Introduction
Sodium–glucose cotransporter 2 (SGLT2) inhibitors block the SGLT2 within the proximal renal tubule, reducing glucose and sodium reabsorption and increasing glycosuria and fluid loss. Dapagliflozin is a new SGLT2 inhibitor indicated alongside diet and exercise for improving glycaemic control in adults with type 2 diabetes (licensed in Europe in 2012 [
1] and the USA in 2014 [
2]). In randomised controlled trials (RCTs) [
3‐
10], dapagliflozin was found to improve glycaemic control, with mean difference in HbA
1c of ~5.5 mmol/mol (0.52%) vs control groups [
11,
12]. Although not an indication for use, RCTs of dapagliflozin have demonstrated weight loss and improved systolic blood pressure (SBP) [
3,
5,
6,
9,
10,
13]. In large-scale placebo-controlled cardiovascular disease (CVD) outcome trials, other SGLT2 inhibitors (empagliflozin [
14] and canagliflozin [
15]) were shown to reduce major CVD events. Although the results for the dapagliflozin DECLARE CVD outcome trial have not yet been published it has been reported that the primary safety endpoint of non-inferiority for major adverse cardiovascular events was met and that there was a significant reduction in one of two primary efficacy CVD endpoints [
16,
17].
Over 3 years of real-world observational data are available for dapagliflozin users in a large national electronic healthcare record-derived dataset of individuals with type 2 diabetes in Scotland, allowing effects on continuously distributed outcomes HbA
1c, BMI, body weight, SBP and kidney function (as eGFR) to be evaluated. First, we aimed to determine whether the effects of dapagliflozin on HbA
1c and other variables in RCTs are obtained in real-world practice. Second, we aimed to undertake safety event outcome analyses, since safety concerns about SGLT2 inhibitors exist, to establish whether an increased rate of these could be observed in dapagliflozin users. Specifically, the Canagliflozin Cardiovascular Assessment Study Programme demonstrated an unexpected increased risk of lower-limb amputation (LLA) in patients treated with canagliflozin [
15] and the US Food and Drug Administration (FDA)’s Adverse Reporting System showed a disproportionately increased reporting ratio for canagliflozin and LLA. It is unclear whether increased LLA risk is a class effect of SGLT2 inhibitors, is restricted to canagliflozin or is a chance effect [
18]. Case reports exist detailing the development of (often euglycaemic) diabetic ketoacidosis (DKA) in individuals with type 2 diabetes following initiation of SGLT2 inhibitor therapy, with increased disproportionality signalling in both European Medicines Agency (EMA) and FDA pharmacovigilance databases [
19,
20]. It is unclear whether this is a true drug effect.
It is important to understand the extent to which drug effects in RCTs are achieved in real clinical recipients who may have a wider range of characteristics [
21,
22]. Dapagliflozin is licensed for those between 18 and 75 years of age, with an eGFR ≥60 ml min
−1 [1.73 m]
−2 and not receiving pioglitazone or loop diuretics. Some individuals not meeting these criteria are nevertheless prescribed the drug.
We focus on dapagliflozin use in Scotland, as there are sufficient dapagliflozin users to adequately power our analyses (8566 dapagliflozin users, 1782 canagliflozin users and 2385 empagliflozin users in current data extract). As data accrues, other SGLT2 inhibitors will be evaluated.
Methods
Data sources
Anonymised data were extracted from the Scottish Care Information-Diabetes (SCI-Diabetes) collaboration database. This database has been described in detail previously [
23,
24] and, in brief, comprises a nationwide register of e-health-records containing extensive clinical data and issued prescriptions for 99% of Scottish diabetes patients. These data are linked using the Community Health Index, an identifier used in all Scottish records, to mortality data from the General Registrar and hospitalisation records available from the Informatics Service Division of the National Health Service in Scotland.
Study period and population
Data were available from 2004 until mid-2016 for all analyses. Those eligible for inclusion into the study met the following criteria: (1) alive with a diagnosis of type 2 diabetes at any time since the introduction of dapagliflozin; (2) had no diagnosis of type 1 diabetes and (3) were aged 18–80 years upon study entry. For the safety analyses, since we focus on cumulative drug effect, a further criterion was imposed that persons had to be fully evaluable for drug exposure since the date of introduction of dapagliflozin or date of onset of diabetes, whichever was later. For both analyses, individuals’ contributed person-time to the study upon the latest of study start date, date of diagnosis of type 2 diabetes or becoming observable within the dataset. Individuals ceased contributing person-time to the study upon the earliest of death, becoming unobservable within the dataset (i.e. lost due to emigration) or study end date. For the safety analysis, individuals were censored following exposure to other SGLT2 inhibitors.
The study was carried out in accordance with the ethical principles in the Declaration of Helsinki as revised in 2008.
Drug exposure
Issued prescription data were used to define drug exposures. All prescriptions were assigned Anatomical Therapeutic Chemical Classification System (ATC) codes; dapagliflozin exposure was defined as ATC code A10BX09/A10BK01. Dapagliflozin ever-users were those with any initiation of dapagliflozin between November 2012 (the first date of dapagliflozin availability) and study end date. Drug exposure start date was defined as the date of initial prescription and drug exposure end dates were extrapolated based on dosage, frequency and directions. Dapagliflozin users were stratified to those receiving dapagliflozin ‘on-licence’ (defined as age 18–75 years, eGFR ≥60 ml min−1 [1.73 m]−2, not receiving pioglitazone and not receiving loop diuretics) and ‘off-licence’ for individuals not fulfilling these criteria. Never-users were those who never received a prescription for dapagliflozin throughout the study period.
Clinical measures including outcome measures
The SCI-Diabetes database contains demographic data, captures all HbA1c, serum creatinine and other biochemical results, as well as all routine clinical measures such as blood pressure, height and body weight. For baseline comparisons of demographic and clinical characteristics of dapagliflozin users vs never-users, measurements for users were taken as those closest to (but no earlier than 24 months before) dapagliflozin initiation. For never-users, equivalent measurements were taken as those closest to (but no earlier than 24 months before) the median initiation date among users.
CVD, DKA and LLA were captured using linkage to national hospitalisation records and death data. ICD-10 codes (
http://apps.who.int/classifications/icd10/browse/2016/en) for cause of admission and operative codes for amputations and revascularisation surgeries were used to define events. CVD codes included chronic ischaemic heart disease, cerebrovascular disease, heart failure, cardiac arrhythmia or coronary revascularisation. See electronic supplementary material (ESM) Table
1 for details.
Statistical methods
Simple descriptive statistics and linear or logistic regressions adjusted for age, sex and diabetes duration were used to compare characteristics of users and never-users. To evaluate the effect of dapagliflozin on continuous clinical outcomes of interest, we first described the distribution of within-person absolute and percentage changes following dapagliflozin initiation at regular intervals of 3 months throughout follow-up among users. For this analysis, clinical outcomes were assigned to time windows by applying a caliper of ±1.5 months (e.g. the 3 month time point contained measurements observed between 1.5 and 4.5 months). For continuous variable analyses, person-time was right-censored when dapagliflozin was ceased, a diabetes drug that was co-prescribed at dapagliflozin initiation was ceased or a new diabetes drug was started that was not already being received at the dapagliflozin initiation date. Where another diabetes drug was dropped at the time of initiation of dapagliflozin, the record was included since that will be conservative with respect to the apparent dapagliflozin effect. For analyses with outcomes of SBP and for CVD events, person-time was also right-censored upon initiating a new drug for CVD (all drugs with first-level ATC code C) that was not received at dapagliflozin initiation or ceasing a CVD drug that was co-prescribed at dapagliflozin initiation.
Discussion
We describe usage trends of dapagliflozin in individuals with type 2 diabetes in Scotland. Almost all (84.4%) of those prescribed dapagliflozin were on-licence users. Dapagliflozin was largely prescribed as add-on therapy on top of one or two drugs and was continued throughout follow-up for the majority of people. As expected, dapagliflozin appeared to be preferentially prescribed for younger individuals who had poorer glycaemic control and longer duration of diabetes and who were on more than one additional oral glucose-lowering medication. Dapagliflozin use was associated with substantial improvements in HbA1c and with slight improvements in BMI, body weight and SBP. Based on follow-up values, the greatest absolute improvements in glycaemic control were observed for users with poorer baseline glycaemic control, as well as users with shorter duration of diabetes and higher kidney function. As well as an initial reduction in these outcomes, dapagliflozin appeared to stabilise HbA1c and SBP so that expected rises in these through time were prevented across this median of 210 days of follow-up.
Real-world evidence can help corroborate findings from RCTs by testing the generalisability of their reported treatment effects and conclusions within a broader and more heterogeneous population who are less supervised in their healthcare management. At 3–6 months, the crude and the modelled estimated treatment effects on HbA
1c were slightly higher than the effect sizes observed in previous RCTs. For example, at 3 months and 6 months, the observed crude mean reduction in HbA
1c from baseline was 10–12 mmol/mol (or −1.0 to −1.1% units), compared with RCT estimates of −0.61% to −0.85% at 3 months and −0.5% to −1.4% at 6 months, subject to dosage and additional drug therapies [
3,
6,
27‐
31]. The US FDA estimates the treatment effect of dapagliflozin on HbA
1c to be in the range −0.40% to −0.84%, which is smaller than our findings [
2,
32]. The National Institute for Health and Care Excellence (NICE) in the UK estimates the same to be −0.39% to −0.84%, which is also smaller than our findings and similar to the US FDA estimate [
33]. However, our estimated effects of dapagliflozin upon HbA
1c at 3–6 months were consistent with reported follow-up values from a recent UK-wide real-world retrospective study [
34].
One potential reason for apparently higher treatment effects in an observational study is regression to the mean. Where there is considerable short-term within-person variation (or ‘noise’) in clinical measures, whether due to true short-term biological variability or to measurement error, a new drug is more likely to be prescribed in response to extreme or outlying clinical observations such as unusually high HbA1c for the respective individual. Even where a drug is ineffective post- vs pre-initiation, comparisons of data might show an apparent treatment effect, as after a given extreme observation subsequent measurements might be expected to regress to the mean. It is also the case that true biological worsening of the clinical measure such as HbA1c will also precede new drug intervention. The combined effect of these two phenomena is that HbA1c at the time of drug initiation is likely to be systematically higher than expected given an individual’s prior measurements, their current characteristics of age, sex, diabetes duration and other characteristics relevant to expected HbA1c. Precisely how much of the observed treatment effect is attributable to regression-to-the-mean effects is not directly estimable. By examining the residuals of the last measurements prior to dapagliflozin initiation in a model of pre-initiation trajectories, we have provided a crude estimate of the magnitude of such effects as being an apparent reduction of about 10% of the apparent treatment effect. The treatment effects we observed on HbA1c at 3–6 months were 0.15–0.30% units higher than in clinical trials but half of this difference might be explained by regression to the mean. The apparently greater effects could also be because any changes in lifestyle that reduce HbA1c could co-occur upon drug initiation.
Our observed treatment effects at 3–6 months upon BMI, body weight and SBP were highly consistent with those from RCTs wherein reported effects on body weight were −1.50 kg to −3.2 kg and effects on SBP were −2.19 to −3.9 mmHg [
3,
5,
6,
27‐
29,
31]. The effects on SBP are consistent with the mechanisms of action of the drug, which encourages renal sodium and glucose loss.
An important aspect of our analyses is the persistence of the apparent drug effect. Since HbA1c and SBP tend to worsen over time in diabetes in the absence of drug exposure, a stable absolute difference from baseline over longer follow-up is consistent not only with the drug not only improving the HbA1c and SBP but also preventing their worsening over time. This is illustrated in the mixed model where a much larger net effect of dapagliflozin on HbA1c of −16 mmol/mol (−1.47%) given its underlying time trend was estimated at 24 months of exposure.
The vast majority of people receiving dapagliflozin respond to the drug but there is considerable variation in the magnitude of the response. We are not able to evaluate in this study to what extent such variation reflects true biological variation in response vs differences in compliance. Some of the variation on an absolute scale reflects that the largest reductions in HbA
1c during follow-up were seen for those users in the highest tertile for baseline HbA
1c (ESM Table
5). Individuals also generally exhibited wide variation in responses in clinical trials (SD for HbA
1c effect ranging from 0.61% to 0.92%), though the wider variation seen here may reflect the broader diversity of individuals’ characteristics in our real-world dataset as well as more diverse compliance [
3‐
7,
10].
Currently, the most common reported adverse effect reported in RCTs of dapagliflozin is a higher risk of urinary and genital tract infections [
9,
10,
35]. There is also some evidence that dapagliflozin is associated with a risk of decline in kidney function, though this association did not persist in subgroups with long-term treatment (>24 months), consistent with our observation of an initial decline in eGFR, which by 12 months was consistent with the annual decline expected in the absence of drug (Fig.
1e) [
36]. Dapagliflozin treatment is not currently recommended for individuals with an eGFR below 60 ml min
−1 [1.73 m]
−2 [
1,
14,
15]. Our focus here was on effects on continuous outcomes for which there is adequate power rather than on CVD and other safety-events analyses for which power is very low. Nonetheless, we included such analyses for completeness. No significant safety signals were found. While there was a significantly lower CVD event rate in those ever vs never exposed, there was no significant cumulative effect of exposure on CVD. As we have described previously, such ever vs never comparisons, while providing some reassurance, cannot be interpreted as proof of a protective causal effect since they remains subject to allocation bias. As further follow-up data accrues in this dataset, we will be able to test for cumulative effects on events with more power. In addition, we intend to explore effects of other SGLT2 inhibitors that were licensed later as further data accrue.
We acknowledge the limitations of our analysis, the most important of which is that unbiased control comparisons cannot be achieved as they are in clinical trials. In addition, as described, within-person analyses can fail to take into account regression to the mean and underlying calendar time trends. Nevertheless, we have made extensive efforts to estimate the likely magnitude of these latter two biases, going well beyond many observational studies of this nature.
In conclusion, the effectiveness of dapagliflozin on HbA1c and other clinical outcomes observed in clinical trials was apparent in this real-world effectiveness study; treatment effect estimates were at least as large as in clinical trials even when likely observational analysis biases are considered. Dapagliflozin lowers HbA1c and SBP shortly after treatment initiation but also appears to prevent worsening of these outcomes over the ensuing 2 years. Dapagliflozin also lowered BMI and body weight.
Duality of interest
LBr, SJM, PMM and HMC declare a grant from AstraZeneca for the work under consideration for publication. The following authors have disclosed declarations of interest outside the submitted work: NS received grant and personal fees from Boehringer Ingelheim, personal fees from Janssen, Eli Lilly and Novo Nordisk and a grant from Astra Zeneca; JRP received grant and personal fees from Sanofi Aventis, Quintiles and Janssen, personal fees from ACI clinical, Pfizer, Lilly and Novo Nordisk and non-financial support from Itamar Medical and Merck (Germany); RJM received personal fees for advisory boards from Novo Nordisk, Sanofi Aventis and Lilly and HMC received grants (as part of EU Innovative Medicines programme collaborations) from AstraZeneca, Boehringer Ingelheim, Eli Lilly & Company, Pfizer, Roche Pharmaceuticals and Sanofi Aventis and grants from Novo Nordisk. HMC is a shareholder in Bayer and Roche Pharmaceuticals. HMC is on trial steering committees or safety monitoring committees with Eli Lilly, Sanofi and Regeneron, Novartis Pharmaceuticals and Novo Nordisk and receives remuneration via her institution for this. She has received speaker fees and travel expenses for presenting trials she has helped design or other research she has led from Pfizer, Eli Lilly, Sanofi and Regeneron. JAM received grants from Novo Nordisk, Lilly, Merck, Boheringer and GSK and non-financial support from Novo Nordisk. ERP received personal honorarium fees from Lilly, MSD, Novo Nordisk and Astra Zeneca. TMC received a grant from Diabetes UK and the British Heart Foundation. All other members of the writing committee declare that there is no duality of interest associated with their contribution to this manuscript.
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