Race- and gender-based equations may better predict obesity-related disease risk

Important points:

• Predict abdominal fat and distribution from simple demographic questions and waist circumference.
• Future studies will examine the accuracy of these equations in different populations.

Combining race- and gender-based questions with circumference measurements may provide a more accurate assessment of obesity-related disease risk compared to typical measures, recent research reveals became.

Abdominal fat, which “has a worse health impact than fat stored elsewhere in the body,” can be assessed using dual-energy X-ray absorptiometry (DXA), MRI, and CT scans. costly clinical measurements are required. Dr. Jacob Earp An assistant professor in the Department of Exercise at the University of Connecticut told Helio.

Earp and colleagues therefore developed an abdominal body mass index based on demographic questions such as age, gender and ethnicity, and anthropometric measurements such as height and weight. The researchers then evaluated how well these measures predicted abdominal fat and its distribution in patients.

Research published in nutrition, metabolism, cardiovascular diseaseused National Health and Nutrition Examination Survey data from 2011 to 2018 on nearly 12,000 DXA scans of healthy adults aged 20 to 59 years. Of these scans, 6,589 were included in the final analysis.

The researchers found that more than 93% of the predicted Abdominal Body Mass Indexes were calculated correctly within concordance.

“From a few simple demographic questions (age, gender, ethnicity) and measurements (height, weight, waist circumference), we can predict people’s abdominal fat and fat distribution with reasonable accuracy. , the results of these equations better represent the risk of obesity-related diseases. than traditional measures such as BMI Or just girth,” Earp said. “Most notably, these equations were used to identify individuals who are not considered obese based on BMI, but who actually have increased obesity-related illnesses due to their abdominal fat/fat distribution. it can be done.”

Earp also spoke with Healio about how this equation could be implemented in the clinical setting and future research in this area.

Helio: What inspired you to do this research?

arp: Body composition is currently a big issue in terms of how fat is distributed throughout the body and how it relates to the risk of obesity-related diseases.

BMI is deeply flawed because it simply sees weight as bad. Weight is not a bad thing you can have. You may have lean body mass or you may be overweight. You may be considered obese, but you are actually very fit and healthy.

This tool allows us to classify people across a population, but many people are misclassified. So this muscle-to-fat balance is an important part of the equation for how we get better diagnostic criteria that people can very easily use to gauge someone’s risk of obesity and obesity-related diseases. is one of

Hello: How can doctors implement these equations?

arp: Visceral fat content can now be better predicted. There are these simple equations that you can put in the numbers you already have available, such as height, weight, self-identified ethnicity and gender, and put them into a very simple calculator. gives a fairly accurate measure of the actual risk of obesity-related diseases. Or you can flag someone as misclassified. Or they are told they are overweight but are not at high risk of disease. There may be people who say they are, but really, they are the least healthy people out there.

Hello: Where does research go from here?

arp: These equations were performed on people who fall into the normal weight category. We want to get a better handle on what’s going on with underweights. overweight and obese populationWe also hope to begin investigating specific disease populations that may affect fat distribution differently, such as diabetes, which causes various metabolic problems.

We’re also trying to find ways to look at fat distributed in muscle as another potentially harmful source. But when that muscle is infiltrated with fat, we know that muscle infiltration causes a lot of dysfunction and disability, especially in older and sedentary people.

References:

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