The China Study: My Response to Campbell

Alright folks, I’ll be honest. I was not expecting my China Study critique, which started as a nerdy personal project pursued in the wee hours of the morn, to generate much interest. Like most of my weird projects, I figured it would be briefly perused by a few number-lovers before fading quietly into the abyss of cyberspace.

Instead, it went viral and racked up 20,000 page views within 24 hours.

I’m surprised, but equally thrilled. My self-marketing skills are pretty dismal, and it was only by the grace of all the bloggers who featured my critique that this page-view boom occurred. Thank you to everyone who helped spread the word. I owe y’all!

This post is going to be quite long (no shocker there) and, in places, a bit more technical than the last. I know not everyone digs science mumbo-jumbo, so I’ll try to keep that to a minimum and explain things like journal quotes in simpler terms.

First, I’d like to address a couple points I’ve seen crop up in reader comments and emails I’ve received.

One: My graphs and simple statistical explanations. The graphs I posted were not intended to stand as new hypotheses or conclusions about the data. I apologize if I didn’t make this abundantly clear. Their sole purpose was to demonstrate, to the general layperson, how raw correlations (in the instances Campbell used them) can be misleading—as well as show how dramatically a single confounder can affect a correlation and make a positive trend appear where there may not be one at all. The graphs and explanations were meant to be illustrative, not exhaustive.

Two: Bias in Campbell’s representation of the data. This is a point I feel has been overlooked by some critics who’ve myopically targeted my use of statistics.

My biggest concern is with the way data appears to be cherry-picked to create a “plant foods are good” and “animal foods are bad” dichotomy when the actual data from the China Study (as well as from Campbell’s own research) does not reflect this.

For instance, when citing the anti-disease effects of plant foods, Campbell points to inverse correlations with biomarkers for plant food consumption as well as plant food intake itself. One example is in Claim #5 when he notes stomach cancer is inversely associated with plasma concentrations of beta-carotene and vitamin C (biomarkers) as well as with green vegetable intake (a plant food). (Both of these claims are based on uncorrected correlations, by the way.)

Yet when citing the purportedly harmful effects of animal foods, Campbell relies on blood markers (usually total cholesterol or apo-B) but fails to find direct relationships between disease and the animal foods themselves. He still indicts animal foods as harmful, but comes to this conclusion by enlisting the help of intermediary variables. And as I explained in the last post and will continue explaining in this one, the link between cholesterol levels and the diseases Campbell links them to are not even as straightforward as he suggests.

To those who approach this discussion already believing animal foods are generally unhealthy, this bias is subtle and might not be obvious. But to those who approach this discussion from a place of neutrality, the bias is unmistakable.

Response to Campbell

Now, onto business.

In case you haven’t heard yet, the much-discussed T. Colin Campbell wrote a response to my critique of his book. If you haven’t already done so, hop on over and read it on

Let me preface this with something important. When it comes to science, my motto is an old line from Dragnet (which, having no TV, I’ve never actually watched): “Just the facts, Ma’am.” Or sir. Science itself should be cool, neutral, and somewhat soulless. As far as I’m concerned, personal conflicts, drama, mudslinging, grudges, and other flurries of emotion should be locked out of science’s doors and banned for life.

For this reason, I want to make it clear that even though I disagree with Campbell’s interpretation of the China Study data, I have no interest in launching any personal vendetta against him. I know some readers are none too pleased with the man, but I do believe he’s trying to promote a message he deeply believes will help others. I won’t be participating in any character attacks, regardless of how I feel about his interpretations.

That said, I’m a bit disappointed Campbell didn’t offer a more revealing glimpse into his own methods of analysis. Here’s a secret: If he wants to silence his critics, all he has to do is publish the details of his process—which, apparently, he has already written up:

A more appropriate method is to search for aggregate groups of data, as in the ‘affluent’ vs. ‘poverty’ disease groups … I actually had written material for our book, elaborating some of these issues but was told that I had already exceeded what is a resonable [sic] number of pages. There simply were not enough pages to go into the lengthy discussions that would have been required–and I had to drop what I had already written.

I’ve emailed Mr. Campbell and asked him to consider publishing this material somewhere as a downloadable PDF or in another accessible form. Then the rest of us can study his methodology, look for oversights, and hopefully replicate his findings. I’ll update this when or if he responds.

UPDATE: Campbell has informed me via email:

To go back and fetch the material that I had previously written would take a lot of time that I don’t have. Also, much of it is in my peer-reviewed 300+ scientific papers.

Well, shucky darns. Although he doesn’t have time to fetch already-written material, he does have time to craft a more thorough response to my critique than the one published on, which he’ll be posting on his website sometime soon. Hopefully he’ll provide more details about his methods there.

Some responses to specific parts of Campbell’s letter

To clarify who’s saying what, quotes will always be italicized and indented. All bold parts of the quotations are my own emphasis and not the original author’s.

Campbell: She claims to have no biases–either for or against–but nonetheless liberally uses adjectives and cutesy expressions that leaves me wondering.

News flash: I was an English major with a creative writing emphasis. Cutesy is my thang. When the occasion calls for it, I can become Formal Monotone Academic Denise—but seeing as this is a blog and I needed to keep readers hooked for 9,000 words, I figured a more colloquial tone would be best.

Also, I wasn’t aware adjectives indicated bias, and if that’s the case, boy am I ever in trouble. You know what else? I sometimes use adverbs. That’s right. Evil adverbs. I learned them from Stalin when we worked together in the ’40s (oops, did I say that out loud?).

Campbell: As far as her substantive comments are concerned, almost all are based on her citing univariate correlations in the China project.

Actually, they’re based on the univariate correlations that Campbell cited first.

If you read my critique, you’ll see that Campbell’s claims align with the raw and uncorrected data, which—as I tried to illustrate—can be misleading due to the influence of other variables implicated with disease.

But it seems my critique wasn’t enough to convince some Campbell supporters that he did not use exhaustive analytical methods under some important circumstances, so I’ll present examples straight from his peer-reviewed papers.

First, let’s look at “Diet, Lifestyle, and the Etiology of Coronary Artery Disease: The Cornell China Study” published in the November 1998 issue of the American Journal of Cardiology. One statement from the paper, from the section discussing “Diet-Coronary Artery Disease Relations,” notes the following:

The combined coronary artery disease mortality rates for both genders in rural China were inversely associated with the frequency of intake of green vegetables (r = -0.43, p<0.01)…

Remember the “Green Veggie Paradox” from my last post, which pointed out that frequency of green vegetable consumption may be a geographical marker for southern regions where heart disease rates are lower, instead of an actual protective agent against heart disease? Well, here’s that paradox again. In a peer-reviewed article. Co-authored by Campbell. Using the raw data (-0.43). And neither him nor the rest of his team made adjustments, ran more sophisticated analyses to account for confounding variables, or even mentioned other factors that could explain the correlation between frequent green-vegetable consumption and healthier hearts.

Had Campbell tried to understand the apparent discrepancy between frequency of green vegetable consumption (which had a strong inverse association with coronary heart disease) and the amount of green vegetables consumed (which had a weak positive association with coronary heart disease), he may have realized there was more to our fibrous friends than meets the eye. For instance, geography is closely tied to heart disease in the China Study data, with lower latitudes exhibiting lower rates. And if frequency of green vegetable consumption strongly reflects geography, it seems any researcher committed to accuracy would want to tease apart these variables before citing them in a scientific paper.

In this article, Campbell also employs several other unadjusted correlations straight from the monograph:

The combined coronary artery disease mortality rates for both genders in rural China were inversely associated with … plasma erythrocyte monounsaturated fatty acids (r = 0.64, p<0.01), but positively associated with a combined index of salt intake plus urinary sodium (r = 0.42, p<0.01) and plasma apolipoprotein B (r = 0.37, p<0.01).

These numbers are all raw correlations. Campbell didn’t conduct a deeper statistical analysis on any of it to account for potential confounders, such as lifestyle habits or other dietary factors that might accompany specific biomarkers.

These apolipoproteins, in turn, are positively associated with animal protein intake (r = 0.26, p <0.05) and the frequency of meat intake (r = 0.32, p<0.01) and inversely associated with plant protein (r = 0.37, p <0.01), legume (r = 0.26, p<0.05), and light colored vegetable intake (r = 0.25,  p <0.05).

Again, we have a match with the uncorrected data. And again, Campbell and his team didn’t appear to run multiple variable regressions or any other analyses to see if the raw data was accurate. (And notice how Campbell can’t say animal protein itself associates with heart disease, but has to pull a connecting variable into the picture to make his theory fit.)

Why didn’t Campbell pay more attention to the role of confounders? Why did he accept the raw data, which showed plant foods as protective and an animal-food biomarker as harmful, without conducting deeper analyses?

This might be the answer:

The principal hypothesis of this study was that the greater the dietary proportion of a variety of good-quality plant-based foods, the lower the rate of chronic degenerative diseases.

Essentially, Campbell and his team approached the data set specifically looking for trends showing plant foods to protect against disease (and, perhaps, showing animal foods to be harmful).

If you’ll recall, the China Study has 8,000 statistically significant correlations. That’s a lot. Enough, in fact, to find pretty much anything you want if you look hard enough—especially if you use a bit of sloppy science and cite raw correlations or chains of variables when they suit your needs.

Of course, that’s not the only China-Study-based  paper showcasing analytical shortcomings. Let’s look at “Fish consumption, blood docosahexaenoic acid and chronic diseases in Chinese rural populations” published in the September 2003 issue of Comparative Biochemistry and Physiology. This paper examines the role of fish and the essential fatty acid DHA in relation to several diseases.

Campbell and his crew’s methodology for studying the variables:

Pearson’s correlation coefficient was used to explore the relationship between variables. The two-tailed test of significance was used to examine the significant differences within variables.

Alright, this is your standard high school stuff: examining the linear relationship between two variables. No multiple variable regressions. No adjustments for confounding variables. And from these rudimentary correlations, Campbell and his team cite a number of observations about the relationships between fish, other meat, total lipids, blood markers, and disease, ultimately concluding:

[T]he protective nature of DHA or aquatic foods is intrinsic and global, with implications for health world wide. The decline in sea and fresh water food consumption in many regions last century could be an adverse, contributory factor to the increasing risk of chronic diseases and the rise in mental ill health …

Researchers concluded from these raw correlations that the DHA is associated with lower risk of many chronic diseases. But might this effect become even more pronounced through different statistical models—namely ones that account for confounding variables?

It seems likely, and here’s why. In this paper, Campbell and his team noted that diabetes is positively associated with DHA in the China Study data, despite other research showing the opposite:

Diabetes showed a positive but non-significant relation with DHA in Fig. 2, which meant no clear-cut conclusion about the efficacy of lower DHA level in diabetes even though a negative association has been found between DHA and triglycerides in plasma [in previous research]. … [We] have no explanation for the positive correlation with diabetes.

No explanation, eh? I’ve got one. In this paper, Campbell and other researchers determine that fish is the most significant source of DHA in the studied counties. And we know from the China Study monograph that fish-eating regions tended to have high intakes of processed starch and sugar compared to other counties—a correlation of 0.58. Could processed sugar and starch intake be skewing the relationship between DHA and diseases like diabetes? If so, why didn’t Campbell et al run more appropriate analyses to account for this?

Still not convinced Campbell’s methods are less than perfect? Here’s some more. From “Diet and chronic degenerative diseases: perspectives from China,” published in the May of 1994 issue of the American Journal of Clinical Nutrition:

Intakes of 14 complex carbohydrate and fiber fractions were obtained in this study to determine whether particular fiber fractions were associated with particular diseases, especially cancers of the large bowel. … Based on an overview of the univariate correlations, colon and rectal cancer mortality rates were consistently inversely correlated with all fiber and complex carbohydrate fractions except for pectin, which showed no correlation.

So here we have Campbell and his team using univariate correlations to look at the relationship between fiber and colorectal (large bowel) cancer. No adjustments made for potential confounding variables. And from these correlations, he concludes:

[T]here is evidence of a weak inverse relationship between cancer of the large bowel and the intake of multiple complex carbohydrate and dietary fiber fractions.

In other words, the fiber fractions seemed to protect against colorectal cancer across the board. But is this an accurate inference?

Had Campbell looked more closely at the data (instead of assuming the raw figures were accurate, as he seems fond of doing when it supports anti-disease properties of plants), he would’ve noticed something striking. The correlations between those 14 fiber fractions and colorectal cancer seem to mirror the correlations between the fiber fractions and schistosomiasis infection.

Okay, I know what you’re thinking. “What’s Denise blathering on about this time?” Let’s back up for a minute.

Schistosomiasis (also called bilharzia) is a parasitic disease known to raise risk of colorectal cancers. If you get infected with one of these lovely worms, they’ll lay eggs that travel to your liver, intestine, or bladder, where they can cause permanent damage and inflammation. How fun!

The link with colorectal cancers isn’t something I’m just pulling out of the air, by the way. It’s pretty well established. Some references:

With that in mind, it seems pretty obvious that Campbell would want to look at a schistosomiasis infection in relation to colon cancer occurrence, especially since 1) it’s pretty common in Asia and 2) it could be a confounding variable. In fact, schistosomiasis has a correlation of 0.89 with colorectal cancer mortality in the China Study data. (If you’re having a déjà vu moment, you’re not crazy: I wrote about this in the previous entry as well.)

So what does this have to do with fiber?

The fiber fractions Campbell cites as having a “weak inverse relationship” with “cancer of the large bowel” also have a somewhat stronger inverse relationship with schistosomiasis. In other words, fiber is already likely to be associated with less colorectal cancer simply because those who eat more of it tended to have less of another significant risk factor.

It might help to represent this visually, so here’s a graph plotting each fiber fraction’s correlation with schistosomiasis and colorectal cancer. These are the fiber fractions corresponding to the x-axis numbers:

  1. Total fiber
  2. Total neutral detergent fiber
  3. Hemi-cellulose fiber
  4. Cellulose fiber
  5. Lignins remaining after cutin removed
  6. Cutin
  7. Starch
  8. Pectin
  9. Rhamnose
  10. Fucose
  11. Arabinose
  12. Xylose
  13. Mannose
  14. Galactose

Bottom line: Is the inverse relationship between fiber and colorectal cancer legitimate, or is that correlation influenced by schistosomiasis rates? Given the relationship between these variables, shouldn’t Campbell have run a more thorough analysis on the data?

I sure think so. But he didn’t. Again, he seems to readily accept uncorrected correlations when they prove his theory.

So, what happens when we do adjust for confounding variables? Let’s look at another of Campbell’s peer-reviewed papers: “Erythrocyte fatty acids, plasma lipids, and cardiovascular disease in rural China” published in the December 1990 issue of the American Journal of Clinical Nutrition. Here were their statistical methods:

To adjust for the effect of other factors in the relationship between two variables, ordinary least-squares multiple-regression analysis was used. Natural logarithmic transformations of the mortality rates (the dependent variable in the models) were used to obtain a normal distribution of the outcome variable for reliable statistical significance testing of the regression coefficients.

No uncorrected correlations here. And the results:

Within China neither plasma total cholesterol nor LDL cholesterol was associated with CVD [cardiovascular disease]. The results indicate that geographical differences in CVD mortality within China are caused primarily by factors other than dietary or plasma cholesterol.

Did you catch that? After adjusting for confounding variables, researchers found that cholesterol was not associated with cardiovascular disease in the China Study data. And that includes both blood cholesterol and cholesterol from food.

Let that sink in for a moment.

Nah, this is pretty big: Give it two moments.

Or three.

Now, let’s look at Campbell’s next point, which flows quite nicely from the last:

Diseases of affluence and diseases of poverty

Campbell: A more appropriate method is to search for aggregate groups of data, as in the ‘affluent’ vs. ‘poverty’ disease groups, then examine whether there is any consistency within groups of biomarkers, as in considering various cholesterol fractions.

If you’re unfamiliar with Campbell’s disease-clustering strategy, you can read “From Diseases of Poverty to Diseases of Affluence” to get a feel for it (although be warned, the formatting is a little wonky). In essence, Campbell examined the China Study data and identified two distinct groups of diseases that were generally associated with each other—with one group representing diseases common to developing nations and the other representing “Western” afflictions.

In the article linked above, Campbell et al describe the first group:

As expected, diseases of poverty are associated more with agricultural than with industrial activity. Areas where these diseases are common are located further inland where mean elevation is higher and overall economic activity, literacy and population density are lower.

And the second group:

In contrast, diseases of affluence are found in the more densely populated rural areas nearer the seacoast where industrial activity and literacy rates are higher and more fish, eggs, soy sauce, beer and processed starch and sugar products are consumed.

More specifically, Campbell defines the “diseases of poverty” as:

  • Pneumonia
  • Intestinal obstructions
  • Peptic ulcer
  • Other digestive disorders
  • Nephritis
  • Pulmonary tuberculosis
  • Infectious diseases (other than schistosomiasis)
  • Eclampsia
  • Rheumatic heart disease
  • Metabolic and endocrine disease (other than diabetes)
  • Diseases of pregnancy and birth (other than eclampsia)

And “diseases of affluence” include:

  • Stomach cancer
  • Liver cancer
  • Colon cancer
  • Lung cancer
  • Breast cancer
  • Leukemia
  • Diabetes
  • Coronary heart disease
  • Brain cancer (ages 0-14 years)

Again, the diseases in each cluster tend to associate positively with each other but inversely with the diseases in the opposite group.

It’s not a bad strategy, really. Campbell uses this two-group method to identify general factors (such as nutritional patterns) related to each disease cluster, taking a holistic view of disease rather than examining ailments through reductionism. This approach aligns with something I very much agree with: that diseases don’t happen in isolation, but that multiple forms of chronic disease can spring from the same cause (poor nutrition, processed foods, unhealthful living, and so forth).

But while I agree with this general method, it’s not without flaws—and the way Campbell employs it to study nutrition and disease requires a few leaps of faith.

First, some problems with the groups Campbell created:

  1. Not all of the “diseases of affluence” are actually common in affluent countries, raising questions about whether these disease clusters apply outside of China. For instance, the two most prevalent diseases of affluence in the China Study data are liver cancer and stomach cancer—but in the US, a decidedly affluent nation, these diseases make up less than 5% of all cancer deaths.
  2. Where’s “stroke” on either list? Nowhere to be found. Campbell had to create a third group called “Other” for a few diseases that didn’t fit cleanly into the other two clusters. According to the American Heart Association, stroke is currently the third leading cause of death in America. So what explains its lack of correlation with other diseases of affluence? Campbell offers no insights.

Perhaps more importantly, Campbell makes some excellent observations about the nutritional variables correlating with diseases of affluence, but then dismisses them without any satisfying or even logical explanation. He lists the following correlations between several foods and his affluent disease cluster:

  • Processed starch and sugar: 0.51
  • Fish (g/day): 0.56
  • Beer: 0.59
  • Eggs (times per year): 0.31

Since the industrialized areas with diseases of affluence tended to be near the coast, it’s not surprising fish consumption was high. But that’s a pretty hefty correlation with processed starch and sugar, too. Could those refined carbs contribute to diseases of affluence? Eh? Eh?

Apparently not. Campbell doesn’t consider them significant in the China Study data. He states that “beer and processed starch and sugar products are also consumed in much lower quantities [than in the US],” and therefore “consumption of these foods is probably more indicative of general economic conditions and other local circumstances than of biological relationships to disease.” And that’s the last we hear about ’em.

That’s right, folks.

Here we have evidence that areas in China with the highest rates of Western-type diseases also eat the most processed starch and sugar. Maybe not in the grotesque amounts that Americans eat them, but then again, China’s “affluent disease” rates were also lower than America’s.

But instead of examining the relationship between processed carbohydrates and poor health, Campbell zeros in on another variable associated with industrialized nations and diseases of affluence. And if you’ve been paying attention to this post and the last, that variable won’t surprise you: It’s cholesterol.

By the way, the correlation between Campbell’s affluent diseases (in the aggregate) and cholesterol is 0.48, slightly less than the correlation with processed starch and sugar. And if you’ll recall, Campbell’s own analysis showed that cholesterol levels in the China Study data didn’t associate with cardiovascular disease, a major cause of “affluent” mortality. But I guess that doesn’t matter, because Campbell says so and Campbell has lots of credentials.

But back to Campbell’s response. His statement that a more appropriate method of analysis is to “search for aggregate groups of data, as in the ‘affluent’ vs. ‘poverty’ disease groups, then examine whether there is any consistency within groups of biomarkers” is something I can at least partially agree with. Yet in examining Campbell’s own use of these disease groups, I smell another whiff of bias: He immediately implicates cholesterol (and, as a consequence, animal products) as causative of disease, when at least four other diet variables (most notably processed starch and sugar) are also heavily implicated with diseases of affluence.

Now, for something completely different:

The “Mysterious Tuoli” not so mysterious?

Campbell: [W]e discovered after the project was completed that meat consumption for one of the counties, Tuoli, was clearly not accurate on the 3 days that the data were being collected. On those days, they were essentially eating as if it were a feast to impress the survey team but on the question of frequency of consumption over the course of a year, it was very different.

I’m glad Campbell pointed this out (and I’ll be updating the Tuoli page to reflect it), but meat was not the component I found notable with the Tuoli diet: dairy was. Assuming the frequency questionnaire was more reliable than the three-day diet survey, the Tuoli still consumed dairy most days of the year and still consumed nearly no vegetables (twice per year), nearly no fruit (once per year), and ate wheat as their primary plant food. Not exactly a balanced diet—yet, compared to the rest of China, they remained in good health.

(By the way, a number of you have asked for help finding more information about the Tuoli. A Google search for “Tuoli” doesn’t reel in a whole lot of relevant hits, so you can try the alternative English spelling of “Toli,” or a search for a related group of people called “Uyghur” or “Uygur” in the Xinjiang Autonomous Region of China.)

However, Campbell’s statement about the unreliability of the diet survey for the Tuoli also calls into question the validity of the three-day diet survey as a whole—as well as the significant observations Campbell gleaned from it. For instance, on page 99 of “The China Study,” Campbell notes:

Average calorie intake, per kilogram of body weight, was 30% higher among the least active Chinese than among average Americans. Yet, body weight was 20% lower. How can it be that even the least active Chinese consume more calories yet have no overweight problems? What is their secret?

If Tuoli is any indication, there may not be a secret at all. Since Campbell drew his calorie data from the three-day diet survey, suppose multiple counties tried to impress researchers by “feasting” or otherwise altering their eating habits to reflect greater wealth, prosperity, or food abundance than they actually had. The result? Calorie intake during those three days would be higher than for the rest of the year, leading to an overestimated average calorie intake for the 65 counties studied.

Did Campbell consider this, especially given his awareness about the unreliable records for the Tuoli? Apparently not. On page 101, he states:

Chinese consume more calories both because they are more physically active and because their adoption of low-fat, low-protein diets shifts conversion of these calories away from body fat to body heat. This is true even for the least physically active Chinese.

Physical activity certain plays a role in higher calorie requirements, but eating a low fat, low protein diet may not increase thermogenesis as Campbell suggests—at least not based on the China Study data. Some counties may have simply been showing off by stuffing themselves silly, leading to high average calorie intakes. We’ve got Campbell’s assertion that at least one place did this: How do we know others didn’t as well?

Again, let me highlight what appears to be another link in a chain of bias: Campbell dismisses the low disease rates and high animal protein intake of the Tuoli because the three-day diet survey was inaccurate, yet doesn’t account for potential shortcomings in that diet survey when it helps score brownie points for plant foods.

Moving on.

Campbell: One final note: she repeatedly uses the ‘V’ words (vegan, vegetarian) in a way that disingenuously suggests that this was my main motive.

I understand—and respect—that Campbell was trying to avoid the ethical implications of the word “vegan,” since the term often conveys a complete lifestyle choice rather than just a diet. However, my intent was definitely not disingenuous, nor was I trying to peg a motive on Campbell. My own use of the term “vegan” was simply to describe a completely animal-product-free diet. I apologize if this wasn’t clear from my post.

Campbell: One further flaw, just like the Weston Price enthusiasts, is her assumption that it was the China project itself, almost standing alone, that determined my conclusions for the book (it was only one chapter!).

I guess Campbell missed the 2,135 words I dedicated to his research on casein, including the problems with extrapolating its effects to all animal protein. And the citation of his own research showing it’s a full spectrum of amino acids, not just animal protein, that apparently spurs cancer in aflatoxin-exposed rats. And the insight that a vegan diet provides all amino acids (and thus complete protein) if you eat a variety of plant foods, thereby posing similar purported risks as omnivory in terms of cancer growth. And the question about the apparent unhealthfulness of breastfeeding and exposing young, delicate-bodied children to casein. And the glaring example of bias in Campbell’s treatment of animal versus plant protein in relation to body size and disease.

Easy oversight, I guess. It was a pretty formidable post. As is this one, apparently.

By the way, if anyone had trouble following my train of thought in the casein/wheat/lysine/complete protein section of the critique, Chris Masterjohn has written a more “digestible” article (pun definitely intended) expanding on this subject and probably explaining it better than I did. Yep, that’s the same Chris who wrote a well-known critique of “The China Study” five years ago.

Next up, a very serious and momentous subject:

Does Denise work for the meat and dairy industry/is Denise a cyborg/is Denise a figment of your imagination/is Denise actually Campbell’s employee, son, dog, long-lost daughter, or alter-ego?

Campbell: I find it very puzzling that someone with virtually no training in this science can do such a lengthy and detailed analysis in their supposedly spare time.


Campbell: I have no proof, of course, whether this young girl is anything other than who she says she is, but I find it very difficult to accept her statement that this was her innocent and objective reasoning, and hers alone. If she did this alone, based on her personal experiences from age 7 (as she describes it), I am more than impressed.

Then thank you for the compliment, Mr. Campbell! I’m definitely a singular person, so I’m glad to more-than-impress you.

Initially, I didn’t want to muddy this post with retorts to statements like this, but really. What’s so hard to believe about a 23-year-old Super Nerd deciding to tackle a project out of personal interest? What do I need to show to prove I’ve got a brain in this noggin? College transcripts? 4.0, three scholarships, dean’s list, top 1% of the class? I can say the alphabet backwards, too. That has to count for something.

In all seriousness, I can understand why Campbell would express skepticism that a young person would have the resources or repertoire of knowledge necessary to tackle this sort of project. And I think it may largely be a generational issue. When Campbell was a young’un, he didn’t have access to the internet or online books or PubMed or Google Scholar or any of the other self-educational tools most of us now take for granted. For him, education did necessitate sitting in a room with a teacher, pouring over textbooks, showing up to a physical classroom, and accumulating credentials to prove you’d survived the journey. These days, education can manifest in numerous other forms.

In other words, I’m more flattered than offended.

Other odds and ends

Several readers have raised an issue that probably deserves more attention than I’ve given it so far: the limitations of the China Study itself. Although I’ve focused on examining the errors and biases in Campbell’s conclusions, the fact of the matter is, this study itself is just a big ol’ epidemiological survey—and any analyses it produces, no matter how thorough, are inherently limited due to the nature of the data.

In fact, before Campbell’s “The China Study” was even released, Thomas Billings of wrote an excellent overview of the shortcomings of the study itself. I recommend reading this if you want a better understanding of what a study like the China Project can and cannot do.

A note on wheat

I know many of you are particularly interested in the correlation between wheat and heart disease. In my critique’s gargantuan cascade of words, the two little paragraphs about wheat pinged on many readers’ radar (or, perhaps, grain-dar). I’ve already seen the “correlation of 67” statistic thrown around the ‘net as if it’s solid evidence. Holiest of molies, that spread fast!

Indeed, I feel the China Study may hold important clues—ones that research thus far has simply not explored—about the role of wheat or wheat flour in human health. However, we can’t jump the gun yet. I will be doing some more analysis of the China Study data regarding wheat and other grains, but even if this manages to paint our glutenous friends as the most malicious of dietary villains, it doesn’t prove a darn thing.

Bummer, right?

As someone who’s massively allergic to wheat, I’d love nothing more than to shove this grain in the corner with a dunce cap and revel in my victory. Karma’s a… female dog in heat. But I can’t do that. Not yet, anyway. Bottom line, this is epidemiological data we’re working with, and it can only show correlations—not causation. Not proof. Not irrefutable evidence.

What I do hope occurs—and feel free to cross your fingers with me—is that this information snags the eye of other nutritional researchers and leads to controlled experiments about the health effects of wheat.

At any rate, I have a couple more China-Study-related posts coming up (including one with the results of multiple variable regressions), and wheat will probably be the subject of the next one. Keep your eyes peeled if this is a subject that interests you.

Summary of this post

For those of you who skipped over everything above and scrolled directly to this part, well… I don’t blame you. However, there’s really only one thing you need to know about this whole ordeal, and this is it:

  1. Data sets are like people. If you torture them long enough, even when they’re innocent, you’ll eventually squeeze out a false confession.

Some final thoughts, for those who haven’t clicked the “back” button on their browser yet

Although the vast majority of the feedback I’ve received (both positive and negative) has been intelligent, respectful, and ultimately constructive, I’ve received a few very fiery emails that have made me realize what a deep nerve diet debates can strike. For those whose lives have been profoundly affected—for better or for worse—by food and nutrition, diet can become a personal issue inextricably bound with identity. And as someone who’s already run through a gamut of eating styles due to allergies, ethical goals, and the pursuit of vibrant health, I know how this goes. I’ve been there. In many ways, I’m still there. For this reason, I can wholly empathize with the emotional response my critique triggered in some readers, and I understand why a backlash is apt to occur.

By the same token, I think it’s important to look at what that impassioned response signifies. Are we trying to be healthy, or are we trying to be right? Are we trying to learn, or do rigid beliefs deafen our ears to new knowledge? Have the open minds that led us to search for the truth in nutrition suddenly slammed shut, clamping tight around an ideology that may or may not truly serve us?

Critical thinking isn’t a privilege reserved for the elite; it’s a birthright. My goal is not to tell people what to think, but to show them how to think. How to sift through the vast expanse of nutritional litter and pull out the gems. How to stop blindly following the advice of so-called authorities who may not have our best interest at heart. How to think independently.

To everyone who’s taken the time to plod through this post and the last, to read, to write, to comment, to think, or to reconsider any limiting beliefs you hold about diet, I extend my deepest gratitude and wish you nothing but health and happiness.

Thank you for reading.

Diet, Lifestyle, and the Etiology of
Coronary Artery Disease: The Cornell
China Study


  1. I know the China Study was flawed and you are correct in your analysis, but Campbell was right in stating that your use of adjectives and, yes, adverbs demonstrated something other than simply reporting facts and refuting conclusions. The use of these modifiers revealed attitude, if not bias. As an English major, I am pretty sure you know that is right. Facts don’t need modifiers, generally speaking, and their use, while making for better readability, dilutes the starkness of the facts. I do agree with your analysis of his work, however.

  2. Denise was not born in 1987. She mentions “…first thing in the morning when I look like the lovechild of Cousin It and the Swamp Thing)

    Do you know anyone younger than 30 that even knows who cousin It or Swamp thing is?


  3. As if I needed another example of Wikipedia editors destroying all the foundations[dubious-discuss] and principles [which?] which that site allegedly works under[NPOV!!!]

    Thank you for your work, Denise. I appreciate it.

  4. Dr. Campbell said numerous times that he wasn’t going to specifically address every critics questions in detail:

    “I also know that critics like her would like nothing better than to get me to spend all my time answering detailed questions, but I simply will not do this.”

    Yet in your ‘response’ you still flamed him over being too vague or not taking the time to peruse over his journals just to provide you with additional data.

    Additionally, you quoted what he had said about Tuoli, and criticized him for not including any data dairy on dairy consumption:

    “I’m glad Campbell pointed this out (and I’ll be updating the Tuoli page to reflect it), but meat was not the component I found notable with the Tuoli diet: dairy was.”

    When literally right above the Tuoli meat statement he addressed the dairy issue:

    “She also makes big issues out of some matters that we had no intent to include because we knew well certain limitations with the data. For example, only 3 counties (of the 65) consumed dairy and the kind of dairy consumed (much of it very hard sun-dried cheese) was much different from dairy in the West. It makes no sense to do that kind of analysis and we did none,”

    It’s dishonest and misleading to openly criticize someone for not addressing something that they clearly addressed literally a sentence before the quote you posted on Tuoli’s meat consumption and use that to suggest that he’s somehow being deceitful. You even take it further by implying that since the Tuoli consume dairy on a regular basis and are healthy that Campbell’s methods are questionable, when this is blatantly ignoring his response. The dairy consumption in Tuoli is drastically different than here in the states, and is therefore statistically irrelevant (and is why it wasn’t included).

    I agree with Dr. Campbell that it is puzzling that someone with no formal education on the subject can somehow find enough “spare time” to collect, tabulate, and analyze over 20 years of research (yes, the China study was conducted over a span of 20 years) and tout that his research findings were false. Even for a self-proclaimed “super-nerd”, it’s a bit of a stretch that someone who “bounced around between science majors” so you could “feed your brain” (from your “about me” section) before settling on an English major could have taken enough upper-division science courses to challenge the research methods of a post-doc researcher with 40+ years under his belt. Especially considering you’re only 23. I live in AZ, and NAU programs aren’t that rigorous, if you had taken a significant amount of science courses or participated in scientific research then you would’ve at least walked away with a minor.

    In your blog you discuss your thoughts on formal education as largely “unnecessary” for someone with the drive/initiative to learn on their own; unless they’re learning dentistry, or surgery since it’s more “hands on”. Scientific research falls into this category, in order to know what you’re doing you need to spend some time doing hands-on research. Simply taking honors math and majoring in English doesn’t mean you’re qualified to analyze and interpret scientific research findings.

    Having a silver tongue and a proficiency in mathematics doesn’t equate to being a scientist. This is something I wish the general public understood, and explains why so many people jump on the bandwagon of “scientists are stupid hurr, they have no proof! Qualitative research is meaningless! blah blah blah”

    Evolutionary evidence for example, isn’t just quantitative; it’s largely qualitative, and began as being ONLY qualitative. Yet this is one of the most fundamental truths of our existence. Like Dr. Campbell stated in his response, by focusing on nit-picking every little inconsistency to disprove a claim you miss over-arching themes and messages. This is the mark of the inexperienced, and it shows when you limit your criticism to being solely on mathematical inconsistencies.

    1. Anon, she’s not doing research science. She is debunking a pop diet book. Learn to read. Someone with a sophomore level of math and science should be able to follow her arguments fairly easily.

      Campbell used simple univariate correlations to justify his arguments while ignoring simple univariate correlations and more advanced analyses that disputed his arguments. In the past he claimed it was because he was using “corrected” or “adjusted” correlations while claiming that his critics did not. Campbell’s defenders claimed he used more advanced multivariate analyses than his critics. Denise went through and showed that his “corrected” correlations were the just the same simple univariate correlations.

      It turns out of course, Campbell invented a whole new “holistic” science that enables him to cherry-pick the simple univariate correlations he likes and disregard the ones he doesn’t. He published these arguments in a book because he could not make these same arguments in a peer-reviewed science journal. He in fact has lamented the fact that modern “reductionist” science doesn’t appreciate his new holistic science.

      This is from Campbell’s “Primer on Statistics” where he details how his new ‘holistic science’ works:

      “In summary, I agree that using univariate correlations of population databases should not be used to infer causality, when one adheres to the reductionist philosophy of nutritional biology and/or when one ignores or does not have prior evidence of biological plausibility beforehand. In this case, these correlations can only be used to generate hypotheses for further investigation, that is, to establish biological plausibility. If in contrast, we start with explanatory models that represent the inherent complexity of nutrition and is accompanied by biological plausibility, then it is fair to look for supportive evidence among a collection of correlations…”

      He is saying because “meat is bad” sounds like a groovy idea to his scientifically trained brain, then he is free to pick all the simple univariate correlations he likes as evidence while disregarding the ones he doesn’t like. Apparently even when the ones he doesn’t like include more advance multivariate analyses and confounder analysis.

      This just doesn’t fly in real science journals. Does it fly with you, Anon?

      So Campbell published a book, and Denise pointed out the flaws in this book. Then everybody in the world that has bug up their ass about meat all of a sudden starts talking about Campbell’s scientific credentials. Why don’t we talk about Campbell’s new holistic science instead?

  5. I encourage you to check out Dr. Doug McGuff’s writings, SuperSlow materials, and Drew Baye’s blog for a taste of some ingredients you might find tempting to use in cooking the work of Kenneth Cooper/The Cooper Institute, the American College of Sports Medicine, the Amercian Society of Exercise Physiologists, etc. That would be *epically awesome* in a quest to rescue good health from bad science. ijs

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  7. “Are we trying to be healthy, or are we trying to be right? Are we trying to learn, or do rigid beliefs deafen our ears to new knowledge? ”

    In your last paragraph you made the above statement. I think this is so important. I am in the “china study camp”, so to speak. I am a doctor and have seen, anecdotally amazing results and will continue to recommend a whole foods plant based diet. My interpretation of the data is, basically agreeing with you, is that a plant based diet does not cause cancer! Whether it protects is a fun thought and tantalizing from the china data. I think that most data says that it does not cause disease makes it a good diet to recommend because , very simply, if you are eating only whole food plant based with no oil, there is no junk food available to you! More than the diet being protective, I just find in my practice that the junk food is so very destructive.

    I appreciate your statement so much because some people in my “camp” have seem to shut off their ears. We complain of the arrogance in the medical community but have similar blinders. Its frustrating. We must always keep our minds open to encounter new researcher.

    I think that your comments are intelligent and well researched. As a person with an advanced degree, I think it is so unfair that people would belittle you for trying to simply seek truth. I saw a comment which criticized where you went to school, which I thought was super silly . When someone starts doing that it instantly signifies that they don’t have anything to say. If you were an english major, would you belittle an MD for writing a work of literary fiction saying that he does not have the appropriate degree.?

    Anyway. long winded for short message. Lets attempt seek the truth .

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  10. Did anyone consider how accurate the data in the China study is? After all, most studies done in China during that time have a political context associated with the data.

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  12. Reblogged this on paleonto|illogical and commented:
    This is well worth a read and some commentary from these quarters is in the making, because there is more to be said about the Gentleman Scientist perspective that what concerns bias and bad science. Domination rears its ugly head in the text and between its lines is a narrative of colonial proportions. For now, enjoy this:

  13. This remarkably weak response to Campbell’s total obliteration of your earlier effort just emphasises that you’re a charlatan. Thanks for the laffs homie

    1. Care to elaborate a bit on “remarkably weak”, where the whole scientific community was quite impressed with the depth of analysis. I am quite hesitant to ask about how Campbell totally obliterated the critique, afraid you might embarrass yourself even further.

  14. I realize I’m kind of late jumping into this, but having been confined to bed with a flu bug, and accidentally stumbling on this blog, I’ve ended up having one of the most entertaining sick days ever! I’ve had time to read Denise’s posts, Campbell’s replies, follow various links, and rummage through the comments – and enough bed rest for a reply to ruminate, so here goes (and if it’s too long, just skip over it – freedom is just a click away).

    First off – what a tour-de-force of writing! Anyone who can turn statistics into a page-turner deserves some sort of writing award. I loved the buffoonish comment from the self-described “writing professor” who “failed” Denise’s writing (insert gob-smacked emoticon here). If anyone told me they intended to write a rip-snort’n Internet bog on the topic of “misapplied univariate correlations in a Chinese diet study”, I’d be lighting a candle to Saint Jude (Google it) but she pulled it off and, even if she’s wrong in her analysis, still deserves top marks; however, the question remains – is she wrong? I had hoped the comments and replies to her posts would help, one way or the other, by pointing out specific errors or misunderstanding in her analysis. Unfortunately most, if not all, were ad hominem – either “yah Denise” or “boo vile blasphemer” – lots about her character and almost nothing about her content. Odd (or sadly, perhaps not) for what should be a scientific discussion. After reading over most of the reactions to her posts, some themes did emerge and are worth exploring.

    “Denise is too young and has no formal scientific training. She has no business commenting on this important topic.” This theme came up in a number of comments. There is, of course, ample precedent for the uninitiated to make important contributions to the scientific dialog. Almost all the great scientists of the Enlightenment were, by today’s standards, rank amateurs. Emily Rosa, a nine-year-old with no evidence of any advanced scientific training (or puberty for that matter) developed an elegant experiment debunking Therapeutic Touch, a technique taught at several well-known academic institutions. And let’s not forget the 1970s, when a number of physicists, PhD anointed attendants to the Queen of Science herself, accepted at face-value the spoon-bending antics Uri Geller and his like. It was stage magicians, academic sans-culottes if there ever were, who provided the debunking. The physicists donned their academic robes, bristled their beards, and waggled their peer-reviewed papers to no avail – the guys with the theater capes and sparky-assistants were right.

    “Campbell is an academic Big Boy with real credentials, years of experience and lots of published papers – he can’t be wrong.” Unfortunately the history of science (and pseudoscience) is rife with acclaimed researchers who either strayed down the wrong path or let their passions get the better of them – just consider Lord Kelvin. Lord Kelvin isn’t just an academic Big Boy, he’s a veritable academic zeppelin. Yet, at the end of his career, he championed a young earth theory. Kelvin was a strong Christian and, with the complex mathematical gymnastics only he was capable of, ‘proved’ the earth was young. He was, of course, wrong. This doesn’t invalidate all his other work, just that in this, he was wrong. Having published 300+ papers doesn’t automatically make your 301st correct.

    “Denise isn’t a person, she is a sockpuppet for big meat.” It shouldn’t matter if Denise’s work was the result of a thousand dairy cows typing on a thousand keyboards for a thousand years – are her findings accurate or not? I find the idea that we can ignore dissenting voices because they come from a source we don’t like disturbing. If Monsanto said “two plus two is four”, do we dismiss it on the grounds that Monsanto always lies? Don’t tell me they must be wrong, show me where they are wrong. Too often these discussions become an echo chamber because we dismiss dissenting voices. We need to listen and study the view of our harshest opponents and demonstrate where they are wrong or, if they prove right, accept our mistakes and adjust accordingly. This is how science is supposed to work, otherwise, it becomes an unending exercise in confirmation bias.

    “Campbell’s response settles the issue.” It did for me – it convinced me he had something to hide. I spent some time reading his comments here and elsewhere, and while I might not be able to spot a correlated coefficient, I can certainly detect obfuscation when I read it, although his comments here: (see comment #63 by Campbell) sealed the deal. If, as Orwell said, obfuscation is like a cuttlefish squirting ink, then Campbell shows what a giant squid can do when he sets his mind to it (as if his son’s med school grades really have any to do with the matter at hand). Nowhere does he ever provide what I needed: detail answers to the specific issues raised in the review. At first, he trivializes the issue by claiming he is too busy to reply (too busy doing with what? Isn’t the China Study one of his major accomplishments?), elsewhere he hints at Dark Forces at work (but what a strange way for these Dark Forces to operate – hire a bunch of top-notch researchers to do a detail critique of his study, and then use an unknown English major’s obscure blog as a front). When he does finally descend from Mount Olympus with a more detailed reply, it’s just more ink. He alludes to all sorts of better data that he didn’t have time or space to publish (we only put the questionable stuff in the book, the good stuff is on the top shelf behind the cookie jar. I can’t reach it now, but trust me, it’s really good stuff). He misrepresents Denise’s criticisms (she uses univariate correlations – odd, I thought that what she accuses him of doing), damns with faint praise (she writes well for someone with no experience or training) and resorts to much self-puffery (did I mention my 300+ articles and many awards, why don’t I list them again just in case you missed the first several times I alluded to them). He finally leaves the scientific world altogether and resorts to a sort of magical thinking in which univariate correlations are a bad idea for most people but OK when he uses them because he is special enough to know when they are correct. At the end, he simply resorts to exhortation (try it – you will see that it works) which is simply the clarion cry of the quack.

    The only conclusion I can draw from all this is a reaffirmation of the scientific process. We must be ever-watchful of the tendency we all have to bend reality to meet our deeply held beliefs. And we must be open to dissenting voices in whatever odd form they may take.

  15. You meat eaters have too much fat clogging your brains. This lady is full of it. What she claims is T.Colin Camp else response is nothing of the sort. Learn how to use your brain and read something for yourself. All she’s saying is she doesn’t agree with the methods and admits she doesn’t understand them and he could clear all this up if he would just post them, then the rest of the paper she assumes she knows his methodology. Listen to an Internet major blogger instead of thousands of doctors and 30 years worth of research. Hilarious. Wake up. Click the linka. It’s not Campbell’s writings she is talking about. Don’t just listen to some scientific sounding person without even checking up on her claims. Cause newsflash, when you do that you’re just believing lies to make yourself especially feel better about your meat and dairy addictions. Another thing, how in the world does she pretend to call Campbell out for his biases and act like as an ex vegan, she herself has none. Making yourselves look really dumb with this one. Again click the link and check this research. She is NOT even using Campbell’s writings like she lies and claims she is!

    1. Also, there is no such thing as an “Ex-Vegan”. If you are truly vegan, there is no going back. Vegan doesn’t mean plant based diet as this person is attacking. She doesn’t even know the real definition of vegan and she has a following? This blogger is a moron who likes to internet fight and have a bunch of brainless zombie like humans following him/her. I agree with you Claire.

    2. TOO BAD that the protein and all from MEAT is necessary for coherent intelligent thought. Your idiocy clearly demonstrates that you have been vegan too long and probably look like a cow in a thunderstorm.
      Denise was QUITE CLEAR to INTELLIGENT Readers and quite spot on with her comments and analysis of Campbell’s, BS and well slanted vegan conclusions.
      Do wake up, take your meds and crawl back into your hole.

  16. Some detractors suggest that Ms. Minger has no scientific “degree” . Im my opinion as a medical doctor , she certainly sounds and writes in a very well educated and articulate fashion. In my expreience interest usually trumps initials any day. For those who sound offended that she even consider to question what Doctor Camp states it would re-inforce my observatoin that we can convince ourselves of almost anything if we desire. Questioning thins is fundamental to the scientific method.

  17. I think it is strange to have such an overwhelming amount of positive feedback for Denis’ work. All “intentions” aside, I think it is iresponsible to give any unqualified person who spent less than a fraction of the time on analyzing this raw data, than Dr. Campbell and his team did, as much credibility than your positive comments imply. I also believe that Denis is an incredibly intelligent person, but mor

  18. Denise,

    Let it be. You are smart, yes, but not qualified to draw any credible conclusions from the data gathered by Dr. Campbell and his team of researchers. He spent decades on this study and years to draw conclusions from the data. Unless you are willing to spend a fraction of this time on even educating yourself on the principles applied for analyzing the raw data gathered from his study, you should stop disrespecting decades of research through your intelegent yet “amatuer” interpretation of his findings. As smart and convincing as they might sound, they are only the interpretation of an unqualified attention seeker. I can’t believe I wasted my time on your posts. And thanks for deleting my earlier posts. Seems fair.

    1. Hi Chris, I don’t delete comments unless they’re spam or gratuitously rude/crude. What did you write that you think was deleted? I have “first comment moderation” on to prevent spam from going through, and it’s possible there are some comments from you that are still awaiting moderation — but when I search all submitted comments for your email address, only this post and the one from a few minutes earlier show up.

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