Ori Ben Yossef
Ori, a sophomore Mathematics and Computer Science double-major, proud LGBT Studies minor, and Humanities Scholar at Cornell University, analyzed metabolomic variation across ethnic groups in the context of type 2 diabetes.
In the United States, type 2 diabetes affects Black, Hispanic, Native American, Asian American, and Pacific Islander individuals at significantly higher rates than it does White individuals. I feel so lucky and grateful to be here. My time with the Broad has given me so much to reflect on. I’ve made so many kind, special, motivated, hardworking friends whom I endlessly admire. My mentor guided me through several difficult but necessary moments that taught me how to handle stagnation, burnout, and time pressure while researching. Our Leadership Development classes gave me the intellectual tools I needed to become more comfortable with difficult conversations and uncertainty in my future. BSRP is truly outstanding. Although we know that genetic, environmental, and social factors can all have unique and compounding effects on human disease, we are not quite sure how this dynamic plays out in the case of type 2 diabetes. Metabolomics, or the measure of molecules (“metabolites”) involved in biochemical processes, quantifies how environmental perturbations like diet, physical activity, and medications manifest on a molecular level in the body. We hope metabolomics can help us disentangle the overlapping causes of differing type 2 diabetes rates. The goal of the project was to describe how metabolomic profiles vary across self-identified racial and ethnic groups for reasons other than genetics. This project conducts the first metabolomic analysis that jointly considers genetic and environmental factors, especially across diverse populations in the context of differences in disease rates. We analyzed data from about 5,000 individuals within the Women’s Health Initiative (WHI) and Multi-Ethnic Study of Atherosclerosis (MESA) cohorts, two diverse ancestry cohorts from the NHLBI’s TOPMed program. We used principal component analyses (PCA) to model the “lines” along which metabolomic data varied within each cohort. Then, we conducted association tests to determine which biology- and study-design-related variables substantially contributed to variation along those lines. Lastly, using a linear regression model that included the variables from the previous step, we identified metabolites whose abundances had statistically significant correlations with self-identified race and ethnicity after accounting for differences in genetic ancestry. We found that 87 named metabolites, as well as hundreds more that have not previously been identified, had significantly differing frequencies in people with different self-identified races and ethnicities for reasons that were not solely genetic. For example, we found significant differences in some triacylglycerols with high carbon numbers and double bonds, which previous studies (PMID 35349649) have associated with increased type 2 diabetes risk. Our findings can get researchers to start thinking about how the metabolites we identified could relate to type 2 diabetes, what lifestyle differences and social stressors could be causing the differences we discovered, and how we can ensure healthier chemical balances in the body. Ultimately, understanding the relationship between type 2 diabetes, environment, and metabolomics gives us an avenue to brainstorm interventions that reduce health disparities in type 2 diabetes.
Project: Comprehensive analysis of metabolomic signatures across ethnic groups and their association with type 2 diabetes
Mentors: Magdalena Sevilla González
PI: Manning Lab, Programs in Metabolism and Medical & Population Genetics