Body Composition Differentiates Prediabetes Phenotype Clusters in the Diabetes Prevention Program Study
Authors' conclusion
Data-driven clustering of patients with prediabetes allows for identification of prediabetes phenotypes at greater risk for disease progression and responses to risk reduction interventions. Further investigation into phenotypic differences in treatment response could enable better personalization of prediabetes and T2D prevention and treatment choices.
Study context
- Population
- Subset of Diabetes Prevention Program participants with prediabetes and body composition measureswith body composition measures
- Participants
- 994 participants
- Primary endpoint
- Optimal number of participant clusters by unsupervised k-means clustering, based on common clinical risk factors alone or plus comprehensive measures of glucose tolerance and body composition
Methodological detailsunsupervised k-means clustering
- Unsupervised k-means clustering
Primary result
As reported in the abstract
Prediabetes phenotype clusters
- Clusters derived from both clinical-only and comprehensive (glucose tolerance + body composition) models
- 5
Further findings
Time to T2D by treatment arm
- For both models, greatest differentiation in time to T2D was in the metformin arm
Original publication
Body Composition Differentiates Prediabetes Phenotype Clusters in the Diabetes Prevention Program Study. J Clin Endocrinol Metab. Published 11 March 2025. doi:10.
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