Study Population

The Nurses' Health Study (NHS) is a prospective cohort study of 121,700 female nurses from 11 U.S. states; participants were enrolled in 1976. The Health Professionals Follow-up Study (HPFS) is a prospective cohort study of 51,529 male health professionals from all 50 states, enrolled in 1986. Follow-up questionnaires are sent biennially to update medical and lifestyle information. Follow-up rates exceed 90% in each 2-year cycle for both cohorts.

For this analysis, we defined baseline as the year of the first validated food-frequency questionnaire in each study — 1980 for the NHS and 1986 for the HPFS. At baseline, 92,468 women in the NHS and 49,934 men in the HPFS completed the dietary questionnaire. We excluded 5611 women and 5939 men with a history of cancer, heart disease, or stroke; 1113 women and 340 men who did not provide information on nut intake; and 9280 women and 1157 men who did not provide information on anthropometric measures or physical activity. The final analyses included 76,464 women in the NHS and 42,498 men in the HPFS.

The authors assume full responsibility for the analyses and interpretation of the data in this study. The funders of the study had no role in its design or conduct; in the collection, management, analysis, or interpretation of the data; or in the preparation, review, or approval of the manuscript. The study was approved by the human subjects committees at Brigham and Women's Hospital and Harvard School of Public Health, and all participants provided written informed consent. In addition, the study was approved by the Connecticut Department of Public Health Human Investigations Committee, and some data used in the study were obtained from the Connecticut Department of Public Health.

Dietary Assessment

Dietary intake was measured with the use of validated food-frequency questionnaires administered every 2 to 4 years. In the 1980 and 1984 dietary questionnaires, we asked the participants how often they had consumed a serving of nuts (serving size, 28 g [1 oz]) during the preceding year: never or almost never, one to three times a month, once a week, two to four times a week, five or six times a week, once a day, two or three times a day, four to six times a day, or more than six times a day. In the subsequent dietary questionnaires, the question regarding nuts was split into two items: peanuts and other nuts. Total nut consumption was defined as the intake of peanuts and other nuts. A validation study of the food-frequency questionnaire indicated that nut intake was reported reasonably accurately; the correlation coefficient was 0.75 for the correlation between intake assessed on the baseline dietary questionnaire and intake assessed on four 1-week dietary records.32

Ascertainment of Deaths

Our primary end point was death from any cause. We performed systematic searches of the vital records of states and of the National Death Index. This search was supplemented by reports from family members and postal authorities. With the use of these methods, we were able to ascertain more than 98% of the deaths in each cohort.33

A physician who was unaware of the data on nut consumption and other risk factors reviewed death certificates and medical records to classify the cause of death according to the eighth and ninth revisions of the International Classification of Diseases. Deaths were grouped into nine major categories (Table S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org).

Statistical Analysis

To better represent long-term diet and to minimize any effects of within-person variation, we calculated the cumulative average of nut consumption. Because participants may alter dietary patterns after the diagnosis of a major illness, we suspended further updating of all dietary variables when participants reported a diagnosis of stroke, heart disease, angina, or cancer, although follow-up continued until death or the end of the study period.

We used Cox proportional-hazards models to estimate hazard ratios and 95% confidence intervals. Multivariate models were adjusted for known or suspected predictors of death. P values for trend were calculated with the use of the Wald test of a score variable based on the median number of servings of nuts consumed per day for each category of nut consumption. We also used restricted-cubic-spline regression to flexibly model the association.

We conducted several sensitivity analyses to test the robustness of the results. To minimize the influence of smoking or an extremely low or high body-mass index (BMI; the weight in kilograms divided by the square of the height in meters) on the results, we excluded participants who had ever smoked or who had a BMI of less than 18.5 or more than 40. We also excluded participants who had diabetes at baseline, and we suspended updating of dietary variables after a diagnosis of diabetes during study follow-up. To assess the influence of total sodium intake, adherence to a Mediterranean diet (as assessed by the Mediterranean-diet score34), and olive-oil intake on the results, we conducted separate analyses with adjustment for each of these variables. Finally, we conducted an analysis in which updating of dietary variables was continued even after a participant reported a diagnosis of a major chronic disease.

To address the concern that occult chronic diseases in the years that preceded diagnosis may have influenced dietary patterns, in the analysis in which we continuously updated dietary information after diagnosis of chronic disease, we excluded the first 2 years of follow-up data and added a 2-year lag period between nut-intake assessment and each follow-up period (e.g., in the NHS, we used nut intake from the 1980 questionnaire for the follow-up period from 1982 to 1984, and so forth).

To address the possibility of residual confounding by measured variables, we further adjusted for a propensity score that reflected associations of nut consumption with the other variables in the multivariate-adjusted model.35 In addition, to estimate the influence of unmeasured confounding on the results, we used the array-approach sensitivity analysis described by Schneeweiss36 in order to determine how strong and imbalanced the confounder would need to be to reduce an association to nonsignificance. We performed separate secondary analyses for peanuts and tree nuts, as well as analyses stratified by other risk factors. For these analyses, we combined categories of high nut intake to maintain statistical power. The likelihood-ratio test was used to test for interaction.

The hazard ratios from multivariate models in each cohort were pooled with the use of the random-effects model, which allowed for between-study heterogeneity. P values for heterogeneity were calculated with the use of the Q statistic. Analyses were performed with the SAS statistical package (version 9.1, SAS Institute). Statistical tests were two-sided, and P values of less than 0.05 were considered to indicate statistical significance.