Conventional wisdom, along with boatloads of scientific evidence, point to obesity being universally unhealthy, leading to diabetes, cancer, heart disease, and many more problems. But in recent years, that conventional wisdom has been challenged by a “U.”
The obesity paradox
That “U” appeared on graphs charting the link between body-mass index — a common but imperfect gauge of whether or not someone’s weight is healthy, calculated simply by dividing their mass by the square of their body height in meters — and their risk of death. Numerous epidemiological studies have found that people in the “overweight” category (BMI 25-30) surprisingly have the lowest mortality risk, while those categorized as “obese” (30-35) have little or no increased risk over the “healthy” (18.5-25). At the extreme ends of the BMI spectrum, both the “underweight” (less than 18.5) and the extremely obese (35+) have a greatly increased risk of death. Furthermore, numerous studies also have suggested that obesity might lower the risk of death for older people and patients with various chronic diseases.
Considering what we know about the health pitfalls of increased body fat, one would expect a mostly straight line of rising mortality risk as one goes from a BMI of healthy to obese. That’s why the “U-shaped” mortality curve has been dubbed the “obesity paradox.”
But in recent years, that paradox, and the studies that created it, have come under fire. Critics chiefly contend that BMI is a flawed way to determine whether someone has obesity. That’s because it does not measure the composition of one’s body mass — that is, how much is fat and how much is muscle. Nor does BMI measure where fat is located, which can make a big difference. Visceral fat jammed among internal organs is much worse than subcutaneous fat stored just beneath the skin. For example, an extremely fit and muscular individual could easily make it into the obese BMI category. At the same time, a “skinny” individual with a lot of body fat nestled dangerously around their mid-section could be categorized as “healthy.”
Why has BMI been so frequently used in epidemiological studies? Because it’s convenient, readily calculated based on self report. On the other hand, measuring body fat requires subjects to take a trip to a lab or to conduct the measurement on themselves, which can be quite difficult for a layperson to do accurately.
Replace BMI with body fat
When a team of researchers adjusted BMI to take muscle mass into account back in 2018, then associated this corrected measure with mortality risk, they found that the “U” mostly transformed into a straight line. Extremely obese individuals went from having only a marginally increased risk of death compared to healthy individuals to about a 70% increased risk.
More recently, Ryan Masters, an associate professor of sociology at the University of Colorado, tried to resolve the obesity paradox by taking more confounding variables into account. He examined nearly 40 years of data from almost 18,000 subjects, and he not only considered subjects’ distribution of body fat, he also tallied the amount of time that they spent at a high or low BMI.
“I would argue that we have been artificially inflating the mortality risk in the low-BMI category by including those who had been high BMI and had just lost weight recently,” he explained in a statement. “The health and mortality consequences of high BMI are not like a light switch,” he added. “There’s an expanding body of work suggesting that the consequences are duration-dependent.”
Obesity paradox debunked
After accounting for the potential biases in the data, Masters found that obesity boosts one’s risk of death by as much as 91%, vastly more than previous studies suggested. The U-shaped curve disappeared, and the paradox along with it. He further estimated that about 1 in 6 U.S. deaths are related to excess weight.
“Paradoxes should be met with skepticism,” a pair of public health experts wrote in a 2017 op-ed in the International Journal of Obesity. “Counterintuitive results should be discussed with colleagues and collaborators with different areas of expertise. The only ‘paradox’ we can see here is why researchers continue to claim to have evidence of a paradox without careful consideration of potential methodological explanations.”
This article was first published at Big Think.
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