Body mass index, or BMI, is a common weight-for-height measure used to classify individuals by weight status—as underweight, normal weight, overweight, or obese. Because of its relevance to longevity, weight status is a key measure in the Sightlines project.

Many trends in the Sightlines data on BMI merit investigation, one of which is the finding that Americans in the oldest age group (adults 75+) are most likely to have healthy BMI’s. Why might this be the case? Are older adults adopting better lifestyle habits? Is it easier to maintain a healthy body weight as one ages? Actually, research suggests these numbers may be misleading. BMI, while convenient for large sample sizes, does not distinguish between fat mass and lean mass, the latter typically diminishing as we age. Loss of lean mass may be the culprit behind the lower BMI’s observed in older adults. Furthermore, assessing lean mass in older individuals is crucial—maintaining lean mass is important for functionality and overall health in the older years. It may be prudent to consider different weigh status measures for older adults, especially on an individual basis, to account for age-related changes in body composition.

BMI is calculated by taking an individual’s weight (in kilograms) divided by height squared (in meters). As might be supposed, this measure does not always provide an accurate account of body composition. Weight, as a measure, lumps fat mass and lean mass into a single number, failing to distinguish between the two. Consequently, BMI may miscategorize patients. A recent study found that in 41% of patients, BMI mislabeled body fat status. Moreover, the study showed that although BMI was positively correlated with body fat, it had a higher correlation with lean body mass.

These findings suggest that a high body mass index may often be the result of higher muscle density, which is actually beneficial to health. Studies such as these indicate that health professionals should look to measures beyond BMI to provide a more accurate account of body weight status, especially when lean mass is a measure of interest.

Lean mass is particularly relevant to older adults. Monitoring lean mass, while important in the younger years, is more salient as we age, when skeletal muscle mass that is key for functionality declines in a condition called sarcopenia. As part of the organic aging process, sarcopenia affects virtually every older adult. Consequences of sarcopenia can be devastating, and include disability, loss of function, frailty, chronic disease, and consequently, mortality. To date, exercise and nutrition are the most effective treatments in combating sarcopenia. It is important that medical professionals assess older adults’ lean mass to provide effective treatment for this prevalent condition. Moreover, even in younger adults, monitoring lean mass is important for taking preventative action against sarcopenia.

New studies echo the importance of lean mass analysis in older adults. One study found that muscle mass, rather than total body mass index, was a better predictor of mortality in older adults. Higher muscle mass was correlated with lower mortality, suggesting that medical professionals should focus special energy on assessment of lean body mass, favoring more detailed measures over BMI.

Dr. Preethi Srikanthan, one of the study’s authors, states, “As there is no gold-standard measure of body composition, several studies have addressed this question using different measurement techniques and have obtained different results. So many studies on the mortality impact of obesity focus on BMI. Our study indicates that clinicians need to be focusing on ways to improve body composition, rather than on BMI alone, when counseling older adults on preventative health behaviors.”

That is not to say that BMI may not be a helpful measure, especially when trying to collect data on weigh status on a large scale. However, the measure itself may have to be

Surprisingly, the Sightlines data suggest exactly the opposite: despite the strong correlation between education and healthy eating, there is not a correlation between Whiteness and healthy eating. The rate of eating five or more fruits and vegetables each day is comparable for Blacks (23.1%), Hispanics (23.4%), and Whites (24.3%), and significantly higher Asians (29.0%). If healthy eating were primarily driven by privilege markers such as education, we might except the higher healthy eating rate for Asians (the “model minority” who have the higher average income and education than Whites),⁸ but we would expect the rate for Whites to also be higher than the rates for Hispanics and Blacks.

our sample it is possible that the non-White populations surveyed live predominantly in agriculturally-productive parts of the U.S., like California, and therefore have easier access to fresh produce. Further research is necessary to explore these possibilities.

Second: the conclusions we draw from Sightlines data could be limited by sample size and presentation. Our data are not behavioral; they derive from a national survey that asked people to self-report their eating habits. Furthermore, the sample sizes of different demographic groups are not the same, so there are limits to the conclusions we can draw in these data from comparing rates of healthy eating across demographics.

These possibilities raise a broader issue of this type of demographic analysis: ethnicity is not nearly as clean of a variable as is something like education. The concepts of race and ethnicity are categorical variables that confound identity with several continuous dimensions of human variation. Measuring a person’s race is much more complicated than measuring how much money that person makes. It is important to consider that complexity in order to truly understand variation across demographic dimensions.⁹

The Sightlines data are unexpected: contrary to what we would predict based on status, healthy eating is associated with being well-educated, but not with being White. Further research is needed to understand the relationships between healthy eating, status, and race. Such research could be very valuable to applications designed to increase healthy eating among Americans, and to further understand the variation of experience among Americans.

Why might this be? There two primary possibilities:

First: healthy eating might be driven by a hidden third variable. For example, perhaps cultural variation in cuisine explains the unexpected rates of eating fruits and vegetables. Or, perhaps there are regional differences in healthy eating: for example, in our sample it is possible that the non-White populations surveyed live predominantly in agriculturally-productive parts of the U.S., like California, and therefore have easier access to fresh produce. Further research is necessary to explore these possibilities.

Second: the conclusions we draw from Sightlines data could be limited by sample size and presentation. Our data are not behavioral; they derive from a national survey that asked people to self-report their eating habits. Furthermore, the sample sizes of different demographic groups are not the same, so there are limits to the conclusions we can draw in these data from comparing rates of healthy eating across demographics.

These possibilities raise a broader issue of this type of demographic analysis: ethnicity is not nearly as clean of a variable as is something like education. The concepts of race and ethnicity are categorical variables that confound identity with several continuous dimensions of human variation. Measuring a person’s race is much more complicated than measuring how much money that person makes. It is important to consider that complexity in order to truly understand variation across demographic dimensions.⁹

The Sightlines data are unexpected: contrary to what we would predict based on status, healthy eating is associated with being well-educated, but not with being White. Further research is needed to understand the relationships between healthy eating, status, and race. Such research could be very valuable to applications designed to increase healthy eating among Americans, and to further understand the variation of experience among Americans.

amended to account for age-related changes in body composition. Indeed, because muscle mass decreases with age, it might be wise to shift the healthy BMI range for older adults. The same BMI could mean very different things at age 25 versus age 75. A recent meta-analysis illuminated this conundrum; researchers found that an overweight BMI did not increase risk of mortality in older individuals, as might be expected, but being underweight did. The authors suggest that World Health Organization amend the healthy weight guidelines for older adults, whose bodies are notably different from those of their younger counterparts.

If BMI is not always the best measure, then what are some alternatives that distinguish between fat mass and lean mass? The DEXA scan, involving X-ray of body tissues, is one. Another is skin calipers, in which sections of skin are clamped and fat and muscle content are assessed, although this measure also has age related issues. Hydrostatic weighing is another option, which involves complete submersion in water. Notably, all of these techniques demand an in- person assessment, whereas BMI can be calculated through self reports of height and weight, making it an easier measure for large-scale studies.

Until a more accurate, self-reportable technique can be developed, BMI may remain the predominant measure of weight status in such studies. However, when assessing older adults on an individual basis—and younger adults too, for that matter—the alternatives to BMI deserve serious consideration in light of their implications for health, preventative and otherwise.

Oreopoulos, Antigone, Justin A. Ezekowitz, Finlay A. Mcalister, Kamyar Kalantar-Zadeh, Gregg C. Fonarow, Colleen M. Norris, Jeffery A. Johnson, and Raj S. Padwal. “Association Between Direct Measures of Body Composition and Prognostic Factors in Chronic Heart Failure.” Mayo Clinic Proceedings 85, no. 7 (2010): 609-17. doi:10.4065/mcp.2010.0103.
Phillips, Edward M., and Roger Fielding. “Sarcopenia in Older Adults.” Encyclopedia of Lifestyle Medicine & Health. doi:10.4135/9781412994149.n310.
Srikanthan, Preethi, and Arun S. Karlamangla. “Muscle Mass Index As a Predictor of Longevity in Older Adults.” The American Journal of Medicine 127, no. 6 (2014): 547-53. doi:10.1016/j.amjmed.2014.02.007.
Winter, J. E., R. J. Macinnis, N. Wattanapenpaiboon, and C. A. Nowson. “BMI and All-cause Mortality in Older Adults: A Meta-analysis.” American Journal of Clinical Nutrition 99, no. 4 (2014): 875-90. doi:10.3945/ajcn.113.068122.