If ≥10 trial comparisons were available, then sources of heterogeneity were explored by subgroup analyses. Sensitivity analyses were also conducted using different correlation coefficient values for cross-over trials (0.25 and 0.75) to test for the robustness of the effect size, conducting analyses using fixed-effects models and restricting analyses to those trials for which pasta intake could be quantified. Interstudy heterogeneity was assessed by the Cochran Q statistic, where P 10%.
Paired analyses were applied to all cross-over trials with the use of a within-individual correlation coefficient between treatments of 0.5 as described by Elbourne et al. Random-effects models were used even in the absence of statistically significant interstudy heterogeneity, as they yield more conservative summary effect estimates in the presence of residual heterogeneity. A generic inverse-variance method with random-effects models was used to calculate pooled mean differences and 95% CIs. Published abstracts were not included.ĭata analyses were conducted using Review Manager (RevMan) V.5.3 (Copenhagen, Denmark: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) for primary analyses and Stata V.13 (College Station, TX: StataCorp LP) for subgroup analyses. When multiple publications existed for the same study, the article with the most information was included (n=6). Trials were excluded if they had diet duration of <3 weeks, did not intend to use a calorie-matched and macronutrient-matched comparator arm that was higher in GI, included pregnant or breastfeeding women or children, or did not provide suitable end-point data. Trials were included if the intervention arm assessed the effect of pasta consumed alone or assessed the effect of a low-GI diet that emphasised pasta as part of the low-GI dietary advice. We included RCTs that investigated the effect of pasta consumed alone or in the context of low-GI dietary patterns that emphasised pasta in comparison with higher-GI diets that did not include pasta on body weight or other measures of global (BMI, body fat) or abdominal (waist circumference, waist-to-hip ratio, sagittal abdominal diameter or visceral adipose tissue as assessed by imaging modalities) adiposity in participants of all health backgrounds. We undertook a systematic review and meta-analysis of RCTs using the GRADE approach to quantify the effect of pasta alone or in the context of low-GI dietary patterns on body weight and measures of adiposity relevant to the prevention and management of overweight and obesity. We are not aware of any systematic reviews and meta-analyses that have synthesised the evidence of the effect of pasta on body weight outcomes. It remains unclear whether pasta alone or in the context of a low-GI dietary pattern shares the advantages of other low-GI foods or on the contrary contributes to weight gain. Pasta is an important example of a food that is considered a refined carbohydrate but has a low GI, a property that has been exploited extensively in studies of low-GI dietary patterns.
2 7 Although systematic reviews and meta-analyses of randomised controlled trials (RCTs) of dietary patterns that include these foods but are low in glycaemic index (GI), 13 14 high in whole grains 15 16 and/or high in dietary fibre have shown advantages for weight-related outcomes, 17 18 there has been a general lack of recognition of the importance of carbohydrate quality.
11 12 Much of the attention has focused on sugars, but traditional carbohydrate staples like pasta, rice and breads are increasingly being implicated in the epidemics of overweight and obesity. As the role of saturated fat in chronic disease has been called into question, carbohydrates have come under attack in the media, 1 2 popular books, 3–9 statements of health advocacy groups 10 and commentaries in leading medical journals.
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