Mary Rooney
Johns Hopkins University, MD·Public Health & Epidemiology·Early-career
Prediabetes, a stage of slightly high blood sugar, affects many adults, but people in this group differ widely. Some have extra body fat, liver problems or high blood lipids, and these traits strongly predict who goes on to develop diabetes and its complications.
- Fields
Public Health & Epidemiology
Medicine & Clinical Research
Mathematics & Statistics
- Topics
prediabetes
type 2 diabetes
diabetes prediction
- Grants
- 2 known grants, through Aug 2030
What they work on
- 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.
- Test whether these prediabetes subtypes differ in later rates of diabetes, cardiovascular disease, chronic kidney disease and mortality.
- Harmonize and pool participant-level data from 13 epidemiologic cohorts to support large-scale analysis of diabetes risk.
- Refine prediabetes definitions by combining clinical factors, proteomic biomarkers (blood proteins) and polygenic risk scores (combined inherited risk), then measure how the resulting risk groups relate to diabetes complications.
More about the research
Yet prediabetes is defined only by blood sugar. This work asks how to sort people with prediabetes into more meaningful risk groups so that prevention can reach those who need it most. Both projects reanalyze data from existing US epidemiologic cohorts, which are large groups of people followed over time.
Why it matters
Current blood-sugar-only definitions of prediabetes do not identify well which adults face high risk of diabetes. Better risk groups could help doctors and public health programs aim screening, lifestyle programs and medications at the people with prediabetes most likely to develop diabetes, heart disease or kidney disease. This could make primary prevention of diabetes and its complications more efficient.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Michael Bancks
Wake Forest University, NC·Public Health & Epidemiology
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.
Ginger Nicol
Washington University in St Louis, MO·Genetics
Identify genetic variants linked to antipsychotic-induced weight gain and cardiometabolic risk through genotyping, GWAS and meta-analysis of about 15,000 biobank participants, using directly measured body fat rather than BMI.
Marguerite Irvin
University of Alabama at Birmingham, AL·Public Health & Epidemiology
Identify genetic variants that predict blood pressure response to chlorthalidone (a diuretic) or lisinopril (an ACE inhibitor) in more than 6,500 African Americans from the randomized GenHAT trial, with validation through the ICAPS consortium.
The school
Johns Hopkins University: 100 labs on PI Pages, $4.1B a year on research, 622 research doctorates a year.
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