New Proof That Being a Vegetarian is Healthier?

According to this blog post from ABC News, the recently published (behind paywall) results of a large prospective cohort study performed in the UK offers "further proof that eating meat can be hazardous to health." That strikes me as sort of an odd way of framing it, and I hope after reading this you'll understand why I think so. The proper conclusion really should be that we have more evidence that being a vegetarian is associated with a reduced risk of heart disease, but it's still difficult to tell how much other lifestyle choices play into this. In other words, as long as you live a healthy lifestyle in general and eat meat a few times a week or less, it's still difficult to assume you'd see a significant benefit by cutting that meat out of your diet.

The study began in 1993 and followed 44,561 men and women in England and Scotland, 34% of whom were vegetarians when the study began. After an average follow-up time of nearly 12 years, the incidence of developing ischemic heart disease (IHD) was compared between the vegetarian group and the meat-eating group. A subset of the study population was also used to measure risk factors related to heart disease such as body-mass index (BMI), non-HDL-cholesterol levels, and systolic blood pressure, and again the vegetarian group came out ahead. The investigators controlled for some obvious potential confounding variables such as age (the vegetarian group was 10 years younger on average), sex, and exercise habits. Overall, I agree with the quoted physician from the ABC post that it's a very good study, and does a pretty good job of trying to estimate the singular effect of whether a person eats meat or not.

This is essentially the type of design I was talking about being needed in the lead/crime post, and while it represents a pretty high level of evidence, there's still some very important limitations to be aware of. That's not to justify being skeptical of the entire claim that being a vegetarian is healthier for the heart than not, but it's not as simple as just saying "meat is hazardous to your health." The two groups being compared are not randomized, so it could very well be that the vegetarians had an overall healthier lifestyle, of course including exercise, but also dietary factors beyond just not eating meat. How would the results compare if you took a vegetarian that ate a lot of whole grains and vegetables vs. an omnivore that ate meat maybe once or twice a week but also ate a lot of whole grains and vegetables? My guess would be that the risks of IHD for the latter individual wouldn't be much higher, if at all. The central issue here is called selection bias, and it refers to the possibility that the two populations are different enough that drawing a firm cause and effect relationship to one specific difference between the groups is suspect.

The perils of reading too much into a cohort study are illustrated by the story of hormone-replacement therapy (HRT) for post-menopausal women. In 1991, a large cohort study based on a population of nurses came to the conclusion that HRT protected women from cardiovascular diseases. This was one out of a series of cohort studies for HRT that found a variety of purported benefits. Immediately, concerns about selection bias were raised, but were largely dismissed. HRT became very common throughout the rest of the decade based on these very well-designed cohort studies that seemed to provide really good evidence. Finally, in 2002, the first double-blinded randomized control trial for HRT vs. placebo was performed. Both the estrogen plus progestin and estrogen only arms of the study were halted early because the risks for heart disease, stroke, and pulmonary embolism were higher than in the placebo groups. The exact opposite of the results of the cohort study were found when the comparison groups were randomized. Again, this isn't to say I dispute that completely eliminating meat is a healthy choice, or that if an RCT were performed, the meat-eaters would turn out to be healthier. I'm just illustrating why you should be always be aware of the possibility of selection bias in non-randomized studies.

Another issue to look out for is how reduced risks are presented in studies. There are essentially two ways to do it: by subtracting the difference in risk in one group vs. the other, or by presenting a risk ratio. Looking at the numbers, only 2.7% of the entire study developed IHD, about 1.6% for meat-eaters vs. 1.1% for vegetarians. Another way of putting this is, "a vegetarian diet is associated with 0.5% less risk of developing IHD." That doesn't sound quite as impressive, but it's completely accurate. It's a lot more eye-opening to divide the 1.1% by the 1.6% and get your 32% reduced risk. I'm not saying anybody did anything inappropriate, but the raw numbers have a way of putting things like this into the proper perspective. 

Ultimately, what does all this amount to? If I were at risk for IHD, I'd adopt a healthy lifestyle of a well-rounded diet with moderate to no meat, as well as to exercise regularly and stay active throughout the day. Hardly an earth shattering piece of advice, but that's what we've got. To put it bluntly, just eat like a person is supposed to eat, goddamnit. You know what I mean.

Sometimes One Article IS Meaningful*

A new meta-analysis published in the latest edition of JNCI provides strong evidence that higher levels of carotenoids in women's bloodstreams may reduce the risk of breast cancer. Here's a pretty accessible article from HuffPo, and another a bit more in-depth about the findings, and what carotenoids are and where you can find them. If you are a woman at risk of breast cancer, it's well worth reading. There's a lot to consider, though, so let's get to it.

In my last post, I said in no uncertain terms that the conclusions of any one study do not represent the full story, but when done properly provide a possible clue. A lot of times, they provide little more than evidence that a hypothesis warrants further investigation. There are two potential exceptions to be on the lookout for when reading articles about health and medicine. Most publications are good about saying what type of analysis was performed.

One is called a systematic review, which is pretty much what it sounds like. The researcher surveys all of the evidence out there on a particular subject and lays it all out in a single article. The other possibility is a meta-analysis, which is similar except that a new statistical analysis is performed on the multiple studies put together, essentially expanding the sample sizes of the people exposed to a treatment or pollutant, and the controls who are not. The latter is what this paper did.

You don't need much experience with data analysis to know that increasing the sample sizes can make results more representative of the general population, thus potentially allowing you to make a stronger conclusion. Every Royals fan knows by now that our April enthusiasm will be sucked dry by Memorial Day, when more games get played and hot starts level off, or revert to the mean, so to speak.

The analysis should also smooth out the variations that can happen in the smaller-scale results due to bias or chance, and provide insight into the "true" effect. Bias, in the sense I'm using it, does not refer to a Fox News-like investigator deviously "cooking the books" for a preferred outcome, but rather a tendency to under or overestimate the effect of the exposure due to characteristics of the study subjects. Any one of those circles could be the result of this bias, and the theory is that looking at the fuller picture minimizes that effect. This particular analysis was done on eight cohort studies, which suffice to say at this point means that the subjects were not randomly selected by the investigator, and are particularly subject to these unintentional biases. This funnel plot, below, provides a visual representation:



Each little circle represents the result of a single published study. Just by chance, there should be some results that do not show a meaningful effect (negative, left of center), and some that do show a meaningful effect (positive, right of center). This is essentially just another visual representation of the bell curve that we're all familiar with.

The big caveat, and this is the case for all reviews and meta-analyses, is that it's possible that literally every published study out there could be over or underestimating the effect, so that your "true" effect may still be questionable. This occurs because a study that doesn't show the hypothesized effect (a negative result) is less likely to be published. Journals like to publish studies that show something interesting, assuming that the alternative is that something interesting didn't occur and not worth reading. Here's a visual representation of what's called publication bias:


When negative results do not get published, you get a funnel plot that skews toward a single direction like the graph on the right. If you look at the circles on both images, you can clearly see that the center (i.e. the mean, a.k.a your result) of them is quite different. The image on the right overestimates the effect of whatever the patients are exposed to. It's certainly plausible that our carotenoids are prone to this situation. How do we know that there aren't 10 studies sitting in various researchers' file cabinets that will never get published because they were negative? They obviously aren't going to be included in a meta-analysis if they aren't published.

So what's the real conclusion to take away from all this?

There seems to be pretty strong evidence that carotenoids may provide some sort of small-ish protective effect in regard to breast cancer, and only breast cancer as far as all of this evidence is concerned. I'll show you how to look at the results section with all the statistics gibberish and gauge the effect for yourself some other time. There's plausible mechanisms for how exactly this would work described in the HuffPo article, so it's not some mysterious shot in the dark. However, we're still well short of definitive proof. Eat fruits with high carotenoid content because they generally are yummy and are healthful in many other ways, too. Do not buy a $20 bottle of carotonoid supplements, and beware anyone trying to sell you on them. And look at a headline such as this one and roll your eyes, now that you know better.

I'll come back to publication bias from time to time, because it's everywhere. The pharmaceutical companies do large controlled experimental trials where the subjects are chosen randomly (i.e. the results are considered more conclusive than a cohort study), and they have plenty of incentive not to publish when the trials have a negative result. Everyone likes a good Big Pharma bashing sesh, and I'm happy to separate the genuine bullshit they pull from the conspiracies that don't really hold up to much scrutiny.