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Beyond Stages: Predicting Individual Time Dependent Risk for Type 1 Diabetes.
Pribitzer, Stephan; O'Rourke, Colin; Ylescupidez, Alyssa; Smithmyer, Megan; Bender, Christine; Speake, Cate; Lord, Sandra; Greenbaum, Carla J.
Afiliación
  • 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.
J Clin Endocrinol Metab ; 2024 May 07.
Article en En | MEDLINE | ID: mdl-38712386
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Endocrinol Metab Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Endocrinol Metab Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos