Online crystal ball may better predict cardiometabolic risk


New York, Mar 31 (IANS): Researchers have developed an online metabolic crystal ball which has the potential to predict a person's risk of developing heart disease and diabetes more accurately than the traditional methods.

The risk for cardiovascular disease, Type 2 diabetes and stroke has traditionally been predicted by looking at a person's level of obesity, high blood pressure, high fasting triglycerides, low HDL (good) cholesterol and high fasting blood sugar.

Patients with abnormalities in at least three of these were diagnosed as having metabolic syndrome and were told that they are at elevated risk for future health problems.

However, the metabolic crystal ball, weighs the traditional risk factors as well as takes race, gender and ethnicity into account to produce an easy-to-understand metabolic severity score, which may prompt patients to make lifestyle changes that would spare them the suffering and expense of avoidable illnesses, the researchers said.

"This boils it down to telling a patient, 'On the risk spectrum, you are here, and you're in a position where we are worried you are going to have a cardiovascular event in the next 10 years'," said Mark DeBoer, from the University of Virginia.

"My hypothesis is that the more specific information you can give to individuals at risk, the more they will understand it and be motivated to make some changes," DeBoer added.

For the study, published in the Journal of the American College of Cardiology, the team looked retroactively at outcomes in more than 13,000 people and found that the new tool was a better risk predictor than the individual risk factors alone.

Such a scoring system could be incorporated in the electronic medical record to calculate someone's risk and that information could be provided both to the physician, who then realises there is an elevated risk, and to the patient, who hopefully can start taking some preventative steps, DeBoer said.

  

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Title: Online crystal ball may better predict cardiometabolic risk



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