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Effect of pasta in the context of low-glycaemic index dietary patterns on body weight and markers of adiposity: a systematic review and meta-analysis of randomised controlled trials in adults.
Chiavaroli, L, Kendall, CWC, Braunstein, CR, Blanco Mejia, S, Leiter, LA, Jenkins, DJA, Sievenpiper, JL
BMJ open. 2018;(3):e019438
Abstract
OBJECTIVE Carbohydrate staples such as pasta have been implicated in the obesity epidemic. It is unclear whether pasta contributes to weight gain or like other low-glycaemic index (GI) foods contributes to weight loss. We synthesised the evidence of the effect of pasta on measures of adiposity. DESIGN Systematic review and meta-analysis using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. DATA SOURCES MEDLINE, Embase, CINAHL and the Cochrane Library were searched through 7 February 2017. ELIGIBILITY CRITERIA FOR SELECTING STUDIES We included randomised controlled trials ≥3 weeks assessing the effect of pasta alone or in the context of low-GI dietary patterns on measures of global (body weight, body mass index (BMI), body fat) and regional (waist circumference (WC), waist-to-hip ratio (WHR), sagittal abdominal diameter (SAD)) adiposity in adults. DATA EXTRACTION AND SYNTHESIS Two independent reviewers extracted data and assessed risk of bias. Data were pooled using the generic inverse-variance method and expressed as mean differences (MDs) with 95% CIs. Heterogeneity was assessed (Cochran Q statistic) and quantified (I2 statistic). GRADE assessed the certainty of the evidence. RESULTS We identified no trial comparisons of the effect of pasta alone and 32 trial comparisons (n=2448 participants) of the effect of pasta in the context of low-GI dietary patterns. Pasta in the context of low-GI dietary patterns significantly reduced body weight (MD=-0.63 kg; 95% CI -0.84 to -0.42 kg) and BMI (MD=-0.26 kg/m2; 95% CI -0.36 to -0.16 kg/m2) compared with higher-GI dietary patterns. There was no effect on other measures of adiposity. The certainty of the evidence was graded as moderate for body weight, BMI, WHR and SAD and low for WC and body fat. CONCLUSIONS Pasta in the context of low-GI dietary patterns does not adversely affect adiposity and even reduces body weight and BMI compared with higher-GI dietary patterns. Future trials should assess the effect of pasta in the context of other 'healthy' dietary patterns. TRIAL REGISTRATION NUMBER NCT02961088; Results.
2.
Fructose intake and risk of gout and hyperuricemia: a systematic review and meta-analysis of prospective cohort studies.
Jamnik, J, Rehman, S, Blanco Mejia, S, de Souza, RJ, Khan, TA, Leiter, LA, Wolever, TM, Kendall, CW, Jenkins, DJ, Sievenpiper, JL
BMJ open. 2016;(10):e013191
Abstract
BACKGROUND The prevalence of hyperuricemia and gout has increased in recent decades. The role of dietary fructose in the development of these conditions remains unclear. OBJECTIVE To conduct a systematic review and meta-analysis of prospective cohort studies investigating the association fructose consumption with incident gout and hyperuricemia. DESIGN MEDLINE, EMBASE and the Cochrane Library were searched (through September 2015). We included prospective cohort studies that assessed fructose consumption and incident gout or hyperuricemia. 2 independent reviewers extracted relevant data and assessed study quality using the Newcastle-Ottawa Scale. We pooled natural-log transformed risk ratios (RRs) using the generic inverse variance method. Interstudy heterogeneity was assessed (Cochran Q statistic) and quantified (I2 statistic). The overall quality of the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. RESULTS 2 studies involving 125 299 participants and 1533 cases of incident gout assessed the association between fructose consumption and incident gout over an average of 17 years of follow-up. No eligible studies assessed incident hyperuricemia as an outcome. Fructose consumption was associated with an increase in the risk of gout (RR=1.62, 95% CI 1.28 to 2.03, p<0.0001) with no evidence of interstudy heterogeneity (I2=0%, p=0.33) when comparing the highest (>11.8% to >11.9% total energy) and lowest (<6.9% to <7.5% total energy) quantiles of consumption. LIMITATIONS Despite a dose-response gradient, the overall quality of evidence as assessed by GRADE was low, due to indirectness. There were only two prospective cohort studies involving predominantly white health professionals that assessed incident gout, and none assessed hyperuricemia. CONCLUSIONS Fructose consumption was associated with an increased risk of developing gout in predominantly white health professionals. More prospective studies are necessary to understand better the role of fructose and its food sources in the development of gout and hyperuricemia. PROTOCOL REGISTRATION NUMBER NCT01608620.
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Effect of fructose on glycemic control in diabetes: a systematic review and meta-analysis of controlled feeding trials.
Cozma, AI, Sievenpiper, JL, de Souza, RJ, Chiavaroli, L, Ha, V, Wang, DD, Mirrahimi, A, Yu, ME, Carleton, AJ, Di Buono, M, et al
Diabetes care. 2012;(7):1611-20
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Abstract
OBJECTIVE The effect of fructose on cardiometabolic risk in humans is controversial. We conducted a systematic review and meta-analysis of controlled feeding trials to clarify the effect of fructose on glycemic control in individuals with diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE, EMBASE, and the Cochrane Library (through 22 March 2012) for relevant trials lasting ≥7 days. Data were aggregated by the generic inverse variance method (random-effects models) and expressed as mean difference (MD) for fasting glucose and insulin and standardized MD (SMD) with 95% CI for glycated hemoglobin (HbA(1c)) and glycated albumin. Heterogeneity was assessed by the Cochran Q statistic and quantified by the I(2) statistic. Trial quality was assessed by the Heyland methodological quality score (MQS). RESULTS Eighteen trials (n = 209) met the eligibility criteria. Isocaloric exchange of fructose for carbohydrate reduced glycated blood proteins (SMD -0.25 [95% CI -0.46 to -0.04]; P = 0.02) with significant intertrial heterogeneity (I(2) = 63%; P = 0.001). This reduction is equivalent to a ~0.53% reduction in HbA(1c). Fructose consumption did not significantly affect fasting glucose or insulin. A priori subgroup analyses showed no evidence of effect modification on any end point. CONCLUSIONS Isocaloric exchange of fructose for other carbohydrate improves long-term glycemic control, as assessed by glycated blood proteins, without affecting insulin in people with diabetes. Generalizability may be limited because most of the trials were <12 weeks and had relatively low MQS (<8). To confirm these findings, larger and longer fructose feeding trials assessing both possible glycemic benefit and adverse metabolic effects are required.