Conclusions
Despite the clear limitations of the existing literature, most organizations that make dietary recommendations for health take the view that they must make recommendations using the best data available and usually disregard the weaknesses of the data upon which the recommendations are formulated.
Therefore, it is incumbent upon scientists to be familiar with the literature that links any dietary component with either beneficial or adverse effects.
Because there are many thousands of papers on this topic, it is impossible to review them all, so this review focused mostly on compiled meta-analyses.
But it needs to be stated that a meta-analysis is only as strong as the studies it summarizes; the computer-speak expression of garbage in, garbage out is particularly relevant to this discussion.
Strong conclusions cannot spring from weak data.
In addition, both the confidence in results and the ability to generalize to all situations is highly subject to the heterogeneity of those results across individual studies that are included and to which confounders are adjusted for in individual studies, which are rarely the same from study to study.
In fact, among meta-analysts, one of the more important factors calculated is I2, the indicator of heterogeneity, and in most diet studies that even include this information, this value leads to considerable uncertainty about conclusions.
Two well-regarded biostatisticians summarized some of problems inherent in observational studies (Young & Karr, 2011).
In their commentary, they listed nutritional factors associated with improved health in epidemiological studies that were later subjected to randomized controlled trials (RCTs) including vitamin E, beta-carotene, low fat diet, calcium and vitamin D, folate and other B vitamins for reduction of heart disease, vitamin C, and selenium.
Twelve clinical trials tested 52 observational claims and not a single observational claim could be replicated in a RCT.
In fact, five of the claims resulted in statistically significant results in the opposite direction from what was observed in the epidemiological studies.
Such a high false discovery rate should temper conclusions from epidemiologic studies, even when repeated observational studies have reported similar results.
A very insightful commentary on the limitations of epidemiology for linking observational data with health outcomes pointed out that epidemiologists and health scientists are different people with different training so that, in the words of the authors, an epidemiologist is essentially an engineer but devotes little thought to the nature of inquiry or scientific truth while the health scientist has not even a basic understanding of epidemiological principles
(Phillips & Goodman, 2006).
This is particularly important in the epidemiology of health because of the complicated models used to interpret data.
The authors of this commentary lamented the lack of interest among epidemiologists in rigorously challenging conclusions and they are far from the only ones critical of such an approach.
Even cancer researchers who have spent years attempting to identify protective factors in food have commented in print that the inability of nutritional epidemiology to identify chemoprotective factors is not simply a problem of quantitation but that the discipline is qualitatively incapable of providing that information (Meyskens & Szabo, 2005).
The rationale is that individual compounds are falsely identified as active agents in which multiple agents or multiple interacting regulatory elements are responsible for the biological effect.
These authors warned that larger cohorts are not able to resolve this issue because of the biases in such studies.
Two recent studies in large cohorts assessed dietary patterns in association with risk of total mortality and cancer incidence or mortality came to similar conclusions — following a recommended dietary pattern in observational studies was associated with similar reductions in any major cause of mortality of cancer incidence (Harmon et al., 2015 and Kabat et al., 2015).
What is abundantly clear in both cohorts is that those participants with the higher diet quality scores measured in one of five different scoring systems, included lower amounts of red or processed meats, but also more fruits, vegetables, whole grains, fish, nuts, legumes, polyunsaturated fats and dairy but less refined grains, sodium, sugar-sweetened beverages and total energy. Harmon et al. (2015)
analyzed data from the Multiethnic Cohort that included over 215,000 male and female subjects who completed a single FFQ and were followed for 13–18 years.
Each of four different indexes of dietary quality was associated with similar RR of 0.64–0.84 in total mortality and mortality from cancer or cardiovascular disease, all of which were significant reductions.
These dietary index scores also correlated with body mass index, age, physical activity, education, smoking, and hormone replacement therapy in women.
Given the large number of covariates, there are undoubtedly others that were not measured so that the confidence in dietary quality score being an independent, causal factor is necessarily low.
In the study from Kabat et al. (2015), over 476,000 adults in the NIH-AARP Diet and Health Study aged 50–71 years at recruitment, completed an FFQ at baseline and were followed for 10–14 years.
This study assessed adherence to American Cancer Society prevention guidelines and related that to cancer incidence, cancer mortality, and total mortality.
Higher adherence was associated with RR of 0.90 in men and 0.81 in women for all cancers combined and 14 of 25 cancer sites showed a significant reduction in risk with higher adherence to the guidelines.
Mortality from cancer had a RR of 0.75 and 0.76 in men and women, and total mortality RR was 0.74 and 0.67 in men and women, respectively.
Not all the same individual dietary factors were reported in this study but diet quality correlated with body mass index, physical activity alcohol intake, and smoking.
Given that obesity and smoking account for large proportions of all cancers, these two factors may be of greater importance than diet.
Of course, whether such relatively small differences in relative risk are truly a signal above the noise of observational studies cannot be determined.
It also needs to be clear that other health habits have been associated with similar reductions in disability and mortality that omit specific foods or nutrients.
This was shown in a U.S. cohort for the following seven health practices: excessive alcohol consumption, smoking cigarettes, being obese, sleeping fewer or more than 7–8 h, having very little physical activity, eating between meals, and not eating breakfast (Breslow & Breslow, 1993).
The higher the number of habits, the lower the disability and mortality.
This finding strongly suggests there are clusters of health habits not normally picked up in most observational studies of diet and health that likely impact the results among those reporting the lowest intake of meat.
Although challenges to current dogma, reproducibility, and refinement of methods are the lifeblood of progress in most fields of scientific inquiry, there is resistance to this from a large segment of researchers debating the discipline of nutritional epidemiology.
A provocative view of the field was summarized when two investigators selected 50 common ingredients from random recipes in a cookbook, searched the medical literature for studies that evaluated the relation of those ingredients to risk of cancer, and found that 80% of the ingredients were associated with either an increased (n = 103) or decreased (n = 88) risk of cancer (Schoenfeld & Ioannidis, 2013).
Meta-analyses of this literature resulted in greatly attenuated risk of cancer being associated with these ingredients; median RR from individual studies were 2.20 and 0.52 for those associated with increased or decreased risk, respectively, but formal meta-analysis yielded a RR of 0.96 with an interquartile range of 0.85–1.10.
The majority of the nine considerations Hill enumerated in 1963 for determining a causal relationship from observational studies have not been fulfilled for meat and any adverse health outcome; although there is no minimum number needed, when only a minority of factors are satisfied the confidence in the relationship being independently causal is necessarily low.
Hill gave examples near the end of his speech that fair evidence would be sufficient to take action on an occupational hazard such as a probably carcinogenic oil in a limited industrial environment but that very strong evidence would be needed to stop eating the fats and sugar that people like.
It is almost impossible to prove a null effect for any dietary component so there will certainly never be a definitive study that clarifies this situation.
This relegates scientists on opposite sides of this issue to continue generating more studies of similar nature to what has been published.
The only way to make progress in this field is to develop entirely new objective methods to accurately capture long-term intake of foods and nutrients which will be aligned with medical and demographic information.
Only then will we be able to begin to tease out whether or not there is truly a causal relationship of meat intake with any adverse health outcome.
Until that time, the non-controversial advice of consuming a balanced diet in moderation approximates the best that can be offered.