Heterogeneity of glycaemic phenotypes in type 1 diabetes
Guy Fagherazzi, Gloria Aguayo, Lu Zhang, H. Hanaire, Sylvie Picard, Laura Sablone, Bruno Vergès, N. Hamamouche, B. Detournay, Michaël Joubert, Brigitte Delemer, Isabelle Guilhem, A. Vambergue, Pierre Gourdy, Samy Hadjadj, Fritz-Line Vélayoudom, Bruno Guerci, Étienne Larger, N. Jeandidier, Jean–François Gautier, Éric Renard, Louis Potier, Pierre‐Yves Benhamou, Agnès Sola, L. Bordier, Élise Bismuth, Gaëtan Prévost, Laurence Kessler, Emmanuel Cosson, Jean‐Pierre Riveline, on behalf of the SFDT1 study team
Abstract
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: 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