A systematic review on the effect of sweeteners on glycemic response and clinically relevant outcomes

Background The major metabolic complications of obesity and type 2 diabetes may be prevented and managed with dietary modification. The use of sweeteners that provide little or no calories may help to achieve this objective. Methods We did a systematic review and network meta-analysis of the comparative effectiveness of sweetener additives using Bayesian techniques. MEDLINE, EMBASE, CENTRAL and CAB Global were searched to January 2011. Randomized trials comparing sweeteners in obese, diabetic, and healthy populations were selected. Outcomes of interest included weight change, energy intake, lipids, glycated hemoglobin, markers of insulin resistance and glycemic response. Evidence-based items potentially indicating risk of bias were assessed. Results Of 3,666 citations, we identified 53 eligible randomized controlled trials with 1,126 participants. In diabetic participants, fructose reduced 2-hour blood glucose concentrations by 4.81 mmol/L (95% CI 3.29, 6.34) compared to glucose. Two-hour blood glucose concentration data comparing hypocaloric sweeteners to sucrose or high fructose corn syrup were inconclusive. Based on two ≤10-week trials, we found that non-caloric sweeteners reduced energy intake compared to the sucrose groups by approximately 250-500 kcal/day (95% CI 153, 806). One trial found that participants in the non-caloric sweetener group had a decrease in body mass index compared to an increase in body mass index in the sucrose group (-0.40 vs 0.50 kg/m2, and -1.00 vs 1.60 kg/m2, respectively). No randomized controlled trials showed that high fructose corn syrup or fructose increased levels of cholesterol relative to other sweeteners. Conclusions Considering the public health importance of obesity and its consequences; the clearly relevant role of diet in the pathogenesis and maintenance of obesity; and the billions of dollars spent on non-caloric sweeteners, little high-quality clinical research has been done. Studies are needed to determine the role of hypocaloric sweeteners in a wider population health strategy to prevent, reduce and manage obesity and its consequences.


Background
Non-caloric sweeteners have been available commercially since the late 1800s [1] and their use in food products and as table-top sweeteners is increasingperhaps due in part to aggressive marketing promoting their capacity to induce weight loss and weight maintenance [2,3]. In 2007, non-caloric and/or high-intensity sweeteners accounted for 16% of the US sweetener market (approximately 0.5 billion USD [4]) and projected sales of these products are expected to exceed one billion USD by 2014 [5].
Sugar alcohols can also be used as sweetener additives and provide less calories per gram than saccharides (sugars). However because sugar alcohols cause gastrointestinal symptoms in some individuals due to incomplete absorption in the small intestine, they may be used less frequently than saccharides. A variety of different saccharides is commonly used to sweeten foods, such as sucrose, fructose, glucose, maltose, isomaltulose, and fructooligosaccharide (FOS). FOS has half the calories per gram than sucrose, fructose, or glucose. Most recently, fructose (a highly commercially used sweetener used in combination with glucose as high fructose corn syrup (HFCS)) has been controversially linked with hypertriglyceridemia [6].
The effects of different sweeteners on clinically relevant outcomes such as weight management, blood glucose and lipids have been incompletely studied. The main metabolic complications of obesity and type 2 diabetes may be prevented and managed in full or in part with dietary modification, including the use of sweeteners that provide little or no calories (hypocaloric sweeteners) [7][8][9][10].
This review systematically summarizes the available randomized trial evidence to determine the comparative effectiveness of sweetener additives (non-caloric, sugar alcohols, and saccharides; Table 1) in food.

Methods
This systematic review was conducted and reported according to guidelines [11].

Data sources and searches
We did a comprehensive search designed by a MLIStrained librarian to identify all randomized controlled trials (RCTs) comparing sweeteners in generally healthy, overweight/obese and/or diabetic participants. We included only trials published in English as full peerreviewed manuscripts. MEDLINE (1950 to January 13, 2011), EMBASE (1980 to January 13, 2011), CENTRAL (January 13, 2011), and CAB (January 13, 2011) were searched. No existing systematic reviews were found. The specific strategies used are provided in Additional File 1. The citations and abstracts were screened by two reviewers to identify pertinent trials. Any study considered potentially relevant by one or both reviewers was retrieved for further consideration.

Study selection
We considered non-caloric sweeteners to include highintensity caloric sweeteners that are functionally noncaloric simply due to extremely low doses (for example, aspartame). Each potentially relevant study was independently assessed by two reviewers for inclusion in the review using predetermined eligibility criteria. Disagreements were resolved by consultation with a third party. Trials with healthy, overweight/obese, and/or diabetic adult (≥ 16 years old) participants meeting the following criteria were eligible for inclusion: parallel or crossover RCTs; weight change, energy intake, lipids, glycated hemoglobin (HbA1C), or insulin resistance were reported; had at least two groups comparing different sweeteners (for example, glucose, fructose, sucrose, other saccharides, sugar alcohols, non-caloric sweeteners: aspartame, saccharin, stevioside, sucralose); and where follow-up was at least one week in duration (see the Box in Additional File 1 for study selection summary). RCTs measuring 2-hour blood (serum or plasma) glucose responses in similar populations without the follow-up requirement were also reviewed. All outcomes selected for study (including weight change) are reversible and thus (providing that order was randomly assigned), a cross-over design should be appropriate. Trials with less than ten participants per group were excluded to improve the efficiency of the work without an appreciable loss of power, and with the possible elimination of some small study bias. Trials aimed at evaluating exercise performance or memory enhancement were excluded. Trials with placebo controls were also excluded as we aimed to investigate comparative effectiveness of different sweeteners, as opposed to exploring the implications of avoiding sweeteners altogether.

Data extraction and quality assessment
A standardized data extraction method was performed by a single reviewer. A second reviewer checked the extracted data for accuracy. The following properties of each trial were recorded in a database: trial characteristics (country, design, sample size, duration of follow-up); participants (age, gender, co-morbidity (obesity, diabetes mellitus -type 1 and 2), baseline body mass index (BMI), diabetic therapy (insulin, oral antihyperglycemic agents, diet, and so on); sweetener characteristics (type, quantity, schedule); diet (that is, daily caloric content by macronutrient/fiber content); and outcomes. Outcomes included weight change (absolute, BMI), energy intake, lipid measures (total cholesterol, triglycerides, high density lipoprotein (HDL), low density lipoprotein (LDL)), HbA1C, insulin resistance (for example, Homeostatic Model Assessment (HOMA) index), and 2-hour blood glucose (with or without meals). Risk of bias was assessed using items known to be associated with the magnitude of results (that is, method of randomization, double-blinding, description of withdrawals/dropouts, and allocation concealment) [12,13]. Source of funding was also extracted given its potential to introduce bias [14].

Data synthesis and analysis
Data were analyzed using Stata 11.1 (http://www.stata. com). Missing standard deviations (SDs) were imputed using the maximum value reported in any included study [15]. Missing correlations for change from baseline and for differences between crossover trial periods were assigned a value of 0.63, the maximum reported value in the included studies. Changes from baseline means were used in place of final means in parallel randomized trials. For weight change, the baseline value prior to the immediate period was used. The mean difference (MD) was used to summarize outcomes. Due to expected diversity between studies, we decided a priori to combine results using a random effects model (Stata command: metan). Additionally we planned to examine the association between certain variables (population, dose, diet, age, gender, and bias criteria) and the effect of specific sweeteners on outcomes, and publication bias with weighted regression [16], however the available comparisons were too sparse to pool trials with outcomes of one week or less. For 2-hour responses, we pooled comparisons by type of sweetener and ordered the matrix tables by expected order of efficacy [17] (that is, non-caloric sweeteners, sugar alcohols, other saccharides, fructose, sucrose, and glucose). Statistical heterogeneity was quantified using the τ 2 statistic (between-study variance) [18]. Furthermore, we explored comparative effectiveness of sweeteners on 2-hour responses using network meta-analysis [19] (specifically, Markov chain Monte Carlo [MCMC] methods within a Bayesian framework) in WinBugs (http://www.mrc-bsu.cam.ac.uk/bugs; code was obtained from Ades et al. [20]). Network analysis extends meta-analysis from simply pooling directly compared treatments (direct evidence) to pooling data from studies not directly compared but linked via one or more common comparators (indirect evidence) by assuming consistency of the evidence [19]. Therefore, this technique facilitates the comparison of any two sweeteners not directly compared in any one study. We used noninformative prior distributions: uniform for the betweenstudy variance (range 0 to 20) and Gaussian for the other parameters (mean 0 and variance 10,000). All chains were run for 10,000 iterations after 1,000 burn-in iterations. Convergence of the MCMC algorithm was assessed using autocorrelation plots. By-population results were generated. Inconsistency in the network (disagreement between direct and mixed evidence) was measured using back-calculations [21]. Ninety-five present Bayesian credible intervals are reported.

Quantity of research available
The searches identified 3,666 unique records with no trials found outside the main literature searches. After initial screening, 491 articles were retrieved for detailed evaluation ( Figure 1) and of these 440 articles were excluded resulting in 53 trials (from 51 publications) that met the selection criteria. Disagreements about the inclusion of studies occurred in 11% of the articles (kappa = 0.71). Fourteen were ultimately included. The remaining were excluded for the following reasons: thirteen with no relevant control group, nine with no relevant population, five with no relevant intervention group, four due to study design, four for small sample size, and one for no usable data. The sweeteners studied in eligible trials are described in Table 1.
Sample size ranged from 10 to 37 (median 17), three studies (8%) had sample sizes ≥ 30 per group and all were randomized crossover trials. The median Jadad score was 1 (range 1 to 4); no studies reported concealing treatment allocation. Table 3 reports the results of the direct meta-analysis for all populations in the lower triangle and the mixed evidence from the Bayesian network ( Figure 2) in the upper triangle. The network included 36 trials and 610 participants. The direct evidence from all nine comparisons was consistent with the mixed evidence from the network. There was large heterogeneity between trials (I 2 's ≥ 77%) for three of seven multi-study direct evidence comparisons. Two of the heterogeneous comparisons included a variety of sweeteners (that is, multiple sugar alcohols (τ 2 = 9.05 (95% CI 2.94,32)), or multiple other sugars (τ 2 = 1.72 (0.37,1.48))) within one category. In the fructose versus glucose comparison, six trials were responsible for the heterogeneity (τ 2 = 1.40 (0.68,1.50)). Three [36,45,50] were subgroups of diabetic participants; they increased the magnitude of the mean difference. The other three trials [32,33,46] showed important differences prior to the 2-hour time point (data not shown) but at two hours showed little or no difference between sweeteners. The single estimate of heterogeneity (τ 2 ) for the network meta-analysis was 0.65 (95% CI 0.35,1.10).

2-hour blood glucose response
Reporting the mixed evidence, two comparisons: fructose versus sucrose (MD -1.12 mmol/L (-1.95,-0.27)), and fructose versus glucose (-1.56 mmol/L (-2.18,-1.02)) were statistically significant, all favoring fructose, but neither of the confidence limits excluded the possibility of non-clinically relevant differences (< 1·15 mmol/Lcalculation based on a clinical important difference of    [17] (with the exception of other sugars) from the expected lowest to highest 2-hour glucose response, with the estimated probability (or rank) listed in the diagonal. Each table cell contains the mean difference (MD) with the accompanying 95% confidence intervals. In the cells with direct evidence, we also list the number of studies, the I 2 (percent of heterogeneity due to between-study heterogeneity) and τ 2 (the between-study variance). Blank cells in the lower triangle indicate that no direct evidence was available. In the cells with mixed evidence, we list whether the mixed evidence was consistent with the available direct evidence. Also, in the first cell of the mixed evidence, we list the single τ 2 estimate for the mixed evidence. Results are the MD of the expected higher-ranked sweeteners compared to the expected lower-ranked sweeteners (for example, MD of sugar alcohols versus sucrose is 0.41 and is in column 2, row 5 for the direct results, and is -0.93 and is in column 5, row 2 for the network analysis results). MDs less than zero favor the expected higher-ranked sweetener (smaller glucose response). For example, sugar alcohols show an increased serum glucose response by 0.41 mmol/L compared to sucrose using the direct evidence. However, sugar alcohols show a decreased serum glucose response by 0.93 mmol/L using the mixed evidence. However, since both confidence intervals include zero, neither analysis allows a confident judgment about which sweetener is preferable. Pooled evidence significant at P < 0.05 are presented in bold font. All nine mixed and direct results are consistent.

Glucose
Non-caloric* Other sugars Fructose 1% for HbA1C) [60]. The weighted regression test for publication bias was not significant.
Non-caloric versus saccharide: effects on weight management, blood glucose and blood lipids Two trials reported change in BMI ( Table 5). The 4week trial in healthy participants [61] did not find a significant loss in BMI in non-caloric sweetener recipients (-0.3 kg/m 2 (-1.1,0.5), 133 participants). The trial in overweight participants [63] found a significantly greater loss in BMI over ten weeks of follow-up in participants consuming the non-caloric sweetener (-0.9 kg/m 2 (-1.5,-0.4), 41 participants). Two trials reported absolute change in weight. One crossover trial was done in type 1 diabetic participants and found no difference in weight loss between groups over four weeks (0.8 kg (-3.3,4.9), ten participants [62]). The other trial in overweight participants [63] found significantly greater weight loss over 10 weeks in the non-caloric sweetener group (-2.6 kg (-3.7,-1.5), 41 participants).
Two trials reported energy intake; both reported a significant effect of non-caloric sweeteners. The 4-week trial in generally healthy participants [61] found a significantly reduced intake of calories in non-caloric sweetener participants (-283 kcal (-414,-153), 133 participants).The trial in overweight participants [63] also found significantly less energy intake (over one day) in the non-caloric sweetener group after ten weeks of follow-up (-491 kcal (-806,-177), 41 participants).
Available trials found no effect of sweetener type on HbA1C (one trial: -0.02% over four weeks (-0.40,0.30),  ten participants [62]) or the HOMA index (one trial: -0.20 over ten weeks (-0.58,0.18), forty-one participants [63]). The trial in ten type 1 diabetic participants [62] found no effect on total cholesterol, HDL cholesterol, or triglycerides over the course of four weeks; the other trial in forty-one overweight participants [63] found no effect on triglycerides over the course of ten weeks.

Saccharide versus saccharide: effects on weight management, blood glucose and blood lipids
Two trials reported change in BMI (Table 6); one comparing honey to sucrose in overweight/obese participants over 4 weeks of follow-up [69]; the other comparing a mixture of isomaltulose and sucrose to sucrose over 12 weeks of follow-up [68] in healthy participants. Neither found a significant difference between sweeteners. One trial compared FOS to glucose [73] (three weeks in twenty diabetic participants) and one trial compared FOS to sucrose [72] (four weeks in twelve healthy participants), respectively. Neither found a difference in absolute weight change. Five other trials done in varying populations (including overweight/obese [69] or healthy populations [64][65][66]68]) found no differences in change in absolute weight between sweeteners.

Discussion
To our knowledge, this is the first systematic review of randomized trial evidence that examines comparative sweetener effectiveness in diabetic, overweight/obese, and healthy populations. Despite tremendous interest in hypocaloric sweeteners as a potential tool to prevent obesity and its complications, we found little evidence to support their health benefits as compared to caloric alternatives. Based on analyses of two trials, we found that the inclusion of non-caloric sweeteners in the diet resulted in reduced energy intake compared to the caloric (sucrose) groups -approximately 500 kcal/day less over 10 weeks or 250 kcal/day over 4 weeks. The longer of these trials found that those in the non-caloric sweetener group also had a decrease in BMI compared to an increase in BMI in the sucrose group (-0.40 versus 0.50 kg/m 2 , and -1.00 versus 1.60 kg, respectively) [63]. Given that the control group was asked to ingest supplemental calories in addition to their regular ad lib diet, a BMI reduction of approximately1 kg/m 2 over 10 weeks (or 0·1 kg/m 2 /week) may be overly optimistic. However, even a reduction in BMI of 0.05 kg/m 2 /week would be clinically relevant if sustained for a year or more. The remaining analyses comparing non-caloric and caloric sweeteners were non-significant.

Main findings
• 53 randomized controlled trials were included -all small and largely short-term (only 13 trials with ≥1 week durations) Head-to-head comparisons between saccharides did not identify any statistically significant differences. The confidence limits of these results either included minimally important differences or the group sizes were too small (< 30) to have good estimates of standard deviation [74]. The one exception was the comparison between sucrose and FOS, which suggested that total cholesterol was reduced to a greater extent with sucrose than with FOS. However, the confidence intervals for this analysis included values that were not clinically relevant (0.03 to 0.59 mmol/L). There was no evidence that HFCS or fructose increased levels of cholesterol relative to other sweeteners.
Although we found that fructose reduced 2-hour blood glucose concentrations by 4.81 mmol/L compared to glucose in diabetic participants, data comparing noncaloric and sugar alcohols to the more commonly used sucrose or HFCS were inconclusive. Contrary to perception and current recommendations, no substantive evidence describing important long-term benefits of hypocaloric sweeteners for diabetic patients were identified. Also, despite popular belief, no high-quality RCT evidence was found indicating that fructose causes or exacerbates hypertriglyceridemia [6].
Although the identified trials were numerous, they were very small and largely short-term. We found 13 trials with participant follow-up longer than 1 week and group sizes ≥ 10: 3 that compared non-caloric sweeteners to sucrose, and 10 that were head-to-head comparisons of saccharides. Ten of 13 trials had a Jadad score of 1 and none adequately concealed treatment assignment prior to assignment. Although blinding the participants would have been impossible in many of the trials due to taste differences between sweeteners [63], the reporting of important design descriptors were largely absent, indicating a substantial risk for bias [12,13]. The longest trial was only 10 weeks -not long enough to determine whether substituting a non-caloric sweetener for a caloric sweetener is sustainable in daily practice. To detect an important reduction in weight over at least one year such as 2.5 kg/m 2 (less than 0.05 kg/m 2 / week) in a RCT would require a minimum of 85 participants (assumptions: 25% loss-to-follow-up, α = 0.05, power = 90%, SD = 3 kg/m 2 ).
Our network meta-analysis had several limitations: 1) the sugar alcohol and other sugar categories contained multiple sweeteners that are likely to have different blood glucose profiles thereby inducing heterogeneity, 2) power to detect inconsistency is limited by the number of trials included in each test, and 3) the back-calculation method used to detect inconsistency involved multiple tests thereby increasing the false-positive rate. However, we did not detect any inconsistency.
Another limitation was that only three studies restricted the total energy consumed by each participant. Therefore, participants may have supplemented energy lost with non-caloric sweeteners with other food products -sweetened or otherwise. However, it may be argued that this is a strength of the trials -in that they reflect what happens in real world self-management diet practices. Lastly, and perhaps most importantly, all studies were small, thereby underestimating standard deviation and as a result underestimating confidence interval widths and increasing the likelihood of false-positive findings [74]. Despite this, the confidence intervals for many analyses were wide and did not exclude a minimally important difference. Small study bias (or publication bias) may also play a role in our findings concerning longer-term outcomes.
In theory, substituting non-caloric and lower caloric sweeteners for simple sugars should reduce energy intake and thereby the risk of obesity and its consequences. However, there are a number of reasons why increasing use of non-caloric and lower caloric sweeteners might not lead to the expected improvements in energy regulation. First, use of hypocaloric sweeteners might not induce weight loss even in the short term. For example, if reductions in calories due to sweeteners are offset by increases in caloric intake from other sources [75,76], or offset by decreases in caloric expenditure [77,78]. Although our data suggest that non-caloric sweeteners may lead to clinically relevant weight loss through reduced energy consumption, this conclusion was driven by a single trial with a total of 41 participants. Unlike caloric sweeteners (which may partially compensate added calories with reduced energy intake from other sources) [79], non-caloric sweeteners are not known to suppress appetite, and therefore would not reduce the motivation to eat. Furthermore, it has been suggested that the psychobiological signals with noncaloric sweeteners may directly influence physiological regulatory mechanisms and thus further reduce their potential for reducing net energy intake [75,80]. Second, if calorie reduction is not maintained, short-term reductions in weight due to the use of hypocaloric sweeteners might not be sustained. Third, it is possible though speculative that any health benefits due to weight loss from non-caloric sweeteners might be wholly or partially offset by currently unrecognized adverse events due to their use. The lack of data on the long-term benefits of non-caloric sweeteners means that it is currently impossible to determine whether these substances will improve public health.

Conclusions
In summary, despite the public health importance of obesity, and obesity-related chronic diseases (for example, diabetes); the clear role of excessive caloric intake in these conditions; and the billions of dollars spent on non-caloric sweeteners [4,5], little high-quality clinical research has been done to identify the potential harms and benefits of hypocaloric sweeteners. Since even small reductions (as little as 6%) in body-weight can prevent chronic disease [81,82], hypocaloric sweeteners could play an important role in a wider population health strategy to prevent, reduce and manage obesity-related comorbidities. Eliminating unnecessary added sweeteners from food products (for example, buns, crackers, and processed meats) and substituting sugars with lower calorie sweeteners in foods such as desserts and drinks could significantly improve health. Long-term, highquality, adequately powered randomized controlled trials are required to confirm this hypothesis by assessing the clinically relevant outcomes reported in this review.

Additional material
Additional file 1: SweetenerSR contains. 1) Appendix: Search strategies, 2) Appendix: Study selection criteria box, 3) Appendix Table 1. Mean difference in serum glucose (mmol/L) at 2 hours post-sweetener consumption and overnight fast in non-diabetic participants, 4) Appendix Table 2. Mean difference in serum glucose (mmol/L) at 2 hours postsweetener consumption and overnight fast in diabetic participants.