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June 7, 2026Diabetes

Phenotype 1 (Metabolic-Driven High Risk) had 3.9-fold higher complication odds than Phenotype 2 (OR 3.94, 95% CI 1.89-8.21) despite being 7 years younger.

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Why the study?

Does unsupervised machine learning identify distinct clinical phenotypes with differential complication burden in patients with Type 2 Diabetes?

Population

217 patients with Type 2 Diabetes Mellitus with complete data from approximately 400 patients across…

Design

Cross-sectional

Authors

SPSHUBHASHREE PATIL

Discussion

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Overview

Unsupervised machine learning identified three distinct T2DM phenotypes driven primarily by triglycerides and BMI rather than HbA1c, revealing significantly different complication burdens that could guide precision treatment.

Key Points

  • To classify type 2 diabetes into distinct phenotypes based on metabolic profiles and complications.
  • K-means clustering applied to 217 T2DM patients with complete data.
  • Analysis included age, gender, BMI, HbA1c, lipid panel, diabetes duration, and metabolic markers.
  • Characterization of metabolic profiles and complication prevalence for each phenotype.
  • Three phenotypes identified: 1) Metabolic-Driven High Risk (32%): higher complications despite younger age; 2) Age-Driven Moderate Risk (44%): typical profile with moderate dyslipidemia; 3) Young Well-Controlled (24%): better control and fewer complications.
  • Phenotype 1 exhibited 3.9-fold higher complications compared to Phenotype 2 (OR 3.94, 95% CI 1.89-8.21).
  • High triglycerides (>200 mg/dL) and BMI (>30) were significant discriminators between phenotypes.

Structured PICO

Does unsupervised machine learning identify distinct clinical phenotypes with differential complication burden in patients with Type 2 Diabetes?

P
Population
217 patients with Type 2 Diabetes Mellitus (T2DM) with complete data from approximately 400 patients across multiple diabetes screening camps.
I
Intervention
Unsupervised machine learning (K-means clustering) using age, gender, BMI, HbA1c, lipid panel (total cholesterol, triglycerides, LDL, HDL, VLDL), diabetes duration, and metabolic markers.
O
Outcome
Identification of distinct phenotypes and their complication prevalence (bone disease, neuropathy, dyslipidemia, hepatic steatosis).

Unsupervised machine learning identified three distinct T2DM phenotypes driven primarily by triglycerides and BMI rather than HbA1c, revealing significantly different complication burdens that could guide precision treatment.

Cite This Study

SHUBHASHREE PATIL (2026) studied this question.

synapsesocial.com/papers/6a250c957def13d035e1ccc0https://doi.org/10.2337/db26-2272-p
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models2025 · 1 citations
  2. 2Phenotypic heterogeneity of type 2 diabetes and risks of all-cause and cause-specific mortality2025 · 5 citations
  3. 3Clinical risk phenotypes in diabetes and their associations with adverse cardiovascular events: A report from the Silesia Diabetes‐Heart Project2025 · 1 citations
  4. 42601-P: Body Composition Clustering and Polygenic Score Analysis in Patients with Early-Onset Newly Diagnosed Type 2 Diabetes2026
  5. 52351-P: Assessment of Phenotypic Clustering of Incident and Prevalent Type 2 Diabetes: A MESA Pilot Study in the Definition, Etiology, Function: Integration to Enhance Type 2 Diabetes Treatment (DEFINE-Type 2 Diabetes) Consortium2026