Racial/Ethnic Differences in the Association Between Perceived Cancer Risk and Epigenetic Aging Among a National Sample of Unaffected U.S. Adults
Faculty Mentor: Dr. Jemar Bather | Graduate Student Mentor: Rophence Ojiambo and Mariana Rodrigues
Cancer is one of the leading causes of death in the United States, and racial/ethnic disparities in cancer incidence, mortality, and screening have persisted despite decades of biomedical advances. Perceived cancer risk, an individual's subjective assessment of their likelihood of developing cancer, is a known predictor of preventive behaviors such as screening uptake, but its psychobiological consequences have received far less attention. Recent work suggests that subjective experiences and chronic psychosocial stressors can become biologically embedded through epigenetic mechanisms, accelerating biological aging. This project will examine whether perceived cancer risk is associated with epigenetic aging among unaffected U.S. adults in the Midlife in the United States (MIDUS) study, and whether this association differs by race/ethnicity.
PQAR Students: TBD
Sleep quality, stress markers, and cognitive aging in midlife and older adults
Faculty Mentor: Dr. Yun Soo Hong | Graduate Student Mentor: Xiaoting Chen
Poor sleep quality is an important contributor to unhealthy aging and cognitive decline. Sleep disturbances have been associated with impaired memory, reduced executive function, and dementia incidence. At the same time, chronic low-grade inflammation and physiologic stress may play critical roles in aging process and the development of aging-related cognitive impairment. Emerging evidence suggests that inflammatory cytokines and stress-related biomarkers may represent key mechanistic pathways between poor sleep and cognitive decline. The goal of this project is to examine the relationship between sleep quality, inflammation and stress biomarkers, and cognitive function in middle-aged and older adults using the Midlife in the United States (MIDUS) study.
We will use the second wave of MIDUS (2004–2009; n = 1,255 participants). Sleep quality will be assessed using measures derived from the Pittsburgh Sleep Quality Index (PSQI). Cognitive outcomes will include standardized measures available within cognitive assessment. Biological markers of inflammation and physiologic stress will include serum interleukin 6 (IL-6), C-reactive protein (CRP), and norepinephrine. We will also see if the associations are different by age and sex.
PQAR Students: TBD
Smoking, Life Expectancy and Chronic Disease in the United States: A Future Elderly Model (FEM) based on Health and Retirement Study (HRS) Data, 1992-2022
Faculty Mentor: Dr. Yajun Mei | Graduate Student Mentor: Madeline McHugh
Smoking is a leading preventable risk factor that adversely affects healthy aging, and it is well- known that heavy smokers face higher risks of chronic disease and premature mortality compared with light smokers and nonsmokers. However, research is limited whether smoking intervention targeted at heavy smokers relative to light smokers lead to disproportionately larger improvement in life expectancy and prevalence of chronic diseases. Kim et al. (2019) examined this question using the data from the Health and Retirement Study (HRS) from 1992-2012.
The objective of this project is to re-examine the health effects of smoking reduction by analyzing an expanded HRS dataset spanning 1992-2022 and by developing a simplified version of the Future Elderly Model (FEM), a powerful microsimulation framework originally developed by Goldman et al. (2004). The FEM is based on Markov chain methods, which model how an individual or system transitions from one state to another over time. Related probabilities ideas also appear in modern machine learning applications such as next-token prediction in large language model. The FEM incorporates individual-level heterogeneity and cohort-level dynamics to evaluate the long-term health impact of lifestyle interventions and public health policies.
Throughout the summer, trainees will not only strengthen their skills in statistical analysis and simulation modeling, but also gain deeper insight into how counterfactual simulations can be used to evaluate health intervention policies and population health outcomes.
PQAR Students: TBD
GWAS Analysis of Alzheimer’s Disease Using False Discovery Rate Control Methods
Faculty Mentor: Dr. Hai Shu | Graduate Student Mentor: Boyu Fan
Alzheimer’s disease (AD) is the most common neurodegenerative disorder and a leading cause of dementia worldwide. Understanding the genetic factors associated with AD progression is important for improving early detection and developing targeted therapies. Genome-wide association studies (GWAS) provide a standard framework for identifying disease-associated single nucleotide polymorphisms (SNPs). However, because millions of SNPs are tested simultaneously, GWAS studies typically use a conventional genome-wide significance threshold, commonly 5 x 10-8 for p-values, to identify significant SNPs, which is often overly conservative and may miss meaningful genetic signals. In this project, we will apply advanced false discovery rate (FDR) control methods to identify significant SNPs associated with AD progression. Specifically, we will compare cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD groups to investigate genetic changes along the progression of Alzheimer’s disease. Unlike standard GWAS approaches based on fixed p-value thresholds, we will focus on state-of-the-art FDR control procedures that improve statistical power by accounting for dependence among SNPs.
PQAR Students: TBD