ArticleBMJ open diabetes research & care2020
Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood.
Article in BMJ open diabetes research & care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07461805 (Caracterización de Subgrupos de Personas Con Diabetes Tipo 1), which is not on this map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Caracterización de Subgrupos de Personas Con Diabetes Tipo 1: análisis de características clínicas y glucométricas Utilizando Una aproximación de Inteligencia Artificial
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- 24-Hour Movement Behaviours (Physical Activity, Sedentary Behaviour and Sleep) Association with Glycaemic Control and Psychosocial Outcomes in Adolescents with Type 1 Diabetes: A Systematic Review of Quantitative and Qualitative Studies.International journal of environmental research and public health · 2023Pooled it
- Effect of Body Weight on Glycaemic Indices in People with Type 1 Diabetes Using Continuous Glucose Monitoring.Journal of clinical medicine · 2024Article
- Endocrine Health and Health Care Disparities in the Pediatric and Sexual and Gender Minority Populations: An Endocrine Society Scientific Statement.The Journal of clinical endocrinology and metabolism · 2023Article
- Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.Scientific reports · 2021Article
- Differential Effects of Empagliflozin on Microvascular Complications in Murine Models of Type 1 and Type 2 Diabetes.Biology · 2020Article
- Functional data analysis and prediction tools for continuous glucose-monitoring studies.Journal of clinical and translational science · 2020Article
Corrections and comments
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Authors and funding
18 authors at 8 institutions in 1 country.
Funding
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 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.
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