|Posted by Chad on October 11, 2010 at 9:57 AM|
"Models with more variables
So apparently comparing wheat + one other independent variable isn’t enough to explain the Wheat Effect. Not even a little bit. But maybe, just maybe, a bigger combination of variables will do the trick. Perhaps wheat-eating regions just host a collection of heart-harming factors (low folate, low vitamin D, low EFAs, and so forth) that, together, are more powerful predictors of disease than the variable wheat.
Here are the variables I’m interested in looking at. Some could be causative and some could be preventative:
Incidentally, one model had the best fit out of all the others for explaining heart disease:
1.Wheat consumption (r = 0.62, p<0.001)
2.Apolipoprotein B (r = 0.38, p<0.001)
3.Total cholesterol (r = -0.22, p<0.05)
Note that the number for total cholesterol is inverse, meaning higher cholesterol was associated with less heart disease—at least in this specific model. Unless you’re an Ancel Keys groupie, this may actually be quite plausible.
Anyway, here’s the important point. No matter what variables I adjust for, I can’t make the correlation between wheat flour and heart disease go away. Sorry, wheat! Neener neener..."
So, here again, another scientist going over numbers brought about by a the China Study, and how the numbers from it's own study, provide basis for a strong case for not eating wheat!
Read for yourself in the above link.