Meta-analysis2015Industry fundedOpen access

Red Meat and Colorectal Cancer: A Quantitative Update on the State of the Epidemiologic Science

Alexander DD, Weed DL, Miller PE, Mohamed MA

Journal of the American College of Nutrition · 67 citations

Review labels

Industry funded

Neutral facts our review recorded about how this study was done. They describe method, never whether we like the result.

How it was studied

Design
Meta-analysis (indexed by PubMed)
Studied in
People
Main outcome
Clinical events such as disease or death
Intake measured by
Not stated

Who paid for it

Funding
Industry funded

Based on full-text disclosure statement.

Publication

Published
2015-05-05 · J Am Coll Nutr · vol. 34 · issue 6 · pp. 521–543
Publisher
Taylor & Francis
Cited
110 citations · more than 99% of similar papers · 14.9× the field average
Impact
Top 10% most cited in its field
References
70 works
Access
Free to read
Research areas
Nutritional Studies and Diet · Colorectal Cancer Screening and Detection · Consumer Attitudes and Food Labeling
Keywords
Red meat, Colorectal cancer, Meta-analysis, Relative risk, Medicine, Random effects model, Prospective cohort study, Cohort study, Cancer, Environmental health, Demography, Publication bias, Confidence interval, Internal medicine, Pathology
MeSH
humans, colorectal neoplasms, diet, risk, risk factors, case-control studies, cohort studies, prospective studies, health status, medline, female, male, red meat

4 authors

From US

  • Dominik D. Alexander · correspondingMichigan United
  • Douglas L. Weed
  • Paula E. MillerMichigan United
  • Muhima A. Mohamed

Abstract

The potential relationship between red meat consumption and colorectal cancer (CRC) has been the subject of scientific debate. Given the high degree of resulting uncertainty, our objective was to update the state of the science by conducting a systematic quantitative assessment of the epidemiologic literature. Specifically, we updated and expanded our previous meta-analysis by integrating data from new prospective cohort studies and conducting a broader evaluation of the relative risk estimates by specific intake categories. Data from 27 independent prospective cohort studies were meta-analyzed using random-effects models, and sources of potential heterogeneity were examined through subgroup and sensitivity analyses. In addition, a comprehensive evaluation of potential dose-response patterns was conducted. In the meta-analysis of all cohorts, a weakly elevated summary relative risk was observed (1.11, 95% CI: 1.03-1.19); however, statistically significant heterogeneity was present. In general, summary associations were attenuated (closer to the null and less heterogeneous) in models that isolated fresh red meat (from processed meat), adjusted for more relevant factors, analyzed women only, and were conducted in countries outside of the United States. Furthermore, no clear patterns of dose-response were apparent. In conclusion, the state of the epidemiologic science on red meat consumption and CRC is best described in terms of weak associations, heterogeneity, an inability to disentangle effects from other dietary and lifestyle factors, lack of a clear dose-response effect, and weakening evidence over time. KEY TEACHING POINTS: •The role of red meat consumption in colorectal cancer risk has been widely contested among the scientific community.•In the current meta-analysis of red meat intake and colorectal cancer, we comprehensively examined associations by creating numerous sub-group stratifications, conducting extensive sensitivity analyses, and evaluating dose-response using several different methods.•Overall, all summary associations were weak in magnitude with no clear dose-response patterns.•Interpretation of findings from epidemiologic studies investigating diet and health outcomes involves numerous methodological considerations, such as accurately measuring food intake, dietary pattern differences across populations, food definitions, outcome classifications, bias and confounding, multicollinearity, biological mechanisms, genetic variation in metabolizing enzymes, and differences in analytical metrics and statistical testing parameters.

Abstract via Europe PMC. Copyright remains with the authors or publisher.

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