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

Abstract

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.

Figure 1.. Unsupervised Machine Learning to Define Unique Phenotypic Subtypes

Schematic of study design. Unsupervised machine learning refers to methods that define associations without relying on a predetermined outcome (eg, death and cardiovascular disease events). CT indicates computed tomography; FHS, Framingham Heart Study; and PC, principal component.

Figure 2.. Principal Components Analysis

Principal components analysis of 11 parameters obtained from chest and abdominal computed tomographic imaging. A, Three principal components (representing 65.7% of the total variance in the imaging data) with loadings of each imaging parameter are presented. The dark blue hue represents imaging variables that have positive loading in a given principal component; the light blue hue represents imaging variables that have a negative loading in a given principal component. Of note, greater liver or muscle attenuation corresponds to decreased liver or intramuscular fat, respectively; therefore, a positive loading for muscle in principal component 3 signifies that a higher principal component 3 score is associated with greater muscle attenuation (lower intramuscular fat content). B, Box plots of principal component scores across decades of age. Details of statistical testing are specified in the text. SAT indicates subcutaneous adipose tissue; VAT, visceral adipose tissue.

Figure 3.. Survival Analysis

Kaplan-Meier survival curves for all-cause mortality (A) and hard cardiovascular disease (CVD) (B) by cluster group. C, Results of multivariable survival analysis (as described in the Methods section). “Reference” indicates the referent group. CAC indicates coronary artery calcium; CT, computed tomography; FRS, Framingham risk score; IDI, integrated discrimination improvement; NRI, net reclassification index; and VAT, visceral adipose tissue.