Heterogeneity of glycaemic phenotypes in type 1 diabetes.

Autor: Fagherazzi G; Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg. Guy.Fagherazzi@lih.lu., Aguayo GA; Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg., Zhang L; Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg., Hanaire H; Department of Diabetology, Metabolic Diseases and Nutrition, CHU Toulouse, University of Toulouse, Toulouse, France.; Francophone Foundation for Diabetes Research, Paris, France., Picard S; Endocrinology and Diabetes, Point Medical, Dijon, France., Sablone L; Francophone Foundation for Diabetes Research, Paris, France., Vergès B; Department of Endocrinology-Diabetology, Inserm LNC UMR1231, University of Burgundy, Dijon, France., Hamamouche N; e-Health Services Sanoïa, Gémenos, France., Detournay B; CEMKA, Bourg-la-Reine, France., Joubert M; Service d'Endocrinologie-Diabétologie (Endocrinology/Diabetes Unit), Centre Hospitalier Universitaire de Caen, Caen, France., Delemer B; Endocrinology, Diabetology and Nutrition Department, Robert Debré University Hospital, Reims, France., Guilhem I; Department of Endocrinology, Diabetes and Nutrition, University Hospital of Rennes, Rennes, France., Vambergue A; Endocrinology, Diabetology, Metabolism and Nutrition Department, Lille University Hospital, Lille, France., Gourdy P; Department of Diabetology, Metabolic Diseases and Nutrition, CHU Toulouse, University of Toulouse, Toulouse, France.; Institute of Metabolic and Cardiovascular Diseases, UMR1297 Inserm/UPS, Toulouse University, Toulouse, France., Hadjadj S; Institut du thorax, INSERM, CNRS, Université Nantes, CHU Nantes, Nantes, France., Velayoudom FL; Department of Endocrinology-Diabetology, University Hospital of Guadeloupe, Pointe-À-Pitre, France.; Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Lille, France., Guerci B; Department of Endocrinology, Diabetology, and Nutrition, Brabois Adult Hospital, University of Lorraine, Vandoeuvre-Lès-Nancy, France., Larger E; University Paris Cité, Institut Cochin, U1016, Inserm, Paris, France.; Diabetology Department, Cochin Hospital, AP-HP, Paris, France., Jeandidier N; Department of Endocrinology, Diabetes and Nutrition, Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, Strasbourg, France., Gautier JF; Institut Necker Enfants Malades, Inserm U1151, CNRS UMR 8253, IMMEDIAB Laboratory, Paris, France.; Centre Universitaire de Diabétologie et de ses Complications, AP-HP, Hôpital Lariboisière, Paris, France., Renard E; Institute of Functional Genomics, University of Montpellier, CNRS, Inserm, Montpellier, France.; Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France., Potier L; Institut Necker Enfants Malades, Inserm U1151, CNRS UMR 8253, IMMEDIAB Laboratory, Paris, France.; Department of Diabetology, Endocrinology and Nutrition, AP-HP, Bichat Hospital, Paris, France., Benhamou PY; Université Grenoble Alpes, Inserm U1055, CHU Grenoble Alpes, Grenoble, France., Sola A; Diabetology Department, Cochin Hospital, AP-HP, Paris, France., Bordier L; Service d'Endocrinologie, Hôpital Bégin, Saint Mandé, France., Bismuth E; Robert-Debré University Hospital, Department of Paediatric Endocrinology and Diabetology, AP-HP, University of Paris, Paris, France., Prévost G; Department of Endocrinology, Diabetes and Metabolic Diseases, Normandie Université, UNIROUEN, Rouen University Hospital, Centre d'Investigation Clinique (CIC-CRB)-Inserm 1404, Rouen University Hospital, Rouen, France., Kessler L; Department of Endocrinology, Diabetes and Nutrition, Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, Strasbourg, France., Cosson E; Department of Endocrinology-Diabetology-Nutrition, AP-HP, Avicenne Hospital, Paris 13 University, Sorbonne Paris Cité, CRNH-IdF, CINFO, Bobigny, France.; Equipe de Recherche en Epidémiologie Nutritionnelle (EREN), Université Sorbonne Paris Nord and Université Paris CitéInserm, INRAE, CNAM, Centre of Research in Epidemiology and StatisticS (CRESS), Bobigny, France., Riveline JP; Institut Necker Enfants Malades, Inserm U1151, CNRS UMR 8253, IMMEDIAB Laboratory, Paris, France.; Centre Universitaire de Diabétologie et de ses Complications, AP-HP, Hôpital Lariboisière, Paris, France.
Jazyk: angličtina
Zdroj: Diabetologia [Diabetologia] 2024 Aug; Vol. 67 (8), pp. 1567-1581. Date of Electronic Publication: 2024 May 23.
DOI: 10.1007/s00125-024-06179-4
Abstrakt: Aims/hypothesis: Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes.
Methods: 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 1c , time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison.
Results: 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 1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA 1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes.
Conclusions/interpretation: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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