicians, researchers, psychologists, educators, and other healthcare professionals.