TY - JOUR
T1 - Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes
T2 - Results from diverse cohorts
AU - Mamtani, Manju
AU - Kulkarni, Hemant
AU - Wong, Gerard
AU - Weir, Jacquelyn M.
AU - Barlow, Christopher K.
AU - Dyer, Thomas D.
AU - Almasy, Laura
AU - Mahaney, Michael C.
AU - Comuzzie, Anthony G.
AU - Glahn, David C.
AU - Magliano, Dianna J.
AU - Zimmet, Paul
AU - Shaw, Jonathan
AU - Williams-Blangero, Sarah
AU - Duggirala, Ravindranath
AU - Blangero, John
AU - Meikle, Peter J.
AU - Curran, Joanne E.
N1 - Funding Information:
This work was supported in part by National Institutes of Health (NIH) grants R01 DK082610 and R01 DK079169; by NIH grants R01 HL045522, R01 MH078143, R01 MH078111 and R01 MH083824 (SAFHS data collection) and by NIH grant R37 MH059490 (analytical methods and software used). The AT&T Genomics Computing Center supercomputing facilities used for this work were supported in part by a gift from the AT&T Foundation with support from the National Center for Research Resources Grant Number S10 RR029392. This investigation was conducted in facilities constructed with support from Research Facilities Improvement Program grants C06 RR013556 and C06 RR017515 from the National Center for Research Resources of the National Institutes of Health. The AusDiab cohort was supported by funding from the Dairy Health and Nutrition Consortium, Australia, The National Health and Medical Research Council of Australia #233200 and #1007544, the OIS Program of the Victorian Government, Australia and by Award Number 1R01DK088972-01 from the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, USA.
Funding Information:
The authors would like to acknowledge the late Dr. Jeremy Jowett. Dr. Jowett was a very close collaborator and colleague for many years and was a key member of the research team at Baker IDI in Melbourne Australia. He helped foster the initial lipidomic collaboration between scientists in the Metabolomics group at Baker IDI and scientists at UTRGV, and worked closely as part of this collaboration until his death in September of 2012. We gratefully acknowledge the expert help with health economic analyses provided by Professor Paul Scuffham, Professor of Health Economics, School of Medicine, Griffith University, Australia. We are also very grateful to the participants of the San Antonio Family Heart Study for their continued involvement in our research programs. The AusDiab study co-coordinated by the Baker IDI Heart and Diabetes Institute, gratefully acknowledges the support and assistance given by: K Anstey, B Atkins, B Balkau, E Barr, A Cameron, S Chadban, M de Courten, D Dunstan, A Kavanagh, S Murray, N Owen, K Polkinghorne, T Welborn, and all the study participants. Also, for funding or logistical support, the AusDiab investigators are grateful to: Australian Government Department of Health and Ageing, Abbott Australasia Pty Ltd, Alphapharm Pty Ltd, Amgen Australia, AstraZeneca, Bristol-Myers Squibb, City Health Centre-Diabetes Service-Canberra, Department of Health and Community Services - Northern Territory, Department of Health and Human Services – Tasmania, Department of Health – New South Wales, Department of Health – Western Australia, Department of Health – South Australia, Department of Human Services – Victoria, Diabetes Australia, Diabetes Australia Northern Territory, Eli Lilly Australia, Estate of the Late Edward Wilson, GlaxoSmithKline, Jack Brockhoff Foundation, Janssen-Cilag, Kidney Health Australia, Marian & FH Flack Trust, Menzies Research Institute, Merck Sharp & Dohme, Novartis Pharmaceuticals, Novo Nordisk Pharmaceuticals, Pfizer Pty Ltd, Pratt Foundation, Queensland Health, Roche Diagnostics Australia, Royal Prince Alfred Hospital, Sydney, Sanofi Aventis, sanofi-synthelabo, and the Victorian Government’s OIS Program.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Diabetes
KW - Diagnostic tools
KW - Endocrine disorders
KW - Genetics
KW - Lipidomics
UR - http://www.scopus.com/inward/record.url?scp=85007569921&partnerID=8YFLogxK
U2 - 10.1186/s12944-016-0234-3
DO - 10.1186/s12944-016-0234-3
M3 - Article
C2 - 27044508
AN - SCOPUS:85007569921
VL - 15
JO - Lipids in Health and Disease
JF - Lipids in Health and Disease
SN - 1476-511X
IS - 1
M1 - 67
ER -