The observational weak relative risk studies can be pumped out infinitely, they do not inform on cause and effect sadly. The best data we have of prewesternized cultures eating meat heavy diets shows no incidence of cancer in these populations (first tribe nomads, inuit, etc). Humans have been eating meat for at least 2.5 million years, yet cancer has only jumped up to the epidemic it is today in the last 150 years. Something in the environment and diet has changed, absolutely. What is the causative factor? The anti-meat papers with weak relative risk tells me that its not the meat, we should be looking for a very strong signal (50% of people born today will have cancer in their lifetimes - 150 years ago basically nobody got cancer).

I could speculate, and I have my own theories, but we are looking for a significant change in the last 150 years as our culprit. Meat is not a new invention. Processed food, fructose, sugar, industrial food oils, pesticides in the food supply - all have bloomed in the last 150 years, I would hazard a guess and say these are the real harbingers of modern disease we need to focus on. Curiously these epidemiological food questionnaire papers don’t look at these factors, maybe because its hard to fill in a survey for sugar (its in all processed food).

Interactive risk curves for the burden of proof inclusion. https://vizhub.healthdata.org/burden-of-proof/

Notes:

One issue involves the assumption of log-linearity, which requires that the hazard ratio for a fixed increment of red meat consumption (for example, 100 g d−1) remains constant across all levels of intake (an increase in consumption from 0 to 100 g d−1 would have the same effect as an increase from 200 to 300 g d−1 ). Yet evidence indicates that the dose–response relationship for many risk factors attenuates at higher doses17,18 (not log linear).

Another notable issue is that meta-analyses attempting to synthesize findings from cohort studies typically do not account for between-study heterogeneity, which can be a prominent source of bias in epidemiological meta-analyses

In most studies (45 of 55), RRs were adjusted for major confounders including age, sex and smoking

This really should include sugar consumption.

The fact there is a weak association between type 2 diabetes and red meat consumption should tell you everything you need to know about healthy user bias in a nutshell. Type 2 Diabetes is a condition of carbohydrate intolerance / overload. Red meat does not increase blood glucose to any meaningful degree. Red meat is not a independent risk factor for T2D. However, the population who eats higher levels of red meat typically does so in the context of a high carbohydrate diet (burgers and fries for example), which is a massive causal factor for T2D.

A key finding of our analysis is that there is substantial between-study heterogeneity and uncertainty for all six risk–outcome pairs included. This may partly reflect the high degree of heterogeneity often present in data sources used for dietary analysis, which typically comprise observational studies. This heterogeneity limited the sensitivity of our analysis to identify clear—and potentially clinically important—relationships between intake and disease end points. Although visual inspection of the mean risk functions suggests a positive (harmful) relationship between unprocessed red meat intake and colorectal cancer, type 2 diabetes, IHD, ischemic stroke and breast cancer and a negative (protective) relationship with hemorrhagic stroke, the large degree of heterogeneity present, coupled with the moderate mean effects, generated wide UIs for the mean risk functions.