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New Equation Bests BMI at Estimating Body Fat Mass

Pam Harrison
August 31, 2018

A new relative fat mass (RFM) equation based on height-to-waist-circumference ratio better predicts the percentage of whole-body fat in men and women than body mass index (BMI), new research indicates. The RFM equation also resulted in fewer instances of misclassification for obesity categories in both sexes and in all ethnic groups tested.

"BMI is widely used to assess body fatness, despite its limited accuracy to estimate body fat percentage ... Thus, simple and low-cost alternatives to BMI with better diagnostic accuracy for obesity in both sexes would be of considerable importance," write Orison Woolcott, MD, and Richard Bergman, MD, both with the Sports Spectacular Diabetes and Obesity Wellness and Research Center at Cedars-Sinai Medical Center in Los Angeles, California.

"In the population studied, the suggested RFM was more accurate than BMI to estimate whole-body fat percentage among women and men and improved body fat-defined obesity misclassification among American adult individuals of Mexican, European, or African ethnicity," they report. The study was published online July 20 in Scientific Reports.

However, obesity expert Lee Kaplan, MD, PhD, director of the Obesity, Metabolism and Nutrition Institute at Massachusetts General Hospital in Boston told Medscape Medical News that although the RFM estimate of body fat mass might prove helpful as a research tool, he doubts it will be more useful than BMI in real-life clinical practice.

He notes that although BMI is used to identify patients who are obese, clinicians should consider what the effect of excess weight is on a patient's health — and the RFM measure, like BMI, doesn't do that.

"As a clinician what you really want to know is, 'What is the clinical impact of the obesity?' That's what you care about," Kaplan elaborated. The question then becomes, Is a higher RFM associated with worse outcomes? This is the same question physicians need to answer if they use BMI to guide clinical decision-making.

"The fact that RFM is more consistent [in predicting whole-body fat percentage] than BMI in men and women is interesting, but not necessarily all that clinically important," Kaplan reiterated.

NHANES Surveys

To develop and validate RFM, the authors used two sets of data from the National Health and Nutrition Examination Survey (NHANES). They used the NHANES survey conducted between 1999 to 2004 for model development. Percentages of whole-body fat were measured by dual-energy X-ray absorptiometry (DXA) in both models. They considered over 350 anthropometric measures in order to arrive at a simple, anthropometric linear equation more accurate than standard BMI for estimating the percentage of whole-body fat in men and women of a variety of ethnic groups.

"Height/waist equation, named as the relative fat mass, was the final model selected because of its simplicity (it requires only two common anthropometrics), it was superior to BMI in predicting body fat percentage among men, had similar predicting ability relative to BMI among women, and had overall better performance than BMI among women and men, independently," Woolcott and Bergman write.

Model Validation

In the validation dataset (the NHANES survey conducted between 2005 to 2007), RFM was significantly more accurate than BMI at estimating body fat mass in women at 91.5% vs 21.6% for BMI (P < .001). It was also more precise than BMI at 4.9% vs 5.8%, the study authors report. Similarly, RFM was more accurate than BMI at estimating percentage of whole-body fat, at 88.9% vs 81.9% (P < .001), and estimating BMI, at 4.2% vs 5.1%.

"Among women, RFM was also more accurate across ethnic groups (P < .001 for all comparisons)," the authors point out. The same held true for men who were European American (P < .001) or African American (P < .001), but not Mexican American.

RFM also performed better than BMI across different age categories and body fat quintiles. Using a definition of obesity of a DXA-measured body fat percentage of ≥ 33.9% for women and ≥ 22.8% for men, investigators also found that whole-body fat estimates using the new equation led to fewer false negatives, at 5.0% in women compared with 72.0% as measured by BMI (P < .001). This was again true for men, although the difference was far less dramatic, whereby the use of RFM resulted in a false-negative rate of 3.8% compared with 4.1% for BMI (P < .001).

Interestingly, RFM led to fewer false positives among men at 32.3% compared with 49.7% when BMI was used (P < .001), whereas false positives among women were much higher at 41% when measured by RFM than BMI, which had no false positives (P < .001).

The use of the new equation was also significantly less likely than BMI to misclassify men and women by obesity category, and this was observed across all ethnic groups studied, as investigators point out.

Obesity Misclassification Rates for RFM vs BMI (All Comparisons P < .001)

Measure Men Women Mexican Americans European Americans African Americans
Misclassification for RFM, % 9.4 12.7 8.2 11.3 9.9
Misclassification for BMI, % 13.0 56.5 35.4 35.2 37.2

"The main aim of the present study was to identify a simple anthropometric equation, which could potentially be used for clinical and epidemiological purposes, as an alternative to BMI to better assess body fatness among adult individuals," Woolcott and Bergman explain. "In the validation dataset, the performance of RFM to estimate DXA-measured body fat percentage was overall more consistent than that of BMI among women and men, across ethnic groups, young, middle-age, and older adults, and across quintiles of body fat percentage, although the accuracy of RFM was lower among individuals with lower body fatness."

"The lower rates of obesity misclassification with RFM compared with BMI...supports the clinical utility of RFM to identify individuals with high body fat percentage, a condition that has been associated with increased mortality," the authors conclude.

Clinical Utility?

Commenting further on why more precise estimates of body fat mass may not be that useful clinically, Kaplan noted that what predicts adverse outcomes in patients with obesity is not the percentage of body fat they have but body fat distribution. "Central body fat is associated with diabetes, fatty liver disease, and all sorts of metabolic abnormalities to a much greater extent than peripheral body fat," he said. "And you are going to make your clinical decisions based on the fact that somebody has adverse outcomes such as diabetes or sleep apnea, not based on their body fat percentage," Kaplan observed.

The other main obstacle to incorporating the RFM equation into clinical practice is the fact that it relies on a highly accurate measurement of a patient's waist circumference. "Waist circumference is reasonably accurate when done in a clinical trial situation, but in a clinical practice situation where you don't have people who've spent hours being trained in how to do it, you can get huge differences in what you measure as waist circumference depending on how and where you measure it," Kaplan noted.

"So I would rather use clinically relevant markers of disease to determine what the level of care needs to be," he added. "RFM may turn out to be a great tool epidemiologically but I'm not convinced it is going to be that much better a tool than BMI clinically," he concluded.

The authors have reported no relevant financial relationships. Kaplan has served as a scientific consultant to AMAG Pharmaceuticals, Gelesis, GI Dynamics, Johnson & Johnson, Novartis, Novo Nordisk, Rhythm, Sanofi, and Zafgen.

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Reviewed on 9/4/2018

SOURCE: Medscape, August 31, 2018. Scientific Reports. Published online July 20, 2018.

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