Research Departments & Organizations
Aging; Algorithms; Biostatistics; Computational Biology; Demography; DNA Methylation; Epidemiology; Gene Expression Profiling; Gene Regulatory Networks; Genetic Variation; Life Expectancy; Longevity; Proportional Hazards Models
Extensive Research Description
My research focuses on the development of high-dimensional biomarkers of human aging, and applying these and other aging outcome measures to study how genetic and social factors coalesce to produce between-person differences in the rate of aging, lifespans, and healthspan.
One of the major goals of geroscience research is to define 'biomarkers of aging'. At the population-level, aging can be measured as the mortality rate doubling time (MRDT), derived from the Gompertz equation. However, there remains a need for individual-level measures of aging that can account for differences in the timing of disease onset, functional decline, and risk of death over the life course. While chronological age is arguably the strongest risk factor for aging-related death and disease, it is important to distinguish chronological time from biological aging. Individuals of the same chronological age may exhibit greatly different susceptibilities to age-related diseases and death, which is likely reflective of differences in their underlying biological aging process. Reliable biomarkers of aging will be crucial for enabling instantaneous evaluation of interventions aimed at slowing the aging process, by providing a measurable outcome other than incidence of death and/or disease, which require extremely long follow-up observation. I have published a number of papers presenting various methods for calculating composite biological age estimates from clinical and/or molecular biomarkers. My most recent work has focused on the use of genome-wide DNA methylation to estimate "epigenetic aging", given that chronological time has been shown to elicit predictable hypo- and hyper-methylation changes at many regions across the genome. Many of the measures I have developed have been shown to predict remaining life expectancy and disease risk significantly better than chronological age. Further, my work has demonstrated that between-person differences in these measures relate to genetic, sociodemographic, and behavioral factors.
Cognitive Aging and Alzheimer’s Disease
My recent work has used multidisciplinary approaches and collaborations to examine the genomics, epigenomic, and transcriptomic networks associated with cognitive aging and Alzheimer’s disease. For instance, I have explored the associations between Alzheimer’s related neuropathology and epigenetic measures of aging in prefrontal cortex. I have also led work on modeling trajectories of cognitive decline for longitudinal studies. Based on this, I developed an easily interpretable composite measure of cognitive age, for which level and change can be contrasted against chronological age to characterize individuals with accelerated decline. Finally, as PI on an R01, I am integrating genetic, transcriptomic, epigenomic, and proteomic data (from three brain regions) to identify multi-scale networks that underlie AD resilience among high risk individuals, such as ApoE e4+.
Biological Resilience Susceptibility to aging and age-related disease may vary across individuals, as a result of complex interactions at the genetic, epigenetic, transcriptomic, proteomic, and environmental levels. Previously I’ve investigated underlying mechanisms of resilience by studying long-lived smokers. This work built upon the phenomena of hidden heterogeneity and mortality selection, showing that current smokers who had survived to age 80 and beyond did not exhibit higher mortality rates compared to never-smokers of the same age (mortality had 'selected for' a resilient group). In a subsequent study, I used network methods to compare the genetic signatures of long-lived smokers and normal-lived smokers, in order to identify mechanisms which allow certain individuals to survive despite repeated exposure to environmental stressors. My work has also examined whether epigenetic measures of aging capture resilience/vulnerability in relation to lung cancer risk among smokers.
Genetics of Complex Traits I have worked on a number of projects examining the genetics of complex traits, with the goal of developing polygenic scores. I have also worked on developing novel network-based methods for identifying pleiotropic gene networks that relate across aging and longevity phenotypes.
The Role of Behaviors and Environment in Aging, Health, and Mortality
I have examined how exogenous factors such as social disadvantage, education, trauma, race/ethnicity, smoking, diet, and air pollution influence health and aging, both in humans and in model organisms. For instance, my work has examined the role of protein consumption in lifespan and cancer, using data from humans, mice, and yeast. This paper was published as the cover article in Cell Metabolism, and to date, the article has the highest impact score in history for the journal. Additionally, my work has investigated alterations in late-life inflammatory gene expression in response to early-life and contemporaneous social environment, and has explored the effects of air pollution exposure on alterations in gene expression networks.