In 1983-84 a massive study was undertaken in China to catalogue the health and habits of rural Chinese living in 65 counties and was dubbed “The China Study”.
Within each of the 65 counties, 2 villages were selected and 50 families in each were randomly chosen for study. One adult from each household (half men and half women), 6500 for the entire survey, participated. Blood, urine and food samples were obtained for later analysis, while a questionnaire and 3-day diet information was recorded.
A total of 367 items of information on these 6500 families eventually were judged to be reliable. These 1983-84 diet and lifestyle data included the 1973-75 mortality rates for about 4 dozen different kinds of cancers and other diseases.
The assumption of the researchers was that “rich Western diets (high in fat and meat, low in dietary fiber) were strongly associated (correlated) with incidence of colon and breast cancer. Which is a commonly held view by most medical professionals today.
In 2005 T. Colin Campbell, one of the researchers involved in the study published a book called “The China Study: Startling Implications for Diet, Weight Loss, and Long-Term Health” which quickly became a “bible” of the vegan/vegetarian movement.
Campbell became interested in the idea that protein (specifically animal protein) might cause cancer after observing in the Philippines that the children from the wealthiest families that ate the most protein had the highest rates of liver cancer.
The book “the China Study” presents Campbell’s proof that animal protein does indeed cause (increase the risk of) cancer as well as a variety of other diseases like heart disease, autoimmune diseases, etc.
The book is divided into two sections. In the first section Campbell discusses experiments he performed on rats showing that after exposure to aflatoxin (a cancer-causing mold) rats fed a high protein diet suffered from more cancer than a low protein diet. A very interesting series of experiments.
However it’s important to recognize that the animal protein that Campbell fed the rats was casein. Casein is one of the protein components of dairy and is not naturally found by itself.
Other research suggests that casein may indeed have a positive (bad) influence on cancers, but that whey the other component of dairy products has a strong negative influence (good).
Campbell incorrectly than goes on to label all animal protein bad from his experiments with one particular type of non-naturally occurring protein, isolated casein. And he generalizes from rats to humans which is very often done in the scientific literature and needs to be taken with a large “grain of salt”. Rats are not furry little humans. While we share many of the same genes, enzymes and biochemistry as rats, we also have a number of important differences which means what is good (or bad) for rats is not always good (or bad) for humans.
In the second part of the book, Campbell then draws on the mass of data gathered in the China Study to prove his assumption that animal protein does “very bad things.”
Since 2005 a small minority have argued that Campbell “cherry picked” the data from the China study to prove his point. That is he started with the conclusion he wanted, that animal protein caused disease, and then sifted through the data looking for data that would prove his point. A new critique shows that he did that very thing.
Richard Nikoley who runs the popular blog ‘Free the Animal’ broke the story to the blogosphere that a blogger named Denise Minger, a self-proclaimed statistics nut, had set out to see if the conclusions of the China Study (the book) were true.
She took the data from “the China Study” (the study, no the book) and began laboriously combing through it to see if the assertions that animal protein causes disease were true. After analyzing the data her conclusion is that Campbell’s conclusion is WRONG.
HERE is her site, where you can read her critique of the China Study as well as some other excellent blog posts. I highly recommend you check it out.
Even more interesting, Minger turned up a STRONG correlation between wheat consumption and heart disease. That is, those people who ate the most wheat suffered from more heart disease. Something that was never mentioned in the China Study (the book).
It’s also important to recognize that the data itself while interesting is only of limited value.
The China Study (the study) is what is known as an epidemiological study, that information was gathered on people and statistical analysis were done to attempt to attempt to relate various factors gathered (in this case what they ate) and diseases they suffered or died from.
As Michael Pollan talks about, this is the most common form of nutritional study performed. Almost without exception when you see a new headline saying “X food linked to Y condition” it is an epidemiological study. Examples include headlines like “processed meat intake linked to increased risks of cancer” and “eggs associated with increased OR not associated with increased risk of…” Epidemiologic studies can show correlation not causation.
Correlation means two things appear together, but cannot say whether one thing causes another thing. Causation implies that one thing causes another.
If I hold a match under my finger it burns my finger, that is causation. Correlation is an entirely different thing. For example we can do a study of obese people and find that increasing belt size is related to obesity, that is, the fatter the person is, the larger the belt they wear. And the statistical significance is very strong, it holds true in almost every case. This would be a correlation between obesity and belt size.
The mistake that is made in the nutritional community would be to imply correlation between the two, that is, the bigger the belt a person chooses to wear the fatter they will become (belts cause obesity). This sounds absurd and it is. But when we look at epidemiologic data (which is the majority of nutritional research currently being done) and we see that consumption of X and disease Y have been correlated with one another we want to jump to the conclusion that X causes Y and the data cannot tell us that. Epidemiologic data is a useful starting point. We can take it, look at correlations that emerge from the data, and then design more focused studies to actually try to test causation.
This concept is crucial to understand because it helps you to make sense of what you see on the Internet and in the news.
If you’re still not clear on the difference between correlation and causation and why it’s so important to make the distinction I recommend you check out the following post.
We owe Denise Minger a debt of gratitude for slogging through reams of data to look at it with an unbiased eye. What she shows is that even the correlations that Campbell drew from the China Study are wrong. If eating vegan/vegetarian works for you, than keep at it, but please stop pointing to the China Study (the book) to justify yourself. It’s wrong. But don’t believe what someone else says, go check it out yourself.
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Another Interesting post: