Body mass index alone is considered an imperfect measure of obesity-associated disease risks, such as for type 2 diabetes. Now, a new study co-authored by Boston University School of Public Health (BUSPH) researchers has identified a pattern of inflammation associated with cardiometabolic risks among Black women. The findings, published online in PLOS ONE, could help underserved patients benefit from precision medicine and personalized profiles of disease risk.
According to the researchers, abnormal, unresolved inflammation in blood and adipose (fat) tissue, rather than obesity per se, is thought to be important for development of disease. Certain biomarkers show promise in predicting obesity-associated diabetes risk; however, the clinical utility of single biomarkers is limited for complex disease phenotypes such as these.
Analyzing plasma samples from participants in the Black Women’s Health Study, the researchers took a systems biology approach to discover six cytokine signatures associated with type 2 diabetes risk in a vulnerable population: African American women with obesity and varying degrees of metabolic health. These six distinct signatures are patterns of 16 cytokines/chemokines that promote or reduce inflammation. The researchers then validated the findings in two separate groups: African American women volunteers with obesity who had donated plasma to the Komen Tissue Bank, and African American women with obesity who were breast reduction surgical patients at a safety net hospital in Greater Boston. The patterns or signatures in the validation cohorts closely resembled the distributions in the discovery cohort.
“These findings are highly relevant to an understudied and underserved population that experiences elevated risks for co-morbidities of obesity,” says lead author Dr. Gerald V. Denis, associate professor of pharmacology and medicine at the Boston University School of Medicine. “The overall impact of this report is high because of the potential utility of the new signatures just discovered and validated, which could assist clinical decision making with more personalized information.”