Heterogeneity of glycaemic phenotypes in type 1 diabetes.

Artificial intelligence Cluster analysis Continuous glucose monitoring Diabetes complications Glycaemia risk index Glycaemic control Glycaemic phenotype Glycaemic variability Insulin pumps Machine learning Type 1 diabetes

Journal

Diabetologia

ISSN: 1432-0428

Titre abrégé: Diabetologia

Pays: Germany

ID NLM: 0006777

Informations de publication

Date de publication:
23 May 2024

Historique:

received: 05 10 2023

accepted: 08 04 2024

medline: 23 5 2024

pubmed: 23 5 2024

entrez: 23 5 2024

Statut: aheadofprint

Résumé

Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes. In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.

Identifiants

DOI: 10.1007/s00125-024-06179-4 PMID: 38780786

pubmed: 38780786

doi: 10.1007/s00125-024-06179-4

pii: 10.1007/s00125-024-06179-4

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Informations de copyright

Références

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