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Original Investigation |

Meat Intake and Mortality:  A Prospective Study of Over Half a Million People FREE

Rashmi Sinha, PhD; Amanda J. Cross, PhD; Barry I. Graubard, PhD; Michael F. Leitzmann, MD, DrPH; Arthur Schatzkin, MD, DrPH
[+] Author Affiliations

Author Affiliations: Nutritional Epidemiology Branch (Drs Sinha, Cross, Leitzmann, and Schatzkin) and Biostatistics Branch (Dr Graubard), Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, Maryland.


Arch Intern Med. 2009;169(6):562-571. doi:10.1001/archinternmed.2009.6.
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Background  High intakes of red or processed meat may increase the risk of mortality. Our objective was to determine the relations of red, white, and processed meat intakes to risk for total and cause-specific mortality.

Methods  The study population included the National Institutes of Health–AARP (formerly known as the American Association of Retired Persons) Diet and Health Study cohort of half a million people aged 50 to 71 years at baseline. Meat intake was estimated from a food frequency questionnaire administered at baseline. Cox proportional hazards regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) within quintiles of meat intake. The covariates included in the models were age, education, marital status, family history of cancer (yes/no) (cancer mortality only), race, body mass index, 31-level smoking history, physical activity, energy intake, alcohol intake, vitamin supplement use, fruit consumption, vegetable consumption, and menopausal hormone therapy among women. Main outcome measures included total mortality and deaths due to cancer, cardiovascular disease, injuries and sudden deaths, and all other causes.

Results  There were 47 976 male deaths and 23 276 female deaths during 10 years of follow-up. Men and women in the highest vs lowest quintile of red (HR, 1.31 [95% CI, 1.27-1.35], and HR, 1.36 [95% CI, 1.30-1.43], respectively) and processed meat (HR, 1.16 [95% CI, 1.12-1.20], and HR, 1.25 [95% CI, 1.20-1.31], respectively) intakes had elevated risks for overall mortality. Regarding cause-specific mortality, men and women had elevated risks for cancer mortality for red (HR, 1.22 [95% CI, 1.16-1.29], and HR, 1.20 [95% CI, 1.12-1.30], respectively) and processed meat (HR, 1.12 [95% CI, 1.06-1.19], and HR, 1.11 [95% CI 1.04-1.19], respectively) intakes. Furthermore, cardiovascular disease risk was elevated for men and women in the highest quintile of red (HR, 1.27 [95% CI, 1.20-1.35], and HR, 1.50 [95% CI, 1.37-1.65], respectively) and processed meat (HR, 1.09 [95% CI, 1.03-1.15], and HR, 1.38 [95% CI, 1.26-1.51], respectively) intakes. When comparing the highest with the lowest quintile of white meat intake, there was an inverse association for total mortality and cancer mortality, as well as all other deaths for both men and women.

Conclusion  Red and processed meat intakes were associated with modest increases in total mortality, cancer mortality, and cardiovascular disease mortality.

Meat intake varies substantially around the world, but the impact of consuming higher levels of meat in relation to chronic disease mortality is ambiguous.16 To increase sample size, pooled analyses of meat intake have been carried out in Seventh-Day Adventists in the United States1,2 and other vegetarian populations in Europe.36 Vegetarian diets differ from nonvegetarian diets in several respects. The main sources of protein in a vegetarian diet are legumes, grains, and nuts. Vegetarian diets also include higher intakes of vegetables, unsaturated fats, dietary fiber, and antioxidants (carotenoids and vitamins C and E), although they contain lower amounts of iron, zinc, and vitamin B12. Furthermore, other lifestyle factors, such as smoking, physical activity, and alcohol consumption among vegetarians and members of select religious groups can differ substantially from the general population.

We prospectively investigated red, white, and processed meat intakes as risk factors for total mortality, as well as cause-specific mortality, including cancer and cardiovascular disease (CVD) mortality in a cohort of approximately half a million men and women enrolled in the National Institutes of Health (NIH)–AARP (formerly known as the American Association of Retired Persons) Diet and Health Study.7 This large prospective study facilitated the investigation of a wide range of meat intakes with chronic disease mortality.

STUDY POPULATION

Individuals aged 50 to 71 years were recruited from 6 US states (California, Florida, Louisiana, New Jersey, North Carolina, and Pennsylvania) and 2 metropolitan areas (Atlanta, Georgia, and Detroit, Michigan) to form a large prospective cohort, the NIH-AARP Diet and Health Study. Questionnaires on demographic and lifestyle characteristics, including dietary habits, were mailed to 3.5 million members of AARP in 1995, as described in detail elsewhere.7 The NIH-AARP Diet and Health Study was approved by the Special Studies Institutional Review Board of the US National Cancer Institute. Completion of the baseline questionnaire was considered to imply informed consent.

DIETARY ASSESSMENT

A 124-item food frequency questionnaire (http://riskfactor.cancer.gov/DHQ/forms/files/shared/dhq1.2002.sample.pdf) was completed at baseline. The food frequency questionnaire collected information on the usual consumption of foods and drinks and portion sizes over the last 12 months. The validity of the food frequency questionnaire was estimated using two 24-hour recalls,8 and the estimated energy-adjusted correlations ranged from 0.36 to 0.76 for various nutrients and attenuation factors ranged from 0.24 to 0.68. Red meat intake was calculated using the frequency of consumption and portion size information of all types of beef and pork and included bacon, beef, cold cuts, ham, hamburger, hotdogs, liver, pork, sausage, steak, and meats in foods such as pizza, chili, lasagna, and stew. White meat included chicken, turkey, and fish and included poultry cold cuts, chicken mixtures, canned tuna, and low-fat sausages and low-fat hotdogs made from poultry. Processed meat included bacon, red meat sausage, poultry sausage, luncheon meats (red and white meat), cold cuts (red and white meat), ham, regular hotdogs and low-fat hotdogs made from poultry. The components constituting red or white and processed meats can overlap because both can include meats such as bacon, sausage, and ham, while processed meat can also included smoked turkey and chicken. However, these meat groups are not used in the same models; thus, they are not duplicated in any one analysis.

To investigate whether the overall composition of meat intake was associated with mortality, we created 3 diet types: high-, medium-, and low-risk meat diet. To form these diet variables, red and white meat consumption was energy adjusted and split into 2 groups using the median values as cut points. Individuals with red meat consumption in the upper half and white meat consumption in the lower half got a score of 1 (high-risk meat diet), those with both red and white meat consumption in the same half got a score of 2 (medium-risk meat diet), and those with red meat consumption in the lower half and white meat consumption in the upper half got a score of 3 (low-risk meat diet).

COHORT FOLLOW-UP AND CASE ASCERTAINMENT

Cohort members were followed-up from the date the baseline questionnaire was returned (beginning 1995) through December 31, 2005, by annual linkage of the cohort to the National Change of Address database maintained by the US Postal Service and through processing of undeliverable mail, other address change update services, and directly from cohort members' notifications. For matching purposes, we have virtually complete data on first and last name, address history, sex, and date of birth. Follow-up for vital status is performed by annual linkage of the cohort to the Social Security Administration Death Master File in the US verification of vital status, and cause of death information is provided by follow-up searches of the National Death Index (NDI) Plus with the current follow-up for mortality covered until 2005.

CAUSE-SPECIFIC CASE ASCERTAINMENT

Cancer (International Classification of Diseases, Ninth Revision [ICD-9] codes 140-239 and International Statistical Classification of Diseases, 10th Revision [ICD-10] codes C00-C44, C45.0, C45.1, C45.7, C45.9, C48-C97, and D12-D48) mortality included deaths due to cancers of the oral cavity and pharynx, digestive tract, respiratory tract, soft tissue (including heart), skin (excluding basal and squamous cell carcinoma), female genital system and breast, male genital system, urinary tract, endocrine system, lymphoma, leukemia, and other miscellaneous cancers.

Cardiovascular disease (ICD-9 codes 390-398, 401-404, 410-438, and 440-448 and ICD-10 codes I00-I09, I10-I13, I20-I51, and I60-I78) mortality was from a combination of diseases of the heart; hypertension without heart disease; cerebrovascular diseases; atherosclerosis; aortic aneurysm and dissection; and other diseases of the arteries, arterioles, and capillaries.

Mortality from injuries and sudden deaths (ICD-9 codes 800-978 and ICD-10 codes U01-U03, V01-Y09, Y35, Y85-Y86, Y87.0, Y87.1, and Y89.0) included deaths due to unintentional injury, adverse effects, suicide, self-inflicted injury, homicide, and legal intervention.

All others deaths included mortality from tuberculosis, human immunodeficiency virus, other infectious and parasitic diseases, septicemia, diabetes mellitus, Alzheimer disease, stomach and duodenal ulcers, pneumonia and influenza, chronic obstructive pulmonary disease and allied conditions, chronic liver disease and cirrhosis, nephritis, nephrotic syndrome and nephrosis, congenital anomalies, certain conditions originating in the perinatal period, ill-defined conditions, and unknown causes of death.

Total mortality is a combination of all of the aforementioned causes of deaths.

STATISTICAL ANALYSIS

A total of 617 119 persons returned the baseline questionnaire; of these, we excluded individuals who moved out of the 8 study areas before returning the baseline questionnaire (n = 321), requested to be withdrawn from the study (n = 829), died before study entry (n = 261), had duplicate records (n = 179), indicated that they were not the intended respondent and did not complete the questionnaire (n = 13 442), provided no information on gender (n = 6), and did not answer substantial portions of the questionnaire or had more than 10 recording errors (n = 35 679). After these exclusions, we further removed individuals whose questionnaire was filled in by someone else on their behalf (n = 15 760). We excluded 4849 subjects reporting extreme daily total energy intake defined as more than 2 interquartile ranges above the 75th percentile or below the 25th percentile and 140 people who had zero person-years of follow-up. After all exclusions, our analytic cohort comprised 322 263 men and 223 390 women.

We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) with time since entry into the study as the underlying time metric using Cox proportional hazards regression models. Quintile cut points were based on the entire cohort, and multivariate-adjusted HRs are reported using the lowest quintile as the referent category. The violation of the proportional hazard assumption was investigated by testing an interaction between a time-dependent binary covariate, which indicated if follow-up was in the first 5 years or in the second 5 years, and the quintile terms for meat consumption. Dietary variables were energy adjusted using the nutrient density method, and meat variables in each model added up to total meat (addition model). For example, one model contained both red and white meat, while the processed meat model also contained a nonprocessed meat variable.

To address confounding, we used forward stepwise variable selection to include covariates to develop the fully adjusted model. Smoking was the largest confounder of the association between meat intake and mortality. Physical activity and education were also important covariates, but not to the same degree as smoking. The final model included age (continuous); education (<8 years or unknown, 8-11 years, 12 years [high school], some college, or college graduate); marital status (married: yes/no); family history of cancer (yes/no) (cancer mortality only); race (non-Hispanic white, non-Hispanic black, Hispanic/Asian/Pacific Islander/American Indian/Alaskan native, or unknown); body mass index (18.5 to <25, 25 to <30, 30 to <35, ≥35 [calculated as weight in kilograms divided by height in meters squared]); 31-level smoking history using smoking status (never, former, or current), time since quitting for former smokers and smoking dose; frequency of vigorous physical activity (never/rarely, 1-3 times/mo, 1-2 times/wk, 3-4 times/wk, ≥5 times/wk); total energy intake (continuous); alcohol intake (none, 0 to <5, 5 to <15, 15 to <30, ≥30 g/d); vitamin supplement user (≥1 supplement/mo); fruit consumption (0 to <0.7, 0.7 to <1.2, 1.2 to <1.7, 1.7 to <2.5, ≥2.5 servings/1000 kcal); vegetable consumption (0 to <1.3, 1.3 to <1.8, 1.8 to <2.2, 2.2 to <3.0, ≥3.0 servings/1000 kcal); and menopausal hormone therapy among women in the multivariate models.

In subanalyses, we investigated the relation between meat intake and mortality by smoking status. We used median values of each quintile to test for linear trend with 2-sided P values. We also calculated population-attributable risks as an estimate of the percentage of mortality that could be prevented if individuals adopted intake levels of participants within the first quintile. This was computed as 1 minus the ratio consisting of the sum of the estimated HR (derived from the Cox proportional hazard regression models) of each member of the cohort divided by the sum of the estimated HR for which meat exposure was assigned to the lowest or highest quintile, depending on which quintile was the ideal level of meat consumption. The population-attributable risk was multiplied by 100 to convert them to a percentage. All statistical analyses were carried out using Statistical Analytic Systems (SAS) software (SAS Institute Inc, Cary, North Carolina).

During 10 years of follow-up, there were 47 976 male deaths and 23 276 female deaths. In general, those in the highest quintile of red meat intake tended to consume a slightly lower amount of white meat but a higher amount of processed meat compared with those in the lowest quintile. Subjects who consumed more red meat tended to be married, more likely of non-Hispanic white ethnicity, more likely a current smoker, have a higher body mass index, and have a higher daily intake of energy, total fat, and saturated fat, and they tended to have lower education and physical activity levels and lower fruit, vegetable, fiber, and vitamin supplement intakes (Table 1).

Table Graphic Jump LocationTable 1. Selected Age-Adjusted Characteristics of the National Institutes of Health–AARP Cohort by Red Meat Quintile Categorya
RED MEAT

There was an overall increased risk of total, cancer, and CVD mortality, as well as all other deaths in both men (Table 2) and women (Table 3) in the highest compared with the lowest quintile of red meat intake in the fully adjusted model. There was an increased risk associated with death from injuries and sudden death with higher consumption of red meat in men but not in women.

Table Graphic Jump LocationTable 2. Multivariate Analysis for Red, White, and Processed Meat Intake and Total and Cause-Specific Mortality in Men in the National Institutes of Health–AARP Diet and Health Studya
Table Graphic Jump LocationTable 3. Multivariate Analysis Red, White, and Processed Meat Intake and Total and Cause-Specific Mortality in Women in the National Institutes of Health–AARP Diet and Health Studya
WHITE MEAT

When comparing the highest with the lowest quintile of white meat intake, there was an inverse association for total mortality and cancer mortality, as well as all other deaths for both men (Table 2) and women (Table 3). In contrast, there was a small increase in risk for CVD mortality in men with higher intake of white meat. There was no association between white meat consumption and death from injuries and sudden death in men or women.

PROCESSED MEAT

There was an overall increased risk of total, cancer, and CVD mortality, as well as all other deaths in both men (Table 2) and women (Table 3) in the highest compared with the lowest quintile of processed meat intake. In contrast, there was no association for processed meat intake and death from injuries and sudden death in either sex.

A lag analysis, excluding deaths occurring in the first 2 years of follow-up, produced results consistent with the main findings in Table 2 and Table 3. For example, the HRs for total mortality in men for red meat was as follows: second quintile HR, 1.05 (95% CI, 1.01-1.09); third quintile HR, 1.13 (95% CI, 1.09-1.17); fourth quintile HR, 1.20 (95% CI, 1.16-1.24); and fifth quintile HR, 1.30 (95% CI, 1.26-1.35). For women, the HRs were as follows: second quintile HR, 1.07 (95% CI, 1.02-1.12); third quintile HR, 1.15 (95% CI, 1.11-1.21); fourth quintile HR, 1.27 (95% CI, 1.21-1.33); and fifth quintile HR, 1.35 (95% CI, 1.28-1.42). Furthermore, we investigated our models for a violation of the proportional hazard assumption. Proportional hazard assumption was not rejected for all analyses except one, the model with red and white meat among the women for total mortality (P = .008). On further examination in that model of the relative HR between the first 5 years of follow-up and the second 5 years of follow-up, the red meat results were consistent between the 2 follow-up periods. However, for white meat, the second 5-year period showed less of an inverse trend compared with the first 5-year period (data not shown).

We investigated whether people who consumed a high-risk meat diet had mortality risk profiles that were different from people who consumed a low-risk meat diet. Both men and women who consumed a low-risk meat diet had statistically significant lower HRs compared with people who consumed a high-risk meat diet for all-cause, cancer, and CVD mortality, as well as all other deaths; for example, for all-cause mortality, the HR for a low-risk meat diet was 0.92 (95% CI, 0.80-0.94) for men and 0.80 (95% CI, 0.78-0.84) for women.

To further explore possible confounding by smoking, we analyzed meat intake and mortality in 2 subgroups—never smokers (15 413 deaths among 190 135 never smokers) and former/current smokers (n = 52 754 deaths among 335 036 former/current smokers). For men, the risks in the fifth quintile of red meat intake for never and former/current smokers were as follows: for total mortality, HR, 1.28 (95% CI, 1.19-1.38), and HR, 1.25 (95% CI, 1.20-1.30), respectively; for cancer mortality, HR, 1.16 (95% CI, 1.02-1.33), and HR, 1.17 (95% CI, 1.09-1.24), respectively; and for CVD mortality, HR, 1.43 (95% CI, 1.25-1.63), and HR, 1.17 (95% CI, 1.10-1.26), respectively. In women, the risks in the fifth quintile of red meat intake for never and former/current smokers were as follows: for total mortality, HR, 1.36 (95% CI, 1.25-1.48), and HR, 1.28 (95% CI, 1.21-1.35), respectively; for cancer mortality, HR, 1.10 (95% CI, 0.95-1.27), and HR, 1.16 (95% CI, 1.06-1.27), respectively; and for CVD mortality, HR, 1.63 (95% CI, 1.38-1.93), and HR, 1.34 (95% CI, 1.18-1.51), respectively. Risks were similar for the 2 smoking categories in most instances for processed meat except for cancer mortality, for which we found a null relation for both sexes in never smokers (men: HR, 1.01 [95% CI, 0.88-1.15]; women: HR, 1.02 [95% CI, 0.89-1.17]), but in former/current smokers we found higher risks (men: HR, 1.12 [95% CI, 1.05-1.19]; women: HR, 1.11 [95% CI, 1.02-1.21]). Intriguingly, there was increased risk with higher intake of white meat for CVD mortality in never smokers (men: HR, 1.24 [95% CI, 1.10-1.40]; women: HR, 1.20 [95% CI, 1.03-1.41]).

We calculated the population attributable risks, representing the percentage of deaths that could be prevented if individuals adopted red or processed meat intake levels of participants within the first quintile. For overall mortality, 11% of deaths in men and 16% of deaths in women could be prevented if people decreased their red meat consumption to the level of intake in the first quintile. The impact on CVD mortality was an 11% decrease in men and a 21% decrease in women if the red meat consumption was decreased to the amount consumed by individuals in the first quintile. The median red meat consumption based on men and women in the first quintile was 9.8 g/1000 kcal/d compared with 62.5 g/1000 kcal/d in the fifth quintile. For women eating processed meat at the first quintile level, the decrease in CVD mortality was approximately 20%. The median processed meat consumption based on men and women in the first quintile was 1.6 g/1000 kcal/d compared with 22.6 g/1000 kcal/d in the fifth quintile.

We examined total and cause-specific mortality in relation to meat consumption in a large prospective study. We found modest increases in risk for total mortality, as well as cancer and CVD mortality, with higher intakes of red and processed meat in both men and women. In contrast, higher white meat consumption was associated with a small decrease in total and cancer mortality in men and women.

The principal strength of this study is the large size of the cohort, which provided us the ability to investigate the relationship of many deaths (47 976 male deaths and 23 276 female deaths) within the context of a single study with a standardized protocol and a wide range of meat consumption. In contrast, other reports investigating meat intake in relation to mortality have pooled data from different studies conducted in California, the United Kingdom, and Germany because the numbers of events were limited in each study.16,914 The protocols and questionnaires in these studies were different, as were the populations: Seventh-Day Adventists in California and vegetarians and nonvegetarians in Europe. Pooled analyses of specialized populations with distinct healthy lifestyles are subject to unmeasured confounding. Furthermore, recall bias and reverse causality were minimized in our study because diet was assessed prior to the diagnosis of the conditions that led to death.

There is a possibility that some residual confounding by smoking may remain; however, we used a detailed 31-level smoking history variable and repeated the analyses within smoking status strata. Within smoking subgroups, we found consistent results for red, white, and processed meat intakes; however, there were some intriguing differences that could be further investigated. We found a positive association for processed meat intake and cancer mortality among former/current smokers but not among never smokers. This may be because we were still not able to fully statistically adjust for residual confounding of smoking because people who eat processed meat may also smoke. An additional reason could be that in addition to being exposed to N-nitroso compounds from processed meats, smokers inhale carcinogenic chemicals. The possible reason why there was an increased risk with white meat consumption among never smokers is not readily apparent.

Because our cohort was predominantly non-Hispanic white, more educated, consumed less fat and red meat and more fiber and fruits and vegetables, and had fewer current smokers than similarly aged adults in the US population, caution should be applied when attempting to generalize our findings to other populations,7 although this caution is somewhat tempered because it is unlikely that the mechanisms relating meat to mortality differ quantitatively between our study population and other white populations older than 50 years. Furthermore, the population-attributable risks in our cohort may be conservative estimates because red and processed meat consumption may be higher in the general population than in our cohort.

The inherent limitations of measurement error in this study are similar to those of any nutritional epidemiologic study that is based on recall of usual intake over a given period. We attempted to reduce measurement error by adjusting our models for reported energy intake.15 The correlations for red meat consumption assessed from the food frequency questionnaire compared with two 24-hour recall diaries were 0.62 for men and 0.70 for women, as reported previously by Schatzkin et al.7 The problem of residual confounding may still exist and could explain the relatively small associations found throughout this study despite the care taken to adjust for known confounders.

Overall, we did not find statistically significant association between meat consumption and deaths from injury and sudden deaths in most instances. The relative HRs of meat consumption with the other causes of death (total, cancer, and CVD mortality) were similar in magnitude in some cases to those of deaths from injury and sudden deaths; however, the number of deaths from injury and sudden deaths was less than the other causes of deaths, and thus the HRs were generally not statistically significant. We observed a higher risk with the category that included “all other deaths”; this is a broad category with many heterogeneous conditions (eg, diabetes mellitus, Alzheimer disease, stomach and duodenal ulcers, chronic liver disease, cirrhosis, nephritis, nephrotic syndrome, and nephrosis), some of which may be positively related to meat intake.

There are various mechanisms by which meat may be related to mortality. In relation to cancer, meat is a source of several multisite carcinogens, including heterocyclic amines and polycyclic aromatic hydrocarbons,1621 which are both formed during high-temperature cooking of meat, as well as N-nitroso compounds.22,23 Iron in red meat may increase oxidative damage and increase the formation of N-nitroso compounds.2427 Furthermore, meat is a major source of saturated fat, which has been positively associated with breast2830 and colorectal cancer.31

In relation to CVD, elevated blood pressure has been shown to be positively associated with higher intakes of red and processed meat, even though the mechanism is unclear, except that possibly meat may substitute for other beneficial foods such as grains, fruits, or vegetables.32 Mean plasma total cholesterol, low-density lipoprotein cholesterol, very-low-density lipoprotein cholesterol, and triglyceride levels were found to be decreased in subjects who substituted red meat with fish.33,34 Vegetarians have lower arachidonic, eicosapentaenoic, and docosahexaenoic acid levels and higher linoleate and antioxidant levels in platelet phospholipids; such a biochemical profile may be related to decreased atherogenesis and thrombogenesis.3436

Red and processed meat intakes, as well as a high-risk meat diet, were associated with a modest increase in risk of total mortality, cancer, and CVD mortality in both men and women. In contrast, high white meat intake and a low-risk meat diet was associated with a small decrease in total and cancer mortality. These results complement the recommendations by the American Institute for Cancer Research and the World Cancer Research Fund to reduce red and processed meat intake to decrease cancer incidence.31 Future research should investigate the relation between subtypes of meat and specific causes of mortality.

Correspondence: Rashmi Sinha, PhD, Division of Cancer Epidemiology and Genetics, National Cancer Institute–Nutritional Epidemiology Branch, 6120 Executive Blvd, Rockville, MD 20852 (sinhar@nih.gov).

Accepted for Publication: October 24, 2008.

Author Contributions: Drs Sinha and Cross had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors have given full approval to the final manuscript. Study concept and design: Sinha, Cross, and Graubard. Acquisition of data: Sinha and Schatzkin. Analysis and interpretation of data: Sinha, Cross, Graubard, Leitzmann, and Schatzkin. Drafting of the manuscript: Sinha, Cross, and Graubard. Critical revision of the manuscript for important intellectual content: Sinha, Cross, Graubard, Leitzmann, and Schatzkin. Statistical analysis: Sinha, Graubard, and Leitzmann. Obtained funding: Schatzkin. Administrative, technical, and material support: Cross and Schatzkin.

Financial Disclosure: None reported.

Funding/Support: This research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute (NCI).

Additional Contributions: Adam Risch, Leslie Carroll, MA, and Dave Campbell from Information Management Services Inc, and Traci Mouw, MS, from NCI, assisted in data management. We are indebted to the participants in the NIH-AARP Diet and Health Study for their outstanding cooperation. Cancer incidence data from the Atlanta metropolitan area were collected by the Georgia Center for Cancer Statistics, Department of Epidemiology, Rollins School of Public Health, Emory University. Cancer incidence data from California were collected by the California Department of Health Services, Cancer Surveillance Section. Cancer incidence data from the Detroit metropolitan area were collected by the Michigan Cancer Surveillance Program, Community Health Administration. The Florida cancer incidence data used in this report were collected by the Florida Cancer Data System under contract to the Department of Health (DOH) (the views expressed herein are solely those of the authors and do not necessarily reflect those of the contractor or the DOH). Cancer incidence data from Louisiana were collected by the Louisiana Tumor Registry, Louisiana State University Medical Center in New Orleans. Cancer incidence data from New Jersey were collected by the New Jersey State Cancer Registry, Cancer Epidemiology Services, New Jersey State Department of Health and Senior Services. Cancer incidence data from North Carolina were collected by the North Carolina Central Cancer Registry. Cancer incidence data from Pennsylvania were supplied by the Division of Health Statistics and Research, Pennsylvania Department of Health, Harrisburg (the Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions).

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Sinha  RRothman  NSalmon  CP  et al.  Heterocyclic amine content in beef cooked by different methods to varying degrees of doneness and gravy made from meat drippings. Food Chem Toxicol 1998;36 (4) 279- 287
PubMed
Skog  KSteineck  GAugustsson  KJagerstad  M Effect of cooking temperature on the formation of heterocyclic amines in fried meat products and pan residues. Carcinogenesis 1995;16 (4) 861- 867
PubMed
Sugimura  TWakabayashi  KOhgaki  HTakayama  SNagao  MEsumi  H Heterocyclic amines produced in cooked food: unavoidable xenobiotics. Princess Takamatsu Symp 1990;21279- 288
PubMed
Kazerouni  NSinha  RHsu  CHGreenberg  ARothman  N Analysis of 200 food items for benzo[a]pyrene and estimation of its intake in an epidemiologic study. Food Chem Toxicol 2001;39 (5) 423- 436
PubMed
Hughes  RCross  AJPollock  JRBingham  S Dose-dependent effect of dietary meat on endogenous colonic N-nitrosation. Carcinogenesis 2001;22 (1) 199- 202
PubMed
Cross  AJSinha  R Meat-related mutagens/carcinogens in the etiology of colorectal cancer. Environ Mol Mutagen 2004;44 (1) 44- 55
PubMed
Kato  IDnistrian  AMSchwartz  M  et al.  Iron intake, body iron stores and colorectal cancer risk in women: a nested case-control study. Int J Cancer 1999;80 (5) 693- 698
PubMed
Kabat  GCMiller  ABJain  MRohan  TE A cohort study of dietary iron and heme iron intake and risk of colorectal cancer in women. Br J Cancer 2007;97 (1) 118- 122
PubMed
Lee  DHJacobs  DR  JrFolsom  AR A hypothesis: interaction between supplemental iron intake and fermentation affecting the risk of colon cancer: the Iowa Women's Health Study. Nutr Cancer 2004;48 (1) 1- 5
PubMed
Wurzelmann  JISilver  ASchreinemachers  DMSandler  RSEverson  RB Iron intake and the risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev 1996;5 (7) 503- 507
PubMed
Bingham  SALuben  RWelch  AWareham  NKhaw  KTDay  N Are imprecise methods obscuring a relation between fat and breast cancer? Lancet 2003;362 (9379) 212- 214
PubMed
Thiébaut  ACKipnis  VChang  SC  et al.  Dietary fat and postmenopausal invasive breast cancer in the National Institutes of Health–AARP Diet and Health Study cohort. J Natl Cancer Inst 2007;99 (6) 451- 462
PubMed
Midthune  DKipnis  VFreedman  LSCarroll  RJ Binary regression in truncated samples, with application to comparing dietary instruments in a large prospective study. Biometrics 2008;64 (1) 289- 298
PubMed
The World Cancer Research Fund/American Institute for Cancer Research, Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective.  Washington, DC AICR2007;
Steffen  LMKroenke  CHYu  X  et al.  Associations of plant food, dairy product, and meat intakes with 15-y incidence of elevated blood pressure in young black and white adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Clin Nutr 2005;82 (6) 1169- 1177
PubMed
Gascon  AJacques  HMoorjani  SDeshaies  YBrun  LDJulien  P Plasma lipoprotein profile and lipolytic activities in response to the substitution of lean white fish for other animal protein sources in premenopausal women. Am J Clin Nutr 1996;63 (3) 315- 321
PubMed
Wolmarans  PBenade  AJKotze  TJDaubitzer  AKMarais  MPLaubscher  R Plasma lipoprotein response to substituting fish for red meat in the diet. Am J Clin Nutr 1991;53 (5) 1171- 1176
PubMed
Jacques  HGascon  ABergeron  N  et al.  Role of dietary fish protein in the regulation of plasma lipids. Can J Cardiol 1995;11 ((suppl G)) 63G- 71G
PubMed
Sanders  TAOakley  FRMiller  GJMitropoulos  KACrook  DOliver  MF Influence of n-6 versus n-3 polyunsaturated fatty acids in diets low in saturated fatty acids on plasma lipoproteins and hemostatic factors. Arterioscler Thromb Vasc Biol 1997;17 (12) 3449- 3460
PubMed

Figures

Tables

Table Graphic Jump LocationTable 1. Selected Age-Adjusted Characteristics of the National Institutes of Health–AARP Cohort by Red Meat Quintile Categorya
Table Graphic Jump LocationTable 2. Multivariate Analysis for Red, White, and Processed Meat Intake and Total and Cause-Specific Mortality in Men in the National Institutes of Health–AARP Diet and Health Studya
Table Graphic Jump LocationTable 3. Multivariate Analysis Red, White, and Processed Meat Intake and Total and Cause-Specific Mortality in Women in the National Institutes of Health–AARP Diet and Health Studya

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Appleby  PNThorogood  MMann  JIKey  TJ Low body mass index in non-meat eaters: the possible roles of animal fat, dietary fibre and alcohol. Int J Obes Relat Metab Disord 1998;22 (5) 454- 460
PubMed
Mann  JIAppleby  PNKey  TJThorogood  M Dietary determinants of ischaemic heart disease in health conscious individuals. Heart 1997;78 (5) 450- 455
PubMed
Snowdon  DAPhillips  RLFraser  GE Meat consumption and fatal ischemic heart disease. Prev Med 1984;13 (5) 490- 500
PubMed
Kipnis  VSubar  AFMidthune  D  et al.  Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol 2003;158 (1) 14- 21
PubMed
Knize  MGDolbeare  FACarroll  KLMoore  DH  IIFelton  JS Effect of cooking time and temperature on the heterocyclic amine content of fried beef patties. Food Chem Toxicol 1994;32 (7) 595- 603
PubMed
Sinha  RKnize  MGSalmon  CP  et al.  Heterocyclic amine content of pork products cooked by different methods and to varying degrees of doneness. Food Chem Toxicol 1998;36 (4) 289- 297
PubMed
Sinha  RRothman  NSalmon  CP  et al.  Heterocyclic amine content in beef cooked by different methods to varying degrees of doneness and gravy made from meat drippings. Food Chem Toxicol 1998;36 (4) 279- 287
PubMed
Skog  KSteineck  GAugustsson  KJagerstad  M Effect of cooking temperature on the formation of heterocyclic amines in fried meat products and pan residues. Carcinogenesis 1995;16 (4) 861- 867
PubMed
Sugimura  TWakabayashi  KOhgaki  HTakayama  SNagao  MEsumi  H Heterocyclic amines produced in cooked food: unavoidable xenobiotics. Princess Takamatsu Symp 1990;21279- 288
PubMed
Kazerouni  NSinha  RHsu  CHGreenberg  ARothman  N Analysis of 200 food items for benzo[a]pyrene and estimation of its intake in an epidemiologic study. Food Chem Toxicol 2001;39 (5) 423- 436
PubMed
Hughes  RCross  AJPollock  JRBingham  S Dose-dependent effect of dietary meat on endogenous colonic N-nitrosation. Carcinogenesis 2001;22 (1) 199- 202
PubMed
Cross  AJSinha  R Meat-related mutagens/carcinogens in the etiology of colorectal cancer. Environ Mol Mutagen 2004;44 (1) 44- 55
PubMed
Kato  IDnistrian  AMSchwartz  M  et al.  Iron intake, body iron stores and colorectal cancer risk in women: a nested case-control study. Int J Cancer 1999;80 (5) 693- 698
PubMed
Kabat  GCMiller  ABJain  MRohan  TE A cohort study of dietary iron and heme iron intake and risk of colorectal cancer in women. Br J Cancer 2007;97 (1) 118- 122
PubMed
Lee  DHJacobs  DR  JrFolsom  AR A hypothesis: interaction between supplemental iron intake and fermentation affecting the risk of colon cancer: the Iowa Women's Health Study. Nutr Cancer 2004;48 (1) 1- 5
PubMed
Wurzelmann  JISilver  ASchreinemachers  DMSandler  RSEverson  RB Iron intake and the risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev 1996;5 (7) 503- 507
PubMed
Bingham  SALuben  RWelch  AWareham  NKhaw  KTDay  N Are imprecise methods obscuring a relation between fat and breast cancer? Lancet 2003;362 (9379) 212- 214
PubMed
Thiébaut  ACKipnis  VChang  SC  et al.  Dietary fat and postmenopausal invasive breast cancer in the National Institutes of Health–AARP Diet and Health Study cohort. J Natl Cancer Inst 2007;99 (6) 451- 462
PubMed
Midthune  DKipnis  VFreedman  LSCarroll  RJ Binary regression in truncated samples, with application to comparing dietary instruments in a large prospective study. Biometrics 2008;64 (1) 289- 298
PubMed
The World Cancer Research Fund/American Institute for Cancer Research, Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective.  Washington, DC AICR2007;
Steffen  LMKroenke  CHYu  X  et al.  Associations of plant food, dairy product, and meat intakes with 15-y incidence of elevated blood pressure in young black and white adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Clin Nutr 2005;82 (6) 1169- 1177
PubMed
Gascon  AJacques  HMoorjani  SDeshaies  YBrun  LDJulien  P Plasma lipoprotein profile and lipolytic activities in response to the substitution of lean white fish for other animal protein sources in premenopausal women. Am J Clin Nutr 1996;63 (3) 315- 321
PubMed
Wolmarans  PBenade  AJKotze  TJDaubitzer  AKMarais  MPLaubscher  R Plasma lipoprotein response to substituting fish for red meat in the diet. Am J Clin Nutr 1991;53 (5) 1171- 1176
PubMed
Jacques  HGascon  ABergeron  N  et al.  Role of dietary fish protein in the regulation of plasma lipids. Can J Cardiol 1995;11 ((suppl G)) 63G- 71G
PubMed
Sanders  TAOakley  FRMiller  GJMitropoulos  KACrook  DOliver  MF Influence of n-6 versus n-3 polyunsaturated fatty acids in diets low in saturated fatty acids on plasma lipoproteins and hemostatic factors. Arterioscler Thromb Vasc Biol 1997;17 (12) 3449- 3460
PubMed

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