Abstract Background Tree nut consumption has been associated with reduced diabetes risk, however, results from randomized trials on glycemic control have been inconsistent. Objective To provide better evidence for diabetes guidelines development, we conducted a systematic review and meta-analysis of randomized controlled trials to assess the effects of tree nuts on markers of glycemic control in individuals with diabetes. Data Sources MEDLINE, EMBASE, CINAHL, and Cochrane databases through 6 April 2014. Study Selection Randomized controlled trials ≥3 weeks conducted in individuals with diabetes that compare the effect of diets emphasizing tree nuts to isocaloric diets without tree nuts on HbA1c, fasting glucose, fasting insulin, and HOMA-IR. Data Extraction and Synthesis Two independent reviewer’s extracted relevant data and assessed study quality and risk of bias. Data were pooled by the generic inverse variance method and expressed as mean differences (MD) with 95% CI’s. Heterogeneity was assessed (Cochran Q-statistic) and quantified (I2). Results Twelve trials (n = 450) were included. Diets emphasizing tree nuts at a median dose of 56 g/d significantly lowered HbA1c (MD = −0.07% [95% CI:−0.10, −0.03%]; P = 0.0003) and fasting glucose (MD = −0.15 mmol/L [95% CI: −0.27, −0.02 mmol/L]; P = 0.03) compared with control diets. No significant treatment effects were observed for fasting insulin and HOMA-IR, however the direction of effect favoured tree nuts. Limitations Majority of trials were of short duration and poor quality. Conclusions Pooled analyses show that tree nuts improve glycemic control in individuals with type 2 diabetes, supporting their inclusion in a healthy diet. Owing to the uncertainties in our analyses there is a need for longer, higher quality trials with a focus on using nuts to displace high-glycemic index carbohydrates. Trial Registration ClinicalTrials.gov NCT01630980

Citation: Viguiliouk E, Kendall CWC, Blanco Mejia S, Cozma AI, Ha V, Mirrahimi A, et al. (2014) Effect of Tree Nuts on Glycemic Control in Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Dietary Trials. PLoS ONE 9(7): e103376. https://doi.org/10.1371/journal.pone.0103376 Editor: C. Mary Schooling, CUNY, United States of America Received: January 12, 2014; Accepted: June 22, 2014; Published: July 30, 2014 Copyright: © 2014 Viguiliouk et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the International Tree Nut Council Nutrition Research & Education Foundation and (Davis, California) and the Canadian Institutes of Health Research (funding reference number, 129920) through the Canada-wide Human Nutrition Trialists’ Network (NTN). The Diet, Digestive tract, and Disease (3-D) Centre, funded through the Canada Foundation for Innovation (CFI) and the Ministry of Research and Innovation’s Ontario Research Fund (ORF), provided the infrastructure for the conduct of this project. VH and AC were funded by Province of Ontario Graduate Scholarships. AC was also funded by a Canadian Institutes of Health Research (CIHR)-Fredrick Banting and Charles Best Canada Graduate Scholarship and Banting and Best Diabetes Centre (BBDC)-Novo Nordisk Studentship. RJdS was funded by a CIHR Postdoctoral Fellowship Award. DJAJ was funded by the Government of Canada through the Canada Research Chair Endowment. None of the sponsors had a role in any aspect of the present study, including design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, approval of the manuscript or decision to publish. Competing interests: CWCK has received research support from the Advanced Foods and Material Network, Agrifoods and Agriculture Canada, the Almond Board of California, the American Pistachio Growers, Barilla, the California Strawberry Commission, the Calorie Control Council, CIHR, the Canola Council of Canada, the Coca-Cola Company (investigator initiated, unrestricted grant), Hain Celestial, the International Tree Nut Council Nutrition Research and Education Foundation, Kellogg, Kraft, Loblaw Companies Ltd., Orafti, Pulse Canada, Saskatchewan Pulse Growers, Solae and Unilever. He has received travel funding, consultant fees and/or honoraria from Abbott Laboratories, the Almond Board of California, the American Peanut Council, the American Pistachio Growers, Barilla, Bayer, the Canola Council of Canada, the Coca-Cola Company, Danone, General Mills, the International Tree Nut Council Nutrition Research and Education Foundation, Kellogg, Loblaw Companies Ltd., the Nutrition Foundation of Italy, Oldways Preservation Trust, Orafti, Paramount Farms, the Peanut Institute, PepsiCo, Pulse Canada, Sabra Dipping Co., Saskatchewan Pulse Growers, Solae, Sun-Maid, Tate and Lyle, and Unilever. He is on the Dietary Guidelines Committee for the Diabetes Nutrition Study Group of the European Association for the Study of Diabetes and has served on the scientific advisory board for the Almond Board of California, the International Tree Nut Council, Oldways Preservation Trust, Paramount Farms and Pulse Canada. VH has received research support from the CIHR and the World Health Organization (WHO) for work on a systematic review and meta-analysis commissioned by WHO of the relation of saturated fatty acids with health outcomes. She received a travel award to attend a science day hosted by PepsiCo Inc. and the New York Academy of Sciences. LC has received research support from the CIHR and the Agricultural Bioproducts Innovation Program through the Pulse Research Network (PURENet), and Saskatchewan Pulse Growers. She is also a casual clinical research coordinator at Glycemic Index Laboratories. RJdS is funded by a CIHR Postdoctoral Fellowship Award and has received research support from the CIHR, the Calorie Control Council, the Canadian Foundation for Dietetic Research and the Coca-Cola Company (investigator initiated, unrestricted grant). He has served as an external resource person to WHO’s Nutrition Guidelines Advisory Group and received travel support from WHO to attend group meetings. He is the lead author of 2 systematic reviews and meta-analyses commissioned by WHO of the relation of saturated fatty acids and trans fatty acids with health outcomes. DJAJ has received research grants from Saskatchewan Pulse Growers, the Agricultural Bioproducts Innovation Program through the Pulse Research Network, the Advanced Foods and Material Network, Loblaw Companies Ltd., Unilever, Barilla, the Almond Board of California, the Coca-Cola Company (investigator initiated, unrestricted grant), Solae, Haine Celestial, the Sanitarium Company, Orafti, the International Tree Nut Council Nutrition Research and Education Foundation, the Peanut Institute, the Canola and Flax Councils of Canada, the Calorie Control Council, the CIHR, the Canada Foundation for Innovation and the Ontario Research Fund. He has been on the speaker’s panel, served on the scientific advisory board, and/or received travel support and/or honoraria from the Almond Board of California, Canadian Agriculture Policy Institute, Loblaw Companies Ltd, the Griffin Hospital (for the development of the NuVal scoring system), the Coca- Cola Company, Saskatchewan Pulse Growers, Sanitarium Company, Orafti, the Almond Board of California, the American Peanut Council, the International Tree Nut Council Nutrition Research and Education Foundation, the Peanut Institute, Herbalife International, Pacific Health Laboratories, Nutritional Fundamental for Health, Barilla, Metagenics, Bayer Consumer Care, Unilever Canada and Netherlands, Solae, Kellogg, Quaker Oats, Procter & Gamble, the Coca-Cola Company, the Griffin Hospital, Abbott Laboratories, the Canola Council of Canada, Dean Foods, the California Strawberry Commission, Haine Celestial, PepsiCo, the Alpro Foundation, Pioneer Hi- Bred International, DuPont Nutrition and Health, Spherix Consulting and WhiteWave Foods, the Advanced Foods and Material Network, the Canola and Flax Councils of Canada, the Nutritional Fundamentals for Health, Agri-Culture and Agri-Food Canada, the Canadian Agri-Food Policy Institute, Pulse Canada, the Saskatchewan Pulse Growers, the Soy Foods Association of North America, the Nutrition Foundation of Italy (NFI), Nutra- Source Diagnostics, the McDougall Program, the Toronto Knowledge Translation Group (St. Michael’s Hospital), the Canadian College of Naturopathic Medicine, The Hospital for Sick Children, the Canadian Nutrition Society (CNS), the American Society of Nutrition (ASN), Arizona State University, Paolo Sorbini Foundation and the Institute of Nutrition, Metabolism and Diabetes. He received an honorarium from the United States Department of Agriculture to present the 2013 W.O. Atwater Memorial Lecture. He received the 2013 Award for Excellence in Research from the International Nut and Dried Fruit Council. He received funding and travel support from the Canadian Society of Endocrinology and Metabolism to produce mini cases for the Canadian Diabetes Association. His wife is a director and partner of Glycemic Index Laboratories, and his sister received funding through a grant from the St. Michael’s Hospital Foundation to develop a cookbook for one of his studies. JLS has received research support from the CIHR, Calorie Control Council, the Coca-Cola Company (investigator initiated, unrestricted grant), Pulse Canada, and the International Tree Nut Council Nutrition Research and Education Foundation. He has received travel funding, speaker fees, and/or honoraria from the American Heart Association, the American Society for Nutrition, the National Institute of Diabetes and Digestive and Kidney Diseases, the Canadian Diabetes Association, the Canadian Nutrition Society, the Calorie Control Council, the Diabetes and Nutrition Study Group of the European Association for the Study of Diabetes, the International Life Sciences Institute North America, the International Life Sciences Institute Brazil, the University of South Carolina, the University of Alabama at Birmingham, the Canadian Sugar Institute, Oldways Preservation Trust, the Nutrition Foundation of Italy, Abbott Laboratories, Pulse Canada, Dr. Pepper Snapple Group, Corn Refiners Association, and the Coca-Cola Company. He is on the Clinical Practice Guidelines Expert Committee for Nutrition Therapy of both the Canadian Diabetes Association and the European Association for the Study of Diabetes, and he is on the American Society for Nutrition writing panel for a scientific statement on the metabolic and nutritional effects of fructose, sucrose and high-fructose corn syrup. He is a member of the Carbohydrate Quality Consortium and an unpaid scientific advisor for the Food, Nutrition and Safety Program of the International Life Science Institute North America. His wife is an employee of Unilever Canada. No competing interests were declared by EV, SBM, AIC, AM, VHJ, LSAA, and LL. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Introduction Tree nuts are a healthy source of vegetable protein [1], unsaturated fatty acids [1], [2], fibre [3], antioxidants [4], vitamins (i.e. folic acid, vitamin B6, niacin, tocopherols), minerals (i.e. magnesium, potassium, calcium), and phytochemicals (i.e. phytosterols) [5]. Over the last two decades, a large body of evidence regarding tree nut consumption and related health outcomes has emerged from both epidemiological and controlled trials [1]. A recent large pooled analysis of two of the Harvard cohorts, as well as a recent meta-analysis of prospective cohort studies support an all-cause mortality benefit [6], [7]. There is also strong evidence that tree nuts lower LDL-cholesterol [8], which has resulted in an FDA qualified health claim [9] and their inclusion in heart association guidelines for cardiovascular risk reduction [10], [11]. The data for diabetes related outcomes have not been as consistent. Although some cohort studies show that frequent nut consumption is associated with lower incidence of type 2 diabetes [12], [13], other cohort studies do not [14], [15]. In addition, 2 recent systematic review and meta-analysis of prospective cohort studies showed no overall significant association between nut consumption and type 2 diabetes risk [7], [16], which was consistent with a subgroup analysis of the PREDIMED trial that showed a non-significant reduction in diabetes risk for individuals consuming a Mediterranean diet supplemented with nuts in comparison to a low fat diet [17]. There has been relatively few controlled trials that have specifically investigated the effects of tree nuts on glycemic control. Despite coronary heart disease being a major cause of death in individuals with diabetes, consumption of tree nuts alone have not been included as part of the recommendations in most diabetes guidelines [18]–[20], with the exception of their recent inclusion as part of various dietary/eating patterns (DASH, Mediterranean, vegetarian and vegan, and low carbohydrate diets) in American (ADA) and Canadian (CDA) diabetes association clinical practice guidelines [19], [20]. Primary prevention and management of diabetes through diet and lifestyle modification remains the cornerstone of therapy [21], [22]. In order to provide better evidence-based guidance on the role of tree nuts on glycemic control, a systematic review and meta-analysis of randomized controlled dietary trials was performed to assess the effect of tree nuts under isocaloric conditions on the endpoints HbA1c, fasting glucose, fasting insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) in individuals with diabetes. The primary outcome and measurement of this study consists of a pooled analysis for each glycemic endpoint.

Methods The Cochrane Handbook for Systematic Reviews of Interventions was followed for the planning and conduct of this meta-analysis [23]. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [24]. The review protocol is available at ClinicalTrials.gov (registration number: NCT01630980). Data Sources and Searches We searched the databases MEDLINE, EMBASE, CINAHL, and the Cochrane Central Register of Controlled Trials through 6 April 2014 using the search strategy shown in Table S1. Manual searches of references also supplemented the electronic search. Study Selection We included randomized controlled dietary trials that compared a diet emphasizing intake of tree nuts (almonds, Brazil nuts, cashews, hazelnuts, macadamia nuts, pecans, pine nuts, pistachios and walnuts) [25] on HbA1c, fasting glucose, fasting insulin, and HOMA-IR in comparison to diets without tree nuts matched for energy (isocaloric) for a follow-up period ≥3 weeks in people with diabetes. Trials that consisted of a non-randomized treatment allocation, <3 weeks of follow-up duration, non-isocaloric comparisons, lacked a suitable control, were not conducted in individuals with diabetes, or did not provide suitable endpoint data were excluded. No restrictions were placed on language. Data Extraction and Quality Assessment Two investigators (EV and SB) independently reviewed all reports that met the inclusion criteria. A standardized form was used to extract relevant information on sample size, subject characteristics (health status, gender, age, weight, etc.), study setting, study design, level of feeding control, nut dose, nut type and form (whole or meal form), comparator, macronutrient breakdown of background diet(s), energy balance, follow-up duration, and funding source. The mean ± SD values were extracted for HbA1c, fasting glucose, fasting insulin, and HOMA-IR. Trials that did not report SD’s were derived from available data (95% CI, P-values, t or F statistics, SE) using standard formulae [23]. The quality of each trial was assessed using the Heyland Methodological Quality Score (MQS) where a maximum score of 13 points could be received on the basis of the trials methods, sample, and intervention [26]. Trials receiving scores of ≥8 were considered to be of higher quality. Disagreements on Heyland MQS scores were reconciled by consensus. Study quality was not assessed for those trials reported exclusively in a published abstract. Trials were assessed for risk of bias using the Cochrane Risk of Bias Tool [23]. Domains of bias assessed were sequence generation, allocation concealment, blinding, outcome data, and outcome reporting. Trials were marked as high risk of bias when the methodological flaw was likely to have affected the true outcome, low risk of bias if the flaw was deemed inconsequential to the true outcome, and unclear risk of bias when insufficient information was provided to permit judgment. All disagreements were resolved by consensus. Authors were contacted for additional information where necessary [27]–[30]. Data Synthesis and Analysis Data were analyzed using Review Manager (RevMan), version 5.2 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark) for primary analyses. The difference between the intervention and control arm’s change from baseline value was derived from each trial for the endpoints HbA1c, fasting glucose, fasting insulin, and HOMA-IR. If change from baseline values were not available, end-of-treatment values were used. For trials containing multiple intervention or control arms a weighted average was applied to combine them in order to create single pair-wise comparisons and to mitigate the unit-of-analysis error. Paired analyses were conducted for all crossover trials [31]. Where necessary, a pooled correlation coefficient was derived and used for calculation of an imputed SD for the between-treatment difference for some crossover trials. Correlation coefficients between baseline and end-of-treatment values within each individual crossover trial were derived from the reported within and between treatment SD according to a published formula [31]. These correlation coefficients were transformed into z-scores ± SD, meta-analyzed using inverse-variance weighing, and back transformed to derive the pooled correlation coefficient. Where we could not derive a calculated pooled correlation coefficient for imputing missing SDs we assumed a correlation coefficient of 0.5, as it is a conservative estimate for an expected range of 0–1. A correlation coefficient of 0.5 was assumed in the primary analysis for HbA1c due to insufficient data and in the primary analyses for fasting glucose and insulin owing to considerable heterogeneity between the derived correlation coefficients (only 2 available correlation coefficients available for pooling in both analyses). The values derived from each trial were pooled and analyzed for each endpoint (HbA1c, fasting glucose, fasting insulin, and HOMA-IR) using the generic inverse variance method with random effects models, which was used even in the absence of statistically significant between-study heterogeneity, as they yield more conservative summary effect estimates in the presence of residual heterogeneity. Exceptions were made for the use of fixed-effects models where there were <5 included trials irrespective of heterogeneity or small trials being pooled with larger more precise trials in the absence of statistically significant between-study heterogeneity. Data were expressed as mean differences (MD) with 95% CI. A two-sided p-value <0.05 was set as the level of significance for comparisons of MD. Inter-study heterogeneity was tested using the Cochran Q-statistic and quantified using the I2-statistic with a significance level set at p-value <0.10. An I2<50%, I2≥50% and I2≥75% were considered to be evidence of “moderate”, “substantial” and “considerable” heterogeneity, respectively [23]. Sources of heterogeneity were explored using sensitivity and subgroup analyses. To determine whether a single trial exerted an undue influence on the overall results, sensitivity analyses were performed in which each individual trial was removed from the meta-analysis and the effect size recalculated with the remaining trials. Sensitivity analyses were also undertaken using correlation coefficients of 0.25, 0.50 and 0.75 to determine whether the overall results were robust to the use of different derived correlation coefficients in paired analyses of crossover trials. A priori subgroup analyses (continuous and categorical) were conducted for baseline values of HbA1c, fasting glucose, fasting insulin and HOMA-IR within the intervention arm, nut type, absolute fiber and saturated fat intake within the intervention arm, difference in fiber and saturated fat intake between the intervention and control arm, change in fiber and saturated fat intake from baseline within the intervention arm, dose, design, follow-up, and study quality (MQS). Post-hoc subgroup analyses were conducted for the difference in percent carbohydrate intake between the control and intervention arm (carbohydrate displacement), sex and BMI. Meta-regression was performed to assess the significance of the subgroup effects with STATA software, version 12.0 (StataCorp, College Station, TX) with a significance level set at p-value <0.05. Publication bias was investigated by visual inspection of funnel plots and quantitatively assessed using Egger’s and Begg’s tests, where a p-value <0.05 was considered evidence of small study effects.

Discussion To our knowledge this is the first systematic review and meta-analysis of randomized controlled trials to assess the effect of tree nut consumption on HbA1c, fasting glucose, fasting insulin, and/or HOMA-IR in individuals with type 2 diabetes. We included 12 randomized controlled dietary trials looking at the effect of tree nuts on these 4 endpoints in 450 predominantly middle-aged adults. Pooled analyses showed an overall significant lowering of HbA1c of −0.07% and fasting glucose of −0.15 mmol/L at a median dose of 56 g/d over a median duration of ∼8 weeks. Although significant advantages for fasting insulin and HOMA-IR were not seen, the direction of effect favoured the tree nut intervention. There is limited existing research looking at the ability of nuts to improve glycemic control over the long term. Previous studies looking at dietary patterns including nuts, such as the Mediterranean and the Dietary Approaches to Stop Hypertension (DASH) dietary pattern, are consistent with our findings. In a systematic review of 5 randomized controlled trials [39], as well as several individual randomized controlled trials [40]–[43] including people with type 2 diabetes, a Mediterranean dietary pattern emphasizing nuts showed decreases in HbA1c (from −0.1% to −0.6% absolute reduction), fasting glucose, and the need for antihyperglycemic drugs over a 4 year period [40], [44] in comparison to a conventional dietary pattern. Improvements in other markers related to glycemic control, such as the adiponectin/leptin ratio, have also been reported [45]. Similar findings were found regarding the DASH dietary pattern emphasizing nuts, where a randomized controlled trial conducted in people with type 2 diabetes showed that compared with a control diet (matched for a moderate sodium intake), the DASH dietary pattern was able to improve HbA1c (an absolute reduction of −1.2%) and fasting glucose (−0.92 mmol/L) over an 8 week period [46]. The ability of tree nuts to improve glycemic control may relate to a carbohydrate displacement mechanism by which tree nuts reduce the glycemic load of the diet by displacing high glycemic-index carbohydrates. Of the 3 trials that showed a significant lowering in HbA1c, the two trials contributing the greatest amount of weight to the analysis (>80% collectively) [29], [33] investigated the effect of tree nuts as a means of displacing carbohydrate by ≥5% of energy [33]. The addition of similar trials in future meta-analyses would be expected to strengthen our results, however, it is not clear whether this lowering would reach a clinically meaningful threshold of ≥0.3% [10]. Other proposed factors relate to the micro- and macronutrient profile of nuts, such as magnesium and monounsaturated fat (MUFA) content. Magnesium content of tree nuts can range from 121–376 mg and MUFA from 9–59 g per 100 g [1], providing approximately between 30% to 94% and 14% and 91% of the Daily Value (DV) for magnesium and total fat, respectively [47]. Meta-analyses of prospective cohort studies and randomized double-blind controlled trials looking at magnesium intake in individuals with type 2 diabetes support decreases in diabetes risk [48], [49], as well as benefits for glycemic control [50]. Magnesium is thought to play a key role in insulin-mediated glucose uptake [51], [52], where animal studies have shown poor intracellular magnesium concentrations to result in defective tyrosine-kinase activity at the insulin-receptor level and therefore impairing insulin action [52], [53]. Similarly, in a meta-analysis of randomized controlled trials looking at the effects of MUFA on glycemic control in individuals with abnormal glucose metabolism, diets high in MUFA were shown to significantly reduce HbA1c [54]. Human trials and animal studies suggest that MUFA may also be involved in the insulin-signaling pathway by playing a role in membrane translocation of glucose transporters in skeletal muscle, as well as by buffering β-cell hyperactivity and insulin resistance [54]–[56]. Although our results do not show significant improvements in insulin resistance by HOMA or fasting insulin levels, there were a limited number of trials and a significant amount of heterogeneity present in the primary analyses. In addition, neither endpoint is a good marker of peripheral insulin sensitivity [57]. Overall, these proposed mechanisms suggest that carbohydrate displacement, magnesium and MUFA content of nuts may be contributing factors in facilitating the effect seen on glycemic control. Heterogeneity was not explained by majority of the subgroup analyses, where only two subgroups, follow-up and study design in the categorical subgroup analyses for HbA1c, approached significance. Trials of ≥12 weeks showed a greater lowering-effect of tree nuts on HbA1c than trials of <12 weeks. These results suggest that tree nut consumption over a longer period (from 12 weeks to 24 months among available trials) may lead to greater improvements in glycemic control. Similarly, trials using a parallel design showed a greater lowering-effect of tree nuts on HbA1c than those using a crossover design. The smaller effect of tree nuts in crossover designs may be attributed to carry-over effects [23]. In our meta-analysis, all 5 trials with a crossover design contained a washout period ranging from 1–8 weeks. Since HbA1c reflects mean glycemia for the previous 3 months [58], it is not certain whether this is long enough to diminish any potential carry-over effects. Several limitations exist in the present meta-analysis that complicates the interpretation of our results. First, it is uncertain whether the length of the follow-up period in these trials is enough time for tree nuts to significantly alter glycemic control. HbA1c levels reflect blood glucose levels in the preceding 3 months (∼90 days or 12 weeks) [58], whereas more than half of the trials (54%) were less than 12 weeks in duration. Second, there was evidence of substantial and considerable between study heterogeneity in the overall primary analyses for fasting insulin and HOMA-IR, respectively, which was not explained by any of the a priori and post-hoc subgroup analyses. In addition, majority of subgroup analyses were underpowered and it was not possible to assess the effect of other factors that may influence glycemic control (i.e. growing conditions of tree nuts) due to unavailability of data. Third, the majority of the trials (81%) were of poor study quality (MQS<8), however, no effect modification by study quality was found in the subgroup analyses. Fourth, a portion of the trials (27%) did not focus on glycemic control endpoints as their primary outcome. In conclusion, the present systematic review and meta-analysis of randomized controlled trials shows that a daily median intake of 56 g (∼2 ounces or ∼½ cup) of tree nuts over a median duration of 8 weeks significantly reduces HbA1c and fasting glucose in individuals with type 2 diabetes. Although significant advantages were not seen for fasting insulin and HOMA-IR, the direction of effect favoured tree nuts. The greatest advantages appear to be seen in trials using tree nuts to displace high-glycemic index carbohydrate to affect a low-glycemic load diet. To address the sources of uncertainty in our analyses, there is a need for large, longer, higher quality trials using tree nuts to lower the glycemic load of the diet by displacing high-glycemic index carbohydrates with a specific focus on glycemic endpoints as a primary outcome. The inclusion of such trials in future meta-analyses will help guide the development of nutrition recommendations and health claims, as well as the planning of future trials. Overall, our data support the inclusion of tree nuts as part of a healthy diet for the management of glycemia in individuals with type 2 diabetes.

Supporting Information Figure S1. Cochrane Risk of Bias Graph. Risk of bias graph: review authors’ judgments about each risk of bias item presented as percentages across all included studies (with the exception of Sauder et al. [29]). https://doi.org/10.1371/journal.pone.0103376.s001 (TIFF) Figure S2. Categorical a priori and post-hoc subgroup analyses for HbA1c. CHO = carbohydrate; N = number of subjects; MQS = Heyland Methodological Quality Score; SFA = saturated fatty acid. Point estimates for each subgroup level (diamonds) are the pooled effect estimates. The dashed line represents the pooled estimate for the overall (total) analysis. The residual I2 value indicates heterogeneity unexplained by the subgroup. Pairwise between-subgroup mean differences (95%CIs) for nut type were as follows: 0.15 [−0.20, 0.49] (1 vs. 4); 0.18 [−0.18, 0.55] (1 vs. 5); 0.04 [−0.33, 0.40] (1 vs. 6); −0.03 [−0.23, 0.16] (4 vs. 5); 0.11 [−0.09, 0.31] (4 vs. 6); 0.14 [−0.09, 0.37] (5 vs. 6). Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline (B) values within the treatment arm (E–B). *Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s002 (PDF) Figure S3. Categorical a priori and post-hoc subgroup analyses for fasting glucose. CHO = carbohydrate; N = number of subjects; MQS = Heyland Methodological Quality Score; SFA = saturated fatty acid. Point estimates for each subgroup level (diamonds) are the pooled effect estimates. The dashed line represents the pooled estimate for the overall (total) analysis. The residual I2 value indicates heterogeneity unexplained by the subgroup. Pairwise between-subgroup mean differences (95%CIs) for nut type were as follows: −0.81 [−2.41, 0.79] (1 vs. 2); −0.65 [−2.03, 0.73] (1 vs. 3); 0.23 [−0.15, 0.61] (1 vs. 4); 0.73 [−0.14, 1.60] (1 vs. 5); 0.09 [−0.46, 0.64] (1 vs. 6); −0.16 [−2.23, 1.91] (2 vs. 3); −1.04 [−2.64, 0.56] (2 vs. 4); −1.54 [−3.32, 0.23] (2 vs. 5); −0.90 [−2.55, 0.75] (2 vs. 6); −0.88 [−2.25, 0.49] (3 vs. 4); −1.38 [−2.97, 0.20] (3 vs. 5); −0.74 [−2.17, 0.69] (3 vs. 6); −0.50 [−1.37, 0.36] (4 vs. 5); 0.14 [−0.40, 0.68] (4 vs. 6); 0.64 [−0.31, 1.59] (5 vs. 6). Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline (B) values within the treatment arm (E–B). *Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s003 (PDF) Figure S4. Categorical a priori and post-hoc subgroup analyses for fasting insulin. CHO = carbohydrate; N = number of subjects; MQS = Heyland Methodological Quality Score; SFA = saturated fatty acid. Point estimates for each subgroup level (diamonds) are the pooled effect estimates. The dashed line represents the pooled estimate for the overall (total) analysis. The residual I2 value indicates heterogeneity unexplained by the subgroup. Pairwise between-subgroup mean differences (95%CIs) for nut type were as follows: −13.00 [−65.37, 39.37] (1 vs. 2); 6.45 [−38.57, 51.47] (1 vs. 4); 20.81 [−19.98, 61.59] (1 vs. 5); −19.45 [−79.26, 40.36] (2 vs. 4); −31.81 [−90.50, 22.88] (2 vs. 5); −14.36 [−64.34, 35.63] (4 vs. 5). Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline (B) values within the treatment arm (E–B). * Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s004 (PDF) Figure S5. Categorical a priori and post-hoc subgroup analyses for HOMA-IR. CHO = carbohydrate; N = number of subjects; MQS = Heyland Methodological Quality Score; SFA = saturated fatty acid. Point estimates for each subgroup level (diamonds) are the pooled effect estimates. The dashed line represents the pooled estimate for the overall (total) analysis. The residual I2 value indicates heterogeneity unexplained by the subgroup. Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline (B) values within the treatment arm (E–B). * Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s005 (PDF) Table S1. Search strategy. For all databases, the original search was 23 May 2012; updated searches were performed 14 May 2013 and 6 April 2014. https://doi.org/10.1371/journal.pone.0103376.s006 (DOCX) Table S2. Study Quality Assessment using the Heyland MQS*. HF = high fat diet; LF = low fat diet; MQS = Methodological Quality Score. * The Heyland MQS assigns a score of 0 or 1 or from 0 to 2 over 9 categories of quality related to study design, sampling procedures, and interventions for a total of 13 points. Trials that scored ≥8 were considered to be of higher quality [25]. † Randomization was scored 2 points for being randomized with the methods described, 1 point for being randomized without the methods described, or 0 points for being neither randomized nor having the methods described. Blinding was scored 1 point for being double-blind or 0 points for “other.” Analysis was scored 2 points for being intention-to-treat; all other types of analyses scored 0 points. ‡ Sample selection was scored 1 point for being consecutive eligible or 0 points for being preselected or indeterminate. Sample comparability was scored 1 point for being comparable or 0 points for not being comparable at baseline. Follow-up was scored 1 point for being 100% or 0 points for <100%. § Treatment protocol was scored 1 point for being reproducibly described or 0 points for being poorly described. Co-interventions were scored 2 points for being described and equal, 1 point for being described but unequal or indeterminate, or 0 points for not being described. Treatment crossovers (where participants were switched from the control treatment to the experimental treatment) were scored 2 points for being <10%, 1 point for being >10%, and 0 points for not being described. || Study quality for this study was not assessed since data for this study was limited (the study’s conferences abstract and correspondence with the authors were the only sources of available data). https://doi.org/10.1371/journal.pone.0103376.s007 (DOCX) Table S3. Continuous a priori and post-hoc subgroup analyses for HbA1c. BMI = body mass index; CHO = carbohydrate; E = energy; M = males; N = number of subjects; No. = number; SFA = saturated fatty acid. β is the slope derived from subgroup analyses on meta-regression analyses and represents the treatment effect of tree nuts for each subgroup. The residual I2 value indicates heterogeneity unexplained by the subgroup. Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline (B) values within the treatment arm (E–B). *Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s008 (DOCX) Table S4. Continuous a priori and post-hoc subgroup analyses for fasting glucose. BMI = body mass index; CHO = carbohydrate; E = energy; M = males; N = number of subjects; No. = number; SFA = saturated fatty acid. β is the slope derived from subgroup analyses on meta-regression analyses and represents the treatment effect of tree nuts for each subgroup. The residual I2 value indicates heterogeneity unexplained by the subgroup. Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline (B) values within the treatment arm (E–B). * Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s009 (DOCX) Table S5. Continuous a priori and post-hoc subgroup analyses for fasting insulin. BMI = body mass index; CHO = carbohydrate; E = energy; M = males; N = number of subjects; No. = number; SFA = saturated fatty acid. β is the slope derived from subgroup analyses on meta-regression analyses and represents the treatment effect of tree nuts for each subgroup. The residual I2 value indicates heterogeneity unexplained by the subgroup. Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline (B) values within the treatment arm (E–B). * Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s010 (DOCX) Table S6. Continuous a priori and post-hoc subgroup analyses for HOMA-IR. BMI = body mass index; CHO = carbohydrate; E = energy; N = number of subjects; No. = number; SFA = saturated fatty acid. β is the slope derived from subgroup analyses on meta-regression analyses and represents the treatment effect of tree nuts for each subgroup. The residual I2 value indicates heterogeneity unexplained by the subgroup. Absolute intakes represent intakes within the treatment arm. Between arm differences represent the difference between the treatment (T) and control (C) arm (T–C). Within arm differences represent the difference between end (E) and baseline(B) values within the treatment arm (E–B). * Statistically significant between subgroups (P<0.05). https://doi.org/10.1371/journal.pone.0103376.s011 (DOCX) Checklist S1. CONSORT checklist. https://doi.org/10.1371/journal.pone.0103376.s012 (DOC) Protocol S1. Trial protocol. https://doi.org/10.1371/journal.pone.0103376.s013 (PDF)

Acknowledgments We wish to thank Teruko Kishibe of Li Ka Shing’s International Healthcare Education Centre at St. Michael Hospital for her help in the development of the search strategy.

Author Contributions Conceived and designed the experiments: CWCK DJAJ JLS. Analyzed the data: EV SBM AIC VH AM RJdS JLS. Wrote the paper: EV JLS. Interpretation of the data: EV SBM AIC VH AM VHJ RJdS LAL. Critical revision of the article for important intellectual content: EV CWCK SBM AIC VH AM VHJ LSAA LC LAL RJdS DJAJ JLS. Final approval of the article: EV CWCK SBM AIC VH AM VHJ LSAA LC LAL RJdS DJAJ JLS. Obtaining of funding: CWCK DJAJ JLS. Administrative, technical, or logistic support: SBM VHJ LC LSAA. Collection and assembly of data: EV SBM. Guarantors: CWCK DJAJ JLS.