Jamie L Felton1,2, David Cuthbertson3, Megan Warnock3, Kuldeep Lohano2, Farah Meah4, John M Wentworth5,6, Jay Sosenko7, Carmella Evans-Molina8,9,10,11,12. 1. Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA. 2. Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA. 3. Health Informatics Institute, University of South Florida, Tampa, FL, USA. 4. Hines VA Medical Center, Hines, IL, USA. 5. Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia. 6. Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, VIC, Australia. 7. Department of Medicine and the Diabetes Research Institute, Leonard Miller School of Medicine, University of Miami, Miami, FL, USA. 8. Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA. cevansmo@iu.edu. 9. Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA. cevansmo@iu.edu. 10. Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. cevansmo@iu.edu. 11. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA. cevansmo@iu.edu. 12. Roudebush VA Medical Center, Indianapolis, IN, USA. cevansmo@iu.edu.
Abstract
AIMS/HYPOTHESIS: Methods to identify individuals at highest risk for type 1 diabetes are essential for the successful implementation of disease-modifying interventions. Simple metabolic measures are needed to help stratify autoantibody-positive (Aab+) individuals who are at risk of developing type 1 diabetes. HOMA2-B is a validated mathematical tool commonly used to estimate beta cell function in type 2 diabetes using fasting glucose and insulin. The utility of HOMA2-B in association with type 1 diabetes progression has not been tested. METHODS: Baseline HOMA2-B values from single-Aab+ (n = 2652; mean age, 21.1 ± 14.0 years) and multiple-Aab+ (n = 3794; mean age, 14.5 ± 11.2 years) individuals enrolled in the TrialNet Pathway to Prevention study were compared. Cox proportional hazard models were used to determine associations between HOMA2-B tertiles and time to progression to type 1 diabetes, with adjustments for age, sex, HLA status and BMI z score. Receiver operating characteristic (ROC) analysis was used to test the association of HOMA2-B with type 1 diabetes development in 1, 2, 5 and 10 years. RESULTS: At study entry, HOMA2-B values were higher in single- compared with multiple-Aab+ Pathway to Prevention participants (91.1 ± 44.5 vs 83.9 ± 38.9; p < 0.001). Single- and multiple-Aab+ individuals in the lowest HOMA2-B tertile had a higher risk and faster rate of progression to type 1 diabetes. For progression to type 1 diabetes within 1 year, area under the ROC curve (AUC-ROC) was 0.685, 0.666 and 0.680 for all Aab+, single-Aab+ and multiple-Aab+ individuals, respectively. When correlation between HOMA2-B and type 1 diabetes risk was assessed in combination with additional factors known to influence type 1 diabetes progression (insulin sensitivity, age and HLA status), AUC-ROC was highest for the single-Aab+ group's risk of progression at 2 years (AUC-ROC 0.723 [95% CI 0.652, 0.794]). CONCLUSIONS/ INTERPRETATION: These data suggest that HOMA2-B may have utility as a single-time-point measurement to stratify risk of type 1 diabetes development in Aab+ individuals.
AIMS/HYPOTHESIS: Methods to identify individuals at highest risk for type 1 diabetes are essential for the successful implementation of disease-modifying interventions. Simple metabolic measures are needed to help stratify autoantibody-positive (Aab+) individuals who are at risk of developing type 1 diabetes. HOMA2-B is a validated mathematical tool commonly used to estimate beta cell function in type 2 diabetes using fasting glucose and insulin. The utility of HOMA2-B in association with type 1 diabetes progression has not been tested. METHODS: Baseline HOMA2-B values from single-Aab+ (n = 2652; mean age, 21.1 ± 14.0 years) and multiple-Aab+ (n = 3794; mean age, 14.5 ± 11.2 years) individuals enrolled in the TrialNet Pathway to Prevention study were compared. Cox proportional hazard models were used to determine associations between HOMA2-B tertiles and time to progression to type 1 diabetes, with adjustments for age, sex, HLA status and BMI z score. Receiver operating characteristic (ROC) analysis was used to test the association of HOMA2-B with type 1 diabetes development in 1, 2, 5 and 10 years. RESULTS: At study entry, HOMA2-B values were higher in single- compared with multiple-Aab+ Pathway to Prevention participants (91.1 ± 44.5 vs 83.9 ± 38.9; p < 0.001). Single- and multiple-Aab+ individuals in the lowest HOMA2-B tertile had a higher risk and faster rate of progression to type 1 diabetes. For progression to type 1 diabetes within 1 year, area under the ROC curve (AUC-ROC) was 0.685, 0.666 and 0.680 for all Aab+, single-Aab+ and multiple-Aab+ individuals, respectively. When correlation between HOMA2-B and type 1 diabetes risk was assessed in combination with additional factors known to influence type 1 diabetes progression (insulin sensitivity, age and HLA status), AUC-ROC was highest for the single-Aab+ group's risk of progression at 2 years (AUC-ROC 0.723 [95% CI 0.652, 0.794]). CONCLUSIONS/ INTERPRETATION: These data suggest that HOMA2-B may have utility as a single-time-point measurement to stratify risk of type 1 diabetes development in Aab+ individuals.
Authors: K Decochez; I H De Leeuw; B Keymeulen; C Mathieu; R Rottiers; I Weets; E Vandemeulebroucke; I Truyen; L Kaufman; F C Schuit; D G Pipeleers; F K Gorus Journal: Diabetologia Date: 2002-11-12 Impact factor: 10.122
Authors: Justyna E Gołębiewska; Julia Solomina; Celeste Thomas; Mark R Kijek; Piotr J Bachul; Lindsay Basto; Karolina Gołąb; Ling-Jia Wang; Natalie Fillman; Martin Tibudan; Kamil Ciepły; Louis Philipson; Alicja Dębska-Ślizień; J Michael Millis; John Fung; Piotr Witkowski Journal: Am J Transplant Date: 2018-01-21 Impact factor: 8.086
Authors: Jay S Skyler; Carla J Greenbaum; John M Lachin; Ellen Leschek; Lisa Rafkin-Mervis; Peter Savage; Lisa Spain Journal: Ann N Y Acad Sci Date: 2008-12 Impact factor: 5.691
Authors: Carmella Evans-Molina; Emily K Sims; Linda A DiMeglio; Heba M Ismail; Andrea K Steck; Jerry P Palmer; Jeffrey P Krischer; Susan Geyer; Ping Xu; Jay M Sosenko Journal: JCI Insight Date: 2018-08-09
Authors: Katarzyna Siewko; Anna Popławska-Kita; Beata Telejko; Rafał Maciulewski; Anna Zielińska; Agnieszka Nikołajuk; Maria Górska; Małgorzata Szelachowska Journal: Endokrynol Pol Date: 2014 Impact factor: 1.582
Authors: Anette G Ziegler; Marian Rewers; Olli Simell; Tuula Simell; Johanna Lempainen; Andrea Steck; Christiane Winkler; Jorma Ilonen; Riitta Veijola; Mikael Knip; Ezio Bonifacio; George S Eisenbarth Journal: JAMA Date: 2013-06-19 Impact factor: 56.272
Authors: Liping Yu; David C Boulware; Craig A Beam; John C Hutton; Janet M Wenzlau; Carla J Greenbaum; Polly J Bingley; Jeffrey P Krischer; Jay M Sosenko; Jay S Skyler; George S Eisenbarth; Jeffrey L Mahon Journal: Diabetes Care Date: 2012-03-23 Impact factor: 19.112