Q&A: Race- and sex-based equations may better predict risk for obesity-related diseases

Key takeaways:

• Simple demographic questions and waist circumference can predict abdominal fat and distribution.
• Future research will examine the accuracy of these equations in different populations.

Race- and sex-based questions combined with circumference measurements could deliver more accurate assessments for obesity-related disease risks compared with typical indicators, a recent study found.

While fat in the abdominal region “has worse health implications than fat stored anywhere else in the body,” evaluating it requires costly clinical measurements like dual-energy X-ray absorptiometry (DXA), MRI and CT scans, Jacob Earp, PhD, an assistant professor in the department of kinesiology at the University of Connecticut, told Healio.

Thus, Earp and colleagues developed abdominal adiposity indices based on demographic questions — such as age, sex and ethnicity — and anthropometric measures like height and weight. The researchers then assessed how accurately these indices predict abdominal fat and its distribution in patients.

The study, published in Nutrition, Metabolism & Cardiovascular Disease, utilized 2011 to 2018 National Health and Nutrition Examination Survey data on close to 12,000 DXA scans of healthy adults aged 20 to 59 years. Of those scans, 6,589 were included in the final analysis.

The researchers found that 93% or more of the predicted abdominal adiposity indices were correctly calculated within the limits of agreement.

“From a few simple demographic questions (age, sex and ethnicity) and measurements (height, weight and waist circumference), we can predict with reasonable accuracy the abdominal fat of people and the distribution of their fat. The results from these equations therefore better represent a person's risk for obesity-related illnesses than convention measures like BMI or waist circumference alone,” Earp said. “Most notably, these equations can be used to identify people who aren’t considered obese based on BMI, but who are actually at an increased for obesity-related illnesses because of abdominal adiposity/fat distribution.”

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

Healio: What drove you to conduct this study?

Earp: Body composition is a big question right now as far as how fat is being distributed throughout the body and how that relates to someone’s risk for obesity-related diseases.

BMI is massively flawed because it simply looks at weight as being a bad thing. Weight is not a bad thing that you can have. You can have lean body mass and you can be overweight. You can be considered obese, but actually be very healthy and very fit.

Across the population, if you use this tool, you can classify people, but there’s a lot of people who are misclassified. So, this balance between muscle and fat is one of those key pieces of the equation of how we can get a better diagnostic criteria that can be used very easily by people to measure someone's obesity risk or risk for obesity-related diseases.

Healio: How can physicians implement these equations?

Earp: We put together equations that are now out there that can better predict visceral fat content. We have these simple equations that could get put onto a very simple calculator that you enter in the numbers that you already have available, like height, weight and self-identified ethnicity and sex. You put that in and add in waist circumference and hip circumference and ... you get a fairly accurate measurement of what someone's actual risk for obesity-related disease is. Or, you can flag them as someone who is misclassified, which means that you’re telling them they're overweight but they’re not at an increased risk for disease. Or more likely, you have someone who is being told that they are at a healthy weight but their lean body mass is low enough that now they're that person who we tell are healthy, but really, they’re some of the least healthy people out there.

Healio: Where does research go from here?

Earp: These equations were done in people who are classified as normal weight. We want to get a better grasp at what’s happening to the underweight, overweight and obese populations. We also want to start looking at specific disease populations that that might affect fat distribution differently such as diabetes, which causes a whole host of different metabolic issues.

We're also trying to figure out how to look at fat that's distributed into the muscle as another potentially detrimental source. By most of our scientific measurements, we say, “oh, they have muscle.” But if that muscle has fat infiltration, we know that infiltration to the muscle is going to be what's causing a lot of the dysfunction and disability, particularly with age populations and sedentary populations.

References:

• Tools for more accurate obesity risk assessment based on sex, ethnicity. https://today.uconn.edu/2023/03/tools-for-more-accurate-obesity-risk-assessment-based-on-sex-ethnicity/. Published March 23, 2023. Accessed April 4, 2023.
• Xu F, et al. Int J Environ Res Public Health. 2022;doi:10.3390/ijerph192315521.
• Xu F, et al. Nutr Metab Cardiovasc Dis. 2023;doi:10.1016/j.numecd.2023.03.001.