Abstract
OBJECTIVE: Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics. MATERIALS AND METHODS: Life's Essential 8 (LE8) variables, demographics, and CVD events were queried over 15 ears in 5060 CARDIA participants with 18 years of subsequent follow-up. LE8 subgraphs were mined and a SANMF algorithm was applied to cluster frequently occurring subgraphs. K-fold cross-validation and diagnostics were performed to determine cluster assignment. Cox proportional hazard models were fit for future CV event risk and logistic regression was performed for cluster phenotyping. RESULTS: The cohort (54.6% female, 48.7% White) produced 3 clusters of CVH metrics: Healthy & Late Obesity (HLO) (29.0%), Healthy & Intermediate Sleep (HIS) (43.2%), and Unhealthy (27.8%). HLO had 5 ideal LE8 metrics between ages 18 and 39 years, until BMI increased at 40. HIS had 7 ideal LE8 metrics, except sleep. Unhealthy had poor levels of sleep, smoking, and diet but ideal glucose. Race and employment were significantly different by cluster (P < .001) but not sex (p =" .734)." for 301 incident cv events, multivariable hazard ratios (hrs) for his and unhealthy were 0.73 (0.53-1.00, p =" .052)" and 2.00 (1.50-2.68, p >< .001), respectively versus hlo. a 15-year event survival was 97.0% (his), 96.3% (hlo), and 90.4% (unhealthy, p >< .001). discussion and conclusion: sanmf of le8 metrics identified 3 unique clusters of cvh behavior patterns. clustering of longitudinal le8 variables via sanmf is a robust tool for phenotypic risk assessment for future adverse cardiovascular events. objective: changes in cardiovascular health (cvh) during the life course are associated with future cardiovascular disease (cvd). longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (sanmf) could create phenotypic risk profiles of clustered cvh metrics. materials and methods: life's essential 8 (le8) variables, demographics, and cvd events were queried over 15 ears in 5060 cardia participants with 18 years of subsequent follow-up. le8 subgraphs were mined and a sanmf algorithm was applied to cluster frequently occurring subgraphs. k-fold cross-validation and diagnostics were performed to determine cluster assignment. cox proportional hazard models were fit for future cv event risk and logistic regression was performed for cluster phenotyping. results: the cohort (54.6% female, 48.7% white) produced 3 clusters of cvh metrics: healthy & late obesity (hlo) (29.0%), healthy & intermediate sleep (his) (43.2%), and unhealthy (27.8%). hlo had 5 ideal le8 metrics between ages 18 and 39 years, until bmi increased at 40. his had 7 ideal le8 metrics, except sleep. unhealthy had poor levels of sleep, smoking, and diet but ideal glucose. race and employment were significantly different by cluster (p >< .001) but not sex (p =" .734)." for 301 incident cv events, multivariable hazard ratios (hrs) for his and unhealthy were 0.73 (0.53-1.00, p =" .052)" and 2.00 (1.50-2.68, p >< .001), respectively versus hlo. a 15-year event survival was 97.0% (his), 96.3% (hlo), and 90.4% (unhealthy, p >< .001). discussion and conclusion: sanmf of le8 metrics identified 3 unique clusters of cvh behavior patterns. clustering of longitudinal le8 variables via sanmf is a robust tool for phenotypic risk assessment for future adverse cardiovascular events.>
