학술논문
Beyond Stages: Predicting Individual Time Dependent Risk for Type 1 Diabetes.
Document Type
Academic Journal
Author
Pribitzer S; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.; O'Rourke C; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.; Ylescupidez A; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.; Smithmyer M; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.; Bender C; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.; Speake C; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.; Lord S; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.; Greenbaum CJ; Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA.
Source
Publisher: Oxford University Press Country of Publication: United States NLM ID: 0375362 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1945-7197 (Electronic) Linking ISSN: 0021972X NLM ISO Abbreviation: J Clin Endocrinol Metab Subsets: MEDLINE
Subject
Language
English
Abstract
Background: Essentially all individuals with multiple autoantibodies will develop clinical type 1 diabetes. Multiple AABs and normal glucose tolerance define Stage 1 diabetes; abnormal glucose tolerance defines Stage 2. However, the rate of progression within these stages is heterogeneous, necessitating personalized risk calculators to improve clinical implementation.
Methods: We developed 3 models using TrialNet's Pathway to Prevention data to accommodate the reality that not all risk variables are clinically available. The Small model included AAB status, fasting glucose, HbA1c and age, while the Medium and Large models added predictors of disease progression measured via oral glucose tolerance testing.
Findings: All models markedly improved granularity regarding personalized risk missing from current categories of stages of T1D. Model derived risk calculations are consistent with the expected reduction of risk with increasing age and increase in risk with higher glucose and lower insulin secretion, illustrating the suitability of the models. Adding glucose and insulin secretion data altered model predicted probabilities within Stages. In those with high 2-hour glucose, a high C-peptide markedly decreased predicted risk; lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of rate of disease progression within Stage 2.
Conclusions: While essentially all those with multiple AABs will develop type 1 diabetes, the rate of progression is heterogeneous and not explained by any individual single risk variable. The model-based probabilities developed here provide an adaptable personalized risk calculator to better inform decisions about how and when to monitor disease progression in clinical practice.
(© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Methods: We developed 3 models using TrialNet's Pathway to Prevention data to accommodate the reality that not all risk variables are clinically available. The Small model included AAB status, fasting glucose, HbA1c and age, while the Medium and Large models added predictors of disease progression measured via oral glucose tolerance testing.
Findings: All models markedly improved granularity regarding personalized risk missing from current categories of stages of T1D. Model derived risk calculations are consistent with the expected reduction of risk with increasing age and increase in risk with higher glucose and lower insulin secretion, illustrating the suitability of the models. Adding glucose and insulin secretion data altered model predicted probabilities within Stages. In those with high 2-hour glucose, a high C-peptide markedly decreased predicted risk; lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of rate of disease progression within Stage 2.
Conclusions: While essentially all those with multiple AABs will develop type 1 diabetes, the rate of progression is heterogeneous and not explained by any individual single risk variable. The model-based probabilities developed here provide an adaptable personalized risk calculator to better inform decisions about how and when to monitor disease progression in clinical practice.
(© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)