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 citas
Etiquetas de revisión
Financiado por la industriaDatos neutrales que nuestra revisión registró sobre cómo se hizo este estudio. Describen el método, nunca si nos gusta el resultado.
Cómo se estudió
- Diseño
- Metaanálisis (indexado por PubMed)
- Estudiado en
- Personas
- Resultado principal
- Eventos clínicos como enfermedad o muerte
- Consumo medido con
- No se indica
Quién lo pagó
- Financiación
- Financiado por la industria
Según la declaración de financiación del texto completo.
Publicación
- Publicado
- 2015-05-05 · J Am Coll Nutr · vol. 34 · número 6 · págs. 521–543
- Editorial
- Taylor & Francis
- Citado
- 110 citas · más que el 99% de estudios similares · 14.9× el promedio de su campo
- Impacto
- Entre el 10 % más citado de su campo
- Referencias
- 70 trabajos
- Acceso
- Lectura gratuita
- Áreas de investigación
- Nutritional Studies and Diet · Colorectal Cancer Screening and Detection · Consumer Attitudes and Food Labeling
- Palabras clave
- 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 autores
- Dominik D. Alexander🇺🇸 · autor de correspondenciaMichigan United
- Douglas L. Weed
- Paula E. Miller🇺🇸Michigan United
- Muhima A. Mohamed
Resumen
El texto original del estudio, en el idioma en que se publicó.
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.
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