Red Meat Consumption and Mortality
Last week, I coincidentally posted on the health benefits of grass-fed beef, with my conclusion basically stating that grass-fed, pastured beef and grain-fed beef are so vastly different in their nutritional values, that they cannot realistically be considered the same food source. Yet after reading the recent article “Red Meat Consumption and Mortality”1 which is all over the news, and seeing my inbox full of about 100 emails, I quickly thought to myself: “Here we go again”.
More food-frequency questionnaires, biases, lumping of foods together, and then placing the cross-hairs on red meat.
The gist of this study is that the more meat you eat, the larger your mortality risk. In fact, they state that 9.3% of deaths in men and 7.6% in women could be prevented by consuming a half less per serving of red meat per day. The strength of this study is a large population of participants, and I admire the authors’ honesty in admitting their results may be a little shaky (in the manuscript though, not the abstract). However there are some definite strikes against the study, and therefore basing important dietary decisions based on this data is unwise (to me). Unfortunately every media outlet has blindly accepted their results and conclusions, spreading the claim red meat is harmful.
In this study, the authors assessed dietary intake of meat in participants of the Health Professionals Follow-up Study through food frequency questionnaires (FFQ) with:
“In each FFQ, we asked the participants how often, on average, they consumed each food of a standard portion size. There were 9 possible responses, ranging from “never or less than once per month” to “6 or more times per day.” Questionnaire items about unprocessed red meat consumption included “beef, pork, or lamb as main dish” (pork was queried separately beginning in 1990), “hamburger,” and “beef, pork, or lamb as a sandwich or mixed dish.” The standard serving size was 85 g (3 oz) for unprocessed red meat. Processed red meat included “bacon” (2 slices, 13 g), “hot dogs” (one, 45 g), and “sausage, salami, bologna, and other processed red meats” (1 piece, 28 g).”
STRIKE 1: Lumping many food-types together, even though in practice they are very different.
This may be obvious to many of you, but clearly the nutritonal benefit (or detrimental effect) of a hotdog full of processed meat and nitrates is NOT THE SAME as grass-fed beef. Similarly, processed bacon made from chronically-stressed pigs (see my previous posts on the living conditions of animals in industrial farms) is NOT THE SAME as ethically and responsibly raised pork. All meat is not created equal, so to study industrially produced meat products and apply that data to free-range, grass-fed meat is scientifically false.
STRIKE 2: Basing an entire study on food questionnaires and self-reporting, both recording methods fraught with error and bias.
Shortly after explaining this, the authors state that validity of these questionnaires have been described elsewhere, referring to several papers which analyzed these techniques2,3. One thing they fail to mention is that these same techniques have been intensely criticized for years for inadequacy and inaccuracy4-6, even including an article titled “Is It Time to Abandon the Food Frequency Questionnaire?7”, yet they base their strong conclusions on them. Self-reporting is also fraught with biases8 as those in poorer health may attribute their issues to those health hazards which have been preached to them their entire life, i.e. red meat is the cause of all evil…
Also, while studies show that the drastic differences in food consumptions throughout the year as seasons and growing periods change requiring data to be collected several times throughout the year for adequacy9,10, yet in this study, not only was information not collected multiple times throughout the year, but it wasn’t even collected on a yearly basis. Data was in fact only collected in 1986, 1990, 1994, 1998, 2002, and 2006. Also, when they asked patients questions regarding their diet and they failed to answer, here is how they solved this problem:
“We replaced missing values in each follow-up FFQ with the cumulative averages before the missing values.”
STRIKE 3: Using a poor method of dietary recording and assessment to begin with, and adding insult to injury by further inadequate methods of data collection.
While the food-frequency questionnaires and the averaging of missed values alone will cause significant errors in recording and calculations, lumping all of these meat types together would be the equivalent of grouping leafy green vegetables and bold-colored fruits packed with antioxidants with grain-based bread and pasta then quoting the ill-effects of all plant-based products. Also, while more time-consuming, I like when patients update their diet in real-time via web sites like FitDay, at which point I can discuss the diets with them to see what they are eating, as opposed to a questionnaire where they try to piece together what they ate over the last year, not to mention a site like Fit Day has actual nutritional values for each type of food, and doesn’t lump a large mixture of very different food types as one, per say “red meat”. Finally, they also claim to adjust for nutrients, yet the nutrients in grass-fed and wild animals (all red meat) are several times higher than those of grain-fed beef. This was neither accounted for nor discussed in the article.
STRIKE 4: Finding out there may be several other factors in these groups that could lead to your findings and then using the magic wand of “controlling” for these variables to make them disappear.
They also do state that:
“Men and women with higher intake of red meat were less likely to be physically active and were more likely to be current smokers, to drink alcohol, and to have a higher body mass index”
They note that these participants also ate less vegetables. This is often the case in these studies – several other variables stand in the way of the findings you want. What usually happens in statistics is that you somehow account or control for this, which has been aptly labeled “statistical alchemy”, or making gold out of sub-par results.
Red meat (and not accounting for whether it is grain, hormone, pesticide, and antibiotic-laden), using poor recording mechanisms, inherent biases, and intricate statistical methods, as well as being physically inactive, drinking more alcohol, smoking, and being overweight show that there may be an increase in mortality. Frankly, the techniques in the article above have been criticized a thousand times over in past studies that have used similar methods.
Cutting out red meat, especially healthy, nutrient-dense grass-fed beef and free-ranging animals will only lead to their replacement with more carbohydrate sources, including the basis of the food pyramid: grains. We can use all the poor reporting methods and intricate calculations possible to prove otherwise, but thirty years of skyrocketing rates of obesity, diabetes, and chronic disease have shown us the results of these dietary recommendations, yet we continually cling to them.
It’s time for the medical field to admit our mistakes and move on.
1. Pan A, Sun Q, Bernstein AM, et al: Red Meat Consumption and Mortality: Results From 2 Prospective Cohort Studies. Arch Intern Med:archinternmed.2011.2287, 2012
2. SALVINI S, HUNTER DJ, SAMPSON L, et al: Food-Based Validation of a Dietary Questionnaire: The Effects of Week-to-Week Variation in Food Consumption. International Journal of Epidemiology 18:858-867, 1989
3. Hu FB, Rimm E, Smith-Warner SA, et al: Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. The American journal of clinical nutrition 69:243-249, 1999
4. Schaefer EJ, Augustin JL, Schaefer MM, et al: Lack of efficacy of a food-frequency questionnaire in assessing dietary macronutrient intakes in subjects consuming diets of known composition. The American journal of clinical nutrition 71:746-751, 2000
5. Kipnis V, Midthune D, Freedman L, et al: Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutrition 5:915-923, 2002
6. Kipnis V, Subar AF, Midthune D, et al: Structure of Dietary Measurement Error: Results of the OPEN Biomarker Study. American Journal of Epidemiology 158:14-21, 2003
7. Kristal AR, Peters U, Potter JD: Is It Time to Abandon the Food Frequency Questionnaire? Cancer Epidemiology Biomarkers & Prevention 14:2826-2828, 2005
8. Adams A, Soumerai S, Lomas J, et al: Evidence of self-report bias in assessing adherence to guidelines. International Journal for Quality in Health Care 11:187-192, 1999
9. Capita R, Alonso-Calleja C: Differences in reported winter and summer dietary intakes in young adults in Spain. International Journal of Food Sciences and Nutrition 56:431-43, 2005
10. Joachim G: The influence of time on dietary data: differences in reported summer and winter food consumption. Nutrition and health 12:33-43, 1997
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