Heterogeneity of glycaemic phenotypes in type 1 diabetes

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.

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.

Fig. 1

Visual representation of the heterogeneity of glycaemic control and variability in type 1 diabetes across the phenotypic tree (SFDT1 cohort, N=618). (a–f) Spatial distribution of glycaemic variables on the tree. (g) Attribution of a number to the seven glycaemic phenotypes (from 1 to 7)

Fig. 2

Association of glycaemic variables with the two dimensions of the tree and spatial autocorrelation MI (SFDT1 cohort, N=618). (a) MI of spatial autocorrelation of each variable. (b) Linear regression of glycaemic variables and the two dimensions of the tree. The blue and red lines represent the coefficient and 95% CI of Dim1 (horizontal) and Dim2 (vertical), respectively. ***p<0.001

Fig. 3

Determinants of glycaemic phenotype heterogeneity, according to the two dimensions of the phenotypic tree (SFDT1 cohort, N=618). Age and sex were residualised. CIs were calculated according to Rubin’s rules. Pump only: treatment with insulin pump only. Pump plus other: pump plus opened loop sensor or hypo minimiser. The blue and red lines are coefficients and 95% CI of Dim1 (horizontal) and Dim2 (vertical), respectively

Fig. 4

Clusters identified by k-means (SFDT1 cohort, N=618). Cluster 1 (‘Euglycaemia’), Cluster 2 (‘Hyperglycaemia’) and Cluster 3 (‘Hypoglycaemia’) are represented in grey, blue and red, respectively. (a) Cluster visualisation in the two PCA coordinates. (b–h) Cluster localisation in the six variables assessing glycaemic phenotypes. (i) Cluster distribution overlaying the glycaemic phenotypic tree. PCA1, component 1; PCA2, component 2