Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts

Lipids in Health and Disease

Research

Dianna J. Magliano¹  Peter J. Meikle¹  Gerard Wong¹  Jonathan Shaw¹  Paul Zimmet¹  Jacquelyn M. Weir¹  Christopher K. Barlow¹  Anthony G. Comuzzie²  David C. Glahn³  Thomas D. Dyer⁴  Michael C. Mahaney⁴  John Blangero⁴  Sarah Williams-Blangero⁴  Ravindranath Duggirala⁴  Laura Almasy⁴  Hemant Kulkarni⁴  Joanne E. Curran⁴  Manju Mamtani⁴ 

[1] Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia;Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA;Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA;Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, 200 Retreat Avenue, New Haven, CT, USA;South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, 78520, Brownsville, TX, USA;

关键词: Diabetes;    Endocrine disorders;    Lipidomics;    Diagnostic tools;    Genetics;   

DOI  :  10.1186/s12944-016-0234-3

 received in 2016-01-08, accepted in 2016-03-24,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundDetection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening.MethodsPlasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia – the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D.ResultsThe LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention.ConclusionsConsidering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.

【 授权许可】

CC BY   
© Mamtani et al. 2016