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The Cochrane Database of Systematic Reviews logoLink to The Cochrane Database of Systematic Reviews
. 2011 Feb 16;2011(2):CD009001. doi: 10.1002/14651858.CD009001

Dapagliflozin for adults with type 2 diabetes mellitus

Amit D Raval 1,✉, Ketan Chovatiya 2, Ankit B Bhavsar 2, Megha H Patel 2
Editor: Cochrane Metabolic and Endocrine Disorders Group
PMCID: PMC8715906

Objectives

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To assess the effects of dapagliflozin for type 2 diabetes mellitus.

Background

Description of the condition

Diabetes mellitus (DM) is a metabolic disorder resulting from a defect in insulin secretion, insulin action, or both. A consequence of this is chronic hyperglycaemia (that is elevated levels of plasma glucose level) with disturbances of carbohydrate, fat and protein metabolism. Long‐term complications of diabetes mellitus include retinopathy, nephropathy and neuropathy. The risk of cardiovascular disease and cancer is increased. For a detailed overview of diabetes mellitus, please see under 'Additional information' in the information on the Metabolic and Endocrine Disorders Group in The Cochrane Library (see 'About', 'Cochrane Review Groups (CRGs)'). For an explanation of methodological terms, see the main glossary in The Cochrane Library.

Diabetes is a global health issue in terms of prevalence, healthcare cost, and overall complications. The world prevalence of diabetes among adults (aged 20 to 79 years) is expected to be 6.4%, affecting 285 million adults in 2010 and will increase to 7.7% and 439 million adults by 2030 (Shaw 2010). Eventually, the global health expenditure on diabetes is expected to total at least USD 376 billion (approx. 273 billion EUR) in 2010 and is projected to be USD 490 billion (approx. 356 billion EUR) in 2030. Globally, 12% of the health expenditures and USD 1330 (approx. 966 EUR) per person are anticipated to be spent on diabetes in 2010 (Zhang 2010). With the rise in global epidemic, diabetes is also a major cause of mortality and morbidity. The total number of excess deaths attributable to diabetes worldwide was estimated to be 3.96 million in the age group 20 to 79 years, 6.8% of total (all ages) mortality. Diabetes is a considerable cause of premature mortality, a situation that is likely to worsen, particularly in low and middle income countries as diabetes prevalence increases (Roglic 2010). Thus, diabetes imposes a significant socio‐economical burden globally.

The burden of diabetes demands for developing effective strategies to prevent or control diabetes immediately. Many studies showed that increased glycosylated haemoglobin A1c (HbA1c) levels are associated with an increased risk of diabetes related complications, heart disease and death (Hill 2010; Kelly 2009; Patel 2008). Lowering HbA1c to or below 7% has been shown to reduce microvascular and neuropathic complications of diabetes. American Diabetes Association (ADA) recommends that the HbA1c goal for nonpregnant adults should be less than 7% for prevention of microvascular complications (ADA 2010). Systematic reviews showed significant effects of glycaemic control on non‐fatal myocardial infarction, however intensive glycaemic control did not show significant reductions in overall mortality or macrovascular complications other than myocardial infarction (Marso 2010; Ray 2009; Turnbull 2009; Zhang 2010a). To prevent or reduce the vascular complications and mortality in patients with type 2 diabetes remains a major challenge. Despite 'lifestyle' changes and available pharmacological therapies, glycaemic control often remains above predefined guideline targets (Harrish 2000) and vascular complications are major contributors of making the disease more complex to manage. Major prospective studies suggest that less than 50% of patients with type 2 diabetes achieve targets for HbA1c less than 7% (Cheung 2009; Gaede 2008; UKPDS 1998). Therefore, alternative agents that help to reduce hyperglycaemia that act via novel mechanisms are needed to help slow the disease progression, reduce disease complications, or both.

Description of the intervention

Dapagliflozin is novel molecule which acts via sodium‐coupled glucose co‐transporter 2 (SGLT2) inhibition. It is designed to improve glucose control in diabetes. The indication of SGLTs inhibitors in diabetic patients were provided earlier in 18th century with the first natural SGLT2 inhibitor, phlorizin, obtained from the root bark of the apple tree. Phlorizin showed promising efficacy over glycaemic control in animal models but failed to proceed further for human use as it is rapidly degraded by lactase‐phlorizin hydrolase, is poorly absorbed from the gastrointestinal tract, and lacks selectivity for SGLT2, thus possessing potential gastrointestinal adverse effects (Ehrenkranz 2005). Until the characterisation of SGLT2 in the 1990s, the role of phlorizin was considered a potential target for diabetic research and early insight into the potential value of this therapeutic approach (Dudash 2004).

How the intervention might work

For centuries, although significantly contributing to glucose homoeostasis, the role of the kidneys has not been of major focus for treating diabetes. The kidneys play a vital role in the body's glucose homoeostasis in three ways: gluconeogenesis (formation of glucose from non carbohydrate sources in the body), utilization of glucose and reabsorption of glucose. In healthy individuals, around 180 g of glucose is filtered by the kidneys in 24 hrs but nearly all of this is reabsorbed such that only 1% of glucose is excreted in urine (Gerich 2010; Stumvoll 1995). The tubular glucose load is approximately 120 mg/min; no glucose is lost in the urine. However, when the glucose load exceeds the ‘glucose threshold’ and rises to a level greater than 220 mg/min, a small amount of glucose begins to appear in the urine (Butterfield 1967). Inhibition of this reabsorption process is predicted to reduce the renal threshold for glucose, allowing the excretion of glucose in the urine and thus lowering plasma glucose levels.

The sodium‐glucose transporter (SGLT) is the major mediator for the process of reabsorption of glucose by the kidney. SGLTs encompass a family of membrane proteins that are responsible for the transport of glucose, amino acids, vitamins, ions across the brush‐border membrane of proximal renal tubules as well as the intestinal epithelium (Bakris 2009). These transporters are present in two important isoforms: SGLT1 which is highly expressed in the gastrointestinal tract with presence in other organs such as liver, lung, and kidney and is the major transporter of dietary glucose and galactose; SGLT2 for which glucose is the primary substrate, appears to be selectively expressed in the brush border membrane of the first segment of the proximal tubule of the kidney (Bakris 2009; Wright 2007). Around 90% of glucose reabsorption in the kidney is achieved by SGLT2 which is located in the proximal tubule (Wood 2003). Inhibition of reabsorption of glucose through SGLT inhibitors could lead to decrease in glucose load in the blood. However, inhibition of SGLT1 causes the glucose‐galactose malabsorption syndrome (GGM) which ends up in life‐threatening dehydration (Wright 2007). Hence, it is necessary to selectively inhibit SGLT2 to inhibit reabsorption of glucose by the kidney.

Dapagliflozin inhibits renal glucose reabsorption through inhibition of SGLT2 (Han 2008; Meng 2008). It has several advantages for the treatment of diabetes. Firstly, the selectivity of dapagliflozin for SGLT2 decreases renal reabsorption of glucose without a discernable effect on the function of SGLT1 in the small intestines; hence theoretically, it is free from the life‐threatening adverse effects of inhibition of SGLT1 like GGM and other gastrointestinal adverse effects that occur due to SGLT1inhibition (Han 2008). Secondly, this mechanism of action does not require insulin secretion or insulin action to effect glucose lowering; it could be efficacious in a wide variety of diabetic patients. Also, it is independent from the beta‐cell capacity and insulin resistance, the two major pathogenic determinants for progression of glucose intolerance. As hyperglycaemia is associated with a reduction in insulin sensitivity and impaired beta‐cell function, correction of hyperglycaemia may improve these pathophysiological changes in type 2 diabetes (Blondel 1990; Kahn 1991; Jonas 1999; Jellinger 2007; Rossetti 1987). Furthermore, the glucosuria associated with SGLT2 inhibition can result in a loss of calories which may provide the potential benefit of weight loss (Kipnes 2009).

Adverse effects of the intervention

Familial renal glucosuria (FRG) is a condition that is associated with mutations of SGLT2 and results in chronic glucosuria. Glucose excretion in the urine has been reported to range up to 30 to 144 g/day in FRG. However, these individuals are typically asymptomatic, showing normal blood glucose levels, renal function and histology (Francis 2004; Oemar 1987; Scholl‐Burgi 2004). Moreover, these individuals do not appear to suffer from adverse clinical consequences, including diabetes, renal failure or increased incidences of urinary tract infection and have largely benign phenotypes with normal life expectancies and no long‐term renal deterioration or known health consequences (Santer 2003; Scholl‐Burgi 2004). However, there are reports of renal sodium wasting and mild volume depletion in these patients (Calado 2006). Given the relatively small number of patients with FRG compared to type 2 diabetes, it is crucial to monitor for various safety outcomes. There is great deal of concern regarding the adverse events related to polyuria and polydipsia (increased urinary glucose excretion could theoretically cause an osmotic diuresis) that may lead to hypotension. It appears especially necessary to investigate the use of dapagliflozin in elderly patients with type 2 diabetes who are at a higher risk of dehydration and in those with mild renal insufficiency. Secondly, it is always a challenge to control infections in diabetic patients due to limited choices of antibiotics. With high sugar levels in the urine the incidence of genitourinary infections (in particular vulvo‐vaginal candidiasis) may increase (Kipnes 2009). Therefore, these parameters currently appear as the major safety concerns for the patients on dapagliflozin.

Why it is important to do this review

Drugs with proven efficacy and without major adverse events on long‐term use become a major objective for diabetes management. Confirmation of the efficacy of dapagliflozin in terms of reducing the vascular complications or mortality in diabetic patients alone or in combination would have potential implications for the treatment of type 2 diabetes mellitus. Currently, there is no health technology assessment review on dapagliflozin for type 2 diabetes mellitus. One systematic review on dapagliflozin for type 2 diabetes mellitus was published (Brooks 2010). However, there are some limitation of this review as it did not address potential clinical outcome like mortality or vascular complications. Since the date of this publication, randomised clinical trials exploring the use of dapagliflozin in diabetes mellitus have been published, many are ongoing. Our aim in doing this review is to identify and aggregate good quality evidence on dapagliflozin for type 2 diabetes mellitus with the major focus on core clinical and patient‐related outcomes ‐ as these aspects are important contributors to the strength of recommendations for all therapies and also the use of dapagliflozin in diabetes mellitus.

Objectives

To assess the effects of dapagliflozin for type 2 diabetes mellitus.

Methods

Criteria for considering studies for this review

Types of studies

Randomised controlled clinical trials.

Types of participants

Patients aged 18 years and over with type 2 diabetes mellitus.

Diagnostic criteria

To be consistent with changes in classification and diagnostic criteria of diabetes mellitus through the years, the diagnosis should be established using the standard criteria valid at the time of the beginning of the trial (for example ADA 1999; ADA 2008; ADA 2010; WHO 1980; WHO 1985; WHO 1998). Ideally, diagnostic criteria should have been described. If necessary, we will use authors' definition of diabetes mellitus. We will plan to subject diagnostic criteria to a sensitivity analysis.

Types of interventions

Intervention

1a) dapagliflozin
1b) dapagliflozin with another antihyperglycaemic agent

Control

2a) placebo or any antihyperglycaemic agent other than dapagliflozin
2b) antihyperglycaemic agent other than dapagliflozin

Types of outcome measures

Primary outcomes

  • mortality (all‐cause and diabetes‐related, including death from vascular disease, renal disease and hypoglycaemia);

  • diabetic complications (any micro‐ or macrovascular complication);

  • glycaemic control (measured by glycosylated haemoglobin A1c (HbA1c) and glucose levels (fasting and post‐prandial)).

Secondary outcomes

  • weight (kg) or body mass index (BMI);

  • blood levels of insulin and C‐peptide;

  • blood pressure;

  • blood levels of lipids: total cholesterol, LDL‐cholesterol, HDL‐cholesterol, triglycerides;

  • adverse effects of dapagliflozin (all expected and unexpected serious and non‐serious adverse events [for example hypoglycaemia, urinary tract infections, genital infections (especially vaginal candidiasis), electrolyte imbalances, hypotension]; hypoglycaemic events will be graded as: mild (symptoms easily controlled by the individual), moderate (normal activities interrupted but not requiring assistance), severe (individual requiring assistance and associated with blood glucose levels less than 50 mg/dL (4 mmol/L) or prompt recovery after carbohydrate or glucagon administration) (DCCT 1993). Classification of hypoglycaemic events as defined by clinical trial protocols will also be recorded;

  • health‐related quality of life, measured by a validated instrument;

  • costs.

Possible covariates, effect modifiers, confounders

  • age;

  • gender;

  • ethnicity;

  • baseline HbA1c levels;

  • hypertension;

  • compliance;

  • co‐medications (for example antihypertensive drugs, aspirin).

Timing of outcome measurement

We will assess outcomes in the short term (equal to or more than 12 weeks to less than 18 weeks), medium term (equal to or more than 18 weeks to less than 52 weeks) and long term (equal to or more than 52 weeks). Data from long‐term extension trials of randomised controlled trials will be considered only if participants are maintaining the same sequence allocation as in the initial feeder trial.

Search methods for identification of studies

Electronic searches

We will use the following sources for the identification of trials:

  • TheCochrane Library (last issue);

  • MEDLINE (until recent);

  • EMBASE (until recent);

  • CINAHL (until recent);

  • LILAC (until recent).

We will also search national and international research registers for ongoing clinical trials; ''Current Controlled Trials''(www.controlled-trials.com); International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch); USA ‐ CenterWatch Clinical Trials Listing Service (http://www.CenterWatch.com/); USA ‐ National Institutes of Health (http://clinicalstudies.info.nih.gov/).

For detailed search strategies please see under Appendix 1. If additional key words of relevance are detected during any of the electronic or other searches we will modify the electronic search strategies to incorporate these terms. We will include studies published in any language.

Searching other resources

We will try to identify additional studies by searching the reference lists of included trials and (systematic) reviews, meta‐analyses and health‐technology assessment reports noticed. We will thoroughly search the U.S. Food and Drug Administration (U.S. FDA) site and European Medicines Agency (EMA) for data on pre‐approval trials. Documentation submitted for drug approval will be appraised, matched with published reports, and appraised for unpublished data. If needed, the authors of the review will contact the FDA/EMA medical officer signing the FDA/EMA medical review for clarification and details.

Known experts will be contacted regarding any unpublished material. In addition, citation searches on key papers to identify citing publications will be completed. Further, web sites of the manufacturer for dapagliflozin will be searched to find relevant publication details.

Data collection and analysis

Selection of studies

To determine the studies to be assessed further, two authors (KC, AB) will independently scan the abstract, title or both sections of every record retrieved. All potentially relevant articles will be investigated as full text. We will measure interrater agreement for selection of potentially relevant studies using the kappa statistic (Cohen 1960). Differences will be marked and if these studies are later on included, we will study the influence of the primary choice by means of a sensitivity analysis. Where differences in opinion exist, they will be resolved by a third party. If resolving disagreement is not possible, the article will be added to those 'awaiting assessment' and we will contact authors for clarification. We will attach an adapted PRISMA (preferred reporting items for systematic reviews and meta‐analyses) flow‐chart of study selection (Liberati 2009).

Data extraction and management

For studies that fulfil inclusion criteria, two authors (KB, AB) will independently abstract relevant population and intervention characteristics using standard data extraction templates (for details see 'Characteristics of included studies', Table 1, Appendix 2, Appendix 3, Appendix 4, Appendix 5) with any disagreements to be resolved by discussion, or if required by a third party. Any relevant missing information on the trial will be sought from the original author(s) of the article, if required. In addition, data extraction for the adverse events in clinical studies will also be collected by third author (MP).

1. Overview of study populations.
Study ID Intervention(s) & control(s) [n] screened [n] randomised [n] safety [n] ITT [n] finishing study [%] of randomised participants
finishing study Comments
ID1 I1:
I2:
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ID2 I1:
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ID3 I1:
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ID3 I1:
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ID4 I1:
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ID5 I1:
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ID6 I1:
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ID7 I1:
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ID8 I1:
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ID9 I1:
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Total     I1:
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C: control; I: intervention; ITT: intention‐to‐treat

Dealing with duplicate publications

In the case of duplicate publications and companion papers of a primary study, we will try to maximise yield of information by simultaneous evaluation of all available data. In cases of doubt, the original publication (usually the oldest version) will obtain priority.

Assessment of risk of bias in included studies

Two authors (KC, AB) will assess each trial independently. Possible disagreements will be resolved by consensus, or with consultation of a third party. Interrater agreement for key bias indicators (e.g. allocation concealment, incomplete outcome data) will be calculated using the kappa statistic (Cohen 1960). In cases of disagreement, the rest of the group will be consulted and a judgement will be made based on consensus.

We will assess risk of bias using the Cochrane Collaboration’s tool (Higgins 2009). We will use the following criteria:

  • was the allocation sequence adequately generated?

  • was the allocation adequately concealed?

  • was knowledge of the allocated intervention adequately prevented during the study?

  • were incomplete outcome data adequately addressed?

  • were reports of the study free of suggestion of selective outcome reporting?

  • was the study apparently free of other problems that could put it at a high risk of bias?

In all cases, an answer 'Yes' indicates a low risk of bias and an answer 'No' indicates high risk of bias. If insufficient details on what happened in the study are reported, the judgement will be ‘Unclear’. An ‘Unclear’ judgement will also be made if what happened in the study is known but the risk of bias is unknown; or if an entry is not relevant to the study at hand (particularly for assessing blinding and incomplete outcome data, when the outcome being assessed was not measured in the study report). We will use these criteria for a judgement of ‘Yes’, ‘No’ and ‘Unclear’ for individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009). A 'risk of bias graph' figure and 'risk of bias summary' figure will be attached.

We will make explicit judgements about the risk of bias for outcomes both within and across studies. Highly subjective outcomes like health‐related quality of life, adverse events like pain, or maybe some biochemical test also, blinding of participants as well as investigator or monitor are critical while with the core clinical outcomes like mortality, macrovascular complications like myocardial infraction, stroke or biochemical parameters, blinding will not be a critical issue as for observation bias. We will also evaluate the precautions to be taken to prevent observation bias, such as in case of dapagliflozin the patient obviously will have high levels of urine glucose so the urine glucose data may bleach the blinding. In this condition either blinding of assessor or investigator to the lab test for urine sugar would provide the best value in determining the level of bias risk at study level. We will present summary assessments of risk of bias at three level; Summarizing risk of bias for a study across outcomes; summarizing risk of bias for an outcome within a study (across domains); summarizing risk of bias for an outcome across studies; These are the main summary assessments that will be made by us and incorporated into judgements about the ‘quality of evidence’ in the ‘Summary of findings’ table, as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009).

Measures of treatment effect

Dichotomous data will be expressed as odds ratio (OR) or risk ratio (RR) with 95% confidence intervals (CI). Continuous data (for example change of HbA1c from baseline, change in weight, decrease of postprandial glycaemic values) will be summarised as weighted mean difference (WMD) with 95% CI. For studies addressing the same outcome but using different outcome measures we will use the standardized mean difference (SMD).Time‐to‐event outcomes (for example time until development of microvascular or macrovascular complications) will be expressed as hazard ratios (HR) 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.

Dealing with missing data

We will obtain relevant missing data from authors, if feasible and carefully perform evaluation of important numerical data such as screened, randomised patients as well as intention‐to‐treat (ITT), as‐treated and per‐protocol (PP) populations. We will investigate attrition rates, for example drop‐outs, losses to follow up and withdrawals and critically appraise issues of missing data and imputation methods (for example last‐observation‐carried‐forward (LOCF)).

Assessment of heterogeneity

In the event of substantial clinical or methodological or statistical heterogeneity we will not report study results as meta‐analytically pooled effect estimates. We will identify heterogeneity by visual inspection of 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 specifically will examine heterogeneity employing the I2 statistic which quantifies inconsistency across studies to assess the impact of heterogeneity on the meta‐analysis (Higgins 2002; Higgins 2003), where an I2 statistic of 75% and more indicates a considerable level of inconsistency (Higgins 2009).

When heterogeneity is found, we will attempt to determine potential reasons for it by examining individual study and subgroup characteristics.

Assessment of reporting biases

We will use funnel plots to assess for the potential existence of small study bias. There are a number of explanations for the asymmetry of a funnel plot (Sterne 2001) and we will carefully interpret results (Lau 2006).

Data synthesis

Data will be summarised statistically if they are available, sufficiently similar and of sufficient quality. We will perform statistical analyses according to the statistical guidelines referenced in the newest version of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009).

Subgroup analysis and investigation of heterogeneity

We will mainly carry out subgroup analyses if one of the primary outcome parameters demonstrates statistically significant differences between intervention groups. In any other case subgroup analyses will be clearly marked as a hypothesis generating exercise.

The following subgroup analyses are planned:

  • age;

  • gender;

  • ethnicity;

  • BMI;

  • nephropathy.

Sensitivity analysis

We will perform sensitivity analyses in order to explore the influence of the following factors on effect size:

  • repeating the analysis excluding unpublished studies;

  • repeating the analysis taking account risk of bias, as specified above;

  • repeating the analysis excluding very long or large studies to establish how much they dominate the results;

  • repeating the analysis excluding studies using the following filters: diagnostic criteria, language of publication, source of funding (industry versus other), country.

We will also test the robustness of the results by repeating the analysis using different measures of effect size (relative risk, odds ratio etc.) and different statistical models (fixed‐effect model and random‐effects model).

History

Protocol first published: Issue 2, 2011

Notes

This protocol has been withdrawn as it will not be possible to complete the review within adequate deadlines. 

Acknowledgements

None.

Appendices

Appendix 1. Search strategies

Search terms
Unless otherwise stated, search terms are free text terms; MeSH = Medical subject heading (Medline medical index term); exp = exploded MeSH; the dollar sign ($) or asterisk (*) stand for any character(s); the question mark (?) = to substitute for one or no characters; ab = abstract; adj = adjacent; ot = original title;  pt = publication type; rn = Registry number or Enzyme Commission number; sh = MeSH; ti = title; tw = text word.
The Cochrane Library:
#1MeSH descriptor Diabetes mellitus, type 2 explode all trees
#2MeSH descriptor Insulin resistance explode all trees
#3( (impaired in All Text and glucose in All Text and toleranc* in All Text) or (glucose in All Text and intoleranc* in All Text) or (insulin* in All Text and resistanc* in All Text) )
#4(obes* in All Text near/6 diabet* in All Text)
#5(MODY in All Text or NIDDM in All Text or TDM2 in All Text or TD2 in All Text)
#6( (non in All Text and insulin* in All Text and depend* in All Text) or (noninsulin* in All Text and depend* in All Text) or (non in All Text and insulindepend* in All Text) or noninsulindepend* in All Text)
#7(typ? in All Text and (2 in All Text near/6 diabet* in All Text) )
#8(typ? in All Text and (II in All Text near/6 diabet* in All Text) )
#9(non in All Text and (keto* in All Text near/6 diabet* in All Text) )
#10(nonketo* in All Text near/6 diabet* in All Text)
#11(adult* in All Text near/6 diabet* in All Text)
#12(matur* in All Text near/6 diabet* in All Text)
#13(late in All Text near/6 diabet* in All Text)
#14(slow in All Text near/6 diabet* in All Text)
#15(stabl* in All Text near/6 diabet* in All Text)
#16(insulin* in All Text and (defic* in All Text near/6 diabet* in All Text) )
#17(plurimetabolic in All Text and syndrom* in All Text)
#18(pluri in All Text and metabolic in All Text and syndrom* in All Text)
#19MeSH descriptor Glucose Intolerance explode all trees
#20(typ?2 in All Text near/3 diabet* in All Text)
#21(keto in All Text and (resist* in All Text near/3 diabet* in All Text) )
#22(non in All Text and (keto* in All Text near/3 diabet* in All Text) )
#23(nonketo* in All Text near/3 diabet* in All Text)
#24(#1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10)
#25(#11 or #12 or #13 or #14 or #15 or #16 or #17 or #18 or #19 or #20 or #21 or #22 or #23)
#26(#24 or #25)
#27MeSH descriptor Diabetes insipidus explode all trees
#28(diabet* in All Text and insipidus in All Text)
#29(#27 or #28)
#30(#26 and not #29)
#31dapagliflozin* in All Text
#32(sodium in All Text and (glucose in All Text near/6 inhibitor* in All Text) )
#33(SGLT2 in All Text and inhibitor* in All Text)
#34(#31 or #32 or #33)
#35(#30 and #34)


Medline:
1 Dapagliflozin.mp.
2 (sodium glucose adj6 inhibitor*).tw,ot.
3 SGLT2 inhibitor*.tw,ot.
4 or/1‐3
5 exp Diabetes Mellitus, Type 2/
6 exp Insulin Resistance/
7 exp Glucose Intolerance/
8 (impaired glucos$ toleranc$ or glucos$ intoleranc$ or insulin resistan$).tw,ot.
9 (obes$ adj3 diabet$).tw,ot.
10 (MODY or NIDDM or T2DM or T2D).tw,ot.
11 (non insulin$ depend$ or noninsulin$ depend$ or noninsulin?depend$ or non
insulin?depend$).tw,ot.
12 ((typ? 2 or typ? II or typ?2 or typ?II) adj3 diabet$).tw,ot.
13 ((keto?resist$ or non?keto$) adj6 diabet$).tw,ot.
14 (((late or adult$ or matur$ or slow or stabl$) adj3 onset) and diabet$).tw,ot.
15 or/5‐14
16 exp Diabetes Insipidus/
17 diabet$ insipidus.tw,ot.
18 16 or 17
19 15 not 18
20 4 and 19
21 (animals not (animals and humans)).sh.
22 20 not 21

Embase:
1 exp dapagliflozin/
2 dapagliflozin*.tw,ot.
3 SGLT2 inhibitor*.tw,ot.
4 (sodium glucose adj6 inhibitor*).tw,ot.
5 or/1‐4
6 exp Diabetes Mellitus, Type 2/
7 exp Insulin Resistance/
8 (MODY or NIDDM or T2D or T2DM).tw,ot.
9 ((typ? 2 or typ? II or typ?II or typ?2) adj3 diabet*).tw,ot.
10 (obes* adj3 diabet*).tw,ot.
11 (non insulin* depend* or non insulin?depend* or noninsulin* depend* or
noninsulin?depend*).tw,ot.
12 ((keto?resist* or non?keto*) adj3 diabet*).tw,ot.
13 ((adult* or matur* or late or slow or stabl*) adj3 diabet*).tw,ot.
14 (insulin* defic* adj3 relativ*).tw,ot.
15 (insulin* resistanc* or impaired glucos* toleranc* or glucos* intoleranc*).tw,ot.
16 or/6‐15
17 exp Diabetes Insipidus/
18 diabet* insipidus.tw,ot.
19 17 or 18
20 16 not 19
21 5 and 20
22 limit 21 to human
Open in a new tab

Appendix 2. Description of interventions

Characteristic study ID1 study ID2 study ID3 study ID4 study ID5 study ID6 study ID7 study ID8 study ID9
Intervention(s)
[route, frequency, total dose/day]		 I1:
I2:		 I1:
I2:		 I1:
I2:		 I1:
I2:		 I1:
I2:		 I1:
I2:		 I1:
I2:		 I1:
I2:		 I1:
I2:
Control(s)
[route, frequency, total dose/day]		 C1:
C2:		 C1:
C2:		 C1:
C2:		 C1:
C2:		 C1:
C2:		 C1:
C2:		 C1:
C2:		 C1:
C2:		 C1:
C2:
Footnotes
C: control; I: intervention
Open in a new tab

Appendix 3. Baseline characteristics

Characteristic study ID1 study ID2 study ID3 study ID4 study ID5 study ID6 study ID7 study ID8 study ID9
Intervention(s) & control(s) I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:
Participating population                  
Sex [female% / male%]                  
Age [mean years (SD)]                  
HbA1c [mean % (SD)]                  
Duration of disease [mean years (SD)]                  
Ethnic groups [%]                  
Duration of intervention                  
Duration of follow up                  
Footnotes
C: control; HbA1c: glycosylated haemoglobin A1c; I: intervention
Open in a new tab

Appendix 4. Matrix of study endpoints

Characteristic study ID1 study ID2 study ID3 study ID4 study ID5 study ID6 study ID7 study ID8 study ID9
Intervention(s) & control(s) I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:
Primary1 endpoint(s)                  
Secondary2 endpoint(s)                  
Other3 endpoint(s)                  
Footnotes
1,2 as stated in the publication; 3 not stated as primary or secondary endpoint(s) in the publication
C: control; I: intervention
Open in a new tab

Appendix 5. Adverse events

Characteristic study ID1 study ID2 study ID3 study ID4 study ID5 study ID6 study ID7 study ID8 study ID9
Intervention(s) & control(s) I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:		 I1:
I2:
C1:
C2:
Deceased participants [n] I1: n / N
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Adverse events [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Serious adverse events [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Drop‐outs due to adverse events [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Hospitalisation [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Out‐patient treatment [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Hypoglycaemic episodes [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Severe hypoglycaemic episodes [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Definition of severe / serious hypoglycaemia                  
Nocturnal hypoglycaemic episodes [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Hyponatraemia [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Urinary tract infections [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Hypotension [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Increased creatinine level [n / %] I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Others unexpected serious and
non‐serious adverse events [n / %]		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:		 I1:
I2:
C1:
C2:
Total:
Footnotes
C: control; I: intervention
Open in a new tab

Contributions of authors

AMIT RAVAL: protocol draft, search strategy development, trial selection, data extraction, data analysis, data interpretation, review draft, update draft.
KETAN CHOVATIYA: protocol draft, search strategy development, acquirement of trial copies, trial selection, data extraction, review draft, update draft.
ANKIT BHAVSAR: protocol draft, trial selection, data extraction, data analysis, data interpretation, review draft, update draft.
MEHGA PATEL: safety perspective with following work ‐protocol draft, data extraction, review draft.

Sources of support

Internal sources

  • No sources of support provided

External sources

  • No sources of support provided

Declarations of interest

None known.

Edited (no change to conclusions)

References

Additional references

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