Observational data, FFQs, epidemiology cannot establish causation only correlation, it's hypothesis generating.
They corrected for confounders, which assumes they can establish a causal correction factor (pre-supposing the solution to the research question) - Age, sex, geographic region, recruitment method, education, living with a partner, ethnicity, height, smoking status, smoking intensity, tobacco chewing (CARRS-1 only), alcohol intake, physical activity, BMI, diabetes history, parity, hormone replacement therapy use, age at menarche (female-specific cancers), age at first birth (female-specific cancers), menopausal status (female-specific cancers), oral contraceptive use (female-specific cancers), PSA screening history (prostate cancer).
However, with all of that, they did not control for carbohydrates or underlying metabolism.
They included the 7da health studies that define vegetarian as meat at most once per week, and vegan as at most once per month... but didn't mention that in their definition section.
They exclude datasets because of low adherence to a vegetarian diet, then at the end applaud the vegetarians for having a high adherence rate. You can't do both in good faith.
They keep confusing causal statements in the paper, where they also caution against making causal conclusions... they keep using risk, when they need to say association.
It's a interesting dataset, i would recommend only looking at the hr forest plots and not the opinion around it.
If red meat gives you colon cancer, why do vegans have a higher association when compared to omnivores?
They spend alot of time speculating about that, calcium, micronutrients, etc, etc, they never mention sugar, and carbs as possible factors.
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