January 26, 2018

Understanding science

Is red meat killing us?

I wrote this post almost six years ago (March 21, 2012), but it’s the gift that keeps on giving.

Read Time 11 minutes

I wrote this post almost six years ago (March 21, 2012), but it’s the gift that keeps on giving.

These days, I feel a lot like Bill Murray in Groundhog Day, where at least a few times a year, my inbox is stuffed with concerned individuals forwarding me a paper (or, much more the case, a story about a paper) implying red meat is going to send me to an early grave. The irony is that being stuck in some sort of sadistic red-meat time loop probably will do the trick. But at least I have meditation to help with that.

To be fair, the red-meat-studies are not the only culprit. As I mentioned in a Nerd Safari on epidemiology, John Ioannidis and Jonathan Schoenfeld picked 50 ingredients at random out of a cookbook and determined if each was associated with cancer. They found that at least one study was identified as showing an association for an increase or decrease in cancer for 40 out of the 50 ingredients. (The 10 that didn’t make the list were more “obscure,” as the authors put it: bay leaf, cloves, thyme, vanilla, hickory, molasses, almonds, baking soda, ginger, and terrapin. Thank heavens I can still have my terrapin.)

It’s probably not unfair to say that I put together this post as a coping mechanism. Fight fire with fire. You want a time loop? You may see the following post make its way to the front of the queue several times a year. While it’s of course not the best tack for me to close my eyes and block my ears to the latest article that forces a visceral reaction, it’s important to put things in context first.

This time around, I’m posting not because a new study just came out on red meat and mortality (although I haven’t checked my email in the past five minutes), but because we’re doing a series on this very topic of observational epidemiology.

Studying Studies: Part I – relative risk vs. absolute risk

Studying Studies: Part II – observational epidemiology

Studying Studies: Part III – the motivation for observational studies

I must admit, re-reading this post for the first time, I thought to myself, ‘Wow, Peter. Chill out…you really wrote that?’ Kinda like when I look at a picture of me from the 90’s. Dude, you wore that?

—P.A., January 2018




“For the greatest enemy of truth is very often not the liedeliberate, contrived and dishonestbut the mythpersistent, persuasive, and unrealistic. Too often we hold fast to the clichés of our forebears. We subject all facts to a prefabricated set of interpretations. We enjoy the comfort of opinion without the discomfort of thought.”

– John F. Kennedy, Yale University commencement address (June 11, 1962)

I’m going to devote this post to a discussion on what I like to call the Scientific Weapon of Mass Destruction: observational epidemiology, at least for public health policy

I had always planned to write about this most important topic soon enough, but the recent study out of Harvard’s School of Public Health generated more than enough stories like this one such that I figured it was worth putting some of my other ideas on the back-burner, at least for a week.  If you’ve been reading this blog at all you’ve hopefully figured out that I’m not writing it to get rich. What I’m trying to do is help people understand how to think about what they eat and why.  I have my own ideas, shared by some, of what is “good” and what is “bad,” and you’ve probably noticed that I don’t eat like most people.

However, that’s not the real point I want to make.  I want to help you become thinkers rather than followers, at least on the topic of health sciences.  And that includes not being mindless followers of me or my ideas, of course. Being a critical thinker doesn’t mean you reject everything out there for the sake of being contrarian.  It means you question everything out there.  I failed to do this in medical school and residency.  I mindlessly accepted what I was taught about nutrition without ever looking at the data myself.  

Too often we cling to nice stories because they make us feel good, but we don’t ask the hard questions.  You’ve had great success improving your health on a vegan diet?  No animals have died at your expense. Great! But, why do you think it is you’ve improved your health on this diet?  Is it because you stopped eating animal products?  Perhaps.  What else did you stop eating?  How can we figure this out?  If we don’t ask these questions, we end up making incorrect linkages between cause and effect.  This is the sine qua non of bad science.

Most disciplines of science—such as physics, chemistry, and biology—use something called the Scientific Method to answer questions. A simple figure of this approach is shown below:


Scientific Method

The figure is pretty self-explanatory, so let me get to the part that observational epidemiology inherently omits: “Conduct an experiment.”  There is no shortage of observations, questions, or hypotheses in the world of epidemiology and public health—so we’re doing well on that front.  It’s that pesky experiment part we’re getting hung up on. Without doing controlled experiments it is not possible to distinguish the relationship between cause and effect.   


What is an experiment?

There are several types of experiments and they are not all equally effective at determining the cause and effect relationship.  Climate scientists and social economists (like one of my favorites, Steven Levitt), for example, often carry out natural experiments. Why? Because the “laboratory” they study can’t actually be manipulated in a controlled setting.  For example, when Levitt and his colleagues tried to figure out if swimming pools or guns were more dangerous to children—i.e., Was a child more likely to drown in a house with a swimming pool or be shot by a gun in a home with a gun?—they could only look at historical, or observational, data.  They could not design an experiment to study this question prospectively and in a controlled manner.

How would one design such an experiment?  In a “dream” world you would find, say, 100,000 families and you would split them into two groups—group 1 and group 2.  Group 1 and 2 would be statistically identical in every way once divided.  Because of the size of the population, any differences between them would cancel out (e.g., socioeconomic status, number of kids, parenting styles, geography).  The 50,000 group 1 families would then have a swimming pool installed in their backyard and the 50,000 group 2 families would be given a gun to keep in their house.

For a period of time, say 5 years, the scientists would observe the differences in child death rates from these two causes (accidental drownings and gunshot wounds).  At the conclusion, provided the study was powered appropriately, the scientists would know which was more hazardous to the life of a child, a home swimming pool or a home gun.

Unfortunately, questions like this (and the other questions studied by folks like Levitt) can’t be studied in a controlled way. Such studies are just impractical, if not impossible, to do.

Similarly, to rigorously study the anthropogenic CO2 – climate change hypothesis, for example, we would need another planet earth with the same number of humans, cows, lakes, oceans, and kittens that did NOT burn fossil fuels for 50 years.  But, since these scenarios are never going to happen the folks that carry out natural experiments do the best they can to statistically manipulate data to separate as many confounding factors as possible in every effort to identify the relationship between cause and effect.

Enter the holy grail of experiments: the controlled experiment. In a controlled experiment, as the name suggests, the scientists have control over all variables between the groups (typically what we call a “control” group and a “treatment” group). Furthermore, they study subjects prospectively (rather than backward-looking, or retrospectively) while only changing one variable at a time.  Even a well-designed experiment, if it changes too many variables (for example), prevents the investigator from making the important link: cause and effect.

Imagine a clinical experiment for patients with colon cancer.  One group gets randomized to no treatment (“control group”). The other group gets randomized to a cocktail of 14 different chemotherapy drugs, plus radiation, plus surgery, plus hypnosis treatments, plus daily massages, plus daily ice cream sandwiches, plus daily visits from kittens (“treatment group”).  A year later the treatment group has outlived the control group, and therefore the treatment has worked.  But how do we know EXACTLY what led to the survival benefit?  Was it 3 of the 14 drugs?  The surgery?  The kittens? We cannot know from this experiment.  The only way to know for certain if a treatment works is to isolate it from all other variables and test it in a randomized prospective fashion.

As you can see, even doing a prospective controlled experiment is not enough, like the one above, if you fail to design the trial correctly.  Technically, the fictitious experiment I describe above is not “wrong,” unless someone—for example, the scientist who carried out the trial or the newspapers who report on it—misrepresented it.

If the New York Times and CNN reported the following: New study proves that kittens cure cancer! would it be accurate? Not even close.  Sadly, most folks would never read the actual study to understand why this bumper-sticker conclusion is categorically false. Sure, it is possible, based on this study, that kittens can cure cancer.  But the scientists in this hypothetical study have wasted a lot of time and money if their goal was to determine if kittens could cure cancer.  The best thing this study did was to reiterate a hypothesis. Nothing more.  In other words, this experiment (even assuming it was done perfectly well from a technical standpoint) learned nothing other than the combination of 20 interventions was better than none because of an experimental design problem.


So what does all of this have to do with eating red meat?

In effect, I’ve already told you everything you need to know.  I’m not actually going to spend any time dissecting the actual study published last week [March 12, 2012] that led to the screaming headlines about how red meat eaters are at greater risk of death from all causes (yes, “all causes,” according to this study) because it’s already been done a number of times by others this week alone.  Three critical posts on this specific paper can be found here, here, and here.

I can’t suggest strongly enough that you read them all if you really want to understand the countless limitations of this particular study, and why its conclusion should be completely disregarded.  If you want bonus points, read the paper first, see if you can understand the failures of it, then check your “answer” against these posts.  As silly as this sounds, it’s actually the best way to know if you’ve really internalized what I’m describing.

Now, I know what you might be thinking:  Oh, come on Peter, you’re just upset because this study says something completely opposite to what you want to hear.

Not so.  In fact, I have the same criticism of similarly conducted studies that “find” conclusions I agree with.  For example, on the exact same day the red meat study was published online (March 12, 2012) in the journal Archives of Internal Medicine, the same group of authors from Harvard’s School of Public Health published another paper in the journal Circulation.  This second paper reported on the link between sweetened beverage consumption and heart disease, which “showed” that consumption of sugar-sweetened beverages increased the risk of heart disease in men.

I agree that sugar-sweetened beverages increase the risk of heart disease (not just in men, of course, but in women, too) along with a whole host of other diseases like cancer, diabetes, and Alzheimer’s disease.  But, the point remains that this study does little to nothing to add to the body of evidence implicating sugar because it was not a controlled experiment.  


This problem is actually rampant in nutrition

We’ve got studies “proving” that eating more grains protect men from colon cancer, that light-to-moderate alcohol consumption reduces the risk of stroke in women, and that low levels of polyunsaturated fats, including omega-6 fats, increase the risk of hip fractures in women.  Are we to believe these studies? They sure sound authoritative, and the way the press reports on them it’s hard to argue, right?

How are these studies typically done?

Let’s talk nuts and bolts for a moment.  I know some of you might already be zoning out with the detail, but if you want to understand why and how you’re being misled, you actually need to “double-click” (i.e., get one layer deeper) a bit.  What the researchers do in these studies is follow a cohort of several tens of thousands of people—nurses, health care professionals, AARP members, etcetera—and they ask them what they eat with a food frequency questionnaire (FFQ) that is known to be almost fatally flawed in terms of its ability to accurately acquire data about what people really eat.  Next, the researchers correlate disease states, morbidity, and maybe even mortality with food consumption, or at least reported food consumption (which is NOT the same thing). So, the end products are correlations—eating food X is associated with a gain of Y pounds, for example. Or eating red meat three times a week is associated with a 50% increase in the risk of death from falling pianos or heart attacks or cancer.

The catch, of course, is that correlations hold no causal information.  Just because two events occur in step does not mean you can conclude one causes the other.  Often in these articles you’ll hear people give the obligatory, “correlation doesn’t necessarily imply causality.” But saying that suggests a slight disconnect from the real issue. A more accurate statement is “correlation does not imply causality” or “correlations contain no causal information.”

So what explains the findings of studies like this (and virtually every single one of these studies coming out of massive health databases like Harvard’s)?

For starters, the foods associated with weight gain (or whichever disease they are studying) are also the foods associated with “bad” eating habits in the United States—french fries, sweets, red meat, processed meat, etc.  Foods associated with weight loss are those associated with “good” eating habits—fruit, low-fat products, vegetables, etc.  But, that’s not because these foods cause weight gain or loss, it’s because they are markers for the people who eat a certain way and live a certain way.

Think about who eats a lot of french fries (or a lot of processed meats). They are people who eat at fast food restaurants regularly (or in the case of processed meats, people who are more likely to be economically disadvantaged).  So, eating lots of french fries, hamburgers, or processed meats is generally a marker for people with poor eating habits, which is often the case when people are less economically advantaged and less educated than people who buy their food fresh at the local farmer’s market or at Whole Foods.  Furthermore, people eating more french fries and red meat are less health conscious in general (or they wouldn’t be eating french fries and red meat—remember, those of us who do eat red meat regularly are in the slim minority of health-conscious folks).  These studies are rife with methodological flaws, and I could devote an entire Ph.D. thesis to this topic alone.


What should we do about this?

I’m guessing most of you—and most physicians and policy makers in the United States for that matter—are not actually browsing the American Journal of Epidemiology (where one can find studies like this all day long).  But occasionally, like last week, the New York Times, Wall Street Journal, Washington Post, CBS, ABC, CNN, and everyone else gets wind of a study like the now-famous red meat study and comments in a misleading fashion. Health policy in the United States—and by extension much of the world—is driven by this. It’s not a conspiracy theory, by the way.  It’s incompetence.  Big difference.  Keep Hanlon’s razor in mind—Never attribute to malice that which is adequately explained by stupidity.

This behavior, in my opinion, is unethical and the journalists who report on it (along with the scientists who stand by not correcting them) are doing humanity no favors.

I do not dispute that observational epidemiology has played a role in helping to elucidate “simple” linkages in health sciences (e.g., contaminated water and cholera or the linkage between scrotal cancer and chimney sweeps).  However, multifaceted or highly complex pathways (e.g., cancer, heart disease) rarely pan out, unless the disease is virtually unheard of without the implicated cause.  A great example of this is the elucidation of the linkage between small-cell lung cancer (SCLC) and smoking—we didn’t need a controlled experiment to link smoking to this particular variant of lung cancer because nothing else has ever been shown to even approach the rate of this type of lung cancer the way smoking has (reported relative risk of SCLC in current smokers of more than 1.5 packs of cigarettes a day was 111.3 and 108.6, respectively—over a 10,000% relative risk increase). As a result of this unique fact, Richard Doll and Austin Bradford Hill were able to design a clever observational analysis to correctly identify the cause and effect linkage between tobacco and lung cancer.  But this sort of example is actually the exception and not the rule when it comes to epidemiology.

Whether it’s Ancel Keys’ observations and correlations of saturated fat intake and heart disease in his famous Seven Countries Study, which “proved” saturated fat is harmful or Denis Burkitt’s observation that people in Africa ate more fiber than people in England and had less colon cancer “proving” that eating fiber is the key to preventing colon cancer, virtually all of the nutritional dogma we are exposed to has not actually been scientifically tested.   Perhaps the most influential current example of observational epidemiology [circa 2012] is the work of T. Colin Campbell, lead author of The China Study, which claims, “the science is clear” and “the results are unmistakable.”  Really?  Not if you define science the way scientists do.  This doesn’t mean Colin Campbell is wrong (though I wholeheartedly believe he is wrong on about 75% of what he says based on current data).  It means he has not done sufficient science to advance the discussion and hypotheses he espouses.  If you want to read detailed critiques of this work, please look to Denise Minger and Michael Eades.  I can only imagine the contribution to mankind Dr. Campbell could have given had he spent the same amount of time and money doing actual scientific experiments to elucidate the impact of dietary intake and chronic disease. [For example, Campbell would have designed a prospective study following subjects randomized to one of two different types of diets for 10 years: plant-based and animal-based, but with all other factors controlled for.] This is one irony of enormous observational epidemiology studies.  Not only are they of little value, in a world of finite resources, they detract from real science being done.

Featured Image credit: Design by K. Pauley (CC BY-SA 2.0)

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  1. Peter, thank you for your work. To base nutritional decisions and policy recommendations on real science would be an invaluable advance. I understand that you are saying that the entire field lacks rigorous studies. I have recently embarked on a low carb way of eating, and I have been trying to absorb the best of what information is available.

    It seems to me that to be fair-minded in making my own personal dietary decisions, I ought to read the best of the literature that opposes low carb eating. Surely there are some bright, well-informed people who are writing in good faith and have serious criticisms of this style of eating?

    Do you believe this is so, and if so is there anything you would particularly recommend to balance out the perspective of someone who has only been reading low carb advocates? I am not looking for a defense of a low fat diet, as much as critique of low carb. Thanks for your thoughts!

    • Honestly, I have not come across ANY valuable work defending the science of “fat is bad and carbs are good.” It’s all observational work and poorly done statistics. Most of the rejections of “low carb” I have seen are ad hominem attacks (e.g., on Atkins, Gary Taubes). Sad, but true.

    • The only counter-argument I can see is that some individuals may have genetic mutations which result in enhanced cholesterol absorption from the gut. Since this usually can’t be predicted in advance, it may be worthwhile to follow an advanced lipid/lipoprotein panel such as VAP, LDL-P, apoB, etc, on a low carb high fat diet. I do wonder if such individuals should be treated with cholesterol absorption inhibitors (of which there are several molecular species), rather than abandon their diet, which will have other beneficial effects on CRP, blood pressure, HDL/triglyceride axis, insulin sensitivity etc.

    • I have a hypothesis (is that the right terminology?) that some people might be genetically better able to to handle carbohydrates because of their ancestors diet e.g. maybe their ancestors spent the majority of their evolutionary development in a carbohydrate rich environment. It is a possible reason that some people are able to eat high carb diets and remain healthy (like my girlfriend) and others eating high carb diet develop health issues (like me). I think that could be an argument for a low carb diet not being ideal for everybody but I have not put in any effort to backup these claims and I can’t really find any well structured studies defending high carb.

      • Nicky, that is exactly the right terminology and for what it’s worth, I think it’s a pretty solid one. The thing to keep in mind is that very few of us are genetically “traceable” to one region. So it may not be as simple as “my family is from Norway and I believe the people who descended from Norway 10,000 years ago ate X, therefore I should eat X.” But I think the framework could be helpful.

  2. Hi Peter,

    My point is that.for example on what basis do you think fruits and vegetables are good or healthy?

    I haven’t come across any RCT showing Fruits & vegetables lowering mortality or cancer.And we have no question that everyone should eat those.

  3. Also from your article” I agree that sugar-sweetened beverages increase the risk of heart disease (not just in men, of course, but in women, too) along with a whole host of other diseases like cancer, diabetes, and Alzheimer’s disease”.

    On what basis do you think sugar-sweetened beverages increase the risk of heart disease”.Do you have any RCT’s showing sugar consumption increase the risk of heart disease or mortality?


  4. Hi Peter,

    Thanks for the study link, Peter.

    I am not sure why you think this is great study. The fructose used is 25% of total calories. This study is just completely unrealistic! The average US consumption of fructose is 10%! The average US sugar intake is around 16% percent. A 2 week study is too short to make any conclusions about any disease.

    This study would be a good study for someone who consumes 8-10 coke bottles a day.

    I hope you apply the same skeptical thinking to every study.

    • Anoop, this is a good study for several reasons:

      1. It’s well controlled (this group is currently publishing even better, more controlled studies, but this one is better than most).

      2. 25% fructose is not as unreasonable as you suggest (25% of average caloric intake of 2400 kcal/day = 0.25 x 2400 = 600 kcal = 150 gm/day of fructose = 55 kg/year = 120 lb/year. While this is more that what some people consume, it’s actually not far out. Don’t forget the dose-response. No one is saying eating 10 gm/day of fructose is harmful. The purpose of a study like this is not to look at the 10th percentile of consumption. Do you know how many Americans do drink the equivalent of 8-10 Coke’s per day? Factor in the low-fat this and the low-fat that, and you’ll get there pretty quick.

      3. It’s the fact that it was so short that makes it so impressive! I think you may have missed this point, Anoop. If in just two weeks these patients witnessed such a shift towards metabolic syndrome, what do you think it would like if this study continued for 2 years?

      4. There is no credible dispute between metabolic syndrome and the diseases linked to it. Of course, 2 weeks is too short to show a “hard” outcome like more cancer, diabetes, or heart disease. But showing the link to metabolic syndrome is logical equivalent.

  5. I hypothesise that people who die earliest tend to eat more, drink more, smoke more because they have less time left to do it all in than other people.
    See, correlation is causation after all!

  6. “No one is saying eating 10 gm/day of fructose is harmful”
    thanks for that Peter, at one point I thought you were!
    John Yudkin discusses this point, canvases various opinions to say that almost everyone should be OK with 25-35g sugar daily. That’s 12.5-17.5 g fructose.
    RD feinman makes the point that on low-carb, more fructose can be tolerated as it is converted to liver glycogen when glucose is low.
    Early low-carb diets were generous with fruit, and worked well; Yudkin, Mackarness, etc.

    • Sure. All of this is correct. Just keep 3 things in mind:
      1) 25-35 gm/day of sugar is less than 25 pounds per year per person. The last time Americans consumed that amount of sugar annually, George Washington was President. Today we’re about 6x that amount.
      2) These points do not diminish the argument that fructose is a toxin — just as most things are toxic at a high enough dose, though, as you suggest, it’s probably not the fruit that’s killing us.
      3) If one is aspiring to be the absolute leanest they can be, it’s worth at least considering a reduction in fruit (especially if currently eating 3 apples, 2 bananas, 3 oranges, and a watermelon each day…you laugh, but some folks think this is “healthy”).

  7. The link to the study does not offer much. Do we have to pay money to see the actual article/study?

  8. Peter,

    My stepdaughter is at HSPH and her study about exercise, TV watching, and sperm quality was just published. As I read the study and her comments, I was proud to see how careful she was to point out that these were merely correlations. She noted that the “right” way to do a sudy would be with a prospective, randomized trial using a cross-over design. Only then would we have a good idea for whether less TV and/or more exercise would really help a men’s sperm quality.

    I’ve only read the abstract from this “red meat” study, but I was blown-away by their “substitution” comment and their “estimates” for how many lives could have been saved if everyone had eaten less red/processed meat. We are used to reporters’ crossing the line from correlation to causality, but here the researchers did that for us. Could they have done any more to imply or state causality, without actually using that word? It seems that they violated one of their own cardinal rules.

  9. “This doesn’t mean Colin Campbell is wrong (though I wholeheartedly believe he is wrong on about 75% of what he says based on current data). It means he has not done any real science to advance the discussion and hypotheses he espouses. If you want to read the most remarkable and detailed critiques of this work, please look no further than here (Denise Minger) and here (Michael Eades).”

    Wow, that’s really scientific: we should no further than a documented fraud and plagiarist with no training in the sciences and a demonstrably bitter man with zero scientific publications to his name to critique a project conducted by scientists from Cornell and Oxford. *Great* advice.

    • Ah…the classy tried and true ad hominem approach. Great job! Try coming up with an actual argument. Or some logic. Or even a scientific fact or two. Amazing how many people criticize Denise because of her lack of MD or PhD. No need to respond, of course. This is all rhetorical.

    • Kojak, huh? Belgian Beer, in my experience the probability of someone being an honest intellectual when they begin their “debate” with personal insults (you’ve managed 3 personal insults in 2 comments) is somewhere between zero and, say, zero percent. So while I love debates, I only engage with, you guessed it, intellectuals. Just as I don’t play chess with kittens or puppies, as much as I love both, I don’t debate with folks who lack the ability and honesty to have them based on data and without resorting to insult. Obviously, I’m trying to politely tell you to troll elsewhere. If you’d actually read this post and thought about it, you might appreciate that epidemiology, while a great tool to establish hypotheses, can’t establish cause, save the 2 or 3 times in history the hazard ratios were over 10. You do know what a Cox Proportional Hazard is, right? And presumably you’re familiar with Bradford Hill’s criteria, which don’t come close to being met with the stuff you reference. If you care enough, why don’t you start your own blog? I’m sure you’ll attract an enlightened readership.

    • Frankly, I’m sure that Peter would be more enamored of your Kojak quip, if you did not hide behind a handle/Pseudonym. I’m imagining a huge beer gut here, actually.

  10. I hate to post to an old thread, but I just discovered your site. Which is fascinating, by the way — I lean low carb, myself, based on my own readings through the literature and experience with patients, and I’ve noticed standard of care in my field inching slowly in that direction. I think your TED talk is the first time I’ve heard the problem characterized so directly as insulin resistance leads to obesity, rather than the other way around, which is intriguing to think about.

    My question for you is about a particular mechanism by which red meat could be increasing risk of CVD and cancer, as we see in some large and well-controlled epidemiological studies (cited at the end). I, personally, love red meat, and would love to go back to eating it, so if you find anything wrong with this mechanism, that would make me very happy. I don’t know if you’re familiar with the work on Neu5Gc (N-glycolylneuraminic acid) by Ajit Varki, one of the directors of Glycobiology at UCSD. Neu5Gc is a cell-surface sialic acid produced in mammals via an enzyme that humans have lost. As a result, it is found in all mammals except humans (and one other primate). The kicker is that we still produce the enzymes that process Neu5Gc for display on cells, and our immune system makes antibodies to it and will deposit complement in response to its presence. It is incorporated from the diet, confirmed by a feeding test, and within the body it concentrates in vascular endothelial cells and in cancerous tumors. Seeing micrographs of tissue labeled for Neu5Gc, where the dye basically outlines the vascular structure in the tissues, and contimplating my immune cells attacking the lining of my blood vessels in response, has frankly put me off red meat. The antibodies are not numerous, and the response is mild, but presumably enough to provoke inflamation and damage that could accelerate atherosclerosis. The response is strong enough to cause angiogenesis that feeds tumors that are expressing Neu5Gc, causing tumor growth in a mouse model fed a source of Neu5Gc.

    Like I said, if I’m interpretting the literature incorrectly, please set me straight so that I can go back to enjoying lamb. Meanwhile, here are a few citations:

    The paper with the scary micrographs:
    “Evidence for a novel human-specific xeno-auto-antibody response against vascular endothelium”:

    Feeding study:

    Association with tumor growth:

    And there’s more, but if you pull up any of these, you’ll probably stumble upon the rest on your own. And to back up my statement at the beginning, here are two large epidemiological studies that compare red meat vs poultry vs fish, etc., where they control for other dietary factors. I’m waiting for direct studies on red meat vs poultry consumption while tracking Neu5Gc antibody expression and atherosclerotic progression, but these sorts of studies would be complicated and expensive (in part due to the need to control for saturated fat in the diet, which is the reason most people think red meat is a problem), so I’m not holding my breath.

    Epidemiological studies I mentioned:

    • This is a very interesting topic, Holly, though Varki himself has admitted that what he reports in these papers has not be directly linked to human disease. My gut (sorry for the pun) instinct is the the role of Neu5Gc and/or the myriad of other potentially pro-inflammatory factors is highly path- and context-dependent. So it’s quite likely that under some dietary conditions that are otherwise pro-inflammatory, Neu5Gc could mediate disease, while in an otherwise low-inflammation environment, the effect is dwarfed by other factors. It seems impossible to imagine that red meat is universally harmful (or universally benign, for that matter). Context mattes, and most people seem to miss that. As for the epidemiology, my feelings are actually quite strong. The weakness of it suggests a lot. If red meat were universally harmful (say, like tobacco), we would expect to see hazard ratios 10 times — literally — higher than we do.

    • Compared to what?

      What is the order of importance of Neu5Gc in the human diet? Sure, perhaps it is one POTENTIALLY harmful chemical in red meat… but there are potentially harmful chemicals in almost (?) everything humans eat. What evidence exists to assert that Neu5Gc in red meat ought to be more of a concern than the thyroid-suppressing goitrogens in broccoli, soy, peanuts, and cabbage, for instance? Evidence does exist that foods high in goitrogens can indeed harm human health. While absence of evidence is not evidence of absence, why is it rational to focus on Neu5Gc instead of all of the other potentially harmful components of food? I am certain there are potentially-harmful components in whatever you would substitute in your diet for red meat.

      And, yet, we need to eat something or we’ll starve to death. It is simply impossible to avoid any potentially harmful component of food while eating a diet of natural, whole foods – and most evidence I’m aware of indicates that purified processed foods are much more unhealthy for humans on a macro scale, even if the exact mechanisms are not clear in every case.

      So in discussing the risk, if any, of Neu5Gc from red meat, another question must be: compared to what?

  11. Peter,

    Once again – great article – nailed it! For an interesting view on pseudo-science and getting things bass-ackwards on causality check out (Nobel Prize winner) Richard Feynman’s “Cargo Cult Science” – it was a commencement address at Caltech.

    All the best

  12. Peter, I read with great interest your blog posts on limiting sugar and carbs. I am a 33yo physician, and I have been self-experimenting (along with my wife) for the last few months but have been disappointed with the lack of evidence to guide my choices. My wife and I are both quite healthy and fit (no chronic medical problems). I have low body fat (<10%) and exercise vigorously 5x/wk (heavy weights and high-level soccer). My BMI is 21 and my waist circumference is 28". I have no family history of CV disease. My resting HR is 55 and my BP is <120 systolic. I have not measured my cholesterol in 7y, but at that time it was wnl. Given these facts, I wonder, just how important are my nutritional choices? As long as I am not eating too many calories or quite unhealthy food (I try to limit sugar, carbs, and processed food in general, but I do enjoy 'cheating' on the wknd), do you think there is much of an effect on my long term health by my nutritional choices? Also, how often do you think it's ok to eat read meat, and meat of any kind? I previously had tried to limit my meat (and esp read meat) intake, but after reading your blog and taking a look at the studies am beginning to wonder if there is some real anti-meat bias out there. I'd appreciate your thoughts, thanks!

  13. Peter – I wanted to ask if you have read the blog of Stephan Guyenet – https://wholehealthsource.blogspot.com/#uds-search-results

    I have read him for a few years now and before including you, he was were I went for my true scientific “fix” on these topics. So, I thought I would search his blog and sure enough you came up. I’m going to go back and see what he has to say about you. I’m guessing since he knows you, you know of him.

    Just spending some time of late reading through many of your posts, I am interesting in seeing your points of difference or agreement with Stephan.


    • Yes, I know Stephan personally. Agree on some things, disagree on some things, but a very cordial relationship. I like Stephan and consider him very thoughtful.

  14. Hi Peter,

    Wanted to ask if you have had the chance to discuss with dr Rosedale regarding his stance on sturated fat as he suggests to limit it for weight loss? He actually calls it “a second generation of carbohyderates”, he seems to be kind of ok with it once one is fat adapted.

  15. I’m not sure if this has been explicitly stated here, but I’m just sort of discovering all of these things, and I am trying to understand about the penetration of the endothelial layers in, and subsequent artheriosclerosis.

    My understanding is that, at the foundation of the low carbohydrate/high fat argument, is the assertion that the lipid hypothesis is incorrect, and something else creates the situation where the endothelial wall is vulnerable to penetration. I’ve heard and read several people make the case that one of the problems is the ratio between omega 3 and omega 6 PUFAs.

    Is there a definitive answer on this?

    • I share the same interest. The science and increasing real world evidence backs low carb high fat so the next question has to be about the quality of fats in the diet. The various opinions about mono/poly/saturated/omega/dha whatever – I’d really like to see some authoritative information as it’s driving me nuts!

  16. Hi, I am from Slovakia (in Central Europe) and I am in my last year of studying mathematical statistics and applied economics… I ran into your blog maybe a week ago (already swallowed all the articles) searching for deeper info about nutritional ketosis and what made me to continue reading was the rigorous scientific method you used (besides all of the useful and interesting information). Me and my girlfriend (from Czech republic) got on the path of ketosis mere six weeks ago firstly because of the weight loss, but as I was digging deeper I made a decision that being low-carb is evolutionary “normal” for my body a that it makes me feel much better. I am looking forward to sharing my story of successful life-change in two year’s time, but today is too early.

    What I want to comment on and ultimately to praise you for is your scientific approach to this topic. I conduct research in the field of theoretical and applied economics where variables are (from the logic of things) not fixed and it is even harder or mostly impossible to make an experiment. Although some “natural” experiments occurs (as it is with the special mutations in very small groups of patients which the researchers may utilize), most of the time I rely on advanced mathematical methods to somehow (if possible) filter the white noise of millions of souls doing their everyday decisions. The world would be a much better place (from economical point) if that alone was enough.

    The problem is that huge (sometimes for me even disgusting) number of researchers, professors and teachers…virtually all those who should be conveying thoughts only after the rigorous scrutiny are not doing that, even worse, they are doing the exact opposite. The thing is that you are always able to get a number, whether it is a correlation coefficient, regression coefficient or a deep parameter of some exotic probability distribution, but when you don’t know what this number means you have got nothing to say. You ought to be silent, unless you want to ask a question “What does it mean in the context of what have I been researching?”.

    The scientific method ethics always makes me emotional and upset. I perfectly know and understand the party situations where my girlfriend is kicking me under the table just to stop me making this exact point. The world is full of logical fallacies and you can not confirm any hypothesis, you can just reject it. This why it is always more important to find out where the hypothesis is weak and not to be afraid of saying “This paper doesn’t by any means answer all of the questions.” or humble “I don’t know, but I am studying this issue.” The topic of statistical misinterpretations and logical fallacies is a story for a whole new blog (with a very poor number of readers I suppose though).

    Excuse me for the very long comment, but I am very glad that finally someone is giving people the hard painful and lengthy knowledge about this topic. As it gives me hope that such people will occur more and more in all fields of human gnosis. Just a big thank you Peter for the scientific ethic, your argumentation rigour and the well-explained warnings concerning statistical phenomenon.

    Have a nice day or more 🙂

    • Hi Vladimir, it’s been Four years now since your original 6 weeks so I’d love to know your reflections on your low carb dieting over the medium term? Many thanks, Oliver Wilkinson

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