Developing a risk tool with clinical measures and multi-omics to predict dyslipidemias in low-income, Latino children with overweight or obesity: Analysis of a 4-year prospective cohort
Grantee: Thomas Robinson, The Irving Schulman, M.D. Professor of Child Health, Professor of Medicine (Stanford Prevention Research Center) and, by courtesy, of Epidemiology and Population Health
Nicole Gladish, Postdoctoral Fellow Epidemiology & Population Health
Jennifer Li-Pook-Than, Research Associate, Genetics Department
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality among Latino individuals in the US, affecting nearly half of Latino adults. Risk factors such as dyslipidemia, high glucose levels, and high BMI are prevalent among Latinos, and early intervention is crucial. Low-income, racial/ethnic minority populations are often under-represented and the last to benefit from research on new biomedical technologies. This study proposes to use a unique, deeply, and broadly characterized 4-year longitudinal cohort of low-income, high-risk, Latino youth to counter this norm. With data from the Stanford GOALS trial of low-income Latino children in northern California, this study will use extensive anthropometric, physiological, behavioral, psychological, socio-cultural, and comprehensive omics profiling to identify predictive markers of multifactorial dyslipidemia (MD) in children, utilizing standard and machine learning methods to assess the predictability of clinical measures and multi-omics profiles individually and in combination. Additionally, we will investigate the association between modifiable psychosocial and behavioral measures and MD predictors over time to inform potential interventions. These findings could contribute to reducing CVD risk disparities by guiding personalized risk profiling and interventions.