A team led by a UF Health researcher has developed a new, more accurate tool to predict the long–term health effects of metabolic syndrome, a convergence of heart–health risk factors in certain individuals that can turn into a perfect storm of health problems over time.

The tool, developed by Matthew Gurka, PhD, at the University of Florida, and Mark DeBoer, MD, at the University of Virginia in Charlottesville, does a better job of gauging a patient’s risk of developing heart disease and Type 2 diabetes than the standard Adult Treatment Panel III, or ATP–III, definition of the metabolic syndrome.

Preliminary studies by Gurka and DeBoer suggest that patients with more severe risk factors – determined by waist circumference, blood pressure, and levels of blood sugar, triglycerides and protective HDL cholesterol – faced greater long–term health risks than those with milder risk factors. There has been some debate in the scientific community about the utility of metabolic syndrome to predict an individual’s long–term risk of disease, particularly heart disease. The latest studies by Gurka and DeBoer use a novel score to measure the severity of metabolic syndrome.

To test the power of the tool to predict heart disease, the researchers analyzed combined health data from 11,004 non–Hispanic white and African–American participants in the Atherosclerosis Risk in Communities, or ARIC, study and 2,137 participants in the Jackson Heart Study, or JHS, the largest longitudinal study of African–Americans ever undertaken.

In a research brief published in the March issue of the Journal of the American College of Cardiology, Gurka and DeBoer reported that nearly 25 percent of patients with the highest levels of metabolic syndrome severity at their initial visit had coronary heart disease within 25 years. By comparison, only 6.5 percent of those with the lowest levels of metabolic syndrome severity developed coronary heart disease within 25 years.

The risk of heart disease associated with metabolic syndrome severity remained elevated even after accounting for the individual risk factors that comprise the syndrome, such as obesity and high cholesterol, suggesting that metabolic syndrome severity has added predictive value. In contrast, they found no significant association between metabolic syndrome and future heart disease using the standard ATP–III definition of metabolic syndrome after accounting for its component risk factors.

In another paper published online ahead of print this week in Diabetologia, the researchers examined the ability of the new tool to predict Type 2 diabetes in the same study participants. The researchers reported similar results: Metabolic syndrome severity can be a useful tool for predicting Type 2 diabetes. They also found that study participants whose metabolic syndrome severity worsened between their first two visits to the clinic also predicted future Type 2 diabetes.

“Once again, these results show the clinical utility of a score that can be monitored over time,” Gurka said. Gurka and DeBoer have incorporated the tool into an online metabolic syndrome severity calculator (mets.health–outcomes–policy.ufl.edu) that patients and their doctors can use to gauge their risk. They have developed and validated a similar score to use for adolescents as well. The metabolic syndrome severity calculator also may help improve health outcomes by providing patients and their caregivers a measure of their heart disease risk to track over time.