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Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework.
Metwally, Ahmed A; Perelman, Dalia; Park, Heyjun; Wu, Yue; Jha, Alokkumar; Sharp, Seth; Celli, Alessandra; Ayhan, Ekrem; Abbasi, Fahim; Gloyn, Anna L; McLaughlin, Tracey; Snyder, Michael.
Afiliación
  • Metwally AA; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
  • Perelman D; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
  • Park H; Department of Medicine, Stanford University, Stanford, CA 94305, USA.
  • Wu Y; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
  • Jha A; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
  • Sharp S; Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
  • Celli A; Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
  • Ayhan E; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
  • Abbasi F; Department of Medicine, Stanford University, Stanford, CA 94305, USA.
  • Gloyn AL; Department of Medicine, Stanford University, Stanford, CA 94305, USA.
  • McLaughlin T; Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
  • Snyder M; Stanford Diabetes Research Centre, Stanford University, Stanford, CA 94305, USA.
medRxiv ; 2024 Sep 09.
Article en En | MEDLINE | ID: mdl-39108516
ABSTRACT
Type 2 diabetes (T2D) and prediabetes are classically defined by the level of fasting glucose or surrogates such as hemoglobin HbA1c. This classification does not take into account the heterogeneity in the pathophysiology of glucose dysregulation, the identification of which could inform targeted approaches to diabetes treatment and prevention and/or predict clinical outcomes. We performed gold-standard metabolic tests in a cohort of individuals with early glucose dysregulation and quantified four distinct metabolic subphenotypes known to contribute to glucose dysregulation and T2D muscle insulin resistance, ß-cell dysfunction, impaired incretin action, and hepatic insulin resistance. We revealed substantial inter-individual heterogeneity, with 34% of individuals exhibiting dominance or co-dominance in muscle and/or liver IR, and 40% exhibiting dominance or co-dominance in ß-cell and/or incretin deficiency. Further, with a frequently-sampled oral glucose tolerance test (OGTT), we developed a novel machine learning framework to predict metabolic subphenotypes using features from the dynamic patterns of the glucose time-series ("shape of the glucose curve"). The glucose time-series features identified insulin resistance, ß-cell deficiency, and incretin defect with auROCs of 95%, 89%, and 88%, respectively. These figures are superior to currently-used estimates. The prediction of muscle insulin resistance and ß-cell deficiency were validated using an independent cohort. We then tested the ability of glucose curves generated by a continuous glucose monitor (CGM) worn during at-home OGTTs to predict insulin resistance and ß-cell deficiency, yielding auROC of 88% and 84%, respectively. We thus demonstrate that the prediabetic state is characterized by metabolic heterogeneity, which can be defined by the shape of the glucose curve during standardized OGTT, performed in a clinical research unit or at-home setting using CGM. The use of at-home CGM to identify muscle insulin resistance and ß-cell deficiency constitutes a practical and scalable method by which to risk stratify individuals with early glucose dysregulation and inform targeted treatment to prevent T2D.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MedRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos 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: MedRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos