Guy Fagherazzi, Gloria A Aguayo, Lu Zhang, Hélène Hanaire, Sylvie Picard, Laura Sablone, Bruno Vergès, Naïma Hamamouche, Bruno Detournay, Michael Joubert, Brigitte Delemer, Isabelle Guilhem, Anne Vambergue, Pierre Gourdy, Samy Hadjadj, Fritz-Line Velayoudom, Bruno Guerci, Etienne Larger, Nathalie Jeandidier, Jean-François Gautier, Eric Renard, Louis Potier, Pierre-Yves Benhamou, Agnès Sola, Lyse Bordier, Elise Bismuth, Gaëtan Prévost, Laurence Kessler, Emmanuel Cosson, Jean-Pierre Riveline
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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.</p><p><strong>Results: </strong>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<sub>1c</sub> (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<sub>1c</sub> (-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.</p><p><strong>Conclusions/interpretation: </strong>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.</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":null,"pages":null},"PeriodicalIF":8.4000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343912/pdf/","citationCount":"0","resultStr":"{\"title\":\"Heterogeneity of glycaemic phenotypes in type 1 diabetes.\",\"authors\":\"Guy Fagherazzi, Gloria A Aguayo, Lu Zhang, Hélène Hanaire, Sylvie Picard, Laura Sablone, Bruno Vergès, Naïma Hamamouche, Bruno Detournay, Michael Joubert, Brigitte Delemer, Isabelle Guilhem, Anne Vambergue, Pierre Gourdy, Samy Hadjadj, Fritz-Line Velayoudom, Bruno Guerci, Etienne Larger, Nathalie Jeandidier, Jean-François Gautier, Eric Renard, Louis Potier, Pierre-Yves Benhamou, Agnès Sola, Lyse Bordier, Elise Bismuth, Gaëtan Prévost, Laurence Kessler, Emmanuel Cosson, Jean-Pierre Riveline\",\"doi\":\"10.1007/s00125-024-06179-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims/hypothesis: </strong>Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes.</p><p><strong>Methods: </strong>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<sub>1c</sub>, 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.</p><p><strong>Results: </strong>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<sub>1c</sub> (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<sub>1c</sub> (-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.</p><p><strong>Conclusions/interpretation: </strong>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. 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Heterogeneity of glycaemic phenotypes in type 1 diabetes.
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.
期刊介绍：
Diabetologia, the authoritative journal dedicated to diabetes research, holds high visibility through society membership, libraries, and social media. As the official journal of the European Association for the Study of Diabetes, it is ranked in the top quartile of the 2019 JCR Impact Factors in the Endocrinology & Metabolism category. The journal boasts dedicated and expert editorial teams committed to supporting authors throughout the peer review process.
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