The China Study: Fact or Fallacy?

Disclaimer: This blog post covers only a fraction of what’s wrong with “The China Study.” In the years since I wrote it, I’ve added a number of additional articles expanding on this critique and covering a great deal of new material. Please read my Forks Over Knives review for more information on what’s wrong with the conclusions drawn from Campbell’s casein/aflatoxin research, and if you’d rather look at peer-reviewed research than the words of some random internet blogger, see my collection of scientific papers based on the China Study data that contradict the claims in Campbell’s book. I’ve also responded to Campbell’s reply to my critique with a much longer, more formal analysis than the one on this page, which you can read here.

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:

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.


  1. A friend of mine in stage 4 cancer had given me “The China Study” to read so I could give her my opinion on the research given. Upon comletetion I wanted to burn it. Our bodies are not made from cookie cutters and to say so bodly that animal fats and dairy are bad for mankind is lunacy.
    Look at history and what mankind has been successfully eating for thousands of years Greed and the love of the almighty dollar is the main reason our diet has become such an issue. Government, food production and big pharma work hand in hand. Dr Campbell, very proudly, listed all of the grants he recieved for his studies!
    For those who are educated beyond their intelligence, I am thankful for Denise’s great work. But as for me with a PhD (plain high school diploma),The truth or rather the “untruths” about the China Study didn’t take charts and graphs. It was as clear as the nose on my face.
    The bible says “Everything in moderation”, no truer words have ever been spoken.

    1. All things in moderation indeed!
      Remember that originally, in Genesis 1:29-30, both man and beast was given “every green plant for food”. Surely, for healing, it would seem to make sense to “go back to the basics”.

      1. Common sense and religion don’t mix. I fail to see how it makes sense to “go back to basics” when the basics are religion. Unless, of course, we’re talking about faith. Are we talking about faith?

      2. Yeah, every green plant. If you like the Garden of Eden idea, remember it had plants, and animals, not grains. In fact…Genesis 3:19, when they were kicked from the garden:

        “In the sweat of thy face shalt thou eat bread, till thou return unto the ground; for out of it wast thou taken: for dust thou art, and unto dust shalt thou return.”

        Sounds like bread was part of the punishment. No bread for me, thanks!

  2. The most curious thing about the whole exercise is that Ms. Minger’s correlations made with the uncorrected data apparently match Dr. Campbell’s.

    I would imagine that Ms. Minger made a few mistakes in her analysis along the way. She is only human, was burning the midnight oil, and has no world class epidemiologists on hire to help check her work. Dr. Campbell, as a full professor at Cornell, no doubt has some very fine epidemiologists at his disposal. If he chose to publish the correlations from the uncorrected data, I cannot credit that it was a mistake.

    How many “correlations” and “associated withs” in the current up-for-debate 2010 guidelines come from the China Study data and the papers published by Dr. Campbell’s group?

    The China Study data set is problematic, as explained by the epidemiologist in the comments, and of course by Ms. Minger herself. Nevertheless such a large data set is important, and could have some meaningful information. If the data is processed correctly.

  3. Rayna suggested:

    “At the very least, you need to model the data using regression analyses so that you can account for multiple factors at one time.”

    Engineer Richard Kroeker did just that two years ago. You can see his results on an Amazon discussion thread here .

  4. John wrote:

    “We discussed data use and misuse on pp. 54-82 of the China Project monograph that curiously was overlooked by Masterjohn and Jay’Y’.”

    I located the monograph here . I read the foreward and the study description and methods but could not find any information about data use and misuse that would be relevant to this debate. I also have a copy of The China Study. I would very much appreciate it if you or someone else could direct me to text in the book or online that would explain how Campbell and others connected data from the monograph to their conclusions about animal and plant protein and fat. The monograph itself states in the section “Study description and methods” that no conclusions can be drawn from varying data on fat consumption, noting that fat consumption raises (in its words) both protective HDL and harmful LDL. Links or page numbers will do.

  5. Thank you Denise!

    My friend Greg Glassman once wrote “Truth is like a beach ball, it takes a lot of effort to hold it underwater and eventually, it will rise to the surface.”

  6. Just need to thank you, Denise, for this well-researched post. Pretty much closes the book on Campbell’s erroneous, biased “scientific” conclusions.

  7. Hi Denise,

    Thank you for your wonderful analysis. Like others before me I am interested in a more detailed parsing of the data concerning wheat and whether the wheat consumed was refined processed wheat. Even though he is a vegan, my guess is T. Colin Campbell would agree with any correlations throwing modern refined processed wheat under the bus.

    Look forward to more of your work (and I also sent you an email).

    Take care..

    1. In the monograph, wheat is identified in English and Chinese as wheat flour. There is no information about whether the wheat flour was whole, unbleached white, or bleached.

      1. Yes, I was aware of that, and those two links I mentioned below were helpful. I also asked Chris Masterjohn and he wasn’t sure but going from memory he thought it was refined (which may also include bromating, treating with transglutaminase etc.).

      2. You can read Chinese, too? : )

        I wonder how Masterjohn would know what kind of flour was consumed during a study that took place in the 70s. Bleached flour was certainly around in China then, and today nearly all flour sold in stores is refined white flour. However, I do not know how rural collectivized farms treated wheat flour back in the 70s. If I can find a Chinese internet forum where foreign nationals can post, I might ask.

      3. I looked up the email addresses of a few Chinese professors of nutrition, contacted them, and one has responded (it’s morning in China). Dr. Duo Li of Zhejiang University stated briefly in his reply that rural Chinese ate whole wheat products in the 70s and that refined wheat was rarely consumed then.

      4. Actually Michael what I wrote to that particular question was “I don’t think the China Study really collected details about kinds and processing of wheat. I think there were only two districts that consumed wheat and dairy and they were modernized, but that’s off memory.”

        (And when I say memory, I mean it has been five years since I looked at the monograph.)

        My comments about the processing were a question to you about the type of wheat flour Dr. Davis was using in his self-experiment.


      5. Chris,

        My bad. When you referred to the two districts as being modernized, I assumed you meant in your recollection you thought they were consuming refined wheat and pasteurized dairy.

  8. Okay, two of the links posted in this comment thread, Brad Marshall and Richard Kroeker, point to the consumption of refined wheat flour, i.e. white flour, which changes the landscape considerably. Brad Marshall seems to dismiss this issue because white rice is also consumed, but white rice does not have the same impact on the body as white flour (refined wheat flour). Nor does this take into account all the processing other than refining that typical modern day refined wheat flour undergoes.

    Both however are outstanding links.

    1. I scrolled up the thread and couldn’t find the link to Marshall. Kroeker’s information comes from a present-day Chinese initiative to enrich flour. I looked online for information about Chinese wheat production and processing and couldn’t find any information specifying one way or the other how flour was processed back in the 70s. I will probably send emails to some middle-aged Chinese who might be able to recall.

  9. Denise, I think you would be very interested in this paper:

    Siri-Tarino, Sun Q, Hu F B, Krauss, R M. “Meta-Analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease”, American Journal of Clinical Nutrition 2010, vol. 91, pp535-546

    It is a meta-analysis of just over 20 papers that looked at the link between saturated fat intake and heart disease. Very good statistical analysis, and I think at one point the authors show that in two studies the researchers misinterpreted their own data.

  10. Denise, I think you would be very interested in this paper:

    Siri-Tarino, Sun Q, Hu F B, Krauss, R M. “Meta-Analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease”, American Journal of Clinical Nutrition 2010, vol. 91, pp535-546

    It is a meta-analysis of just over 20 papers that looked at the link between saturated fat intake and heart disease. A very good statistical analysis.

  11. Hi Denise,

    As promised, I’m posting my response to your email on your site. You asked that I provide some tips on where to start and how to proceed. BTW, you mentioned “epidemiology secrets” and I just want to say: no “secrets”!! Epidemiology is just critical thinking, but with numbers. It’s no different from many other disciplines. Maybe some time you can help me with writing (scientists are generally terrible writers, hehe).

    Note: I’ve included some comments on what went wrong and how it can be corrected merely for demonstrative purposes – not at all malicious attacks, OK? This is how we all learn after all. In caps, I will highlight steps in the action plan for you.

    STEP 0: Do a literature search. I find it helpful to keep an excel spreadsheet with columns for author, title, journal, year, summary of paper, strengths of the study, weaknesses, and concluding remarks. This is essential, as one shouldn’t just blindly go into an analysis without having at least some background information on the subject matter. No need to be an expert, but good to know what’s already out there, and what needs to be done.

    1. Correlations:
    For this discussion, the outcome will be colorectal cancer, since you used it on your post. Similarly, the primary exposure of interest will be total cholesterol. By by basing your conclusions on uncorrected correlations alone, you’ve made a huge leap that doesn’t have much ground to stand on. The simple correlations are biased, as you yourself pointed out when evaluating total cholesterol, schistomiasis, and colorectal cancer. As such, if you don’t adjust for potential confounders via multiple regression, the association you observe is biased. We almost always need to adjust for confounders, and this is very true in your case.

    STEP 1: It’s a good habit to evaluate the correlations between all exposures and also between all exposures and the outcome at the individual level. So, for *every* analysis you plan on doing, run create scatterplots for every X against X and every X against Y, using the *individual* data (where possible), and provide the correlation + 95% confidence interval for each.

    STEP 2: Create histograms for every exposure of that is categoric and density plots (or you can create histograms with very narrow bars) for every exposure that is continuous. This will tell you how the variables are distributed and what the appropriate summary statistics for them would be. For example, if total cholesterol is not normally distributed (follow a bell curve) then *median* total cholesterol might be a better summary statistic then *mean* total cholesterol (good to know when you present descriptive statistics of the data you’re using). Sometimes it’s useful to present different stats for a single variable.

    2. Individual data vs. aggregated data:
    You stated you didn’t see much curvature, but keep in mind that you were presenting with aggregated data (eg. average total cholesterol for all individuals) instead of including individual-level data (the exposure and outcome for a single individual). Consequently, there was a big loss in information, and you can’t make accurate decisions on how to model your data if you plot aggregated data. Related to this, your analysis was ecologic (used aggregated/grouped data) but you made individual-level conclusions when you used the term “risk factor.” This is referred to as an ecologic fallacy – and it’s just that. A fallacy. For example, all we can say based on your cholesterol-colorectal cancer example (the one that doesn’t account for schistomiasis) is that the counties with higher mean total cholesterol tend to have higher incidence rates of colorectal cancer. We can’t make the leap to calling cholesterol a *risk factor* for colorectal cancer.

    STEP 3: Don’t aggregate your data in your analysis. Why? You lose A LOT of information when you aggregate data and you can bias your results. So keep that data at the individual-level. For descriptive tables, by all means, aggregated data is necessary for obvious reasons. But in your analysis, individual-level data when you’ve got it is essential.

    3. The right regression model:
    One of your outcomes was incidence rates of colorectal cancer. When you do your analysis with individual-level data, with incidence rates of colorectal cancer as your outcome, linear regression = WRONG model. Make sure you know which models to use and when. To start – when modeling “raw” rates (case counts and person time), we almost always use Poisson regression, and often we need to account for overdispersion as well. Get to know some of the other common regression models as well.

    STEP 4: Write out all of the primary exposures of interest you want to investigate and the corresponding outcome of interest and how you’re setting up your outcome variable (are you interested in colorectal cancer *incidence rates*, *prevalence*, a simple yes/no the person has colorectal cancer?)

    STEP 5: Write out what the appropriate regression model would be for the different analyses you plan to conduct.

    4. Confounders:
    These are factors that are related to the exposure and the outcome of interest such that *not* adjusting for them will produce a biased association between exposure and outcome. As you saw, schistomiasis might be a confounder. And in fact, county might be too – and is actually upstream of schistomiasis in some sense, right? Two confounders that almost *always* must be included in a model are AGE and SEX (provided your analysis isn’t restricted to one sex). This is especially true for chronic disease (eg. cardiovascular disease and cancer). In this particular case, body mass index (BMI) would be very important to include as well. County may also be important.

    STEP 6: For every analysis you do, write out all potential confounders you can think of and why. You know the data better than I do as you’ve worked with it extensively. And, from STEP 0, you’ll know your context.

    STEP 7: Write out *how* the confounders are related to the exposure and outcome. Is the confounder protective (i.e. decrease risk) for the outcome? Or is it a risk factor? How is it associated with the primary exposure of interest? This is where those scatterplots in STEP 1 come in handy! The purpose of this is to give you an idea of *how* an observed association might be biased if you *don’t* adjust for certain confounders. It is tedious, but thorough and, like STEP 6, will allow you to approach your analyses with more contextual background.

    5. “Cleaning” and “recoding” your data:
    Raw data is not *in and of itself* a bad thing. It is simply the data in its original form. But in order to be useful for analysis we often need to “clean” it and “recode” it. When I say “clean” it, I mean setting up the *dataset* that is free (to the greatest extent possible) of unnecessary data (for example, if you’re interested in ovarian cancer, you wouldn’t include men), or mistakes (for example, if an individual in the data was coded as being a man with ovarian cancer, this is clearly wrong). In this case, you might either omit it since you don’t have a way to check which is correct or, based on other data for that individual choose to change “man” to “woman” or “ovarian cancer” to “no ovarian cancer.” “Recoding” means setting up the *variables* to be useful. For example, we might recode BMI in categories of underweight, normal, overweight, and obese rather than leave it as continuous. Some variables may already be categoric, if the corresponding data were collected that way.

    STEP 8: Clean your data. You will likely need to set up multiple datasets.

    STEP 9: Write out *how* you’ve cleaned your data. (This is good record keeping.)

    STEP 10: Recode your data. This might include combining variables too.

    STEP 11: Create a “data dictionary” similar to the one on the Oxford site. But in addition, include a description of how you’ve coded your data (eg. 1=underweight, 2=normal, 3=overweight, 4=obese). Again, good for record keeping, but also “keeps you honest” so others know how you set up your data. This will often be apparent when you present your results, but not always. It’s a good habit to keep track of this, in any event.

    STEP 12: Replot all newly *categorized* variables against the outcome(s) of interest. Why? Because the categorized data may reveal non-linear relationships with the outcome (in fact, this is a strength of categorizing data – that we can account for some non-linear relationships). For example, underweight might be a risk for something, whereas normal BMI is protective, while overweight and obese are a risk (“U-shaped”).

    6. Exploration of your data through descriptive statistics:
    Almost all scientific papers start out with a “Table 1” which presents a description of the data. It tells us things like What’s the % of women and men in our data, What is the proportion of people with and without the exposure and with and without the outcome?

    STEP 13: Create descriptive tables of all relevant variables. This includes your primary exposure of interest, confounders, and outcome. Obviously, you will have different tables for each analysis as you’re interested in different primary exposures (cholesterol? meat? total caloric intake?) and outcomes (cardiovascular disease? colorectal cancer? bladder cancer?). To save time, you might include all relevant exposures and confounders in rows, and cross-classify them with all outcomes of interest in columns.

    6. Analysis:
    The fun part.

    STEP 14: Run your models. Keep track of what you include in your models b/c oftentimes we will evaluate several models for each analysis depending on what’s called “fit statistics.” Since you are familiar with p-values and I assume interpretation of beta coefficients, use these to help inform you of which variables to include in your final model *within the context of the analysis at hand* (this is key – if you have reason to believe that a confounder is important to include, keep it in the model even if it’s non-significant).

    STEP 15: Create tables for results from *all* analyses (including the models you decide to can in favor for another one) and what regression model was used. This is much more transparent than simply producing your final model.

    There’s more “post-analysis” stuff that should be done, but really Steps 1-15 is a pretty thorough.

    7. Publish:
    I can’t stress this enough. This is a long-term goal for sure, especially as you will likely end up with multiple papers! But once you think you’ve got the data set-up and analyses down, you need to write it up and send it on for peer-review. Peer-review is not perfect for sure, but it is the best measure we have for good science. It gives credibility to your efforts. Besides, you *do* want to be acknowledged for your efforts, right? By publishing in a peer-reviewed journal, you’re more likely to gain more widely publicized attention, which I think should be the goal of most epidemiological studies; we want to improve public health through informing not only our peers, but also the public.

    As a last note, I know this is a huge undertaking, but these are steps to a thorough analysis. I have no doubt you’re capable of tackling it.

    Best wishes.

    1. PS. I’m sure you already planned to do this, but make all of the above available. With your large readership, you can make this a collaborative effort.

    2. R,

      I haven’t read it yet, so perhaps you can tell me, which of your 16 steps did TCC follow for his China Project publication?

      If your argument is that anything less than your complete process is bad science and not to be trusted, what might this say about TCC’s work?

      1. i’m not trying to defend TCC’s work by this post, i’m merely providing a framework for denise since i offered help, and she accepted.

    3. Hi R,

      Thanks for sharing all that information on data analysis. I think data analysis is very interesting, and I appreciate you sharing your knowledge.

      It is amusing though that some people are so simple-minded to think that you are somehow defending the China Study by critiquing Minger’s work here. I believe that a vegan website is prominently displaying your comments as some type of defense of the China Study.

      They are so blind to see how much your thoughtful commentary is so incredibly damning of Campbell’s work itself. Every criticism you have found with Minger’s work applies also to Campbell’s work, and in most cases it applies even more frequently and strongly to Campbell’s.

      The key thing is, Minger’s work is just an analysis used to bring to light the problems with Campbell’s work, whereas apparently many people are using Campbell’s “findings” from this ecological data to base and support (and defend) their views on optimal nutrition. These findings that Campbell derived from the uncorrected raw data are so important to some people that they feel the need to attack anybody that questions the analysis and interpretations that were used to reach these findings.

      1. fair points. but i think it’s important to remember that “the china study” relied not only on the data from the china project, but hundreds of other studies as well.

  12. Denise, you might not want to trust this Rayna woman. She is part of a group called Debunking the China Study Critics here-

    Where she posted this-
    My response to Denise’s acceptance of my offer to assist, Its purpose was to re-articulate the limitations of her analysis, but also to inform. Good science should prevail, after all. Also, I think it’s good to attack with kindness. 🙂

    Clearly she is not approaching this with intent to help you but to prove you wrong and make it look as though your argument is weak.

    I encourage everyone to go to the first link and see what the insane vegans are doing. They aren’t interested in science, they’re interested in pushing their own agenda. This is plainly obvious. They aren’t even interested in you doing more analysis, they just want to destroy your hard work because they don’t like its outcome.


    1. I’m really sorry for using the “wrong” words. I can see how they can be misconstrued. Anyhow, I think it’s important to be kind, whether I agree. When I wrote it out “attack”, I was thinking of a shirt my husband has with bouncing Buddhas acting as “bullets” as a funny way to portray pacificism. I genuinely wished to offer help and guidance to Denise as she is clearly a smart, ambitious woman who simply needed a little push to expand what she’s already done. In any event, I can see I’m not at all welcome here, so I will close out and wish everyone luck.

      1. Perhaps you could send an email to Campbell and offer to help him with his statistics. He clearly needs a refresher course.

        It seems that the China study is nothing but a large data set waiting for someone to properly analyse it. With your experience and statistical knowledge you should try it. You would certainly do a better job than Campbell.

      2. R-
        Too bad others have made personal attacks on you. Denise did an impressive job. But as you pointed out, it needs to be checked by others who are qualified to do so. A mistake she may have made would be almost impossible for her to identify herself.
        I hope she follows your suggestions and submits her work for peer-reviewed publication.

  13. Fantastic. This is analyzing data done right. If only all our scientists were this diligent and competent we would be living in a much better world. A big thanks for all the hard work.

  14. My husband had throat cancer last year and lost most of his swallowing capacity due to the procedures done. He was advised to go vegan to keep the cancer from returning. We believe this was successful, as well as doing his best to get his system back to a better PH. However, veganism, and he was practicing it in good form using high quality foods, still left him in a weakened state. When we became the pick-up location for raw milk products in our area, he met a nutritionist that advised him to add some raw eggs and raw, high quality animal protein to his diet. He started with the raw eggs in his blends, as he is now mostly tube fed from the cancer procedures. What a difference! The addition of just 1 raw egg in each of his tube blends made a huge difference in his energy levels. Of course he uses eggs from a trusted source from our local farmers market, with very clean conditions and healthy hens that are allowed for forage for themselves and we can tell the eggs are of good quality. He also found that adding a small amount of high quality, grass feed meat to his diet made another difference in his energy level. So while he continues to alkalize his system, he has now balanced his dietary needs so that he is now able to return to work in the trades and even lift his floor sander once again as he now finds his strength returning, which it wasn’t doing on a vegan diet.

    1. Catherine, I saw this and just had to respond. I am so happy to hear your husband is doing better. I hope he doesn’t have to have any more chemotherapy or radiation therapy – horrible stuff!! Best wishes to you both.

  15. Denise, I don’t know if you’ve actually heard from Campbell directly, but he is bad-mouthing you to third-parties, and questioning whose water your carrying. However, you ought to read it so you can pre-emptively address his velveteen slander:

    It is a nasty piece of work, probably the most impressive array of back-handed compliments I’ve ever read.

  16. There are plenty of good reasons to abstain from meat eating. But maximizing nutrition and health is not one of them.

    Humans evolved eating meat. In fact, it’s pretty much a universal truth that all societies of humans seek out animal products and consume them on as regular a basis as they are able.

    However, it’s perfectly valid to abstain from meat for ethical or environmental reasons. Most Buddhists, for example, refrain from meat eating because reverence for all life is central to Buddhism.

    Just don’t expect to have your choice validated on nutritional grounds. As a poster noted above, John Robbins, who is vegan, let his honesty overcome his bias and wrote a book called “Healthy At 100” about the most healthy societies on earth. All of them consumed some degree of animal products.

    I’m baffled by the need that people have to seek endless validation for their choices. If vegans decide to stop eating meat because they believe that factory farms are barbaric or because they believe that industrial meat production is very destructive of the environment why isn’t that enough?

    Personally, I would like to be a vegan. But I can’t. I want good health. I want to live a moral life but I don’t want to sacrifice my health to do so.

    1. Just pointing out that the current Dalai Lama eats meat. He is not happy about it, but eats it because he is not healthy otherwise. He is from a region that is traditionally at a very high altitude and experiences very little sun. This makes it very important to eat meat for the Vitamin D.
      Infact he is the first Dalai Lama to be Vegetarian ;-).

      Farming is bad period. That includes Vegetables and grains grown in Farms. We should return to eating only that which grows in the Wild. But I guess that is not practically possible. So everybody tries to determine their own ways.

      1. If the current Dalai Lama eats meat, then he is not a vegetarian. Also, there are very few natural sources of vitamin D, and beef is not one of them (minus the liver). Cholesterol in our skin (cholesterol helps give our cells structure) is a precursor for vitamin D, and is converted into vitamin D through direct contact to sun light.
        Sustainable farming is our way of using what our beautiful-life-giving Earth has to offer without destroying it. So, farming is not bad as long as it doesn’t upset the Earth’s natural balances. I grow organic vegetables in a space I made in my garden. Does that make me a “demon farmer” that’s destroying our planet? No. That’s Monsanto’s job.

    2. Yes humans evolved eating meat, but only when it didn’t eat them first, and they didn’t eat it on a daily basis, and only after countless years of not eating meat, and it was real game, not the industrialized, genetically modified, hormone induced meat we eat today. And no, not all societies eat meat; India and China eat very little meat today, yet they have the lowest rates of heart disease, high blood pressure, cancer, diabetes, angina, obesity. If you think it’s just a freaky coincedence, buy more stock in McDonald’s.

  17. I don’t have time to review every comment but did quickly scan the text. This analysis seems very impressive, especially given the writer’s young age with no training in nutritional science (see her web page).

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

    As far as her substantive comments are concerned, almost all are based on her citing univariate correlations in the China project that can easily mislead, especially if one of the two variables does not have a sufficient range, is too low to be useful and/or is known to be a very different level of exposure at the time of the survey than it would have been years before when disease was developing. There is a number of these univariate correlations in the China project (associations of 2 variables only) that do not fit the model (out of 8000, there would be) and most can be explained by one of these limitations.

    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. This is rather like using metanalysis to obtain a better overview of possible associations. 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 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. This book was not meant to be an exhaustive scientific treatise. It was meant for the public, while including about as much scientific data and discussion that the average reader would tolerate.

    She also makes big issues out of some matters that we had no intent to include because we knew well certain limitations with the data. For example, only 3 counties (of the 65) consumed dairy and the kind of dairy consumed (much of it very hard sun-dried cheese) was much different from dairy in the West. It makes no sense to do that kind of analysis and we did none, both because of the limited number of sample points and because we 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. Still, the reviewer makes a big issue of our not including the data for this county as if I were being devious.

    In short, she has done what she claims that should not be done–focusing on narrowly defined data rather than searching for overarching messages, focusing on the trees instead of the forest.

    I very carefully stated in the book that there are some correlations that are not consistent with the message and, knowing this, I suggested to the reader that he/she need not accept what is said in the book. In this very complex business it is possible to focus on the details and make widely divergent interpretations but, in so doing, miss the much more important general message. In the final analysis, I simply asked the reader to try it and see for themselves. And the results that people have achieved have been truly overwhelming.

    One final note: she repeatedly uses the ‘V’ words (vegan, vegetarian) in a way that disingenuously suggests that this was my main motive. I am not aware that I used either of these words in the book, not once. I wanted to focus on the science, not on these ideologies.

    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. I know how agricultural lobbying organizations do it–like the Weston A Price Foundation with many chapters around the country and untold amounts of financial resources. Someone takes the lead in doing a draft of an article, then has access to a large number of commentators to check out the details, technical and literal, of the drafts as they are produced.

    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. But she suffers one major flaw that seeps into her entire analysis by focusing on the selection of univariate correlations to make her arguments (univariate correlations in a study like this means, for example, comparing 2 variables–like dietary fat and breast cancer–within a very large database where there will undoubtedly be many factors that could incorrectly negate or enhance a possible correlation). She acknowledges this problem in several places but still turns around and displays data sets of univariate correlations. 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!). She, and others like her, ignore much of the rest of the book. Can any other diet match the findings of Drs. Esslestyn, Ornish and McDougall, who were interviewed for our book (and now an increasing of other physicians have done with their patients)? No diet or any other medical strategy comes close to the benefits that can be achieved with a whole foods, plant based diet.

    I also know that critics like her would like nothing better than to get me to spend all my time answering detailed questions, but I simply will not do this. As we said in our book, no one needs to accept at face value what I say. Rather, as we said in the book, “Try it” and the results will be what they are. So far, the reports of positive benefits have been nothing less than overwhelming.

    I hope this helps, although it was written in haste.


    1. Young girl? She looks like an adult to me. How would you like being called “gramps”? Ageism cuts both ways. If Denise Minger is a “young girl,” then your co-author of the China Study was a ‘young boy” when he helped you write the book, Dr. Campbell.

    2. Mark/Colin says: “I know how agricultural lobbying organizations do it–like the Weston A Price Foundation with many chapters around the country and untold amounts of financial resources.”

      The Weston A Price Foundation is a non-profit. As such, their books are open to the public. Their budget for 2009 was…$1,406,000. This hardly qualifies as “untold amounts of financial resources”.

      (or check their actual IRS filing).

      The fact that you failed to check the facts on this, and instead resorted to ad hominem (feminem?) attacks, undermines any claim you might have to objectivity or sound research methodology.

      1. Yup, he’s definitely taking the low road. Not only by attacking the Weston A. Price Foundation, which is a tiny, underfunded, understaffed group composed mainly of volunteers, but by insinuating that the author of this piece is a shill. I wonder how much he’s being paid by the soy/corn/cotton industry?

    3. The harshness of the comments directed at this book are quite suspect. To ignore the “whole sum of the book” with flippant demeaning statements is more diversion than science. … ? Trying to elevate one-self by putting down others perhaps?

      Campbell’s work looks at some real anomalies in the health/nutrition /science fields of today, and they are not minor, and should not be dismissed. Thank you Colin Campbell for your science, and your bravery in revealing some of these situations to the public. I’m aware of more such actions, and I think you may have understated it.

      I think some of your analyses are intriguing, Denise. I hope the statistical methods suggested by others will strengthen and refine your observations, and further good scientific findings.

  18. You know, instead of worrying about WPF boogey-men and telling people to just try the diet espoused in the book and see if it works, it would be nice if Campbell simply explained how he performed his analysis of this particular data set and reached his conclusions, and then he could make this publicly available, say in a scientific journal where it could undergo peer-review…or at least on the internet so that interested parties could review his methods. Just sayin’…

    1. LOL! I agree CPM! I was just about ready to make a similar point, but you beat me to the punch. His post was mostly hand-waving and ad hominem attacks, as Alex stated. I do think he made some valid points, too bad it was surrounded by nonsense.

    2. Ms. Minger, thank you for sharing the fruits of your study of this research. It has clearly generated a great deal of conversation and that usually is a good thing. I hope that it helps you move towards the graduate studies in nutrition that you state elsewhere on your blog interest you and that you might become equipped with a professional toolbox for this kind of analysis.

      @CPM – I’m not interested in taking “sides” in the conclusions that people are debating here, but regarding your comment Campbell has published extensively on these topics. You might start with “Diet and chronic degenerative diseases: perspectives from China” by Campbell and Junshi, American Journal of Clinical Nutrition, Vol 59, 1153S-1161S, available for free download at . Unlike the third party summary of results without discussion of methodology that Ms. Minger used to structure her analysis, a choice that makes sense for presentation purposes, this summary of research includes description of the analyses and some reasoning behind the choices made in them, as well as references to the individual studies for further study.

      There’s room for discussion and debate about the conclusions reached based on any experimental results, but Dr. Campbell has been exceedingly transparent and professional in carrying out his research and there is no cause for accusations of obfuscation or fraud as I see in many of these comments.

      1. Interesting to see that Campbell’s own published work seems to rely on nothing but univariate correlations. At one point, he says, “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.” He never mentions any terms or concepts (such as “regression” or “multivariate”) anywhere else that suggest anything other than univariate correlations.

      2. Hi N,

        I’m not that experienced at this sort of thing, but in the paper you reference it appears all of his correlations are univariate. That has been a criticism by some of Minger. There has been the implication in some of Campbell’s responses to critics that he used a more sophisticated analysis than his critics did, but as of yet no one has pointed out any evidence of this as far as I can tell.

        Like I said, I’m not that experienced in this sort of stuff and I might be missing something, but the paper you referenced appears to be very lacking in the epidemiological requirements that some are demanding of Minger and in fact appears less sophisticated than Minger in the analysis of confounding variables.

      3. With the amount of research in discussion here there’s no way around looking at specific studies if that is the level of detail you desire. A summary can be only that. I provided that citation as more detailed summary (but still only a general summery) that also included references that would allow one to identify the literature where these studies were discussed in full detail. It is simply a more informative starting point than the paper Ms. Minger used for presentation purposes.

        For example, using the academic summary rather than the popular one, it is easy to follow up with “Nonassociation of Aflatoxin with Primary Liver Cancer in a Cross-Sectional Ecological Survey in the People’s Republic of China”, in CANCER RESEARCH 50, 6882-6893, and see the multivariate regression analysis that examines the relationship between the variables of interest that cannot be answered by the univariate approaches given in the summary and examined in further detail by Ms. Minger.

        While methods such as this multivariate regression analysis can be found in several of those specific studies, I do think that Dr. Campbell relies a bit heavily on simple correlations. However he is attempting to map out mechanisms of disease that are not easily modeled with conventional statistics alone, and this employs other areas of expertise in addition to the simple data analysis. Again, his conclusions are open for debate, but his approach here is uncontroversial. There is a whole discipline of computational biology dedicated to that kind of modeling that came of age after Dr. Campbell’s research career came to a close that might further explore these ideas in a more data intensive way. At the level of the individual study, however, his methods meet professional standards and are described in detail that should be taken into account before leveling the kinds of accusations seen throughout this discussion.

      4. N — the fact that Campbell used multiple regression in a previous article on alfatoxins doesn’t excuse his failure to do so as to The China Study; it makes it even more inexplicable.

        Moreover, this just doesn’t make sense: “However he is attempting to map out mechanisms of disease that are not easily modeled with conventional statistics alone.” Not true at all.

      5. @JD The paper mentioned *is* part of The China Study. The China Study is not a single work, except perhaps as a reference to the data set gathered. Whatever summary of The China Study we look at, whether it is the popular book, the popular summary on the Cornell website that Ms. Minger used, or the journal article I provided, in the end it hangs on those individual studies such as the article an alflatoxins. It is a large body of work.

        And quite I’m curious why you would say that the complex systems involved in disease development are easily modeled by conventional statistics alone.

      6. OK, so what you’re saying is that Campbell was somehow able to use multiple regression for the aflatoxin part of the China Study, but when it came to his much-publicized conclusions about plants vs. meat, he was suddenly forced to use univariate correlations because of . . . something about conventional statistics? Unbelievable. Univariate correlations are conventional statistics themselves, just of a fairly worthless sort. Nor is there anything about disease development that prevented Campbell from controlling for numerous other factors.

      7. @JD No, that’s not what I’m saying at all. And it’s become clear that you’re not interested in real discussion on the matter. But I will clarify one last time. He’s clearly fond of summarizing information with correlations, and I suggested that this was perhaps overdone, but he is able to back that up with references to the in-depth analysis on which his conclusions are based. Those lists of sources at the end of these articles and taking up a good 35 pages at the end of his book aren’t for decoration, they provide supporting information. Debate his conclusions, but these methods are not suspect.

        1. Actually, many of Campbell’s references don’t actually support what he cites them for. For example, in Chapter 1, Campbell states “Heart disease can be prevented and even reversed by a healthy diet” and lists two references. Both those references are for studies that use diet *in conjunction* with other lifestyle changes (quitting smoking, stress management, exercise) or drugs (cholesterol lowering medications), making it unclear whether diet alone or the other changes improved heart health. And both studies were merely preliminary. Other examples of misleading citations abound in the book. Footnotes make things look authoritative, but even they require further investigation to validate.

      8. Indeed, attention to references is always a must. Both for the information they provide and the information may they fail to provide. In my attempts to clarify my meaning I probably overstated the confidence one could place in the unchecked content of any given reference in order to explain that they are present to provide information that cannot be included in such a high level summary of results. My point is simply that there is room to debate his conclusions, but that this should be done based on a full appraisal of the work and not just the summarized information. I think this should be a pretty uncontroversial position!

  19. “Can you please increase the font size? Very difficult to read and hard on eyes.”

    Mark, you can use your browser’s ‘zoom’ function to increase the text size on your end.

  20. LOL. Classic Campbell there. Can you imagine anyone else in a position of prominence behaving the way he does? How can anyone take this buffoon seriously?

  21. Hi all,

    Once again — a gigantic “thank you!” for the feedback, questions, and other comments that keep pouring in. I’m up to my nose in a sea of emails (anyone have a snorkel handy?), and if you’ve written to me, I do promise a response is on the way! Maybe not very soon. But it’ll come. Cross my heart.

    I was thrilled to see that Mr. Campbell took the time to reply to my critique. Also thrilled to see he called it “impressive” and insinuated I may have a research team (nope, just me), although I wish he had gotten a little deeper into the methodology he himself used. I’ll be addressing all of the points he raised in my next blog post, which will be a combination of responses to both his reply and to some questions I’ve received from readers.

    It seems the main criticisms against my review so far are that I’m using raw data and univariate correlations. This misses the point completely, as I’m trying to point out that it’s Campbell whose claims, in every single instance, align perfectly with the raw data but become erroneous once major confounders have been adjusted for. I’ll try to explain this point better in my reply, as perhaps I didn’t make it clear enough in the critique.

    In addition, as I’ll explain in my reply, univariate correlations weren’t the only ones I used in my analysis — they’re just the only ones I chose to include in this post. I felt they’d be effective for getting my message across to a standard audience who may not be too interested in stats jargon, since they’re a simple way to illustrate the effects of confounding variables and they’re easy to graph visually. I also ran multiple variable regressions on the data I used and it corroborated with what I achieved through the more “crude” methods highlighted in this critique. In the not-too-distant future, I’ll be writing a separate post with the results of these regressions — and maybe including downloadable spreadsheets with some data, so any skeptics can test for themselves what I’ve done.

    Again, I can’t even express how grateful I am for all the responses. I have not replied to most of the comments left on this post due to time constraints, but I *have* read each and every one of them.

    So, thank you. And stay tuned. 🙂

  22. I am disappointed that Campbell decided to make rude insinuations instead of simply explaining how he got his numbers. My prediction is he and some associates will be creating a continuous stream of hoops for Ms Minger to jump through which they will claim are needed in order to be ‘professional.’ They will claim they are doing this as a kind favor to ‘help’ her. The more hoops she jumps through, the more byzantine and laborious the hoops that will be presented. Meanwhile, they themselves will continue to not give any explanation for their own erroneous looking conclusions. But hey I could be wrong and if the truth varies greatly from that prediction, it should become vastly more interesting. I will stay tuned to see how it comes out!

  23. As one who tends to ‘glaze over’when it comes to long winded posts,i (almost ) read every word you wrote..Again Denise you are freakn amazing woman,finally some one with a balanced view point on that stinking China study,thanks for a balanced view point you are sharing with us here.
    I am also a reformed raw vegan,thank goodness,before my health totally melted in a puddle in front of me!
    keep up the good work babe 🙂

  24. there goes my, planned, internet hiatus 😦 :D:D

    i was pretty sure you wouldn’t have put so much work into this without first checking that you had something more to offer, in terms of critique, than previous attempts

    maybe you showed your hand too early and maybe it was a mistake to pander to those, like myself, who needed the simplified version 🙂 but at least we got the rather disappointing and inevitable (and dismissive/patronising) campbell ‘rebuttal’ out of the way

    to watch the masses scurry around on the sidelines condemning you at all stages, disregarding your – stated – intention and your, still, valid doubts regarding campbell’s own methodology is more than a little amusing….such entertainment and no one is charging for tickets

    whatever the outcome, it is inspirational to see the huge effort you have put into this and the way you have handled yourself through it all….

    it seems there is more to come…looking forward to it

  25. Fantastic job Denise! I’ve finally gotten through your material and I’m glad you’ve taken the critiques offered thus far to a new and much more in-depth level. I’ve posted a link to your work on my blog.


  26. Hello Denise,

    Thank you for sharing your analysis. A question: did you use age adjusted data, that is, compare the prevalence of diseases in similarly aged participants across counties and countries?

    1. Does anyone recall reading whether Denise used age-adjusted data for her analysis? I’ve looked, but haven’t been able to find any reference to this on the site. Thanks.

  27. 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’ve chosen to eat animal products for health reasons. 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.

    If you had said, data doesn’t support the plant based diet’s claim, would have been fine by me. Here you are saying you’ve chosen to eat animal products for health reasons. Animal products are dangerous to neutral is one thing, you infact go to an extent of terming them as healthy. Can you tell us how animal protein is health protective?

    You write you’ve respect for those who follow plant based diet but wouldn’t do it yourself. I smell some fish here!

    1. Hi Mark,

      That’s not quite what I said. “I eat animal products for health reasons” means I reintroduced them into my diet and discovered the health problems I experienced as a vegan vanished. In repeated experiments of going back to veganism and then back to non-veganism, I’ve found that, for me, the animal products are the factor keeping those problems from cropping up again. I prefer to eat whole foods than to supplement, so non-vegan is the route I’ve gone rather than stuffing my cupboard with pills.

      For me, this is what helped. I also believe the human body is incredibly adaptable and can survive on numerous combinations of foods, so I don’t feel I have the authority to say that what does (or doesn’t) work for me will (or won’t) work for others.

      I just want to give people access to information, and then let them use it how they wish. I have no interest in delineating a single optimal diet. There are plenty of other folks tackling that goal. 🙂

    2. I have respect for people who choose to live their life as Buddhist monks, but I wouldn’t do it myself. I have respect for firefighters, but I wouldn’t do it myself.

  28. After reading Lynne McTaggart’s book The Field, I’d have to agree with your end summary that Campbell, and all researchers to some degree, find what they are looking for. The fact that they are looking for results skews the possibility on non-biased data. Thank you for the very comprehensive breakdown. Impressive.

  29. “…In fact, when running MRA the protective trends for animal foods were even more accentuated in most cases (I recall a -70 between animal protein and cardiovascular diseases) ”

    I find this very interesting. Campbell’s dug his own grave with his response and can’t wait for more on this series. I also find his claims of the Tuoli data being unreliable to be disingenuous, as if they spent an entire year in every other county to get their raw data. Also, thanks to the innanets, we now have visual proof of what some these nomadic tribesmen actually eat.

    Granted this is Mongolia and not Tuoli but I’d imagine the diet is similar all across the wide swath of land from Mongolia to the Central Asian countries. Great work, as usual Denise!

  30. Amazing work Denise! A very outstanding analysis of the China Study. I am amazed by the amount of work you put into this. I’ve read critiques of the study but your work is the best so far in my opinion.

    While I somewhat agree with your skeptics that it would be ideal to use multiple regression analysis on your data and show it to us, it would be better for them to prove to us that Campbell used only or mainly multiple regressions to construct his conclusions, and also show us that by using multiple regression on the data, it will lead to a different conclusion than your’s.

  31. A number of people have pointed out that the criticisms of Denise’s analysis apply to Campbell as well, and since they seem to be at least somewhat familiar with statistics, I’ll expand on my initial critique (Denise, I hope this will be helpful to you as you build on your initial analysis).

    First and foremost Denise did not take into account potential confounders. I think everyone understands at this point that confounders can bias the observed correlation towards or away from the null (i.e., correlation=0). She only *partially* took schistosomiasis into account by restricting her analysis to counties without schistosomiasis. Her p-values only reflect the test of whether the correlation was significantly different from zero. *Not* if there was a statistically significant change in the exposure-outcome correlation after taking schistomiasis into account.

    Let me repeat that. The p-values Denise provides reflect whether correlation=0. They do not tell us whether or not schistosomiasis is a potential confounder. To determine this, we need to know if the correlation of +33 for all counties was statistically significantly different from the correlation of +13 for just the counties without schistosomiasis. This is where 95% confidence intervals would be helpful, but Denise doesn’t provide these. Nor does she tell us what the correlation is only among counties *with* schistosomiasis. There are several ways to tease out whether we should include a factor in our analysis, but here are two commonly used methods, using the schistosomiasis/cholesterol/colorectal cancer example:

    Method 1:
    1. Calculate correlation for entire sample
    –> Denise calculated this to be +33.

    2. Now stratify on the variable you think is a potential confounder, i.e., schistosomiasis, and calculate the correlation within each stratum.
    –> Denise stratified on county but we’ll let this slide b/c this was probably her only choice. For counties with no schistosomiasis, the correlation was +13. What about the correlation for counties with schistosomiasis? Denise does not provide this.

    3. Compare the within-strata correlations (+13 and ??) to the correlation for the the entire sample (+33), and test whether they are statistically significantly different from each other (not whether they are significantly different from 0). One should first perform a global test, and if the result is significant, proceed with pair-wise tests.
    –> Denise did not do this.

    4. If the correlations are significantly different from each other, then there is evidence that there may be confounding. If they are not significantly different from each other, there is evidence for no confounding.
    –> Denise did not do this.

    5. Bonus step: if the pair-wise tests between stratum-specific correlations are significant, this is evidence for effect modification.
    –> Denise did not do this.

    Method 2:
    1. Run a full model that includes cholesterol and schistosomiasis as exposures (ideally, the model would include more than just this, but we’ll keep it simple) and colorectal cancer as the outcome. Obtain the adjusted correlation, and make a note of the residual deviance or log likelihood for the model.

    2. Run a reduced model that does not include the variable you think is a potential confounder, i.e., just include cholesterol as an exposure. Make a note of the residual deviance or log likelihood for this reduced model.

    3. Now take the difference of the deviance or the -2 times the difference in the log likelihoods. This is your chi-square test statistic with k degrees of freedom (in our example, the degrees of freedom=1). Calculate the corresponding p-value. A significant/small p-value strongly suggests that the we should stick with the full model (i.e., the one with cholesterol and schistosomiasis). A large/non-significant p-value suggests that the full model doesn’t add much more information and therefore we would opt for the more parsimonious model. In other words, the reduced model (i.e., the one with cholesterol only) is probably sufficient.

    I’m assuming Denise did none of this since there was no mention of it. To her credit, Denise does mention why she took a look at schistosomiasis.

    When people criticize Campbell for not including schistomiasis, it is very possible that upon further inspection, it was *not* a potential confounder as Denise concluded based on her results. A factor is a confounder if and only if it:
    1. Is associated with the exposure (cholesterol)
    2. Is a risk factor or protective factor for the outcome (colorectal cancer), and
    3. Is not on the causal pathway between the exposure and outcome.

    Perhaps criterion 1 was not met and therefore not included in Campbell’s final analysis. Only Campbell and colleagues know for sure what the detailed analyses were; a final presentation will always include only the most salient points.

    As for many of Campbell’s conclusions being drawn from purely ecologic data, I think this ignores the fact that while the China-Cornell-Oxford Project was a large component of the book “The China Study,” the book’s thesis is based on hundreds (in fact, nearly 1000) of additional references that corroborate the Project’s findings.

    1. This is all very interesting, but it does nothing to disprove Minger’s criticisms of Campbell’s findings.

      Nor is this accurate:

      the book’s thesis is based on hundreds (in fact, nearly 1000) of additional references that corroborate the Project’s findings.

      Impossible. There are not hundreds or 1,000 valid studies that would corroborate the conclusion that plants are so superior to meat. For example, a recent systematic review of valid studies found no evidence that meat, eggs, dairy, or saturated fat have any relation to heart disease. See

    2. Seriously?

      Does your browser not show italicized text? Maybe the font size is too small? Perhaps you simply lack the ability to read or comprehend the intent?

      No offense if it wasn’t your intent (or rather, no offense at all- just take this as an opportunity to reevaluate your critical thinking skills and writing style)- Your observations are nothing more than pedantic bigotry. Logically, pigeonholing only works if you do so relative to the intent. You don’t seem to show an understanding of her intent, so your pigeonholing seems disingenuous and rude.

      If you understand the intent, you might recognize that she is using a similar methodology as Campbell. Campbell doesn’t explicitly outline how he comes to his conclusions, but it is possible to extrapolate his methodology based upon his final numbers and the original data set. Campbell uses data acquired using a specific methodology and then uses that data to support a claim. Denise seems to be emulating the same methodology

      1. Oops, premature ‘enter’ing–

        … same methodology and found that there is plenty of data contrary to Campbell’s claims.

        Your observations, by derivative, only suggest that Campbell’s methodology wasn’t sufficient to begin with, which support Denise’s findings. I find it hard to believe that you are some kind of champion of proper analysis when you have little foresight in the logical conclusion of your own observations.

        1. The health authorities are fully aware of the serious flaws and omissions in this meta-analysis. This study was funded by the National Dairy Council, dairy being the number one contributor of saturated fat in the U.S. and many other parts of the world. It was also conveniently published just before the USDA lowered the dietary recommendations of saturated fat for the first time in 20 years, from 10% to 7% of total calories.

          Below is a section from the statement released by the European Heart Network in regards to their opinion of this meta-analysis, titled “European Heart Network position piece: Impact of saturated fat on cardiovascular disease obscured by over‐adjustment in recent meta‐analysis”

          “However, the meta‐analysis (and an accompanying opinion piece by the same authors (4)) is compromised by a number of serious flaws and omissions. These are enumerated and discussed in detail in an editorial from Jeremiah Stamler (5). The most serious of these flaws is an over‐adjustment for serum cholesterol levels. The meta‐analysis involves data from 16 studies that evaluate the impact of saturated fat intake on CHD incidence or mortality, and 8 studies that evaluate the impact of saturated fat intake on stroke incidence or mortality. The authors state that ‘wherever possible, risk estimates from the most fully adjusted models were used in the estimation of the pooled [relative risks]’. It is well‐established that saturated fat intake is associated with increased level of serum cholesterol (6), and that serum cholesterol levels are associated with CHD and CVD (7). Therefore, serum cholesterol levels lie on the causal chain between saturated fat intake and CHD and CVD, and to adjust for serum cholesterol levels in a meta‐analysis will obscure the impact of saturated fat intake on these health outcomes. Yet 7 of the 16 studies included in the meta‐analysis of CHD events, and 4 of the 8 studies included in the meta‐analysis of stroke events were adjusted for serum cholesterol levels. These studies accounted for nearly half of all CHD and CVD events included in the meta‐analyses. Adjustment for serum cholesterol levels will inevitably bias the results of the meta‐analyses towards finding no association between dietary saturated fat intake and cardiovascular disease, but the authors do not mention this limitation in their article. As Jeremiah Stamler asserts in his editorial, what was actually found by the meta‐analysis was ‘a statistically non‐significant relation of SFA [saturated fat] to CHD… independent of other dietary lipids, serum lipids, and other covariates’ (5). A more appropriate and informative analysis would have included non‐adjusted associations between saturated fat and cardiovascular disease. An examination of the forest plots provided in the article shows that those cohort studies that did not adjust for serum cholesterol levels were more likely to find a positive association between saturated fat intake and cardiovascular disease, suggesting that a meta‐analysis of unadjusted data would likely produce positive results. “

          References 5-7
          (5) Stamler J. Diet‐heart: a problematic revisit. American Journal of Clinical Nutrition, 2010; 91: 497‐499.
          (6) Clarke R, Frost C, Collins R, Appleby P, Peto R. Dietary lipids and blood cholesterol: quantitative meta‐analysis of metabolic ward studies. BMJ, 1997; 314: 112.
          (7) Prospective Studies Collaboration. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta‐analysis of individual data from 61 prospective studies with 55,000 vascular deaths. The Lancet, 2007; 370: 1829‐1839.

          The full statement from the European Heart Network can be found here:

          Below is a published study showing reversal of severe heart disease backed up with angiogram evidence.

  32. It is amazing how many vegans are insistent that Denise do her analysis their way, while, for Campbell, opacity and “just trust me” serve just fine.

    “r”, you’d be waiting forever before Campbell could jump through those hoops…in his recent “defense”, he relies on conspiracy theorizing, snarky remarks about girlishness and — his big finish! — “try it; you’ll like it.”

    (Of course, his defense was “written in haste” — no doubt he would demolish Denise with actual data if only he weren’t so very, very busy.)

    1. It seems to me that there are not many only one. They all seem to reason in the same way. It maybe that they all are into group think.

      When I was reading the 30BAD site. I noticed that Apple-Man (the second poster to this article) was trying to reason with the people there. It was so funny and saddening. I am not sure how he felt about banging his head into the void. I can only pity the pure vegans as to what it does to their brains. I am surprised that some eventually survive with their critical thinking abilities still alive.

      Lierre Kieth’s book The Vegetarian Myth is amazing when you understand that she survived the crippling effects of Tofu.

  33. still confused as to why criticisms of campbell’s findings would be answered by critiquing denise’s work, further….

  34. —>”It is amazing how many vegans are insistent that Denise do her analysis their way, while, for Campbell, opacity and “just trust me” serve just fine.””n his recent “defense”, he relies on conspiracy theorizing, snarky remarks about girlishness and — his big finish! — “try it; you’ll like it.”<—-

    makes my hair stand on end

  35. Thanks for the highly informative post. I have two hopefully simple questions – first, it seems that the data in the study regarding cancer rates was actually rates of mortality from various cancers. Even in the 1970s, I believe some cancers were successfully treated, but that there would have been quite a strong effect of access to regular medical care (ie, early diagnosis) that would favour populations in industrialized areas. Is this accounted for, or is the assumption in the analyses (both Minger’s and Campbell’s) that mortality rate from cancer = incidence of cancer? Is this also the case for other diseases referenced?

    Second, my impression from both the China Study book and Minger’s post was that individual data was not actually available to be used in the manner suggested by the previous poster? I thought that aggregate data was all that existed; ie, individuals were surveyed regarding dietary habits, and this data was plotted against data from hospitals and health authorities on cancer and disease rates for the local populations as a whole. If this is the case, then surely no more accurate analysis can be done because there is no data on what the individuals who developed cancer or various cardiovascular ailments typically ate. I’m no expert in any of this – I took stats in university so I can follow the arguments – but it seems that the data itself, while certainly suggestive of further lines of inquiry, is insufficient for even identifying real correlations, because there only established link between individuals who participated in the dietary survey and individuals who suffered from various diseases is geographical. How big are the counties? Do they have similar population sizes? China is certainly not a genetically homogeneous country, either – were differences in ethnic distributions accounted for?

    I guess what I’m saying is that I’m unconvinced that this data set deserves all the attention it’s been given. I think Minger’s analysis is good in that it points out the shortcomings of “The China Study”, but it should NOT be used (and I believe she would agree) to argue the opposite point. In the same way that the data doesn’t really support the assertion that an all-plant diet is healthy, neither does it support the assertion that an omnivorous, high-meat, or any other type of diet is healthy.

  36. Denise. Such a great effort.

    Why don’t you submit a paper on your findings to a respected peer reviewed journal? Given the popularity of your blog, it probably won’t be hard for you to collect enough money from readers (donations!) to fund the expenses required for the whole process, if your paper successfully ends up being published.

    In science, peer review by qualified, relatively unbiased judges is the most important process. You haven’t proven anything unless your work stands that test. Unfortunately blog is just about the worst place for this kind of process, since most are just interested in promoting their agenda. I cannot trust any of the commenter here, because I have no way of knowing their qualifications.

    The implication of nutrition science is huge for the public. As such, if your critique of the China Study really stands the test of rigorous scientific reviews, that would be very important. Since I have no knowledge of nutrition, I wish to see how your work is received in the nutrition science community.

    Please do consider my suggestion seriously.

  37. nomo17k,

    you apparently think that a review of someone’s published conclusions (not peer reviewed) have to be peer reviewed.

    those of us that reside on planet earth find this somewhat odd.

    the book is in the public domain.

    denise minger reviews the book.

    you demand peer review???????????????

    you appear to be a particularly bad pr flack for vegans.

    oh, btw, “nutrition science” is a non sequiter.

    I am bemused by the thought that book reviews have to be “peer reviewed”.

    I doubt that any book review has ever involved new research.

    the book is there ,we can all read it.
    the review is there, we can all read it.

    you want “peer review”?

    time you started eating meat and getting the brain cells activated.

    1. Let me just say a junk comment like yours only serves to taint the serious work that Denise is trying to do — exactly the reason why peer review by qualified experts instills a bit more confidence in people who want real information, not religious beliefs from zealots.

      By the way I eat meat. Just not the kinds of shit you may be eating at McDonald’s.

  38. While Denise’s effort is excellent, she too may be biased. As she states, she wanted to critique Campbell’s China study. So you can not rule some element of bias, without malicious intent.

    I’m not smart enough and stat wizard to tell where Denise is making mistake if at all she made any mistake. Her thought process is pretty cool though i.e. If high meat and dairy = high cholesterol = higher rate of chronic diseases such as cancer and CVD then high rate of meat consumption should be equal to high rate of chronic diseases.

    Mathematically speaking, if A=B=C then A should be equal to C.

    Other way to look at this stuff is, there are many world class nutritionist believe that high cholesterol is a risk factor for CVD. Many health organizations specifically focuses and advises people to use less meat, less saturated fat, less dairy along with exercises to reduce the risk of CVD. Framing ham study specifically established risk between high cholesterol and higher rate of CVD.. so Independent of Campbell’s China come Denise’s analysis show it otherwise? Logically thinking it sounds something is a miss? Isn’t it?

    1. Mark, I think you are trying to mean “A=>B and B=>C”, so A=>C. So are you trying to say “Meat and saturated fat leads to a higher LDL, High LDL leads to a higher incidence of heart diseases” so “meat and saturates fat leads to a higher incidence of heart disease”?

      Is not exactly A=B=C. From this logic we cannot really deduce “no meat and saturated fat => no heart disease”. There could also be other factors that cause heart disease. Not to mention that meat or saturated fat actually increases the large fluffy LDL which is harmless and not the small dense ones. Here’s a meta-analysis on saturated fats:

      The reason why these authorities are advising people to cut out meat and dairy is because of the Ancel Key’s 7 countries study which like Campbell’s study, is guilty of some omissions. Countries like France that consumed a large amount of animal products has less CVD than in the US for example.

  39. Hey Denise,

    you might want to give vegetarianism a go again with sprouting + boiling grains(specifically brown rice – GBR) and legumes before consuming them to dilute the phytic acid concentration in them which might be inhibiting the micro nutrient uptake by your body .

    The idea of veganism is mainly practised in the east but sometimes the west takes it without considering the traditional eastern processes prior to consuming the grains and pulses.

    Conclusions which do not consider the traditional cooking practices can’t be entirely relied on!

    1. Pallav, I am an Indian too. And no I don’t believe that the Vegetarianism of India can apply to other people.

      There are three very distinct qualities that are required for being a vegetarian.
      1) You must live in a tropical place with year round sun. Vitamin D3 is an absolute requirement. And if you don’t get it from sun you must get it from Meat. This applies to people staying indoors and to people living in Northern Climates or high altitudes. This is not negotiable. Nowadays you can supplement.

      2) You need to eat a lot of Dairy. The dairy will provide a lot of missing nutrients, like Vitamin B12, Zinc, Iron, Vitamin K2, etc. If you cannot handle Dairy you cannot be vegetarian period. This is also not negotiable.

      3) You need to eat a lot of vegetables, particularly greens.

      Rest of all is fluff. Even Indians in this age are not taking care of the 3 points. You should know that heart disease, cancer and diabetes are growing like a wild fire in India.

      The reason is the lack of the above 3 cultural necessities, which people have stopped, due to changed occupations, and doctor’s advice. Unfortunately you cannot even get good quality dairy in the cities.

      Bottomline you remain Vegetarian in the present circumstances at a great peril. Move to a village and take up some job that requires staying in the sun most of the day, get a source of grassfed dairy, eat a lot of traditionally grown vegetables, and eat less, then you can be a vegetarian.

      Incidently eating less calories, but highly dense nutrients helps get rid of a lot of problems. The way Denise is structuring her diet contains a lot of eating less calories (although it involves eating a lot of quantity of food).

      1. Hi Anand,

        It’s good to see other indians taking interest in nutrition and understanding doctors advice is not always right. For all the points you raise i’m on the side of population aware of them from health blogs like this!

        Why i bring eastern cooking practises in the debate is because like a sea saw we are move in favour of plant based diets or animal based diets. weston price versus campbell.

        Considering the effort put in this research i’d certainly hope a more balanced take on nutrition can be had giving due credit to both plant and animal based diet (a combination of both) and not allow irrational reductionism to creep in.

        Giving plant portion of the diet credit is not possible without considering the holistic traditional cooking practice which goes into making it nutritious.

      2. @Pallav – Since you brought it up, are you aware that the WAPF doesn’t endorse a carnivorous, but rather, omnivorous, diet? That it encourages people to eat grains that have been soaked, sprouted, or fermented, just like you recommend?

        I often hear people talk about the WAPF as if they were advocating an exclusively meat-based diet. That is simply not true. It makes me wonder if people who think that have actually bothered to read their literature.

  40. “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.”

    I’m wondering how you got that correlation. According to monograph maps and data for green vegetable intake per day, the counties with the highest intakes, Wuhua (monograph code UF 434.9g), Echeng (OB 360.4g), Panyu (UB 341.9g), and Qianshan (JB 311.2) are all located at mid to low latitudes. On the other hand, counties with the very highest latitudes, Baoching (GA 98.0g), Tuoli (WA 26.0g), Changling (FA 86.3g), xinyuan (WB 69.2g), all have very low daily intakes. There are some ups and downs. Some counties at higher latitudes have higher consumption than counties at lower latitudes, and some counties near each other have very different consumption amounts.

    Speaking as someone who grew up in Michigan and ate out of a garden every summer and as someone who lived in northern China, I cannot visualize how people in a northerly climate with a three-month growing season could possibly eat more green vegetables per day on average than people with access to vegetables year round. Of course, vegetables can be canned and pickled, but salted vegetables are a separate category in the monograph. I’m interested in knowing how you got that correlation.

    1. I’m wondering how you got that correlation.

      It’s already calculated on the “Green vegetable intake (per day)” page in the monograph. Look in the table on the most far-right column next to the variable for latitude.

    2. According to the monograph, p557, titled D043 GREENVEG

      -33 G001 LATITUDE

      Look at the dots on the map on the preceding page, p556, diet survey GREEN VEGETABLE INTAKE (g/day/reference man, fresh weight). The black dots representing highest intake are concentrated on or near the coast at mid to lower latitudes with a few further inland at mid to lower latitudes. The two mid inland dots are around Beijing, I believe. I notice that the clusters of black dots representing high intakes are around Shanghai and Guangzhou. As I recall from the monograph, while most counties were rural, some suburban counties were included.

      1. Hi Whatsonthemenu,

        Any chance you’re looking at the book “Mortality, Biochemistry, Diet and Lifestyle in Rural China” rather than “Diet, Life-style and Mortality in China?” The former actually features the China Project II, which studies additional counties and Taiwan, rather than the first China Project. Many of the numbers may be different.

        If you’re not looking at a book but using the data from Oxford’s website, this is also the China Study II data and will be different than what I (and Campbell) used. The overall trends should mostly be the same, but specific correlations could be different between the data sets. I’ll put a disclaimer about that on the page where I link to it.

        If you are using “Diet, Life-style and Mortality in China,” let me know and I’ll check to see if I’ve made an error. I try to be scrupulous about that and check my work 80 times, so I’m hoping this isn’t the case. 😉

      2. Yes, I am. That explains the discrepancy.

        You might want to revisit this hypothesis, however:

        “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.”

        Would you agree that foods which confer some health benefits, such as omega-3 rich fish, offer more protection if eaten regularly than if eaten only a few months of the year? Eating, say, 2 servings of fresh green vegetables a day for four months isn’t the same as eating those 2 servings for a period of 10 months.

        If you believe that latitude-dependent vitamin D is a protective factor, I would agree with you completely. However, regular consumption of fresh green vegetables may also offer some protection.

  41. Now all Denise has to answer is‘s anecdotal evidence that moving from meat eating to vegan diet helps many people get off diabetes and heart medication. Watch the testimonial videos…

    Meanwhile, she’s still got to answer all the global warming problems with the way we raise livestock now (according to a recent U.N. report, raising livestock accounts for more global warming than all human transportation combined…see: for one footnote).

    1. Too much of anything is bad! balance between plant/animal diet has to be maintained. people on either extreme of the spectrum will suffer a variety of problems.

    2. I think we can call agree that there are big problems with the way we raise livestock now–environmental problems and health problems.

      I disagree that Denise has to answer those issues in the present article. That’s a whole other issue.

      Having said that, I would add that raising livestock using non-CAFO, polyculture methods, grass-fed for cattle, is better in every respect than what most animal operations are doing now. Managed properly, animals are a net positive for the environment, not a net negative. And we have the “technology”–we’ve had it for thousands of years. We just don’t have the political will and proper economic incentives to make the changes.

  42. Back again. The green vegetable page on the monograph lists a correlation of -33 with latitude. This shocked me. Some southwest inland provinces are extremely poor, and I wondered if some very poor lower latitude outliers are throwing the statistics. I noticed that the correlation with elevation is almost the same at -30. Rocky, acidic soils are not good for crop cultivation. Looking at provinces with extremely low green vegetable intakes, I noticed Huguan (CB 0.0, midnorth inland), Shangshui (DA 15.3 mid inland), Xuanwei (RA 6.8 low inland), Longde (XB 17.6 midnorth inland), and Jingxing (BB 8.4, midnorth, listed as in a coastal province but appears to be inland on map). By looking at the locations on a map, I know that some of these counties with very low daily green vegetable intakes are located in mountainous areas, but I do not know their exact elevations, and I’m not ambitious enough to check them out.

    My point is that I think we can learn more by looking at the trees than the forest with regard to daily green vegetable intake and many other food items as well.

    1. Oops, I misinterpreted latitude. A negative correlation is expected for green vegetables if people consume less of them the further north they live.

  43. Here is a rebuttal to Denise’s article that accuses her of a naive understanding of statistical analysis. Here is one of the most damning of the criticisms: “when she refers to “statistical significance”, all that’s being tested is the “null hypothesis” that there is no correlation (i.e. correlation = 0). it is not testing whether an exposure is or is not a risk factor for the outcome, even though Denise uses this term loosely.” Also, we all know that correlation is not causality and risk factors are not the same as causes.

    1. As I’ll explain better in my next post (hopefully up by tomorrow), I’m simply replicating the methods Campbell used — which were mainly univariate correlations straight from the raw data — and showing how they failed to account for confounders. The criticisms RE: my statistical methods apply directly to him. They can nail me for taking a too simplistic approach, but in doing so, they’re cutting of their foot to spite their leg, so to speak. 😉

      1. “I’m simply replicating the methods Campbell used, which were mainly univariate correlations. The criticisms RE: my statistical methods apply directly to him.”

        Not according to the article I cited. Campbell did not perform his analysis on raw data and any good scientist would adjust for confounding variables. Are you saying Campbell didn’t do that? Campbell’s study is flawed, but that has already been noted by Anthony Colpo, Chris Masterjohn, and many others. The article also accused you of deleting a comment.

        QUOTE. Your analysis is completely OVER-SIMPLIFIED. Every good epidemiologist/statistician will tell you that a correlation does NOT equal an association. By running a series of correlations, you’ve merely pointed out linear, non-directional, and unadjusted relationships between two factors. I suggest you pick up a basic biostatistics book, download a free copy of “R” (an open-source statistical software program), and learn how to analyze data properly. I’m a PhD cancer epidemiologist, and would be happy to help you do this properly. While I’m impressed by your crude, and – at best – preliminary analyses, it is quite irresponsible of you to draw conclusions based on these results alone. At the very least, you need to model the data using regression analyses so that you can account for multiple factors at one time. UNQUOTE

        1. Not according to the article I cited. Campbell did not perform his analysis on raw data and any good scientist would adjust for confounding variables. Are you saying Campbell didn’t do that?

          Yes. For the most part, anyway. As I’ll show you in the next post, Campbell’s correlations all perfectly match the raw data. I don’t know how I can state that any more clearly…

          His way of ‘adjusting’ was primarily to separate disease groups into two clusters (diseases of affluence and diseases of poverty), and cite the (raw) correlations between those disease groups and various blood markers and foods as a way of linking them to diet. He zeroed in on the (raw) correlations with cholesterol and disease, and based many conclusions about animal food consumption off of that. He did not thoroughly adjust for confounders for the disease groups, as far as I can tell, presumably because he was more interested in the “forest” than the “trees,” as he himself stated in his response to me.

          I will be discussing all of this in the post I’m working on, so just sit tight, ‘kay?

          I haven’t deleted a single comment, by the way, although I’ve had to “free” a couple of valid ones from the spam folder.

      2. Hi Ian,

        The person that you claim to be defending Campbell has not said that she has reviewed any of his papers. There is one available free online at :

        If you look at this paper, and then look at your comment about “any good scientist would adjust for confounding variables”, you must deduce that Campbell is not a good scientist because there is no mention of confounding variables.

        And as has been said over and over and over again, the only documentation that seems to be available shows that Campbell performed simple univariate correlations on the RAW DATA.

      3. @CPM That is a high level summary where, for good or for bad, Campbell has chosen to summarize information with simple correlations. What is important is that where appropriate the studies in question are carried out including more sophisticated methods. And in general, where I’ve followed the references in that article, he does.

      4. Hi N,

        The thing is, he did use those simple correlations without mention of confounding variables, and Ian just damned Denise for this (even though she did use confounding variables to a degree) and said that a good scientist would never do this. I think people are going a little overboard in their defense of Campbell.

        I believe he chose to use these simple correlations in his book as well, and these have been given a certain weight by the general public because the publisher chose to make a big deal about the China data.

        The thing is, if someone has problems with Campbell’s conclusions and think he might be overly biased, you have to begin your critique somewhere, and you have to kind of take it one argument at a time.

        Many of his defenders are claiming that these are more than simple correlations, and he himself has said or strongly implied this when replying to critics. You have to address these simple correlations that he did choose to use or people will keep throwing them out there like they mean something.

      5. Hi N,

        It depends. The China Study was a book for the general public. Some of the arguments in the book were based on studies (and apparently some of the studies did not exactly say what Campbell claimed), but apparently some of the arguments in the book were just simple correlations from raw China data.

        Most of Denise’s critics won’t even admit that Campbell ever used simple correlations in any manner.

        I also think if one of your larger arguments is going to be that Campbell is misusing science in this book, it is a valid point to discuss his use of simple correlations to support his points when apparently other correlations are contradictory. Some people are apparently ready to burn other people at the stake for using simple correlations, so maybe this is a worthwhile topic for a critic to at least explore.

      6. Well, I’m much more interested in cutting through the noise and addressing the scientific matters at hand than letting angry mobs dictate how analysis should or should not be done.

        There’s nothing inherently wrong with using correlations, although care must be exercised. Like any statistic they must be used with demonstration that some understanding of what is conveyed is had. But people generally understand what a correlation means. In my experience that often dictates usage more than what may or may not be the most mathematically appropriate statistic for a given task even among scientists. Campbell uses simple correlation often, especially when summarizing results, probably for these very reasons. Where appropriate, as would be demanded in any of the peer reviewed setting where his individual studies are published, more sophisticated methods are used when necessary to demonstrate a given relationship. Being able to back up a relationship described using a correlation with a study that contains more in depth analysis is a perfectly valid way of conveying scientific information. A lack of such demonstration would be much more suspect. Along these lines, I hope Ms. Minger will follow up with the additional analysis that she has mentioned a few times now is coming.

        A book targeted toward a lay population is going to be even more casual in its descriptions of experimental results, although to his credit he still provides complete references. I have not found his references to be of poorer quality than average in the scientific community, although that is not to say I’m surprised to hear that some can be found that seem questionable. But on the topic of the book, as far as I can tell Ms. Minger’s discussion is not about the book but a Cornell news article summarizing a presentation given that summarized a wide range of research. Unfortunately this is even less informative, lacking references or any description of methodology at all. That is why I have suggested that the academic summary linked a few times here be used to advance the discussion, because at least it allows one to follow up with the appropriate studies in question.

        And to be sure at times Campbell does use correlations alone. Sometimes documenting an association is all that the situation requires. At other times complex mechanisms may not have conventional statistics that are well designed for testing them. An unproven computational or statistical model may be even more suspect than an expert’s manual mapping out of the most parsimonious relationship between associations. This isn’t to say that it’s at all foolproof, but there comes a point where we approach the limits of our ability to extract and share information from a complex dataset. It’s perfectly reasonable to set one’s level of skepticism in the results accordingly.

        Skepticism in Campbell’s conclusions is healthy. I am by no means convinced that the evidence convincingly brings us to the end point he suggests. But Campbell has been exceeding transparent and professional in his work. To assert otherwise is an insult to the whole scientific community.

      7. Babe,don’t exhaust yourself.Rest and recover you are putting so much effort in here.I for one appreciate every single word you have written,you are very enlightening,others may not and it’s not worth wearing yourself out for,ever!

  44. Guys,

    I didn’t mean to say Denise said if A=b and B=c then A has to be equal to C. This is what Dr Campbell is accused of doing. I don’t fully understand Dr Cambell’s methodology so I can’t say for sure whether he simply used above logic or there was more than this that was involved that led him to implicate animal protein.

    What Denise has done is from Raw data she tried to see if A equals C or if there is positive correlation between A and C. Just want to make sure I’m understood properly.

  45. And one more thing regarding the weak positive and negative correlations between average green vegetable intake and heart diseases and stroke. You wondered why average intake does not have strong negative correlations indicative of a protective effect but frequency of green vegetable consumption does. You speculated geography might hold the answer, but then you headed in the direction of latitude. As I clarifed, the correct correlation between average green veggie intake and latitude is -33, not +5. Geography holds the answer, but it’s differences in latitude. It’s suburban versus rural. Look at the green veggie intake map on p556. Notice the black dot clusters on the coast in the middle and in the south. The midcoastal cluster is around Shanghai. The south coastal cluster is around Guangzhou. If you check out these two hot spots on other maps, you’ll see these folks ate a varied diet with a bit of everything back in the 70s and 80s, and they still do today. The area that appears to have eaten a diet approaching a modern one with processed foods is around Shanghai with the highest consumption of added sugars and starches (like cornstarch as a sauce thickener) and of vegetable oils, no surprise to anyone who’s been served a plate of bokchoy drowning in oily sauce.

    If you’re still interested in the green veggie paradox – why frequency is negatively correlated with those disease but average intake is not – you might explore differences between suburban and rural veggie eaters.

  46. “But Campbell has been exceeding transparent and professional in his work. To assert otherwise is an insult to the whole scientific community.”

    My answer: An insult to one man is not an insult to all men. An insult to one violin player is not an insult to all violin players. And an insult to one researcher is certainly not in any way an insult to a whole scientific community. And if Campbell wants to be transparent, then he can tell us how he came to his conclusions instead of making excuses for doing the exact opposite.

    I have nothing but respect for those who try to do what is morally right and those who care about animals, but the predator/prey relationship is a natural part of the animal ecosystem and we are animals too. If we are to truly understand what is healthiest, we cannot think of diet as a religion and researchers that got some papers published as Gods that cannot be questioned. Vegans can be the best vegans only if they are willing to be totally objective about what may or may not be true. What you eat is a choice. What we are designed to eat is not. Maybe in a perfect world, we would always be designed exactly for what we think we should be designed for, but this may not be a perfect world. If we are designed to eat some meat, then understanding that will only help vegans understand better how to avoid problems when they choose not to eat those things anymore. It will allow them to find better vegan substitutes. It will allow them to be healthier.

    Diet issues are particularly tricky to understand because the addition of any type of food necesarily means the reduction in other foods. If you eat more fish, and your caloric intake remains the same, then you are eating less of something else. Is it meat that is healthier or is it less grains that are healthier? Or maybe it is saturated fat that is healthier? There are plenty of vegan sources of saturated fat. None of us know all the answers yet. SOme of what we now think will later turn out to be wrong. But which parts? Now is not the time to think we have all the answers and attack all who disagree with religious zeal. Both the meateaters and the vegans still have a lot to learn. The sooner we all admit that, the faster we will learn more about the truth.

  47. I am looking forward to your next post. I am very interested by the connection of wheat with health in China. By the way, I found this in the amazon comment section to the book China Study. Richard Kroker, an engineer with a PHD, has done a multiple variable regression analysis on the China Study raw data. Did you obtain a similar result?

  48. Might Denise be employed by T Colin Campbell in order to drum up publicity and sales of The China Study?

    Nothing sells books like a good controversy.

    I find it hard to believe that an untrained blogger could come up with such a rigorous analysis without professional help, and who better to provide that help than TCC himself. Also, the “beginner mistakes” in stats that Denise makes are probably a setup so that later T Colin Campbell can write a scathing defense of the book and sell even more copies.

    1. *Sigh*

      I admit it. You caught me.

      I’m actually a Japanese spy who infiltrates the meat and dairy industry headquarters Monday through Wednesday, collaborates with T. Colin Campbell Thursday and Friday, and spends Saturday fashioning my top-secret plans for world domination (which include implanting dairy cows with human embryos to breed an army of half-bovine ninja children).

      On Sundays I rest.

      It’s a tough life, I tell ya.


    2. The world is full of autodidactic folks, and thank God for them. This is why I specifically tackled the credential issue in my post about Denise’s work.

  49. Denise,

    Once you said, you introduced meat for health reasons, that really stopped many folks from reading it any further. This is what I read at some other forum where I frequent. Take it for whatever it is worth.

    1. Probably a good thing Mark. An inability to read a critique of one’s position usually means you are still knee deep in the grip of ideology. Of course it doesn’t mean if you do read you aren’t, but for those can’t or won’t it almost certainly means that.

      1. An inability to read a critique of one’s position usually means you are still knee deep in the grip of ideology.

        Damn good words of wisdom

  50. I haven’t been this entertained in a geeky statistics way since grad school. And the fight in the comments reminds me of many a technical conference between two parties whose entire existence apparently revolved around their hypothesis being the One True Way, which require all doubters to be struck down.

    Congrats Denise on a simple and elegant (though no doubt time-consuming and painstaking) analysis. Your post lives up to exactly what you said you were doing – no more, no less, whereas the people posting “helpful hints” are out there tap-dancing on the edge of the stage.

  51. oooops, sorry denise

    just discovered that you were only 23 years old (one of campbell’s criticisms that actually finds its way to print)…all bets are off..

    you are far too young to have anything of value to say….i mean campbell’s gotta be 40 years your senior….ever heard of respecting your elders

    trust, and respect, automatically defaults to the older dude

    sorry, that’s just how it is

    i’m sure this essay of yours will look good in your scrapbook, denise, but leave the science to the older folk


  52. Denise,

    I just wanted to show some support for what you’re doing here, and say a big “thanks!” I’m impressed with what you’ve done so far and look forward to your upcoming posts. I think it’s sad that the 30bad folks clearly don’t understand what your work is showing (or even what you set out to do), and insist you jump through hoops. I can tell you that nothing short of peer review will satisfy them (they’re really geared up about this, and apparently Campbell is going to offer up a more detailed–and let’s hope less snarky and more professional–reply). I would recommend you didn’t waste your time if I weren’t so interested in what your analysis shows! Anyway, don’t let the haters get you down!

    Best wishes –

  53. Denise, you concluded your article with: “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.”

    However, The China Study, on page 243 states in pertinent part that salmon, tuna, and cod may be eaten; only meat, poultry, dairy, and eggs should be avoided. Moreover, The China Study plainly states that the science shows that animal protein may be eaten without causing adverse health problems if the amount is 10% or less of one’s daily calories; for the typical 2000 calorie eater that means that 50 grams of animal protein may be eaten daily.

    I do agree however that on page 242 of his book Dr. Campbell makes a leap when he opines that “it’s not unreasonable to assume that the optimum percentage of animal-based products is zero, at least for anyone with a predisposition for a degenerative disease. But this has not been absolutely proven. Certainly it is true that most of the health benefits are realized at very low but non-zero levels of animal-based foods.” Why he wrote the foregoing and advises the reader “to try to eliminate all animal-based products from your diet, but not obsess over it,” is beyond me.

    We read The China Study and find example after example of why it is okay to eat 50+ grams of animal-based protein daily without jeopardizing our health, and why we should eat a variety of whole, unrefined plant-based foods, and in his list of foods to eat, he includes in pertinent part salmon, tuna, cod (fish), but then he throws in his “assumptions” which he admits “has not been absolutely proven” that we should avoid animal-based protein. What? This assumption without any scientific basis should not have been included in The China Study — I don’t know why Dr. Campbell threw this in his book on pages 242 – 244. In answer to his questions, “What Does Minimize Mean?” and “Should You Eliminate Meat Completely?” the research in The China Study answers: minimize means eating a serving of animal-based protein daily, and no, we should not eliminate meat completely.

    So The China Study does support Denise’s hypotheses; it does not support a vegan life-style! I think Denise would do us all a service if she pointed out the foregoing regarding Dr. Campbell’s advice to eat salmon, etc.

    1. Hey Mike:

      While I congratulate you on the large weight loss and it’s certainly got to be healthier than where you were, man have you lost a LOT of lean mass.

      Those wanting to lose fat while PRESERVING, even building lean mass & strength might want to consider a paleo-styled diet including lots of animal protein & fat.

      Here’s my 60 lb weight loss, but it was 100% fat loss.

      That’s of about a year ago. Here’s yesterday 6th & 7th pictures down:

      y’know, long as we’re haulin’ out anecdotes & all.

    2. Mike T,

      Been there, done that and came to the same conclusion as Denise. Reintroduction of animal foods, albeit less in volume than in previous years, has resulted in an improved state of health and one I think is optimal for me. My diet is plant-based with some meat to supplement it plus a drastic reduction in grain consumption. Glad you found something that works for you.


  54. Hi everyone,

    Quick update: I’ll be posting my response to Campbell either tonight or tomorrow. Alas, “day job” duties have stood in the way of cranking this out as quickly as I’d hoped.

    I’ve also been informed that Campbell is writing a more thorough response to my critique and will be posting it on his website,, in the next day or so. Mr. Campbell has also released a newsletter asking the graduates of his course in plant-based nutrition to come to my blog (and others linking to it) and “read and respond in a way befitting of Dr. Campbell and his message,” so for those of you arriving here via that avenue, welcome!

    Thanks again for the comments, feedback, and occasional seething, embittered character attacks; I appreciate all of it. 😉


  55. I anxiously await the updates from both Denise and TCC.

    I hope TCC will refrain from:

    1) Any mention of his own qualifications and experience.
    2) Any reference to Denise’s age and/or qualifications.

    If TCC has good science on his side (and I believe he probably does), he need not clutter up the argument with storytelling. Just the facts, please!

    This is not a typical lay audience. This is an audience that craves understanding. We don’t want to be persuaded you’re a credible source. We want to UNDERSTAND The China Study. Help us understand exactly how you analyzed the data. Help us understand what you found. Help us understand what you did not find.

    Advice for Denise: Please refrain from any snarky comments or little jabs at TCC. You might even owe the man an apology.


    1. Denise has been elegant throughout. It was Campbell who sank to ad hominems.

      BTW, “You might even owe the man an apology” is pretty snotty yourself.

  56. I enjoy snarky, so please don’t refrain, and it is your blog after all.

    Apology? I don’t think so. If TCC had presented and defended all of his findings in a much more transparent manner from the get go this wouldn’t be an issue at all. If his findings and conclusion are indeed all justified and scientifically correct great, I’m sure Denise will be more than civil about it.

    “I’m sorry you didn’t give us all the information the first time around.”

    That might work though.

  57. I am glad that you feel you have found a diet that works for you, however, I must question how a 23 year old English major has the knowledge or qualifications to even begin to evaluate epidemiological studies and scientific research?

    I believe that each individual should research health and nutrition and try to find accurate information and well conducted studies to support that information. However, It is also important to recognize your own limitations in interpreting data and research and avoid proclaiming yourself an expert who can adequately evaluate research when you clearly do not have the education or experience to do so.

    The statistical analysis you have tried to accomplish is extremely misleading not to mention inaccurate and naive. You do not have any experience in epidemiological research and therefore do not even begin to understand how data is to be evaluated and how conclusions should be drawn. To believe you can discredit scientific research based on personal nutrition study is arrogant to say the least.

    While I applaud your persistence in striving to justify your own personal beliefs about an optimal diet, this analysis is far from ‘proof’ that the China Study is a fallacy. You don’t have any experience in scientific research or epidemiological studies so how can you even begin to proclaim that you can prove any scientific research to be valid or invalid?

    Dr. Campbell has conducted research for over 40 years. Dr. Campbell is the Jacob Gould Schurman Professor Emeritus of Nutritional Biochemistry at Cornell University. He has more than seventy grant-years of peer-reviewed research funding and authored more than 300 research papers. What credentials do you have that qualify you to discredit his research? What credentials do you have that qualify you to even evaluate this research?

    I realize that you want to justify your own dietary beliefs but it is irresponsible to try to discredit scientific research merely because it goes against your own personal beliefs, when you clearly do not have the knowledge, education or experience to do so. I believe you owe Dr. Campbell a sincere apology for recklessly and naively disparaging his work without any knowledge, education, qualifications or experience to do so.

    1. Hi Tandi,

      Credentials blah, blah, blah…Campbell used simple correlations of the raw data to make some of his key arguments; you don’t need any experience in epidemiological studies and scientific research to see that. Denise simply took it one step further and introduced confounding variables to show how flawed the simple correlations were. She was not publishing her own epidemiology study; she was using hard numbers to criticize the one that had been published.

      If Campbell had just come out and said,” yes, I used simple correlations and this is my reason why (and maybe in hindsight I should not have used these in my book)” in replying to his critics maybe he would not catch so much flak, but instead he has belittled anybody who has questioned his use of these simple correlations, called them agents of the Weston Price Foundation, and claimed that his simple correlations of raw data were not in fact simple correlations of the raw data. How esteemed is that?

      The possibly bigger issue though is if Campbell has misrepresented the findings of his references, which Denise and others have claimed he has done. From what I understand that is almost a mortal sin among scientists. A rational person would have a hard time defending this, but many of his defenders do not seem to be so rational sometimes.

      1. Hi N,

        I don’t have access to most of the scientific papers, and I don’t have time to really look into it in that great of detail. Hopefully as Denise proceeds she can maybe go in that direction.

        Using the paper that has been used here before ,
        I can however pick out a few statements such as the one below where it appears to me that while references might be important information, they do not represent a more in-depth analysis of the China data beyond Campbell’s simple correlations. I could be wrong without accessing the actual papers (just sometimes very limited abstracts), but I believe for example one of the studies referenced below is for Israeli data.

        “Plasma cholesterol concentration was associated directly with all-cancer mortality rates measured in this study. Most notably, these associations were statistically significant for eight different cancers, including colon cancer (P < 0.01 for males and P < 0.001 for females) (55, 56, 66)."

        Some of his correlations may have better analysis to back them up, but some of them don't.

        Again, you can maybe argue whether the use of simple correlations were justified or not, but Campbell has bashed other people for doing this when they come up with contradictory correlations to his own, and he has said or implied that he has not used simple correlations of the raw data.

        As I said before, you got to start somewhere (and I know we disagree on where to start), but Campbell has seemingly always responded to his critics by claiming that he has always used a more sophisticated analysis than them and that his critics cannot be trusted because they used simple correlations of the raw data (and that they are all possibly agents of the Weston Price Foundation out to get him).

      2. PS –

        “Are you unwilling or unable to follow references to supporting studies?

        According to the person I originally responded to, I am not qualified to even look at his Campbell’s scientific papers anyway. I am not epidemiologist or a scientific researcher.

      3. Some of us have spent long hours in libraries to become familiar with these matters, and so don’t see a lack of personal access as a reason not to investigate as far as it takes to answer these questions. And nobody said you weren’t qualified to read about this. A few statistics courses allow interpretation of results. I would hope that a student coming out of one of my statistics classes could recognize some of the problems in Ms. Minger’s analysis for example. To do it yourself is another matter. Even with years of statistical expertise I do not have the epidemiological skills to carry out a proper analysis on this data.

      4. ” I would hope that a student coming out of one of my statistics classes could recognize some of the problems in Ms. Minger’s analysis for example. ”

        I would hope that someone who supposedly teaches statistics classes would realize that Denise isn’t the one with the problem here — she’s just pointing out that Campbell’s univariate correlations are bunk.

    2. Tandi and others,

      Pointing out someone’s age or credentials is generally an implicit admission that one cannot competently address the person’s argument. Denise has been extremely respectful of Dr. Campbell in a personal sense and has dealt with data. I think she deserves equal respect and those criticizing her should criticize her data and data analysis directly.

      It does not take a long time, not even a few statistics classes, to learn how to generate pearson correlation coefficients and multiple linear regression models. One can gain a very excellent understanding of these things from reading half of a statistics book, and one can gain sufficient understanding to perform them correctly by purchasing software and reading the tutorials. Besides which, someone who began college at 16 as stated on her web site and is now 23 could easily have taken plenty of statistics courses if she so chose to do, again, not that it would have been necessary.

      It is clear to me that the “cancer epidemiologist” that you are quoting, who has posted here and has been quoted many times over, neither read the China Study nor read Denise’s review, except perhaps by casual skim. This epidemiologist critcizes Denise for using ecological data and analyzing by county, conceding at one point that perhaps this is all she had access to. Had this person read either the book The China Study or read the original monograph, they would know that the China Study is an ecological study and pooled all the blood of all individuals’ in a local unit into a large vat so the investigators could measure more biochemical markers.

      Had Dr. Campbell only published in peer-reviewed journals, the discussion of his work would be limited primarily to peer-reviewed journals. When he chose to write a best-selling book, however, he opened up his arguments to criticism by the public. This was the course he chose.

      As he acknoweldges in his own brief response to Denise that has been posted in the comments section here, he believes the better choice is to correlate animal food intake, cholesterol levels, and so on, with multiple depenedent variables such as “diseases of poverty” and “diseases of affluence,” but this was excised from the book due to space. Obviously, this is a novel approach, debatable, and would be controversial among researchers. In any case, it is not the same thing as adjusting for confounding variables by having multiple independent variables — and that type of data was also not presented in The China Study. One need not follow 700 references to find this out. It is up to Campbell to state in the text “after adjusting for…” when he presents his correlations, and he never states this because it isn’t true.

      Denise’s analysis was very simple, and that is part of its strength. Many people have been floating the idea that any reputable scientist would “adjust for confounding variables.” This is, first, nonsense. Any reputable scientist would first and foremost present raw data. Look in ANY peer-reviewed publication where multiple regression is used and you will see that the first thing presented is the unadjusted data. As the “cancer epidemiologist” pointed out, one of the criteria of a confounding variable is that it must not be on the chain of causation between the thing whose effect it is confounding and the dependent variable. This is not something that can be determined by statistics. It requires discussion, argumentation, and subjective judgment. Ten researchers will present ten judgments on what may or may not lie in a chain of causation because frequently we just don’t know. Adjusting for confounding variables is a partially subjective process subject to much uncertainty and disagreement, and this is why usually in a peer-reviewed paper the raw correlations are presented along with several different multiple regression models.

      In The China Study, there are over 8000 statistically significant correlations. There are many different factors one could put into a multiple regression model. Which ones do you pick? Ten researchers will give ten answers. There is no “correct” multiple regression model.

      Before one adjusts, one must make a case for it.

      What Denise did here is take simple correlations that Campbell was using and make a strong, well-developed argument that Campbell did not take into account many compelling confounding factors.

      And she did, contrary to many statements found within the comments section, analyze some of his references. The most remarkable of these was showing that his claim that animal protein uniquely promotes cancer in aflatoxin-treated animals is based on his own study showing that plant protein is just as effective as animal protein as long as the missing amino acids are provided as would occur on a mixed vegetarian diet.

      My analysis of why this contribution of Denise’s was so important can be found here:


    3. I think Dr. Campbell owes me an apology for ostensibly being a scientist and penning his hack, biased conclusions in “The China Study,” and also for being a total unhealthy-appearing asshole in any video I’ve seen of him. Also, his whole argument about “The China Study” being only one chapter of his book – it was the TITLE of his book. Also, let’s examine the subtitle: “the most comprehensive study of nutrition ever conducted and the startling implications for diet, weight loss and long-term health” – PARDON ME if we take that seriously, Dr. Campbell. Seems that your argument that the actual study itself was fairly unimportant seems, well, disingenuous.

      1. RICH said: “his whole argument about “The China Study” being only one chapter of his book – it was the TITLE of his book.”>>

        It’s very common for author’s to have the title of their books chosen by their publisher (who chose titles to be provocative and sell books, which is their only goal). As Dr. Campbell notes, they submitted 200 title suggestions, not one of them was the title the publisher chose to use. To quote:

        “We suggested 200 possible titles, not one of which was ‘The China Study’. But when we objected, he said that we already had signed the contract and this was his right and responsibility.”

    4. @Tandi – Your attitude is a tad elitist, don’t you think? So the “experts” are there to tell us lowly peons how to eat and think and live our lives and we must never question their advice, uh?

    5. This is why most people are confused by what they read in the papers. English majors writing about subjects they do not understand. If data is something that makes Denise happy than she should learn how to use it. Correlation is not Causation. If it was wearing clothes would be the cause of urination. The simple fact that the monograph is available and all of the data is contained in it will allow many more of these fools to come up with ridiculous correlations and claim they are causes.

      1. @ SupremePundit

        It is kind of funny you talking about other people being confused by what they read…Dunning-Kruger…

        P.S. While Denise has said repeatedly that “Correlation is not Causation”, the eminent Dr. Campbell has said that he can cherry-pick simple correlations to prove that his hypotheses are correct (not just develop hypotheses, but to prove that hypotheses are correct.)

    6. A title after your name, a degree and $1 will buy you a soda at McDonalds.

      My great grandfather who had a third grade education, was one of the smartest men my father ever knew. People who had no formal education came up with all kinds of scientific discoveries. Until recently that is, when all of a sudden it doesn’t matter how smart you are, or how much you study on your own, but instead “credentials” are dragged out as if it means something. It doesn’t mean shit though. I’ve seen plenty of men who have PhDs who couldn’t really think their way out of a paper bag.

  58. Tandy, it is just as irresponsible to take Campbell’s word as is without question. Do you take Campbell’s word as is without question?

    1. One doesn’t need to take his word for it when following references to the specific studies in question to answer further questions. Most researchers are also much more responsive to questions when it’s evident that you’ve done this too.

      1. But when one follows the references and finds they refute the word, as Denise has done, then one must start to doubt the word. Most researchers would soon realize that one doesn’t need to be a researcher to figure that one out.

  59. Did I take Dr. Campbell’s research alone and decide that was all the evidence I needed, absolutely not. Research scientists are not all honest, and you can pretty much get any conclusion you want if you set it up correctly. So obviously you cannot just take a study at face value. However, I wouldn’t necessarily blindly believe an oversimplified evaluation of research by some random person either.

    Quote from an epidemiologist on this evaluation:

    “Your analysis is completely OVER-SIMPLIFIED. Every good epidemiologist/statistician will tell you that a correlation does NOT equal an association. By running a series of correlations, you’ve merely pointed out linear, non-directional, and unadjusted relationships between two factors. I suggest you pick up a basic biostatistics book, download a free copy of “R” (an open-source statistical software program), and learn how to analyze data properly. I’m a PhD cancer epidemiologist, and would be happy to help you do this properly.

    While I’m impressed by your crude, and – at best – preliminary analyses, it is quite irresponsible of you to draw conclusions based on these results alone. At the very least, you need to model the data using regression analyses so that you can account for multiple factors at one time.”

    1. Is the book that Ms. Minger is analyzing a compendium of observational studies?
      If I understand her logic correctly she’s not making assertions that certain variables have causal relationships, quite the contrary, she’s highlighting the fact that the associations between those variables are weak.

      Moreover she’s quite clearly outlining her methods which, while simple, don’t seem egregiously flawed.

      Per the suggestion she should be using regression…. check out the definition of “correlation”, (wikipedia is excellent).

      “…account for multiple factors at one time.”, please explain, especially why this is _necessary_ for her critique.

      I have to say that I’m strongly put off by the suggestion that expert supervision is necessary for a clean statistical analysis…. but if she wants help….

      On a more positive note I (a random stranger) also heartily endorse R. It’s awesome:

      1. ///“…account for multiple factors at one time.”, please explain, especially why this is _necessary_ for her critique.///

        It’s necessary so they can make data look like it’s something it isn’t.

        They do it in real estate and unemployment statistics too. They “adjust” the numbers so that they take the season (or some other factor) into account. It can make the data look better or worse. And I don’t need a math degree or a science degree to figure that one out.

    2. A correlation _is_ an “association”, in particular a simple linear association. I think what you mean is “Correlation does not imply causation.” In many, if not most, if not all (I’d have to reread) of the cases above she was pointing out a _lack_ of correlation…

  60. Denise,
    You also forget some possible relationships. Quite normal, because it is impossible to see/know everything.
    f.i.: you correlate wheat-consumption with the occurence of cancers.
    But how do we consume wheat:
    – bread is consumed with meat/cheese, containing preservatives, colouring agents and other suspicious stuff, or with sweet stuffs – wich also correlate to cancers.
    – there is a big difference between ‘old fashioned’ bread with sourdough, and bread with yeast.
    – cereals contain additives, maybe in overdose (f.i. Kellog’s All-Bran contains metallic (!) iron and other stuff ) and are consumed with milk (a bad combination, because nutrientuptake, other then calcium is inhibited)

    So also your story is far from (statistical) complete…

    1. Yes, Denise, your analysis is invalid because you did not do a multiple regression analysis of the consumption of Kellogg’s All-Bran in Xuanwei province.

      1. anon: you don’t mention the other 2 points I pinpointed.
        beside that:
        – bread also contains additives like cysteine that may influence the results (the thiol side chain often participates in enzymatic reactions)
        – wheat in non-western countries is (often) more contaminated with aflatoxins.

  61. denise, i read your reply to campbell first and was stunned, then i read this and was even more stunned. i think you might be the smartest 23 year old person i’ve ever seen. your writing style is also fantastic. if you wrote a book i would buy it immediately no matter what it was about.

    any chance you’re single? heh heh heh… 🙂

  62. Say Hey-

    Nice work. Beware the truism “no good deed goes unpunished”.

    It was a shock to me at first to find out the depth of the chasm between what is known and what is accepted wisdom. After some time I got over it, as this is (sadly) not the only area of knowledge under assault by emotionally involved people with a predetermined answer. Those of us who do this type of work (data analysis) for a living are trained to see this bias in ourselves and we try hard to use tools that are resistant to manipulation. Just bear in mind that no honest researcher will withhold the raw data (nor their methods) from you.

    There is one thing I see mentioned above that I wanted to expand upon a bit. While this data set was a massive amount of work to accumulate, and even though it may be the best data generally available on mortality and nutrition; the data set does not hold answers so much as it helps us form the right questions. It is easy to over analyze a data set with fancy math tools; the hard work is to follow up on the initial (clear and obvious) questions.

    A correlation is a correlation. We are actually after cause and effect. You see this caution repeated many times above in one form or another. The heart of this issue is that the answers we seek can not be found in the numbers. The numbers guide you as to how to spend your limited time available in investigating cause and effect in matters of interest to you.

    This data set screams “wheat is bad for you.” After chasing previously formed questions about plant vs animal protein issues, cholesterol, vitamins and the like – you have to come back the the big surprise; “wheat is bad”.
    At first you may not want to believe it – even if you have a gluten allergy that has progressed to celiac disease. To make progress on this issue you have to go outside the data set to find a well supported mechanism that explains the nature of the problem.

    If you think the argument about plant vs animal foods is “difficult”, you don’t know the meaning of the word. I have been studying the problem with wheat for two years now. Good luck with your quick follow up in a “few days”. I fell down the rabbit hole. The problem is not that nobody knows why wheat is bad for you. The problem is that “we” have not been listening.

    Check out the relationship in the china data set between Vitamin C blood levels and wheat consumption. I missed its importance at first, but it is very helpful in understanding why wheat has to be fortified with vitamins. But depleting vitamins is not the only problem with wheat; those who are interested should check out the newly discovered hormone zonulin that wheat can mimic. I also recommend “Dangerous Grains” by James Braly.

    The core of the issue with wheat are polypeptides that we can not digest completely. We are known to be rapidly genetically adapting to wheat – and there is no clearer proof that it is a dangerous thing to eat.

    After two years (and counting…) of investigating the health effects of wheat consumption I am fully aware of one salient fact. Assuming plant foods are naturally good for you is absolutely childish. Put in a very general way, all food kills you. The problem is just that you die much faster if you don’t eat any. Your particular genes allow you to digest your “historic foods” better that “novel foods”. There isn’t going to be one right answer.


    PS. I also recommend Sally Fallon and Mary Enig’s simple discussions about why it is necessary to wash seeds (such as wheat) before eating them.

  63. Brava! This is one of the most objective and honest analyses I have read in many years. Denise, you have the heart and mind of a TRUE scientist. Please, disregard the snooty comments of those fools who confuse credentials with capability or integrity. As a layperson (non-statistician) who has read many medical studies while researching personal issues, I have encountered many an epidemiological study where the conclusion drawn did not account for many variables that could potentially affect the results. It is a pleasure to read the results of your hard work, that provide an effective counterbalance to shoddy science.

    Thank you.

  64. I’m SO glad that you did this. Just before finding the Paleo diet and lifestyle, I bought and read Dr. Joel Fuhrman’s books, Eat to Live etc., which are heavily based on the China Study data. I felt pretty confused, knowing that I eat butter, dairy, and meats and have perfect blood pressure and cholesterol. Thank you for cutting through the BS, and pointing out what should have been obvious. I hadn’t gotten as far as reading the China Study itself, and most likely would have had a very hard time wading through it to draw the conclusions that you did in such a clear unbiased way! thank you, thankyou!

  65. Absolutely brilliant!!
    I have been looking for an intelligent, objective critique of the China study for a long time and this definitely fits the description, without some of the biases of the surprisingly few other critiques to date. I used to be a big fan of Dr. Campbell and the China Study, having read it three times, believing that such an extensive and comprehensive study led by a Scientist with Dr. Campbells credentials had finally revealed the “truth” abiut diet, nutrition and disease. Having recently looked into the other side of the issue; paleolithic nutrition, low carb, high fat etc.. I started to have many questions and the direct correlation between animal protein and cancer made less and less sense. I was therefore very pleased to find this critique that meticulously takes apart Dr. Campbells theory by using the actual data from the original China Study itself. From this we can see that the conclusions Dr. Campbell arrives at and which he repeats incessantly in his book, namely that animal protein causes or rather facilitates the progression of cancer, are selective conclusions with no statistical basis from the data of the actual China Study.
    I would therefore like to thank Denise for her excellent work in helping to expose yet another hidden agenda attempt at misinformation.

    1. Moox, just keep eating animals and you will learn the truth the hard way. Denise Minger is a rank amateur and not even close to Dr. Campbells league. The China Study is a peer review work that we are fortunate to have available to us.

      1. Hi Frank,

        Campbell’s book, the China Study, is not a peer-reviewed work. It is just a book where some guy argues his hypothesis, one that he did not feel was getting enough ‘air-time’ in peer-reviewed scientific journals.

    2. I said the China Study, not his book. His hands were tied on the title. And he is not some guy, he is a true scientist, he does not need “air time”. If you want vibrant health read his book, be grateful he produced something for you to criticize when you should instead be quiet if that is all you have to offer.

    1. Vegans do not need to hear there’s “scientific rationale for avoiding all animal foods.” They know from living it and enjoy excellent health and do not need to rely on the propaganda of the monied interests that have made this country the sickest nation on the planet. Here is a clue for you: have you noticed that all the name calling and inflammatory words couple with personal attacks never come from the ones providing information like that found in the China Study? That speaks volumes.

      1. “have you noticed that all the name calling and inflammatory words couple with personal attacks never come from the ones providing information like that found in the China Study? That speaks volumes”

        actually no, I have not noticed any such thing.

        Go back and read any rebuttal written by TCC himself. Count the ad-hominem attacks. (btw, that means attacks on the messenger not the message). He works fromt the same template every time. 1) Question the credentials of his critic 2) Remind you of his own impressive CV 3) Mention that his diet has been proven to eliminate disease 4) Belittle the critic some more 5) Complain about being called names like buffoon (that one seems to have really gotten to him 6) Implore you to “just try it? and most importantly 7) Never, ever respond to the actual criticisms in any meaningful way.

        step away from the kool-aid dude…

      2. Correct that: accurate. But I think it is obvious that my statement is true. It’s something I noticed over 30 years ago when I first got into this; it stuck out like a sore thumb. As i said; it speaks volumes.

      3. Wow, you are in all the way. You can have the last comment if you need it, this will be my last. You comment makes me think you are reading something else all together and there is no way to reach you. After 30 years of vibrant health i think I’ll keep drinking the kool-aid dude.

      4. If vegans have “excellent health,” then why is it every vegan I’ve ever known — and I’ve known at least a dozen — looked like they were on the verge of dying, sometimes after only a few weeks on their new diet?

  66. I have to commend your effort and time spent studying the data behind Campbell’s book, and you do raise some very good points (such as Campbell being unable to prove a direct link between animal proteins and cancer).

    I have not read the book yet, but have ordered a copy and am looking forward to making up my own mind about the study.

    What I would really like to know, but have been unable to find in any source, is whether Campbell was a vegan/vegetarian before embarking on this nutritional study. Many detractors imply this as a way of reducing Campbell’s credibility, accusing him of going into the project with an agenda. The accounts of his life I have read seemed to suggest that he was originally a believer in the meat and dairy industry due to family ties, but it was this research that changed his mind, which is not suggestive of going into the project with bias, but rather the opposite. I really wish there was a credible source which stated when he became a vegetarian/vegan.

    I also wish nutrition didn’t need to be such a political minefield…

    1. What you will find out after recieving your book, is that he was neither vegetarian or vegan when he started all this. He grew up on a dairy farm, eating the very foods that are considered “American”. He also started out trying to find better sources of protein to fix an apperant “defiency”.

      It’s great that Mrs. Minger set out to work so hard at refuting Dr. Campbell’s work, but she is no scientist with no credentials and no peer reviewed work. This is her opinion and her alone. I will gladly follow the doctors and scientists that not only have all those above, but live the life and are healthy for doing so. Not one takes medication for a chronic disease like so many others in the nation.

      Nutrition wouldnt be such a political minefield if it were left up to the dietians and doctors. Government and politicians let big dairy, meat, egg and pharma control most of what we read and hear and how we hear it through the regualr media.

      1. Hi Deb,

        There are plenty of doctors and scientists that think eating meat and fat is beneficial, and they live the life and are healthy doing so too.

        Campbell said that basically one of the reasons that he published the book is because he felt that similar views were under-represented in the scientific literature. In essence, Campbell is saying the majority of scientists don’t share his views. That does not mean his hypothesis is wrong, but it does mean that credentials are kind of a pointless issue to bring to the argument.

        Especially when all Denise has done is point out flaws in his arguments and evidence in his book used to support his hypothesis. The book is not peer-reviewed scientific literature. It is some guy arguing his hypothesis to a jury of laypeople.

      2. Hi CPM. I agree with your statement. I’d like to add that “lay-people” should also read and (loudly) critique “peer reviewed” literature.

      3. She did not point out flaws. Read Dr. Cambells reply. And there were plenty of doctors and scientists who told us smoking was good for us too. They live the life and are healthy doing so too? Who are they. If they consume meat and dairy for years and years it will degrade their health; they are not immune to the laws of phisiology.

      4. Frank,

        Campbell is not an omnipotent being so of course there will be flaws in his work, and Denise pointed out quite a few. You can’t hope to understand that, and the weakness of Campbell’s response, if you don’t read this post and the next. Read really slow if you have to. Just read it. Oh wait that’s right, you don’t need scientific rationale to defend a vegan diet. Seems strange you would even bother spamming up the comments if that truly were the case.

      5. Try this Kat,

        These attacks are a clear sign of the widespread success of Dr Campbell’s work. His 35 years of research is nothing to sneeze at. And the positive results so many have had from changing to a plant-based diet after reading his book, is frightening to his opponents. The results are real and undeniable…backed up by standard medical tests. Remove the cause of illnesses & they go away. An animal food-based diet is the cause of the majority of common diseases. A low-fat plant-based diet is the answer.

        His findings on the direct effect of dairy casein
        on cancer markers is astounding…add casein, markers go up, subtract casein, the markers go down. It doesn’t get any clearer than that. Get dairy completely off your plate if you want to be well. And while you’re at it, remove the dead carcasses as well.

        Dr Campbell is a gentle & humble man, with all the best intentions of sharing his findings with the world, in order to help people. He is not out for fame or wealth. He simply is sharing the truth.

      6. Frank,

        Seriously man, read the post. It’s obvious that you haven’t. If you want to debate the issues that Denise raised then cool, we’ll do that. But you simply can’t do that without first reading the post. Coming here and trying to discuss these things based off of Campbell’s response is like trying to discuss Crime and Punishment by reading a cliff notes version of Romeo and Juliet. It ain’t the same.

        “Dr Campbell is a gentle & humble man…” Well now I’m not so sure you’ve actually read his responses, either.

      7. Seriously man, I read them both. What Denise raised really amounts to somebody wanting attention, there is nothing to discuss. As wrote “we were mildly surprised that Dr. Campbell felt he needed to take the time to dignify Minger’s musings with a response”.

      8. @deb,

        Yes, the dietitians and doctors are doing a wonderful job. Just look at the amazingly slim and healthy people all around! I’m glad we’re not in the midst of any sort of chronic illness epidemic, like diabetes, obesity, cancer, and heart disease.

      9. @Frank,

        I don’t suppose you have any actual scientific points to make, as opposed to vague inferences about sociology and psychology?

  67. What is up with that obsession with WAPF? Too bad he didn’t read Minger’s response to his initial response! And that he hangs his hat on the “mysterious missing comment” that never really went missing. Who does his fact-finding? Unimpressive.

  68. I mentioned some points before, here are some others:

    1: relativism: the ‘fact’ that wheat (in some form, in a certain context) causes relative more cancers doesn’t mean that meat (in larger quantities) won’t.
    And what is meat ? There is a difference between USA and EU (Chinese ?) meat, between fresh meat and ‘fabricated’ meat.
    Or between organic and ‘conventional’. What other products do consumers (who consume a lot of, or less, meat/wheat) eat, that are’nt taken into account ? (legumes & fruit, nuts, (un)refined oils, diary), have they been adapted to the ‘western’ way of life ?

    2: environment: what are the environmental factors ? Living in an unhealthy environment may be correlated to wheat consumption. Environmental factors play a very important role in the development of cancers.

    3: smoking: do the big wheat consumers in China also smoke more ?

    So again my point: Denise (with all respect for her energy and superiour intelligence),
    you cannot make any definite conclusions from this data – and you have to explain this limitations and possible other correlations in your publication to be taken serious in your ‘overall’ analysis.

      1. ok. ‘definite’ is too hard. the definite conclusions are taken by others…
        but with Denise’s analysis as excuse.
        When you’re trying to superevaluate statistical data analysis, you have to break through the boundaries of the data themselves – and through your own boundaries.
        Because life is very complex – in all respects. And you have to account for that in your conclusions.

        Can someone tell me, why the so (scientifically) praised ‘Mediterrenean’ diet is that healthy ? They consume a lot of wheat overthere (what I saw): bread, macaroni, cous-cous, bulgur…
        Some other factors play a role….

  69. Dr Campbell has done an amazing job with the China Study exposing the dangerous of consuming animal products.

    Just answer me one question , how long does meat stay in your body before is eliminated, and how long does vegetables take before they come out. I work at a cancer clinic and see the results of poor diet. kids as little as 5 years old already with cancer. Once the patient is switch to a plant base diet Miracles they get better!!!

    I myself I’m a cancer survivor. I grow up in farm in Nicaragua My mother force me to eat meat not realizing the damage she was causing me. I had asthma, constipation , depression , parasites. And many other illness associated with meat and dairy. First time I had cancer was 17 and later at 27. I made the switch to plant base diet and my world change .

    Meat causes inflammation, constipation , retardation, meat has not fiber. How can this be good.

    If the number and chart are not right who cares the bottom line is plant base diet is best for any one who is looking for good health . I think that some times we complicate thing. Specially when it comes to food!!!.
    Even in the bible talks about a plant base diet. The test of the food if you care to look is on the book of Daniel.

    1. Actually sugar and refined carbohydrates cause inflammation. Low carb and paleo diets are anti-inflammation.

      Fiber may irritate the lining of the colon and bowels. We’re not cows or horses after all and cannot digest it.

      To say that people are sick from eating meat when they’re eating a standard American diet that is high in both sugar and refined carbs is naive. People in the bible ate copious meat. I suggest you not cherry pick verses.

  70. Denise,
    You are 23 years old.
    Who wrote this material for you?
    Your extracts from The China Study (book), which is just a meta analysis, is not working with all the data. Your critique is therefore a distortion.

    Note I don’t know Campbell, nor do I particularly care about whether he is right or wrong – I have been doing research independently in this field for 35 years myself and have no answers or any axe to grind.

    However, he is a person who has worked his life to help people be healthy.

    As a professional researcher however, I believe certain protocols must be followed or the field of nutrition on the web will become like a Rush Limbaugh hour – full of ugly insinuations with very bad fact finding.

    I am surprised that if you are truly interested in fact finding, you would attack like this. Campbell is neither a crank nor crazy. He is a 72 year old scientist who has taught at MIT, Cornell and worked for the National Institute of Health.

    Where are your credentials? Just being a web blogger hardly qualifies you to make these conclusions, as nicely as you write.

    I hope you take your conscience into account before you publish material provided by others in the future.

    I am quite sure that your materials are well intended, but they are completely unscientific in their methodology and their conclusions – as with Mr. Limbaugh.


    1. Hi Elizabeth,

      Rational thought requires no credentials. The results speak for themselves. The same goes for reading comprehension ability…

      Also, it is very scientific of you to criticize Denise’s age, claim that somebody else wrote this for her with no evidence at all, and harp on credentials instead of content. You must be a student of Campbell’s.

      It is also very scientific of many of Campbell’s supporters such as yourself that feel that the intended audience of Campbell’s book are unqualified to judge its merits and must simply bow down to his credentials as though he is the only scientist in the world that has every published a book on nutrition, let alone published a scientific paper.

    2. Your credentials and $.75 will buy you a cup of coffee.

      You don’t critique anything she wrote, you don’t say it’s wrong. You resort to ad hominem attack, equating her to Rush Limbaugh and accusing her of plagiarism.

      You say you’re a researcher? Heh. No surprise there.

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