BACKGROUND: New electronic cohort (e-Cohort) study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-Cohort study without direct, in-person participant contact can achieve successful participation rates. OBJECTIVE: The objective of this study was to compare 2 distinct enrollment methods for setting up mobile health (mHealth) devices and to assess the ongoing adherence to device use in an e-Cohort pilot study. METHODS: We coenrolled participants from the Framingham Heart Study (FHS) into the FHS-Health eHeart (HeH) pilot study, a digital cohort with infrastructure for collecting mHealth data. FHS participants who had an email address and smartphone were randomized to our FHS-HeH pilot study into 1 of 2 study arms: remote versus on-site support. We oversampled older adults (age > /=65 years), with a target of enrolling 20% of our sample as older adults. In the remote arm, participants received an email containing a link to enrollment website and, upon enrollment, were sent 4 smartphone-connectable sensor devices. Participants in the on-site arm were invited to visit an in-person FHS facility and were provided in-person support for enrollment and connecting the devices. Device data were tracked for at least 5 months. RESULTS: Compared with the individuals who declined, individuals who consented to our pilot study (on-site, n=101; remote, n=93) were more likely to be women, highly educated, and younger. In the on-site arm, the connection and initial use of devices was > /=20% higher than the remote arm (mean percent difference was 25% [95% CI 17-35] for activity monitor, 22% [95% CI 12-32] for blood pressure cuff, 20% [95% CI 10-30] for scale, and 43% [95% CI 30-55] for electrocardiogram), with device connection rates in the on-site arm of 99%, 95%, 95%, and 84%. Once connected, continued device use over the 5-month study period was similar between the study arms. CONCLUSIONS: Our pilot study demonstrated that the deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared with offering only remote support. Once connected, the device use was similar in both groups.

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.

BACKGROUND: Visceral adipose tissue (VAT) and fatty liver differ in their associations with cardiovascular risk compared with subcutaneous adipose tissue (SAT). Several biomarkers have been linked to metabolic derangements and may contribute to the pathogenicity of fat depots. We examined the association between fat depots on multidetector computed tomography and metabolic regulatory biomarkers. METHODS AND RESULTS: Participants from the Framingham Heart Study (n=1583, 47% women) underwent assessment of SAT, VAT, and liver attenuation. We measured circulating biomarkers secreted by adipose tissue or liver (adiponectin, leptin, leptin receptor, fatty acid binding protein 4, fetuin-A, and retinol binding protein 4). Using multivariable linear regression models, we examined relations of fat depots with biomarkers. Higher levels of fat depots were positively associated with leptin and fatty acid binding protein 4 but negatively associated with adiponectin (all P < 0.001). Associations with leptin receptor, fetuin-A, and retinol binding protein 4 varied according to fat depot type or sex. When comparing the associations of SAT and VAT with biomarkers, VAT was the stronger correlate of adiponectin (beta=-0.28 [women]; beta=-0.30 [men]; both P < 0.001), whereas SAT was the stronger correlate of leptin (beta=0.62 [women]; beta=0.49 [men]; both P < 0.001; P < 0.001 for comparing VAT versus SAT). Although fetuin-A and retinol binding protein 4 are secreted by the liver in addition to adipose tissue, associations of liver attenuation with these biomarkers was not stronger than that of SAT or VAT. CONCLUSIONS: SAT, VAT, and liver attenuation are associated with metabolic regulatory biomarkers with differences in the associations by fat depot type and sex. These findings support the possibility of biological differences between fat depots.

BACKGROUND: Excess accumulation of abdominal subcutaneous (SAT) and visceral adipose tissue (VAT) is associated with adverse levels of adipokines and cardiovascular disease risk. Whether fat quality is associated with adipokines has not been firmly established. This study examined the association between abdominal SAT and VAT density, an indirect measure of fat quality, with a panel of metabolic regulatory biomarkers secreted by adipose tissue or the liver independently of absolute fat volumes. METHODS AND RESULTS: We evaluated 1829 Framingham Heart Study participants (44.9% women). Abdominal SAT and VAT density was estimated indirectly by adipose tissue attenuation using computed tomography. Adipokines included adiponectin, leptin receptor, leptin, fatty acid-binding protein 4 (FABP-4), retinol-binding protein 4 (RBP-4), and fetuin-A. Fat density was associated with all the biomarkers evaluated, except fetuin-A. Lower fat density (ie, more-negative fat attenuation) was associated with lower adiponectin and leptin receptor, but higher leptin and FABP-4 levels (all P < 0.0001). SAT density was inversely associated with RPB-4 in both sexes, whereas the association between VAT density and RPB-4 was only observed in men (P < 0.0001). In women, after additional adjustment for respective fat volume, SAT density retained the significant associations with adiponectin, leptin, FABP-4, and RBP-4; and VAT density with adiponectin only (all P<0.0001). In men, significant associations were maintained upon additional adjustment for respective fat volume (P < 0.005). CONCLUSIONS: Lower abdominal fat density was associated with a profile of biomarkers suggestive of greater cardiometabolic risk. These observations support that fat density may be a valid biomarker of cardiometabolic risk.