Non‐nutritive sweeteners for diabetes mellitus
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effects of non‐nutritive sweeteners for diabetes mellitus.
Background
Description of the condition
Diabetes mellitus is a metabolic disorder impeding the pancreas from producing enough insulin, body cells from responding properly to the insulin produced, or both. This leads to chronic hyperglycaemia (i.e. elevated plasma glucose levels) and disturbances of carbohydrate, fat, and protein metabolism. In the long term, this condition leads to complications such as retinopathy, nephropathy, neuropathy, and an increased risk for cardiovascular diseases.
Diabetes is one of the most common diseases in the world, and its burden is increasing globally: the number of people with diabetes rose from 108 million in 1980 to 422 million in 2014 (WHO 2016). The global prevalence of diabetes in adults over 18 years of age was 8.5% in 2014 (WHO 2016). Diabetes was the direct cause of 1.5 million deaths in 2012 (WHO 2016). The global cost of diabetes is now about USD 825 billion per year (NCD‐RisC 2016).
A healthy diet, regular physical activity, and pharmacotherapy are key components of diabetes management. For many individuals with diabetes, the most challenging part of the treatment plan is determining what to eat.
Today, nutrition therapy is recommended for all people with type 1 and type 2 diabetes as a component of their overall treatment plan (Evert 2013). Goals of nutrition therapy are to promote and support healthy eating patterns with a variety of nutrient‐dense foods in appropriate portion size to achieve individualised glycaemic, blood pressure, and lipid goals; attain and maintain body weight goals; and delay or prevent complications of diabetes. A further goal is to maintain the pleasure of eating by providing positive messages about food choices and practical tools for day‐to‐day meal planning (Evert 2013).
Description of the intervention
Non‐nutritive sweeteners (NNS) are defined as sweetening agents having higher sweetening intensity and lower calorie content per gram compared to caloric sweeteners like sucrose or corn syrups (Chattopadhyay 2014). Both the general population and individuals with type 1 or type 2 diabetes use NNS as a caloric sweetener replacement to control their carbohydrate and energy intake.
Most of the NNS approved for human consumption are synthetic (artificial sweeteners); however, more and more natural non‐caloric sweeteners are becoming available for human consumption.
Products sweetened with NNS are widely available on the market: diet beverages, diet yoghourts, desserts, and chewing gums are the most common forms. Additionally, NNS are also available as table‐top sweeteners for use by consumers at home as a sweetening agent for beverages and for cooking and baking.
With regard to the range of approved artifical sweeteners, there are important differences among countries. In the USA, the Food and Drug Administration (FDA) has to date approved six artificial sweeteners for human consumption: acesulfame‐K, aspartame, neotame, saccharin, sucralose, and advantame. Additionally, steviol glycosides, thaumatin, and luo han guo fruit extracts (mogrosides) are approved NNS of natural origin (FDA 2015a). In the European Union, the following 11 NNS are approved for use in foods and drinks by the European Food Safety Authority: acesulfame‐K (E950), advantame (E969), aspartame (E951), aspartame‐acesulfame salt (E962), cyclamate (E952), neohesperidine DC (E959), neotame (E961), saccharin (E954), steviol glycosides (E960), sucralose (E955), and thaumatin (E957) (FSA 2016).
The approved NNS are described in more detail below. Table 1 lists the acceptable daily intake levels defined by the main regulatory bodies (JECFA 2010).
| Sweetener | FDA (mg/kg body weight) (FDA 2015a) | SCF/EFSA (mg/kg body weight) (Mortensen 2006) | JECFA (mg/kg body weight) (JECFA 2010) |
| ACE‐K | 15 | 9 | 15 |
| Advantame | 32.8 | 5 | 5 |
| Aspartame | 50 | 40 | 40 |
| Cyclamate | Not approved | 7 | 11 |
| Luo han guo fruit extracts | Not specified | Not specified | Not specified |
| Neohesperidine DC | Not approved | 5 | Not evaluated |
| Neotame | 0.3 | 2 | 2 |
| Saccharin | 15 | 5 | 5 |
| Sucralose | 5 | 15 | 15 |
| Steviol glycosides | 4 | 4 | 4 |
| Thaumatin | Not approved | Not specified | Not specified |
Note: aspartame and acesulfame moieties in aspartame‐acesulfame salt are covered by acceptable daily intake values established for aspartame and acesulfame potassium
ACE‐K: acesulfame potassium; DC: dihydrochalcone; EFSA: European Food Safety Authority;FDA: Food and Drug Administration; JECFA: Joint FAO/WHO Expert Committee on Food Additives; SCF: Scientific Committee on Food (European Commission)
Acesulfame‐K (acesulfame potassium) is a combination of an organic acid and potassium and was first approved for general use as an NNS in 1988. It contains 0 kcal/g and is 200 times sweeter than sucrose (Chattopadhyay 2014). The estimated daily intake (EDI; i.e. the presumed daily consumption of NNS) ranges from 0.2 to 1.7 mg/kg of body weight (Fitch 2012; Gardner 2012).
Advantame is an N‐substituted derivative of aspartame made from aspartame and vanillin (Otabe 2011). It is approximately 20,000 times sweeter than sucrose (FDA 2015a).
Aspartame is the methyl ester of the dipeptide of the amino acids aspartic acid and the essential amino acid phenylalanine. It was approved for general use in 1981 and is 180 to 200 times sweeter than sucrose (Chattopadhyay 2014). Although it has 4 kcal/g, the intensity of sweet taste means that very small amounts are required to achieve desired sweetness levels. The EDI ranges from 0.2 to 4.1 mg/kg of body weight (Fitch 2012; Gardner 2012).
Cyclamate (cyclamic acid) is used as an NNS in two forms: sodium cyclamate and calcium cyclamate. It is 30 times sweeter than sucrose and contains zero calories (Chattopadhyay 2014). It is used in more than 50 countries (Fitch 2012); however, cyclamate and its salts are currently prohibited from use in the USA (FDA 2015a).
Luo han guo (also known as Siraitia grosvenori) fruit extract is a traditional Chinese herb containing varying levels of mogrosides. Depending on the mogroside content, it is reported to be 100 to 250 times sweeter than sucrose (FDA 2015a).
Neohesperidine dihydrochalcone (DC) is a non‐nutritive sweetener derived from the flavones of citrus fruit. The customary concentration is 400 to 600 times sweeter than sucrose.
Neotame is a dipeptide methyl ester derivate. It has a sweetness factor approximately 7000 to 13,000 times greater than that of sucrose and approximately 30 to 60 times greater than that of aspartame, depending on the food application (Aguilar 2007).
Saccharin is the oldest NNS, first discovered and used in 1879 (FDA 2015b). It is an organic chemical compound (O‐sulfobenzimide), which can be artificially synthesised in various ways. It has no calories and is about 300 times sweeter than sucrose (Chattopadhyay 2014); however, it has an unpleasant bitter or metallic taste. The EDI ranges from 0.1 to 2.0 mg/kg of body weight (Fitch 2012).
Stevia rebaudiana‐based products are the best‐known NNS of natural origin. Steviol glycosides, extracted from the plant stevia, contain stevioside and rebaudioside A as well as other glycosides (Ceunen 2013). Steviol glycosides are 10 to 15 times sweeter than sucrose. Stevia has been used as a sweetener in some countries (e.g. Japan) for decades, while it was approved as a food additive in 2011 by the European Food Safety Authority (EC 2011).The FDA first recognized the use of certain steviol glycosides as a sweetener as generally safe in 2008 (FDA 2008).
Sucralose is an organic chemical compound (trichlorosucrose), approved for general use as a non‐nutritive sweetener since 1999 (Gardner 2012). It is 450 to 650 times sweeter than sucrose and has 0 kcal/g. The quality and intensity of sweet taste is very close to that of sucrose (Chattopadhyay 2014). The EDI ranges from 0.1 to 2.0 mg/kg of body weight (Fitch 2012).
Thaumatin is a mixture of sweet‐tasting polypeptides that can be extracted from the skin surrounding the seeds of the West African katemfe fruit.
Adverse effects of the intervention
Food safety agencies consider consumption of NNS up to the acceptable daily intake to be safe; however, the effects of NNS on glucose metabolism are not clearly understood (Romo‐Romo 2016). Individuals with diabetes may consume NNS for very long periods (i.e. years or even decades) on a daily basis, possibly at an amount exceeding the acceptable daily intake levels (Ilbäck 2003). There has been little research on the negative health outcomes arising as a consequence of consuming such considerable amounts of NNS over long periods, and even less focusing specifically on people with diabetes.
A potentially increased risk for cancer is a starting point for many debates around the safety of NNS (Gallus 2007).
Additionally, some studies indicated that NNS consumption might lead to weight gain instead of the expected weight loss (Mattes 2009), which in people with diabetes could lead to the worsening of glycaemic control, blood pressure, and lipid profile (ADA 2016).
Furthermore, some researchers have also raised the question whether NNS (consumed without caloric sweeteners) could enhance the cephalic phase of insulin secretion (the early increase of insulin secretion immediately following gustatory stimulation, prior to the rise of blood glucose) by evoking the recognition of the sweet taste, sight, smell, and expectation of food, and whether in the absence of caloric sweetener intake it could lead to exercise‐induced hypoglycaemia (Ferland 2007; Just 2008).
It is also important to note that Greenwood 2014 found a positive association between artificially sweetened soft drink intake and type 2 diabetes risk in their systematic review and dose‐response meta‐analysis of prospective studies.
How the intervention might work
The mechanisms by which NNS might influence health outcomes in people with diabetes include the improvement in glycaemic control and the facilitation of weight management.
One of the key elements in nutrition therapy for type 1 diabetes is carbohydrate‐counting meal planning and adjustments to insulin doses based on carbohydrate intake, in order to maintain blood glucose levels within the normal range. A simple diabetes meal planning approach such as portion control may be an appropriate nutrition strategy for individuals with type 2 diabetes. Use of NNS has the potential to reduce the overall caloric and carbohydrate intake if they substitute for caloric sweeteners, without compensation by intake of additional calories from other food sources (Evert 2013).
If people with diabetes use NNS to replace caloric sweeteners without caloric compensation, then NNS may also be useful in weight management. Since being overweight and obese may worsen glycaemic control and increase cardiometabolic risk, it is considered important to prevent weight gain in individuals with diabetes. Dietary changes can result in modest and sustained weight loss, and they may produce clinically meaningful reductions in glycosylated haemoglobin A1c (HbA1c) and triglycerides (ADA 2016; Pastors 2002).
Why it is important to do this review
One systematic review focusing on the effects of FDA‐approved NNS in individuals with diabetes found that NNS do not appear to affect glycaemic control (Timpe Behnen 2013). However, that systematic review had some limitations: it included only studies published in English and considered only NNS available in the USA. New trials have been published since then that could provide additional relevant evidence. Furthermore, it is important to focus on determining the effects of regular NNS use on patient‐important outcomes, such as morbidity, mortality, and adverse effects, which Timpe Behnen 2013 did not address.
Non‐nutritive sweeteners as part of nutrition therapy represent a simple and cheap intervention that might help decrease the need for antidiabetic drugs, insulin, or both, delaying possible complications. As diabetes is a major public health problem worldwide, such an intervention might have huge benefits for health systems in terms of reducing burden and costs.
Objectives
To assess the effects of non‐nutritive sweeteners for diabetes mellitus.
Methods
Criteria for considering studies for this review
Types of studies
We will include randomised controlled trials (RCTs).
Types of participants
Individuals with type 1 or type 2 diabetes mellitus.
Diagnostic criteria for diabetes mellitus
In order to be consistent with changes in the classification and diagnostic criteria for diabetes mellitus over the years, the diagnosis should be established using the standard criteria valid at the time of trial commencement (for example ADA 2003; ADA 2008; WHO 1998). Ideally, trials should describe diagnostic criteria. If necessary, we will use the trial authors' definition of diabetes mellitus. We plan to subject diagnostic criteria to a sensitivity analysis.
Types of interventions
We plan to investigate the following comparisons of intervention versus control/comparator.
Intervention
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Any type of NNS, either alone or in combination with another NNS.
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NNS plus a behaviour‐changing intervention such as diet, exercise or both.
Comparisons
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Usual diet versus NNS.
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No intervention versus NNS.
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Placebo versus NNS.
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Water versus NNS.
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NNS versus a different NNS.
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NNS versus NNS of a differing dose.
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Behaviour‐changing intervention such as diet, exercise or both versus NNS plus behaviour‐changing intervention.
Concomitant interventions will have to be the same in both the intervention and comparator groups to allow fair comparisons and to isolate the effect of NNS on health outcomes.
Minimum duration of intervention
We will consider RCTs in which the intervention had a minimum duration of four weeks.
Minimum duration of follow‐up
Minimum duration of follow‐up will be four weeks after start of the intervention.
We will define extended follow‐up periods (also called open‐label extension studies) as follow‐up of participants once the original trial as specified in the trial protocol had been terminated.
Summary of specific exclusion criteria
None.
Types of outcome measures
We will include outcomes that are measured for as long as follow‐up is carried out at any given time point. We will classify the outcome measurement as medium‐ and long‐term. We will define medium term as at least four weeks to less than six months and long term as six months or more. We will use the data at the longest follow‐up available for the meta‐analyses.
Primary outcomes
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HbA1c.
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Body weight (kg).
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Adverse events.
Secondary outcomes
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Diabetes complications.
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All‐cause mortality.
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Health‐related quality of life.
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Anthropometric measures other than body weight (kg).
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Lipid profile.
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Glucose levels (fasting and postprandial).
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Serum insulin.
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Insulin sensitivity.
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Socioeconomic effects.
We will include trials reporting at least one of the listed primary or secondary outcome measures in the publication. In case a trial reports none of our primary or secondary outcomes, we will not include this trial but provide relevant, basic information in an additional table.
Method of outcome measurement
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HbA1c: measured in % (mmol/mol).
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Body weight: measured in kilograms (kg).
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Adverse events: such as hypoglycaemic episodes, abdominal discomfort, flatulence or diarrhoea measured at any time after participants were randomised to intervention/comparator groups.
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Diabetes complications: defined as diabetic nephropathy, diabetic neuropathy, diabetic retinopathy, and cardiovascular events.
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All‐cause mortality: defined as death from any cause and measured at any time after participants were randomised to intervention/comparator groups.
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Health‐related quality of life: evaluated by a validated instrument such as Audit of Diabetes‐Dependent Quality of Life (ADDQoL) or 36‐Item Short Form Health Survey (SF‐36).
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Anthropometric measures other than body weight (kg): defined as BMI (body mass index; kg/m²), waist circumference (cm), percent of body fat (%) or waist‐to‐hip ratio.
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Lipid profile: analysed by total cholesterol, HDL‐cholesterol, LDL‐cholesterol and triglycerides (TG).
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Glucose levels: fasting blood glucose levels (mg/dL) and postprandial blood glucose levels in (mg/dL).
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Serum insulin: measured in microU/mL.
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Insulin sensitivity: analysed by the homeostasis model assessment of insulin resistance (HOMA‐IR).
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Socioeconomic effects: such as direct costs defined as admission/readmission rates, average length of hospital stay, visits to general practitioner, visits to the emergency department; medication consumption; indirect costs defined as resources lost due to illness by the participant or their family member or absence from work.
Timing of outcome measurement
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With the exception of adverse events and all‐cause mortality (measured at any time after participants were randomised to intervention/comparator groups) we will consider outcomes measured after a minimum follow‐up of four weeks.
Search methods for identification of studies
Electronic searches
We will search the following sources from the inception of each database and will place no restrictions on the language of publication.
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Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Register of Studies Online (CRSO, crso.cochrane.org).
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MEDLINE Ovid (Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, Ovid MEDLINE (R) Daily and Ovid MEDLINE (R); from 1946 onwards).
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Scopus (www.scopus.com).
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ClinicalTrials.gov (www.clinicaltrials.gov).
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World Health Organization International Clinical Trials Registry Platform (ICTRP, www.who.int/trialsearch).
We will continuously apply a MEDLINE (via Ovid SP) email alert service established by the Cochrane Metabolic and Endocrine Disorders (CMED) Group to identify newly published trials using the same search strategy as described for MEDLINE (for details on search strategies, see Appendix 1). After we submit the final review draft for editorial approval, the CMED Group will perform a complete search update on all databases available at the editorial office and will send the results to the review authors. Should we identify new trials for inclusion, we will evaluate these, incorporate the findings into our review and resubmit another Cochrane Review draft (Beller 2013).
Searching other resources
We will try to identify other potentially eligible trials or ancillary publications by searching the reference lists of included trials, (systematic) reviews, meta‐analyses, and health technology assessment reports. In addition, we will contact authors of included trials to identify any additional information on the retrieved trials and to determine if there are further trials that we may have missed.
We will not use abstracts or conference proceedings for data extraction unless full data are available from trial authors because this information source does not fulfil the CONSORT requirements, which consist of "an evidence‐based, minimum set of recommendations for reporting randomized trials" (CONSORT 2010; Scherer 2007). We will list key data of abstracts in an appendix.
We additionally will search the database from the regulatory agency US Food and Drugs Administration (FDA; www.fda.gov/Food).
Data collection and analysis
Selection of studies
Pairs of review authors (SL, IT, DK) will independently screen the abstract, title or both, of every record retrieved by the literature searches, to determine which trials we should assess further. We will obtain the full text of all potentially relevant records. We will resolve any disagreements through consensus or by recourse to a third review author (SL, IT, DK, or JM). If we cannot resolve a disagreement, we will categorise the trial as a 'study awaiting classification' and will contact the trial authors for clarification. We will present a PRISMA flow diagram to describe the process of trial selection (Liberati 2009). We will list all articles excluded after full‐text assessment in a 'Characteristics of excluded studies' table and will provide the reasons for exclusion.
Data extraction and management
For trials that fulfil our inclusion criteria, pairs of review authors (SL, IT, DK) will independently extract key participant and intervention characteristics. We will describe interventions using the 'template for intervention description and replication' (TIDieR) checklist (Hoffmann 2014; Hoffmann 2017).
We will record data on efficacy outcomes and adverse events using standardised data extraction sheets from the CMED Group. We will resolve any disagreements by discussion or, if required, by consultation with a third review author (Sl, IT, DK, or JM).
We will provide information about potentially relevant ongoing trials, including the trial identifiers, in the 'Characteristics of ongoing trials' table and in a joint appendix 'Matrix of trial endpoint (publications and trial documents)'. We will try to find the protocol for each included trial, and we will report primary, secondary, and other outcomes in comparison with data in publications in a joint appendix to assess risk of selective outcome reporting.
We will email all authors of included trials to enquire whether they would be willing to answer questions regarding their trials. We will present the results of this survey in an appendix. We will thereafter seek relevant missing information on the trial from the primary trial author(s), if required.
Dealing with duplicate and companion publications
In the event of duplicate publications, companion documents, or multiple reports of a primary trial, we will maximise the information yield by collating all available data, and we will use the most complete data set aggregated across all known publications. We will list duplicate publications, companion documents, multiple reports of a primary trial, and trial documents of included trials (such as trial registry information) as secondary references under the study ID of the included trial. Furthermore, we will also list duplicate publications, companion documents, multiple reports of a trial, and trial documents of excluded trials (such as trial registry information) as secondary references under the study ID of the excluded trial.
Data from clinical trial registers
If data from included trials are available as study results in clinical trial registers such as ClinicalTrials.gov or similar sources, we will make full use of this information and extract the data. If there is also a full publication of the trial, we will collate and critically appraise all available data. If an included trial is marked as a completed study in a clinical trial register but no additional information (study results, publication or both) is available, we will add this trial to the table 'Characteristics of studies awaiting classification'.
Assessment of risk of bias in included studies
Pairs of review authors (SL, IT, DK) will independently assess the 'Risk of bias' of each included trial. We will resolve any disagreements by consensus or by consultation with a third review author (SL, IT, DK, or JM). In case of disagreement, we will consult the rest of the author team and make a judgment based on consensus. If adequate information is not available from publications, trial protocols, or other sources, we will contact the trial authors to request missing data on 'Risk of bias' items.
We will use the Cochrane 'Risk of bias' assessment tool (Higgins 2011a; Higgins 2011b), and assign assessments of low, high or unclear risk of bias. We will evaluate individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions according to the criteria and associated categorisations contained therein(Higgins 2011b).
Random sequence generation (selection bias due to inadequate generation of a randomised sequence)
For each included trial, we will describe the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.
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Low risk of bias: the trial authors achieved sequence generation using computer‐generated random numbers or a random numbers table. Drawing of lots, tossing a coin, shuffling cards or envelopes, and throwing dice are adequate if an independent person performed this who was not otherwise involved in the trial. We will consider the use of the minimisation technique as equivalent to being random.
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Unclear risk of bias: insufficient information about the sequence generation process.
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High risk of bias: the sequence generation method was non‐random or quasi‐random (e.g. sequence generated by odd or even date of birth; sequence generated by some rule based on date (or day) of admission; sequence generated by some rule based on hospital or clinic record number; allocation by judgment of the clinician; allocation by preference of the participant; allocation based on the results of a laboratory test or a series of tests; or allocation by availability of the intervention).
Allocation concealment (selection bias due to inadequate concealment of allocation prior to assignment)
We will describe for each included trial the method used to conceal allocation to interventions prior to assignment and we will assess whether intervention allocation could have been foreseen in advance of or during recruitment, or changed after assignment.
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Low risk of bias: central allocation (including telephone, interactive voice‐recorder, web‐based and pharmacy‐controlled randomisation); sequentially numbered drug containers of identical appearance; sequentially numbered, opaque, sealed envelopes.
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Unclear risk of bias: insufficient information about the allocation concealment.
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High risk of bias: using an open random allocation schedule (e.g. a list of random numbers); assignment envelopes were used without appropriate safeguards; alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure.
We will also evaluate trial baseline data to incorporate assessment of baseline imbalance into the 'Risk of bias' judgment for selection bias (Corbett 2014). Chance imbalances may also affect judgments on the risk of attrition bias. In the case of unadjusted analyses, we will distinguish between trials we rate as being at low risk of bias on the basis of both randomisation methods and baseline similarity versus trials we rate as being at low risk of bias on the basis of baseline similarity alone (Corbett 2014). We will re‐classify judgments of unclear, low, or high risk of selection bias as specified in Appendix 2.
Blinding of participants and study personnel (performance bias due to knowledge of the allocated interventions by participants and personnel during the trial)
We will evaluate the risk of detection bias separately for each outcome (Hróbjartsson 2013). We will note whether endpoints were self‐reported, investigator‐assessed, or adjudicated outcome measures (see below).
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Low risk of bias: blinding of participants and key study personnel is ensured, and it is unlikely that the blinding could have been broken; no blinding or incomplete blinding, but we judge that the outcome is unlikely to have been influenced by lack of blinding.
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Unclear risk of bias: insufficient information about the blinding of participants and study personnel; the trial does not address this outcome.
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High risk of bias: no blinding or incomplete blinding, and the outcome is likely to have been influenced by lack of blinding; blinding of trial participants and key personnel attempted, but likely that the blinding could have been broken, and the outcome is likely to be influenced by lack of blinding.
Blinding of outcome assessment (detection bias due to knowledge of the allocated interventions by outcome assessment)
We will evaluate the risk of detection bias separately for each outcome (Hróbjartsson 2013). We will note whether endpoints were self‐reported, investigator‐assessed, or adjudicated outcome measures (see below).
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Low risk of bias: blinding of outcome assessment is ensured, and it is unlikely that the blinding could have been broken; no blinding of outcome assessment, but we judge that the outcome measurement is unlikely to have been influenced by lack of blinding.
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Unclear risk of bias: insufficient information about the blinding of outcome assessors; the trial did not address this outcome.
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High risk of bias: no blinding of outcome assessment, and the outcome measurement is likely to have been influenced by lack of blinding; blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement is likely to be influenced by lack of blinding.
Incomplete outcome data (attrition bias due to amount, nature or handling of incomplete outcome data)
For each included trial and or each outcome, we will describe the completeness of data, including attrition and exclusions from the analyses. We will state whether the trial reported attrition and exclusions, and the number of participants included in the analysis at each stage (compared with the number of randomised participants per intervention/comparator groups). We will also note if the trial reported the reasons for attrition or exclusion and whether missing data were balanced across groups or were related to outcomes. We will consider the implications of missing outcome data per outcome such as high dropout rates (e.g. above 15%) or disparate attrition rates (e.g. difference of 10% or more between trial arms).
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Low risk of bias: no missing outcome data; reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to introduce bias); missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk is not enough to have a clinically relevant impact on the intervention effect estimate; for continuous outcome data, plausible effect size (mean difference or standardised mean difference) among missing outcomes is not enough to have a clinically relevant impact on observed effect size; appropriate methods, such as multiple imputation, were used to handle missing data.
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Unclear risk of bias: insufficient information to assess whether missing data in combination with the method used to handle missing data were likely to induce bias; the trial did not address this outcome.
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High risk of bias: reason for missing outcome data is likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk enough to induce clinically relevant bias in the intervention effect estimate; for continuous outcome data, plausible effect size (mean difference or standardised mean difference) among missing outcomes enough to induce clinically relevant bias in observed effect size; 'as‐treated' or similar analysis done with substantial departure of the intervention received from that assigned at randomisation; potentially inappropriate application of simple imputation.
Selective reporting (reporting bias due to selective outcome reporting)
We will assess outcome reporting bias by integrating the results of the appendix 'Matrix of trial endpoints (publications and trial documents)' (Boutron 2014; Jones 2015; Mathieu 2009), with those of the appendix 'High risk of outcome reporting bias according to ORBIT classification' (Kirkham 2010). This analysis will form the basis for the judgment of selective reporting.
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Low risk of bias: the trial protocol is available, and all of the trial's pre‐specified (primary and secondary) outcomes that are of interest in the review have been reported in the pre‐specified way; the study protocol is unavailable, but it is clear that the published reports include all expected outcomes (ORBIT classification).
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Unclear risk of bias: insufficient information about selective reporting.
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High risk of bias: not all the trial's pre‐specified primary outcomes were reported; one or more primary outcomes were reported using measurements, analysis methods or subsets of the data (e.g. subscales) that were not pre‐specified; one or more reported primary outcomes were not pre‐specified (unless clear justification for their reporting was provided, such as an unexpected adverse effect); one or more outcomes of interest in the Cochrane Review were reported incompletely so that we cannot enter them in a meta‐analysis; the trial report failed to include results for a key outcome that we would expect to have been reported for such a trial (ORBIT classification).
Other bias
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Low risk of bias: the trial appeared to be free from other sources of bias.
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Unclear risk of bias: there was insufficient information to assess whether an important risk of bias existed; insufficient rationale or evidence that an identified problem introduced bias.
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High risk of bias: the trial had a potential source of bias related to the specific trial design used; the trial was claimed to be fraudulent; or the trial had some other serious problem.
Summary assessment of risk of bias
We will present a 'Risk of bias' graph and a 'Risk of bias' summary figure.
We will distinguish between self‐reported and investigator‐assessed outcome measures.
We will consider the following self‐reported outcomes.
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Health‐related quality of life.
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Adverse events.
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Body weight (kg).
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Glucose levels.
We will consider the following investigator‐assessed outcomes.
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HbA1c.
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Body weight (kg).
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All‐cause mortality.
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Diabetes complications.
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Anthropometric measures other than body weight (kg).
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Lipid profile.
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Glucose levels.
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Serum insulin.
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Insulin sensitivity.
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Socioeconomic effects.
Risk of bias for a trial across outcomes: some 'Risk of bias' domains, such as selection bias (sequence generation and allocation sequence concealment), affect the risk of bias across all outcome measures in a trial. In case of high risk of selection bias, we will mark all endpoints investigated in the associated trial as high risk. Otherwise, we will not perform a summary assessment of the risk of bias across all outcomes for a trial.
Risk of bias for an outcome within a trial and across domains: we will assess the risk of bias for an outcome measure by including all entries relevant to that outcome (i.e. both trial‐level entries and outcome‐specific entries). We consider low risk of bias to denote a low risk of bias for all key domains, unclear risk to denote an unclear risk of bias for one or more key domains, and high risk to denote a high risk of bias for one or more key domains.
Risk of bias for an outcome across trials and across domains: these are the main summary assessments that we will incorporate into our judgments about the quality of evidence in the 'Summary of finding' tables. We define outcomes as being at low risk of bias when most information comes from trials at low risk of bias, unclear risk when most information comes from trials at low or unclear risk of bias, and high risk when a sufficient proportion of information comes from trials at high risk of bias.
Measures of treatment effect
When at least two included trials are available for a comparison and a given outcome, we will try to express dichotomous data as a risk ratio (RR) or odds ratio (OR) with 95% confidence interval (CI). For continuous outcomes measured on the same scale (e.g. weight loss in kg), we will estimate the intervention effect using the mean difference with 95% CI. For continuous outcomes measuring the same underlying concept (e.g. health‐related quality of life) but using different measurement scales, we will calculate the standardised mean difference (SMD) with its 95% CI. We will express time‐to‐event data as a hazard ratio with 95% CI.
Unit of analysis issues
We will take into account the level at which randomisation occurred, such as cross‐over trials, cluster‐randomised trials, and multiple observations for the same outcome. If more than one comparison from the same trial is eligible for inclusion in the same meta‐analysis, we will either combine groups to create a single pair‐wise comparison or appropriately reduce the sample size so that the same participants do not contribute data to the meta‐analysis more than once (splitting the 'shared' group into two or more groups). While the latter approach offers some solution to adjusting the precision of the comparison, it does not account for correlation arising from the same set of participants being in multiple comparisons (Higgins 2011c).
We will attempt to reanalyse cluster‐RCTs that have not appropriately adjusted for potential clustering of participants within clusters in their analyses. The variance of the intervention effects will be inflated by a design effect. Calculation of a design effect involves estimation of an intracluster correlation coefficient (ICC). We will obtain estimates of ICCs through contact with authors or impute them, either using estimates from other included trials that report ICCs or using external estimates from empirical research (e.g. Bell 2013). We plan to examine the impact of clustering using sensitivity analyses.
Dealing with missing data
If possible, we will obtain missing data from the authors of the included trials.We will carefully evaluate important numerical data such as screened, randomly assigned participants as well as intention‐to‐treat, as‐treated and per‐protocol populations. We will investigate attrition rates (e.g. dropouts, losses to follow‐up, withdrawals), and we will critically appraise issues concerning missing data and use of imputation methods (e.g. last observation carried forward) if individuals are missing from the reported results.
When change from baseline is the outcome of interest, missing standard deviations for changes from baseline constitute a special case. If authors do not explicitly present these data, and we cannot obtain them from trial authors, we will calculate the mean change in each group by subtracting the final mean from the baseline mean. When baseline and final standard deviations are available, we will impute the missing standard deviation using an imputed value for the correlation coefficient (Abrams 2005; Follmann 1992). Here, we will use a correlation coefficient of zero (Higgins 2011c, see 16.1.3.2 'Imputing standard deviations for changes from baseline') and check in sensitivity analyses whether the overall result of the analysis is robust to the use of different correlation coefficients. We will report per outcome which trials were included with imputed SDs.
Assessment of heterogeneity
In the event of substantial clinical or methodological heterogeneity, we will not report trial results as the pooled effect estimate in a meta‐analysis.
We will identify heterogeneity (inconsistency) by visually inspecting the forest plots and by using a standard Chi² test with a significance level of α = 0.1. In view of the low power of this test, we will also consider the I² statistic, which quantifies inconsistency across trials to assess the impact of heterogeneity on the meta‐analysis (Higgins 2002; Higgins 2003).
When we find heterogeneity, we will attempt to determine the possible reasons for it by examining individual trial and subgroup characteristics.
Assessment of reporting biases
If we include 10 or more trials that investigate a particular outcome, we will use funnel plots to assess small‐trial effects. Several explanations may account for funnel plot asymmetry, including true heterogeneity of effect with respect to trial size, poor methodological design (and hence small trial bias), and publication bias. Therefore, we will interpret the results carefully (Sterne 2011).
Data synthesis
We plan to undertake (or display) a meta‐analysis only if we judge participants, interventions, comparisons, and outcomes to be sufficiently similar to ensure an answer that is clinically meaningful. Unless good evidence shows homogeneous effects across trials of different methodological quality, we will primarily summarise low risk of bias data using a random‐effects model (Wood 2008). We will interpret random‐effects meta‐analyses with due consideration to the whole distribution of effects and present a prediction interval (Borenstein 2017a; Borenstein 2017b; Higgins 2009). A prediction interval needs at least three trials to be calculated and specifies a predicted range for the true treatment effect in an individual trial (Riley 2011). For rare events such as event rates below 1%, we will use Peto's odds ratio method, provided that there is no substantial imbalance between intervention and comparator group sizes and intervention effects are not exceptionally large. In addition, we will perform statistical analyses according to the statistical guidelines presented in the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2011).
Subgroup analysis and investigation of heterogeneity
We expect the following characteristics to introduce clinical heterogeneity, and we plan to carry out the following subgroup analyses including investigation of interactions (Altman 2003).
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Type 1 or type 2 diabetes.
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Age groups (children: 0 to 18 years; adults: 19 to 64 years; elderly: 65 years or older).
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Length of non‐nutritive sweetener intervention (medium versus long term).
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Different types of non‐nutritive sweeteners used.
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Different types of sources of non‐nutritive sweeteners (liquid, mixed, solid).
Sensitivity analysis
We plan to perform sensitivity analyses to explore the influence of the following factors (when applicable) on effect sizes by restricting analysis to the following.
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Published trials.
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Effect of risk of bias, as specified in the Assessment of risk of bias in included studies section.
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Very long or large trials to establish the extent to which they dominate the results.
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Trials based on the following filters: diagnostic criteria, imputation, language of publication, source of funding (industry versus other), or country.
We will also test the robustness of results by repeating the analyses using different measures of effect size (RR, OR, etc.) and different statistical models (fixed‐effect and random‐effects models).
Summary of findings table
We will present a summary of the evidence in a 'Summary of findings' table. This will provide key information about the best estimate of the magnitude of the effect, in relative terms and as absolute differences, for each relevant comparison of alternative management strategies, numbers of participants and trials addressing each important outcome and a rating of overall confidence in effect estimates for each outcome. We will create the 'Summary of findings' table based on the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2011) by means of the Review Manager (RevMan 5.3) table editor (RevMan 2014). We will report the following outcomes, listed according to priority.
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Health‐related quality of life.
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Diabetes complications.
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All cause‐mortality.
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Adverse events.
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HbA1c.
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Body weight (kg).
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Socioeconomic effects.
We will present the overall quality of the evidence for each outcome specified under 'Types of outcome measures: Summary of findings' according to the GRADE approach, which takes into account issues related not only to internal validity (risk of bias, inconsistency, imprecision, publication bias) but also to external validity, such as directness of results. Pairs of review authors (SL, IT, DK) will independently rate the quality of evidence for each outcome. Differences in assessment will be solved by discussion or consultation with a third researcher (SL, IT, DK, JM).
We will include an appendix titled 'Checklist to aid consistency and reproducibility of GRADE assessments', to help with standardisation of the 'Summary of findings' tables (Meader 2014). Alternatively, we will use the GRADEpro Guideline Development Tool (GDT) software (GRADEproGDT 2015) and present evidence profile tables as an appendix. We will present results for the outcomes as described in the Types of outcome measures section. If meta‐analysis is not possible, we will present the results in a narrative format in the 'Summary of findings' table. We will justify all decisions to downgrade the quality of trials using footnotes, and we will make comments to aid the reader's understanding of the Cochrane Review where necessary.
| Sweetener | FDA (mg/kg body weight) (FDA 2015a) | SCF/EFSA (mg/kg body weight) (Mortensen 2006) | JECFA (mg/kg body weight) (JECFA 2010) |
| ACE‐K | 15 | 9 | 15 |
| Advantame | 32.8 | 5 | 5 |
| Aspartame | 50 | 40 | 40 |
| Cyclamate | Not approved | 7 | 11 |
| Luo han guo fruit extracts | Not specified | Not specified | Not specified |
| Neohesperidine DC | Not approved | 5 | Not evaluated |
| Neotame | 0.3 | 2 | 2 |
| Saccharin | 15 | 5 | 5 |
| Sucralose | 5 | 15 | 15 |
| Steviol glycosides | 4 | 4 | 4 |
| Thaumatin | Not approved | Not specified | Not specified |
| Note: aspartame and acesulfame moieties in aspartame‐acesulfame salt are covered by acceptable daily intake values established for aspartame and acesulfame potassium ACE‐K: acesulfame potassium; DC: dihydrochalcone; EFSA: European Food Safety Authority;FDA: Food and Drug Administration; JECFA: Joint FAO/WHO Expert Committee on Food Additives; SCF: Scientific Committee on Food (European Commission) | |||
