Body mass index (BMI) purports to be a measure of adiposity (body fat), and its association with mortality and cardiovascular disease has been amply shown in the general medical literature as well as in publications specific to the life insurance industry¹. However, because BMI is generated only from the relationship between height and weight, it can sometimes be high or low due to factors other than body fat. For instance, highly trained athletes, particularly male strength athletes, may have high BMI due to muscle mass. Pregnant women may also have elevated BMI due to fluid shifts and weight of the fetus. Children often have lower BMIs due to lack of muscle and bone density. In fact, the median BMI is under 17 for children below age 11, whereas a BMI of 17 for an adult is very low.
While there are much more technical and accurate ways to measure body composition like MRI and hydrostatic weighing, BMI has become the most pervasive parameter because it is so accessible in a public health setting. Most people know their height and weight, so BMI can even be determined using simple questions without having to measure anything. In a public health setting, it is reasonable to assume that high BMI is indicative of adiposity since, as any trip to a local mall will show, highly trained athletes are much less common than are overweight individuals.
Nonetheless, the shortcomings of BMI have spurred researchers to come up with other measurement-based indicators of body composition. Some of these measures include the waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), waist circumference (WC) and the body adiposity index (BAI). The BAI is calculated as follows: BAI = [(hip circumference (cm)/height (m)1.5) − 18] × 100. These are all fairly easy to obtain, though many people would not know their waist or hip circumference if asked, and taking proper measurements would require instruction regarding placement of a measuring tape.
In a recent cohort study², these new measures were compared to BMI as a mortality predictor in both men and women. It showed that BMI had the expected U-shaped curve with high levels indicating increased risk of cardiovascular death and low levels indicating increased risk from cancer. WHR had a more consistent positive slope with lower levels associated with lower mortality across the board. The pattern of BAI was the most like BMI of the examined markers. WHtR was also like BMI, but with higher overall magnitude of effects.
Another study³, this one in an American cohort, showed that WHtR was more strongly associated with mortality than BMI, BAI or WHR, though the methods used did not allow for J-shaped or U-shaped relationships.
It is not unusual in life insurance to run into individuals who are offered less-than-best class due to mild elevations in BMI. Often there is pushback indicating that the applicant is an athlete of some kind and more muscular than usual. The above studies show that it may well be the case that these individuals have more favorable mortality as indicated by the alternate measures of body composition. Of course, the waist and/or hip measurements would need to be available to justify this conclusion.
About the Author
Dr. Steven J. Rigatti is a consulting medical director with Clinical Reference Laboratory, with 12 years’ experience in the life insurance industry. He is the current chair of the Mortality Committee of the American Academy of Life Insurance Medicine.
References — Click to View Full Reports:
- Fulks M, Dolan VF, Stout RL. 2014 CRL Build Study of Life Insurance Applicants. J Insur Med 2016;46:13–19.
- Rost S, Freuer D, Peters A, et al. New Indexes of Body Fat Distribution and Sex-Specific Risk of Total and Cause-Specific Mortality: A Prospective Cohort Study. BMC Public Health. 2018 Apr 2;18(1):427.
- Katzmarzyk PT, Mire E, Bray GA, et al. Anthropometric Markers of Obesity and Mortality in White and African American Adults: The Pennington Center Longitudinal Study Obesity (2013) 21, 1070–1075.