Genetic Composition and Autoantibody Titers Model the Probability of Detecting C-Peptide Following Type 1 Diabetes Diagnosis

• Authors: MacKenzie D Williams, University of Florida; Rhonda Bacher, University of Florida; Daniel J Perry, University of Florida; Ramsey C Grace, University of Florida; Kieran M McGrail, University of Florida; Amanda L Posgai, University of Florida; Andrew Muir, Emory University; Srikar Chamala, University of Florida; Michael J Haller, University of Florida; Desmond A Schatz, University of Florida; Todd M Brusko, University of Florida; Mark A Atkinson, University of Florida; Clive H Wasserfall, University of Florida
• Journal Title: DIABETES
• Volume: Volume 70, Number 4
• Publisher: AMER DIABETES ASSOC | 2021-04-01, Pages 932-943
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.2337/db20-0937
• Subjects: Health Sciences, Pathology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Endocrinology & Metabolism; BETA-CELL FUNCTION; ZINC TRANSPORTER 8; RISK SCORE; INSULIN-SECRETION; MAJOR AUTOANTIGEN; PREDICTION; CHILDREN; PROGRESSION; ONSET; AGE

Abstract

We and others previously demonstrated that a type 1 diabetes genetic risk score (GRS) improves the ability to predict disease progression and onset in at-risk subjects with islet autoantibodies. Here, we hypothesized that GRS and islet autoantibodies, combined with age at onset and disease duration, could serve as markers of residual β-cell function following type 1 diabetes diagnosis. Generalized estimating equations were used to investigate whether GRS along with insulinoma-associated protein-2 autoantibody (IA-2A), zinc transporter 8 autoantibody (ZnT8A), and GAD autoantibody (GADA) titers were predictive of C-peptide detection in a largely cross-sectional cohort of 401 subjects with type 1 diabetes (median duration 4.5 years [range 0-60]). Indeed, a combined model with incorporation of disease duration, age at onset, GRS, and titers of IA-2A, ZnT8A, and GADA provided superior capacity to predict C-peptide detection (quasi-likelihood information criterion [QIC] = 334.6) compared with the capacity of disease duration, age at onset, and GRS as the sole parameters (QIC = 359.2). These findings support the need for longitudinal validation of our combinatorial model. The ability to project the rate and extent of decline in residual C-peptide production for individuals with type 1 diabetes could critically inform enrollment and benchmarking for clinical trials where investigators are seeking to preserve or restore endogenous β-cell function.