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Original Investigation
November 2017

Association of Multiorgan Computed Tomographic Phenomap With Adverse Cardiovascular Health Outcomes: The Framingham Heart Study

Author Affiliations
  • 1Cardiovascular Research Center, Massachusetts General Hospital, Boston
  • 2Department of Cardiology, University of Michigan, Ann Arbor
  • 3Department of Statistics, Boston University, Boston, Massachusetts
  • 4Department of Mathematics and Statistics, Boston University, Boston, Massachusetts
  • 5University of Massachusetts, Worcester
  • 6Framingham Heart Study, Framingham, Massachusetts
  • 7Section of Gastroenterology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
  • 8Merck Research Laboratories, Boston, Massachusetts
  • 9Cardiology and Preventive Medicine and Epidemiology Sections, Boston University School of Medicine, and Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
  • 10Cardiology Section, Department of Medicine, Boston VA Healthcare, Boston, Massachusetts
  • 11Associate Editor, JAMA Cardiology
  • 12Department of Radiology, Massachusetts General Hospital, Boston
JAMA Cardiol. 2017;2(11):1236-1246. doi:10.1001/jamacardio.2017.3145
Key Points

Question  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.