• nuts;
  • fiber;
  • overweight;
  • longitudinal analysis;
  • epidemiology


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Objective: To assess the association, in a Mediterranean population, between nut consumption and risk of weight gain (at least 5 kg) or the risk of becoming overweight/obese.

Research Methods and Procedures: The Seguimiento Universidad de Navarra project is a prospective cohort of 8865 adult men and women who completed a follow-up questionnaire after a median of 28 months. Dietary habits were assessed with a previously validated semiquantitative food-frequency questionnaire.

Results: Nine hundred thirty-seven participants reported a weight gain of ≥5 kg at follow-up. After adjusting for age, sex, smoking, leisure time physical activity, and other known risk factors for obesity, participants who ate nuts two or more times per week had a significantly lower risk of weight gain (odds ratio: 0.69; 95% confidence interval: 0.53 to 0.90, p for trend = 0.006) than those who never or almost never ate nuts. Participants with little nut consumption (never/almost never) gained an average of 424 grams (95% confidence interval: 102 to 746) more than frequent nut eaters. Nut consumption was not significantly associated with incident overweight/obesity in the cohort.

Discussion: Frequent nut consumption was associated with a reduced risk of weight gain (5 kg or more). These results support the recommendation of nut consumption as an important component of a cardioprotective diet and also allay fears of possible weight gain.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Nuts are an integral part of the Mediterranean food pattern (MFP),1 which includes a substantial intake of fat (up to 35% to 40% of total energy intake) (1). Adherence to the MFP has resulted in protection against mortality for all causes and against the development of coronary heart disease, stroke, hypertension, and cancer (2, 3, 4, 5, 6, 7, 8, 9).

Previous studies have consistently shown an association between nut consumption and reduced risk of cardiovascular diseases (CVD) (10, 11, 12, 13), as well as an improvement in serum lipid and lipoprotein profiles (11, 14, 15). For this reason, the American Heart Association has recommended nut consumption since 2000 (16). Nevertheless, there is continued concern that an increase in consumption of this energy-dense, high-fat food will lead to excessive weight gain. As a result, the belief that nuts are unhealthful because of their fat content is still widely accepted by the general public and health professionals alike. Adding to this concern is that obesity is a growing public health problem and a known risk factor for CVD (17, 18). In Europe, rates of obesity are higher in Mediterranean countries compared with Nordic countries (19). Therefore, there is the concern that nuts, as a part of the Mediterranean diet (20), could explain the obesity trend in the Mediterranean region.

Current epidemiological evidence on this issue, which could affect public health recommendations, is scarce and based mostly on indirect studies (10, 13). However, the association between nut consumption and the risk of weight gain (or becoming overweight/obese) has, to our knowledge, never been assessed in a free-living Mediterranean population.

Therefore, we examined the association between frequency of nut consumption and the risk of weight gain (or becoming overweight/obese) in a Mediterranean cohort of university graduates.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Study Population

The SUN project [Seguimiento Universidad de Navarra (University of Navarra Follow-up)] is a prospective cohort study designed to establish associations between diet and the occurrence of several diseases and chronic conditions including obesity (21). Information is collected through self-administered questionnaires sent by mail every 2 years.

The recruitment of participants, all of whom are university graduates, started in December 1999 and is permanently open, because the study was designed to be a dynamic cohort. As of December 2005, the dataset of the SUN Project included 16,378 participants. All participants who completed a baseline assessment (Q_0) before December 31 2003 were eligible for these analyses (n = 11,714). Among them, 1474 did not answer the 2-year follow-up questionnaire (Q_2); after five mailings, they were considered lost to follow-up. We retained 10,240 participants (89.2%) who successfully completed the Q_2. Participants who reported extreme (low or high) values for total energy intake (<800 kcal/d for men, <500 kcal/d for women or >4000 kcal/d for men, >3500 kcal/d for women; n = 1039) were excluded, as were subjects with missing values in variables of interest and participants with biologically implausible values for weight and/or height. Ultimately, data from 8865 participants were used in the statistical analyses.

The study was approved by the Institutional Review Board at the University of Navarra. Informed consent was implied by the voluntary completion of the baseline questionnaire.

Assessment of Dietary Exposure

Dietary habits were assessed through a baseline semiquantitative food-frequency questionnaire that has been validated in Spain (22).

The questionnaire was based on typical portion sizes and had nine options for the average frequency of intake in the previous year of 136 food items (ranging from never/almost never to at least six times per day).

Questions on nut consumption included walnuts, almonds, hazelnuts, and peanuts. These four types represent >95% of the total nut intake in the Spanish population (23, 24). In an on-going validation study with more detailed data related to nut consumption (food records), walnuts were found to be the most frequently consumed nut, followed by hazelnuts, almonds, and peanuts, respectively. Nutrient intake scores were computed using an ad hoc computer program. A trained dietitian updated the nutrient data bank using the most up-to-date food composition tables for Spain (25, 26).

Assessment of Non-dietary Variables

The baseline assessment also included other questions (46 items for men and 54 for women) to assess medical history, health habits, and lifestyle and sociodemographic variables. Participants were classified as non-smoker, former smoker, or current smoker. A physical activity questionnaire, measuring participants’ involvement in 17 activities, was also completed at baseline. To quantify the volume of activity exerted during leisure time, an activity metabolic equivalent (MET) index was computed. A multiple of resting metabolic rate (MET score) was assigned to each activity (27), and time spent engaging in each activity was multiplied by the MET score specific to that activity. The MET-hours for all activities were combined to obtain a value of total weekly MET-hours, which adequately correlated (Spearman ρ = +0.51; p = 0.002) with energy expenditure measured in a validated subsample of the cohort (28).

Assessment of the Outcome

Participants’ weight was recorded at baseline and Q_2, which was completed at least 2 years after baseline (median follow-up time = 28 months). The reliability and validity of self-reporting weight was assessed in a representative subsample of the cohort. The mean relative error in self-reported weight was 1.45%, and the correlation coefficient between measured and self-reported weight was 0.99 [95% confidence interval (CI): 0.98 to 0.99] (29).

The outcomes were 1) an increase in body weight of at least 5 kg during follow-up categorized as a dichotomous variable (cut-off point ≥ 5 kg); 2) change in body weight during follow-up as a continuous variable [weight in Q_2 minus weight in Q_0 (kg)], and 3) incident overweight/obesity (participants with a BMI value lower than 24.9 kg/m2 at baseline and a BMI ≥ 25 kg/m2 at follow-up). We also repeated the analyses using only participants who showed incident obesity (participants with a BMI < 29.9 kg/m2 at baseline and >30 kg/m2 at follow-up).

Statistical Analyses

Nonconditional logistic regression models were fit to assess the relationship between the frequency of nut consumption (categorized as never/almost never, one to three times per month, once a week, or at least two times per week, based on the frequency distribution of the variables) and the risk of weight gain (at least 5 kg at follow-up), as well as the risk of becoming overweight/obese (BMI ≥ 25 kg/m2). Odds ratios (ORs) and their 95% CIs were calculated, using participants who indicated little nut consumption (never/almost never) as the reference category. Tests of linear trend across increasing categories of consumption were conducted by assigning medians for the frequency of intake of each category and treating them as a continuous variable. Least squares regression models were used to assess the association between frequency of nut consumption and weight change at follow-up. β regression coefficients (and their 95% CIs) for the three other categories of participants were estimated using never/almost never consumption as the reference category. These coefficients represent the absolute difference in weight gain between participants in the lowest consumption category and the other three categories.

We fit a crude (univariate) model, an age- and sex-adjusted model, and a multivariate model after additional adjustment for the following variables: baseline BMI, leisure time physical activity (METs-hours per week), smoking status (non-smoker, smoker, former smoker), snacking between meals (yes, no), and TV watching (hours per week). We evaluated all first-order multiplicative interactions (effect modification) through product terms.

All p values presented are two-tailed; p < 0.05 was considered statistically significant, unless otherwise specified.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

The results did not reveal a positive relationship between frequency of nut consumption and baseline BMI in the cross-sectional analyses (Table 1). Subjects with more frequent nut consumption were less likely to smoke and more likely to be physically active. Nut consumption was positively associated with higher values of total fat intake and higher values of total energy intake. However, participants with more frequent nut consumption reported lower saturated fat and higher mono- and polyunsaturated fat intake. Fiber intake was positively associated with frequent nut consumption. Overall, the average weight of participants increased at follow-up (Table 2).

Table 1.  Characteristics* of participants according to baseline frequency of nut consumption
 Frequency of nut consumption (50-g serving)
  • *

    Mean and SD unless otherwise stated.

  • Adjusted for sex and age.

  • SFA, saturated fatty acids; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids.

 Never/ almost neverOne to three times per monthOnce per weekAt least two times per week
Total nut consumption (g/d)<3.33.3–7.07.1–21.3≥21.4
Age (years)35.6 (11.9)36.7 (11.8)37.6 (12.0)41.5 (13.1)
Men (%)33.140.846.951.0
BMI (kg/m2)23.6 (2.9)23.5 (2.9)23.2 (2.9)22.9 (2.9)
Baseline weight (kg)67.5 (9.4)67.1 (9.4)66.6 (9.4)65.5 (9.4)
Television watching (h/wk)12.4 (10.5)12.1 (10.1)11.5 (9.9)12.4 (10.8)
Physical activity (MET-h/wk)22.5 (19.7)23.5 (19.9)24.4 (20.5)27.1 (22.8)
Current smokers (%)26.324.926.721.1
Former smokers (%)26.426.324.332.3
Total energy intake (kcal/d)2172 (580)2354 (578)2510 (579)2674 (583)
Total fat intake (% of energy)36.3 (7.1)37.0 (6.3)37.4 (6.1)38.0 (6.3)
 SFA (% of energy)12.7 (3.4)12.9 (3.0)13.0 (3.0)11.7 (3.1)
 MUFA (% of energy)15.6 (4.0)15.9 (3.5)16.0 (3.4)16.4 (3.5)
 PUFA (% of energy)5.0 (1.7)5.2 (1.5)5.4 (1.4)6.1 (1.5)
Protein intake (% of energy)18.7 (3.6)18.1 (3.0)17.6 (2.8)17.2 (2.7)
Carbohydrate intake (% of energy)43.2 (7.9)43.0 (7.2)43.0 (6.9)42.6 (7.2)
Fiber intake (g/d)24.6 (11.4)25.7 (10.9)27.7 (10.4)34.3 (13.1)
Alcohol intake (g/d)5.6 (9.9)6.2 (9.6)7.1 (9.6)8.3 (11,6)
Snacking between meals (%)32.833.337.233.8
Table 2.  Mean weight change (g; 95% CI) at 28-month follow-up
 Age groups (median)
 <30 years (25)30 to 50 years (38)>50 years (56)
 Men (n = 855)Women (n = 2253)Men (n = 1799)Women (n = 2420)Men (n = 1045)Women (n = 493)
Weight change (g)+974+627+332+908+32+335
95% CI+585 to +1363+471 to +782+136 to +529+724 to +1092−218 to +281+35 to +634

We documented 937 incident cases of weight gain (≥5 kg), as reported at the 28-month follow-up (10.6% of the cohort). Frequency of nut consumption at baseline was inversely associated with weight gain in the age- and sex-adjusted analyses (Table 3). The OR in participants consuming nuts at least two times per week compared with those who never/almost never ate nuts was 0.61 (95% CI: 0.47 to 0.79; p for trend < 0.001). In multivariate models, baseline BMI was the strongest confounder. When we controlled for baseline BMI (continuous variable), the OR was slightly attenuated to 0.69 (95% CI: 0.53 to 0.90; p for trend = 0.006). The OR was virtually unchanged after controlling for other potential confounders, including leisure time physical activity, smoking status, snacking between meals, and television watching. When we additionally adjusted for total energy intake or fiber intake, the inverse association remained [0.71 (95% CI: 0.54 to 0.93); p for trend = 0.013 and 0.73 (95% CI: 0.55 to 0.96); p for trend = 0.026, respectively]. We did not find evidence of a threshold effect when analyzing a more detailed categorization of energy-adjusted nut consumption (data not shown).

Table 3.  ORs and 95% CIs for weight gain ≥5 kg at follow-up according to frequency of nut consumption
 Frequency of nut consumption (50-g serving)
 Never/ almost neverOne to three times per monthOnce per weekAt least two times per weekp for trend
  • *

    The model included age (years); sex; baseline BMI (kg/m2); leisure-time physical activity (MET-h/wk); smoking status (non-smoker, smoker, former smoker); snacking (no, yes); television watching (h/wk). An interaction term (age × sex, p < 0.001) was also added.

Incident cases of weight gain ≥5 kg, n (%)214 (11.6)444 (10.8)194 (10.9)85 (7.4) 
Crude OR (95% CI)1.00 (Ref.)0.93 (0.78 to 1.10)0.94 (0.76 to 1.15)0.61 (0.47 to 0.79)<0.001
Age- and sex-adjusted OR (95% CI)1.00 (Ref.)0.93 (0.78 to 1.11)0.94 (0.77 to 1.16)0.66 (0.51 to 0.86)0.002
Multivariate-adjusted OR* (95% CI)1.00 (Ref.)0.94 (0.79 to 1.12)0.95 (0.77 to 1.11)0.69 (0.53 to 0.90)0.006
Additionally adjusted for total energy intake OR* (95% CI)1.00 (Ref.)0.94 (0.80 to 1.13)0.96 (0.78 to 1.19)0.71 (0.54 to 0.93)0.013
Additionally adjusted for total fiber intake OR* (95% CI)1.00 (Ref.)0.95 (0.79 to 1.13)0.97 (0.78 to 1.19)0.73 (0.55 to 0.96)0.026

Using weight gain as a continuous outcome variable, the results from least squares regression showed that participants with a low frequency of consumption (never/almost never) gained 350 grams (95% CI: 29 to 672 grams) more than participants with a higher frequency. The estimate for the difference in weight gain was 424 grams (95% CI: 102 to 746 grams) after adjusting for relevant confounders in the multivariate model (Table 4). This observed inverse association was attenuated when no exclusion caused by outlying limits of energy intake was made (i.e., when all participants, including those with implausible values for total energy intake, were analyzed). The statistical significance for the multivariate adjusted p for trend in Table 4 was lost because it changed from p = 0.018 to p = 0.153 when all participants were included (regardless of total energy intake).

Table 4.  Estimates (regression coefficients and 95% CIs) for the subsequent weight gain (g) according to frequency of nut consumption
 Frequency of nut consumption (50-g serving) 
 Never/ almost neverOne to three times per monthOnce per weekAt least two times per weekp for trend
  • *

    The model included age (years); baseline BMI (kg/m2); leisure-time physical activity (MET-h/wk); smoking status (non-smoker, smoker, former smoker); snacking (no, yes); television watching (h/wk). An interaction term (age × sex, p < 0.001) was also added.

Absolute weight change+684+552+752+3330.210
[g, mean (95% CI)](+490 to +878)(+409 to +694)(+556 to +947)(+120 to +547) 
Differences in weight change     
 Crude (Regression coefficient, β)0 (Ref.)−132 (−372 to +108)+68 (−217 to +352)−350 (−672 to −29)0.068
 Age- and sex-adjusted (Regression coefficient, β)0 (Ref.)−98 (−338 to +142)+128 (−157 to +413)−225 (−550 to +100)0.301
 Multivariate adjusted model* (Regression coefficient, β)0 (Ref.)−136 (−373 to +100)+11 (−271 to +293)−424 (−746 to −102)0.018
 Additionally adjusted for total energy intake (Regression coefficient, β)0 (Ref.)−131 (−369 to +107)+20 (−266 to +307)−410 (−740 to −80)0.026
 Additionally adjusted for fiber (Regression coefficient, β)0 (Ref.)−126 (−362 to +111)+43 (−240 to +326)−331 (−661 to −1)0.091

To assess the association between frequency of nut consumption and the risk of becoming overweight/obese (BMI ≥ 25 kg/m2), we analyzed 6300 subjects who were not overweight or obese at baseline. After follow-up (28 months later), we observed 434 incident cases of participants who became overweight/obese. Frequency of nut consumption was inversely associated with the risk of becoming overweight/obese. When we adjusted for age and sex, participants who consumed nuts at least two times per week reduced their odds of becoming overweight/obese by 43% (OR = 0.57; 95% CI: 0.39 to 0.83) compared with those who never/almost never consumed nuts. Nevertheless, the inverse association was weaker (OR = 0.73; 95% CI: 0.49 to 1.10) after adjusting for several potential confounders (Table 5). When obesity was assessed (BMI ≥ 30 kg/m2) as the single outcome, we observed 139 incident cases of obesity among 8487 participants who did not exhibit obesity at baseline. In the sex- and age-adjusted model, the OR of becoming obese for participants who consumed nuts at least two times per week was 0.50 (95% CI: 0.25 to 0.99), but this did not remain statistically significant after multivariate adjustment (OR = 0.88; 95% CI: 0.41 to 1.86). Both in the models for the risk of weight gain ≥5 kg at follow-up and in the least-squares models, the age × sex interaction was the only product term found to be statistically significant.

Table 5.  ORs and 95% CIs for the risk of becoming overweight/obese at follow-up in 6300 participants according to frequency of nut consumption
 Frequency of nut consumption (50-g serving)
 Never/ almost neverOne to three times per monthOnce per weekAt least two times per weekp for trend
  • *

    The model included age (years); sex; baseline BMI (kg/m2); leisure time physical activity (MET-h/wk); smoking status (non-smoker, smoker, former smoker); snacking (no, yes); television watching (h/wk). When we additionally adjusted for total fiber intake, the results did not change substantially.

Incident cases of overweight/obesity, n (%)99 (7.3%)195 (6.7%)96 (7.7%)44 (5.6%) 
Crude OR (95% CI)1.00 (Ref.)0.92 (0.71 to 1.18)1.06 (0.79 to 1.42)0.75 (0.52 to 1.09)0.202
Age- and sex-adjusted OR (95% CI)1.00 (Ref.)0.84 (0.65 to 1.08)0.91 (0.68 to 1.22)0.57 (0.39 to 0.83)0.007
Multivariate-adjusted OR* (95% CI)1.00 (Ref.)0.87 (0.66 to 1.15)0.97 (0.70 to 1.34)0.73 (0.49 to 1.10)0.206
Additionally adjusted for total energy intake1.00 (Ref.)0.87 (0.66 to 1.15)0.97 (0.70 to 1.35)0.73 (0.48 to 1.11)0.214


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

In this prospective study of a Mediterranean population, frequent nut consumption was associated with a significantly reduced risk of weight gain at follow-up (a median of 28 months after baseline). We observed an increase in average weight after follow-up in all participants, regardless of frequency of nut consumption. However, participants with a higher frequency of nut consumption gained the least amount of weight. Participants who consumed nuts at least two times per week were 30% less likely to gain weight (5 kg or more) in comparison with those who rarely ate nuts. The proportion of total energy intake caused by nuts across categories of weight change (weight loss ≥ 5 kg; weight loss < 5 kg; no change; weight gain < 5 kg; weight gain ≥ 5 kg) was homogeneous (1.5%, 1.8%, 1.8%, 1.7%, and 1.4%, respectively; data not shown). Nuts do provide additional energy intake. The percentage of total energy intake of nuts among participants in the highest consumption category (at least two times per week) was 7.2%. Total energy intake was not adjusted for in some models, because adjusted models hold the assumption of isocaloric intake. The inverse relationship between nut consumption and weight gain, even when not adjusted for calorie intake, further supports our hypothesis. However, because energy intake can also confound this association, analyses adjusting for total energy intake were also conducted. The energy-adjusted estimates were similar to the unadjusted estimates.

While two times a week may seem like a low frequency, when this category was split into two (two to four times per week and at least five times per week), the results went in the same direction. The crude ORs for weight gain ≥5 kg were 0.63 (95% CI: 0.47 to 0.85) among participants who consumed two to four times per week and 0.58 (95% CI: 0.38 to 0.88) among those who consumed at least five times per week. When models were multivariately adjusted, the ORs continued to be protective but did not achieve statistical significance; this is most likely because of poor statistical power, because only 27 incident cases of obesity among participants who ate nuts at least five times per week were observed.

Confounding may account for the observed inverse association, because participants with frequent nut consumption also reported healthier lifestyles and better overall diet than those who rarely ate nuts. After adjusting for potential confounders (i.e., leisure time physical activity, smoking status, snacking between meals, television watching, fiber intake, and total energy intake), the associations, as expected, were attenuated; however, a clear and statistically significant risk reduction did remain. While some residual confounding is possible, it is unlikely to fully explain the strong inverse association that we observed.

Selection bias could provide an alternative explanation for the inverse association between nut consumption and weight change. However, this would hold true only if the probability of being lost to follow-up were associated with nut consumption, which it is not (30).

Another potential concern is the possibility of an inaccurate assessment of nut consumption. We acknowledge that our estimates of nut consumption may present some degree of measurement error, but this is to be expected in nutritional epidemiology (31, 32). Nevertheless, the semiquantitative food-frequency questionnaire completed by the cohort shows reasonable accuracy (22). Misclassification of exposure would most likely bias the ORs toward the null value. However, when there are more than two categories in the exposure variable, the bias from non-differential misclassification of exposure may be away from the null value. Regardless, this is unlikely, because trend reversal cannot occur if the mean exposure measurement increases with true exposure (33).

Another possible limitation is the self-reporting of participants’ weight. The validity of self-reported weight was shown in a representative subgroup of the cohort, which revealed a 1.45% mean relative error in self-reporting (29).

We acknowledge that our cohort is non-representative of the general population, as it consists exclusively of university graduates, and in Spain, a high educational level is associated with a lower prevalence of obesity (34). While we cannot extend our findings to the population with lower educational levels, this potential limit does not affect the internal validity of our study, because it is biologically implausible to deduce that the effect of nut consumption on weight gain can be modified by educational levels (33).

Our results are consistent with other epidemiological studies. In a recent randomized cross-over trial, Sabaté et al. (35) found a minimal weight gain among subjects who ate 28 to 56 grams of walnuts daily for 6 months. Moreover, there was no weight change when it was controlled for energy intake. In 2002, Fraser et al. (36) conducted a cross-over study of 81 subjects, which showed that the daily incorporation of a modest quantity of almonds for 6 months did not lead to significant changes in body weight. Other small trials have found that when nuts are added to one's diet, there is no associated weight gain, even when total energy intake increases substantially (37, 38). In other studies, the observed weight gain associated with nut consumption was considerably lower than predicted (39). However, these trials were relatively short term and, therefore, gave no indication of the lasting effect that nut intake may have on weight.

When the association between nut consumption and type 2 diabetes was assessed in the Nurses’ Health Study, the authors reported that women who consumed more nuts tended to weigh less (40). When the association between nut consumption and coronary heart disease was examined in the Adventist Health Study cohort, a statistically significant inverse cross-sectional association was found between nut consumption and BMI (10). In the Physicians’ Health Study, men who consumed nuts two or more times per week had lower BMIs (cross-sectional assessment) (13).

Other dietary intervention studies have revealed similar findings to ours (14, 41, 42, 43, 44, 45). However, the majority were not designed to evaluate body weight, and some controlled for total energy intake, making it difficult to draw definitive conclusions.

There are various mechanistic hypotheses that could explain the biological plausibility of the association between nut consumption and a lack of weight gain, despite the higher total energy intake among frequent nut eaters. Although nuts are high in fat, the fat is mostly unsaturated. The combination of unsaturated fat and high protein content found in nuts can lead to an increase in resting energy expenditure and diet-induced thermogenesis (46, 47, 48). Nuts may increase satiety, because they are a good source of fiber and vegetable proteins, both of which are known to increase satiety ratings (49, 50, 51, 52). Our estimates (Table 4) consistently showed that adjusting for fiber lessened the strength of the association and resulted in a lack of statistical significance. Therefore, we posit that a high supply of fiber may be a mechanistic explanation of why nuts may aid in the prevention of weight gain. This idea is supported by the results of a study by Lairon et al. (53) that examined fiber intake in a cross-sectional study. Wien et al. (54) have also shown that the substitution of nuts for carbohydrates improves insulin sensitivity, leading to weight loss. The structure of lipid-storing granules in nuts, together with their various fiber components and incomplete mastication, may cause a low level of fat absorption that could result in a loss of available energy (55, 56, 57). In addition, other mechanistic explanations may be related to the several bioactive compounds that are present in nuts (58).

There is great controversy as to whether fat, compared with other sources of calories, is more likely to cause weight gain (59, 60, 61). This idea has come to the forefront of debate after recently published results from the Women's Health Initiative Dietary Modification Trial, which showed no substantial weight loss after adhering to a low-fat diet (62). McManus et al. (63) reported that obese subjects who followed a moderate-fat weight-reduction diet (including a variety of nuts, peanut butter, and olive oil) experienced greater and more sustained weight loss than obese subjects who followed a low-fat diet. The moderate-fat diet also had a higher compliance.

Our study has many strengths, including its prospective design, which avoids the possibility of inverse causation effects of the reported associations. Another is the previous validation of the methods used to analyze the main variables. In addition, our cohort comes from a cultural dietary environment that had never before been studied using a prospective assessment (35, 36).

We did not control for socioeconomic status (SES) because we were confident that the SES of the participants was not a major confounder, because the cohort was relatively homogeneous with regard to SES. All participants attained the same educational level (i.e., restriction was used to control for SES as a confounding factor). Although similar educational level does not guarantee similar income, educational level has proven to be influential in the evaluation of SES (64). Analyses that have taken into account education, occupation, income, and employment status have shown that education is the strongest determinant of socioeconomic differences (65).

Because our results come from an observational study, we cannot prove a causal relationship. Nevertheless, we were able to identify an association between frequent nut consumption and a reduced risk of weight gain in a cohort of highly educated participants from a Mediterranean country.

A large primary prevention trial of CVD with MFP (the PREDIMED Study) is on-going. The pilot study of this trial (n = 772) showed no weight gain after 3 months in participants assigned to nuts (n = 257) (66). Our results reveal the importance of conducting such a trial regarding weight maintenance. According to this research, nut consumption does not seem to be the cause of the high rates of obesity in southern Europe. Moreover, we found an inverse association between weight gain and nut consumption, although it was attenuated when outliers in total energy intake were included. Other factors that could play an important role in obesity trends are a sedentary lifestyle and the adoption of Westernized dietary patterns (67, 68). A Westernized diet is more prevalent among children and young adults (69). We found that people who consumed more nuts were usually older (Table 1), meaning that our findings could be caused by a generational effect. However, when we adjusted for age and other potential confounders, the inverse association remained. Further studies are necessary to evaluate possible long-term effects of nut consumption on weight change. These data and those of other epidemiological and clinical studies could lead to the recommendation of frequent nut consumption, which, in turn, could lead to a decreased risk of cardiovascular disease. It is important to emphasize the recommendation of nuts as a substitute for other energy-dense snacks that lack nutritional value to facilitate beneficial changes in dietary habits.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

We thank the participants of the SUN Study for continued cooperation and participation. We thank the other members of the SUN Study Group: C de la Fuente, A Sánchez-Villegas, J de Irala, M Seguí-Gómez, M Delgado-Rodríguez, M Serrano-Martínez, A Tortosa, Z Vázquez, F Pastor, A Marti, M Muñoz, F Guillen-Grima and I Aguinaga. We thank Joan Fernandez-Ballart for help in assessing nut consumption in the Valfreco validation study. We also thank our advisors from the Harvard School of Public Health, who helped to design the SUN cohort study. This research was supported by the Department of Health of the Navarra Regional Government (PI41–2005) and the Spanish Ministry of Health (Instituto de Salud Carlos III, Fondo de Investigaciones Sanitarias, Projects PI042241, PI040233, PI050976 and G03–140, Red Temática de Dieta y Enfermedad Cardiovascular).

  • 1

    Nonstandard abbreviations: MFP, Mediterranean food pattern; CVD, cardiovascular disease; SUN, Seguimiento Universidad de Navarra; MET, metabolic equivalent; CI, confidence interval; OR, odds ratio; SES, socioeconomic status.

  • The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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