Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood

Abstract

IntroductionIndividuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups.Research design and methodsParticipants with T1D in the SEARCH study cohort (n=1817, 6.0–30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively.ResultsThe sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01).ConclusionsThere are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.

Differential Effects of Empagliflozin on Microvascular Complications in Murine Models of Type 1 and Type 2 Diabetes

“…db /+ HFD animals had impaired glucose tolerance at 16 ( Figure S1C ) and 36 weeks ( Figure S1D ). These results show that the BKS db /+ mouse background upon metabolic challenge, such as HFD, acquires metabolic features now routinely seen in patients with T1D [ 30 ] advocating it as a novel and more biologically relevant mouse model for STZ-induced T1D.…”

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“…Cardiometabolic health relies on complex, intricate, physiological relationships between all the considered parameters in this work. These results imply a move from a “one-size fits-all” vision to a precision cardiometabolic prevention approach to tackle cardiometabolic diseases according to the variety of phenotypes observed in the general population 14 .…”

“…Besides, clustering approaches used in the litterature so far were mostly unsupervised where it is assumed that there is no outcome variable nor is anything known about the relationships between the observations in the data set, which is not a reliable hypothesis with respect to cardiometabolic prevention. Semi-supervised clustering techniques may therefore be more adapted to derive meaningful groups 13 , similarly to what has been recently suggested in people with type 1 diabetes 14 , to redefine the way we consider, prevent and treat cardiometabolic diseases in the general population, not as independent entities but rather with a more comprehensive, patient-centered, approach.…”