Study population

A cohort of 112 participants (31 men and 81 women) was recruited from the Lower Naugatuck Valley in Connecticut through flyers and newspaper advertisements. Interested participants (n=678) were prescreened over the telephone. This study included participants aged 25–75 years who were non-smokers and had a high risk for diabetes, which was defined as meeting at least one of the following criteria: overweight with increased waist circumference; prediabetes with fasting blood glucose >100 and <126 mg/dL or HbA1c 5.7–6.4%; metabolic syndrome, that is, meeting three out of five of the following criteria: (1) blood pressure ≥130/85 mm Hg or currently taking antihypertensive medication; (2) fasting plasma glucose >100 mg/dL (6.1 mmol/L); (3) serum triglycerides level >150 mg/dL (1.69 mmol/L); (4) HDL cholesterol <40 mg/dL (1.04 mmol/L) in men and<50 mg/dL (1.29 mmol/L) in women; (5) overweight (body mass index (BMI) ≥25 kg/m²) with waist circumference of more than 40 inches (102 cm) for men and more than 35 inches (88 cm) for women. Exclusion criteria included: allergy to walnuts or any other nuts; anticipated inability to complete study protocol for any reason; current eating disorder; restricted diets by choice (ie, vegetarian, vegan); receiving pharmacotherapy for obesity, including appetite suppressants; unstable use of lipid-lowering, antihypertensive medications or aspirin (ie, dose that had changed in the 3 months prior to enrollment) or unwilling to refrain from taking medication for 12 h prior to EF scanning; regular use of high doses of vitamin E (>400 IU/day) or vitamin C (>500 mg/day); intake of fish oil, flaxseed oil, ω-3 fatty acid, or fiber supplements unless welling to discontinue supplementation for the study duration; use of insulin, glucose-sensitizing medication, or vasoactive medications (including glucocorticoids, antineoplastic agents, psychoactive agents, or bronchodilators); diagnosed diabetes; diagnosed sleep apnea; established cardiovascular disease (including symptomatic coronary artery disease, myocardial infarction, peripheral vascular disease, congestive heart failure, or carotid stenosis); coagulopathy, known bleeding diathesis, or history of clinically significant hemorrhage; current use of warfarin; regular exercise defined as participation in moderate-intensity exercise ≥150 min/week; substance abuse (chronic alcoholism or other chemical dependency); any unstable medical condition (eg, cancer, AIDS, tuberculosis, psychotic disorder) that would limit the ability to participate fully in the trial; pregnant or lactating women; women receiving Depo-Provera shots; and/or women receiving hormone replacement therapy.

Participants who passed the telephone screening (n=333) underwent clinical screening examination consisting of the assessment of height, weight, BMI, and waist circumference; blood pressure measurements; and laboratory testing, including fasting serum lipids, fasting serum glucose, and HbA1c. The study protocol and consent form were approved by the Griffin Hospital (Derby, Connecticut, USA) Institutional Review Board. Signed informed consent was obtained from all study participants. All participants received monetary compensation for their participation. Participant participation and flow are shown in figure 1.

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Figure 1

Participant flow diagram.

Study design

This study was a randomized, controlled, modified Latin square parallel design study with two treatment arms. The study participants were randomized using a SAS-generated random table. The eligible participants were randomized using a permuted design in a 1:1 ratio between a walnut intake arm with dietary counseling to adjust calorie intake or a walnut intake arm without dietary counseling to adjust calorie intake, and within each arm they were further assigned to one of two different sequence permutations: walnut-included diet/walnut-excluded diet or walnut-excluded diet/walnut-included diet. For the duration of the study, participants underwent an intervention, control, and washout phase. During the intervention phase, participants consumed 392 g of walnuts per week for 6 months, and during the control phase, they excluded walnuts from their diet for 6 months. A 3-month washout phase was incorporated between the two phases. Participants were evaluated on five occasions during the study: immediately prior to phase I, at the mid-point of phase I (3 months), at the end point of phase I (6 months), at the mid-point of phase II (12 months), and at the end point of phase II (15 months).

Intervention

Walnut intake without calorie regulation: Participants were provided 392 g of walnuts per week (56 g or 2 oz/day providing 366 kcal) to include in their diet. Their caloric intake was not monitored or regulated, and thus was allowed to float ad libitum.

Walnut intake with caloric regulation: The intervention group participants met with a registered dietitian and received instructions and recipes for inclusion of 392 g of walnuts per week (56 g or 2 oz/day providing 366 kcal) in their meal plan. Participants received instruction to preserve an isocaloric condition after the addition of walnuts. The study dietitian customized dietary adjustments to make room for walnuts in the diet, while accommodating the priorities of each study participant. The general approach emphasized general reduction in portion sizes; participants also received advice, based on baseline dietary intake analysis, of food eliminations that they might want to consider. While the isocaloric condition was encouraged and monitored by the dietitian, participants were provided latitude in determining how they make room for the walnut calories, to better approximate real-world conditions.

Control diet: During the control phase (ie, walnut-excluded phase), participants received instructions to consume an ad libitum diet while avoiding walnuts and specific walnut-containing products.

Outcome measures

Diet quality: Diet quality was the primary outcome measure for the study. It was assessed using the Healthy Eating Index 2010 (HEI-2010). Participants completed 24 h recalls using a web-based Automated Self-Administrated 24-Hour Recall.16 The participants completed one 24 h recall at each time point of assessment. The HEI-2010 is a tool used for assessing diet quality as specified by the 2010 Dietary Guidelines for Americans. The HEI-2010 is used to assess changes in diet quality over time and the efficacy of nutritional interventions. It is also used to better understand relationships between nutrients, foods, dietary patterns, and health-related outcomes. The basic steps we used to determine HEI-2010 scores include: identifying the set of foods under consideration; determining the amount of each relevant food group, subgroup, and nutrient in the set of foods; deriving the pertinent ratios; and scoring each component using the appropriate standard.17

EF assessment: The brachial artery reactivity studies’ methodology used was as described in the published ‘Guidelines for Ultrasound Assessment of Endothelial-dependent Flow-mediated Vasodilation of the Brachial Artery’.18 EF was measured as flow-mediated dilation (FMD), the percentage change of brachial artery diameter from before cuff inflation to 60 s after cuff release. In addition to brachial diameter at 60 s after cuff release, flow after cuff deflation within the first 15 s was used as an indicator of stimulus strength, hyperemic flow being the stimulus for endothelial reactivity. To account for potential variability in stimulus strength, FMD was divided by flow at 15 s after cuff deflation to create a stimulus-adjusted response measure.

Lipid profile, fasting blood glucose and HbA1c: The lipid profile was determined as follows: Total cholesterol (Tchol), triglycerides (TRIG) and HDL were obtained by direct measurements. Very-low-density lipoprotein (VLDL) and LDL were obtained by calculation: VLDL=TRIG/5; and LDL=Tchol−(VLDL+HDL). HDL:Tchol ratio. Fasting blood glucose and HbA1c were also measured at each visit.

Body composition: Body composition was measured using bioelectrical impedance analysis, which uses the resistance of electrical flow through the body to estimate body fat. The Tanita SC-240 Body Composition Analyzer was used to measure body composition. The SC-240 Body Composition Analyzer measured weight and calculated body fat percentage and total body water percentage in addition to BMI.

Anthropometric measures: Body weight was measured using a calibrated digital scale and height was measured by using a calibrated stadiometer. BMI was calculated as weight (kg) divided by height in meters (m) squared. Waist circumference was measured using the guidelines of the National Obesity Expert Panel Report.

Blood pressure: Blood pressure was measured using an automatic blood pressure monitor at each visit. Systolic and diastolic blood pressure was measured using an approved automated device. Blood pressure was measured (average of three measurements with 3 min between measurements) with the participants sitting in a quiet room.

Physical activity: Physical activity was measured using the International Physical Activity Questionnaire (IPAQ),19 a valid, comprehensive, and reliable tool used to assess physical activity in adults. The IPAQ was used to collect information on weekly involvement in household and yard-work activities, occupational activity, transport, leisure time physical activity, and sedentary behavior.

Statistical analysis

Descriptive and exploratory analyses of all measured outcomes were conducted before embarking on modeling or hypothesis testing procedures. Distributions of variables met the criteria for analysis with parametric statistics. Log transformation of data or non-parametric analytic techniques was employed. Linear mixed model regressions were used to analyze the design. Other factors were incorporated into the regression models in order to adjust for potential confounding factors (ie, covariate imbalance between the treatment groups), such as individual characteristics (ie, age, gender, caloric intake, fiber intake, monounsaturated fat (MUFA) intake, PUFA intake, ω-3 intake, and physical activity level). All analyses of end points were based on the intention-to-treat principle. A p value of <0.05 was considered statistically significant. SAS software for Windows V.9.3 (SAS Institute, Cary, North Carolina, USA) was used to carry out all statistical analyses. Results are expressed as means±SD in text and tables.

The sample size was estimated to allow for 25% attrition and non-compliance and to provide ≥80% power to detect a minimal difference of 7.5 in HEI score20 between treatment groups, with a SD of 10 points in the HEI scale and maximum allowable type I error of 5% adjusted for three pair-wise comparisons.

Results

Of the 112 participants (31 men and 81 women), 97 completed the study. Two participants withdrew from the study for medical reasons unrelated to walnut consumption, four due to relocation, six due to loss to follow-up, one due to an allergic reaction to walnuts, one due to an inability to comply with the study protocol, and one due to mental and family issues. The study participants randomized to receive walnuts with dietary counseling to adjust their caloric intake were comparable in terms of demographics, diet quality, body composition, and marker of cardiovascular risk to those randomized to receive walnuts without advice to control their caloric intake. Demographic characteristics and baseline information of the study participants are presented in tables 1 and 2.

When compared with a walnut-excluded diet, a walnut-included diet for 6 months, with or without dietary counseling to adjust caloric intake, significantly improved diet quality as measured by the HEI-2010 (9.14±17.71 vs 0.40±15.13; p=0.02 and 7.02±15.89 vs −5.92±21.84; p=0.001, respectively).

EF, total and LDL cholesterol significantly improved from baseline after a walnut-included diet daily for 6 months with or without dietary counseling to adjust caloric intake. However, EF, total and LDL cholesterol did not significantly differ between the walnut-included diet for 6 months, with or without dietary counseling to adjust caloric intake compared with walnut-excluded diet. When compared with a walnut-excluded diet, a walnut-included diet for 6 months, with or without dietary counseling to adjust caloric intake, did not significantly improve (p>0.05) BMI, percent body fat, percent body water, or visceral fat. The walnut-included diet, when consumed without advice to adjust caloric intake, led to significant increases in percent body fat and visceral fat relative to baseline. Waist circumference significantly improved from baseline for the walnut-included diet for 6 months with dietary counseling to adjust caloric intake as well as in the walnut-excluded diet.

A walnut-included diet for 6 months, with or without dietary counseling to adjust caloric intake, did not improve (p>0.05) blood pressure and fasting blood glucose level in this sample of adults at risk for diabetes. HbA1c significantly increased from baseline after daily consumption of walnuts for 6 months with or without dietary counseling to adjust caloric intake. HbA1c level significantly increased from baseline also in the walnut-excluded diet.

Dietary counseling to adjust caloric intake to keep it constant with the addition of walnuts to the diet did not (p>0.05) enhance the beneficial effects of walnut consumption on diet quality, body composition, and vascular function.

Our findings persisted after controlling for age, gender, caloric intake, fiber intake, MUFA intake, PUFA intake, ω-3 intake, and physical activity level in regression models.