Posts Tagged ‘Meat Intake and Mortality’

Red meat and cancer

Posted: November 10, 2015 in Cancer, Epidemiology, Evidence Based Medicine, misuse of statistics, Observational Studies, Red Meat
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As the nutrition world implodes, there are a lot of accusations about ulterior motives and personal gain. (A little odd, that in this period of unbelievable greed — CEO’s ripping off public companies for hundreds of millions of dollars, congress trying to give tax breaks to billionaires — book authors are upbraided for trying to make money). So let me declare that I am not embarrassed to be an author for the money — although the profits from my book do go to research, it is my own research and the research of my colleagues. So beyond general excellence (not yet reviewed by David Katz), I think “World Turned Upside Down” does give you some scientific information about red meat and cancer that you can’t get from the WHO report on the subject.

The WHO report has not yet released the evidence to support their claim that red meat will give you cancer but it is worth going back to one of the previous attacks.  Chapters 18 and 19 discussed a paper by Sinha et al, entitled “Meat Intake and Mortality.”    The Abstract says “Conclusion: Red and processed meat intakes were associated with modest increases in total mortality, cancer mortality, and cardiovascular disease mortality,” I had previously written a blogpost about the study indicating how weak the association was. In that post, I had used the data on men but when I incorporated the information into the book, I went back to Sinha’s paper and analyzed the original data. For some reason, I also checked the data on women. That turned out to be pretty surprising:

Sinha_Table3_Chapter18_Apr22

I described on Page 286: “The population was again broken up into five groups or quintiles. The lower numbered quintiles are for the lowest consumption of red meat. Looking at all cause mortality, there were 5,314 deaths [in lowest quintile] and when you go up to quintile 05, highest red meat consumption, there are 3,752 deaths. What? The more red meat, the lower the death rate? Isn’t that the opposite of the conclusion of the paper? And the next line has [calculated] relative risk which now goes the other way: higher risk with higher meat consumption. What’s going on? As near as one can guess, “correcting” for the confounders changed the direction….” They do not show most of the data or calculations but I take this to be equivalent to a multivariate analysis, that is, red meat + other things gives you risk. If they had broken up the population by quintiles of smoking, you would see that that was the real contributor. That’s how I interpreted it but, in any case, their conclusion is about meat and it is opposite to what the data say.

So how much do you gain from eating red meat? “A useful way to look at this data is from the standpoint of conditional probability. We ask: what is the probability of dying in this experiment if you are a big meat‑eater? The answer is simply the number of people who both died during the experiment and were big meat‑eaters …. = 0.0839 or about 8%. If you are not a big meat‑eater, your risk is …. = 0.109 or about 11%.” Absolute gain is only 3 %. But that’s good enough for me.

Me, at Jubilat, the Polish butcher in the neighborhood: “The Boczak Wedzony (smoked bacon). I’ll take the whole piece.”

Wedzony_Nov_8

Boczak Wedzony from Jubilat Provisions. 

Rashmi Sinha is a Senior Investigator and Deputy Branch Chief and Senior at the NIH. She is a member of the WHO panel, the one who says red meat will give you cancer (although they don’t say “if you have the right confounders.”)

So, buy my book: Amazon, Alibris, or

Direct:  Personalized, autographed copy $ 20.00 free shipping USA only.  Use coupon code: SEPT16

 

Making Americans Afraid of Meat.

Posted: May 13, 2012 in Association and Causality, Crisis in Nutrition, Red Meat, saturated fat
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TIME: You’re partnering with, among others, Harvard University on this. In an alternate Lady Gaga universe, would you have liked to have gone to Harvard?

Lady Gaga: I don’t know. I am going to Harvard today. So that’ll do.

— Belinda Luscombe, Time Magazine, March 12, 2012

There was a sense of déja-vu about the latest red meat scare and I thought that my previous post as well as those of others had covered the bases but I just came across a remarkable article from the Harvard Health Blog. It was entitled “Study urges moderation in red meat intake.” It describes how the “study linking red meat and mortality lit up the media…. Headline writers had a field day, with entries like ‘Red meat death study,’ ‘Will red meat kill you?’ and ‘Singing the blues about red meat.”’

What’s odd is that this is all described from a distance as if the study by Pan, et al (and likely the content of the blog) hadn’t come from Harvard itself but was rather a natural phenomenon, similar to the way every seminar on obesity begins with a slide of the state-by-state development of obesity as if it were some kind of meteorologic event.

When the article refers to “headline writers,” we are probably supposed to imagine sleazy tabloid publishers like the ones who are always pushing the limits of first amendment rights in the old Law & Order episodes.  The Newsletter article, however, is not any less exaggerated itself. (My friends in English Departments tell me that self-reference is some kind of hallmark of real art). And it is not true that the Harvard study was urging moderation. In fact, it is admitted that the original paper “sounded ominous. Every extra daily serving of unprocessed red meat (steak, hamburger, pork, etc.) increased the risk of dying prematurely by 13%. Processed red meat (hot dogs, sausage, bacon, and the like) upped the risk by 20%.” That is what the paper urged. Not moderation. Prohibition. Who wants to buck odds like that? Who wants to die prematurely?

It wasn’t just the media. Critics in the blogosphere were also working over-time deconstructing the study.  Among the faults that were cited, a fault common to much of the medical literature and the popular press, was the reporting of relative risk.

The limitations of reporting relative risk or odds ratio are widely discussed in popular and technical statistical books and I ran through the analysis in the earlier post. Relative risk destroys information.  It obscures what the risks were to begin with.  I usually point out that you can double your odds of winning the lottery if you buy two tickets instead of one. So why do people keep doing it?  One reason, of course, is that it makes your work look more significant.  But, if you don’t report the absolute change in risk, you may be scaring people about risks that aren’t real. The nutritional establishment is not good at facing their critics but on this one, they admit that they don’t wish to contest the issue.

Nolo Contendere.

“To err is human, said the duck as it got off the chicken’s back”

 — Curt Jürgens in The Devil’s General

Having turned the media loose to scare the American public, Harvard now admits that the bloggers are correct.  The Health NewsBlog allocutes to having reported “relative risks, comparing death rates in the group eating the least meat with those eating the most. The absolute risks… sometimes help tell the story a bit more clearly. These numbers are somewhat less scary.” Why does Dr. Pan not want to tell the story as clearly as possible?  Isn’t that what you’re supposed to do in science? Why would you want to make it scary?

The figure from the Health News Blog:

Deaths per 1,000 people per year
    1 serving unprocessed meat a week   2 servings unprocessed meat a day
    Women    

7.0

 8.5
    3 servings unprocessed meat a week   2 servings unprocessed meat a day
    Men

12.3

13.0

Unfortunately, the Health Blog doesn’t actually calculate the  absolute risk for you.  You would think that they would want to make up for Dr. Pan scaring you.   Let’s calculate the absolute risk.  It’s not hard.Risk is usually taken as probability, that is, number cases divided by total number of participants.  Looking at the men, the risk of death with 3 servings per week is equal to the 12.3 cases per 1000 people = 12.3/1000 = 0.1.23 = 1.23 %. Now going to 14 servings a week (the units in the two columns of the table are different) is 13/1000 = 1.3 % so, for men, the absolute difference in risk is 1.3-1.23 = 0.07, less than 0.1 %.  Definitely less scary. In fact, not scary at all. Put another way, you would have to drastically change the eating habits (from 14 to 3 servings) of 1, 429 men to save one life.  Well, it’s something.  Right? After all for millions of people, it could add up.  Or could it?  We have to step back and ask what is predictable about 1 % risk. Doesn’t it mean that if a couple of guys got hit by cars in one or another of the groups whether that might not throw the whole thing off? in other words, it means nothing.

Observational Studies Test Hypotheses but the Hypotheses Must be Testable.

It is commonly said that observational studies only generate hypotheses and that association does not imply causation.  Whatever the philosophical idea behind these statements, it is not exactly what is done in science.  There are an infinite number of observations you can make.  When you compare two phenomena, you usually have an idea in mind (however much it is unstated). As Einstein put it “your theory determines the measurement you make.”  Pan, et al. were testing the hypothesis that red meat increases mortality.  If they had done the right analysis, they would have admitted that the test had failed and the hypothesis was not true.  The association was very weak and the underlying mechanism was, in fact, not borne out.  In some sense, in science, there is only association. God does not whisper in our ear that the electron is charged. We make an association between an electron source and the response of a detector.  Association does not necessarily imply causality, however; the association has to be strong and the underlying mechanism that made us make the association in the first place, must make sense.

What is the mechanism that would make you think that red meat increased mortality.  One of the most remarkable statements in the original paper:

“Regarding CVD mortality, we previously reported that red meat intake was associated with an increased risk of coronary heart disease2, 14 and saturated fat and cholesterol from red meat may partially explain this association.  The association between red meat and CVD mortality was moderately attenuated after further adjustment for saturated fat and cholesterol, suggesting a mediating role for these nutrients.” (my italics)

This bizarre statement — that saturated fat played a role in increased risk because it reduced risk— was morphed in the Harvard News Letters plea bargain to “The authors of the Archives paper suggest that the increased risk from red meat may come from the saturated fat, cholesterol, and iron it delivers;” the blogger forgot to add “…although the data show the opposite.” Reference (2) cited above had the conclusion that “Consumption of processed meats, but not red meats, is associated with higher incidence of CHD and diabetes mellitus.” In essence, the hypothesis is not falsifiable — any association at all will be accepted as proof. The conclusion may be accepted if you do not look at the data.

The Data

In fact, the data are not available. The individual points for each people’s red meat intake are grouped together in quintiles ( broken up into five groups) so that it is not clear what the individual variation is and therefore what your real expectation of actually living longer with less meat is.  Quintiles are some kind of anachronism presumably from a period when computers were expensive and it was hard to print out all the data (or, sometimes, a representative sample).  If the data were really shown, it would be possible to recognize that it had a shotgun quality, that the results were all over the place and that whatever the statistical correlation, it is unlikely to be meaningful in any real world sense.  But you can’t even see the quintiles, at least not the raw data. The outcome is corrected for all kinds of things, smoking, age, etc.  This might actually be a conservative approach — the raw data might show more risk — but only the computer knows for sure.

Confounders

“…mathematically, though, there is no distinction between confounding and explanatory variables.”

  — Walter Willett, Nutritional Epidemiology, 2o edition.

You make a lot of assumptions when you carry out a “multivariate adjustment for major lifestyle and dietary risk factors.”   Right off , you assume that the parameter that you want to look at — in this case, red meat — is the one that everybody wants to look at, and that other factors can be subtracted out. However, the process of adjustment is symmetrical: a study of the risk of red meat corrected for smoking might alternatively be described as a study of the risk from smoking corrected for the effect of red meat. Given that smoking is an established risk factor, it is unlikely that the odds ratio for meat is even in the same ballpark as what would be found for smoking. The figure below shows how risk factors follow the quintiles of meat consumption.  If the quintiles had been derived from the factors themselves we would have expected even better association with mortality.

The key assumption is that the there are many independent risk factors which contribute in a linear way but, in fact, if they interact, the assumption is not appropriate.  You can correct for “current smoker,” but biologically speaking, you cannot correct for the effect of smoking on an increased response to otherwise harmless elements in meat, if there actually were any.  And, as pointed out before, red meat on a sandwich may be different from red meat on a bed of cauliflower puree.

This is the essence of it.  The underlying philosophy of this type of analysis is “you are what you eat.” The major challenge to this idea is that carbohydrates, in particular, control the response to other nutrients but, in the face of the plea of nolo contendere,  it is all moot.

Who paid for this and what should be done.

We paid for it. Pan, et al was funded in part by 6 NIH grants.  (No wonder there is no money for studies of carbohydrate restriction).  It is hard to believe with all the flaws pointed out here and, in the end, admitted by the Harvard Health Blog and others, that this was subject to any meaningful peer review.  A plea of no contest does not imply negligence or intent to do harm but something is wrong. The clear attempt to influence the dietary habits of the population is not justified by an absolute risk reduction of less than one-tenth of one per cent, especially given that others have made the case that some part of the population, particularly the elderly may not get adequate protein. The need for an oversight committee of impartial scientists is the most important conclusion of Pan, et al.  I will suggest it to the NIH.

Red Meat and the New Puritans

Posted: April 7, 2011 in Association and Causality, Protein, The Nutrition Story
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“Dost thou think, because thou art virtuous, there shall be no more cakes and ale?”

— William Shakespeare, Twelfth Night.

Experts on nutrition are like experts on sexuality.  No matter how professional they are in general, in some way they are always trying to justify their own lifestyle.  They share a tendency to think that their own lifestyle is the one that everybody else should follow and they are always eager to save us from our own sins, sexual or dietary. The new puritans want to save us from red meat. It is unknown whether Michael Pollan’s In Defense of Food was reporting the news or making the news but it’s coupling of not eating too much and not eating meat is common.  More magazine’s take on saturated fat was very sympathetic to my own point of view and I probably shouldn’t complain that tacked on at the end was the conclusion that “most physicians will probably wait for more research before giving you carte blanche to order juicy porterhouse steaks.” I’m not sure that my physician knows about the research that already exists or that I am waiting for his permission on a zaftig steak.

“Daily Red Meat Raises Chances Of Dying Early” was the headline in the Washington Post last year. This scary story was accompanied by the photo below. The gloved hand slicing roast beef with a scalpel-like instrument was probably intended to evoke CSI autopsy scenes, although, to me, the beef still looked pretty good if slightly over-cooked.  I don’t know the reporter, Rob Stein, but I can’t help feeling that we’re not talking Woodward and Bernstein here.  For those too young to remember Watergate, the reporters from the Post were encouraged to “follow the money” by Deep Throat, their anonymous whistle-blower. A similar character, claiming to be an insider and  identifying himself or herself as “Fat Throat,” has been sending intermittent emails to bloggers, suggesting that they “follow the data.”

The Post story was based on a research report “Meat Intake and Mortality” published in the medical journal, Archives of Internal Medicine by Sinha and coauthors.  It got a lot of press and had some influence and recently re-surfaced in the Harvard Men’s Health Watch in a two part article called, incredibly enough, “Meat or beans: What will you have?” (The Health Watch does admit that “red meat is a good source of iron and protein and…beans can trigger intestinal gas” and that they are “very different foods”) but somehow it is assumed that we can substitute one for the other.

Let me focus on Dr. Sinha’s article and try to explain what it really says.  My conclusion will be that there is no reason to think that any danger of red meat has been demonstrated and I will try to point out some general ways in which one can deal with these kinds of reports of scientific information.

A few points to remember first.  During the forty years that we describe as the obesity and diabetes epidemic, protein intake has been relatively constant; almost all of the increase in calories has been due to an increase in carbohydrates; fat, if anything, went down. During this period, consumption of almost everything increased.  Wheat and corn, of course went up.  So did fruits and vegetables and beans.  The two things whose consumption went down were red meat and eggs.  In other words there is some a priori reason to think that red meat is not a health risk and that the burden of proof should be on demonstrating harm.  Looking ahead, the paper, like analysis of the population data, will rely entirely on associations.

The conclusion of the study was that “Red and processed meat intakes were associated with modest increases in total mortality, cancer mortality, and cardiovascular disease mortality.”  Now, modest increase in mortality is a fairly big step down from “Dying Early,” and surely a step-down from the editorial quoted in the Washington Post.  Written by Barry Popkin, professor of global nutrition at the University of North Carolina it said: “This is a slam-dunk to say that, ‘Yes, indeed, if people want to be healthy and live longer, consume less red and processed meat.'” Now, I thought that the phrase “slam-dunk” was pretty much out after George Tenet, then head of the CIA, told President Bush that the Weapons of Mass Destruction in Iraq was a slam-dunk.  (I found an interview with Tenet after his resignation quite disturbing; when the director of the CIA can’t lie convincingly, we are in big trouble).  And quoting Barry Popkin is like getting a second opinion from a member of the “administration.” It’s definitely different from investigative reporting like, you know, reading the article.

So what does the research article really say?  As I mentioned in my blog on eggs, when I read a scientific paper, I look for the pictures. The figures in a scientific paper usually make clear to the reader what is going on — that is the goal of scientific communication.  But there are no figures.  With no figures, Dr. Sinha’s research paper has to be analyzed for what it does have: a lot of statistics.  Many scientists share Mark Twain’s suspicion of statistics, so it is important to understand how it is applied.  A good statistics book will have an introduction that says something like “what we do in statistics, is try to put a number on our intuition.”  In other words, it is not really, by itself, science.  It is, or should be, a tool for the experimenter’s use. The problem is that many authors of papers in the medical literature allow statistics to become their master rather than their servant: numbers are plugged into a statistical program and the results are interpreted in a cut-and-dried fashion with no intervention of insight or common sense. On the other hand, many medical researchers see this as an impartial approach. So let it be with Sinha.

What were the outcomes? The study population of 322, 263 men and 223, 390 women was broken up into five groups (quintiles) according to meat consumption, the highest taking in about 7 times as much as the lower group (big differences).  The Harvard News Letter says that the men who ate the most red meat had a 31 % higher death rate than the men who ate the least meat.  This sounds serious but does it tell you what you want to know? In the media, scientific results are almost universally reported this way but it is entirely misleading.  (Bob has 30 % more money than Alice but they may both be on welfare). To be fair, the Abstract of the paper itself reported this as a hazard ratio of 1.31 which, while still misleading, is less prejudicial. Hazard ratio is a little bit complicated but, in the end, it is similar to odds ratio or risk ratio which is pretty much what you think: an odds ratio of 2 means you’re twice as likely to win with one strategy as compared to the other.  A moment’s thought tells you that this is not good information because you can get an odds ratio of 2, that is, you can double your chances of winning the lottery, by buying two tickets instead of one.  You need to know the actual odds of each strategy.  Taking the ratio hides information.  Do reporters not know this?  Some have told me they do but that their editors are trying to gain market share and don’t care.  Let me explain it in detail.  If you already understand, you can skip the next paragraph.

A trip to Las Vegas

Taking the hazard ratio as more or less the same as odds ratio or risk ratio, let’s consider applying odds (in the current case, they are very similar).  So, we are in Las Vegas and it turns out that there are two black-jack tables and, for some reason (different number of decks or something), the odds are different at the two tables (odds are ways of winning divided by ways of not winning).  Table 1 pays out on average once every 100 hands.  Table 2 pays out once in 67 hands. The odds are 1/99 or close to one in a hundred at the first table and 1/66 at the second.  The odds ratio is, obviously the ratio of the two odds or 1/66 divided by 1/99 or about 1.55.  (The odds ratio would be 1 if there were no difference between the two tables).

Right off, something is wrong: if you were just given the odds ratio you would have lost some important  information.  The odds ratio tells you that one gambling table is definitely better than the other but you need additional information to find out that the odds aren’t particularly good at either table: technically, information about the absolute risk was lost.

So knowing the odds ratio by itself is not much help.  But since we know the absolute risk of each table, does that help you decide which table to play?  Well, it depends who you are. For the guy who is at the blackjack table when you go up to your hotel room to go to sleep and who is still sitting there when you come down for the breakfast buffet, things are going to be much better off at the second table.  He will play hundreds of hands and the better odds ratio of 1.5 will pay off in the long run.  Suppose, however, that you are somebody who will take the advice of my cousin the statistician who says to just go and play one hand for the fun of it, just to see if the universe really loves you (that’s what gamblers are really trying to find out).  You’re going to play the hand and then, win or lose, you are going to go do something else.  Does it matter which table you play at?  Obviously it doesn’t.  The odds ratio doesn’t tell you anything useful because you know that your chances of winning are pretty slim either way.

Now going over to the Red Meat article the hazard ratio (again, roughly the odds ratio) between high and low red meat intakes for all-cause mortality for men, for example, is 1.31 or, as they like to report in the media 31 % higher risk of dying which sounds pretty scary.  But what is the absolute risk?  To find that we have to find the actual number of people who died in the high red meat quintile and the low end quintile.  This is easy for the low end: 6,437 people died from the group of  64,452, so the probability (probability is ways of winning divided by total possibilities) of dying are 6,437/64,452 or just about 0.10 or 10 %.  It’s a little trickier for the high red meat consumers.  There, 13,350 died.  Again,  dividing that by the number in that group, we find an absolute risk of 0.21 or 21 % which seems pretty high and the absolute difference in risk is an increase of 10 % which still seems pretty significant.  Or is it?  In these kinds of studies, you have to ask about confounders, variables that might bias the results.  Well, here, it is not hard to find.  Table 1 reveals that the high red meat group had 3 times the number of smokers. (Not 31 % more but 3 times more).  So the authors corrected the data for this and other effects (education, family history of cancer, BMI, etc.) which is how the final a value of 1.31 was obtained.  Since we know the absolute value of risk in the lowest red meat group, 0.1 we can calculate the risk in the highest red meat group which will be 0.131.  The absolute increase in risk from eating red meat, a lot more red meat, is then 0.131 – 0.10 = 0.031 or 3.1 % which is quite a bit less than we thought.

Now, we can see that the odds ratio of 1.31 is not telling us much — and remember this is for big changes, like 6 or 7 times as much meat; doubling red meat intake (quintiles 1 and 2) leads to a hazard ratio of 1.07.  What is a meaningful odds ratio?  For comparison, the odds ratio for smoking vs not smoking for incidence of lung disease is about 22.

Well, 3.1 % is not much but it’s something.  Are we sure?  Remember that this is a statistical outcome and that means that some people in the high red meat group had lower risk, not higher risk.  In other words, this is what is called statistically two-tailed, that is, the statistics reflect changes that go both ways.  What is the danger in reducing meat intake.  The data don’t really tell you that.  Unlike cigarettes, where there is little reason to believe that anybody’s lungs really benefit from cigarette smoke (and the statistics are due to random variation), we know that there are many benefits to protein especially if it replaces carbohydrate in the diet, that is, the variation may be telling us something real.  With odds ratios around 1.31 — again, a value of 1 means that there is no difference — you are almost as likely to benefit from adding red meat as you are reducing it.  The odds still favor things getting worse but it really is a risk in both directions. You are at the gaming tables.  You don’t get your chips back. If reducing red meat does not reduce your risk, it may increase it.  So much for the slam dunk.

What about public health? Many people would say that for a single person, red meat might not make a difference but if the population reduced meat by half, we would save thousands of lives.  The authors do want to do this.  At this point, before you and your family take part in a big experiment to save health statistics in the country, you have to ask how strong the relations are.  To understand the quality of the data, you must look for things that would not be expected to have a correlation.  “There was an increased risk associated with death from injuries and sudden death with higher consumption of red meat in men but not in women.”  The authors dismiss this because the numbers were smaller (343 deaths) but the whole study is about small differences and it sounds like we are dealing with a good deal of randomness.  Finally, the authors set out from the start to investigate red meat.  To be fair, they also studied white meat which was slightly beneficial. But what are we to compare the meat results to? Why red meat?  What about potatoes?  Cupcakes?   Breakfast cereal?  Are these completely neutral? If we ran these through the same computer, what would we see?  And finally there is the elephant in the room: carbohydrate. Basic biochemistry suggests that a roast beef sandwich may have a different effect than roast beef in a lettuce wrap.

So I’ve given you the perspective of a biochemistry professor.  This was a single paper and surely not the worst, but I think it’s not really about science.  It’s about sin.

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Nutrition & Metabolism Society