Most of us are familiar with the term, Body Mass Index, or BMI, as an index to identify obesity. Obesity is generally classified as a BMI equal to or greater than 30 kg/m2. One of BMI’s limitations is that it can’t differentiate between muscle, fat and bone, so even Olympic athletes can be classified as obese. On the other end of the spectrum, people with high levels of visceral fat who are at significant health risk can pass entirely under the BMI radar.
Waist circumference provides a unique indicator of body fat distribution, which can identify patients who are at risk for obesity-related diseases more effectively than the current BMI based classification. To correct the limitations of BMI, I, George Fernandez, a senior analytical consultant at SAS Institute and a retired professor of applied statistics at the University of Nevada, Reno, am proposing a new Body Fat Index (BFI) which incorporates waist circumference, in addition to body weight and height measurements in BFI computation.
Using SAS software and statistical procedures, I discovered a much-improved way of classifying obesity-overweight, which closely corresponds to BMI charts. For example, BMI wrongly classify an athlete’s body measurements (e.g. weight: 242 lbs., height: 75 inches and waist: 32 inches) as obese (BMI of 30.24). In contrast, the BFI calculation correctly classifies this athlete’s body measurements as a healthy weight, with an equivalent BMI of 22.02. Using another example, BFI classifies the following body measurements of 160 lbs. weight, 68 inches height and 38 inches waist as obese (a BMI equivalent of 24.32); however, the BMI chart for these same measurements wrongly suggests a healthy weight. A user-friendly SAS macro BFI is included in this post. Please download the attached user-friendly SAS macro and compute both BMI and BFI both in metric and in imperial units by providing body weight, height, and waist measurements.
Traditional BMI calculation classify the following athlete’s body measurements ( WT:248 lbs, HT:76 inches, Waist: 34 inches) as obese. In this new BFI classification, we substitute HT with waist corrected height which is 85”. Thus, this new BFI classification classify this athlete’s body measurements as Healthy weight.
To calculate BFI in metric and imperial units, the height is corrected by waist measurement, then this corrected physiological height is substituted in place of the actual height; therefore, a similar formula to that of BMI can be used. Additionally, BFI uses the same obesity classification as the Body Mass Index: Underweight: < 18.5, Healthy weight: 18.5 to 24.9, Overweight: 25 to 29.9 and Obese: > 30. To download user-friendly customized height specific BFI charts, please follow this link (https://sites.google.com/view/improvedobesityclassification/home).
Advanced statistical analysis using SAS Survey procedures and data from the National Health and Nutrition Examination Surveys (NHANES) revealed that BFI based obesity-overweight classification accurately estimate national overweight and obesity percentages. Furthermore, BFI based obesity-overweight classification is a better predictor than BMI in predicting type 2 diabetes after controlling for age, gender, and race – ethnicity.
I have been actively researching the obesity epidemic for the last 10 years during which I have released findings on the maximum weight limit concept for adults, overweight-obesity screening tools for children and customized waist charts for adults (http://www.max-weight-limit.com). My studies and efforts in this area has culminated to this simple-to-use BFI formula and user-friendly charts, which will be useful in medically underserved areas of the world especially during this Covid-19 pandemic, and for individuals without access to advanced statistical software. Anyone, anywhere, can calculate their BFI if they know their weight, height and waist measurements using this simple formula or from the downloadable user-friendly charts.
George Fernandez (2010) A New and Simpler Way to Compute Body's "Maximum Weight Limit" SAS Global Forum Proceedings https://www.lexjansen.com/wuss/2010/posters/2948_5_POS-Fernandez1.pdf
George Fernandez (2011) User-Friendly Childhood Obesity Screening Charts Using SAS/STAT® Graphics SAS Global Forum Proceedings https://support.sas.com/resources/papers/proceedings11/235-2011.pdf
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