BACKGROUND: This study compares a previously developed Diabetes Risk Score to commonly used clinical tools for type 2 diabetes risk evaluation in the Insulin Resistance Atherosclerosis Study (IRAS) cohort, a multi-ethnic US cohort. Available as a clinical test, the PreDx® Diabetes Risk Score uses fasting concentrations of adiponectin, C-reactive protein, ferritin, interleukin-2 receptor alpha, HbA(1c) , glucose and insulin, plus age and gender to predict 5-year risk of diabetes. It was developed in a Northern European population. METHODS: The Diabetes Risk Score was measured using archived fasting plasma specimens from 722 non-diabetic IRAS participants, 17.6% of whom developed diabetes during 5.2 years median follow-up (inter-quartile range: 5.1-5.4 years). The study included non-Hispanic whites (41.8%), Hispanics (34.5%) and African Americans (23.7%). Performance of the algorithm was evaluated by area under the receiver operating characteristic curve (AROC) and risk reclassification against other tools. RESULTS: The Diabetes Risk Score discriminates participants who developed diabetes from those who did not significantly better than fasting glucose (AROC = 0.763 versus 0.710, p = 0.003). The Diabetes Risk Score performed equally well in subpopulations defined by race/ethnicity or gender. The Diabetes Risk Score provided a significant net reclassification improvement of 0.24 (p = 0.01) when comparing predefined low/moderate/high Diabetes Risk Score categories to metabolic syndrome risk factor counting. The Diabetes Risk Score complemented the use of the oral glucose tolerance test by identifying high risk patients with impaired fasting glucose but normal glucose tolerance, 33% of whom converted. CONCLUSIONS: Measuring the Diabetes Risk Score of elevated-risk US patients could help physicians decide which patients warrant more intensive intervention. The Diabetes Risk Score performed equally well across the ethnic subpopulations present in this cohort.
BACKGROUND: This study compares a previously developed Diabetes Risk Score to commonly used clinical tools for type 2 diabetes risk evaluation in the Insulin Resistance Atherosclerosis Study (IRAS) cohort, a multi-ethnic US cohort. Available as a clinical test, the PreDx® Diabetes Risk Score uses fasting concentrations of adiponectin, C-reactive protein, ferritin, interleukin-2 receptor alpha, HbA(1c) , glucose and insulin, plus age and gender to predict 5-year risk of diabetes. It was developed in a Northern European population. METHODS: The Diabetes Risk Score was measured using archived fasting plasma specimens from 722 non-diabetic IRAS participants, 17.6% of whom developed diabetes during 5.2 years median follow-up (inter-quartile range: 5.1-5.4 years). The study included non-Hispanic whites (41.8%), Hispanics (34.5%) and African Americans (23.7%). Performance of the algorithm was evaluated by area under the receiver operating characteristic curve (AROC) and risk reclassification against other tools. RESULTS: The Diabetes Risk Score discriminates participants who developed diabetes from those who did not significantly better than fasting glucose (AROC = 0.763 versus 0.710, p = 0.003). The Diabetes Risk Score performed equally well in subpopulations defined by race/ethnicity or gender. The Diabetes Risk Score provided a significant net reclassification improvement of 0.24 (p = 0.01) when comparing predefined low/moderate/high Diabetes Risk Score categories to metabolic syndrome risk factor counting. The Diabetes Risk Score complemented the use of the oral glucose tolerance test by identifying high risk patients with impaired fasting glucose but normal glucose tolerance, 33% of whom converted. CONCLUSIONS: Measuring the Diabetes Risk Score of elevated-risk US patients could help physicians decide which patients warrant more intensive intervention. The Diabetes Risk Score performed equally well across the ethnic subpopulations present in this cohort.
Authors: Valeria Lyssenko; Torben Jørgensen; Robert W Gerwien; Torben Hansen; Michael W Rowe; Michael P McKenna; Janice Kolberg; Oluf Pedersen; Knut Borch-Johnsen; Leif Groop Journal: Diab Vasc Dis Res Date: 2011-11-04 Impact factor: 3.291
Authors: Janice A Kolberg; Torben Jørgensen; Robert W Gerwien; Sarah Hamren; Michael P McKenna; Edward Moler; Michael W Rowe; Mickey S Urdea; Xiaomei M Xu; Torben Hansen; Oluf Pedersen; Knut Borch-Johnsen Journal: Diabetes Care Date: 2009-07 Impact factor: 19.112
Authors: S M Haffner; R D'Agostino; M F Saad; M Rewers; L Mykkänen; J Selby; G Howard; P J Savage; R F Hamman; L E Wagenknecht Journal: Diabetes Date: 1996-06 Impact factor: 9.461
Authors: Catherine C Cowie; Keith F Rust; Earl S Ford; Mark S Eberhardt; Danita D Byrd-Holt; Chaoyang Li; Desmond E Williams; Edward W Gregg; Kathleen E Bainbridge; Sharon H Saydah; Linda S Geiss Journal: Diabetes Care Date: 2008-11-18 Impact factor: 17.152
Authors: Ingrid D Santaren; Steven M Watkins; Angela D Liese; Lynne E Wagenknecht; Marian J Rewers; Steven M Haffner; Carlos Lorenzo; Andreas Festa; Richard P Bazinet; Anthony J Hanley Journal: J Lipid Res Date: 2017-09-19 Impact factor: 5.922
Authors: Maureen R Courtney; Edward J Moler; John A Osborne; Geoff Whitney; Scott E Conard Journal: Diabetes Metab Syndr Obes Date: 2015-09-18 Impact factor: 3.168