We integrate social and biological data to better understand how different life experiences and public policies change people at a cellular level. We seek to understand not only the social mechanisms that affect health but also when in the life course people are most sensitive to adverse exposures biologically. Our goal is to identify policy targets that can increase quality of life and extend human healthspan.
Life course approach to aging research
We explore how exposure to adverse conditions, from as early as conception, can accelerate the aging process and lead to earlier mortality.
Bridging disciplines for novel understanding
We integrate theory, methods, and data from multiple disciplines to study the molecular imprint of our social environment.
socioeconomic status as an ecosystem
Social class affects everything from the air we breathe to the job we work. We study the effects of these exposures on health and aging.
We look at how social adversity impacts health across the lifecourse.
Research on maternal-fetal epigenetic programming argues that adverse exposures to the intrauterine environment can have long-term effects on adult morbidity and mortality. However, causal research on epigenetic programming in humans at a population level is rare and is often unable to separate intrauterine effects from conditions in the postnatal period that may continue to impact child development.
In this study, we used a quasi-natural experiment that leveraged state-year variation in economic shocks during the Great Depression to examine the causal effect of environmental exposures in early life on late-life accelerated epigenetic aging for participants in the US Health and Retirement Study (HRS). HRS is the first population-representative study to collect epigenome-wide DNA methylation data that has the sample size and geographic variation necessary to exploit quasi-random variation in state environments, which expands possibilities for causal research in epigenetics.
Our findings suggest that exposure to changing economic conditions in the 1930s had lasting impacts on next-generation epigenetic aging signatures that were developed to predict mortality risk and physiological decline. We show that these effects are localized to the in utero period specifically as opposed to the preconception, postnatal, childhood, or early adolescent periods.
To date, biosocial research severely underrepresents non-white populations and is almost exclusively in high-income countries, even though low- and middle-income countries comprise 84% of the global population. Research that integrates biological and social data from diverse social, cultural, and economic contexts can help us learn more about what factors contribute to accelerated physical and cognitive aging.
To address this disparity in global biosocial research, I am co-leading (with Hans-Peter Kohler) a large NIH grant initiative that is collecting genomic and epigenomic data on individuals in the Malawi Longitudinal Study of Families and Health (MLSFH)—the longest running cohort study in a Sub-Saharan African low-income country (LIC) (NIA R01 AG079527). Over 25 years of existing social, contextual, and health data in MLSFH will be supplemented with epigenetic aging biomarkers, genetic data, and additional longitudinal measures of cognition to study the risk and resilience factors that shape the evolution of accelerated aging and cognitive decline in an LIC context.
This is the lowest-income context for which population-based epigenetic and genetic data will be available alongside detailed, longitudinal health information. Our long-term goal is to yield generalizable evidence that can inform policy intervention for millions of older adults who live in similar contexts with mostly subsistence-agricultural economies and inadequate health systems.
Genetic predispositions interact with the social environment to promote or impede health and social mobility across the lifespan, but identifying these interactions is challenging.
We use experimental variation in policy environments to distinguish between gene-environment interactions (G × E) and genetic selection into environments, or gene-environment correlation (rGE). In the context of social policy, differentiating between the two is crucial because in the case of G × E, there is room for policy intervention because modifications to the environment may mitigate genetic risk. To capture the genetic architecture of complex traits while maximizing statistical power, we use results from genome wide association studies (GWAS) to construct polygenic indices (PGI, also known as genetic risk scores or polygenic scores). PGIs aggregate millions of single nucleotide polymorphisms (SNPs) across the genome and weight them by the strength of their association to construct a single measure of genetic risk.
Published research documents an increase in lifetime smoking behavior and lower educational attainment for genetically-at-risk individuals who were drafted into Vietnam; potentially harmful reductions in body mass index (BMI) after a job loss from a plant or business closure for older workers with less plastic genotypes; and higher rates of smoking persistence for female smokers due to sex differences in the genetic overlap between variants for smoking behavior, depression, and hypothalamic-pituitary-adrenocortical (HPA) axis function.
Ongoing work is examining heterogenous treatment effects of state-wide cigarette tax policies in adolescence and adulthood on genetic risk for smoking behavior and related comorbidities; identifying the effect of childhood socioeconomic status (SES) and genetic risk on the health and schooling outcomes of Korean adoptees with random family placement; and the effects of statewide investments in education and genetic potential for educational attainment on schooling and lifetime earnings.
