Project Summary/Abstract
Cardiometabolic (CM) diseases including cardiovascular (CV) and metabolic diseases are the leading
cause of preventable death in the United States and Worldwide. CM diseases are interconnected and positively
associated with multi-domain Cardiometabolic Risk Factors (CMRFs) such as metabolic dysregulation, obesity,
physical inactivity, poor nutrition, and other emerging factors (including and especially sleep disorders). CMRFs
are highly and increasingly prevalent in adolescents and young adults, which foreshadows a future
epidemic of incident CM diseases as they age. However, existing studies have primarily focused on the adult
and senior population with little to no knowledge on the young population.
CM data hold great promise to facilitate CM subgroup discovery for early risk stratification and precise
prognosis. However, significant gaps exist in fully leveraging CM data. Gap I: Lack of inclusion of multi-
domain CMRFs (especially sleep health). Gap II: Lack of “outcome-predictive” CM subgroups in early risk
stratification. Gap III: Lack of “subgroup-specific” precise prognosis of “multi-dimensional” CM outcomes. Gap
IV: Under-utilization of the rich but “incomplete” multi-domain CM data in NHANES and NSRR. We propose a
multi-study multi-domain secondary analysis for CM subgroup discovery and risk prediction in U.S. adolescents
(11-18) and young adults (19-39). The objective is to create 2 combined NHANES and NSRR datasets and
examine multi-domain CMRFs including metabolic dysregulation, physical inactivity, poor nutrition, and multi-
dimensional sleep measures for CM subgroup discovery and risk prediction in the large and diverse U.S.
adolescent and young adult population of Hispanics/Latinos, African Americans, Caucasians, and Asian
Americans. Aim 1. CM risk subgroup discovery at baseline for U.S. adolescents & young adults. (1.1) Develop
a novel sparse Incomplete Multi-domain Mixed-typed Factor Mixture Model (IM2-FMM) for subgroup discovery
from incomplete multi-domain mixed-typed CMRFs. (1.2) Apply IM2-FMM to identify, characterize, and evaluate
CM subgroups in adolescents and young adults from incomplete multi-domain mixed-typed CMRFs at baseline
including: (a) self-reported sleep measures in NHANES; (b) self-reported and objective sleep measures in NSRR.
Aim 2. Subgroup-specific prediction of multi-dimensional longitudinal CM outcomes for young adults. (2.1)
Develop a novel sparse Transfer Learning-based Generalized Multi-level Model (TL-GMM) to predict multi-
dimensional longitudinal CM outcomes from clustered CMRFs at baseline. (2.2) Apply TL-GMM to young adults
in NSRR to: (1) examine fixed effects and random effects of baseline CMRFs on CM outcomes; (2) provide
subgroup-specific multi-dimensional prognosis of CM health from clustered CMRFs at baseline. Impact: Our
study will generate novel insights into CM subgroup discovery to facilitate early and targeted interventions and
help establish health promoting behaviors in adolescents and young adults, eventually improving CM health care
in their transition to adulthood and reducing CM health disparities and costs as the young population ages.
