title: Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts.
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.
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA. manju.mamtani@utrgv.edu.
authors.forename: Manju
authors.initials: M
authors.lastname: Mamtani
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: Hemant
authors.initials: H
authors.lastname: Kulkarni
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
authors.forename: Gerard
authors.initials: G
authors.lastname: Wong
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
authors.forename: Jacquelyn M
authors.initials: JM
authors.lastname: Weir
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
authors.forename: Christopher K
authors.initials: CK
authors.lastname: Barlow
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: Thomas D
authors.initials: TD
authors.lastname: Dyer
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Almasy
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Mahaney
authors.affiliation: Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA.
authors.forename: Anthony G
authors.initials: AG
authors.lastname: Comuzzie
authors.affiliation: 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.
authors.forename: David C
authors.initials: DC
authors.lastname: Glahn
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
authors.forename: Dianna J
authors.initials: DJ
authors.lastname: Magliano
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Zimmet
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Shaw
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: Sarah
authors.initials: S
authors.lastname: Williams-Blangero
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: Ravindranath
authors.initials: R
authors.lastname: Duggirala
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: John
authors.initials: J
authors.lastname: Blangero
authors.affiliation: Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Meikle
authors.affiliation: South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
authors.forename: Joanne E
authors.initials: JE
authors.lastname: Curran
mesh_terms: D015415:Biomarkers / Q000097:blood; D015331:Cohort Studies; D003362:Cost-Benefit Analysis; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000191:economics* / Q000209:etiology; D006801:Humans; D007333:Insulin Resistance; D008055:Lipids / Q000097:blood*; D015203:Reproducibility of Results; D012307:Risk Factors
chemical_list: D015415:Biomarkers; D008055:Lipids
keywords: Diabetes; Diagnostic tools; Endocrine disorders; Genetics; Lipidomics