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Recognising, quantifying and accounting for classification uncertainty in type 2 diabetes subtypes.

Tim Mori1,2, Oana P Zaharia3,4,5, Klaus Straßburger6,3

  • 1Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany. tim.mori@ddz.de.

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Summary

This study introduces a novel method to quantify classification uncertainty in type 2 diabetes subtypes. Accounting for this uncertainty improves the prediction of cardiovascular disease risk in individuals with type 2 diabetes.

Keywords:
Classification uncertaintyClustersGerman Diabetes StudyPrecision medicineRelative entropySubtypesType 2 diabetes mellitus

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Area of Science:

  • Endocrinology
  • Precision Medicine
  • Diabetes Subtyping

Background:

  • Precision diagnostics for type 2 diabetes subtypes are of interest, but classification uncertainty remains a challenge.
  • Existing methods lack a robust way to quantify and address individual classification uncertainty within diabetes subtypes.

Purpose of the Study:

  • To introduce and validate a novel method for quantifying classification uncertainty in type 2 diabetes subtypes.
  • To assess how accounting for classification uncertainty impacts the prediction of cardiovascular disease (CVD) risk across different subtypes.

Main Methods:

  • Utilized normalised relative entropy (NRE) to quantify classification uncertainty, derived from distances to cluster centroids.
  • Analyzed a cohort of 859 recent-onset type 2 diabetes patients from the German Diabetes Study (GDS).
  • Evaluated the impact of classification uncertainty on the predictive power of subtypes for 10-year CVD risk (SCORE2-Diabetes).

Main Results:

  • Classification uncertainty (NRE) varied across subtypes, being lower in severe insulin-resistant and insulin-deficient diabetes compared to mild age-related and obesity-related subtypes.
  • Accounting for classification uncertainty significantly increased the proportion of variation in predicted 10-year CVD risk explained by the subtypes (from 17.4% to 31.5%).
  • The predicted 10-year CVD risk for the mild age-related diabetes subtype increased when classification certainty was considered.

Conclusions:

  • The normalised relative entropy (NRE) effectively quantifies and compares classification uncertainty in type 2 diabetes subtypes.
  • Classification uncertainty is a significant factor that varies among individuals and subtypes.
  • Incorporating classification certainty into analyses enhances the predictive accuracy of type 2 diabetes subtypes for long-term CVD risk.