ESPEYB19 10. Type 1 Diabetes New paradigms (4 abstracts)
10.6. Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories
Kwon BC , Anand V , Achenbach P , Dunne JL , Hagopian W , Hu J , Koski E , Lernmark Æ , Lundgren M , Ng K , Toppari J , Veijola R & Frohnert BI
T1DI Study Group. Nat Commun. 2022 Mar 21;13(1):1514. https://pubmed.ncbi.nlm.nih.gov/35314671/
Brief Summary: This study of 5 birth cohorts of individuals at high risk for type 1 diabetes (T1D) used machine learning methods to explore trajectories from autoantibodies appearance to T1D progression. They identified 11 distinct latent health states and individuals progressed according to one of three distinct trajectories (TR1, TR2 and TR3), with an associated 5-year cumulative diabetes-free survival of 40%, 62%, and 88%, respectively.
T1D autoantibodies are markers of the autoimmune process and there is strong evidence that their presence predicts the risk of developing T1D (1). However, their temporal appearance and progression shows heterogonous patterns, which could further support and refine risk stratification. To better understand these patterns, the researcher investigated the presence or absence of three islet autoantibodies (GADA, IAA, and IA-2A), prior to the onset of clinical diabetes, in a large cohort of over 24,000 participants at risk of T1D recruited in 5 prospective studies (DAISY, DiPiS, DIPP, DEW-IT, BABYDIAB).
Using machine learning methods, a model containing 11 latent states was discovered that best fits the data and was subsequently applied to all autoantibody positive participants. Over 15 years of follow up, 643 participants developed T1D and the analysis identified three trajectories, TR1, TR2, and TR3, each characterized by a distinct sequence of 11 identified latent states. The three trajectories were associated with a different risk of developing T1D. Participants in TR1 progressed faster to diabetes than those in TR2, who progressed faster than those in TR3. Age, sex, and HLA-DR status further refined the progression rates within trajectories, improving risk stratification.
The defined trajectories and associated visual representation of the latent status within each of them could support screening strategies and be implemented in clinical practice. Clinicians can use autoantibodies patterns and age to estimate the trajectory of their patient and therefore their risk for developing T1D.
Reference: 1. Ziegler AG, Rewers M, Simell O, Simell T, Lempainen J, Steck A, et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA 2013;309:2473–9.
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ESPE Yearbook of Paediatric Endocrinology
ISSN 1662-4009 (Online)
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