Objective: This study performed individual-centric, data-driven calculations of propensity for coronary
heart disease (CHD) and type 2 diabetes (T2D), utilizing magnetic resonance imaging-acquired body
composition measurements, for sub-phenotyping of obesity and nonalcoholic fatty liver disease (NAFLD).
Methods: A total of 10,019 participants from the UK Biobank imaging substudy were included and analyzed
for visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat. An adaption
of the k-nearest neighbors algorithm was applied to the imaging variable space to calculate individualized
CHD and T2D propensity and explore metabolic sub-phenotyping within obesity and NAFLD.
Results: The ranges of CHD and T2D propensity for the whole cohort were 1.3% to 58.0% and 0.6% to
42.0%, respectively. The diagnostic performance, area under the receiver operating characteristic curve
(95% CI), using disease propensities for CHD and T2D detection was 0.75 (0.73-0.77) and 0.79 (0.77-0.81).
Exploring individualized disease propensity, CHD phenotypes, T2D phenotypes, comorbid phenotypes,
and metabolically healthy phenotypes were found within obesity and NAFLD.
Conclusions: The adaptive k-nearest neighbors algorithm allowed an individual-centric assessment of
each individual’s metabolic phenotype moving beyond discrete categorizations of body composition. Within
obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and consequently enable optimization of treatment.