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An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.

Roderick C Slieker1,2,3,4, Magnus Münch1, Louise A Donnelly5

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Summary

Predicting insulin initiation in type 2 diabetes is possible using clinical factors and molecular markers. Machine learning models show that adding proteins and metabolites modestly improves prediction accuracy for faster disease progression.

Keywords:
Machine learningPrediction modelProgressionType 2 diabetes

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Area of Science:

  • Endocrinology and Metabolism
  • Computational Biology
  • Biomarker Discovery

Background:

  • Type 2 diabetes progression is heterogeneous, with varying rates of insulin initiation.
  • Classical biomarkers like HbA1c and age predict glycaemic progression but their ability to predict insulin initiation is unclear.
  • The added predictive value of novel molecular markers for insulin requirement is unknown.

Purpose of the Study:

  • To investigate the predictive value of clinical variables and molecular markers (metabolites, lipids, proteins) for time to insulin requirement in type 2 diabetes.
  • To compare the performance of different machine learning approaches in predicting insulin initiation.
  • To determine if molecular markers improve prediction beyond established clinical factors.

Main Methods:

  • Two prospective cohorts (IMI-RHAPSODY study) with 585 (DCS) and 571 (GoDARTS) individuals were analyzed.
  • Machine learning models (lasso, ridge, GRridge, random forest) were employed to predict time to insulin requirement.
  • Models incorporated clinical variables (age, sex, HbA1c, HDL-cholesterol, C-peptide) and molecular markers (metabolites, lipids, proteins).
  • Model performance was evaluated using Harrel's C statistic.

Main Results:

  • Clinical variables, particularly HbA1c, age, and C-peptide, were frequently selected in predictive models.
  • Base models with clinical variables showed moderate predictive performance (C-statistic ~0.71).
  • Inclusion of HDL-cholesterol and C-peptide improved model performance.
  • Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were consistently selected and slightly enhanced prediction.

Conclusions:

  • Machine learning models can modestly predict insulin requirement risk in type 2 diabetes, primarily using clinical variables.
  • Incorporating molecular markers, especially proteins, can improve prognostic performance by up to 5%.
  • These predictive models can help identify individuals with type 2 diabetes at higher risk of rapid disease progression requiring treatment intensification.