Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Mamtani, Manju; Kulkarni, Hemant; Wong, Gerard; Weir, Jacquelyn; Barlow, Christopher; Dyer, Thomas; Almasy, Laura; Mahaney, Michael; Comuzzie, Anthony; Glahn, David; Magliano, Dianna; Zimmet, Paul; Shaw, Jonathan; Williams-Blangero, Sarah; Duggirala, Ravindranath; Blangero, John; Meikle, Peter; Curran, Joanne
• Is Part Of: Lipids in health and disease. Volume 15:Issue 1(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-12
• Subjects: Diabetes -- Endocrine disorders -- Lipidomics -- Diagnostic tools -- Genetics; Lipids -- Periodicals; Lipids in human nutrition -- Periodicals; Lipids -- Metabolism -- Disorders -- Periodicals; 616.3997
• DOI: 10.1186/s12944-016-0234-3
• Languages: English
• ISSNs: 1476-511X

Abstract

Abstract Background Detection 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. Methods Plasma 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. Results The 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. Conclusions Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.