Key PointsQuestion
Can an unbiased evaluation of multiorgan phenotypes identify favorable and unfavorable clusters within a community-based cohort, especially among elderly American adults?
Findings
Using 11 quantitative computed tomography–based phenotypes, this cohort study found that unsupervised machine learning identified a favorable multiorgan phenotype characterized by increased calcification, adiposity, and muscle fat, which was associated with a 2-fold increased hazard of death at 10 years, robust to adjustment for age, sex, risk factors, and individual phenotypes.
Meaning
This proof-of-concept analysis demonstrates that unsupervised machine learning identifies an unfavorable multiorgan phenotype associated with adverse health outcomes, especially in elderly American adults.
Importance
Increased ability to quantify anatomical phenotypes across multiple organs provides the opportunity to assess their cumulative ability to identify individuals at greatest susceptibility for adverse outcomes.
Objective
To apply unsupervised machine learning to define the distribution and prognostic importance of computed tomography–based multiorgan phenotypes associated with adverse health outcomes.
Design, Setting, and Participants
This asymptomatic community-based cohort study included 2924 Framingham Heart Study participants between July 2002 and April 2005 undergoing computed tomographic imaging of the chest and abdomen. Participants are from the offspring and third-generation cohorts.
Exposures
Eleven computed tomography–based measures of valvular/vascular calcification, adiposity, and muscle attenuation.
Main Outcomes and Measures
All-cause mortality and cardiovascular disease (myocardial infarction, stroke, or cardiovascular death).
Results
The median age of the participants was 50 years (interquartile range, 43-60 years), and 1422 (48.6%) were men. Principal component analysis identified 3 major anatomic axes: (1) global calcification (defined by aortic, thoracic, coronary, and valvular calcification); (2) adiposity (defined by pericardial, visceral, hepatic, and intrathoracic fat); and (3) muscle attenuation that explained 65.7% of the population variation. Principal components showed different evolution with age (continuous increase in global calcification, decrease in muscle attenuation, and U-shaped association with adiposity) but similar patterns in men and women. Using unsupervised clustering approaches in the offspring cohort (n = 1150), we identified a cohort (n = 232; 20.2%) with an unfavorable multiorgan phenotype across all 3 anatomic axes as compared with a favorable multiorgan phenotype. Membership in the unfavorable phenotypic cluster was associated with a greater prevalence of cardiovascular disease risk factors and with increased all-cause mortality (hazard ratio, 2.61; 95% CI, 1.74-3.92; P < .001), independent of coronary artery calcium score, visceral adipose tissue, and 10-year global cardiovascular disease Framingham risk, and it provided improvement in metrics of discrimination and reclassification.
Conclusions and Relevance
This proof-of-concept analysis demonstrates that unsupervised machine learning, in an asymptomatic community cohort, identifies an unfavorable multiorgan phenotype associated with adverse health outcomes, especially in elderly American adults. Future investigations in larger populations are required not only to validate the present results, but also to harness clinical, biochemical, imaging, and genetic markers to increase our understanding of healthy cardiovascular aging.