TY - JOUR
T1 - Heterogeneity of glycaemic phenotypes in type 1 diabetes
AU - Fagherazzi, Guy
AU - Aguayo, Gloria A.
AU - Zhang, Lu
AU - Hanaire, Hélène
AU - Picard, Sylvie
AU - Sablone, Laura
AU - Vergès, Bruno
AU - Hamamouche, Naïma
AU - Detournay, Bruno
AU - Joubert, Michael
AU - Delemer, Brigitte
AU - Guilhem, Isabelle
AU - Vambergue, Anne
AU - Gourdy, Pierre
AU - Hadjadj, Samy
AU - Velayoudom, Fritz Line
AU - Guerci, Bruno
AU - Larger, Etienne
AU - Jeandidier, Nathalie
AU - Gautier, Jean François
AU - Renard, Eric
AU - Potier, Louis
AU - Benhamou, Pierre Yves
AU - Sola, Agnès
AU - Bordier, Lyse
AU - Bismuth, Elise
AU - Prévost, Gaëtan
AU - Kessler, Laurence
AU - Cosson, Emmanuel
AU - Riveline, Jean Pierre
AU - on behalf of the SFDT1 study team
N1 - Funding:
This work was made possible thanks to the institutional support from the Fondation Francophone pour la Recherche sur le Diabète (FFRD), the Société Francophone du Diabète (SFD) and the Luxembourg Institute of Health, as well as from the following partners: Aide aux Jeunes Diabétiques (AJD), Fédération Française des Diabétiques, Lilly, Abbott, Air Liquide Healthcare, Novo Nordisk, Sanofi, Insulet, Medtronic, Dexcom, Ypsomed, Lifescan and Sur les Pas de So. The study sponsors/funders were not involved in the design of the study; the collection, analysis and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/8
Y1 - 2024/8
N2 - 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.)
AB - 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.)
KW - Artificial intelligence
KW - Cluster analysis
KW - Continuous glucose monitoring
KW - Diabetes complications
KW - Glycaemia risk index
KW - Glycaemic control
KW - Glycaemic phenotype
KW - Glycaemic variability
KW - Insulin pumps
KW - Machine learning
KW - Type 1 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85193955159&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/38780786
U2 - 10.1007/s00125-024-06179-4
DO - 10.1007/s00125-024-06179-4
M3 - Article
C2 - 38780786
AN - SCOPUS:85193955159
SN - 0012-186X
VL - 67
SP - 1567
EP - 1581
JO - Diabetologia
JF - Diabetologia
IS - 8
ER -