Investigating the Interactions between Inflammatory Biomarkers and Gut Microbiome in the Wisconsin Longitudinal Study
PI: ZhengZheng Tang
Persistent inflammation has emerged as a consistent marker of aging and aging-related diseases. This pilot project seeks to investigate the connection between these inflamed biomarkers and changes in the gut microbiome among older adults, using the Wisconsin Longitudinal Study, a rich longitudinal data source that has tracked individuals from the 1957 high school class throughout Wisconsin.
Adverse Childhood Experiences and Physiological, Affective, and Cardiovascular Reactivity to Family Caregiving Stress
PI: Jooyoung Kong
Longer life spans have helped advent the “sandwich generation” – middle-aged adults who find themselves caring for their children and their own aging parents. This position, stressful for anyone, may be especially stressful for adults who had Adverse Childhood Experiences (ACEs) while growing up. Primarily using the data from the Midlife in the United States (MIDUS) study, this project will explore how caregiving adults with ACES in their histories respond and react to daily stressors.
Understanding Socio-economic Influences on Health and Longevity
Scientists have already discovered that low socio-economic status (SES) is linked to an array of physical and mental health conditions and reduced longevity, but the mechanisms behind these correlations have proven elusive thus far. If they could be discovered, scientists may be able to predict and prevent some of the poor outcomes associated with low SES. This project will utilize the over 500,000 genomic samples within the UK Biobank to complete analyses.
Improving Biomarkers of Metabolic Disease in African American Populations
PI: Judith Simcox
Studies have shown that the most widely used clinical markers for diabetes — HDL, LDL, and triglycerides — are not accurate predictors of the disease in African-Americans. This pilot project will examine the promise of two potential alternatives in African-American females: C reactive protein and arachidonic acid containing lipids. African Americans are 60% more likely than Caucasians to have diabetes and twice as likely to die from diabetes-related complications. With better diagnostic tools, healthcare professionals have a better chance of bringing about positive health care outcomes.
Deploying Machine Learning Tools to Predict Mortality Outcomes in the General Population
PI: Jason Fletcher
Utilizing machine learning to predict mortality outcomes at the population level has thus far proven a tough nut to crack: the existing small samples sizes too small, the large samples lacking the proper type of data. This project aims to find the middle ground between these extremes by linking large population-based data from the National Longitudinal Mortality Study and the American Community Survey (ACS) to data from the National Death Index (NDI) and Medicare claims. Utilizing these linkages, the researchers will look for interactions that lead to mortality, and create risk scores based off of these interactions.