The goal would be to examine how body fat, pre-pregnancy, and activity levels before and during pregnancy were related to pregnancy outcomes (e.g., miscarriage, full-term, low birthweight, etc.) in athletes with a normal or high BMI and normal ovulatory function; prior studies focus solely on BMI, which is often an inaccurate measure for athletes. All of Us should collect data on body fat preconception, physical activity ...more »
The goal of this study is to research underlying causes of health disparities using existing data and novel data sources for example is diagnosis and treatment of diabetes different across ethnic groups, does a1c vary by G6PD genotype and does follow up and a1c target differ by diversity standards.
The goal of this study is to use existing data such as billing codes and laboratory values to predict the presence of a genetic condition before it is diagnosed by traditional medical practice.
The goal of this study is to understand if mobile technologies can contribute clinically meaningful metrics. By analyzing mobile technology over time and comparing to available clinical health records, supervised and unsupervised machine learning can be applied to understand potential of this technology in clinical care and outcomes.
The goal of this study is to understand pharmacogenetic and other predictors of adverse effects of medications and therapeutic failure enabled by aggregation of claims data and pharmacy fill data from multiple sources over many years. For example, does treatment failure of proton pump inhibitors in genetic hypermetabolizers cause increased surgical procedures and how do drugs interact in patients treated with multiple ...more »
The goal of this study is to ascertain clinical implications of rare genetic variants predicted to be deleterious discovered in individuals not carrying the diagnosis. Using existing phenotypes or potentially with recontact to participants physiological impact of variants on health and disease can be understood.
The goal of this study is to ascertain clinical implications of rare variants associated with rare disease such as autoimmune polyglandular syndrome type 1. Whole genome sequencing will discover variants known to cause disease, likely in individuals not carrying the diagnosis. Using existing phenotypes or potentially with recontact to participants physiological impact of variants on health and disease can be understood. ...more »
Characterize phenotypes of participants with rare deleterious variants of unknown significance discovered by deep sequencing through available surveys and EHR data or through recontact with additional studies.
Estimate risk for common disease or health outcomes based on known genetic factors combined with novel environmental data types such as proximity to food sources and pollution and laboratory measures of pollutant exposure, for example, what is the relative risk of genetic factors for diabetes compared to dietary habits and proximity to grocery stores?
Cohort study will be done by recruiting mothers at their first year of giving birth to their baby. The intervention can be both health center oriented or home based physical exercise coaching. Weekly Edinburgh post Natal depression scale will be used to followup how their feelings and physical as well as emotional health is being improved. The result can be compared against previously conducted research on mother's or ...more »
Acute exacerbation accounts for frequent and recurrent ER visits in children with asthma. Genetic susceptibility and interaction with factors such as infection/inflammation, exposure to environmental pollutants may contribute to acute decompensation. Predictive modeling using multiple data points including genetic variants, environmental exposure, and self monitored spirometry may help with early detection and intervention ...more »