Heterogeneity of glycaemic phenotypes in type 1 diabetes,Diabetologia

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Heterogeneity of glycaemic phenotypes in type 1 diabetes
Diabetologia ( IF 8.4 ) Pub Date : 2024-05-23 , DOI: 10.1007/s00125-024-06179-4
Guy Fagherazzi ₁ , Gloria A Aguayo ₁ , Lu Zhang ₂ , Hélène Hanaire _{3,

4} , Sylvie Picard ₅ , Laura Sablone ₄ , Bruno Vergès ₆ , Naïma Hamamouche ₇ , Bruno Detournay ₈ , Michael Joubert ₉ , Brigitte Delemer ₁₀ , Isabelle Guilhem ₁₁ , Anne Vambergue ₁₂ , Pierre Gourdy _{3,

13} , Samy Hadjadj ₁₄ , Fritz-Line Velayoudom _{15,

16} , Bruno Guerci ₁₇ , Etienne Larger _{18,

19} , Nathalie Jeandidier ₂₀ , Jean-François Gautier _{21,

22} , Eric Renard _{23,

24} , Louis Potier _{21,

25} , Pierre-Yves Benhamou ₂₆ , Agnès Sola ₁₉ , Lyse Bordier ₂₇ , Elise Bismuth ₂₈ , Gaëtan Prévost ₂₉ , Laurence Kessler ₂₀ , Emmanuel Cosson _{30,

31} , Jean-Pierre Riveline _{21,

22} ,

Affiliation

Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg.
Department of Diabetology, Metabolic Diseases and Nutrition, CHU Toulouse, University of Toulouse, Toulouse, France.
Francophone Foundation for Diabetes Research, Paris, France.
Endocrinology and Diabetes, Point Medical, Dijon, France.
Department of Endocrinology-Diabetology, Inserm LNC UMR1231, University of Burgundy, Dijon, France.
e-Health Services Sanoïa, Gémenos, France.
CEMKA, Bourg-la-Reine, France.
Service d'Endocrinologie-Diabétologie (Endocrinology/Diabetes Unit), Centre Hospitalier Universitaire de Caen, Caen, France.
Endocrinology, Diabetology and Nutrition Department, Robert Debré University Hospital, Reims, France.
Department of Endocrinology, Diabetes and Nutrition, University Hospital of Rennes, Rennes, France.
Endocrinology, Diabetology, Metabolism and Nutrition Department, Lille University Hospital, Lille, France.
Institute of Metabolic and Cardiovascular Diseases, UMR1297 Inserm/UPS, Toulouse University, Toulouse, France.
Institut du thorax, INSERM, CNRS, Université Nantes, CHU Nantes, Nantes, France.
Department of Endocrinology-Diabetology, University Hospital of Guadeloupe, Pointe-À-Pitre, France.
Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Lille, France.
Department of Endocrinology, Diabetology, and Nutrition, Brabois Adult Hospital, University of Lorraine, Vandoeuvre-Lès-Nancy, France.
University Paris Cité, Institut Cochin, U1016, Inserm, Paris, France.
Diabetology Department, Cochin Hospital, AP-HP, Paris, France.
Department of Endocrinology, Diabetes and Nutrition, Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, Strasbourg, France.
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.
Institute of Functional Genomics, University of Montpellier, CNRS, Inserm, Montpellier, France.
Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France.
Department of Diabetology, Endocrinology and Nutrition, AP-HP, Bichat Hospital, Paris, France.
Université Grenoble Alpes, Inserm U1055, CHU Grenoble Alpes, Grenoble, France.
Service d'Endocrinologie, Hôpital Bégin, Saint Mandé, France.
Robert-Debré University Hospital, Department of Paediatric Endocrinology and Diabetology, AP-HP, University of Paris, Paris, France.
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.
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.

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.

Graphical Abstract

中文翻译：

1 型糖尿病血糖表型的异质性

目标/假设

我们的研究旨在揭示 1 型糖尿病的血糖表型异质性。

方法

在法语 1 型糖尿病协会 (SFDT1) 的研究中，我们通过一组补充指标来描述血糖异质性：HbA _1c 、范围内时间 (TIR)、低于范围时间 (TBR)、CV、Gold 评分和血糖风险指数（GRI）。应用判别性降维树（DDRTree）算法，我们创建了表型树，即二维视觉映射。我们还进行了聚类分析以进行比较。

结果

我们纳入了 618 名患有 1 型糖尿病的参与者（52.9% 为男性，平均年龄 40.6 岁 [SD 14.1]）。我们的表型树确定了七种血糖表型。二维表型树包括近端区域的主分支和远端区域的血糖表型。维度 1（水平维度）与 GRI（系数 [95% CI]）（0.54 [0.52，0.57]）、HbA _1c （0.39 [0.35，0.42]）、CV（0.24 [0.19，0.28]）和TBR (0.11 [0.06, 0.15])，TIR 为负 (−0.52 [−0.54, −0.49])。垂直尺寸与 TBR (0.41 [0.38, 0.44])、CV (0.40 [0.37, 0.43])、TIR (0.16 [0.12, 0.20])、Gold 得分 (0.10 [0.06, 0.15]) 和 GRI 呈正相关 ( 0.06 [0.02, 0.11])，HbA _1c为负值 (−0.21 [−0.25, -0.17])。值得注意的是，社会经济因素、心血管风险指标、视网膜病变和治疗策略是血糖表型多样性的重要决定因素。表型树在揭示 1 型糖尿病患者的临床相关亚组方面比传统聚类具有更大的粒度。