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Article Dans Une Revue Diabetologia Année : 2024

Heterogeneity of glycaemic phenotypes in type 1 diabetes

1 LIH - Luxembourg Institute of Health
2 CHU Toulouse - Centre Hospitalier Universitaire de Toulouse
3 Point médical (Dijon)
4 LNC - Lipides - Nutrition - Cancer [Dijon - U1231]
5 Sanoïa [Gémenos]
6 CEMKA
7 CHU Caen
8 CHU Reims - Hôpital universitaire Robert Debré [Reims]
9 Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Ponchaillou]
10 CHRU Lille - Centre Hospitalier Régional Universitaire [CHU Lille]
11 UPS/Inserm U1297 - I2MC - Institut des Maladies Métaboliques et Casdiovasculaires
12 Institut du Thorax [Nantes]
13 CHU Pointe-à-Pitre / Abymes [Guadeloupe]
14 EGENODIA (GI3M) - Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283
15 Service d'Endocrinologie - Diabète - Nutrition [CHRU Nancy]
16 UL - Université de Lorraine
17 Hôpital Cochin [AP-HP]
18 CHU Strasbourg - Centre Hospitalier Universitaire [Strasbourg]
19 UNISTRA - Université de Strasbourg
20 INEM - UM 111 (UMR 8253 / U1151) - Institut Necker Enfants-Malades
21 IMMEDIAB Lab - Immunité et métabolisme dans le diabète = IMmunity and MEtabolism in DIABetes [CRC]
22 CNR - INSERM U1151 - National Cystic Fibrosis Reference Center [CHU Necker]
23 Hôpital Lariboisière-Fernand-Widal [APHP]
24 CHRU Montpellier - Centre Hospitalier Régional Universitaire [Montpellier]
25 IGF - Institut de Génomique Fonctionnelle
26 Service d'endocrinologie, diabétologie et nutrition [CHU Bichat]
27 LBFA - Laboratory of Fundamental and Applied Bioenergetics = Laboratoire de bioénergétique fondamentale et appliquée
28 HIA Begin - Hôpital d'Instruction des Armées Begin [Saint-Mandé, France]
29 Hôpital Robert Debré
30 AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP)
31 CHU Rouen
32 UNIROUEN UFR Santé - UNIROUEN - UFR Santé
33 NU - Normandie Université
34 CIC Rouen - Centre d'Investigation Clinique [CHU Rouen]
35 Hôpital Avicenne [AP-HP]
36 USPC - Université Sorbonne Paris Cité
37 CRNH-IDF - Centre de Recherche en Nutrition Humaine d'Ile-de-France
38 EREN [CRESS - U1153 / UMR_A 1125] - Nutritional Epidemiology Research Team | Equipe de Recherche en Epidémiologie Nutritionnelle
Guy Fagherazzi
Gloria A Aguayo
Lu Zhang
Sylvie Picard
Laura Sablone
Naïma Hamamouche
Bruno Detournay
Michael Joubert
Samy Hadjadj
Etienne Larger
Agnès Sola

Résumé

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: HbA1c, 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]), HbA1c (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 HbA1c (-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.
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Dates et versions

hal-04594934 , version 1 (30-05-2024)

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Guy Fagherazzi, Gloria A Aguayo, Lu Zhang, Hélène Hanaire, Sylvie Picard, et al.. Heterogeneity of glycaemic phenotypes in type 1 diabetes. Diabetologia, In press, ⟨10.1007/s00125-024-06179-4⟩. ⟨hal-04594934⟩
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