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Elise Kinyanjui ‘25, a St. Mary’s College of Maryland Neuroscience and Psychology alumna, recently published an article in Alzheimer's & Dementia. Her paper, “The influence of midlife multimorbidity clusters and dementia risk: The ARIC Study”, examines how different midlife comorbidities impact dementia risk using a machine‐learning approach. The study uses a machine learning model to analyze data from the Atherosclerosis Risk in Communities (ARIC) cohort, which is an ongoing community‐based prospective study that began in the 1980s.
Kinyanjui emphasized why this research is important to her and to the community, noting that “individuals rarely experience conditions in isolation, and studying a single risk factor may underestimate how combinations of diseases influence long term brain health.” She used an unsupervised machine learning model to group people together based on similarities in their clinical profiles. For example, people who have obesity, diabetes, and hypertension may end up in a cluster together to better capture how overlapping conditions shape dementia risk.
This research began when Kinyanjui worked in Dr. Rebecca Gottesman’s lab as a participant in the National Institutes of Health’s Summer Internship Program after her junior year at St. Mary’s; after graduation, she rejoined the lab as a postbaccalaureate fellow and saw the work through to publication. In between, she continued to write and develop her research through her St. Mary’s Project (SMP). Kinyanjui remarked on how these experiences connected, noting that “My SMP really informed how I framed and interpreted my manuscript findings. It focused on the racial disparities underlying dementia risk, which helped me contextualize my findings and observe how the results differed across diverse populations. ”
As a Postbaccalaureate Fellow at the National Institute of Neurological Disorders and Stroke, Kinyanjui is currently using the ARIC cohort to examine how the co-occurrence of Alzheimer's specific biomarkers and markers of vascular disease may further predict dementia risk. Kinyanjui is interested in continuing to study neuroepidemiology on a large scale and is particularly interested in incorporating different social determinants of health factors into her research. In the future, she hopes to pursue her love of neuroepidemiology in graduate school.
