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Stratification of Pro-Atherogenic Phenotypes in Prediabetes Using Machine Learning.

Liana Signorini1, Waldemar Volanski1,2, Ademir Luiz do Prado1,3

  • 1Graduate Program in Pharmaceutical Sciences, Laboratory of the Research Group on Metabolic Diseases, Federal University of Parana, Curitiba 80210-170, Brazil.

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|March 28, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning identified a pro-atherogenic cluster in prediabetes using routine lab tests. This cluster, characterized by specific lipid profiles, indicates higher cardiovascular disease risk, guiding targeted interventions.

Keywords:
ROC curveartificial intelligencebinomial logistic regressionclusterintermediate hyperglycemiak-meansknowledge discovery databaselipid biomarkers

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Area of Science:

  • Cardiology
  • Metabolic Health
  • Biomarker Analysis

Background:

  • Prediabetes is a metabolic condition with diverse glucose metabolism phenotypes.
  • Prediabetes significantly elevates the risk of cardiovascular disease (CVD).
  • Routine laboratory biomarkers can potentially characterize CVD risk phenotypes in prediabetes.

Purpose of the Study:

  • To utilize machine learning to identify distinct phenotypes of CVD risk within the prediabetic population.
  • To characterize these phenotypes using accessible, routine laboratory biomarkers.
  • To evaluate the efficacy of lipid profile parameters and derived indices in distinguishing CVD risk clusters.

Main Methods:

  • Analysis of laboratory records from over 1,000,000 individuals, focusing on 3024 prediabetic subjects (fasting glucose 100-125 mg/dL, HbA1c 5.7-6.4%).
  • Application of k-means clustering to lipid profile parameters (TC, HDL-C, LDL-C, triglycerides) and derived indices (AIP, TyG, TC/HDL-C, LDL-C/HDL-C).
  • Receiver operating characteristic (ROC) curve analysis and binomial logistic regression to assess biomarker performance.

Main Results:

  • K-means clustering identified two groups: a pro-atherogenic cluster (P-AC; n=1113) and a less-atherogenic cluster (L-AC; n=1911).
  • Triglycerides, Atherogenic Index of Plasma (AIP), and Triglyceride-Glucose index (TyG) demonstrated high diagnostic performance (AUC > 0.97).
  • Logistic regression using AIP and LDL-C/HDL-C achieved an AUC of 0.984 with >93% accuracy.

Conclusions:

  • The k-means algorithm effectively identified a pro-atherogenic cluster for CVD risk among prediabetics using cost-effective biomarkers.
  • Widely available laboratory biomarkers can stratify CVD risk in prediabetic individuals.
  • Individuals in the identified P-AC may benefit from tailored treatment strategies to mitigate CVD risk.