As most of you probably know, a documentary called “Forks Over Knives” recently hit the theaters after months of private screenings. Vegans everywhere are swooning, giddy that their message is now animated, narrated, and on sale for $14.99. Proud meat-eaters are less enthused, sometimes hilariously so. The film’s producers call it a movie that “examines the profound claim that most, if not all, of the degenerative diseases that afflict us can be controlled, or even reversed, by rejecting our present menu of animal-based and processed foods.” Roger Ebert calls it “a movie that could save your life.” I call it a movie that deftly blends fact and fiction, and has lots of pictures of vegetables.

Vilification of animal products aside, “Forks Over Knives” highlights something I strongly believe in—the power of diet and lifestyle to trump illness. When I first heard about this movie, I thought the title described a salad fork conquering a steak knife, but it turns out the imagery actually refers to diet (fork) and medicine (knife, or scalpel). Forks over knives. Food over medicine. Hey, I can get on board with that!

And along those lines, I have a weird confession. I kind of loved this movie. Not because of its scientific accuracy (which was sketchy) or because of its riveting narrative (it’s no Brave Little Toaster), but because I’m a sap when it comes to seeing sick people get healthy. “Forks Over Knives” had no shortage of personal stories from folks who, with a tearful glimmer in their eye, recounted how they evaded death by ditching their pill-popping, fast-food-noshing, insulin-injecting lifestyles. Toss in some animated graphs and gross surgery pictures, and I’m in 96 minutes of nerd heaven.

But there’s a reason I’m a health blogger and not a film critic, and I realize not everyone likes to see coronary arteries slashed open or a hear slew of personal stories intended to pluck at our heartstrings. So this won’t be your standard movie review. In fact, it isn’t a “review” so much as a chronological critique of the scientific claims made throughout the movie. My criticisms are limited to the stuff presented as evidence rather than those weepy personal stories, the filming quality, or other features I’ve got no talent in reviewing.

Why am I doing this? Am I evil?

For the record, I’m not dissecting this movie because I think everything in it is terrible. Quite the opposite, in fact. I believe the “plant-based diet doctors” got a lot of things right, and a diet of whole, unprocessed plant foods (i.e., Real Food) can bring tremendous health improvements for people who were formerly eating a low-nutrient, high-crap diet. Especially short term. But I also believe this type of diet achieves some of its success by accident, and that the perks of eliminating processed junk are inaccurately attributed to eliminating all animal foods. So the goal of this critique is to shed light on the areas where the “plant-based science” is a little, um, wilted.

Some other observations about the movie, both positive and negative, before we dive into the real critique:

• Word choice. This film was very careful about avoiding the term “vegan” and using “plant-based diet” instead—and frankly, it was a smart move. Even though the movie made it clear that no animal foods are good for you ever, the phrase “plant-based diet” sounds flexible, non-dogmatic, and limited to the realm of edible things. “Vegan,” on the other hand, is loaded with ethical and political connotations—evoking images of pamphlet-pushing PETA members, rubbery soy cheese, and Walter Bond.

• You’re good men, Charlie Browns. I’ve written (and spoken) about the “plant-based diet doctor squad” in the past—our enthusiastic Team Asparagus comprised of Dean Ornish, John McDougall, Neal Barnard, Caldwell Esselstyn, and Joel Fuhrman (although he’s a bit of a rebel, eschewing grains and allowing more fat than the rest). In this movie, Esselstyn and McDougall get plenty of camera time, and I’ve got to say, I really like these guys. No joke. They’re sincere, they’re well-intentioned, and they’re passionate about what they do. The world needs more doctors who want their patients to get off their medication, who prescribe food instead of drugs, and who have a sincere interest in changing lives. Way to go, dudes.
• Hey, fatty. A major component of Esselstyn’s heart-disease-reversal diet is the massive reduction in fat—not just from animal sources, but also the elimination of nuts, seeds, avocado, olives, olive oil, canola oil, coconut, and any other forms of concentrated plant fat. Unless I dozed off for something important, this movie barely mentioned this part of Esselstyn’s program, which I think is critical one. By keeping fat under 10% of total calories (which we also see in the disease-fighting programs of McDougall, Ornish, Pritikin, and Barnard), omega-6 intake—particularly the problematic linoleic acid—sinks like a gondola shot with a machine gun. Although these plant-based-diet doctors have a different view of fat than I do (Esselstyn, for instance, believes that any dietary fat damages the endothelial cells and promotes heart disease), it still would’ve been useful to hear about this in the movie, if only for the sake of full disclosure. I almost wonder if the movie’s creators dodged the “uber low fat” message to avoid freaking out the audience. What? We can’t even put olive oil on that ten-pound salad?!
• Go fish. As we’ll see later in this critique, some of the anecdotes used to support a plant-based diet (such as Norway’s war-time cuisine and the traditional Japanese diet) actually point to marine foods being a great addition to your menu. For some reason, no one in the movie says a gosh darn thing about fish. Are they lumping fish into the same “meat” category as Oscar Mayer Weiners? Have they forgotten that fish exists in the food supply? Are they ignoring the health benefits of marine foods that nearly everyone—even the folks who swear on their momma’s grave that red meat will kill you—agrees on? What’s going on here? I sure don’t know, but it seems awfully… fishy. (You totally saw that coming.)
• Welcome to False Dichotomyville—population: you. According to this movie, “plant-based diet” and “Standard American diet” are the only two ways you can possibly eat, and an egg is exactly the same as a bag of Cheetos. A recent pingback led me to this review at DoingSpeed.com (it’s not what you think), which nicely sums up the movie’s flip-flopping description of America’s cuisine: “the definition of the Western diet changes suddenly, one second referring to cake and donuts and the next [to] animal products.” Animal foods, it seems, are synonymous with the Western diet, and meat exists only in industrialized countries. Non-Westernized populations like the Masai, traditional Inuit, Australian aborigines, and countless hunter-gatherers have conveniently vanished for the duration of this movie. It must be awesome to selectively choose reality like that!
• Fast forward. For me, the most interesting part of this movie happened around the 30 minute mark. First, the film discusses a 1973 corn subsidy bill that encouraged a massive increase in corn production—which pretty much explains why so many foods these days are injected full of high-fructose corn syrup or other cheap, corn-based ingredients. It’s all about the money. Shortly after that, the movie gives some camera time to evolutionary psychologist Dr. Doug Lisle, who tells us about a concept called the Pleasure Trap—a motivational triad of “seeking pleasure, avoiding pain, and conserving energy” that all our years of evolution have hardwired us for. Because our modern, processed foods are so rich in calories and easy to access, they provide a high degree of dietary reward with almost no effort. Our bodies freakin’ love this. So much, in fact, that our brains say “eat eat eat!” in the presence of such foods and our natural hunger signals get overridden. That worked well in the wild, when periods of food abundance were interrupted with periods of famine. But these days, it just makes it easy to get fat. And the Pleasure Trap applies to much more than just food. Indeed, we’re biologically driven to seek the easy way out, to avoid pain, and to pursue things that make us feel good.

Critique time!

After a collage of soundbites about how awful and unhealthy Americans are (ya think?), the fun begins around the 13-minute mark, when we get a brief biology lesson on the C-word: cholesterol. Props to the scriptwriter for at least noting that cholesterol is a “natural and essential substance” (per some descriptions, you’d think the stuff was toxic sludge), but the narration goes downhill from there. After outlining cholesterol’s important biological functions, the movie states:

13:06—But when we consume dietary cholesterol, which is only found in animal foods like meat, eggs, and dairy products, it tends to stay in the bloodstream. This so-called plaque is what collects on the inside of our blood vessels and is the major cause of coronary artery disease.

Yikes! Did we slip and fall back into the ’80s?

For starters, cholesterol from animal foods does not have some magical ability to set up permanent camp in your bloodstream and turn into plaque, just by sheer virtue of its animal-foodness. This was a common line of thought decades ago, but as research progressed, we figured out that the body is actually pretty awesome at regulating cholesterol production in response to what we ingest from food. As this paper from 2009 explains, the supposed link between dietary and serum cholesterol stems from studies that had fundamental design flaws, failed to separate the effects of cholesterol different types of fat intake, or were performed on animals that are obligate herbivores (hey there, rabbits!). The doctors in “Forks Over Knives,” it seems, are among the few stragglers who still believe dietary cholesterol is harmful.

Most people (about 70% of the population) are “hypo-responders” when it comes to cholesterol intake—meaning the cholesterol they eat from food has a negligible effect on the total cholesterol in their blood. A smaller slice of the population (“hyper-responders”) see a greater rise in blood cholesterol after eating high-cholesterol foods, but the change is because both LDL and HDL increase proportionally, preserving the cholesterol ratio and leaving heart disease risk the same as what it was before. (As more evidence, a similar study (PDF) found no change in LDL/HDL ratio in either they hypo-responders or hyper-responders, even when feeding folks an extra 640 mg of cholesterol per day.)

Not only that, but some cholesterol-rich foods like eggs have actually been shown to make LDL (the so-called “bad” cholesterol) less atherogenic by increasing its particle size. And in one study of diabetics, a high-protein, high-cholesterol diet improved HDL more than a similar high-protein diet with a low cholesterol content (though it was likely other components of the foods involved, rather than the dietary cholesterol itself, that caused this). It’s a weird, wobbly stretch to paint animal foods as a death knell because they contain cholesterol.

Enter: T. Colin Campbell

Minute 17:01—"We learned that animal protein was really good in turning on cancer." There's an inappropriate joke buried somewhere in there.

Now we’re talkin’! To anyone who’s read (or is moderately familiar with) the book “The China Study,” the next part of the movie is a trip down memory lane. We learn about Campbell’s work in the Philippines, where he was trying to improve the lives of malnourished children by filling their diets with more protein. It was here that the trajectory of his career made its first wild turn:

Minute 15:42—But then Dr. Campbell stumbled upon a piece of information that was extremely important. … The more affluent families in the Philippines … were eating relatively high amounts of animal-based foods. But at the same time, they were the ones who were most likely to have children susceptible to getting liver cancer.

(Gasp! Shock! Horror! Let me insert the requisite “correlation isn’t causation” warning before we continue.)

Minute 16:10—Shortly afterward, Dr. Campbell came across a scientific paper published in a little-known Indian medical journal. It detailed work that had been done on a population of experimental rats that were first exposed to a carcinogen called aflatoxin, then fed a diet of casein, the main protein found in milk. [Campbell:] “They were testing the effect of protein on the development of liver cancer. They used two different levels of protein: They used 20% of total calories, and then they used a much lower level, 5%. Twenty percent turned on cancer; 5% turned it off.”

Although the above is true, it’s only one (misleading) part of the story. We’ll explore exactly what’s wrong with this summary later on, when Campbell’s own research comes to the fore in the film. But for now, let’s just look at one spot where the film lets a figurative cat (err, rat?) out of the bag.

The paper from India that Campbell found is called The Effect of Dietary Protein on Carcinogenesis of Aflatoxin, which appeared in the Archives of Pathology in 1968. Indeed, the researchers discovered that rats fed 5% of their diet as casein were generally free from cancerous growths, whereas the rats fed 20% casein were riddled with ‘em. But at the 16:37-minute mark, we get to see a snippet of this paper that shows us something equally important:

Don’t get distracted by those red letters! What we’re interested in is the sentence near the bottom, which the film’s producers apparently didn’t notice: ”In all, 30 rats on the high-protein diet and 12 on the low-protein diet survived for more than a year.”

Let that sink in for a moment. Maybe it’ll hit a little harder if I told you that in the “high protein vs. low protein” experiments discussed in this paper, 10 low-protein rats died prematurely while all the high-protein rats stayed alive. In other words, the overall survival rate for the 20% casein group was much better than for the 5% casein group, despite the fact they had liver tumors. The low-protein rats were dying rapidly—just not from liver cancer. And as we’ll see later, the reason the non-dead, low-protein rats didn’t get tumors was partly because their liver cells were committing mass suicide. 

In his article “The Curious Case of Campbell’s Rats: Does Protein Deficiency Prevent Cancer?“, Chris Masterjohn explores this oddity further by plowing through the Indian research Campbell talked about. If you haven’t seen this article yet, you owe it yourself to read it now, because it’s kind of mind-blowing—both for Chris’s analysis of the Indian research and his takedown of Campbell’s own rat studies. (And for anyone who’s going to gripe about this article being posted on the Weston A. Price Foundation site (I know you gripers are out there), I encourage you to read it anyway, use your noggin, and check the references for yourself rather than dismissing it sight unseen.)

Regarding that paper from India that sparked Campbell’s “aha protein evil!” moment, Chris notes that “Campbell never tells us … that these Indian researchers actually published this paper as part of a two-paper set, one showing that low-casein diets make aflatoxin much more acutely toxic to rats.” This second paper is called The Effect of Dietary Protein on Liver Injury in Weanling Rats, and indeed, it shows that rats on low-protein diets experience much more actual liver damage than rats on high-protein diets when they’re exposed to aflatoxin. They don’t get cancer, but they’re sicker overall because they’re less capable of detoxifying aflatxoin—leading to fun stuff like fatty liver, liver necrosis (cell death), proliferation of bile duct tissue, and early death. As Chris puts it:

Somehow, I doubt many people would read this study and shout “sign me up!” for a low-protein, plant-based diet if it is going to save them from cancer at the expense of killing them in their youth.

Indeed! As we’ll see later in this critique, Campbell’s own low-protein rats weren’t a rosy picture of health, either. Even more exciting, we’ll look at some more studies conducted in India showing that low-casein diets—but not high-casein diets—promote cancer when aflatoxin dosage is at a lower, real-world-applicable level. Fun times ahead! (If you’re impatient, you can skip to that section right now by clicking here.)

Esselstyn: From operating table to kitchen table

Next up, we get a bigger peek into the life of one seriously cool cat: Dr. Caldwell Esselstyn, physician at the Cleveland Clinic. Although Esselstyn noted—in an earlier segment of the movie—that he loved surgery for its ability to neatly remove a problem from the body, he faced some disillusionment as his career progressed. In 1978, when Esselstyn was chairman of Breast Cancer Task Force at Cleveland Clinic, he was unhappy that he was only treating people who were already ill and doing diddly squat for the “next unsuspecting victim.” He wanted to focus on prevention. So he put on his sleuth cap and set off to investigate—first by shoveling through global statistics for cancer.

Only YOU can prevent forest fires. And heart disease.

For the next few minutes, we get to hear about the alarming discoveries this investigation uncovered. Don’t want breast cancer? Then move to Kenya, where the rates are 82 times lower than in the US (well, at least they were in 1978). Got prostate cancer? Japan doesn’t: In 1958, there were only 18 autopsy-proven deaths from prostate cancer in the whole country. Compare that to the 14,000 in the US for the same year. Heart disease, too, was lower outside of America:

Minute 19:21—Dr. Esselstyn also discovered that in the 1970s, the risk for heart disease in rural China was 12 times lower than it was in the US. And in the highlands of Papau New Guinea, heart disease was rarely encountered. The link he noticed between all the areas he studied was simple. [Esselstyn:] “Virtually the Western diet was nonexistant. They had no animal products. No dairy, they had no meat.”

…And there it is. Again, we have the conflating of “Western diet” with “animal products,” as if meat and dairy are the major dietary difference between Westernized and non-Westernized populations. Oy! (By the way, here’s a friendly reminder that in rural China—at least based on the China Study data—heart disease mortality was actually inversely associated with meat intake, meaning the folks eating the least meat actually died more frequently of heart disease. It doesn’t mean too much as a lowly correlation, but it does fly against the assumption that animal foods are always linked with heart disease.)

Next is where it really gets interesting. About 20 minutes into the movie, we get a fascinating historical tidbit about diet and heart disease in war-time Norway:

Minute 19:50—In World War II, the Germans occupied Norway. Among the first things they did was confiscate all the livestock and farm animals to provide supplies for their own troops. So the Norwegians were forced to eat mainly plant-based foods.

In the movie, Esselstyn eagerly explains how cardiovascular disease went kerplunk when the Germans invaded in 1939, only to zip back up as soon as the war was over—perfectly coinciding with their supposed near-vegan period. How obvious it is! The Norwegians went veggie and healthied up; they returned to their lamb and gjetost and re-clogged their arteries. As Esselstyn puts it: ”With the cessation of hostilities in 1945, back comes the meat, back comes the dairy, back comes the strokes and heart attacks.”

Here’s the graph the movie walks us through. The Nazi flag marks the arrival of the Germans; 1945 is when they left. (Right below it is a similar graph from a 1951 issue of “The Lancet” that’s even more dramatic. After adjusting for an unequal age distribution (and unrealistically low mortality in the ’20s and ’30s), we can see that death from cardiovascular disease really did nosedive to a lower rate than Norway had seen in the past few decades.)

War! What is it good for? Reversing heart disease, apparently.

Oh, Norway; how close you were to cardiovascular salvation! Nice job screwing it up.

The intended point, of course, is that the dip in mortality was from giving up animal foods. When the Germans swiped all sentient creatures from the food supply, Norwegian hearts pumped with atherosclerosis-free ease—proving that going “plant based” will save your ticker. It sounds convincing enough, and the graph is compelling*… but is there more to the story than meets the eye?

*Note: If you look at the numbers on the right side of the graph, you’ll see mortality dropped from 30 to 24 deaths per 10,000—a difference of only six people per 10,000. That’s still nothing to sneeze at (especially if one of the saved was your great-grandpa Bjørn who helped you exist), but the graph gives an exaggerated view of the actual change in mortality.

Luckily, there are a few resources out there that track the war-time diet changes in more detail. One is a paper discussing how nutrition affected Norwegian youngsters during the war, which you can read as a PDF here (spoiler: the kids were shorties). But the part we’re interested in is the table estimating how food intake changed during the war. The numbers represent how much each food increased or decreased during the war (percentage wise) compared to the pre-war values.

Did meat and milk intake go down? Fo’ sho’ (although clearly not to zero). But look what else happened. Sugar consumption was chopped in half. Both butter and margarine intake decreased significantly. Veggie intake shot up. And perhaps most significantly, fish consumption increased by a whopping 200%, a bigger change than seen with any other single food item. Need I mention the eighty gazillion studies showing the benefits of fish, DHA, and an improved omega-3/omega-6 ratio for cardiovascular health?

The paper also notes that total calorie intake decreased by about 20% compared to pre-war levels and weight loss was common. Did calorie restriction and sinking body mass play a role in mortality changes? Definitely maybe.

Oh, but it gets better. There’s a section in a super old issue of “Proceedings of the Nutrition Society” called “Food Conditions in Norway During the War, 1939-45” with even juicier details. I couldn’t find any free copies to link to, so I’ll type out the relevant bits. But first, take another look at that “circulatory disease” graph from the movie and verify with your own eyes that the first (and biggest) drop in mortality happened in 1941.

Now read this:

During the first year [starting in spring of 1940] the rationing included all imported foods, bread, fats, sugar, coffee, cocoa, syrup, and coffee substitute. In the second year [starting in late 1941] all kinds of meat and pork, eggs, milk and dairy products were rationed…

See the problem?

Animal foods didn’t really dwindle from Norwegian kitchens until the end of 1941. Even if we ignore the fact that changes in mortality would naturally lag behind changes in diet, it’s hard to blame the 1941 drop in cardiovascular disease on something that mostly happened in 1942! D’oh. Time-wise, there’s a stronger link between the mortality tailspin and the previous year of food rationing: “imported foods, bread, fats, sugar, coffee, cocoa, syrup, and coffee substitute.” (Or maybe it was just the anticipation of ditching meat that made everyone healthier.)

Despite the dismal record keeping, a few studies were “secretly performed” in Oslo to track changes in food intake during the war. Between 30 and 50 families were surveyed three times annually from 1941 to 1945, giving us a nice little diet portrait encompassing not only rationed food, but also the “black market” items people were eating. Although it’s hard to say how accurately this represents the food intake of Norway’s whole population, it’s at least a place to start. And unlike the last table, it breaks down food consumption year by year, rather comparing only war-time and pre-war values. (Note that the top row is for the years 1936-7 and the next is for 1941—it seems there isn’t any data for the gap between.)

I pity da fool who doesn’t enlarge this image.

From "Proceedings of the Nutrition Society," 1947. Volume 5, issue 4, page 264.

Numbers, numbers, everywhere! Let’s distill the major stuff from that chart so you don’t have to squint at it forever:

• Cod liver oil became a standard addition to war-time diets. (Interestingly, the paper later notes a huge improvement in Norwegian dental health between 1940 and 1945: By the end of the war, the average number of cavities was less than half of what it was before the war. Vitamin A and D, anyone?)
• As we saw earlier, fish intake increased massively. So did ‘taters, roots, and vegetables, particularly in 1942 and 1943.
• Intake of whole milk was actually higher in 1941 compared to before the war, but then gradually diminished.
• Intake of skim milk was higher throughout the war than before it.
• Cheese, cream, and condensed milk started dropping off the radar at the end of 1941.
• Meat hit a major low in 1943 and 1944.
• Added fats like margarine and butter declined, particularly in 1942 and 1943.
• Flour, meal, groats, and bread intake went up slightly, mainly from black-market sources.
• Intake of sugar, coffee, and chocolate declined significantly.
• Fruit also declined significantly, and as we’ll see later, mainly came in the form of locally picked berries.

There’s no doubt about it: In 1941, when cardiovascular disease started plummeting, Norwegians were eating more total dairy (light blue line) than they were before the war, when the death rate was higher.

How about flesh foods? Again, this is in grams per day for your average Norwegian man:

For the families surveyed in Oslo, fish and meat consumption were almost exactly inverse: Fish intake rose in perfect step with the decline of meat. And at its peak, the average man was consuming almost three-quarters of a pound of fish a day! That’s a decent chunk o’ seafood. Because meat and fish intake were so tightly correlated, it’s hard—maybe impossible, given the sparse data available—to separate any mortality effects of meat reduction from the huge spike in marine foods.

One more gem from this paper. In another table, we get yearly data for Norway’s daily intake of total animal protein (in grams) for 1936-7 and then from 1942 to 1945. This should  be fun, right? Here’s a graphed version of that data, paired up with the cardiovascular disease mortality rates from those same years. (To make it easier to see the interplay between the lines, I doubled the mortality figures to make them “per 20,000 people” instead of “per 10,000.”)

Well, golly. In both 1942 and 1943, when mortality made its steepest descent, animal protein intake was actually higher than it was before the war! The major decline in total animal protein intake didn’t happen until 1944 and 1945, well after Norway had already seen cardiovascular disease plummet. Again, this data isn’t rock-solid because of poor record keeping, and correlation isn’t causation anyway, but it sure doesn’t support the argument that Norway got healthier due to a plant-based diet.

For comparison’s sake, this is what a graph would look like if these variables were tightly linked:

One more thing before we emigrate from Norway. After poking around the interwebs, I found a gem of a paper called Food rationing during World War two: a special case of sustainable consumption? The whole thing’s pretty interesting, but the best nuggets are the details about actual foods eaten in Norway during the war (and the reiteration that “sugar rations [were] restricted to 3 kilos per household per year,” which is less than 2% of what a four-person Norwegian family consumes today.)

Minute 21:56—[McDougall:] Their kids, they started to give up the rice and replace it with the animal foods, the dairy products, the meats… and the results were obvious. They got fat and sick. I knew, at that point, what causes most diseases.

The movie goes on to explain that animal protein has some mystical, inexplicable, yet very real ability to promote disease—a property that plant protein lacks. Referencing Campbell’s rat studies, we’re told that “the results were consistent: Nutrients from animal foods promoted cancer growth, while nutrients from plant foods decreased cancer growth.” And yet…

Minute 29:20—Campbell hadn’t identified a specific biological mechanism that caused the effects he observed. “It finally occurred to me that there was no such thing as the mechanism. What we were looking at was a symphony of mechanisms,” he said.

Out of all the moments in the movie, this might have been the biggest face-palmer for me.

It just so happens that Campbell did identify exactly why casein behaved differently than plant proteins in his rat studies. Decades ago. In 1989. The discovery emerged from a study he conducted on “protein quality” and liver tumor growth, which you can find here. Although regular wheat protein didn’t spur tumor growth like casein did,* wheat protein behaved exactly like casein as soon as Campbell added lysine, the amino acid wheat is low in. Basically, any complete set of amino acids—whether from the animal kingdom or plant kingdom—is going to have the same cancer-promoting effects. (At least when aflatoxin dosing is very high. At lower aflatoxin dosing, that same complete protein will protect against oft-deadly liver damage. In fact, in the paper cited above, Campbell notes that the unsupplemented gluten groups and low-casein group—despite getting fewer “foci” that mark the start of cancer—had far worse liver injury than the high-casein group. He writes: ”All animals developed bile duct proliferation, which characterizes the acutely toxic response to aflatoxin B1 (data not presented). The most severe lesions occurred in the experimental groups with the lowest response of foci [5% casein and 20% unsupplemented gluten].”)

*Note: Campbell actually used casein diets that were supplemented with methionine (test diet PDF here), an amino acid that casein is low in. This made the casein a more “complete” protein and may have influenced the cancer-promoting abilities of the casein diets. If we’re going to compare apples and apples, we could look at the casein-supplemented-with-methionine diet right next to the gluten-supplemented-with-lysine diet. And when we do that, we find that both are equally powerful at promoting tumor growth.

The reason this finding is so important is that it shows, fairly convincingly, that Campbell’s findings only apply in a lab setting—where rats are fed a single source of protein for their entire lives. The rats that stayed cancer-free on an unsupplemented gluten diet were the equivalent of a human eating nothing but wheat, every single day, from the moment they’re weaned off Momma’s teat until the day they die. A vegan eating a mixture of plant foods will naturally end up consuming complementary amino acids, and their body will synthesize the “complete protein” that Campbell says is cancer-promoting. For instance, in the common combination of rice and beans, beans supply extra lysine that rice is low in—the same effect as supplementing gluten with this amino acid.

It’s not like Campbell forgot about his discovery, either. In his 2009 response to a critique by Joseph Mercola, Campbell wrote:

The adverse effects of animal protein, as illustrated in our laboratory by the effects of casein, are related to their amino acid composition. … There have been many different kinds of studies for well over a half century showing that the nutritional responses of different proteins are attributed to their differing amino acid compositions. … These differences in nutritional response disappear when any limiting amino acids are restored.

Wheat protein, unlike casein for example, did not stimulate cancer development but when its limiting amino acid, lysine, was restored, it acted just like casein. There have been literally thousands of studies going back many decades showing a similar effect on body growth and other events associated with body growth—all resulting from differences in amino acid composition of different proteins.

Enough said. Let’s look at one more snippet from this segment before we move on:

Minute 29:00—Over the next several years, Campbell initiated more extensive lab studies using various animal and plant nutrients. The results were consistent. Nutrients from animal foods promoted cancer growth, while nutrients from plant foods decreased cancer growth.

Beep! False. Campbell actually discovered that certain animal fats are superior to certain plant fats in terms of cancer protection. In a study published in 1985, he found that fish oil tends to inhibit cancer, and in a couple other studies, found that corn oil appears to promote it (such as here).

Esselstyn: The study cogs start turnin’

But enough about rats and vegetable protein. Next up, the movie returns to one of our movie’s shining (human) stars, Caldwell Esselstyn. In the 1980s, with “prevention!” flashing relentlessly in his mind’s eye, Esselstyn finally got the chance to do what his years of surgery never allowed: Fix heart disease with food instead of scalpels.

Minute 44:11—In the mid-1980s, Dr. Caldwell Esselstyn was struggling to organize a study on coronary artery disease. His plan was to put a group of patients on a diet of low-fat, plant-based foods along with small quantities of low-fat dairy products and minimal amounts of cholesterol-reducing drugs.

Indeed, that’s the gist of it: a low-fat, plant-based diet with a scoop of statins for dessert. But since the film doesn’t dive into the finer details of the diet, let’s look at how Esselstyn describes his program in his book, “Prevent and Reverse Heart Disease.” From pages 5, 6, and 72, we  can see that the diet eliminates:

• Anything with a “mother or a face,” including meat, fish, and poultry
• All dairy*
• All nuts and avocados
• All oils, such as soybean oil, olive oil, corn oil, cottonseed oil, canola oil, and anything else with “oil” in the name
• All solid fats like margarine and butter
• All foods—whether pre-made or prepared at home—that contain even a drop of added fat
• Any grains that aren’t cross-your-heart, swear-on-your-grandmomma’s-grave, 100% whole. According to Esselstyn, this includes eliminating items that have healthy-sounding ingredients like “multigrain, cracked wheat, seven-grain, stone-ground, 100 percent wheat, enriched flour, or degerminated cornmeal”

On the flip side, the diet allows:

• All vegetables, including leafy greens, root veggies, and other veggies encompassing all the beautiful colors of the rainbow
• Legumes such as lentils, peas, and beans
• Whole grains ranging from the commonplace (whole wheat, corn, wild rice) to the exotic (quinoa, millet, amaranth, teff, kamut, spelt, rye)—but only if they contain no added fat, high-fructose corn syrup, or even a smidgen of refined grain
• All fruit

And if you think this diet is flexible and allows some cheat-meal wiggle room, you’re sadly mistaken. Esselstyn is a self-admitted stickler, and insists that a fundamental rule of his program is that “it contains not a single item of any food known to cause or promote the development of vascular disease.” Which, to him, means a life permanently devoid of pot roast, Nutty Buddies, or butter on your endless bowls of steamed kale.

Although his program doesn’t specifically forbid processed foods, adhering to his rules pretty much ensures everything you eat will be Real Food. For instance, his diet manages to eliminate even the “fat free” replacement products we’ve all seen at the store:

If you see any of the following words or phrases on a label—glycerin, hydrogenated, partially hydrogenated, mono or diglycerides—avoid the product. These are all sneaky forms of fat. Snackwell’s devil’s food “fat-free” cookies* list 0 grams of fat on the nutritional chart required on all packages. But if you read the ingredients, you notice that glycerin is listed fifth among them. Similarly, Kraft’s zesty Italian fat-free dressing and Wishbone’s fat-free ranch both list soybean oil and dairy products among their ingredients. But because the portion sizes are small, these products can still be called “fat-free,” under the government’s standard. Read the ingredients. (Page 124)

*Forget glycerin! How ’bout avoiding this junk because the first four ingredients are sugar, refined flour, high-fructose corn syrup, and corn syrup?

Indeed, Esselstyn’s diet categorically eliminates most “fat-free” Frankenfoods—many of which were wildly popular when he conducted his study in the ’80s and ’90s. Apparently, he nixes them not because they contain ingredients so awful they’d make a billygoat puke, but because their microscopic amount of fat is still too much. In a lipid-phobic era when dieters swapped fat for refined carbs, Esselstyn accidentally ‘rescued’ his patients from junk-filled replacement foods, which we now know are often worse than the originals. He got it right for the wrong reasons.

And lastly, despite what it may seem, Esselstyn’s diet is not a whole-grain free-for-all. His book describes the diet as decidedly vegetable-based, and notes that you may need to scale down on the starches to avoid unwanted pounds:

If you are eating a plant-based, no-oil, whole-grain diet filled with leafy greens and all the colorful vegetables, you don’t need to worry about weight. … However, if you let whole grains, starchy vegetables, and desserts dominate, weight can begin to creep back. If that happens, simply cut back on grains and starches, increase your consumption of leafy greens and colorful vegetables, and cut out desserts. (Page 126)

As we can see, Esselstyn’s program entails a lot more than a simple shift to plant foods. Here are the likely culprits behind his success:

• By completely eliminating oils, Esselstyn’s diet causes a massive reduction in the omega-6 fats—particularly linoleic acid—running wild in Western diets. (And more broadly, it slashes intake of polyunsaturated fats, which are the type of fat most likely to promote LDL oxidation because of their unstable chemical structure.) Boom! Down goes polyunsaturated fat intake, down goes omega-6 intake, down goes inflammation, down goes some major components of heart disease. Although Esselstyn achieves this by giving the boot to all fats, the same thing could be achieved by just eliminating foods and oils high in polyunsaturated fats, particularly industrial seed oils like soybean oil and corn oil. (If you’re thinking, “But those are the types of oils the government tells us are healthy,” please read this.)
• Due to its strict no-added-fat rule, Esselstyn’s program eliminates 99% of what you’d find in a gas-station convenience store, a vending machine, or a crinkly silver Frito-Lay bag. In other words, this is a no-junk diet. Sure, animal foods are out—but so are the even wider range of low-nutrient snacks, processed desserts, convenience foods, and other manufactured items that usually fill American kitchens.
• By allowing only 100% whole-grain foods with no added fat or sugars, Esselstyn makes it pretty tough to eat processed wheat products like bread, pasta, cereal, bagels, pastries, crackers, and other grainy goodies. In his book, Esselstyn acknowledges how hard it is to find bread that fits into his diet plan, and endorses sprouted grain products by companies like Ezekiel. As a result, the main starches in this diet are likely to be from roots, starchy vegetables, legumes, squash, and grains that still look like they did when they came off the plant—like corn or wild rice. The movie showed the following display as an example of an Esselstyn-approved feast.

Behold: plants.

Now that we have a better idea of what Esselstyn’s diet entails, let’s hop back into the movie.

Minute 44:32—[Esselstyn:] “Slowly, over the next 18 months, I got the patients that I’d asked for. But the ones they sent me were a little bit sicker than I’d thought! These were patients who had failed their first or second bypass operation, they had failed their first or second angioplasty, and there were five who were told by their expert cardiologist that they would not live out the year.”

We then get to meet one of those so-called lost causes: Evelyn Oswick, who’d been one of Esselstyn’s most “gravely ill” patients. Not only had she already suffered from two heart attacks by the age of 59, but her doctors thought her situation was so hopeless that they told her—quite literally—to go home, sit in a rocking chair, and wait to die. But as evidenced by the fact she appeared in “Forks Over Knives,” she’s not only alive, but quite the bright-eyed and bushy-tailed survivor. Woohoo, Evelyn! Woohoo, Dr. Esselstyn! Woohoo, plant-based diet!

Although we don’t have enough data to really analyze her success, I’ve got to wonder if ditching meat—or even the fat—was really the thing that helped. Here’s how she describes her previous diet:

Minute 45:00—[Oswick:] “I ate all the chocolate I could eat, I ate every doughnut I could get my hands on… oh, I just loved things like that. A lot of gravy.”

"It was that drop of glycerin in the candy that did me in."

Esselstyn then describes how his study was performed. For a full five years, he met with his patients once every two weeks to draw blood, take their blood pressure, measure their weight, and endure the nickname “Dr. Sprouts.” We know Mrs. Oswick is alive, but what happened to the other 23 study subjects? Did they end up back on the operating table, wads of carrots lodged in their veins? Did they miraculously heal? We’ll have to wait to find out, because now it’s time to learn about…

The China Study

I’ll admit it: I was pretty excited to see what “Forks Over Knives” had to say about the China Study—a massive epidemiological project and namesake for Campbell’s bestselling book. Would we get an elaborate, attempted indictment of animal foods by blaming all woes of the human body on high cholesterol? Would the producers sacrifice accuracy for simplicity and just say “animal foods made bad things happen?” Would Campbell warn the audience not to Google around for critiques of his study, because they’re all written by shills for the meat industry, or—worse—liberal arts majors?

Finally, we get to find out. After nearly 50 minutes of nail-biting anticipation for our China Study segment, we see T. Colin Campbell and his colleague, Junshi Chen, thumbing through a copy of “Diet, Life-style, and Mortality in China“—the 900-page tome showcasing the data they spent so many years gathering. Oh, sweet reminiscence! This is the same book that sat on my desk for three months last year, collecting blood, sweat, and sticky-notes.

"Orange you glad I didn't say banana?"

Campbell briefly explains how this study generated a whopping 8,000 to 9,000 statistically significant correlations. “This means that if 19 out of 20 are pointing in the same direction, it’s highly significant, and likely to be true,” he says. (I’d add that “true” isn’t the same as “meaningful”—variables can be strongly and legitimately correlated, but not actually have a cause-and-effect relationship.) After learning a bit more about how the data was presented in that giant book, we get to the good stuff. The summary of it all. The fruit of international labor. The culmination of those 9,000 statistically significant correlations. Are you ready?

Minute 49:30—[Chen:] “I think the major message we got out of this correlation analysis is only one message: The plant-food based diet—mainly cereal grains, vegetables, and fruits, and very little animal food—is always associated with lower mortality of certain cancers, stroke, and coronary heart disease.”

That’s a pretty simple message to get from such a big, complicated study! Too bad it’s baloney.

What Campbell and Chen imply in this movie clip is that all those correlations are, serendipitously, singing the same tune: That plant foods offer protection against diseases, while animal foods tend to promote them. Alas, the trends in this study are anything but straightforward—and as Campbell himself has pointed out, the unadjusted correlations can be quite misleading:

“Use of these correlations … should only be done with caution, that is, being careful not to infer one-to-one causal associations. … First, a variable may reflect the effects of other factors that change along with the variable under study. Therefore, this requires adjustment for confounding factors.”

Agreed, good sir. But since we’ve just been told in “Forks Over Knives” that these correlations generally point in the same direction (and reinforce the idea that animal foods cause disease), let’s put relevance aside and see if that claim is up to snuff.

Note for anyone needing a math catch-up: A correlation is basically a relationship between two things—meaning they both go up at the same time (a positive correlation) or one goes up while the other goes down (a negative or “inverse” correlation). For example, your age is positively correlated with the number of wrinkles on your face, but your age is negatively correlated with the amount of time you spend Googling “Justin Bieber.” Correlations are usually expressed as numbers between 1 and -1, with zero indicating that there’s absolutely no relationship between the variables. The farther away the number is from zero, the stronger the relationship—so a value of 0.54, for instance, would be stronger than a value of 0.12. In the case of our China Study data, strong positive numbers indicate that a certain food is associated with more of a certain disease, while strong negative numbers indicate the food is associated with less of that disease.

Get it? Got it? Good!

In my China Study critique last year, I pulled a bunch of data directly from “Diet, Life-style, and Mortality in China”—the same book Campbell and Chen are huddled around in that last picture—showing just how inconsistent the “plant-based diet is healthier” message really is. For instance, we’ve got peculiar things like this:

• Plant protein has a correlation of 0.21 with heart disease (positive)
• Non-fish animal protein has a correlation of 0.01 with heart disease (neutral)
• Fish protein has a correlation of -0.11 with heart disease (inverse)
• Meat intake has a correlation of -0.28 with heart disease (strongly inverse)
• Fish intake has a correlation of -0.15 with heart disease (inverse)
• Egg intake has a correlation of -0.13 with heart disease (inverse)
• Wheat has a correlation of 0.67 with heart disease (really flippin’ high!)—which is not only the strongest association between any food and heart disease, but remained sky-high even when I tried adjusting for anything that might be confounding it.*

*Our grain-happy “conventional wisdom” might scoff at the idea of wheat being atherogenic, but there’s at least one cardiologist out there who has great success treating his patients’ heart disease by eliminating wheat (and not going low-fat)—and he recently published a fantastic book showing why modern wheat is so problematic.

First, let’s look at how various foods correlate with “death from all medical causes” for adults age 35 to 69. This variable is more interesting to me than “all-cause mortality” because it excludes things like drowning, car accidents, getting mauled by a pack of rabid wolves, and other modes of death that have nothing to do with diet (unless the wolves found you because they smelled your nitrate-free liverwurst).

Correlations with death from all medical causes, ages 35 to 69.

All aboard the Abbreviation Train! Choo-choo. For reference, PLNT =  plant, ANIM = animal, PROT = protein, and CHOL = dietary cholesterol. The variables preceded by the letter “M” are mortality statistics; the ones preceded by “P” are plasma measurements; the ones preceded by “U” are urine measurements; the ones preceded by “D” are foods from the diet survey; and the ones preceded by “Q” are from a questionnaire.

I’ve highlighted the food variables specific to either the plant or animal kingdom, so let’s take a gander at how they correlate with “all medical deaths.” Total plant food, percent of diet as plant protein, and wheat? All strongly positively associated with death from all medical causes, meaning that as intake of these things goes up, so does the risk of keeling over from something body-related. Total animal protein intake, percent of total calories as animal protein, egg intake, meat intake, red meat intake, fish intake, and consumption of dietary cholesterol? All strongly negatively associated with death from all medical causes, meaning that as intake of these foods goes up, medical mortality rates decline. Again, many of these associations may be—and probably are—totally meaningless, but they describe an important trend: For whatever reason, in China, the animal-food-eaters are living longer than their more plant-based counterparts.

…Which brings us to another problem. As we saw with heart disease in Norway, high rates of infectious disease can sometimes obscure the true prevalence of chronic disease—because folks are getting wiped out by short-term illness before they have a chance to die from things like cancer, strokes, or heart attacks. Even if their arteries are plaqued up the wazoo or their bodies riddled with tumors, it’ll be the tuberculosis, or the pneumonia, or the other infectious disease that shows up on the death certificate (and, subsequently, in the data). In the China Study, low animal food intake tends to be associated more with poor counties where malnutrition, unsanitary conditions, less education, and acute “diseases of poverty” prevail. For instance, here are some charts for three mortality variables associated with lower quality of living: death from all respiratory disease, death from all digestive disease, and death from pregnancy and childbirth complications. In each case, you can see the strong inverse associations with animal foods (except milk), and strong positive associations with a greater portion of the diet as plant foods. (For a complete key to all the variable abbreviations, check here.)

Correlations with death from all respiratory diseases, ages 35 to 69.

Correlations with death from all digestive diseases, ages 35 to 69.

Correlations with death from pregnancy and childbirth, women aged 34 and under.

Based on the above, we’d actually expect to see areas with higher animal food consumption also experience higher mortality from long-term diseases. Not because they actually have more of those diseases, but because there are fewer “diseases of poverty” to kill them off prematurely. Again, it’s all about what the death certificate says. And to quote a paper Campbell himself co-authored: “it is the largely vegetarian, inland communities who have the greatest all-risk mortalities and morbidities and who have the lowest LDL cholesterols.”

While we’re at it, here are some other relevant pages from the China Study II monograph—some “diseases of affluence.” If you’re sick of these charts, just keep scrolling ’til it’s over. I won’t be offended! Once again, correlations really don’t mean diddly squat here, but they do paint an interesting picture of how diet and mortality patterns interact… and again, it’s far from damning of animal foods.

Correlations with “death from all cancers.” Strong inverse associations with animal fat (ANIMFAT) and saturated fat (%SATFA); strong positive associations with millet and eggs:

Correlations with death from all cancers, ages 35 to 69.

Correlations with “death from heart disease.” Strong inverse associations with animal fat, rice, legumes, and green vegetables; strong positive associations with wheat flour, light-colored vegetables, fruit, and eggs:

Correlations with death from heart disease, ages 35 to 69.

Correlations with “death from stroke.” Strong inverse associations with percent of diet as animal protein, rice, poultry, fish, dietary cholesterol, legumes, and green vegetables; strong positive associations with wheat, percent of diet as plant protein, and percent of total calories from plant food:

Correlations with death from stroke, ages 35 to 69.

Correlations with “death from diabetes.” Strong inverse associations with milk, meat, red meat, and animal fat; strong positive associations with fruit and eggs:

Correlations with death from diabetes, ages 35 to 69.

And lastly (no, seriously, this is the last thing): Since we already know collections of plain-jane correlations can be totally misleading, here are some of the findings from researchers who analyzed the China Study data beyond the raw correlations—including adjustments for confounders. I wrote about these studies in greater depth in my one-year China Study Anniversary post, but here’s the Reader’s Digest version.

From “Erythrocyte fatty acids, plasma lipids, and cardiovascular disease in rural China” (PDF):

• “Within China neither plasma total cholesterol nor LDL cholesterol was associated with cardiovascular disease”
• “There were no significant correlations between the various cholesterol fractions and the three mortality rates [coronary heart disease, hypertensive heart disease, and stroke]“
• “The consumption of wheat flour and salt … was positively correlated with all three diseases [cardiovascular disease, hypertensive heart disease, and stroke]“
• “Red blood cell total polyunsaturated fats, especially the n-6 fatty acids, were positively correlated with coronary heart disease and hypertensive heart disease”

Just because.

Esselstyn: It’s a plant-based miracle!

Now that we have The One Message from the China Study neatly tucked into our brains, we turn our attention back to Dr. Esselstyn and his revolutionary research.

Minute 52:00—While Dr. Campbell was publishing his China Study, Dr. Esselstyn was getting some powerful data from the research he’d started in 1985. He began with 24 patients. But six had dropped out in the first year, leaving him with a total of 18. [Esselstyn:] “At the end of five years, we had follow-up angiograms, and 11 of the group had halted their disease. There was no progression. And there were four where we had rather exciting evidence of regression of disease.”

As the movie notes, this is pretty darn exciting. Even the most experienced, uber-credentialed doctors often believe that heart disease progression can only be slowed down—not stopped, and certainly not reversed. I salute you, O mighty broccoli!

But there’s something majorly funky with the movie’s description of this study. We’re told that Esselstyn ultimately ended up with 18 patients, 11 of whom halted their disease. Four folks regressed their disease, but we don’t know if these people are included in the 11 who didn’t get worse. And at any rate, 11 plus 4 doesn’t equal 18, so some folks have mysteriously vanished from the head-count. What’s up with the weird math?

After poking around for more detailed results of Esselstyn’s study, I found that—quite fortuitously—he posted the full text his papers right on his website. The five-year results are discussed here: A Strategy to Arrest and Reverse Coronary Artery Disease: A 5-Year Longitudinal Study of a Single Physician’s Practice. (Note the line of links near the top of the article for the full description of methods, results, discussion, and conclusion.)

In contrast to what we’re told in “Forks Over Knives,” Esselstyn’s paper says that he started with 22 patients, five dropped out, and six stayed on the diet but never came back for data collection—leaving Esselstyn with only 11 people in the study. (We’ll talk about why this is a problem in a moment.) Of those 11 folks, all had an “overall” stabilization of their heart disease, although four people did have lesions that slightly progressed. Depending on the method of analysis used (“mean percent stenosis” or “minimal lumen diameter”), either eight people or five people had evidence of regression in some of their arterial lesions. Aye, numbers!

No disrespect to Dr. Esselstyn and his work, but right off the bat, we can see there are some big problems with this study:

1. The drop-out rate was crazy high! Since the initial 22 patients got slashed down to 11, we have to consider why the other half of the group slipped off the radar. Was it because they were feeling bad on Esselstyn’s program? Did they experience repercussions from a plant-based diet that they perceived were even worse than heart disease? Were they sick of getting celery strings stuck between their teeth? When studies have a significant drop-out rate, the folks who stick around tend to be the ones having the most success, while the failures slink away—which ends up skewing the results to make the intervention look more effective than it may have truly been.
2. It was an uncontrolled intervention trial. That means there was a no control group to compare against the folks who got dietary and statin intervention, so we can’t estimate how many of their health changes were due to Esselstyn’s program and how many were due to chance.
3. It was a non-randomized study. The patients volunteered rather than being randomly assigned to treatment, creating a problem called “selection bias.” In this type of research, we know that folks who elect themselves for study may have different characteristics than the rest of the population, which is why many researchers use randomization to choose study subjects instead of letting people choose themselves.
4. A whole bunch of variables changed. This wasn’t a study that examined the effects of one component of diet; it did a complete menu overhaul, changing total fat intake, animal food intake, processed food intake, sugar intake, vegetable oil intake, and about ninety gazillion other things. Combined with that lack of a control group, it’s impossible to determine exactly which diet components had an effect on heart disease, and which were neutral (or even negative).

In addition, some effects of Esselstyn’s diet are a little alarming. In the “results” section of his paper, he displays the following table, which shows how his study subjects’ blood values changed during the intervention.

Let’s ignore the fact that those super-low total cholesterol levels are associated with higher rates of cancer, mental illness, infection, and other fun stuff (yes, your cholesterol can be too low) and focus instead on the other values. Holy triglycerides, Batman! Although Esselstyn’s diet helped lower most of his patients’ triglycerides, a couple still have values in the major danger zone (362?). Some of those HDL numbers are looking pretty sorry as well.

All in all, Esselstyn’s study shows that a whole-foods, plant-based diet is probably infinitely better for cardiovascular health than the junky cuisine many folks eat. But it’s far from conclusive evidence that this diet is the best we can do for reversing heart disease, or that it would generally be effective in a population beyond his 11 self-selected subjects. A diet that reduces triglycerides and increases HDL more than his did, for instance, might have an even better outcome.

That’s all, folks

For sure, “Forks Over Knives” has some other areas I could nitpick, such as Campbell’s statement that “animal protein tends to create an acid-like condition in the body called metabolic acidosis” and leads to osteoporosis (minute 1:03:20)—an unfounded belief that I already debunked in the “dairy” section of this post. But I think this critique covers the meatiest points. (Pun definitely intended.) And if you made it this far, hats off to you!

Now if you’ll excuse me, I have to go tend to my feedlot cows and cash my Meat Industry checks. Oops, did I say that out loud?

First item of business: The Ancestral Health Symposium. Due to some serendipitous events, it turns out I’ll be presenting at this hyperventilation-inducingly-awesome event next week. My lecture is at 10:00 AM on August 6th in the Rolfe 1200 auditorium. If you’re lucky enough to have a ticket, I hope to see you there, and to verify my existence for anyone who still thinks I’m a meat industry puppet. Otherwise, unless PETA pops in and sets fire to UCLA, all the presentations should be available online for free shortly after the symposium is over. Woohoo!

Second item of business: Now that he’s outed the project himself, I feel safe in announcing that Mark Sisson is going to be publishing the book I mentioned working on in an earlier blog post, and that it’ll be released mid-2012. I’m super excited, and couldn’t ask for a better publisher to work with. Or one with more impressive abs (see link above). More details to come in the near future.

Now on to the real point of this post.

One year ago, this happened:

Those are the monthly visits to this blog, according to my top-secret WordPress dashboard. And that crazy jump last July is when I posted my critique of the China Study, which is probably the only reason most of you know this blog exists. Thanks to Richard Nikoley’s smackdown roundup, the critique got passed along by a lot of cyber-hands, eventually reaching Campbell himself.

With the release of the movie Forks over Knives, Campbell’s recent appearance on the Bill Maher show, and continued Wikipedia drama about the peculiar lack of criticism on its “The China Study” page, it seems the China Study is back in the spotlight for awhile.

Since I’m not a vegan anymore, I figure it’s okay for me to beat dead horses. And also to resurrect the ones I buried last year and wallop on their half-rotten, fly-infested carcasses with my fists a few more times. So wallop I will: This post is dedicated to driving a couple more nails into the China Study coffin—and is aimed particularly at the folks out there who would rather listen to peer-reviewed research than some girl with a blog.

So my dear readers, hecklers, and spambots, I present to you a collection of peer-reviewed papers based on the China Study data that contradict or conflict with Campbell’s interpretation in his book, “The China Study.” Some studies you may have seen before; others will be new. Regardless, you can rest assured that these papers—some co-authored by Campbell himself—are by folks generally considered qualified in their field, and that, contrary to the “animal foods are harmful and cholesterol is associated with all Western diseases” message we received in “The China Study,”* other perspectives of the data abound.

*It’s also worth noting that”The China Study”—the one written by Campbell and published by BenBella Books—is not peer reviewed. Shockingly, neither are BenBella’s other books, including “You Don’t Talk About Fight Club,” “Seven Seasons of Buffy,” and “The Psychology of Harry Potter.” Contrary to what’s apparently popular belief, books—even health books—don’t have to pass under the scrutiny of peer review before they hit the stands. Hence why this exists.

But first, a few words on peer review

I want to burst the Peer-Review Bubble of Perfection before we get much further.

Peer review might be the best we’ve got right now—but it’s far from infallible, and its biases are no secret. In an article from 2000, Richard Horton—editor of the uber-peer-reviewed journal “The Lancet”—wrote some rather scathing comments about the peer-review system, stating that it is “biased, unjust, unaccountable, incomplete, easily fixed, often insulting, usually ignorant, occasionally foolish, and frequently wrong.” Even peer-reviewed journals have published papers on the problems with peer review (e.g., this article in “Nature”).

To top that off, history is speckled with some disturbing cases of research fraud that slipped right through the peer-review system. The best example is Scott Reuben, once considered a pioneer in the field of anesthesiology and pain management, who concocted at least 21 “studies” that were pure works of fiction—and managed to get all of them published in peer-reviewed journals. Over the years, he accepted big bucks from pharmaceutical companies to conduct studies on drugs like Celebrex and Effexor, but instead of actually enrolling patients, he made up some numbers and slid his nonexistent findings into major publications. And as it turns out, many of the drugs he convinced the world were beneficial were either ineffective of downright harmful. (You can see a list of his falsified peer-reviewed papers here.)

That’s an extreme example, but it illustrates the point. Even peer-reviewed papers should be taken with a grain of salt instead of held as gospel.

And with that, here are some papers to mull over. The bolded parts within quotes are to highlight the relevant bits.

Erythrocyte fatty acids, plasma lipids, and cardiovascular disease in rural China by Fan Wenxun, Robert Parker, Banoo Parpia, Qu Yinsheng, Patricia Cassano, Michael Crawford, Julius Leyton, Jean Tian, Li Junyao, Chen Junshi, and T. Colin Campbell.

A study involving fat, cholesterol, and cardiovascular disease? Surely they must be talking about how all that nasty saturated fat in animal foods clogs your arteries, right? Oh, snap:

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.

There were no significant correlations between the various cholesterol fractions and the three mortality rates [coronary heart disease, hypertensive heart disease, and stroke]. In contrast, plasma triglyceride had a significant positive association with CHD and HHD but not with stroke.

We’ve even got a cameo appearance from wheat again:

The consumption of wheat flour and salt (the latter measured by a computed index of salt intake and urinary sodium excretion) was positively correlated with all three diseases [cardiovascular disease, hypertensive heart disease, and stroke].

And for those of your leery of industrial oils and polyunsaturated fats, check this out:

Unlike what might be expected from studies on Western subjects, there was no significant inverse correlation between RBC-PC total PUFAs and CVD mortality; in fact, RBC-PC total PUFAs, especially the n-6 fatty acids, were positively correlated with CHD [coronary heart disease] and HHD [hypertensive heart disease].

That one was a little acronym-y, but basically it says that higher levels of polyunsaturated fats (especially omega-6 fats) in red blood cells was associated with more heart disease.

So if you don’t want to take my word for it, take the word of this peer-reviewed paper: The China Study data showed no correlation between cholesterol and heart disease, but did find wheat and polyunsaturated fats to be mighty suspect.

Association of dietary factors and selected plasma variables with sex hormone-binding globulin in rural Chinese women (PDF) by Jeffrey R. Gates, Banoo Parpia, T. Colin Campbell, and Chen Junshi.

Wheat-fearers, you’ll enjoy this one.

This study focuses on sex hormone-binding globulin (SHBG), a molecule sometimes used to test for insulin resistance. (Higher levels are associated with better insulin sensitivity; lower levels are associated with insulin resistance and diabetes.) After fishing out associations between SHBG, fasting insulin, triglycerides, testosterone, and a number of diet and lifestyle variables, the researchers found:

The principal positive food-SHBG correlates in order of magnitude were rice (0.61, P < 0.0001), green vegetables (0.49, P < 0.001), fish (0.42, P < 0.001), and meat (0.38, P < 0.05). The strongest negative food correlate with SHBG (positively correlated with insulin) was wheat (-0.57, P < 0.0001).

In other words, the foods associated with higher SHBG (and lower insulin) were rice, green veggies, fish, and meat. The main food associated with lower SHBG (and higher insulin) was… dun dun dun… wheat. Not only do we have vindication of some animal foods, but we also have another red flag whipping up over our favorite glutenous grain. Although the link with meat diminished in more sophisticated statistical models, the other foods kept their associations with SHGB—and wheat proved to be the strongest predictor of low SHBG, while rice was the strongest predictor of higher SHBG. In discussing their findings, the researchers note that wheat seemed to accompany a number of markers for poor health:

Significant differences in the diet of rural Chinese populations studied suggest that wheat consumption may promote higher insulin, higher triacylglycerol, and lower SHBG values. Such a profile is consistent with that commonly associated with obesity, dyslipidemia, diabetes, hypertension, and heart disease. On the other hand, the intake of rice, fish, and possibly green vegetables may elevate SHBG concentrations independent of weight or smoking habits.

It looks like the post I wrote on wheat and heart disease in the China Study was old news: Campbell and his colleagues already spotted the link back in 1996! But why would wheat have such a vastly different effect than rice? The paper offers a possible explanation:

The effect of rice and wheat on SHBG was remarkable and unexpected. … Nevertheless, there is some evidence to suggest that rice and wheat can have significantly different effects on the biochemical variables we measured. Panlasigui et al (58) found that the high-amylose rice varieties had blood glucose responses that were lower than those of wheat bread. Other varieties, particularly “converted” rice, gave considerably higher values. Miller et al (59) in comparing rice and wheat varieties found that the insulin index (II) was unusually low on the relative scale compared with the glycemic index (GI) of the same foods. For example, Calrose brown rice had a GI = 83 but an II = 51. White bread was used as the reference food (GI = 100, II = 100). Wheat may be unique in its relative capacity to stimulate insulin. Most recently, Behall and Howe (60) reported a significantly lower insulin response curve area in both normal and hyperinsulinemic men consuming a high-amylose diet. The relative differences in the fatty acid proportions and/or amylose content for wheat and rice may thus be responsible for modulating serum SHBG, triacylglycerols, and insulin.

Although it’s still speculative, the amount of amylose (a component of starch) and relative proportion of fatty acids in wheat might make it more problematic than other grains like rice—especially in terms of raising triglycerides and fasting insulin while lowering SHBG. Which is particularly interesting in the context of this paper, because in one of his responses to my critique, Campbell stated:

[The] correlation of wheat flour and heart disease is interesting but I am not aware of any prior and biologically plausible and convincing evidence to support an hypothesis that wheat causes these diseases, as you infer.

Go figure!

Prolonged infection with hepatitis B virus and association between low blood cholesterol concentration and liver cancer (PDF) by Zhengming Chen, Anthony Keech, Rory Collins, Brenda Slavin, Junshi Chen, T. Colin Campbell, and Richard Peto.

This one’s a doozy. But first, some background info to help us understand the full extent of its doozydom.

One of the prevailing themes in “The China Study” is a supposed link between cancer and animal protein (and subsequently, blood cholesterol). Campbell first started chasing this association after finding that casein, a milk protein, raised cholesterol and promoted liver cancer growth in rats exposed to aflatoxin. After reviewing the China Study data, Campbell concluded this link held true in humans: He states that animal protein, as reflected by higher cholesterol levels, promoted liver cancer in folks already at risk for it (namely those who carried the hepatitis B virus). Straight from the book:

In addition to the [hepatitis B] virus being a cause of liver cancer in China, it seems that diet also plays a key role. How do we know? The blood cholesterol levels provided the main clue. Liver cancer is strongly associated with increasing blood cholesterol, and we already know that animal-based foods are responsible for increases in cholesterol. … Individuals who are chronically infected with HBV and who consume animal-based foods have high blood cholesterol and a high rate of liver cancer. The virus provides the gun, and bad nutrition pulls the trigger. (Page 104)

People chronically infected with hepatitis B virus also had an increased risk of liver cancer. But our findings suggested those who were infected with the virus and who were simultaneously eating more animal-based foods had higher cholesterol levels and more liver cancer than those infected with the virus and not consuming animal-based foods. (Page 105)

Seems clear enough. According to Campbell, the data showed that liver cancer went hand-in-hand with high cholesterol in the China Study data.

…Which is what makes this particular study (co-authored by Campbell, nonetheless) so interesting. Campbell et al. set out to investigate “the association between low blood cholesterol concentration and liver disease by studying blood lipid concentrations about middle aged men in rural China.” Already seems fishy, huh? Before even discussing the study results, the paper includes some sections that contradict the notion that high cholesterol is linked with liver cancer:

Several prospective epidemiological studies … have found an inverse relation between cholesterol concentration and the subsequent risk of cancer. … A prospective observational study in a Chinese population … found a significant inverse association between blood concentration of cholesterol and subsequent mortality from non-malignant liver disease or from liver cancer. More recently significant excess risk of death from liver cancer and chronic liver disease has been reported among North Americans with a low blood cholesterol concentration.

In the largest study in a Western population (the multiple risk factor intervention trial) 100 deaths from liver cancer were recorded during the follow up period, and a significantly increased risk of death from liver cancer was found among people in the group with the lowest cholesterol concentrations.

In our previous prospective study of another Chinese population the subsequent risk of death from liver cancer was shown to increase significantly with decreasing blood concentrations of cholesterol.

Whether low cholesterol is a cause or a consequence of cancer is a different story—but either way, there’s no mistaking it: Liver cancer looks pretty solidly linked with low cholesterol in other relevant studies. And this study does nothing to refute that:

We have now shown that prolonged infection with hepatitis B virus is an additional factor contributing to the inverse relation between cholesterol concentration and liver cancer. Chronic hepatitis B, which usually starts in early childhood in China, leads not only to liver disease but also to a lower blood concentration of cholesterol in adulthood. This produces, as observed elsewhere, an inverse relation between cholesterol concentration and the risk of death from liver cancer or from other chronic liver disease. This result may also help to explain, at least in part, the inverse association between cholesterol concentration and liver disease observed in Western populations.

So why did Campbell repeatedly state in “The China Study” that liver cancer was associated with higher cholesterol? Probably because it was—but only after the (more reliable) individual data was aggregated at the county level, which made it easy to succumb to a little somethin’ called the “ecological fallacy.”

Let me explain. The publicly available China Study data—the stuff I used for my critique, and that Campbell pulled correlations from in his book—consists of averaged values for 65 counties, instead of the thousands of data points originally collected. But this particular study used the individual data, before any of it was combined and diluted by averaging. And as the paper explains, that makes this study much more reliable than the ones using aggregated data:

In this study there was a negative correlation between chronic infection with hepatitis B virus and blood concentrations of cholesterol (and apolipoprotein B) when people living in the same village were compared with each other, but the correlation was reversed when average values for different villages were compared with each other.

Correlations between populations based on average measures in groups are subject to the “ecological fallacy” (whereby these correlations may not represent the correlations that would be seen among individual subjects). … In general, comparisons within populations are much more reliable than comparisons between populations when assessing association of variables and diseases in individual subjects. So, in the present instance, the negative correlation observed when people living in the same village were compared with each other provides the most reliable evidence as to the real relation between chronic infection with hepatitis B virus and lipid concentrations in individual subjects.

This is kind of fascinating. Here we’ve got a China-Study-based paper—again, co-authored by Campbell—that explicitly describes why aggregated data can be unreliable, and why positive links between liver cancer variables and cholesterol are probably backwards. This also begs the question of how many other associations in the aggregated data would be reversed at the individual level. (Or whether any seemingly neutral relationships are actually correlated—such as liver cancer and the carcinogen aflatoxin, which are paradoxically unassociated in the aggregated data.)

Here’s an example to help illustrate the concept of “ecological fallacy” as it relates to liver cancer in China.

Say we’ve got two counties, each with 1000 residents. Folks in the first county have total cholesterol ranging from 130 to 150, with the average being 140. These lucky ducks are free from liver cancer or hepatitis B infection! The second county has total cholesterol ranging from 120 to 190, with the average being 170. Much more liver cancer and hepatitis B infection in this place, but the afflicted folks all have cholesterol at the bottom end of the spectrum, around 120.

What happens if we look only at the aggregated data? We’d see that the county with the lower average cholesterol (140) had lower rates of liver cancer, and the county with higher average cholesterol (170) had higher rates of liver cancer. Thus, a liver cancer/higher cholesterol relationship is born. But what happens if we look at the individual data instead? We’d see that the people with liver cancer had lower cholesterol than any of the healthy folks—the exact opposite of what the averaged data showed.

See how tricky numbers can be?

At any rate, this (peer-reviewed!) study blatantly contradicts some of the claims made in “The China Study,” especially the concept that cancer goes hand-in-hand with high cholesterol.

Dietary calcium and bone density among middle-aged and elderly women in China (PDF) by Ji-Fan Hu, Xi-He Zhao, Jian-Bin Jia, Banoo Parpia, and T. Colin Campbell.

True to its title, this paper examines the role of calcium in bone density in the China Study data—with a special focus on the effects of dairy calcium versus plant calcium. Campbell et al. zoomed in on five counties with “distinct lifestyles and diets”: the dairy-and-meat-loving Xianghuangqi, the infamous dairy-full Tuoli, and the rural, nearly-vegan farming towns of Jiexiu, Cangxi, and Changle.

But before we look at the paper itself, let’s see how Campbell summarized its findings in a Cornell Chronicle article in 1994:

Animal protein, including that from dairy products, may leach more calcium from the bones than is ingested, said Campbell, professor of nutritional biochemistry at Cornell and director of the Cornell-China-Oxford Project, the most comprehensive project on diet and disease ever conducted.

Campbell [and other collaborators] analyzed the role of dietary calcium in bone density by following closely the diets of 800 women from five counties that have very different diets in China. … Analyses of these data suggest that increased levels of animal-based proteins, including protein from dairy products, “almost certainly contribute to a significant loss of bone calcium while vegetable-based diets clearly protect against bone loss,” Campbell reported.

Sounds pretty clear: The dairy-eating counties must have had poor bone health due to their animal protein habit, whereas the more plant-based dieters were skeletally superb. In other words, milk does a body bad! But do the summaries above match up with this paper actually found? First, let’s look at what the women in each county were typically eating:

*Lest I get the “you’re trying to justify your dairy addiction” line and/or accusations of dairy industry affiliation, I’d like to remind everyone that dairy hasn’t been part of my diet in over six years, and I believe the dairy most people consume (low-fat, ultra-pasteurized, etc.) is downright nasty stuff. But that doesn’t mean I won’t defend dairy when the science warrants it.

As you can see, Xianghuangqi ate a pretty shabby diet as far as whole-foods veganism is concerned: We’ve got dairy galore, beef, mutton, wheat flour, a mere smattering vegetables, and millet. Their bones should be snapping like peanut brittle! Tuoli’s not much better, what with their milk tea, animal flesh, and decided lack of green leafy veggies. More bone snappage, right?

I’ll let the paper speak for itself:

Analysis by individual for all counties combined showed that [bone mineral content] and [bone mineral density] were correlated positively with total calcium (r = 0.27-0.38, P < 0.0001), dairy calcium (r = 0.34-0.40, P < 0.0001), and to a lesser extent with nondairy calcium (r = 0.06-0.12. P = 0.001-0.100), even after age and/or body weight were adjusted for. The results strongly indicated that dietary calcium, especially from dairy sources, increased bone mass in middle-aged and elderly women by facilitating optimal peak bone mass earlier in life.

Did you catch that? Dairy calcium—far more than plant calcium—was linked with stronger bones. Moreover, the paper notes that “nondairy calcium … showed no association with bone variables after age and/or body weight were adjusted for.”

Continuing on:

Comparison of results in Table 7 reveal that calcium from dairy sources was correlated with bone variables to a higher degree than was calcium from the nondairy sources, probably resulting from the higher bioavailability of dairy calcium.

A comparison of the bone mass of women in the five counties revealed that 20% greater bone mass at the distal radius was observed for all age groups of women in county YA [Xianghuangqi], a pastoral county with high consumption of dairy foods, as compared with the nonpastoral areas with lower calcium intakes.

I’ll add my own unsolicited 2¢ and speculate that calcium probably wasn’t the only protective factor in the dairy-eating counties. Aged cheese, likely consumed at least in Xianghuangqi, is high in vitamin K2—a nutritional superstar when it comes to bone health (among other things). K2 isn’t present in plant foods except for a fermented soy product called natto (not everyone’s cup o’ tea). As the paper notes, the dairy-eating counties also had a higher intake of fat (25% of daily calories, opposed to 9.9 – 13.6% for the other counties), potentially increasing the absorption of fat-soluble vitamins necessary for bone health.

So how did Campbell conclude from this study that “increased levels of animal-based proteins, including protein from dairy products, almost certainly contribute to a significant loss of bone calcium”? The dairy part is unfounded no matter which way you spin it, but the rest of his statement probably stemmed from this:

The associations between bone mass and other nutrients, like dietary protein and phosphorous, were also examined. However, none of these nutrients showed an association with bone mass as significantly as did dietary calcium, although an inverse correlation was observed consistently for nondairy animal protein.

Unfortunately, that’s the only blurb in the entire paper that mentions animal protein in relation to bone mass, so we can’t see the data behind the “consistent inverse correlation.” In the context of this study, though, it makes sense: Protein has a complex relationship with bone formation, serving as a synergist when calcium intake is adequate, but as a potential antagonist when calcium intake is low. In other words, the effects of protein on bone health depend on how much calcium you’re taking in.

So for the counties in this study that ate more animal protein but sparse calcium—such as Changle, which had the highest non-dairy animal food consumption and also the lowest calcium intake (averaging a mere 230 mg per day)—I wouldn’t be surprised if an animal protein/weaker bones connection showed up. Whether that trend would hold at higher calcium intakes is a different story. And either way, this finding doesn’t jive with most other research done on this topic: Most studies show a protective association between animal protein and bone density, formation, and retention:

• Prospective study of dietary protein intake and risk of hip fracture in postmenopausal women. “Protein from animal sources was the nutrient variable with the strongest negative association with risk of hip fracture in this prospective study of Iowa women. Protein from vegetable sources did not appear to protect against hip fractures.”
• Effect of Dietary Protein on Bone Loss in Elderly Men and Women: The Framingham Osteoporosis Study. “Contrary to expectations, elders with animal protein intake up to several-fold greater than the RDA also had the least bone loss after controlling for known confounders. Nonanimal sources of protein were not related to BMD. These results suggest that typical population intakes of animal protein, within the range commonly consumed, do not result in bone loss. Rather animal protein intake appears important in maintaining bone or minimizing bone loss in elderly persons.”
• Protein Consumption and Bone Mineral Density in the Elderly. “Multiple linear regression analyses … showed a positive association between animal protein consumption … and BMD. Conversely, a negative association between vegetable protein and BMD was observed in both sexes. … This study supports a protective role for dietary animal protein in the skeletal health of elderly women.”
• Controlled High Meat Diets Do Not Affect Calcium Retention or Indices of Bone Status in Healthy Postmenopausal Women. “Calcium retention is not reduced when subjects consume a high protein diet from common dietary sources such as meat.”
  

In addition, if animal protein was such a bone-killer and plant protein was bone protective, we’d see vegetarians or vegans having the best outcomes in the bone department. But this just ain’t the case. At best, non-meat-eaters are equally matched with their omnivorous counterparts; at worst, they’re more prone to fracture:

• Veganism and osteoporosis: A review of the current literature. “The findings gathered consistently support the hypothesis that vegans do have lower bone mineral density than their non-vegan counterparts.”
• A Comparison of Bone Mass Measurements of Vegetarians and Omnivores. “In this review of 9 cross-sectional and 1 longitudinal study, little statistical significance between bone density and bone content was found between vegetarians and omnivores.”
• Effect of vegetarian diets on bone mineral density: a Bayesian meta-analysis. “The results suggest that vegetarian diets, particularly vegan diets, are associated with lower BMD, but the magnitude of the association is clinically insignificant.”
• Long-Term Vegetarian Diet and Bone Mineral Density in Postmenopausal Taiwanese Women. “Long-term practitioners of vegan vegetarian were found to be at a higher risk of exceeding lumbar spine fracture threshold … and of being classified as having osteopenia of the femoral neck.”

So, although the “calcium-leeching” properties of animal protein is a common battle cry in the vegan world, the research just doesn’t support it. There are even some interesting (and peer-reviewed!) papers out there looking at how belief systems influence the interpretation and misrepresentation of bone/protein studies. Read that link because it’s awesome.

But back on topic. This paper, with Campbell’s own name on it, suggests a strongly bone-protective role for dairy in the diet. Not quite the message we heard in “The China Study.”

Reply to TC Campbell by Frank B. Hu and Walter Willett.

Short n’ sweet, this one speaks for itself. Frank Hu and Walter Willett responded to Campbell’s criticism of their findings in the Nurses’ Health Study*, and in so doing, offered their take on the China Study.

Campbell questioned the validity of our findings because they contradict the results of international correlation studies on animal product consumption and disease rates. … Correlational studies conducted within a country can usually provide more credible data than international comparisons because of relatively homogeneous populations and the possibility of collecting data on potential confounding variables at individual levels. A survey of 65 counties in rural China, however, did not find a clear association between animal product consumption and risk of heart disease or major cancers.

*The Nurses’ Health Study has its share of problems, and I actually agree with Campbell’s assessment in some cases, but that’s a story for a different day.

Correlation of Cervical Cancer Mortality with Reproductive and Dietary Factors, and Serum Markers in China by Wan-De Guo, Ann W. Hsing, Jun-Yao Li, Jun-Shi Chen, Wong-Ho Chow, and William J. Blot:

As this paper notes, cervical cancer is the second leading cause of cancer-related deaths among Chinese females. There are a few known risk factors (especially herpes infection), but other than that, the reason for its variation across China is a big mystery. Can we blame animal foods for this one?

After identifying the variables that had the strongest correlations with cervical cancer in the China Study data—including herpes infection, serum ferritin, body mass index, cigarette smoking, age at first birth, green vegetable intake, and animal food consumption—the researchers ran multiple regressions to see which correlations were legit. The results?

When these variables were considered in the multiple regression analysis, early age at first birth and higher BMI were positively associated with cervical cancer mortality, while consumption of green vegetables and animal foods were negatively correlated.

Simply put, animal foods were inversely associated with death from cervical cancer—meaning the folks eating more animal products had fewer deaths from this disease. If it were true that animal protein promotes the growth of cancer cells, as Campbell theorized based on his research with casein and liver cancer, then we’d expect to see the opposite. What an anomaly.

Risk Factors for Stomach Cancer in Sixty-Five Chinese Counties (PDF) by Robert W. Kneller, Wan-De Guo, Ann W. Hsing, Jun-Shi Chen, William J. Blot, Jun-Yao Li, David Forman, and Joseph F. Fraumeni, Jr.

In this paper, the authors pulled out variables that had strong correlations with stomach cancer in the China Study data, and then analyzed them in greater depth using some regression models. The most significant associations involved plants—but since the focus of this post is animal foods, let’s see what the researchers uncovered on that front:

Consumption of green vegetables, rice, meat, and fish was associated with reduced mortality. … On the other hand, salt-preserved vegetables, potatoes, wheat, and millet, plus combinations of wheat, corn, and millet, were correlated with significantly increased mortality.

Our finding of a significant inverse association for meat is consistent with a recent case-control report from Turkey. Meat is a common source of selenium, which showed the strongest protective effect among all the plasma micronutrients.

Say what? Not only did meat not seem to increase stomach cancer rates (which we might expect if Campbell’s “animal protein spurs cancer growth” hypothesis held true), but it actually showed the opposite trend. Perhaps the selenium was enough to counteract meat’s general evilness. Also interesting is that the foods associated with increased stomach cancer mortality were mainly of plant origin. Including wheat.

However, this paper did uncover a relationship between stomach cancer and one animal food: eggs. Given the known associations between stomach cancer and salt-preserved foods (and lack of association with any other animal food), I’d wager this link has something to do with the way eggs are eaten in China rather than anything inherent in the eggs themselves. China is known for dishes like salted duck eggs, which are preserved with salt or charcoal, and century eggs, which sit for weeks or months in a mixture of ash, salt, clay, and other ingredients, gradually becoming glossy balls of ammonia stench. It seems likely that preserved eggs could increase stomach cancer risk for the same reason preserved vegetables do.

Century egg. Nom, nom, nom.

The researchers also note that they “know of no previous reports linking egg consumption to increased [stomach cancer] risk” and that the counties with high egg consumption had other confounders not accounted for—including a tendency to be in coastal areas, have a higher percent of industry-employed workers, and have higher indexes of socioeconomic status. In addition, the range of egg intake in China may be too narrow to determine anything meaningful: The eggiest county ate the equivalent of only two or three chicken eggs per week, and the average for all counties was about 1/15th of an egg per day.

Nonetheless, no other animal foods, nor animal protein as a whole, contributed to stomach cancer risk in this analysis. Which is pretty interesting, because Campbell still links stomach cancer with animal foods via blood cholesterol in his book (pages 78 – 79):

What a surprise we got! As blood cholesterol levels decreased from 170 mg/dL to 90 mg/dL, cancers of the liver, rectum, colon, male lung, female lung, breast, childhood leukemia, adult leukemia, childhood brain, adult brain, stomach and esophagus (throat) decreased.

Yet as meat consumption increased, stomach cancer decreased. How curious!

Fish consumption, blood docosahexaenoic acid and chronic diseases in Chinese rural populations by Yiqun Wang, Michael A. Crawford, Junshi Chenb, Junyao Li, Kebreab Ghebremeskel, T. Colin Campbell, Wenxun Fan, Robert Parker, and Julius Leyton.

As an animal food rich in protein, fish should top the list of health villains—at least according to the animal protein/disease theory. Was that the case in this study? From the full text (not linked above):

Our finding that the highest blood cholesterol levels in the Chinese were associated with DHA and fish consumption but with the lowest risk [of heart disease], is also a contradiction of what might be expected.

The higher blood LDL cholesterol levels associated with the marine coastal and lacustrine communities in China as compared with their inland neighbours, needs to be seen as starting from very low levels. In this context, it is the largely vegetarian, inland communities who have the greatest all risk mortalities and morbidities and who have the lowest LDL cholesterols. It could well be that there is a minimum level of LDL cholesterol below which cell membranes are adversely affected.

Translations: In the China Study data, fish-eaters (with higher cholesterol, to boot) were generally healthier than the more vegetarian populations that didn’t consume seafood.

And here’s a nice table showing the associations between red blood cell concentration of DHA (which the researchers determined was mostly due to fish intake) and chronic diseases. (The bars to the left of the center line indicate a negative or “protective” correlation; the bars to the right are positive.)

The researchers note that the liver cancer correlation has a likely explanation:

[It] is not difficult to visualise the reason for the link with liver cancer. The coastal, estuarine and lacustrine regions with the high fish and sea food intakes are also those with the highest humidities. Storage of food in regions of high humidity is known to encourage the spread and growth of hepatitis B virus and Aspergillus flavus which produces aflatoxin, both are major causes of primary carcinoma of the liver.

Diet and Blood Nutrient Correlations with Ischemic Heart, Hypertensive Heart, and Stroke Mortality in China by Wande Guo, J.Y. Li, H. King, and F.B. Locke.

Here we’ve got a paper reporting some of the correlations between blood markers, food intake, and cardiovascular disease in the China Study data. Yep, cardiovascular disease! We should see animal products splattered all over this one, right?

Five variables were positively correlated: triglycerides and herpes antibodies with ischemic heart disease; salt and phosphorus (females) with hypertensive heart disease; and only albumin (males) with stroke. … Some findings confirm those observed in the West (salt, triglycerides, herpes, legumes, oleic acid, and liquor), but molybdenum and age at first pregnancy have not been emphasized previously. Still others significant in the West have not been observed here, such as cholesterol and smoking.

This bears repeating: This correlative study (the kind Campbell drew heavily from to link animal products and disease in the China Study data) found no association between cardiovascular diseases and cholesterol. Nada. Or smoking, which is also pretty interesting. How peculiar that one of the most monstrous “diseases of affluence” was unrelated to blood cholesterol.

Okay, folks: That should be enough to chew on for now. I hope to see some of you at the Ancestral Health Symposium in a few days!

I just learned from a highly reliable source that I am not a “private blogger,” but rather, “very likely a large scale underground defamation campaign against Dr.Campbell.” As a result, all mention of my critique—AKA the Minger Scam—has been yanked from Wikipedia’s “The China Study” page by a vegan editor there. The rationale is as follows:

Just tell me, which “private fun blogger” is able, aside of her alleged full time work and study of “English literature”, to write 36 pages of scientific responses to a professor?!! And again and again??? Either “she” is some sort of very mighty – and very mad and crazy and hate filled – genius, which in itself would be something extremely rare and highly unlikely (really, why would a pretty young girl have so much reason for such a giant ordeal, fight, all that massive work, all that hate???) … Or “she” is in reality another underground [campaign].

Whoops—my bad! I forgot females aren’t supposed to think or write stuff; we’re here to take Home Ec and vacuum in stilettos and learn how to become Good Wives:

On behalf of Minger Scam, Inc., I apologize for any inconvenience we may have caused.

Now onto business.

I’ve got graphs, graphs, graphs galore, but they aren’t really relevant to the upcoming wheat post, so I’m plopping them here instead. In my first China Study critique, I looked at some mortality differences between the five counties that ate the most animal foods and the five counties that ate the least. Here, I’m doing something similar—except this time I’ll be comparing the counties with the super-highest and ultra-lowest heart disease rates and seeing what they do differently in terms of diet.

One of the incredible things about China is the vast difference in heart disease mortality between regions. One county, Fusui, has only 1.5 per 100,000 deaths attributable to heart disease—whereas another county, Dunhuang, has a whoppin’ 184. That’s even more than the US’s figure of 106.

In case graphs freak you out, here’s a summary of what’s below:

• The healthy-hearted regions almost universally had higher intakes of animal fat, animal protein, dietary cholesterol, and saturated fat than the heart-disease-prone regions.
• The healthier regions generally had lower intakes of fiber, light-colored vegetables, plant protein, vegetable oil, and—big surprise—wheat flour.
• Consumption of green vegetables didn’t differ significantly between the high and low heart disease regions. Neither did smoking rates, total cholesterol, or non-HDL cholesterol, although HDL cholesterol looks slightly higher in the regions with excellent heart health.

Does this “prove” anything about diet and heart disease? Nope—there’s the curse of epidemiology again. But we can make the observation that some regions in China exhibited astonishingly low rates of heart disease while eating more animal foods than the Chinese average. And the county with the absolute lowest consumption of animal foods, Longxian, had the second highest rate of heart disease mortality out of all the counties studied. (For the record, I used the China Study II data for this, all of which is available online.)

Key for the graphs:

Red bars, high heart disease rates—left to right:

1. VB = Dunhuang
2. TD = Longxian
3. WC = Tulufan
4. XA = Yongning
5. CC = Jiangxiang

Blue bars, low heart disease rates—left to right:

1. PD = Fusui
2. NC = Qiyang
3. PA = Cangwu
4. NB = Mayang
5. NA = Linwu

Green bar:

Average value for all counties studied in the China Study II.

The graphs should be pretty easy to understand without me yapping away, so without further ado, here ya go.

(Not only is this woefully, frustratingly, absurdly belated, but it’s also not yet finished. But I hate being a blog tease, so here’s part one!)

If you’ve been following along with the previous China Study entries (and the wild drama that ensued), you know that I’ve been promising an entry on wheat for a while now, mostly because this little snippet snagged so many eyes:

Correlation between wheat flour and coronary heart disease: 0.67

That’s a value straight from the original China Study data. Could the “Grand Prix of epidemiology” have accidentally uncovered a link between the Western world’s leading cause of death and its favorite glutenous grain? Is the “staff of life” really the staff of death? Bwah ha ha.

Damning as it seems, a single unadjusted correlation isn’t enough to make that leap. Actually, nothing in this post will be enough to make that leap, because A) it’s epidemiological data and not a controlled study, and B) correlation isn’t causation anyhow. You know the drill.

So my goal here isn’t to prove anything about wheat. Mostly, I want to see if I can find a confounder that’s creating a false association between wheat and heart disease in the China Study data. Something wheat-eating regions have in common that makes them more susceptible to ticker troubles. Because really, folks, this is serious business:

And when we pluck out the wheat variable from the 1989 China Study II questionnaire—which has more recorded data—and consider potential nonlinearity, the outcome is even creepier:

Wowza! By the way, wheat flour also correlates significantly with hypertensive heart disease and stroke, but I’m mainly going to look at coronary heart disease in this post. (And although wheat looks like it could have a nonlinear relationship with heart disease, with the highest wheat eaters having disproportionately steeper rates than non-wheat eaters, I’m going to treat it as linear for the sake of this analysis. That way, the worst that’ll happen is we’ll underestimate the potential effect of wheat, which—for now—is better than overestimating it.)

Since I’m not trying to dissect our friend Campbell’s claims anymore, I’ll be using the China Study II data (from 1989) because it recorded more descriptive variables about diet and blood samples.* And because it’s already available online. (Not that I don’t love typing thousands of numbers onto my computer by hand. Three cheers for data-entry-induced carpal tunnel!)

*Quickie note: If you want to play with the China Study numbers yourself, I recommend not just using the “all vascular diseases” variable, because it includes rheumatic heart disease—a condition spawned by rheumatic fever and generally unrelated to diet. Lumping diseases with different etiologies together dilutes the strong correlations you can find by looking at each disease independently. Try checking out stroke (M065 STROKE), ischaemic heart disease (M063 IHD), and/or hypertensive heart disease (M062 HYPTENS)  along with all vascular diseases (M059 ALLVASC).

Here’s the problem with looking at wheat and heart health. Along with correlating pretty darn strongly with heart disease, wheat-eating regions boast  a number of other factors possibly involved as well—some as protective agents and some as causative. For instance, wheat flour correlates significantly and inversely with:

• Plasma folate concentrations (and consequently, homocysteine status)
• Fish intake and DHA levels
• Yearly green vegetable consumption
• HDL cholesterol
• Vitamin C intake

And it correlates significantly and positively with:

• Height, weight, and BMI
• Blood pressure
• Latitude (as a possible marker for vitamin D status)
• Yearly milk intake
• Polyunsaturated fatty acid intake

Since all of these variables also associate (inversely or positively) with heart disease, it’s possible they could be confusing the “0.67″ figure we’ve cited for wheat. Could some other, non-grain component of the wheat eaters’ diets predispose these folks to heart disease?

On the bright side, China’s wheat eaters are less likely to drown than the wheat-shunners (r = -0.68 for the youngsters under 34). Maybe they’re all buoyant from celiac bloat.

And in case you’re wondering, here are some heart disease risk factors (the ones Conventional Nutritional Wisdom likes to toss around) that don’t positively correlate with wheat. That means we probably can’t blame ‘em for wheat’s dirty deeds. Out of curiosity, though, I’ll still include them in some of my models just to see how they behave in relation to wheat with heart disease.

• All meat intake (r = -0.35)
• Red meat intake (r = -0.30)
• Animal fat intake (r = -0.35)
• Saturated fat intake (r = -0.40)
• Total animal protein intake (r = -0.27)
• Total fat intake (r = -0.43)
• Fat as a percentage of total calories ( r = -0.41)
• Total cholesterol (r = -0.05)
• Apolipoprotein B (r = 0.02)
• Daily alcohol intake  (r = -0.37)

Mostly, what I’m looking for is a little somethin’-somethin’ that both wheat flour and heart disease have in common. A shared variable that could be slyly—and wrongfully—framing wheat as our heart-harming villain.

So how do we untangle all these variables? I’m using two methods: multiple regression analysis and stratification. Multiple regression is a handy way of looking at two or more variables and seeing how each one behaves when the others are held constant, and stratifying data can work similarly by divvying up data into groups that share or exclude a certain variable. (For the stats junkies out there, I’m using ordinary least squares for the regressions, and I’m running each model two times: once with the data as-is, and once with any non-normally-distributed dependent variables transformed (via natural log) for more reliable statistical significance testing. I’m also checking for linearity between the variables before creating each model, since a nonlinear relationship will be underestimated with linear regressions.)

And for anyone not familiar with statistics terminology, here’s a really quick rundown of what you need to know to understand the numbers in this post:

• r = the Pearson product-moment correlation coefficient  between two variables. It can range from -1 to 1. When it’s zero or close to zero, there’s pretty much no relationship between the variables. When it’s a negative number (like r = -0.50), there’s an inverse relationship between the variables, meaning one increases as the other decreases. When it’s a positive number (like r = 0.50), there’s a positive relationship between the variables, meaning they increase and decrease hand-in-hand. The closer to -1 or 1 r is, the stronger the association. R can never prove cause and effect, though—it only indicates an relationship of some sort.
• beta = the standardized coefficient for each variable in the multiple regressions I’ll  be running. This is a lot like r, in the sense that it shows how well a specific variable is predicting the outcome (eg, heart disease) and also ranges from -1 to 1. But in the case of beta, we’re also controlling for the effects of other variables, so this number tends to be more accurate than r.
• p = the probability that our results are just a fluke. P indicates how likely it is that we’d get a value of a test statistic that’s as extreme (or more extreme) as the one we have based on chance alone. Having a p-value of less than 0.05 indicates a high level of significance and means that our results are pretty sound. The lower the number, the more confident we can be that we’ve got something legit.
• r-squared = percent of variance explained. This number shows what proportion of the outcome (eg, heart disease) can be explained by the variables in a particular model (eg, wheat and HDL cholesterol). The higher the number, the more successfully the variables are predicting the outcome. (“Predicting” is a misleading way of putting it, though, since we still aren’t looking at proof of cause-and-effect—only a relationship.)

Preliminary theories

It’s no secret that I’m less-than-enamored with wheat. We parted ways long ago (he got me allergic and then ran off with some floozy—classy, eh?). Nonetheless, I don’t like pointing fingers where they shouldn’t be pointed, so I’ll entertain some alternative theories that could explain wheat’s apparent association with heart disease.

1. Folate deficiency. In northern China, about 40% of the population qualifies as folate deficient (compared to only 6% in the south)—a geographical trend that corresponds nicely with wheat consumption. Being low in folate tends to elevate homocysteine, which—you guessed it—is an independent risk factor for heart disease. So maybe it’s not the wheat itself causing mischief, but the fact that low-vegetable, wheat-centered diets in China tend to breed folate deficiency and hike up homocysteine.

On top of that, in the China Study II data, wheat flour positively correlates (r = 0.30, p<0.05) with childhood death from neural tube defects—a category of birth defects often related to folate deficiency. Although the China Study data didn’t document homocysteine levels (darnit), the 1989 data did measure plasma folate. That means we’ll be able to test whether folate levels could be obscuring the true relationship between wheat and heart disease.

2. Vitamin D deficiency. For the most part, wheat-eating regions in China are in the northern half of the country—a hotspot for vitamin D deficiency, which is strongly linked to heart disease. Given the pretty convincing correlation between latitude and heart disease mortality, it’s possible that vitamin D is playing a role in this mess. Are the wheat-eaters merely suffering from low levels of the ol’ Sunshine Vitamin due to their unfortunate geographical placement, and getting more heart disease as a result? Sure seems possible.

3. Low intake of DHA. In an earlier publication, Campbell and his crew already determined that fish and DHA intake appears protective against heart disease in the China Study data. Not too surprising, since DHA reduces blood viscosity and can lower other factors associated with heart disease (like triglyceride levels). And considering wheat-eating regions don’t consume much seafood (r = -0.43 for daily fish intake), perhaps DHA deficiency—rather than wheat consumption itself—is to blame for higher rates of heart disease.

4. Combo-abombo. Maybe a mix of low folate, vitamin D deficiency, and DHA deficiency are swirling together into a doomful vortex—some horrible, Bermuda-Triangle-esque zone of heart disease. A zone that just happens to overlay areas of wheat consumption.

5. Unexpected mystery variable. If none of the above can explain the wheat-heart disease link, we’ve still got a verdant jungle of China Study variables to plow through. So plow we shall. I’ll try running a number of common-sense models to see if I can find something that explains heart disease better than wheat alone.

Multiple regression results

Folate. Ah, theory numero uno! Like wheat, folate has a strong, statistically significant correlation with heart disease (r = -0.40, p<0.001), so what happens when we run a model using both folate and wheat as exposures? Initially, it looks like wheat clobbers folate as a predictor (beta = 0.59, p<0.001 versus beta = -0.06, p = 0.39)—which would suggest that, although China’s wheat-eaters tend to have lower folate levels, folate deficiency itself isn’t enough to explain the link with heart disease.

But I’m not ready to dismiss this one just yet. As often happens with plasma measurements and health conditions, folate may have a nonlinear relationship with heart disease—which means multiple regressions (of the linear variety) won’t show the full picture. Indeed, when I make a scatter plot for folate levels and coronary heart disease, it looks like a bit of a curve emerges, with folate being most strongly associated with heart disease when the county average dips below 10 micrograms per liter (or thereabouts). Above that, the correlation is far less dramatic.

So how do we deal with this statistical monkey wrench? For starters, I tried transforming the folate data to make it more suitable for linear regressions, but that didn’t do diddly squat to the results: The numbers were beta = 0.58, p<0.001 for wheat and beta = -0.06, p = 0.31 for folate. So then I tried stratifying the data based on “low” and “high” folate levels (10 or less micrograms/liter versus 10.1 or more micrograms/liter), but both subgroups continued showing wheat as strongly and significantly correlated with heart disease while folate was off the hook.

Just to cover my bases (and because I’m a stubborn son-of-a-gun), I kept playing  with the numbers for a while longer to see if I could excavate anything new. Nope. Bottom line: It looks like wheat is predictive of heart disease whether or not folate levels are low, whereas folate is mostly predictive of heart disease only in the presence of high levels of wheat consumption.

So, theory #1 doesn’t pan out. Bugger. But bear in mind, we’re using folate mostly as a marker for elevated homocysteine, so these results don’t mean that homocysteine itself isn’t playing a role. Other causes of high homocysteine, such as B12 deficiency, weren’t documented in the China Study data. So this is an issue that’ll have to remain annoyingly unresolved. Another bugger!

Onto the next theory: latitude. Could the folks living in northern wheat-eating regions have lower vitamin D levels, leading to more heart problems—and creating a false link between wheat and cardiovascular disease? I admit, this was my favored theory after folate, but it ain’t holdin’ water. When I run wheat and latitude together as potential contributors to heart disease, wheat remains strongly predictive (beta = 0.65, p<0.001), while latitude diminishes (beta = -0.01, p=0.96). It’s pretty clear that the raw correlation between heart disease and latitude (which is 0.43, p<0.01) is just an echo of the relationship between heart diseases and wheat-eating regions, which are typically northern.

Okay, so that’s two strikes for Denise’s heart disease theories. What about fish and DHA? Are the wheat eaters suffering due to their fishless (and low-in-DHA) diets rather than from wheat itself? Alas, it doesn’t look likely. When I run these things together as exposures for heart disease, wheat stays strongly predictive (beta = 0.68, p<0.001) while the fishies do not (beta = 0.08, p = 0.47). Likewise, DHA teeters out into statistical insignificance (beta = 0.06, p = 0.30) when used in a model with wheat.

(Wait, I know what you’re thinking! “Why does it look like fish and DHA contribute positively to heart disease?” It’s because many of the fish-eating regions are more industrialized, and—in the absence of wheat—the fish-heart disease relationship is confounded by other factors like more desk work, more smoking (especially manufactured cigarettes), less physical activity, more vegetable oil consumption, and so forth. When we add some more variables to the model that take away the “city effect” associated with fish—such as apo-B, tobacco use, or percentage of the population employed in agriculture—then both fish and DHA turn inverse again. Although wheat, it should be mentioned, stays rock-steady in its high coefficient and statistical significance.)

Other stuff

Milk. Is moo juice a cardiovascular foe obscuring the relationship between wheat and heart disease? Probably not, according to the data—which isn’t surprising, given how few counties even drink the stuff. When running daily milk intake alongside wheat intake, wheat keeps its positive correlation (beta = 0.67, p<0.001) and milk actually turns a bit inverse, though not significantly so (beta = -0.07, p=0.47). No model shows a significant association between milk and cardiovascular disease, so I’m crossing this one off the list of potential confounders.

Blood pressure, BMI, corn, millet, sorghum, rice, added animal fat, added vegetable fat, total fat, total animal food, total carbs, total protein, percent of calories from animal protein, and all the smoking/tobacco variables I tried became statistically nonsignificant (in relation to heart disease) when thrown into a model with wheat.

Income is positively associated with heart disease when wheat is held constant, but it still doesn’t put a ding in wheat’s association with heart disease.

Models with more variables

So apparently comparing wheat + one other independent variable isn’t enough to explain the Wheat Effect. Not even a little bit. But maybe, just maybe, a bigger combination of variables will do the trick. Perhaps wheat-eating regions just host a collection of heart-harming factors (low folate, low vitamin D, low EFAs, and so forth) that, together, are more powerful predictors of disease than the variable wheat.

Here are the variables I’m interested in looking at. Some could be causative and some could be preventative:

• Wheat consumption
• Corn consumption
• Millet consumption
• Rice consumption
• Total blood cholesterol
• LDL cholesterol
• HDL cholesterol
• Apolipoprotein-B
• DHA levels
• Folate levels
• Latitude
• Added vegetable oil
• Blood pressure
• Weight
• BMI
• Total fat intake
• Total monounsaturated fat intake
• Total polyunsaturated fat intake
• Total saturated fat intake
• Percent of calories as fat
• Percent of calories as carbohydrates
• Total animal protein intake
• Total plant food intake (by weight)
• Total animal food intake (by weight)
• Green vegetables (daily, not yearly)
• Vitamin C intake
• Total sodium intake
• Poultry consumption
• Egg consumption
• Red meat consumption
• All meat consumption
• Fish consumption
• Dietary cholesterol intake
• Percent of the population currently smoking
• Percent of the population who have ever smoked tobacco
• Percent of the population smoking manufactured cigarettes
• Percent of the population pipe smoking
• Percent of the population smoking cigars
• Percent of the population working in industry (typically less physical activity)
• Percent of the population working in agriculture (typically more physical activity)

I won’t bore you with the results of every single combination I tried (over 100), so here’s the gist. No matter what model I use, wheat always adds unique variance. That means wheat (or an undocumented variable associated with wheat) is contributing something to heart disease that these other variables can’t account for. No combination out of the above bumped the association between wheat and heart disease out of the “statistically significant” zone.

Incidentally, one model had the best fit out of all the others for explaining heart disease:

1. Wheat consumption (beta = 0.62, p<0.001)
2. Apolipoprotein B (beta  = 0.38, p<0.001)
3. Total cholesterol (beta = -0.22, p<0.05)

Note that the number for total cholesterol is inverse, meaning higher cholesterol was associated with less heart disease—at least in this specific model. Unless you’re an Ancel Keys groupie, this may actually be quite plausible.

Anyway, here’s the important point. No matter what variables I adjust for, I can’t make the correlation between wheat flour and heart disease go away. Sorry, wheat! Neener neener.

Cardiovascular disease: The only “Western” problem without “Western” risk factors

Here’s a mystery for ya.

In the China Study data, most Western diseases (such as breast cancer, colon cancer, lung cancer, and diabetes) are concentrated in areas that share some key characteristics: more industrial employment, less agricultural work, greater population density, and often higher levels of schooling. Folks here eat more processed starch and sugar, use more polyunsaturated vegetable oils, chug down more beer, smoke more manufactured cigarettes, and typically get less physical activity than their neighbors in pastoral communities.

In other words, the Western-disease-prone-regions are like baby Americas—slowly waddling, diapered and naive, towards the motherly lap of disease.

Most likely, these Western ailments aren’t spawned from a single food or activity, but from a tragic mix of diet choices, lifestyle habits, and environmental factors. For problems like breast cancer and colon cancer and lung cancer, it’s pretty easy to see what the matrix of risk-raisers are from looking at the data: It’s the same combination of things spurring disease in Western nations.

But oddly enough, this isn’t the case for heart conditions. The factors shared by other Western illnesses are not, in most cases, associated with heart disease in this data set. If you’ve read some of the earlier China Study posts, you might remember that I took issue with Campbell’s disease-clustering strategy because heart disease doesn’t fit cleanly with the “diseases of affluence” group, despite his insistence on sticking it there anyway. Unlike the other Western problems, heart disease isn’t associated with eating more sugar, working in industry, drinking more alcohol, using vegetable oils, having higher apo-B levels, or any of the other variables uniting the Western diseases and mirroring the traits common to industrialized countries.

What’s the only thing heart-disease-prone regions have in common with Westernized nations? That’s right: consumption of high amounts of wheat flour.

Food for thought. Kinda spooky.

Wheat eaters: fatter with fewer calories

Here’s some more weirdness. In both China Study I and II, wheat is the strongest positive predictor of body weight (r = 0.65, p<0.001) out of any diet variable. And it’s not just because wheat eaters are taller, either, because wheat consumption also strongly correlates with body mass index (r = 0.58, p<0.001):  

How odd! This aligns with a post Stephan Guyenet at Whole Health Source wrote about wheat consumption and obesity in China, speculating that wheat might wreak metabolic havoc wherever it goes—a trend that becomes apparent when comparing similar populations of wheat eaters and non-wheat eaters, such as in China. But perhaps there’s some confounding going on. What about calorie intake? Are the wheat eaters just scarfing down more food in general, leading to higher weight regardless of wheat consumption? Doesn’t look like it. Running wheat and calorie intake together as predictors with BMI as the outcome, wheat takes the weight-gaining gold:

• Wheat: beta = 0.56, p<0.001
• Calorie intake: beta = 0.13, p = 0.19

Unfortunately, we have no way of accounting for energy expenditure through physical activity—but considering wheat-eating regions tend to be pastoral and dominated by agricultural work, it seems they’d be burning through a greater wallop of calories than more sedentary regions. Indeed, independent of calorie intake, there’s a clear association between agricultural work and weight (lower) versus industry work and weight (higher), suggesting these things could be approximate measures of calorie expenditure. So once again, we’ve got a paradox: The wheat eaters are consuming lower or average levels of calories, doing more physical labor, and yet… they’re fatter.

Out of curiosity, I ran a stepwise regression on a bunch of relevant variables to see what combination would best predict BMI. (In statistics, stepwise regression is a really cool, but sometimes totally misleading method for building a statistical model. It involves adding (or winnowing away) variables one by one based on how they behave together and contribute to the outcome—BMI, in this case—until you’ve got a model where each variable offers significant variation and the highest possible percent of explanation (represented as r-squared). Unfortunately, since this process is automated and computers usually don’t understand the whole “biological plausibility” thing, you can wind up with weird models that don’t make sense in the real world. Nonetheless, it can be a worthwhile method if used with caution.)

Setting BMI as the outcome, I chose the following variables as potential exposures:

• Total calories
• Total fat
• Total carbohydrates
• Total plant food
• Total animal food
• Total plant protein
• Total animal protein
• Total monunsaturated fat intake
• Total saturated fat intake
• Total polyunsaturated fat intake
• Red meat
• All meat
• Fish
• Poultry
• Eggs
• Wheat flour
• Corn
• Millet
• Legumes
• Starchy tubers
• Green vegetables (daily, not yearly)
• Agricultural employment
• Industrial employment

(I left out milk because so few counties consumed it.)

The best-fitting model for predicting BMI (at 95% confidence)? Drum roll please. Three variables made the cut.

1. Eating more wheat flour (beta = 0.48, p<0.001)
2. Eating more polyunsaturated fat (beta = 0.44, p<0.001), and
3. Eating fewer green vegetables (beta = -0.29, p<0.01).

This model has an r-squared value of 0.53, meaning it predicts a little over half of the variation in BMI—at least in theory. That’s actually pretty high, considering we haven’t directly factored things like physical activity into the equation.

Interesting, eh? All animal foods and total dietary fat, by the way, were completely insignificant in terms of BMI.

Of course, there could be other variables involved that the China Study didn’t cover. Were the higher-BMI folks also more heavily muscled (perhaps from more physical labor), increasing their body weight but not body fat? Are the wheat eaters, some of whom are ethnic minorities in China (especially Turkic and Mongolian), genetically “bigger” than the Han Chinese? There are plenty of unknowns, and alas, no way to clarify them based on this data.

I guess we’ll leave it as a question mark for now.

Grain damage: Do other studies back it up?

But don’t those peer-reviewed, scientific studies tell us wheat is healthy? Alas, the vast majority of studies on grains—especially wheat—showcase at least one of the following problems:

• They look at the effects of whole grains versus refined grains—not whole grains versus the same diet with no grains at all.
• Study subjects increase their consumption of whole grains, and this displaces some portion of yuckfoods (processed junk, white-flour products, sugary things, and so forth). As a result, it’s hard to tell whether any health perks are due to the addition of whole grains, or from the reduction of truly-awful-for-you foods. This is particularly true in studies that scout out disease patterns in populations rather than controlled studies that measure specific changes that occur with the addition of whole grains.
• They don’t adequately account for other factors that often accompany whole-grain consumption, like a greater level of health consciousness, more exercise, other positive diet choices, and so forth.

However, a few gems are lurking in the massive slush-pile of irrelevant studies. This one’s pretty doggone interesting, and it’s from all the way back in 1959: “Comparisons of atherogenesis in rabbits fed liquid oil, hydrogenated oil, wheat germ and sucrose.” You can click on that for the full-text PDF.

As you might guess from the title, this study examines the effects of diet on the development of atherosclerosis—AKA hardening of the arteries. The researchers took cholesterol-infused rabbit food and supplemented it with liquid corn oil (yuck), hydrogenated corn oil (double yuck), wheat germ (mystery murderer?), and sucrose (sweet poison!). Sorry, I dig hyperbole. Anyway, part of the goal was to create an experiment testing the hypothesis that “the geographic differences in the incidence of coronary disease might be related to selective hydrogenation of polyunsaturated fatty acids or to degermination of cereals.”

So now, the moment of truth: Which group had the most severe atherogenesis? Perhaps the one fed the nasty hydrogenated oil, as hypothesized? Ladies and gentlemen, place your bets. From the article:

The most severe atherogenesis occurred in the animals on the wheat germ diet.

Was it a fluke? Probably not:

In an earlier study, we maintained 5 groups of 5 rabbits each for three months on 500 mg of cholesterol daily and rabbit chow supplemented with different fats or with wheat germ. Here also, the animals on the wheat germ diet showed a significantly greater degree of atheromatous lesions than the animals on rabbit chow plus 20% corn oil, cottonseed oil or hydrogenated cottonseed oil, whereas no significant difference was found between the various fats.

So what made the wheat germ contribute to atherogenesis? The researchers state that it’s “difficult to speculate” about the mechanism, which is a scientific way of saying “We dunno.” They suggest the extra dietary protein from wheat germ could be the cause, but from the literature I’ve skimmed so far, it looks like plant proteins don’t have much effect on bunnies (although animal protein does).

Of course, rabbits are truly terrible models for anything that happens in the human body. They’re hardcore herbivores. A mere billowing of the wind is practically enough to spike their cholesterol. But what explains the specific effect of wheat germ on their poor arteries? Could this have implications for humans?

My answer: It’s “difficult to speculate.”

Other studies

Prefer human studies? Me too. Here’s one that initially looks totally irrelevant but is actually pretty interesting: Flaxseed and cardiovascular risk factors: Results from a double blind, randomized, controlled clinical trial. (This also a stellar example of why it’s important to read full-text articles instead of just abstracts, which often don’t tell you diddly about the stuff you want to know.)

This particular study charted the effects of flaxseed on adults with high cholesterol. One group got food with ground flaxseed; the other group got food with added wheat bran. Other dietary elements were the same. (Low fat, low cholesterol. Fun times!)

The results? Ye Olde Flaxseed Group did pretty well: Compared to their baseline measurements, these folks had lower insulin, lower blood glucose, lower C-reactive protein (a marker for inflammation), and better insulin sensitivity (as calculated by HOMA-IR).

But poor Wheat Group was less fortunate. Since the study was about flaxseed, the results of wheat aren’t specifically discussed, but check out “Table 4″ in the link above to see the numbers for yourself. The wheat-bran eaters had a 14.9% increase in insulin resistance (calculated by HOMA-IR) and a 9.3% increase in C-reactive protein. In other words, they lost some insulin sensitivity and gained some inflammation—two risk factors for heart disease. Hmm. Was the wheat bran to blame? Some other element of the control diet? It’s impossible to say for sure based on this study, but considering the wheat group’s adverse effects were more dramatic than the flaxseed group’s benefits, it seems a little suspect.

(A rather abrupt end of part one! The next post will have some more studies and speculations on potential mechanisms for wheat as causative of heart disease.)

Click here for the HTML version, or head straight to the PDF:

“The China Study”: A Formal Analysis and Response

(Updated noon-ish PST on August 3rd with typo corrections)

If you haven’t done so yet, also read Campbell’s first response and Campbell’s second response, which this is in reply to.

I’ll see what I can do about getting this set up in blog-post form, but I really don’t have the mental capacity to work on it right now. Sorry. In the meantime, here’s the table of contents so you know what you’re getting yourself into:

SECTION 1: Reiteration and Expansion of Criticisms

1. Linkage of animal protein with cancer by way of cholesterol
2. Misleading association of breast cancer with lipid intake and lipid intake with animal protein
3. Supposition that plasma cholesterol increases liver cancer risk
4. Misrepresentation of heart-protective effects of green vegetables, and the three-variable linkage between animal protein, apolipoprotein B, and cardiovascular disease
5. Biased use of unadjusted univariate correlations to confer protective benefits of plant foods but not with animal foods
6. Use of a three-variable chain to connect animal foods with “Western” diseases
7. Unexplored role of blood glucose, insulin, and disease
8. Dismissing relevant variables
9. Errors in the extrapolation of casein to all animal protein

SECTION 2: Biological Models and Cited Papers

1. Breast cancer
2. Liver cancer
3. Energy utilization
4. Affluent-poverty diseases
5. Summary

SECTION 3: Response to Points Raised by Campbell

1. Wheat: confounded variable or legitimate concern?
2. Selection of univariate correlations and confirmation bias
3. Tuoli county and erroneous data
4. Whole-food, plant-based diets versus whole-food diets with animal products
5. Conclusion

Also, this is sort of a pre-final version, and there may be typos (please point them out!) or orphaned punctuation (ditto). If I make any changes, I’ll post the updated version with a note.

Now if you’ll excuse me, I’m going to spend a very, very long time not staring at the computer screen, catching up on a couple weeks’ worth of sleep, and hopefully regrowing the little chunks of my soul that died while writing this. Adieu!

Yeah, yeah, yeah. It’s now Saturday. Gotta love being a multiple-offense deadline breaker. (I tend to value thoroughness over timeliness–so anyone out there who was thinking of hiring me for any time-sensitive job, you’ve been duly warned.) I’m currently adding a final section on wheat to Campbell response #2, and then this puppy WILL be ready to post. Pinky swear! Thanks for bearing with me.

–(end update/start of older post)–

I’m excited to see quite a few people take interest in the China Study data (huzzah, numbers!), and even more excited that some of you are already posting the results of your analyses. To quote reader and blogger Ned Kock:

I hope more people will do their own analyses on the original data, like we have been doing. Then the discussion will move away from X or Y are saying this, to something more like “the data” is saying this.

Right on.

While I’m finishing a fairly laborious (you’ll see what I mean later) response  to Mr. Campbell, I thought I’d post some of the data I already have typed up for those of you who are gettin’ antsy. I’ll be updating this entry frequently as I upload more files, but here’s the first batch.

I’ll also use this post to link to anyone who has posted their results somewhere on the ‘net. Those will be right after the links to the data.

Also feel free to request any variable(s) you’re interested in analyzing, and I’ll type them up when I have a spare moment.

Enjoy!

Myocardial infarction/coronary heart disease:

(includes total cholesterol, HDL cholesterol, green vegetable consumption, animal protein, plant protein, dairy variables, egg variables, meat variables, and fish variables)

• Myocardial infarction variables for Excell 2007
• Myocardial infarction/CHD variables for Excell ’97 – ’03

(Note: included are the variables “amount of green vegetables consumed” and “frequency of green vegetables consumed” to illustrate the Green Veggie Paradox.)

Colorectal cancer:

(includes cholesterol, schistosomiasis, plant protein, and animal protein)

• Colorectal cancer variables for Excell 2007
• Colorectal cancer variables for Excell ’97 – ’03

(A shout out to eds. Chen Junshi, T. Colin Campbell, Li Junyao, and Richard Peto for making this stuff available in book form.)

Reader links:

So far, we have two posts from Ned Kock:

1. The China Study again: A multivariate analysis suggesting that schistosomiasis rules!
2. The China Study one more time: Are raw plant foods giving people cancer? (This one’s particularly interesting: Ned used a nonlinear regression analysis on the data with no schistosomiasis infection, and uncovered a U-curve in the relationship between cholesterol and colorectal cancer. In other words, the counties with the lowest cholesterol and highest cholesterol had higher rates of colorectal cancer than the groups with more mid-range cholesterol, who appear the most protected. Ned offers a great hypothesis for this result in his post. Additionally, while animal protein consumption correlated strongly with total cholesterol, animal protein itself correlated inversely (beta = -0.31, p<0.10) with colorectal cancer, while plant protein correlated positively (beta = 0.47, p<0.01). Remember, of course, that correlation doesn’t equal causation, and this is just a sampling of the dizzying number of variables recorded in the China Study.)

Update 7/22: Reader Ned Kock of “Health Correlator” performed a multivariate analysis on the data for colorectal cancer, animal protein, cholesterol, plant protein, and schistosomiasis from the China Study. Check his blog to read what he discovered. (Any other readers who’ve done something similar, please post and let us know what you’ve found as well.)

In other news:

If you haven’t seen it yet, Campbell has expanded his original response to my critique and posted it in two places:

1. On his website TColinCampbell.org, where it’s available for download as a Word document, and
2. On CampbellCoalition.com, where it’s in HTML format and you can contribute comments and questions.

Word has it that Campbell himself will be replying to at least some of the comments on Campbell Coalition, so this would be a wonderful opportunity for anyone with questions for him to engage in dialogue. Correction 7/22: Campbell has closed this discussion to comments with the following remark:

Based on the response received thus far, we have determined that our prior idea of a reasoned and civil discourse, with participation by Dr. Campbell, is not feasible and have decided to discontinue this discussion thread.

Bummer. Well, if you want to carry a non-reasoned and un-civil discourse, feel free to do it here. First Amendment FTW!

If you submitted comments that weren’t accepted on the Campbell Coalition website, Dave Dixon has created a special entry on his blog “Spark of Reason” where you can post them and still get your voice heard.

Campbell’s longer rebuttal has also been featured on Vegsource.com, in which the editors kindly wrote:

Previously we at VegSource had looked at some of Ms. Minger’s anti-Campbell rhetoric.  One thing we were struck by early on was the fact that Ms. Minger apparently removes comments on her blog from scientific researchers who point out the flaws in her reasoning and in her understanding of accepted research methods.

Huh. All scientific researchers who had their comments removed, please say “aye.” The one and only comment I’ve deleted thus far was one I wrote, although (as I’ve mentioned several times now) some comments do get snagged in the spam or “awaiting approval’” queue, especially if they have links–in which case they don’t show up right away.  I apologize if this has happened to you, but you’re welcome to comment here even if you disagree. Dissenting voices FTW!

Update 7/22: Looks like they edited the above to be marginally nicer but still woefully inaccurate. And, as per tradition, they took a moment to lambaste the Weston A. Price Foundation—’cause really, what China Study article would be complete without randomly evoking something completely irrelevant to the discussion? Non-sequiturs FTW!

I have (another) response to Campbell underway, so for those of you waiting for the wheat post, it just got pushed back farther in the waiting line. Many apologies. Contrary to some circulating hypotheses, I really am just one person, with limited capacity to type and crank out blog entries. When I finish rearing my army of bovine ninja babies, I’ll enslave them and outsource my research and data entry tasks, but that’s a ways off yet.

Carry on.

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 Tynan.net.

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 Tynan.net, 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:

• “Schistosoma japonicum and colorectal cancer: an epidemiological study in the People’s Republic of China“: Prevalence of infestation with Schistosoma japonicum was highly correlated with mortality from colorectal cancer in 89 communes in four counties of Jiangsu province, China (rank correlation coefficient = 0.68) in 1973-75, and with incidence of colorectal cancer in 24 communes of Haining county, Zhejiang province in 1977-79.
• “Correlations of colon cancer mortality with dietary factors, serum markers, and schistosomiasis in China“: [P]revalence of schistosomiasis was significantly correlated with increased colon cancer mortality.
• “Schistosomiasis and its prognostic significance in patients with colorectal cancer. National Cooperative Group on Pathology and Prognosis of Colorectal Cancer“: This paper analyses 430 cases of colorectal cancer complicated with schistosomiasis. The 5 year survival rate was 45.6%, lower than that without schistosomiasis. … The infection of schistosome should be considered as one of the important factors in prognosis. (In other words: Schistosomiasis infection increases the mortality rate of colorectal cancer sufferers.)
• “A cohort study on the causes of death in an endemic area of schistosomiasis japonica in Japan“: These results suggest that schistosomiasis japonica is one of the important risk factors for cirrhosis of the liver, cancer of the liver and cancer of the colon.

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.

And:

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 Beyondveg.com 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.

When I first started analyzing the original China Study data, I had no intention of writing up an actual critique of Campbell’s much-lauded book. I’m a data junkie. Numbers, along with strawberries and Audrey Hepburn films, make me a very happy girl. I mainly wanted to see for myself how closely Campbell’s claims aligned with the data he drew from—if only to satisfy my own curiosity.

But after spending a solid month and a half reading, graphing, sticky-noting, and passing out at 3 AM from studious exhaustion upon my copy of the raw China Study data, I’ve decided it’s time to voice all my criticisms. And there are many.

First, let me put out some fires before they have a chance to ignite:

1. I don’t work for the meat or dairy industry. Nor do I have a fat-walleted roommate, best friend, parent, child, love interest, or highly prodigious cat who works for the meat or dairy industry who paid me off to debunk Campbell.
2. Due to food sensitivities, I don’t consume dairy myself, nor do I have any personal reason to promote it as a health food.
3. I was a vegetarian/vegan for over a decade and have nothing but respect for those who choose a plant-based diet, even though I am no longer vegan. My goal, with the “The China Study” analysis and elsewhere, is to figure out the truth about nutrition and health without the interference of biases and dogma. I have no agenda to promote.

As I mentioned, I’m airing my criticisms here; this won’t be a China Study love fest, or even a typical balanced review with pros and cons. Campbell actually raises a  number of points I wholeheartedly agree with—particularly in the “Why Haven’t You Heard This?” section of his book, where he exposes the reality behind Big Pharma and the science industry at large. I admire Campbell’s philosophy towards nutritional research and echo his sentiments about the dangers of scientific reductionism. However, the internet is already flooded with rave reviews of this book, and I’m not interested in adding redundant praise. My intent is to highlight the weaknesses of “The China Study” and the potential errors in Campbell’s interpretation of the original data.

(IMPORTANT NOTE: My response to Campbell’s reply, as well as to some common reader questions, can be found in the following post: My Response to Campbell. Please read this for clarification regarding univariate correlations and flaws in Campbell’s analytical methods.)

(If this is your first time here, feel free to browse the earlier posts in the China Study category to get up to speed.)

On the Cornell University website (the institution that—along with Oxford University—spawned the China Project), I came across an excellent summary of Campbell’s conclusions from the data. Although this article was published a few years before “The China Study,” it distills some of the book’s points in a concise, down-n’-dirty way. In this post, I’ll be looking at these statements along with other overriding claims in “The China Study” and seeing whether they hold up under scrutiny—including an in-depth look at Campbell’s discoveries with casein.

(Disclaimer: This post is long. Very long. If either your time or your attention span is short, you can scroll down to the bottom, where I summarize the 9,000 words that follow in a less formidable manner.)

(Disclaimer 2: All correlations here are presented as the original value multiplied by 100 in order to avoid dealing with excessive decimals. Asterisked correlations indicate statistical significance, with * = p<0.05, ** = p<0.01, and *** = p<0.001. In other words, the more stars you see, the more confident we are that the trend is legit. If you’re rusty on stats, visit the meat and disease in the China Study page for a basic refresher on some math terms.)

(Disclaimer 3: The China Study files on the University of Oxford website include the results of the China Study II, which was conducted after the first China Study. It includes Taiwan and a number of additional counties on top of the original 65–and thus, more data points. The numbers I use in this critique come solely from the first China Study, as recorded in the book “Diet, Life-style and Mortality in China,” and may be different than the numbers on the website.)

From Cornell University’s article:

“Even small increases in the consumption of animal-based foods was associated with increased disease risk,” Campbell told a symposium at the epidemiology congress, pointing to several statistically significant correlations from the China studies.

Alright, Mr. Campbell—I’ll hear ya out. Let’s take a look at these correlations.

Campbell Claim #1

Plasma cholesterol in the 90-170 milligrams per deciliter range is positively associated with most cancer mortality rates. Plasma cholesterol is positively associated with animal protein intake and inversely associated with plant protein intake.

No falsification here. Indeed, cholesterol in the China Project has statistically significant associations with several cancers (though not with heart disease). And indeed, plasma cholesterol correlates positively with animal protein consumption and negatively with plant protein consumption.

But there’s more to the story than that.

Notice Campbell cites a chain of three variables: Cancer associates with cholesterol, cholesterol associates with animal protein, and therefore we infer that animal protein associates with cancer. Or from another angle: Cancer associates with cholesterol, cholesterol negatively associates with plant protein, and therefore we infer plant protein protects against cancer.

But when we actually track down the direct correlation between animal protein and cancer, there is no statistically significant positive trend. None. Looking directly at animal protein intake, we have the following correlations with cancers:

Lymphoma: -18
Penis cancer: -16
Rectal cancer: -12
Bladder cancer: -9
Colorectal cancer: -8
Leukemia: -5
Nasopharyngeal: -4
Cervix cancer: -4
Colon cancer: -3
Liver cancer: -3
Oesophageal cancer: +2
Brain cancer: +5
Breast cancer: +12

Most are negative, but none even reach statistical significance. In other words, the only way Campbell could indict animal protein is by throwing a third variable—cholesterol—into the mix. If animal protein were the real cause of these diseases, Campbell should be able to cite a direct correlation between cancer and animal protein consumption, which would show that people eating more animal protein did in fact get more cancer.

But what about plant protein? Since plant protein correlates negatively with plasma cholesterol, does that mean plant protein correlates with lower cancer risk? Let’s take a look at the cancer correlations with “plant protein intake”:

Nasopharyngeal cancer: -40**
Brain cancer: -15
Liver cancer: -14
Penis cancer: -4
Lymphoma: -4
Bladder cancer: -3
Breast cancer: +1
Stomach cancer: +10
Rectal cancer: +12
Cervix cancer: +12
Colon cancer: +13
Leukemia: +15
Oesophageal cancer +18
Colorectal cancer: +19

We have one statistically significant correlation with a rare cancer not linked to diet (nasopharyngeal cancer), but we also have more positive correlations than we saw with animal protein.

In fact, when we look solely at the variable “death from all cancers,” the association with plant protein is +12. With animal protein, it’s only +3. So why is Campbell linking animal protein to cancer, yet implying plant protein is protective against it?

In addition, Campbell’s statement about cholesterol and cancer leaves out a few significant points. What he doesn’t mention is that plasma cholesterol is also associated with several non-nutritional variables known to raise cancer risk—namely schistosomiasis infection (correlation of +34*) and hepatitis B infection (correlation of +30*).

Not coincidentally, cholesterol’s strongest cancer links are with liver cancer, rectal cancer, colon cancer, and the sum of all colorectal cancers. As we saw in the posts on meat consumption and fish consumption, schistosomiasis and hepatitis B are the two biggest factors in the occurrence of these diseases. So is it higher cholesterol (by way of animal products) that causes these cancers, or is it a misleading association because areas with high cholesterol are riddled with other cancer risk factors? We can’t know for sure, but it does seem odd that Campbell never points out the latter scenario as a possibility.

Campbell Claim #2

Breast cancer is associated with dietary fat (which is associated with animal protein intake) and inversely with age at menarche (women who reach puberty at younger ages have a greater risk of breast cancer).

Campbell is correct that breast cancer negatively relates to the age of first menstruation—a correlation of -20. Not statistically significant, but given what we know about hormone exposure and breast cancer, it certainly makes sense. And there is a correlation between fat intake and breast cancer—a non-statistically-significant +18 for fat as a percentage of total calories and +22 for total lipid intake. But are there any dietary or lifestyle factors with a similar or stronger association than this? Let’s look at the correlation between breast cancer and a few other variables. Asterisked items are statistically significant:

Blood glucose level: +36**
Wine intake: +33*
Alcohol intake: +31*
Yearly fruit consumption: +25
Percentage of population working in industry: +24
Hexachlorocyclohexane in food: +24
Processed starch and sugar intake: +20
Corn intake: +20
Daily beer intake: +19
Legume intake: +17

Looks to me like breast cancer may have links with sugar and alcohol, and perhaps also with hexachlorocyclohexane and occupational hazards associated with industry work. Again, why is Campbell singling out fat from animal products when other—stronger—correlations are present?

Certainly, consuming dairy and meat from hormone-injected livestock may logically raise breast cancer risk due to increased exposure to hormones, but this isn’t grounds for generalizing all animal products as causative for this disease. Nor is a correlation of +18 for fat calories grounds for indicting fat as a breast cancer risk factor, when alcohol, processed sugar, and starch correlate even more strongly. (Animal protein itself, for the record, correlates with breast cancer at +12—which is lower than breast cancer’s correlation with light-colored vegetables, legume intake, fruit, and a number of other purportedly healthy plant foods.)

Campbell Claim #3

For those at risk for liver cancer (for example, because of chronic infection with hepatitis B virus) increasing intakes of animal-based foods and/or increasing concentrations of plasma cholesterol are associated with a higher disease risk.

Ah, here’s one that may be interesting! Even if animal products don’t cause cancer, do they spur its occurrence when other risk factors are present? That would certainly be in line with Campbell’s research on aflatoxin and rats, where the milk protein casein dramatically increased cancer rates.

So, let’s look only at the counties with the highest rates of hepatitis B infection and see what animal food consumption does there. In the China Study, one documented variable is the percentage of each county’s population testing positive for the hepatitis B surface antigen. Population averages ranged from 1% to 29%, with a mean of 13% and median of 14%. If we take only the counties that have, say, 18% or more testing positive, that leaves us with a solid pool of high-risk data points to look at.

Animal product consumption in these places ranges from a meager 6.9 grams per day to a heftier 148.1 grams per day—a wide enough range to give us a good variety of data points. Liver cancer mortality ranges from 5.51 to 59.63 people per thousand.

Let’s crunch these numbers, shall we? Here’s a chart of the data I’m using.

When we map out liver cancer mortality and animal product consumption only in areas with high rates of hepatitis B infection (18% and higher), we should see cancer rates rise as animal product consumption increases—at least, according to Campbell. That would indicate animal-based foods do encourage cancer growth. But here’s what we really get.

In these high-risk areas for liver cancer, total animal food intake has a correlation with liver cancer of… dun dun dun… +1.

That’s it. One. We rarely get a perfect statistical zero in the real world, but this is pretty doggone close to neutral. Broken up into different types of animal food rather than total consumption, we have the following correlations:

• Meat correlates at -7 with liver cancer in high-risk counties
• Fish correlates at +11
• Eggs correlate at -29
• Dairy correlates at -19

In other words, it looks like animal foods have virtually no effect—whether positive or negative—on the occurrence of liver cancer in hepatitis-B infected areas.

Campbell mentioned plasma cholesterol also associates with liver cancer, which is correct: The raw correlation is a statistically significant +37. If it’s true blood cholesterol is somehow an instigator for liver cancer in hepatitis-B-riddled populations, we’d expect to see this correlation preserved or heightened among our highest-risk counties. So let’s take a look at the same previous 19 counties with high hepatitis B occurrence, and graph their total cholesterol alongside their liver cancer rates.

In the high-risk groups, the correlation between total cholesterol and liver cancer drops from +37 to +8. Still slightly positive, but not exactly damning.

If I were Campbell, I’d look at not only animal protein and cholesterol in relation to liver cancer, but also at the many other variables that correlate positively with the disease. For instance, daily liquor intake correlates at +33*, total alcohol intake correlates at +28*, cigarette use correlates at +27*, intake of the heavy metal cadmium correlates at +38**, rapeseed oil intake correlates at +25*—so on and so forth. All are statistically significant. Why doesn’t Campbell mention these factors as possible causes of increased liver cancer in high-risk areas? And, more importantly, why doesn’t Campbell account for the fact that many of these variables occur alongside increased cholesterol and animal product consumption, making it unclear what’s causing what?

Campbell Claim #4

Cardiovascular diseases are associated with lower intakes of green vegetables and higher concentrations of apo-B (a form of so-called bad blood cholesterol) which is associated with increasing intakes of animal protein and decreasing intakes of plant protein.

Alright, we’ve got a multi-parter here. First, let’s see what the actual correlations are between cardiovascular diseases and green vegetables—an interesting connection, if it holds true. The China Study accounted for this variable in two ways: one through a diet survey that measured how many grams of green vegetables each county averaged per day, and one through a questionnaire that recorded how many times per year citizens ate green vegetables.

From the diet survey, green vegetable intake (average grams per day) has the following correlations:

Myocardial infarction and coronary heart disease: +5
Hypertensive heart disease: -4
Stroke: -8

From the questionnaire, green vegetable intake (times eaten per year) has the following correlations:

Myocardial infarction and coronary heart disease: -43**
Hypertensive heart disease: -36*
Stroke: -35*

A little odd, oui? When we look at total quantity of green vegetables consumed (in terms of weight), we’ve got only weak negative associations for two cardiovascular conditions, and a slightly positive association for heart attacks (myocardial infarction) and coronary heart disease. Nothing to write home about. But when we look at the number of times per year green vegetables are consumed, we have much stronger inverse associations with all cardiovascular diseases. Why the huge difference? Why would frequency be more protective than quantity? What accounts for this mystery?

It could be that the China Study diet survey did a poor job of tracking and estimating greens intake on a long-term basis (indeed, it was only a three-day survey, although when repeated at a later date yielded similar results for each county). But the explanation could also boil down to one word: geography.

Let me explain.

The counties in China that eat greens year-round live in a particular climate and latitude—namely, humid regions to the south.  The “Green vegetable intake, times per year” variable has a correlation of -68*** with aridity (indicating a humid climate) and a correlation of -60*** with latitude (indicating southerly placement on the ol’ map). Folks living in these regions might not eat the most green vegetables quantity-wise, but they do eat them frequently, since their growing season is nearly year-round.

In contrast, the variable “Green vegetable intake, grams per day” has a correlation of only -16 with aridity and +5 with latitude, indicating much looser associations with southern geography. The folks who eat lots of green veggies don’t necessarily live in climates with a year-round growing season, but when green vegetables are available, they eat a lot of them. That bumps up the average intake per day, even if they endure some periods where greens aren’t on the menu at all.

If green vegetables themselves were protective of heart disease, as Campbell seems to be implying, we would expect their anti-heart-disease effects to be present in both quantity of consumption and frequency of consumption. Yet the counties eating the most greens quantity-wise didn’t have any less cardiovascular disease than average. This tells us there’s probably another variable unique to the southern, humid regions in China that confers heart disease protection—but green veggies aren’t it.

Some of the hallmark variables of humid southern regions include high fish intake, low use of salt, high rice consumption (and low consumption of all other grains, especially wheat), higher meat consumption, and smaller body size (shorter height and lower weight). And as you’ll see in an upcoming post on heart disease, these southerly regions also had more intense sunlight exposure and thus more vitamin D—an important player in heart disease prevention.

(And for the record, as a green-veggie lover myself, I’m not trying to negate their health benefits—promise! I just want to offer equal skepticism to all claims, even the ones I’d prefer to be true.)

Basically, Campbell’s implication that green vegetables are associated with less cardiovascular disease is misleading. More accurately, certain geographical regions have strong correlations with cardiovascular disease (or lack thereof), and year-round green vegetable consumption is simply an indicator of geography. Since only frequency and not actual quantity of greens seems protective of heart disease and stroke, it’s safe to say that greens probably aren’t the true protective factor.

So that about covers it for greens. What about the next variable in Campbell’s claim: a “bad” form of cholesterol called apo-B?

Campbell is justified in noting the link between apolipoprotein B (apo-B) and cardiovascular disease in the China Study data, a connection widely recognized by the medical community today. These are its correlations with cardiovascular disease:

Myocardial infarction and coronary heart disease: +37**
Hypertensive heart disease: +35*
Stroke: +35*

And he’s also right about the negative association between apo-B and plant protein, which is -37*, as well as the positive association between apo-B and animal protein, which is +25* for non-fish protein and +16 for fish protein. So from a technical standpoint, Campbell’s statement (aside from the green veggie issue) is legit.

However, it’s the implications of this claim that are misleading. From what Campbell asserts, it would seem that animal products are ultimately linked to cardiovascular diseases and plant protein is ultimately protective of those diseases, and apo-B is merely a secondary indicator of this reality. But does that claim hold water? Here’s the raw data.

Correlations between animal protein and cardiovascular disease:

Myocardial infarction and coronary heart disease: +1
Hypertensive heart disease: +25
Stroke: +5

Correlations between fish protein and cardiovascular disease:

Myocardial infarction and coronary heart disease: -11
Hypertensive heart disease: -9
Stroke: -11

Correlations between plant protein and cardiovascular disease (from the China Study’s “diet survey”):

Myocardial infarction and coronary heart disease: +25
Hypertensive heart disease: -10
Stroke: -3

Correlations between plant protein and cardiovascular disease (from the China Study’s “food composite analysis”):

Myocardial infarction and coronary heart disease: +21
Hypertensive heart disease: 0
Stroke: +12

Check that out! Fish protein looks weakly protective all-around; non-fish animal protein is neutral for coronary heart disease/heart attacks and stroke but associates positively with hypertensive heart disease (related to high blood pressure); and plant protein actually correlates fairly strongly with heart attacks and coronary heart disease. (The China Study documented two variables related to plant protein: one from a lab analysis of foods eaten in each county, and one from a diet survey given to county citizens.) Surely, there is no wide division here between the protective or disease-causing effects of animal-based protein versus plant protein. If anything, fish protein looks the most protective of the bunch. No wonder Campbell had to cite a third variable in order to vilify animal products and praise plant protein: Examined directly, they’re nearly neck-and-neck.

If you’re wondering about the connection between animal protein and hypertensive heart disease, by the way, it’s actually hiked up solely by the dairy variable. Here are the individual correlations between specific animal foods and hypertensive heart disease:

Milk and dairy products intake: +30**
Egg intake: -28
Meat intake: -4
Fish intake: -14

You can read more about the connection between dairy and hypertensive heart disease in the entry on dairy in the China Study.

At any rate, Campbell accurately points out that apo-B correlates positively with cardiovascular diseases. But to imply animal protein is causative of these diseases—and green vegetables or plant protein protective of them—is dubious at best. What factors cause both apo-B and cardiovascular disease risk to increase hand-in-hand? This is the question we should be asking.

Campbell Claim #5

Colorectal cancers are consistently inversely associated with intakes of 14 different dietary fiber fractions (although only one is statistically significant). Stomach cancer is inversely associated with green vegetable intake and plasma concentrations of beta-carotene and vitamin C obtained only from plant-based foods.

This is congruous with conventional beliefs about fiber being helpful for colon health. And as a plant-nosher myself, I hope it’s true—but that’s no reason to omit this claim from critical examination. Here are all of the China Study’s fiber variables as they correlate to colorectal cancer:

Total fiber intake: -3
Total neutral detergent fiber intake: -13
Hemi-cellulose fiber intake: -10
Cellulose fiber intake: -13
Intake of lignins remaining after cutin removed: -9
Cutin intake: -14
Starch intake: -1
Pectin intake: +3
Rhamnose intake: -26*
Fucose intake: +2
Arabinose intake: -18
Xylose intake: -15
Mannose intake: -13
Galactose intake: -24

Surprise, surprise: I agree with Campbell on this one! All but two of the fiber variables have inverse associations with colorectal cancers. The first part of Campbell Claim #5 passes Denise’s BS-o-Meter test. Let us celebrate!

…But before we get too jiggy with it, I do have a nit to pick. Fiber intake also negatively correlates with schistosomiasis infection, a type of parasite. Try Googling “schistosomiasis and colorectal cancer” and you’ll get more relevant hits than you’ll ever have time to read. I’ll elaborate on this in a few paragraphs, so hang tight—but for now, I’ll just point out two things:

1. Schistosomiasis infection is a very strong predictor for colon and rectal cancers, more so than any of the other hundreds of variables studied in the China Project (it has a correlation of +89 with colorectal cancer).
2. The only fiber factions that don’t appear protective of colorectal cancer (pectin and fucose) also have the most neutral associations with schistosomiasis infection (+1 and -5, respectively—whereas other fiber factions had correlations ranging from -9 to -27 with schistosomiasis). In all cases, the correlation between each fiber faction and colorectal cancer parallels its correlation with schistosomiasis.

In other words: Is it the fiber itself that’s protective against colorectal cancer, or is it the fact that the counties eating the most fiber happened to also have the lowest rates of schistosomiasis? It would, I think, be wise to prune these variables apart before declaring fiber itself as protective based on the China Study data.

There is research conducted outside of the China Project suggesting fiber benefits colon health, but often that association dissolves when researchers adjust for other dietary risk factors, such as with the this pooled analysis of colorectal cancer studies published in the Journal of the American Medical Association. Bottom line: It’s never a good idea to go looking for a specific trend just because we believe it should be there. Chains of confirmation bias are often what cause nutritional myths to emerge and persist. Fiber may be beneficial, but we shouldn’t approach the data already expecting to find this—lest we overlook other important influences.

Moving on. Now, what about the second part of this claim: Stomach cancer is inversely associated with green vegetable intake and plasma concentrations of beta-carotene and vitamin C obtained only from plant-based foods.

Is this a fair assessment? Let’s find out. Here are the correlations between stomach cancer and each of these variables.

Green vegetables, daily intake: +5
Green vegetables, times eaten per year: -35**
Plasma beta-carotene: -14
Plasma vitamin C: -13

Ah, looks like we’re facing the Green Veggie Paradox once again. The folks with year-round access to green vegetables get less stomach cancer, but the the folks who eat more green vegetables overall aren’t protected. Once again, I’ll suggest that a geographic variable specific to veggie-growing regions could be at play here.

As for beta-carotene and vitamin C concentrations in the blood, Campbell is correct in noting an inverse association with stomach cancer. However, the correlations aren’t statistically significant, nor are they very high: -14 and -13, respectively.

Campbell Claim #6

Western-type diseases, in the aggregate, are highly significantly correlated with increasing concentrations of plasma cholesterol, which are associated in turn with increasing intakes of animal-based foods.

From his book, we know Campbell defines Western-type diseases as including heart disease, diabetes, colorectal cancers, breast cancer, stomach cancer, leukemia, and liver cancer. And indeed, the variable “total cholesterol” correlates positively with many of these diseases:

Myocardial infarction and coronary heart disease: +4
Diabetes: +8
Colon cancer: +44**
Rectal cancer: +30*
Colorectal cancer: +33**
Breast cancer: +19
Stomach cancer: +17
Leukemia: +26*
Liver cancer: +37*

Perhaps surprisingly, total cholesterol has only weak associations with heart disease and diabetes—weaker, in fact, than the correlation between these conditions and plant protein intake (+25 and +12, respectively). But we’ll put that last point aside for the time being. For now, let’s focus on the diseases with statistical significance, which include all forms of colorectal cancer, leukemia, and liver cancer. (Despite classifying stomach cancer as a “Western disease,” by the way, China actually has far higher rates of this disease than any Western nation. In fact, half the people who die each year from stomach cancer live in China.)

First, let’s dive into the dark, murky chambers of the digestive tract and start with colorectal cancers. Off we go!

What Campbell overlooks about colorectal cancers and cholesterol

As I mentioned earlier, a little somethin’ called “schistosomiasis” is a profoundly strong risk factor for developing colon cancer and rectal cancer. In the China Study data, schistosomiasis correlates at +89*** with colorectal cancer mortality. Yes, 89—higher than any of the other 367 variables recorded.

This, ladies and gentlemen, is what we call a positive correlation.

It just so happens that total cholesterol also correlates with schistosomiasis infection, at a statistically significant rate of +34*:

Basically, this means that areas with higher cholesterol levels also had—for whatever reason—a higher incidence of schistosomiasis infection. It’s hard to say for sure why this is, but it’s likely that the high-cholesterol and high-schistosomiasis groups had a third variable in common, such as infected drinking water or other source of schistosomiasis exposure.

From this alone, it shouldn’t be too shocking that higher cholesterol also correlates with higher rates of colorectal cancer (+33*):

Clearly, we have three tangled-up variables to sort through: total cholesterol, colorectal cancer rates, and schistosomiasis infection. Is it really higher cholesterol that increases the risk of developing colon and rectal cancers, or is the influence of schistosomiasis deceiving us?

To figure this out, let’s look at what cholesterol and colorectal cancer rates look like only in regions with zero schistosomiasis infection. If cholesterol is a causative factor for colorectal cancers, then cancer rates should still increase as total cholesterol rises.

The above graph showcases a correlation of +13. Still positive, but not statistically significant, and a major downgrade from the original correlation of +33*. It does seem schistosomiasis inflates the correlation between cholesterol and colorectal cancers—something Campbell never takes into account. Is blood cholesterol still a risk factor? It’s possible, but we would need more data to know whether the +13 correlation persists or whether there are additional confounding variables at work. For instance, beer intake is another factor correlating significantly with both total cholesterol (+32*) and colon cancer (+40**).  If we remove the three counties that drink the most beer from of the data set above, the correlation between cholesterol and and colorectal cancer drops to -9.

See how tricky the interplay of variables can be?

What Campbell overlooks about leukemia and cholesterol

Next in our lineup of “Western diseases” is leukemia, which has a statistically significant correlation of +26* with total cholesterol. (Although the implication here is that animal product consumption raises leukemia risk, it should be noted that animal protein intake itself has a correlation of -5 with leukemia, whereas plant protein actually has a correlation of +15 with this disease. But let’s humor this claim anyway by looking solely at the role of blood cholesterol.)

If you’ll recall from the post on fish and disease in the China Study, leukemia correlates very strongly with working in industry (+53**) and inversely with working in agriculture (-53**). Although it’s possible the cause is nutritional, it’s also quite likely that an occupational hazard is to blame—such as benzene exposure, which is a major and well-known cause of leukemia in Chinese factory and refinery workers.

Lo and behold, cholesterol also correlates strongly with working in industry (+45**) and inversely with working in agriculture (-46**). If an industry-related risk factor raises leukemia rates, it could very well appear as a false correlation with cholesterol. How can we tell if this is the case?

Let’s try looking at the correlation between leukemia and cholesterol only in counties where few members of the population were employed in industry. If cholesterol itself heightens leukemia risk, our positive trend should still be in place. In the China Study data set, the range for percent of the population working in industry is 1.1% to  41.3%, so let’s try looking at the counties where the value is under 10%:

For the low-industry counties, the correlation between leukemia and total cholesterol is close to neutral—a mere +4. As you can see, this is hardly a damning trend. And in case you’re wondering if higher cholesterol could possibly spur the rates of leukemia in folks who are already at risk, this isn’t the case either: Using only counties that had 20% or more of the population working in industry, presumably the folks who had the most exposure to chemicals like benzene, the correlation between cholesterol and leukemia is a slightly protective -3.

What Campbell overlooks about liver cancer and cholesterol

I may not be vegan, but that doesn’t mean I like beating dead horses. Instead of rehashing the earlier analysis of liver cancer under Campbell Claim #3, I’ll just repeat that cholesterol does not have a significant correlation with liver cancer when you divide the data set into separate groups: areas with high hepatitis B rates an areas with low hepatitis B rates.

From page 104 of his book:

Liver cancer rates are very high in rural China, exceptionally high in some areas. Why was this? The primary culprit seemed to be chronic infection with hepatitis B virus (HBV). …

… But there’s more. In addition to the [hepatitis B] virus being a cause of liver cancer in China, it seems that diet also plays a key role. How do we know? The blood cholesterol levels provided the main clue. Liver cancer is strongly associated with increasing blood cholesterol, and we already know that animal-based foods are responsible for increases in cholesterol.

Campbell connects some of the dots, but misses a very important one. Indeed, hepatitis B associates strongly with liver cancer. Indeed, cholesterol associates with liver cancer. But what he doesn’t mention is that cholesterol also associates with hepatitis B infection. In other words, the groups with higher cholesterol are already at greater risk of liver cancer than groups with lower cholesterol—but it’s not because of diet.

In addition to greater rates of hepatitis B infection, higher-cholesterol areas had additional risk factors for liver cancer, such beer consumption, which also inflated the trend. Despite Campbell’s claims, cholesterol itself does not appear to significantly heighten cancer rates in at-risk populations.

Given Campbell’s casein research and earlier observations about the animal-protein consuming children in the Philippines getting more liver cancer, I wonder if Campbell approached the China Study already expecting a particular outcome. In a massive data set with 8,000 statistically significant correlations, even a smidgen of confirmation bias can cause someone to find a trend that isn’t truly there.

An example of bias in “The China Study”

Body weight, associated with animal protein intake, was associated with more cancer and more coronary heart disease. It seems that being bigger, and presumably better, comes with very high costs. (Page 102)

Consuming more protein was associated with greater body size. … However, this effect was primarily attributed to plant protein, because it makes up 90% of the total Chinese protein intake. (Page 103)

Let’s read between the lines. Here we have Campbell claiming two things, a few paragraphs apart: One, that body weight is associated with more cancer and heart disease, and two, that body size in China is linked not only with a greater intake of animal protein, but also with a greater intake of plant protein. In fact, the link between body size is stronger with plant protein than with animal protein.

Yet notice how Campbell only implicates animal protein in the association between body weight, cancer, and heart disease. If he were to describe the data without bias, Campbell’s first statement would be this:

Body weight, associated with animal protein intake and plant protein intake, was associated with more cancer and more coronary heart disease.

Maybe his editor just overlooked that omission, eh? Right afterward, Campbell notes:

But the good news is this: Greater plant protein intake was closely linked to greater height and body weight. Body growth is linked to protein in general and both animal and plant proteins are effective! (Page 102)

Wait a minute. This is good news? Didn’t Campbell just say being bigger “comes with very high costs” and that it’s associated with “more cancer and coronary heart disease?” Why is body size a bad thing when it’s associated with animal protein, but a good thing when it’s associated with plant protein?

Does less animal foods equal better health?

People who ate the most animal-based foods got the most chronic disease. Even relatively small intakes of animal-based food were associated with adverse effects. People who ate the most plant-based foods were the healthiest and tended to avoid chronic disease.

This oft-repeated quote from “The China Study” is compelling, but is it true? Based on the data above, it seems like an unlikely conclusion—but let’s try once more to see if it could be valid.

As an illustrative experiment, let’s look at the top five Chinese counties with the lowest animal protein consumption and compare them against the top five counties with the highest animal protein consumption. A data set of 10 won’t yield any confident conclusions, of course, and I won’t treat this as representative of the collective body of China Study data. But since animal protein consumption among the studied counties ranged from 0 grams* to almost 135 grams per day, we should see a stark contrast between the nearly-vegan regions and the ones eating considerably more animal foods. That is, assuming it’s true that “even relatively small intakes of animal-based food” yield disease.

*The county averaging zero grams per day wasn’t completely vegan, but the yearly consumption of animal foods was low enough so that the daily average appeared less than 0.01 grams.

Here are the counties I’ll be using. The first five are our near-vegans; the second five are our highest animal product consumers. From both groups, I had to exclude a top-five county due to missing data for most mortality variables (illegible documentation, according to the authors of “Diet, Life-style and Mortality in China”) and replaced it with a sixth county where animal protein consumption matched within a few hundredths of a gram.

Below are the names of each county, as well as values for their daily animal protein intake, the percentage of their total caloric intake coming from fat, and their daily intake of fiber (in case the latter two variables are also of interest).

To give you a visual idea of these quantities, 135 grams of animal protein is the equivalent of 22 medium eggs per day, 24 grams of animal protein is the equivalent of four medium eggs per day, 12 grams is two eggs, and 9 grams is one and a half eggs. Obviously, that’s quite a wide range even among the top consumers of animal foods, so the highest animal-food-eating counties (Tuoli and XIanghuang qi) may be the most important to study in contrast with the near-vegan counties.

Animal protein intake by county:

For reference, some other diet variables:

And now, mortality rates for important variables (as per 1000 people). I’ll save you my commentary and just show you the graphs, which should speak for themselves. Remember, the five left-most bars (Jiexiu through Songxian) on each graph are the near-vegan counties, and the five right-most bars (Tuoli through Wenjiang) are the highest consumers of animal products.

As you can see, the mortality rates for both groups (near-vegan and higher-animal-foods) are quite similar, with the animal food group coming out more favorably in some cases (death from all cancers, myocardial infarction, brain and neurological diseases, lymphoma, cervix cancer). This little comparison might not carry a lot of scientific clout due to its small sample size, but it does blatantly undermine Campbell’s assessment:

People who ate the most animal-based foods got the most chronic disease … People who ate the most plant-based foods were the healthiest and tended to avoid chronic disease.

Sins of omission

Perhaps more troubling than the distorted facts in “The China Study” are the details Campbell leaves out.

Why does Campbell indict animal foods in cardiovascular disease (correlation of +1 for animal protein and -11 for fish protein), yet fail to mention that wheat flour has a correlation of +67 with heart attacks and coronary heart disease, and plant protein correlates at +25 with these conditions?

Speaking of wheat, why doesn’t Campbell also note the astronomical correlations wheat flour has with various diseases: +46 with cervix cancer, +54 with hypertensive heart disease, +47 with stroke, +41 with diseases of the blood and blood-forming organs, and the aforementioned +67 with myocardial infarction and coronary heart disease? (None of these correlations appear to be tangled with any risk-heightening variables, either.)

Why does Campbell overlook the unique Tuoli peoples documented in the China Study, who eat twice as much animal protein as the average American (including two pounds of casein-filled dairy per day)—yet don’t exhibit higher rates of any diseases Campbell ascribes to animal foods?

Why does Campbell point out the relationship between cholesterol and colorectal cancer (+33) but not mention the much higher relationship between sea vegetables and colorectal cancer (+76)? (For any researcher, this alone should be a red flag to look for an underlying variable creating misleading correlations, which—in this case—happens to be schistosomiasis infection.)

Why does Campbell fail to mention that plant protein intake correlates positively with many of the “Western diseases” he blames cholesterol for—including +19 for colorectal cancers, +12 for cervix cancer, +15 for leukemia, +25 for myocardial infarction and coronary heart disease, +12 for diabetes, +1 for breast cancer, and +10 for stomach cancer?

Of course, these questions are largely rhetorical. Only a small segment of “The China Study” even discusses the China Study, and Campbell set out to write a publicly accessible book—not an exhaustive discussion of every correlation his research team uncovered. However, it does seem Campbell overlooked or ignored significant points when discerning the overriding nutritional themes in the China Project data.

What about casein?

Along with trends gleaned from the China Project, Campbell recounts the startling connection he found between casein (a milk protein) and cancer in his research with lab rats. In his own words, casein “proved to be so powerful in its effect that we could turn on and turn off cancer growth simply by changing the level consumed” (page 5 of “The China Study”). Protein from wheat and soy did not have this effect.

This finding is no doubt fascinating. If nothing else, it suggests a strong need for more research regarding the safety of casein supplementation in humans, especially among bodybuilders, athletes, and others who use isolated casein for muscle recovery. Unfortunately, Campbell extrapolates this research beyond its logical scope: He concludes that all forms of animal protein have similar cancer-promoting properties in humans, and we’re therefore better off as vegans. This claim rests on several unproven assumptions:

1. The casein-cancer mechanism behaves the same way in humans as in lab rats.
2. Casein promotes cancer not just when isolated, but also when occurring in its natural food form (in a matrix of other milk substances like whey, bioactive peptides, conjugated linoleic acid, minerals, and vitamins, some of which appear to have anti-cancer properties).
3. There are no differences between casein and other types of animal protein that could impose different effects on cancer growth/tumorigenesis.

Campbell offers no convincing evidence that any of the above are true. We do share some metabolic similarities with rats, so for the sake of being able to entertain the possibility that #2 and #3 are valid, let’s assume that the effect of casein on rats translates cleanly to humans.

How does Campbell justify generalizing the effects of casein to all forms of animal protein? Much of it is based on a study he helped conduct: “Effect of dietary protein quality on development of aflatoxin B[1]-induced hepatic preneoplastic lesions,” published in the August 1989 edition of the Journal of the National Cancer Institute. In this study, he and his research crew discovered that aflatoxin-exposed rats fed wheat gluten exhibited less cancer growth than rats fed the same amount of casein. But get this: When lysine (the limiting amino acid in wheat) was restored to make the gluten a complete protein, the rats had just as much cancer occurrence as the casein group. Jeepers!

Campbell thus deduced that it’s the amino acid profile itself responsible for spurring cancer growth. Because most forms of plant protein are low in one or more amino acids (called “limiting amino acids”) and animal protein is complete, Campbell concluded that animal protein, but not plant protein, must encourage cancer growth. Time to whip out the veggie burgers!

Of course, this conclusion has some gaping logical holes when applied to real life. Unless you consume nothing but animal products, you’ll be ingesting a mixed ratio of amino acids by default, since animal foods combined with plant foods still yield limiting amino acids. The rats in Campbell’s research consumed casein as their only protein source, the equivalent of someone eating zero plant protein for life. An unlikely scenario, to be sure.

Moreover, certain combinations of vegan foods (like grains and legumes) have complementary amino acid profiles, restoring each other’s limiting amino acid and resulting in protein that’s complete or nearly so. Would these food combinations also spur cancer growth? How about folks who pop a daily lysine supplement after eating wheat bread? If Campbell’s conclusions are correct, it would seem vegans could also be subject to the cancer-promoting effects of complete protein, even when eschewing all animal foods.

Also, it seems Campbell never mentions an obvious implication of a casein-cancer connection in humans: breast milk, which contains high levels of casein. Should women stop breastfeeding to reduce their children’s exposure to casein? Did nature really muck it up that much? Are children who are weaned later in life at increased risk for cancer, due to a longer exposure time the casein in their mothers’ milk? It does seem strange that casein, a substance universally consumed by young mammals, is so hazardous for health—especially since it’s designed for a time in life when the immune system is still fragile and developing.

At any rate, Campbell’s theories about plant versus animal protein and cancer are essentially speculation. Despite a single experiment with restoring lysine to wheat gluten, he hasn’t actually offered evidence that all animal protein behaves the same way as casein.

But check this out. After delineating his discovery of the link between casein and cancer, Campbell writes:

We initiated more studies using several different nutrients, including fish protein, dietary fats and the antioxidants known as cartenoids. A couple of excellent graduate students of mine, Tom O’Conner and Youping He, measured the ability of these nutrients to affect liver and pancreatic cancer. (Page 66)

So he did experiment with an animal protein besides casein! Unfortunately, Campbell never mentions what the specific results of this research were. In describing the studies he conducted with his grad students, Campbell says only that a “pattern was beginning to emerge: nutrients from animal-based foods increased tumor development while nutrients from plant-based foods decreased tumor development.” (Page 66)

I don’t know about you, but I’d sure like to see the actual data for some of this.

After a little searching, I found one of the aforementioned experiments conducted by Campbell, his grad student Tom, and two other researchers. It was published in the November 1985 issue of the Journal of the National Cancer Institute: “Effect of dietary intake of fish oil and fish protein on the development of L-azaserine-induced preneoplastic lesions in the rat pancreas.”

(A preneoplastic lesion, by the way, is a fancy term for the growth that occurs before a tumor.)

In this study, Campbell and his team studied three groups of carcinogen-exposed rats: One fed casein plus corn oil, one fed fish protein plus corn oil, and one fed fish protein plus fish oil (from a type of high omega-3 fish called menhaden). All groups received a diet of about 20% protein and 20% fat and ate the same amount of calories.

Providing background for the study, the authors note that previous research has showed fish protein to have anti-cancer properties (emphasis mine):

Gridley et al. [n15,n16] reported on two studies in which intake of fish protein resulted in a reduced tumor yield when compared to other protein sources. Spontaneous mammary tumor development in C3H/HeJ mice was reduced. The incidence of herpes virus type 2-transformed cell-induced tumors in mice was also reduced in animals fed a fish protein diet.

Perhaps this should’ve tipped Campbell off that not all sources of animal protein spur cancer growth like casein does. For reference, the cited studies are “Modification of herpes 2-transformed cell-induced tumors in mice fed different sources of protein, fat and carbohydrate” published in the November-December 1982 issue of Cancer Letters, and “Modification of spontaneous mammary tumors in mice fed different sources of protein, fat and carbohydrate” published in the June 1983 issue of Cancer Letters.

So what were the results of Campbell’s experiment? According to the study, both the casein/corn oil and fish protein/corn oil groups had significant preneoplastic lesions. We don’t know whether to blame this on the protein or the corn oil, since—according to the researchers—“intake of corn oil has previously been shown to promote the development of L-azaserine-induced preneoplastic lesions in rats.” However, the group that ate fish protein plus fish oil exhibited something radically different:

It is immediately apparent that menhaden oil had a dramatic effect both on the development in the number and size of preneoplastic lesions. The number of AACN per cubic centimeter and the mean diameter and mean volume were significantly smaller in the F/F [fish protein and fish oil] group compared to the F/C [fish protein and corn oil] group. Furthermore, no carcinomas in situ were observed in the F/F group, whereas the F/C group had an incidence of 3 per 16 with 6 total carcinomas.

There’s some significant stuff here, so let’s break this down point by point.

One: The cancer-inducing properties of fish protein, if there are any to begin with, were neutralized by the presence of fish oil. This means that even if all animal protein behaves like casein under certain circumstances, its effect on cancer depends on what other substances accompany it. Animal protein is therefore not a universal cancer promoter; only a situational one, at best.

Two: What does “fish protein” plus “fish fat” start to resemble? Whole fish. Campbell just demonstrated that animal protein may, indeed, operate differently when consumed with its natural synergistic components.

Since there wasn’t a rat group eating casein plus fish oil, we don’t know what the effect of a dairy protein plus fish fat would have been. However, it would be interesting to have more studies looking at cancer growth in mice fed diets of casein plus milk fat. If casein loses its cancer-promoting abilities under that circumstance, as fish protein did with fish oil, then we’d have good reason to think the various factions of whole animal products might reduce any cancer-promoting properties a single component has in isolation.

And Campbell and his team conclude:

[A] 20% menhaden oil diet, rich in omega 3 fatty acids, produced a significant decrease in the development of both the size and number of preneoplastic lesions when compared to a 20% corn oil diet rich in omega 6 fatty acids. This study provides evidence that fish oils, rich in omega 3 fatty acids, may have potential as inhibitory agents in cancer development.

Remember how Campbell said, summarizing this research, that “nutrients from animal-based foods increased tumor development while nutrients from plant-based foods decreased tumor development”? Last I checked, fish oil ain’t no plant food.

Why does Campbell avoid mentioning anything potentially positive about animal products in “The China Study,” including  evidence unearthed by his own research? For someone who has openly censured the nutritional bias rampant in the scientific community, this seems a tad hypocritical.

But back to casein and milk for a moment. It’s interesting that the only dairy protein Campbell experimented with was casein, since whey—the other major protein in milk products—repeatedly shows cancer-protective and immunity-boosting effects, including when tested side-by-side with casein. Just a sampling of the literature:

• Diets containing whey proteins or soy protein isolate protect against 7,12-dimethylbenz(a)anthracene-induced mammary tumors in female rats. ” When 100% of the casein-fed rats had at least one tumor, soy-fed rats had a lower tumor incidence (77%) in experiment B (P < 0.002), but not in experiment A (P < 0.12), and there were no differences in tumor multiplicity. Whey-fed rats had lower mammary tumor incidence (54-62%; P < 0.002) and multiplicity (P < 0.007) than casein-fed rats in both experiments. … Furthermore, whey appears to be at least twice as effective as soy in reducing both tumor incidence and multiplicity.” (So much for plant protein being more protective against cancer!)
• Developmental effects and health aspects of soy protein isolate, casein, and whey in male and female rats. We found that SPI [soy protein isolate] accelerated puberty in female rats (p < .05) and WPH [whey protein hydrolysate] delayed puberty in males and females, as compared with CAS (p < .05). … Female rats fed SPI or WHP or treated with genistein had reduced incidence of chemically induced mammary cancers (p < .05) compared to CAS controls, with WHP reducing tumor incidence by as much as 50%, findings that replicate previous results from our laboratory.
• Tp53-associated growth arrest and DNA damage repair gene expression is attenuated in mammary epithelial cells of rats fed whey proteins. “Results indicate that mammary glands of rats fed a WPH [whey protein hydrolysate] diet are more protected from endogenous DNA damage than are those of CAS [casein]-fed rats.”
• A role for milk proteins and their peptides in cancer prevention. “Animal models, usually for colon and mammary tumorigenesis, nearly always show that whey protein is superior to other dietary proteins for suppression of tumour development.”
• A bovine whey protein extract stimulates human neutrophils to generate bioactive IL-1Ra through a NF-kappaB- and MAPK-dependent mechanism. “Our data suggest that WPE [whey protein extract] … has immunomodulatory properties and the potential to increase host defenses.”
• Whey proteins in cancer prevention.
• Whey protein concentrate (WPC) and glutathione modulation in cancer treatment.

Given all this, it seems unlikely that casein’s effects on cancer apply to other forms of milk protein—much less all animal protein at large. Isn’t it possible (maybe even probable) that casein has deleterious effects when isolated, but doesn’t exhibit cancer-spurring qualities when consumed with the other components in milk? Could casein and whey work synergistically, with the anti-cancer properties of whey neutralizing the pro-cancer properties of casein?

I’ll let you be the judge.

In summary and conclusion…

Apart from his cherry-picked references for other studies (some of which don’t back up the claims he cites them for), Campbell’s strongest arguments against animal foods hinge heavily on:

1. Associations between cholesterol and disease, and
2. His discoveries regarding casein and cancer.

For #1, it seems Campbell never took the critical step of accounting for other disease-causing variables that tend to cluster with higher-cholesterol counties in the China Study—variables like schistosomiasis infection, industrial work hazards, increased hepatitis B infection, and other non-nutritional factors spurring chronic conditions. Areas with lower cholesterol, by contrast, tended to have fewer non-dietary risk factors, giving them an automatic advantage for preventing most cancers and heart disease. (The health threats in the lower-cholesterol areas were more related to poor living conditions, leading to greater rates of tuberculosis, pneumonia, intestinal obstruction, and so forth.)

Even if the correlations with cholesterol did remain after adjusting for these risk factors, it takes a profound leap in logic to link animal products with disease by way of blood cholesterol when the animal products themselves don’t correlate with those diseases. If all three of these variables rose in unison, then hypotheses about animal foods raising disease risk via cholesterol could be justified. Yet the China Study data speaks for itself: Animal protein doesn’t correspond with more disease, even in the highest animal food-eating counties—such as Tuoli, whose citizens chow down on 134 grams of animal protein per day.

Nor is the link between animal food consumption and cholesterol levels always as strong as Campbell implies. For instance, despite eating such massive amounts of animal foods, Tuoli county had the same average cholesterol level as the near-vegan Shanyang county, and a had a slightly lower cholesterol than another near-vegan county called Taixing. (Both Shanyang and Taixing consumed less than 1 gram of animal protein per day, on average.) Clearly, the relationship between animal food consumption and blood cholesterol isn’t always linear, and other factors play a role in raising or lowering levels.

For #2, Campbell’s discoveries with casein and cancer, his work is no doubt revelatory. I give him props for dedicating so much of his life to a field of disease research that wasn’t always well-received by the scientific community, and for pursuing so ardently the link between nutrition and health. Unfortunately, Campbell projects the results of his casein-cancer research onto all animal protein—a leap he does not justify with evidence or even sound logic.

As ample literature indicates, other forms of animal protein—particularly whey, another component of milk—may have strong anti-cancer properties. Some studies have examined the effect of whey and casein, side-by-side, on tumor growth and cancer, showing in nearly all cases that these two proteins have dramatically different effects on tumorigenesis (with whey being protective). A study Campbell helped conduct with one of his grad students in the 1980s showed that the cancer-promoting abilities of fish protein depended on what type of fat is consumed alongside it. The relationship between animal protein and cancer is obviously complex, situationally dependent, and bound with other substances found in animal foods—making it impossible extrapolate anything universal from a link between isolated casein and cancer.

On page 106 of his book, Campbell makes a statement I wholeheartedly agree with:

Everything in food works together to create health or disease. The more we think that a single chemical characterizes a whole food, the more we stray into idiocy.

It seems ironic that Campbell censures reductionism in nutritional science, yet uses that very reductionism to condemn an entire class of foods (animal products) based on the behavior of one substance in isolation (casein).

In sum, “The China Study” is a compelling collection of carefully chosen data. Unfortunately for both health seekers and the scientific community, Campbell appears to exclude relevant information when it indicts plant foods as causative of disease, or when it shows potential benefits for animal products. This presents readers with a strongly misleading interpretation of the original China Study data, as well as a slanted perspective of nutritional research from other arenas (including some that Campbell himself conducted).

In rebuttals to previous criticism on “The China Study,” Campbell seems to use his curriculum vitae as reason his word should be trusted above that of his critics. His education and experience is no doubt impressive, but the “Trust me, I’m a scientist” argument is a profoundly weak one. It doesn’t require a PhD to be a critical thinker, nor does a laundry list of credentials prevent a person from falling victim to biased thinking. Ultimately, I believe Campbell was influenced by his own expectations about animal protein and disease, leading him to seek out specific correlations in the China Study data (and elsewhere) to confirm his predictions.

It’s no surprise “The China Study” has been so widely embraced within the vegan and vegetarian community: It says point-blank what any vegan wants to hear—that there’s scientific rationale for avoiding all animal foods. That even small amounts of animal protein are harmful. That an ethical ideal can be completely wed with health. These are exciting things to hear for anyone trying to justify a plant-only diet, and it’s for this reason I believe “The China Study” has not received as much critical analysis as it deserves, especially from some of the great thinkers in the vegetarian world. Hopefully this critique has shed some light on the book’s problems and will lead others to examine the data for themselves.

At any rate, I recommend not quoting this post or citing it as “evidence” for anything simply because of the uncertainty surrounding the Tuoli data in the China Study. Please see the following posts for more information on the issue of Tuoli’s accuracy:

http://rawfoodsos.com/2010/08/03/the-china-study-a-formal-analysis-and-response/

http://rawfoodsos.com/2010/07/16/the-china-study-my-response-to-campbell/

As I mentioned in the previous post on dairy consumption and disease in China, there’s a fascinating little county by the name of “Tuoli” situated in northwest China—a place quite worthy of nutritional study, due to their unique diet.

They live here:

Which looks like this:

Where they eat a lot of this:

But not a lot of this:

The Tuoli diet is so abnormal for China, in fact, that T. Colin Campbell et al omitted this county from analysis in several China Study papers—such as “Vitamin A and cartenoid status in rural China,” published in the British Journal of Nutrition:

One county (Tuoli County in Xinjiang Autonomous Region), composed primarily of an ethnic minority population of herdspeople, had disproportionately high values for retinol, lipid and protein intake due to an exceptionally high intake of animal foods. This ‘outlier’ was not included in the analysis, to characterize more accurately the average intakes of the rural Chinese population and to avoid the undue influence of one data point on the results.

Given the prevailing beliefs about nutrition and health—such as saturated fat and cholesterol as a cause of heart disease, the necessity of fiber for colon health, the immunity-boosting properties of fruits and vegetables, and the dangers of a diet high in animal fat—it would seem the Tuoli should showcase the health woes that come from breaking every rule in the diet book.

But is that the case?

Tuoli diet

First, let’s take a closer look at what he China Project data has to say about these Tuoli folks.

In terms of macronutrients, the Tuoli consumed an average of 185.6 grams of fat, 172.5 grams of protein, and 322 grams of carbohydrates per day. Average energy intake was a whoppin’ 3704 calories, and average fiber intake was 17.9 grams per day—only slightly more than your run-of-the-mill American.

The average diet of all counties studied in the China Project is clearly carb-based, low in fat and protein (as a percent of total calories):

In contrast, the Tuoli diet is nearly half fat:

Main items on the Tuoli menu included:

• Dairy: 856.5 grams per day (almost two pounds)
• Wheat flour: 371.6 grams per day (0.82 pounds)
• Meat: 121 grams per day (a bit over a quarter of a pound)

Sparse and non-existent items included:

• Potatoes: five to six times per year
• Green vegetables: twice per year
• Fruit: less than once per year
• Legumes: never
• Sea vegetables: never
• Nuts: never
• Eggs: never
• Fish: never
• Plant oils (rapeseed, soybean, sesame, corn): never
• Soy sauce: never

Basically, these folks live on dairy, meat, and wheat, and refuse to eat their vegetables. Sounds like some Americans I know.

Tuoli blood markers and diseases

If the Tuoli’s meat-and-dairy-heavy diet is the source of disease, we’d expect to see these folks facing more chronic conditions than the regions eating plant-based diets. To test whether this is the case, let’s compare Tuoli with the 13 counties in the China Project that consumed less than 1 gram of animal protein per day—the closest thing we have to Chinese vegans.

I’ll be putting these all in a bar graphs, but to prevent an uber-cluttered x-axis, I’ll just use numbers corresponding to each county:

1. Cixian
2. Jingxing
3. Huguan
4. Jiangxian
5. Jiexiu
6. Linxian
7. Songxian
8. Jianhu
9. Taixing
10. Qingzhen
11. Cangxi
12. Shanyang
13. Longxian
14. Tuoli

The first 13 counties will always be blue bars; Tuoli will always be red.

Before getting to the mortality statistics, let’s look at some basic blood markers for heart disease. Here we have total cholesterol of the above counties, lined up side-by-side for comparison.

As you might expect, Tuolians* have higher total cholesterol than most of the near-vegan counties, although it’s still a healthy number by American standards. However, the difference between a couple of those counties isn’t all that profound: Tuoli’s cholesterol is tied with that of Shanyang and lags a bit behind Taixing, both of which consume only trivial amounts of animal products. Curious, indeed. Obviously, something other than animal product consumption affects blood cholesterol.

*”Tuolian” may or may not be an actual term.

Next, let’s peek at triglycerides—a type of blood fat that, in high amounts, can raise your risk of heart disease.

It seems Tuoli is pretty much neck-and-neck with the plant-eating counties. By American standards, triglyceride levels between 150 and 199 are considered borderline high, and lower numbers are considered normal—so only one county, a near-vegan one, had values outside a healthy range.

Disease rates

First up: Death from all causes (per 1000 people under the age of 65). Remember, Tuoli county is the red bar; the blue bars represent the near-vegan counties in the China Project that consumed less than 1 gram of animal protein per day on average.

Okay, so the Tuoli don’t have a higher death rate than the near-vegans. In fact, Tuoli’s total mortality rate is lower than 11 of the other counties and higher than only two.

But what about cancer? Let’s look at mortality from all cancers for Tuoli and the plant-lovin’ regions. Again, Tuoli is the red bar, and the near-vegan counties are the blue ones.

How ’bout them apples? Tuoli doesn’t appear to have higher cancer rates than the near-vegan areas. Eight counties have higher rates and only five have lower ones, leaving Tuoli hovering near the lower-middle end of the spectrum.

Next we have mortality from myocardial infarction (heart attacks) and coronary heart disease, per 1000 people. Tuoli is red, near-vegan counties are blue… you know the drill.

Surprised? Despite a massive intake of cholesterol, saturated fat, calories, animal protein, and all those other horrors ascribed to declining heart health, the Tuoli have relatively low levels of coronary heart disease and heart attacks. Seven near-vegan counties have higher rates than Tuoli, and six have lower rates.

And now for stroke mortality (per 1000 people).

Again, no significantly higher stroke rates for the Tuolians. Seven near-vegan counties have more incidences of stroke, and six have fewer incidences of stroke.

And since lack of fiber is supposed to harm colon health, here is a comparison of colon cancer and rectal cancer mortality (per 1000 people) between the plant-noshing counties and the vegetable-phobic Tuolians.

Looks like they’re doing pretty dandy without much fiber, right?

But what about leukemia? Let’s check it out:

As you can see, Tuoli isn’t significantly worse off than the near-vegan counties in terms of chronic disease. Total mortality rate is lower, cancer rates are lower or similar, heart attacks aren’t more common than usual, stroke rates are average. From this data alone, we’d have no basis for claiming that eating two pounds of dairy per day (and minimal vegetation, aside from wheat flour) is less healthful than consuming a mostly vegetarian diet. For sure, this data fails to support Campbell’s claim that chronic disease rates climb when animal protein intake rises.

(And as you’ll see in an upcoming post, it’s pretty surprising that the Tuoli had low rates of cardiovascular disease while eating high levels of wheat—but we’ll get to that later.)

Why aren’t these people sick and diseased?

We have plenty of evidence showing hormone-pumped dairy, grain-fed meat, pasteurized and homogenized milk, processed lunch meats, and other monstrosities are bad for the human body. No debate there. But we do have a woeful lack of research on the effects of “clean” animal products—meat from wild or pastured animals fed good diets, milk that hasn’t been heat-zapped, antibiotic-free cheeses and yogurts, and so forth. Perhaps the best data we have is from observational studies of isolated or primitive peoples (such as those studied by Weston A. Price), but those lack detailed documentation about mortality rates and don’t usually meet standards of scientific rigor.

In other words, this is one area where nutritional research is pretty deficient.

Is it possible the diseases we ascribe to animal products aren’t caused by animal products themselves, but by the chemicals, hormones, and treatment processes we expose them to? If the Tuoli are any indication, this may be the case. Hopefully future research will shed more light on the matter.