Characterizing the potential health effects of exposure to risk factors such as red meat consumption is essential to inform health policy and practice. Previous meta-analyses evaluating the effects of red meat intake have generated mixed findings and do not formally assess evidence strength. Here, we conducted a systematic review and implemented a meta-regression— relaxing conventional log-linearity assumptions and incorporating between-study heterogeneity—to evaluate the relation-ships between unprocessed red meat consumption and six potential health outcomes. We found weak evidence of association between unprocessed red meat consumption and colorectal cancer, breast cancer, type 2 diabetes and ischemic heart disease. Moreover, we found no evidence of an association between unprocessed red meat and ischemic stroke or hemorrhagic stroke. We also found that while risk for the six outcomes in our analysis combined was minimized at 0 g unprocessed red meat intake per day, the 95% uncertainty interval that incorporated between-study heterogeneity was very wide: from 0–200 g d−1. While there is some evidence that eating unprocessed red meat is associated with increased risk of disease incidence and mortality, it is weak and insufficient to make stronger or more conclusive recommendations. More rigorous, well-powered research is needed to better understand and quantify the relationship between consumption of unprocessed red meat and chronic disease.

Full Paper - https://doi.org/10.1038/s41591-022-01968-z

  • jetOPMAEnglish
    2·
    2 days ago

    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.

    • Onomatopoeia@lemmy.cafeEnglish
      3·
      2 days ago

      The medical community is finally starting to notice that glucose instability is causal to cardiovascular conditions, after all we just had 40 years of denigrating meat and fat, promoting high carb diets, ending with a major increase in T2D and cardiovascular disease.

      I mean for crying out loud it’s right in our faces, yet some biochemists were making this very point by the early 90’s.

      Even worse, doctors who treated diabetes (T1) in the early 20th century already understood reducing simple carbs and increasing fat and protein was the way to help manage T1.

      Thanks for the links

      • jetOPMAEnglish
        2·
        2 days ago

        https://vizhub.healthdata.org/burden-of-proof/

        Even the associative papers get the risk factors super low for glucose. The researchers forming the questions on a observational data set are as important as the data itself.

        Even worse, doctors who treated diabetes (T1) in the early 20th century already understood reducing simple carbs and increasing fat and protein was the way to help manage T1.

        Yeah, it’s crazy, we had the Banting diet in 1863!!! And somehow totally forgot about it during the T2D epidemic.

        https://en.wikipedia.org/wiki/William_Banting#Weight_loss_diet