Effect of Lower Versus Higher Red Meat Intake on Cardiometabolic and Cancer OutcomesFREE
- Correction(s) for this article:
- CorrectionsFeb 2020
This article has been corrected. The original version (PDF) is appended to this article as a Supplement.
Few randomized trials have evaluated the effect of reducing red meat intake on clinically important outcomes.
To summarize the effect of lower versus higher red meat intake on the incidence of cardiometabolic and cancer outcomes in adults.
EMBASE, CENTRAL, CINAHL, Web of Science, and ProQuest from inception to July 2018 and MEDLINE from inception to April 2019, without language restrictions.
Randomized trials (published in any language) comparing diets lower in red meat with diets higher in red meat that differed by a gradient of at least 1 serving per week for 6 months or more.
Teams of 2 reviewers independently extracted data and assessed the risk of bias and the certainty of the evidence.
Of 12 eligible trials, a single trial enrolling 48 835 women provided the most credible, though still low-certainty, evidence that diets lower in red meat may have little or no effect on all-cause mortality (hazard ratio [HR], 0.99 [95% CI, 0.95 to 1.03]), cardiovascular mortality (HR, 0.98 [CI, 0.91 to 1.06]), and cardiovascular disease (HR, 0.99 [CI, 0.94 to 1.05]). That trial also provided low- to very-low-certainty evidence that diets lower in red meat may have little or no effect on total cancer mortality (HR, 0.95 [CI, 0.89 to 1.01]) and the incidence of cancer, including colorectal cancer (HR, 1.04 [CI, 0.90 to 1.20]) and breast cancer (HR, 0.97 [0.90 to 1.04]).
There were few trials, most addressing only surrogate outcomes, with heterogeneous comparators and small gradients in red meat consumption between lower versus higher intake groups.
Low- to very-low-certainty evidence suggests that diets restricted in red meat may have little or no effect on major cardiometabolic outcomes and cancer mortality and incidence.
Primary Funding Source:
None (PROSPERO: CRD42017074074).
Observational studies have reported that intake of red meat is associated with cardiometabolic disease and cancer (1–8). Dietary guidelines from the United States, United Kingdom, and the World Cancer Fund/American Institute for Cancer Research recommend limiting intake of red and processed meat (8–10). Such recommendations are primarily based on observational studies that are at high risk for confounding.
Randomized trials generally provide higher-certainty evidence supporting causal relationships (11, 12). The few systematic reviews of trials addressing red meat consumption have evaluated only surrogate outcomes, such as blood pressure and lipid levels (13–15).
In this systematic review of randomized trials, we investigate the effect of lower versus higher red meat intake on the incidence of major cardiometabolic and cancer outcomes. The review was performed by the Nutritional Recommendations (NutriRECS) working group as part of a new initiative to develop trustworthy guideline recommendations in nutrition (16). In addition to this review, we performed 4 parallel systematic reviews that focused on observational studies addressing the effect of red and processed meat consumption on cardiometabolic and cancer outcomes (17–19), and a review of health-related values and preferences related to meat consumption (20). These reviews were used to underpin guideline recommendations for consumption of red and processed meats (21).
We registered the systematic review protocol in PROSPERO (CRD42017074074) on 10 August 2017 (22).
Data Source and Searches
We searched MEDLINE, EMBASE, CENTRAL (Cochrane Central Register of Controlled Trials), CINAHL (Cumulative Index to Nursing and Allied Health Literature), and the Web of Science from inception until July 2018, and MEDLINE from inception through to April 2019, with no restrictions on language or date of publication (Section I of the Supplement). We also searched ProQuest Dissertations and Theses Global (1989 to 2018); trial registries, including ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform Search Portal, to April 2019; and bibliographies of eligible studies and relevant systematic reviews.
We included English-language and non–English-language reports of randomized trials of adults allocated to consume diets that included varying quantities of unprocessed red meat (measured as servings or times/week, or as g/d) or processed meat (meat preserved by smoking, curing, salting, or adding preservatives) for 6 months or more (23). Eligible trials compared diets lower in red or processed meat with diets higher in red or processed meat that differed by a gradient of at least 1 serving per week (Table 1). If a trial reported more than 2 study groups (24, 25), we used the groups with the largest gradient in red meat intake or combined groups if red meat intake was equal. Studies in which more than 20% of the participants were pregnant or had cancer or a chronic health condition, other than cardiometabolic diseases, were excluded.
Outcomes of interest, which were determined a priori and in consultation with the guideline panel, were all-cause mortality, cardiovascular mortality, adverse cardiometabolic events and major morbidity, cancer mortality and incidence, quality of life, and surrogate outcomes (weight, body mass index, blood lipid levels, blood pressure, and hemoglobin level) (22). Pairs of reviewers screened titles and abstracts for initial eligibility and reviewed the full text of potentially eligible studies, independently and in duplicate. Reviewers resolved disagreements by discussion and third-party adjudication if needed.
Data Extraction and Quality Assessment
Using standardized, piloted forms, pairs of reviewers conducted calibration exercises and independently extracted information on study design, participant characteristics, interventions, comparators, and outcomes of interest and resolved disagreement by discussion or, if necessary, third-party adjudication. When details related to methods or results were unavailable or unclear, we contacted study authors for additional information.
Reviewers, independently and in duplicate, assessed the risk of bias of eligible trials by using a modified version of the Cochrane Collaboration's risk of bias instrument for randomized trials (26–28). The modified version categorizes risk of bias as “definitely low,” “probably low,” “probably high,” or “definitely high” for each of the following domains: sequence generation, allocation sequence concealment, blinding, missing participant outcome data, selective outcome reporting, and other bias (for example, prematurely terminated studies). We resolved any disagreements by discussion or, if necessary, third-party adjudication. We collapsed ratings of “probably low” and “definitely low” into “low risk of bias” and ratings of “probably high” and “definitely high” into “high risk of bias.” Among the 8 risk of bias domains, we considered a study to be at high risk of bias if, at the outcome level, 2 or more domains were at high risk of bias (Section I of the Supplement).
Data Synthesis and Analysis
We reported risk ratios (RRs), hazard ratios (HRs), and mean differences (MDs) with their 95% CIs for the lowest versus highest category of red meat intake, at the last reported time point. We used the Hartung–Knapp–Sidik–Jonkman approach to pool data (29, 30). To calculate absolute risk differences, we multiplied the effect estimate for each outcome with the population risk estimates from the Emerging Risk Factors Collaboration study for cardiometabolic outcomes (31) or from GLOBOCAN for cancer outcomes (32, 33) and, when this was not available, the control group estimate from the largest study (Section I of the Supplement).
We investigated heterogeneity by using the Cochran Q test and the I2 statistic (34). We used R Project, version 3.3.0 (R Foundation for Statistical Computing), for all analyses.
To rate the certainty of the evidence for each outcome, we used the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach (11, 35–39). Reviewers, independently and in duplicate, assessed the certainty of evidence for each outcome, and resolved disagreements by discussion.
Role of the Funding Source
This systematic review was conducted without financial support.
Electronic searches yielded 13 190 unique articles (Appendix Figure). Of these, 24 articles (24, 25, 40–62) reporting on 12 unique randomized trials met eligibility criteria. In 2 instances, authors provided clarification about study characteristics or outcomes: Turner-McGrievy and colleagues (24) clarified the aggregated change in weight for vegan/vegetarian and semi-vegetarian/omnivorous groups, and Griffin and associates (44) clarified reported effect estimates.
Trials ranged in size from 32 to 48 835 participants (Table 1). The mean age of participants ranged from 22.4 to 70.9 years. The largest study, the Women's Health Initiative (WHI), enrolled postmenopausal women (45). Five trials, including the WHI, enrolled overweight and obese participants (24, 25, 41, 45, 59, 60); 5 focused on participants with medical conditions, such as diabetes or hypercholesterolemia (42, 43, 57, 58, 61); and 1 enrolled older (>64 years) healthy individuals (41). Only 1 trial explicitly reported participants' consumption of both unprocessed red meat and processed meat (62).
All trials used parallel designs, except for a small crossover trial in patients with hypercholesterolemia (57). Intervention and control diets varied widely. The primary protein intake in the low red meat group was from plant sources in 4 trials (40, 60, 58, 61); from animal protein sources in 5 trials (25, 43, 44, 57, 59); and from a mix of plant and animal protein in 3 trials (24, 41, 42). The largest trial, the WHI trial, compared a low-fat dietary intervention aimed at reducing total dietary fat to 20% with a usual diet group given diet and health-related materials (45–56). The duration of interventions ranged from 6 months (24, 25, 41, 59) to 12 years (51).
Risk of Bias
Trials were most often rated as high risk of bias for lack of blinding (not possible for participants) and missing outcome data overall (Supplement Table 1). However, some trials were rated as low risk of bias for specific outcomes (all-cause mortality, cardiovascular disease, type 2 diabetes, adenocarcinoma) because there were either more outcome events than missing data for dichotomous outcomes or there were less than 10% missing data for continuous outcomes. Selective reporting bias was detected in 4 trials (40, 42, 44, 57). Other biases included a nonpaired analysis of data from a crossover trial (57) and early termination for benefit in the Lyon Diet Heart Study (42).
None of the trials reported on a combination of fatal and nonfatal myocardial infarction, fatal infarction, nonfatal coronary heart disease, prostate cancer, and satisfaction with diet. Only 2 trials, the Lyon Diet Heart Study and the WHI trial (42, 54), addressed all-cause mortality and other patient-important, major morbid cardiovascular outcomes. The Lyon Diet Heart Study reported an implausibly large treatment effect, potentially due to stopping the trial early for benefit, and had a sample size (605 participants) more than 80 times smaller than the WHI trial (48 835 participants); for this reason the 2 trials were not pooled (63). Results presented below and in Table 2 regarding all-cause mortality and cardiovascular outcomes are based on the WHI trial results. Results of the Lyon Diet Heart Study are presented in Section II of the Supplement.
All-Cause Mortality and Cardiometabolic Outcomes
Low-certainty evidence from the WHI trial showed that a diet lower in red meat may have little or no effect on all-cause mortality (HR, 0.99 [95% CI, 0.95 to 1.03]) (54). The certainty of evidence was rated down for serious indirectness. The trial investigated reducing dietary fat intake, which led to reduction of red meat intake (rather than directly investigating reduction of red meat intake). Compared with the usual diet control group, the low-fat dietary intervention group reduced their consumption of red meat by about 20% (approximately 1.4 servings per week).
Evidence showing little or no effect on cardiovascular mortality (HR, 0.98 [CI, 0.91 to 1.06]), fatal and nonfatal cardiovascular disease (HR, 0.99 [CI, 0.94 to 1.05]), nonfatal myocardial infarction (RR, 1.05 [CI, 0.96 to 1.16]), fatal and nonfatal stroke (RR, 0.98 [CI, 0.89 to 1.07]), fatal stroke (HR, 0.97 [CI, 0.69 to 1.36]), nonfatal stroke (HR, 1.03 [CI, 0.90 to 1.17]), and risk for type 2 diabetes (HR, 0.96 [95% CI, 0.90 to 1.03]) was considered of low or very low certainty owing to indirectness, risk of bias, or imprecision (Table 2 and Supplement Table 2).
Because of risk of bias, imprecision, and serious indirectness, the WHI trial (53) provided very-low-certainty evidence that a diet lower in red meat may have little or no effect on cancer mortality (HR, 0.95 [CI, 0.89 to 1.01]) (Table 2). Similarly, the WHI trial provided very-low-certainty evidence that a diet lower in red meat may have little or no effect on colorectal, pancreatic, esophageal, and stomach cancer in women (51, 53, 55). This evidence was rated down to very low certainty owing to risk of bias, imprecision, or serious indirectness (Supplement Table 3). The WHI trial (46, 53, 55) also found that a diet lower in red meat may have little or no effect on the risk for invasive breast cancer (HR, 0.97 [95% CI, 0.90 to 1.04]); breast cancer mortality (HR, 0.91 [95% CI, 0.72 to 1.15]); or risk for gynecologic, ovarian, endometrial cancer, and ductal carcinoma in situ (Table 2 and Supplement Table 3). Such evidence was considered low or very low certainty owing to risk of bias, imprecision or serious indirectness (Supplement Table 3).
One trial of 2079 participants (58) provided very-low-certainty evidence (imprecision and serious indirectness) that a diet lower in red meat may have little or no effect on the risk for adenoma recurrence (HR, 1.04 [CI, 0.98 to 1.09]) (Supplement Table 3).
Quality of Life
The WHI trial (39 416 participants) provided very-low-certainty evidence, owing to risk of bias and serious indirectness, that a diet lower in red meat may have little or no effect on quality of life as measured by the RAND 36-Item Health Survey: general health (MD, 1.7 units [CI, 1.5 to 2.0 units]), physical functioning (MD, 2.0 units [CI, 1.7 to 2.3 units]), vitality (MD, 1.9 units [CI, 1.6 to 2.2 units]), and global quality of life (MD, 0.09 unit [CI, 0.07 to 0.12 units]) (45) (Supplement Table 4). The judgment of little or no effect is based on the minimal important difference estimates for the domain scores on the RAND-36 instrument, which range from 3.5 to 7, whereas the important difference for the global score is 1.7 (64).
Aside from a trivial effect on high-density lipoprotein (HDL) cholesterol based on 6 trials (2320 participants) (0.77 mg/dL [CI, 0.07 to 1.54 mg/dL]; 0.02 mmol/L [CI, 0.002 to 0.04 mmol/L]; I2 = 0%), low- to very low-certainty evidence suggests diets lower in red meat may have little or no effect on surrogate outcomes, such as cholesterol, weight, blood pressure, and hemoglobin (Supplement Table 4).
On the basis of evidence from 24 articles reporting on 12 randomized trials, our review shows that diets lower in red meat may have little or no effect on all-cause mortality, nonfatal cardiovascular disease, and diabetes (low-certainty evidence) and, although we are very uncertain, may have little or no effect on cancer mortality and incidence. Although no effect estimates for the major cardiometabolic or cancer outcomes met conventional criteria for statistical significance, 13 of 21 outcomes demonstrated a trivial to very small absolute risk reduction (range, 1 to 12 fewer events per 1000 persons over 8 to 17 years) in those who consume approximately 1 to 3 fewer servings of red meat per week. We found some improvements in quality of life and HDL cholesterol level, but the effects were very small: For HDL cholesterol level, the MD was 0.77 mg/dL (0.02 mmol/L), and for quality of life, the effects on the RAND-36 Health Survey ranged from 1.7 to 2.0 on 3 domains in which the minimally important differences ranges from 3.5 to 7.0.
Strengths of our review include adherence to a priori methods based on a registered protocol (22); a comprehensive search strategy without language restrictions; and inclusion of evidence on 8 cardiometabolic outcomes, 13 cancer outcomes, and 10 surrogate outcomes. We used explicit eligibility criteria, duplicate screening, abstraction of data, and risk-of-bias assessments with third-party adjudication of discrepancies and GRADE guidance to rate the certainty of evidence for each outcome.
Our review had limitations. First, many of the data were derived from a single large study in postmenopausal women: the WHI trial. Although 12 trials proved eligible, only 2 reported on the most patient-important outcomes—cardiovascular mortality and major morbidity, diabetes, and cancer mortality and incidence—and we considered only the WHI trial to have trustworthy results. Eleven studies proved at high risk of bias overall, primarily because of lack of blinding and substantial missing participant outcome data.
In addition, participants consuming alternative diets may have made different choices regarding smoking, exercise, or other lifestyle factors. In clinical trials of dietary interventions, particularly primary prevention trials, studies must follow participants for decades to capture important outcomes, such as cancer incidence (65). Of trials that met our eligibility criteria, only the WHI and the less trustworthy Lyon Diet Heart Study followed participants for 2 or more years. The choice to substitute red meat with poultry, fish, plant sources of protein, or whole or refined carbohydrates may result in different effects for some outcomes (66, 67). Thus, failure to demonstrate effects of decreased meat consumption may be related to trials' varying sources of protein replacement (for example, fish) in the diets lower in red meat (68). We had planned to address these issues through subgroup analyses (22, 69), but the paucity of trials made this impossible. The trials achieved only small differences between red meat intake in the intervention and control groups, equivalent to about 1 to 3 servings per week. In particular, the WHI study (61), on which we relied for our most important estimates, achieved a difference of 1.4 servings per week between the low-fat and the usual diet group (70). The failure to find differences in outcomes may be a result of the small gradient in red meat intake between the experimental and control groups. Had studies achieved larger gradients in consumption, researchers might have observed statistically significant and possibly an important effect on health outcomes.
Finally, only 1 study specified the proportion of red meat that was consumed as processed (42, 62). Observational studies have suggested that processed meat may have a larger adverse effect than unprocessed red meat (3, 6, 17, 19). Most red meat is, however, consumed as unprocessed (71), and our estimates therefore are likely to apply predominantly to red meat.
Our review of randomized trials relies largely on the WHI trial for estimates of effect on important major morbid cardiometabolic and cancer outcomes. Our results for surrogate outcomes are consistent with those of previous systematic reviews of trials, suggesting that red meat has little or no effect on blood pressure and blood lipids (13–15) (Supplement). Regarding important outcomes, systematic reviews of observational studies assessing diets that vary in red meat have, in contrast, reported positive associations between red meat intake and all-cause (6, 7, 19), cardiovascular (4, 6), and cancer (6, 17) mortality.
The discrepancy between results from randomized trials and observational studies may be explained by unadjusted confounders in the observational studies or by smaller gradients in red meat intake in trials and, thus, lower power, or the shorter follow-up in trials. Furthermore, compared with randomized trials, observational studies do not face the same limitations caused by poor adherence, missing end points, and financing, allowing investigators to better capture and evaluate important outcomes (such as cancer) that often take decades to develop (65).
Our results from the evaluation of randomized trials do not support the recommendations in the United Kingdom, United States, or World Cancer Research Fund guidelines on red meat intake (8–10). One could argue, however, that neither do they seriously challenge those recommendations: We found only low- to very-low-certainty evidence that diets lower in red meat compared with those higher in red meat have minimal or no influence on all-cause mortality, cancer mortality, cardiovascular mortality, myocardial infarction, stroke, diabetes, and incidence of gastrointestinal and gynecologic cancer. Our results highlight the uncertainty regarding causal relationships between red meat consumption and major cardiometabolic and cancer outcomes.
Song Y, Manson JE, Buring JE, et al. A prospective study of red meat consumption and type 2 diabetes in middle-aged and elderly women: the Women's Health Study. Diabetes Care. 2004;27:2108-15. [PMID: 15333470] CrossrefMedlineGoogle Scholar
Sinha R, Cross AJ, Graubard BI, et al. Meat intake and mortality: a prospective study of over half a million people. Arch Intern Med. 2009;169:562-71. [PMID: 19307518] doi:10.1001/archinternmed.2009.6 CrossrefMedlineGoogle Scholar
Micha R, Wallace SK, Mozaffarian D. Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis. Circulation. 2010;121:2271-83. [PMID: 20479151] doi:10.1161/CIRCULATIONAHA.109.924977 CrossrefMedlineGoogle Scholar
Abete I, Romaguera D, Vieira AR, et al. Association between total, processed, red and white meat consumption and all-cause, CVD and IHD mortality: a meta-analysis of cohort studies. Br J Nutr. 2014;112:762-75. [PMID: 24932617] doi:10.1017/S000711451400124X CrossrefMedlineGoogle Scholar
Bouvard V, Loomis D, Guyton KZ, et al; International Agency for Research on Cancer Monograph Working Group. Carcinogenicity of consumption of red and processed meat. Lancet Oncol. 2015;16:1599-600. [PMID: 26514947] doi:10.1016/S1470-2045(15)00444-1 CrossrefMedlineGoogle Scholar
Wang X, Lin X, Ouyang YY, et al. Red and processed meat consumption and mortality: dose-response meta-analysis of prospective cohort studies. Public Health Nutr. 2016;19:893-905. [PMID: 26143683] doi:10.1017/S1368980015002062 CrossrefMedlineGoogle Scholar
Schwingshackl L, Schwedhelm C, Hoffmann G, et al. Food groups and risk of all-cause mortality: a systematic review and meta-analysis of prospective studies. Am J Clin Nutr. 2017;105:1462-73. [PMID: 28446499] doi:10.3945/ajcn.117.153148 CrossrefMedlineGoogle Scholar
- 8. World Cancer Research Fund; American Institute for Cancer Research. Continuous Update Project Expert Report 2019. Meat, fish and dairy products and the risk of cancer. Accessed at https://www.wcrf.org/dietandcancer on 3 July 2019. Google Scholar
U.S. Department of Health and Human Services.. 2015-2020 dietary guidelines for Americans. Washington, DC: U.S. Department of Health and Human Services; December 2015. Google Scholar
- 10. The Eatwell Guide. London, UK: Public Health England; 2016. Accessed at https://www.gov.uk/government/publications/the-eatwell-guide on 3 July 2019. Google Scholar
Guyatt GH, Oxman AD, Vist GE, et al; GRADE Working Group. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336:924-6. [PMID: 18436948] doi:10.1136/bmj.39489.470347.AD CrossrefMedlineGoogle Scholar
Young SS, Karr AF. Deming, data and observational studies: a process out of control and needing fixing. Quality Control Appl Stat. 2013;58:31-2. Google Scholar
Maki KC, Van Elswyk ME, Alexander DD, et al. A meta-analysis of randomized controlled trials that compare the lipid effects of beef versus poultry and/or fish consumption. J Clin Lipidol. 2012;6:352-61. [PMID: 22836072] doi:10.1016/j.jacl.2012.01.001 CrossrefMedlineGoogle Scholar
O'Connor LE, Kim JE, Campbell WW. Total red meat intake of ≥0.5 servings/d does not negatively influence cardiovascular disease risk factors: a systemically searched meta-analysis of randomized controlled trials. Am J Clin Nutr. 2017;105:57-69. [PMID: 27881394] doi:10.3945/ajcn.116.142521 CrossrefMedlineGoogle Scholar
Guasch-Ferré M, Satija A, Blondin SA, et al. Meta-analysis of randomized controlled trials of red meat consumption in comparison with various comparison diets on cardiovascular risk factors. Circulation. 2019;139:1828-45. [PMID: 30958719] doi:10.1161/CIRCULATIONAHA.118.035225 CrossrefMedlineGoogle Scholar
Johnston BC, Alonso-Coello P, Bala MM, et al. NutriRECS (Nutritional Recommendations and accessible Evidence summaries Composed of Systematic reviews): a protocol. BMC Med Res Methodol. 2018;18:162. CrossrefMedlineGoogle Scholar
Han MA, Zeraatkar D, Guyatt GH, et al. Reduction of red and processed meat intake and cancer mortality and incidence. A systematic review and meta-analysis of cohort studies. Ann Intern Med. 2019;171:711-20. doi:10.7326/M19-0699 LinkGoogle Scholar
Vernooij RWM, Zeraatkar D, Han MA, et al. Patterns of red and processed meat consumption and risk for cardiometabolic and cancer outcomes. A systematic review and meta-analysis of cohort studies. Ann Intern Med. 2019;171:732-41. doi:10.7326/M19-1583 LinkGoogle Scholar
Zeraatkar D, Han MA, Guyatt GH, et al. Red and processed meat consumption and risk for all-cause mortality and cardiometabolic outcomes. A systematic review and meta-analysis of cohort studies. Ann Intern Med. 2019;171:703-10. doi:10.7326/M19-0655 LinkGoogle Scholar
Valli C, Rabassa M, Johnston BC, et al. Health-related values and preferences regarding meat consumption. A mixed-methods systematic review. Ann Intern Med. 2019;171:742-55. doi:10.7326/M19-1326 LinkGoogle Scholar
Johnston BC, Zeraatkar D, Han MA, et al. Unprocessed red meat and processed meat consumption: dietary guideline recommendations from the Nutritional Recommendations (NutriRECS) Consortium. Ann Intern Med. 2019;171:756-64. doi:10.7326/M19-1621 LinkGoogle Scholar
- 22. Zeraatkar D, Bala M, Webber-Adams T, et al. Red meat and health outcomes: a systematic review. PROSPERO 2017 CRD42017074074. Accessed at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=74074 on 3 August 2019. Google Scholar
- 23. World Cancer Research Fund; American Institute for Cancer Research. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Accessed at https://www.wcrf.org/sites/default/files/english.pdf on 26 August 2019. Google Scholar
Turner-McGrievy GM, Davidson CR, Wingard EE, et al. Comparative effectiveness of plant-based diets for weight loss: a randomized controlled trial of five different diets. Nutrition. 2015;31:350-8. [PMID: 25592014] doi:10.1016/j.nut.2014.09.002 CrossrefMedlineGoogle Scholar
Yaskolka Meir A, Tsaban G, Zelicha H, et al. A green-Mediterranean diet, supplemented with Mankai duckweed, preserves iron-homeostasis in humans and is efficient in reversal of anemia in rats. J Nutr. 2019;149:1004-11. [PMID: 30915471] doi:10.1093/jn/nxy321 CrossrefMedlineGoogle Scholar
Higgins JP, Altman DG, Gøtzsche PC, et al; Cochrane Bias Methods Group. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928. [PMID: 22008217] doi:10.1136/bmj.d5928 CrossrefMedlineGoogle Scholar
Akl EA, Johnston BC, Alonso-Coello P, et al. Addressing dichotomous data for participants excluded from trial analysis: a guide for systematic reviewers. PloS One. 2013 Feb 25;8(2):e57132. CrossrefGoogle Scholar
- 28. Evidence Partners. CLARITY 2014 risk of bias tool for cohort studies. Accessed at https://www.evidencepartners.com/wp-content/uploads/2014/02/Tool-to-Assess-Risk-of-Bias-in-Cohort-Studies.doc on 3 August 2019. Google Scholar
Hartung J, Knapp G. A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat Med. 2001;20:3875-89. [PMID: 11782040] CrossrefMedlineGoogle Scholar
IntHout J, Ioannidis JP, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14:25. [PMID: 24548571] doi:10.1186/1471-2288-14-25 CrossrefMedlineGoogle Scholar
Sarwar N, Gao P, Seshasai SR, et al; Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375:2215-22. [PMID: 20609967] doi:10.1016/S0140-6736(10)60484-9 CrossrefMedlineGoogle Scholar
- 32. GLOBOCAN. Estimated cancer incidence, mortality and prevalence worldwide in 2012. Accessed at http://globocan.iarc.fr on 3 August 2019. Google Scholar
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359-86. [PMID: 25220842] doi:10.1002/ijc.29210 CrossrefMedlineGoogle Scholar
Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557-60. [PMID: 12958120] CrossrefMedlineGoogle Scholar
Guyatt GH, Oxman AD, Vist G, et al. GRADE guidelines: 4. Rating the quality of evidence—study limitations (risk of bias). J Clin Epidemiol. 2011;64:407-15. [PMID: 21247734] doi:10.1016/j.jclinepi.2010.07.017 CrossrefMedlineGoogle Scholar
Guyatt GH, Oxman AD, Kunz R, et al. GRADE guidelines 6. Rating the quality of evidence—imprecision. J Clin Epidemiol. 2011;64:1283-93. [PMID: 21839614] doi:10.1016/j.jclinepi.2011.01.012 CrossrefMedlineGoogle Scholar
Guyatt GH, Oxman AD, Kunz R, et al; GRADE Working Group. GRADE guidelines: 7. rating the quality of evidence—inconsistency. J Clin Epidemiol. 2011;64:1294-302. [PMID: 21803546] doi:10.1016/j.jclinepi.2011.03.017 CrossrefMedlineGoogle Scholar
Guyatt GH, Oxman AD, Kunz R, et al; GRADE Working Group. GRADE guidelines: 8. rating the quality of evidence—indirectness. J Clin Epidemiol. 2011;64:1303-10. [PMID: 21802903] doi:10.1016/j.jclinepi.2011.04.014 CrossrefMedlineGoogle Scholar
Guyatt GH, Oxman AD, Montori V, et al. GRADE guidelines: 5. Rating the quality of evidence—publication bias. J Clin Epidemiol. 2011;64:1277-82. [PMID: 21802904] doi:10.1016/j.jclinepi.2011.01.011 CrossrefMedlineGoogle Scholar
Benassi-Evans B, Clifton PM, Noakes M, et al. High protein-high red meat versus high carbohydrate weight loss diets do not differ in effect on genome stability and cell death in lymphocytes of overweight men. Mutagenesis. 2009;24:271-7. [PMID: 19264840] doi:10.1093/mutage/gep006 CrossrefMedlineGoogle Scholar
Davis CR, Hodgson JM, Woodman R, et al. A Mediterranean diet lowers blood pressure and improves endothelial function: results from the MedLey randomized intervention trial. Am J Clin Nutr. 2017;105:1305-13. [PMID: 28424187] doi:10.3945/ajcn.116.146803 CrossrefMedlineGoogle Scholar
de Lorgeril M, Salen P, Martin JL, et al. Mediterranean diet, traditional risk factors, and the rate of cardiovascular complications after myocardial infarction: final report of the Lyon Diet Heart Study. Circulation. 1999;99:779-85. [PMID: 9989963] CrossrefMedlineGoogle Scholar
de Mello VD, Zelmanovitz T, Azevedo MJ, et al. Long-term effect of a chicken-based diet versus enalapril on albuminuria in type 2 diabetic patients with microalbuminuria. J Ren Nutr. 2008;18:440-7. [PMID: 18721739] doi:10.1053/j.jrn.2008.04.010 CrossrefMedlineGoogle Scholar
Griffin HJ, Cheng HL, O'Connor HT, et al. Higher protein diet for weight management in young overweight women: a 12-month randomized controlled trial. Diabetes Obes Metab. 2013;15:572-5. [PMID: 23279557] doi:10.1111/dom.12056 CrossrefMedlineGoogle Scholar
Assaf AR, Beresford SA, Risica PM, et al. Low-fat dietary pattern intervention and health-related quality of life: the Women's Health Initiative randomized controlled dietary modification trial. J Acad Nutr Diet. 2016;116:259-71. [PMID: 26384466] doi:10.1016/j.jand.2015.07.016 CrossrefMedlineGoogle Scholar
Chlebowski RT, Aragaki AK, Anderson GL, et al. Low-fat dietary pattern and breast cancer mortality in the Women's Health Initiative randomized controlled trial. J Clin Oncol. 2017;35:2919-26. [PMID: 28654363] doi:10.1200/JCO.2016.72.0326 CrossrefMedlineGoogle Scholar
Gamba CS, Stefanick ML, Shikany JM, et al. Low-fat diet and skin cancer risk: the Women's Health Initiative randomized controlled dietary modification trial. Cancer Epidemiol Biomarkers Prev. 2013;22:1509-19. [PMID: 23697610] doi:10.1158/1055-9965.EPI-13-0341 CrossrefMedlineGoogle Scholar
Howard BV, Manson JE, Stefanick ML, et al. Low-fat dietary pattern and weight change over 7 years: the Women's Health Initiative dietary modification trial. JAMA. 2006;295:39-49. [PMID: 16391215] CrossrefMedlineGoogle Scholar
Howard BV, Van Horn L, Hsia J, et al. Low-fat dietary pattern and risk of cardiovascular disease: the Women's Health Initiative randomized controlled dietary modification trial. JAMA. 2006;295:655-66. [PMID: 16467234] CrossrefMedlineGoogle Scholar
Howard BV, Curb JD, Eaton CB, et al. Low-fat dietary pattern and lipoprotein risk factors: the Women's Health Initiative dietary modification trial. Am J Clin Nutr. 2010;91:860-74. [PMID: 20164311] doi:10.3945/ajcn.2009.28034 CrossrefMedlineGoogle Scholar
Jiao L, Chen L, White DL, et al. Low-fat dietary pattern and pancreatic cancer risk in the Women's Health Initiative dietary modification randomized controlled trial. J Natl Cancer Inst. 2018;110. [PMID: 28922784] doi:10.1093/jnci/djx117 CrossrefMedlineGoogle Scholar
Neuhouser ML, Howard B, Lu J, et al. A low-fat dietary pattern and risk of metabolic syndrome in postmenopausal women: the Women's Health Initiative. Metabolism. 2012;61:1572-81. [PMID: 22633601] doi:10.1016/j.metabol.2012.04.007 CrossrefMedlineGoogle Scholar
Prentice RL, Thomson CA, Caan B, et al. Low-fat dietary pattern and cancer incidence in the Women's Health Initiative dietary modification randomized controlled trial. J Natl Cancer Inst. 2007;99:1534-43. [PMID: 17925539] CrossrefMedlineGoogle Scholar
Prentice RL, Aragaki AK, Van Horn L, et al. Low-fat dietary pattern and cardiovascular disease: results from the Women's Health Initiative randomized controlled trial. Am J Clin Nutr. 2017;106:35-43. [PMID: 28515068] doi:10.3945/ajcn.117.153270 CrossrefMedlineGoogle Scholar
Thomson CA, Van Horn L, Caan BJ, et al. Cancer incidence and mortality during the intervention and postintervention periods of the Women's Health Initiative dietary modification trial. Cancer Epidemiol Biomarkers Prev. 2014;23:2924-35. [PMID: 25258014] doi:10.1158/1055-9965.EPI-14-0922 CrossrefMedlineGoogle Scholar
Tinker LF, Bonds DE, Margolis KL, et al; Women's Health Initiative. Low-fat dietary pattern and risk of treated diabetes mellitus in postmenopausal women: the Women's Health Initiative randomized controlled dietary modification trial. Arch Intern Med. 2008;168:1500-11. [PMID: 18663162] doi:10.1001/archinte.168.14.1500 CrossrefMedlineGoogle Scholar
Hunninghake DB, Maki KC, Kwiterovich PO, et al. Incorporation of lean red meat into a National Cholesterol Education Program Step I diet: a long-term, randomized clinical trial in free-living persons with hypercholesterolemia. J Am Coll Nutr. 2000;19:351-60. [PMID: 10872897] CrossrefMedlineGoogle Scholar
Lanza E, Yu B, Murphy G, et al; Polyp Prevention Trial Study Group. The polyp prevention trial continued follow-up study: no effect of a low-fat, high-fiber, high-fruit, and -vegetable diet on adenoma recurrence eight years after randomization. Cancer Epidemiol Biomarkers Prev. 2007;16:1745-52. [PMID: 17855692] CrossrefMedlineGoogle Scholar
Murphy KJ, Thomson RL, Coates AM, et al. Effects of eating fresh lean pork on cardiometabolic health parameters. Nutrients. 2012;4:711-23. [PMID: 22852059] doi:10.3390/nu4070711 CrossrefMedlineGoogle Scholar
Poddar KH, Ames M, Hsin-Jen C, et al. Positive effect of mushrooms substituted for meat on body weight, body composition, and health parameters: a 1-year randomized clinical trial. Appetite. 2013;71:379-87. [PMID: 24056209] doi:10.1016/j.appet.2013.09.008 CrossrefMedlineGoogle Scholar
- 61. Design of the Women's Health Initiative clinical trial and observational study. The Women's Health Initiative Study Group. Control Clin Trials. 1998;19:61-109. [PMID: 9492970] MedlineGoogle Scholar
de Lorgeril M, Renaud S, Mamelle N, et al. Mediterranean alpha-linolenic acid-rich diet in secondary prevention of coronary heart disease. Lancet. 1994;343:1454-9. [PMID: 7911176] CrossrefMedlineGoogle Scholar
Bassler D, Briel M, Montori VM, et al; STOPIT-2 Study Group. Stopping randomized trials early for benefit and estimation of treatment effects: systematic review and meta-regression analysis. JAMA. 2010;303:1180-7. [PMID: 20332404] doi:10.1001/jama.2010.310 CrossrefMedlineGoogle Scholar
- 64. Carrasco-Labra A, Devji T, Lytvyn L, et al. Minimally important difference estimates and assessment of their credibility for patient-reported outcomes in adults: a systematic survey. Abstracts of the Global Evidence Summit, Cape Town, South Africa. Cochrane Database Syst Rev. 2017;(9 Suppl 1). https://doi.org/10.1002/14651858.CD201702 Google Scholar
Johnston BC, Seivenpiper JL, Vernooij RWM, et al. The philosophy of evidence-based principles and practice in nutrition. Mayo Clin Proc Innov Qual Outcomes. 2019;3:189-99. [PMID: 31193887] doi:10.1016/j.mayocpiqo.2019.02.005 CrossrefMedlineGoogle Scholar
Solyakov A, Skog K. Screening for heterocyclic amines in chicken cooked in various ways. Food Chem Toxicol. 2002;40:1205-11. [PMID: 12067585] CrossrefMedlineGoogle Scholar
Wretling S, Eriksson A, Eskhult G, Larsson B. Polycyclic aromatic hydrocarbons (PAHs) in Swedish smoked meat and fish. J Food Compost Anal. 2010;23:264-72. CrossrefGoogle Scholar
Sinha R, Peters U, Cross AJ, et al. Meat, meat cooking methods and preservation, and risk for colorectal adenoma. Cancer Res. 2005;65:8034-41. [PMID: 16140978] CrossrefMedlineGoogle Scholar
Sun X, Briel M, Busse JW, et al. Subgroup Analysis of Trials Is Rarely Easy (SATIRE): a study protocol for a systematic review to characterize the analysis, reporting, and claim of subgroup effects in randomized trials. Trials. 2009;10:101. [PMID: 19900273] doi:10.1186/1745-6215-10-101 CrossrefMedlineGoogle Scholar
Beresford SA, Johnson KC, Ritenbaugh C, et al. Low-fat dietary pattern and risk of colorectal cancer: the Women's Health Initiative randomized controlled dietary modification trial. JAMA. 2006;295:643-54. [PMID: 16467233] CrossrefMedlineGoogle Scholar
Inoue-Choi M, Sinha R, Gierach GL, et al. Red and processed meat, nitrite, and heme iron intakes and postmenopausal breast cancer risk in the NIH-AARP Diet and Health Study. Int J Cancer. 2016;138:1609-18. [PMID: 26505173] doi:10.1002/ijc.29901 CrossrefMedlineGoogle Scholar
Author, Article, and Disclosure Information
McMaster University, Hamilton, Ontario, Canada (D.Z., B.C.J., J.B., K.C., K.M., B.S., A.M.Z., Y.L., G.H.G.)
Jagiellonian University Medical College, Kraków, Poland (M.M.B.)
Iberoamerican Cochrane Centre Barcelona, Biomedical Research Institute San Pau (IIB Sant Pau), Barcelona, Spain (C.V., M.R., P.A.)
University of British Columbia, Vancouver, British Columbia, Canada (D.S.)
University of Toronto, Toronto, Ontario, Canada (A.A.)
Chosun University, Gwangju, Republic of Korea (M.A.H.)
Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands (R.W.V.)
Institute of Science and Technology, Universidade Estadual Paulista, São José dos Campos, São Paulo, Brazil (R.E.)
Acknowledgment: The authors thank Thomasin Adams-Webber (Hospital for Sick Children) for her help designing our search strategy.
Disclosures: Dr. Johnston received a grant from Texas A&M AgriLife Research to fund investigator-driven research related to saturated and polyunsaturated fats within the 36-month reporting period required by the International Committee of Medical Journal Editors, as well as funding received from the International Life Science Institute (North America) that ended before the 36-month reporting period. Dr. El Dib received a São Paulo Research Foundation (FAPESP) (2018/11205-6) scholarship and funding from the National Council for Scientific and Technological Development (CNPq) (CNPq 310953/2015-4) and the Faculty of Medicine, Dalhousie University. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-0622.
Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that her spouse has stock options/holdings with Targeted Diagnostics and Therapeutics. Darren B. Taichman, MD, PhD, Executive Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Catharine B. Stack, PhD, MS, Deputy Editor, Statistics, reports that she has stock holdings in Pfizer, Johnson & Johnson, and Colgate-Palmolive. Christina C. Wee, MD, MPH, Deputy Editor, reports employment with Beth Israel Deaconess Medical Center. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Yu-Xiao Yang, MD, MSCE, Deputy Editor, reports that he has no financial relationships or interest to disclose.
>Reproducible Research Statement: Study protocol: Available at PROSPERO (CRD42017074074). Statistical code and data set: Available from Dr. Johnston (e-mail, [email protected]
Corresponding Author: Bradley C. Johnston, PhD, Department of Community Health and Epidemiology, Dalhousie University, Centre for Clinical Research, Room 404, 5790 University Avenue, Halifax, Nova Scotia B3H 1V7, Canada; e-mail, [email protected]
Current Author Addresses: Ms. Zeraatkar, Ms. Bartoszko, Drs. Sadeghirad and Guyatt, and Ms. Perdomo: Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada.
Dr. Johnston: Department of Community Health and Epidemiology, Dalhousie University, Centre for Clinical Research, Room 404, 5790 University Avenue, Halifax, Nova Scotia, B3H 1V7, Canada.
Dr. Cheung: 114 Loganberry Crescent, Toronto, Ontario M2H 3H1, Canada.
Dr. Bala: Jagiellonian University Medical College, Department of Hygiene and Dietetics, 7 Kopernika Street, 31-034 Kraków, Poland.
Ms. Valli and Drs. Rabassa and Alonso-Coello: Iberoamerican Cochrane Centre, Instituto de Investigación Biomédica de Sant Pau (IIB Sant Pau-CIBERESP), Carrer de Sant Antoni Maria Claret 167, Barcelona 08025, Spain.
Dr. Sit: University of British Columbia, 107-1165 West 13th Avenue, Vancouver, British Columbia V6H 1N4, Canada.
Mr. Milio: 592 Regal Place, Waterloo, Ontario N2V 2G3, Canada.
Dr. Agarwal: Department of Medicine, University of Toronto, Suite RFE 3-805, 200 Elizabeth Street, Toronto, Ontario M5G 2C4, Canada.
Ms. Zea: Clinica Las Americas, Diagonal 75B, North 2A-80/140, Medellin, Colombia.
Mr. Lee: 30 White Lodge Crescent, Richmond Hill, Ontario L4C 9A1, Canada.
Dr. Han: Department of Preventive Medicine, College of Medicine, Chosun University, 309 Philmun-daero, Dong-gu, Gwangju 61452, Korea.
Dr. Vernooij: Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht 3511 DT, the Netherlands.
Dr. El Dib: Institute of Science and Technology, São José dos Campos, Avenida Engenheiro Francisco José Longo, 777, Jardim São Dimas, São Paulo 12245-000, Brazil.
Author Contributions: Conception and design: D. Zeraatkar, B.C. Johnston, M.M. Bala, P. Alonso-Coello, G.H. Guyatt, R. El Dib.
Analysis and interpretation of the data: D. Zeraatkar, B.C. Johnston, M.M. Bala, M. Rabassa, D. Sit, M.A. Han, P. Alonso-Coello, G.H. Guyatt, R. El Dib.
Drafting of the article: B.C. Johnston, R. El Dib.
Critical revision of the article for important intellectual content: D. Zeraatkar, B.C. Johnston, M.M. Bala, D. Sit, B. Sadeghirad, A.M. Zea, Y. Lee, M.A. Han, R.W.M. Vernooij, P. Alonso-Coello, G.H. Guyatt, R. El Dib.
Final approval of the article: D. Zeraatkar, B.C. Johnston, J. Bartoszko, K. Cheung, M.M. Bala, C. Valli, M. Rabassa, D. Sit, K. Milio, B. Sadeghirad, A. Agarwal, A.M. Zea, Y. Lee, M.A. Han, R.W.M. Vernooij, P. Alonso-Coello, G.H. Guyatt, R. El Dib.
Provision of study materials or patients: B.C. Johnston, R. El Dib.
Administrative, technical, or logistic support: D. Zeraatkar, B.C. Johnston, R. El Dib.
Collection and assembly of data: D. Zeraatkar, B.C. Johnston, J. Bartoszko, K. Cheung, M.M. Bala, C. Valli, M. Rabassa, D. Sit, K. Milio, B. Sadeghirad, A. Agarwal, A.M. Zea, Y. Lee, M. Han, R.W.M. Vernooij, R. El Dib.
This article was published at Annals.org on 1 October 2019.