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Sub-phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large

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This study used body composition data to calculate individual risks for coronary heart disease (CHD) and type 2 diabetes (T2D). This approach helps identify specific metabolic phenotypes within obesity and nonalcoholic fatty liver disease (NAFLD) for personalized treatment.

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Area of Science:

  • Biomedical imaging
  • Metabolic disease research
  • Personalized medicine

Background:

  • Obesity and nonalcoholic fatty liver disease (NAFLD) are complex conditions with varying metabolic risks.
  • Current sub-phenotyping methods often rely on discrete categorizations, potentially missing individual nuances.
  • Accurate assessment of disease propensity is crucial for effective management.

Purpose of the Study:

  • To perform individual-centric, data-driven calculations of coronary heart disease (CHD) and type 2 diabetes (T2D) propensity.
  • To utilize magnetic resonance imaging (MRI)-acquired body composition measurements for sub-phenotyping obesity and NAFLD.
  • To explore metabolic sub-phenotypes within obesity and NAFLD using an adaptive k-nearest neighbors algorithm.

Main Methods:

  • Analysis of 10,019 UK Biobank participants' imaging substudy data.
  • Measurement of visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat.
  • Application of an adaptive k-nearest neighbors algorithm to imaging variables for calculating individualized CHD and T2D propensity.

Main Results:

  • Coronary heart disease (CHD) and type 2 diabetes (T2D) propensity ranged from 1.3% to 58.0% and 0.6% to 42.0%, respectively.
  • The algorithm achieved diagnostic performance (AUC) of 0.75 for CHD and 0.79 for T2D detection.
  • Individualized disease propensity revealed distinct CHD, T2D, comorbid, and metabolically healthy phenotypes within obesity and NAFLD cohorts.

Conclusions:

  • An adaptive k-nearest neighbors algorithm enables individual-centric metabolic phenotype assessment beyond discrete body composition categories.
  • This approach can aid in identifying potential comorbidities in patients with obesity and NAFLD.
  • Personalized assessment may lead to optimized treatment strategies for metabolic diseases.