Michael Bancks
Wake Forest University, NC·Public Health & Epidemiology
Type 2 diabetes (T2D) is not a single disease. In different people, different organs and body processes drive high blood sugar.
- Fields
Public Health & Epidemiology
Medicine & Clinical Research
Mathematics & Statistics
- Topics
type 2 diabetes
diabetes subtypes
precision medicine
- Grants
- 1 known grant, through Aug 2029
What they work on
- Assemble and harmonize clinical, genetic, metabolomic, social determinants of health, and outcome data from 10 US cohorts totaling 55,766 people, using the team's computational methods for estimating missing data.
- Build and cross-validate hard- and soft-clustering models that classify type 2 diabetes subtypes from clinical, genetic, and metabolomic data, both separately and combined.
- Test how type 2 diabetes subtypes relate to risk of cardiovascular disease, chronic kidney disease, retinopathy, and death, and whether social determinants of health change these links.
More about the research
As a result, patients vary in the complications they develop and in which treatments work best. Researchers have begun sorting T2D into subtypes, but this approach is not yet ready for the clinic. Study groups have lacked racial and ethnic diversity, have rarely spanned all ages, and have seldom combined many types of data. This project asks whether combining clinical, genetic, and metabolomic data across diverse populations can define reliable subtypes. Metabolomic data are measurements of the small molecules in blood.
Why it matters
Clearer, physiology-based subtypes of type 2 diabetes could help doctors and patients choose prevention and treatment strategies that fit each person's disease. They could also flag who is at higher risk of heart disease, kidney disease, eye damage, or early death. Because the data include large numbers of Black, Hispanic, female, and young participants, the results may apply more fairly across the US population.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Mary Rooney
Johns Hopkins University, MD·Public Health & Epidemiology·Early-career
Derive and validate prediabetes subtypes from cardiometabolic risk factors such as adiposity, liver function and lipids, using machine-learning clustering in multiple US cardiovascular cohorts.
Tanika Kelly
University of Illinois Chicago, IL·Public Health & Epidemiology
Recruit 300 diverse adults with and without diabetic kidney disease and collect blood and environmental exposure data, such as metals and social conditions, every year for three years.
Pamela Lutsey
University of Minnesota, MN·Public Health & Epidemiology·Early-career
Measure rural versus urban differences in new cardiovascular disease and its management, using MarketScan and Medicare 20% insurance claims classified by rural-urban commuting area codes.
The school
Wake Forest University: 27 labs on PI Pages, $345M a year on research, 54 research doctorates a year.
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