For the video version of this post, click here. Body mass index. Since the term entered the medical consciousness in 1972, it has served admirably as a proxy measure for body fat, since body fat itself is sort of tough to measure. But it is demonstrably imperfect. Since it relates weight to height, it has no ability to distinguish between fat mass and muscle mass, leading to so called “obesity paradoxes” that are really no such thing. We need something better than BMI.
That something better, according to an article appearing in the Annals of Internal Medicine, may be the ratio of waist to hip circumference.
The study has some dramatic results. Using data from the National Health and Nutrition Examination Survey, researchers examined around 15,000 participants. Each participant had a BMI and a waist-to-hip ratio. It turned out that waist-to-hip ratio was a much better predictor of mortality and cardiovascular disease than BMI.
In fact, once you accounted for waist-to-hip ratio, BMI didn’t predict mortality at all. What this suggests is that all those studies linking BMI to bad outcomes were secretly studies linking central obesity to bad outcomes (because BMI and central obesity are correlated). But when you introduce a better measure of central obesity, the utility of BMI goes out the window. It’s a proxy measure without a home.
It turned out that, among men with normal BMIs, those with a high waist-to-hip ratio had an 87% higher risk of death. Women with normal BMIs and central obesity had a 50% higher risk of death. Perhaps more interesting, men with normal BMIs and central obesity had around twice the risk of death of men who were overweight or obese by BMI but who didn’t have central obesity. Women’s results went in the same direction, though the magnitude wasn’t as great.
So, do we give up on BMIs altogether? Not necessarily. Waist-to-hip ratio does seem to be the superior risk marker, but it’s not as easy to measure. These data were collected by individuals trained to do these measurements the same way, every time - it may not be possible to do that in the doctor’s office and get reliable results. Though maybe we could start employing tailors.
Also, remember that BMI still captures a lot of this data. The finding that individuals with normal BMI but high waist-to-hip ratio have increased mortality is compelling, but only 11% of men and 3% of women fit in this category. In other words, chances are if you have a normal BMI you’re fine. That said, it seems clear now that we need to find something better than BMI, something that helps distinguish between fat mass and muscle in a way that BMI can not. Whether a technological solution, such as bioimpedance analysis, or an anthropometric solution like the one in this study takes the baton, my intuition is that BMI now has a shelf-life.
Competing risk is a tough concept. I think the reason people have so much trouble with it is that it sounds intuitive. When you hear "competing risk" you have this immediate sense that you know what it means. But in practice, competing risk works quite a bit differently from what you might expect. In a post written for MedPage today, I outlined the issues in interpretation of competing risk using my foray into nightlife as an analogy.
Check out the full post here.
For the video version of this post, click here. The idea of screening mammography makes a lot of sense. Detect cancers early, treat them early, improve outcomes. In practice, though, screening mammography gets much more complicated. When should screening begin? When should it end? How frequent should it be? Each of these questions has its own fully-developed controversy. Full disclosure: I am married to a breast cancer surgeon. Now a study, appearing in JAMA Oncology tries to crack the frequency question.
The study, by Diana Miglioretti and colleagues, used data from the Breast Cancer Surveillance Consortium, a national group that records data from regional radiology facilities. They identified all women in the dataset aged 40 - 85 who were diagnosed with a new breast cancer - around 15,000 women altogether.
Of those, around 12,000 had gotten annual mammograms, and 3000 or so had gotten biennial mammograms. The question was whether the tumors in the less-frequently-screened women would be bigger, or more advanced than those who were screened more frequently. In other words, does more frequent screening catch cancers when they are smaller?
The answer is a definitive kind of. Overall, there was no difference in tumor characteristics among those screened yearly or every other year. Among premenopausal women, though, annual screening did seem to find tumors with less advanced characteristics, an effect that was statistically significant, provided you don’t account for the multiple hypotheses being tested. But if the association is real, it’s interesting to note that there was no such effect when the cohort was stratified by age - so it seems that biological age, at least in terms of menopause, might be more relevant than chronological age here.
The study is shackled by several big limitations though. #1 is that every woman in this study was diagnosed with cancer. We have no idea how many screenings were done to identify these 15,000 women with cancer and no way to tell if women undergoing every other year screening are treated differently. Perhaps mildly abnormal results get biopsied more often in the every other year group, since waiting to see how things look next year is not an option. The second big problem is that there is no link to any outcomes. Even if we buy that more frequent screening detects cancers earlier, we have no data to tell us whether that matters - ie, whether treatment is more effective at that point.
In the end, the authors, like the guideline organizations, say that the frequency of screening should be decided between a patient and her doctor. But at some point, the decision to switch to biennial screening may be forced by insurance companies or medicare, and, at least according to this study, that might not be a bad thing.
Error. If you're an epidemiologist, you work, live, and breathe it. Some of us loathe it as the source of all our negative studies. Some of us embrace it as a reminder that the universe is, well, just imperfect. And maybe that's OK. But however you feel about error, you best be reporting it in your research studies. And how you report it matters. For a full discussion, check out my latest methods man blog post.