bmi – a conceptual mess, or, andrew perrin i take back what i said earlier

One of the basic measures of health is body mass index (BMI). It is meant to be a simple measure of a person’s obesity which is also correlated with morbidity. Recently, I spent some time researching the validity of BMI. Does it actually measure fatness? The answer is extremely confusing.

If you google “validity of BMI as a measurement of obesity,” you get this article that summarizes a few studies. The original definition of obesity is that you need to have at least 25% body fat for men and 35% for women. This is hard to measure without special equipment, so BMI is the default. Thus to measure BMI validity, you need a sample of people and then compute the BMI and the body fat percentage (BF%). The reports that in number of studies compared BF% and BMI. In some ways, BMI survives scrutiny. In non-obese people, as defined by BF%, BMI works well, but it seems to under-report obesity in others. In a few odd cases, mainly athletes with a lot of muscle weight, obesity is over-reported. Thus, you get a lot of mis-classification: “One meta-analysis on the subject suggests that BMI fails to classify half of persons with excess body fat, reporting them as normal or overweight despite having a body fat percentage classifying them as obese.” Translation, we are much fatter than we appear to be.

Then, soon after I read some of these studies, the LA Times reported on a new study from UCLA that examines the BMI-morbitiy correlation. In that study, researchers measured BMI and then collected data on biomarkers of health. This is done using the NHANES data set. See the study here. Result? A lot of fat people are actually quite healthy in the sense that BMI is not associated with cardio-pulmonary health (i.e., your heart stopping). This reminds me of an earlier discussion on this blog, where there were conflicting estimates of the obesity-mortality link and a meta-analysis kind of, sort of, shows an aggregate positive effect.

How do I approach the BMI issue as of today?

  • BMI is a rough measure of fatness (“adiposity”), but not precise enough for doctors to be making big judgments about patients on a single number/measurement.
  • BMI is not a terribly good predictor of mortality, even if there is a mild overall correlation that can be detected through meta-analysis.
  • BMI is probably not correlated with a lot of morbidity that we care about with some important exceptions like diabetes.

The lead author of the UCLA study said that this was the “last nail in the coffin” for BMI. She might be right.

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Written by fabiorojas

February 18, 2016 at 12:01 am

2 Responses

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  1. There can be a distinction between BMI as a population health measure and BMI as a clinical indicator. As a population-level measure, it tracks pretty well as an early indicator of diseases that influence health policy. Diabetes, for example, can be (largely) treated, but ends up being very expensive. Health policy makers need to have something on which to base incidence and BMI provides a good measure for doing so. In this regard it is not unlike life expectancy: it would be absurd for doctors or financial planners to tell people to plan their lives based on life expectancy because there is so much variation. But, as a population measure, it is really helpful.



    February 19, 2016 at 2:57 pm

  2. Mike’s right about the measurement component. The other issue is that BMI in clinical settings is appropriately understood as a screen, not a measure — it’s a low-cost, easy-to-calculate value that can index risk for clinicians.

    The UCLA study (here’s the actual study instead of the LA Times gloss) is actually about misclassification for insurance purposes. Essentially it’s about the danger of using a screen that has substantial error as an individual-level assessment of risk for actuarial purposes. That’s a common practice, and certainly unfair.

    However, the article really buries the lede — the more important finding is not the misclassification rate, but the relative cardiometabolic risk by BMI category (table 2, last page). Estimated population frequency of unhealthy metabolic status for “normal weight”: 30.1%; overweight: 52.59% (RR 1.7); obese class I: 71.36% (RR 2.3); obese class II/III: 84.11% (RR 2.7). Similar effects (in reverse direction) for healthy metabolic status rates (e.g., RR 0.4 of healthy metabolic status for obese class I compared to normal weight). So in fact, BMI is a good indicator of metabolic status, but one that contains error.

    Liked by 1 person


    February 19, 2016 at 4:18 pm

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