Maria Glymour, ScD, MS

School of Medicine
550 16th. Street
San Francisco, CA 94158
Maria Glymour
Education and Training

University of Chicago, AB - Biology

Harvard School of Public Health, ScD - Social Epidemiology

Harvard School of Public Health, SM - Health and Social Behavior

Columbia University, Postdoctoral Fellowship - Population Health/Aging/Dementia

Research Interests
※ Cognitive aging, Alzheimer's disease, and related causes of dementia
※ Social and geographic determinants of health in aging
※ Social policies and health
※ Causal inference in ADRD research and social epidemiology

My research focuses on how social factors experienced across the lifecourse, from infancy to adulthood, influence cognitive function, dementia, stroke, and other health outcomes in old age. I am especially interested in education and other exposures amenable to policy interventions. The health of current cohorts of elderly individuals in the US reflect a lifetime of social exposures, including educational experiences shaped by major changes in schooling policies. One thread of my research examines how changes in schooling laws and school quality in the 20th century might have influenced the health and cognitive functioning of current cohorts of elderly. Our results suggest that extra schooling has substantial benefits for memory function in the elderly independent of any “innate” characteristics. I have also worked on the influence of "place" on health, for example to understand the excess stroke burden for individuals who grew up in the US Stroke Belt. With my colleague Dr. Adina Zeki Al-Hazzouri, I also have a grant evaluating the long term effects of migrating from Mexico to the US on cognitive outcomes and dementia risk. In a project with colleagues including Drs. Rachel Whitmer, Elizabeth Rose Mayeda, and Paola Gilsanz, we are continuing a unique multi-ethnic cohort of older adults in Northern California, with a wealth of lifecourse biological and social data to offer insight into the reasons for racial/ethnic differences in Alzheimer's and dementia risk (https://rachelwhitmer.ucdavis.edu/khandle).

A separate theme of my research focuses on overcoming methodological problems encountered in analyses of social determinants of health, Alzheimer's disease, and dementia. For many reasons, research focusing on lifecourse epidemiology as well as cognitive aging introduces substantial methodological challenges. Sometimes, these are conceptual challenges, and clear causal thinking can help! Many of these challenges are being addressed in the MELODEM (MEthods in LOngitudinal research on DEMentia) initiative, an international group of researchers focusing on analytic challenges in research on dementia and cognitive aging. MELODEM has working group phone calls on the first and third Thursdays of the month, open to all. Sign up at melodem.org. Related to this, I work with my colleague Dr. Melinda Power (George Washington University) on a project to align evidence from observational and randomized studies of the impact of diabetes on Alzheimer's disease. In collaboration with Dr. Zeki Al Hazzouri, we are linking data sets with detailed information at different lifecourse periods to better evaluate long-term effects of exposures at specific sensitive ages. Many of the methodological problems might be circumvented by clever research designs. To that end, we are evaluating the puzzling pattern of an inverse association between cancer and Alzheimer's Disease to illuminate biological or artefactual explanations.

I have advocated the use of causal directed acyclic graphs (DAGs) as a standard research tool to represent our causal hypotheses and help elucidate potential biases in proposed analyses. In other cases, the methodological problems require more analytical solutions that have been developed elsewhere in epidemiology or in other disciplines, but are rarely applied to these research questions. Instrumental variables analyses of natural or induced experiments are one promising example. Genetic variations have recently been advanced as possible instrumental variables to estimate the health effects of a wide range of phenotypes, an approach sometimes called “Mendelian Randomization.” Using genetic polymorphisms as instrumental variables could provide a very powerful tool for social epidemiology, but the inferences from such analyses rest on strong assumptions. Thus I am currently working with a team to explore approaches to evaluating the plausibility of those assumptions in applications for social epidemiology.

I currently serve as the Director for the UCSF PhD program in Epidemiology and Translational Science (http://epibiostat.ucsf.edu/doctoral-program-epidemiology-translational-science). With Drs. Bob Hiatt and Peggy Cawthon, I co-lead the UCSF T32 training grant on Aging and Chronic Disease (https://epibiostat.ucsf.edu/training-research-aging-and-chronic-disease), which offers financial support for pre- and post-doctoral researchers. With Drs. Aric Prather and Will Brown, I co-lead the UCSF T32 on Data Science Training to Advance Behavioral and Social Science Expertise for Health Disparities Research (DaTABASE) https://epibiostat.ucsf.edu/data-science-training-advance-behavioral-and-social-science-expertise-health-disparities-research, which supports pre-doctoral trainees as part of the national TADA-BSSR consortium.
Trainees interested in research collaborations related to my work are welcome to send me an email directly or contact Bev Bitagon, who coordinates our group.