Heterogeneity of glycaemic phenotypes in type 1 diabetes

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 GautierEric Renard, Louis Potier, Pierre Yves Benhamou, Agnès Sola, Lyse Bordier, Elise Bismuth, Gaëtan Prévost, Laurence Kessler, Emmanuel Cosson, Jean Pierre Riveline, on behalf of the SFDT1 study team

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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. Graphical Abstract: (Figure presented.).

Original languageEnglish
JournalDiabetologia
Early online date23 May 2024
DOIs
Publication statusE-pub ahead of print - 23 May 2024

Keywords

  • 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

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