Data synthesis: Mean differences will be pooled using the generic inverse variance method when data are available from more than 2 trials. Random-effects models will be 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. Fixed-effects models will only be used where there is <5 included studies. Paired analyses will be applied for crossover trials. Heterogeneity will be assessed by the Cochran Q statistic and quantified by the I2 statistic. To explore sources of heterogeneity, the investigators will conduct sensitivity analyses, in which each study is systematically removed. If there are >=10 studies per endpoint, then the investigators will also explore sources of heterogeneity by a priori subgroup analyses by age (children [=<18 years of age], adults), health status (metabolic syndrome criteria, diabetes, overweight/ obese, healthy), comparator type, fructose- containing sugar form (sucrose, HFCS, honey, fructose), dose (=<10% energy, >10% energy), baseline measurements, randomization, study design (parallel, crossover), energy balance (positive, neutral, negative), follow-up (=<8-weeks, >8-weeks), and risk of bias. Meta-regression analyses will assess the significance of categorical and continuous subgroups analyses. When >=10 studies are available, publication bias will be investigated by inspection of funnel plots and formal testing using the Egger's and Begg's tests. If publication bias is suspected, then the investigators will attempt to adjust for funnel plot asymmetry by imputing the missing study data using the Duval and Tweedie trim and fill method.