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Introduction

Prediabetes is a common metabolic condition aetiologically related to obesity [1] and associated with increased risk of type 2 diabetes, CVD and all-cause mortality [2]. Identifying effective strategies to reduce diabetes risk and improve cardiometabolic function in this population is therefore important. Randomised controlled trials have shown that weight loss, lifestyle modification and metformin treatment can reduce the progression to diabetes in individuals with impaired glucose tolerance [3,4,5]. However, evidence is lacking in individuals with impaired fasting glucose or prediabetes assessed by HbA1c [6, 7]. Despite this, the ADA suggests that all people with prediabetes should accomplish and maintain weight loss, increase physical activity and consider metformin treatment [8]. HbA1c is now commonly used for the diagnosis of diabetes and prediabetes, but due to the poor overlap between fasting glucose, 2 h glucose and HbA1c [9,10,11,12], results from intervention studies in individuals with impaired glucose tolerance may not apply to those with prediabetes diagnosed by HbA1c. To address this question, trials focusing on improving blood glucose levels and reducing cardiovascular risk in people with elevated HbA1c levels are needed.

In recent years, sodium–glucose cotransporter 2 (SGLT2) inhibitors have proven efficacious in reducing blood glucose, body weight and cardiovascular events in individuals with type 2 diabetes [3, 13]. A few smaller studies have examined the effect of SGLT2 inhibition in prediabetes [14,15,16], suggesting that these drugs have potential in the prevention of type 2 diabetes. However, the ability of SGLT2 inhibitors to improve overall blood glucose levels, glycaemic variability and other cardiometabolic risk factors in individuals with prediabetes in comparison with other glucose-lowering interventions has not previously been examined.

We conducted a randomised controlled trial comparing the short-term safety and efficacy of three glucose-lowering interventions (dapagliflozin, metformin and exercise) in combination with dietary advice on glycaemic variability, body composition and cardiometabolic risk in overweight or obese individuals with prediabetes defined by HbA1c [17]. The primary outcome was changes in glycaemic variability from baseline to 13 weeks of intervention. Glycaemic variability was assessed by continuous glucose monitoring (CGM) to reflect glucose fluctuations in daily life. Glycaemic variability is an important aspect of glycaemic control, which is associated with morbidity and mortality in type 2 diabetes [18, 19]; however, whether this extends to people with prediabetes is yet unknown. Exercise has previously been shown to reduce glycaemic variability in individuals with type 2 diabetes or who are at increased risk for developing the disease [20, 21]. We hypothesised that after 13 weeks of treatment, exercise would be superior to the pharmacological interventions and dapagliflozin would be superior to metformin in reducing glycaemic variability.

Methods

Trial design

The PRE-D Trial was an investigator-initiated, randomised, controlled, open-label, four-arm (1:1:1:1), superiority trial performed at Steno Diabetes Center Copenhagen, Gentofte, Denmark. The trial was initiated on 24 February 2016 and terminated on 13 January 2019. The design of the PRE-D Trial has previously been published with open access [22]. The trial protocol was approved by the Ethics Committee of the Capital Region (H-15011398) and the Danish Medicines Agency (EudraCT: 2015-001552-30) and registered at ClinicalTrials.gov (NCT02695810). Approval of data storage was obtained from the Danish Data Protection Board (2012-58-0004). The trial was conducted in accordance with the Helsinki II declaration and Good Clinical Practice.

Because of a slow recruitment rate, the inclusion criterion for HbA1c was expanded from 42–47 mmol/mol (6.0%–6.4%) (criterion suggested by the International Expert Committee) to 39–47 mmol/mol (5.7%–6.4%) (criterion suggested by the ADA), effective from 13 April 2016. Also, the number of participants to be included was reduced from 160 to 120 on 11 July 2016.

The trial consisted of a baseline test period (baseline visit and CGM measurement during the subsequent 6 days), 13 weeks of active intervention, and a follow-up period of 13 weeks with no active treatment. Test days and subsequent 6 day CGM measurement periods were scheduled at baseline and at 6, 13 and 26 weeks after intervention start. The trial was designed to examine effects during active treatment (visit at 6 weeks and CGM measurement during the subsequent 6 days) and immediately after the termination of treatment (visit at 13 weeks and CGM measurement during the subsequent 6 days with the primary endpoint measured here). Additionally, potential effects sustained after the end of treatment were examined (follow-up visit at 26 weeks and CGM measurement during the subsequent 6 days).

Participant and public involvement

No participants were involved in defining the research question or the outcome measures, nor were they involved in the implementation of the study. We disseminated the results from the trial to the participants and invited them to participate in focus-group interviews after the end of the study.

Participants

The study included participants aged 30–70 years with overweight or obesity (BMI ≥25 kg/m2) and who had prediabetes based on the HbA1c criterion (39–47 mmol/mol [5.7–6.4%]) [23]. Exclusion criteria were as follows: pregnancy (planned or current); breast feeding; known diabetes; bariatric surgery within the past 2 years; neurogenic bladder disorder; impaired renal function (eGFR <60 ml min−1 [1.73 m]−2); uncontrolled medical issues; use of beta blockers, steroids, loop diuretics, thiazide diuretics or medication affecting glucose metabolism (though treatment with statins was allowed); allergy to one of the medications used in the trial; or being unable to exercise according to the protocol. Participants with hypertension who were taking any of the above-mentioned BP-lowering medications and who were willing to switch to a drug allowed in the trial were enrolled.

Participants were recruited using public advertisements and at the Steno Diabetes Center Copenhagen outpatient clinic (relatives and friends to patients with diabetes). All participants provided oral and written informed consent before taking part in the study. Participants received no incentives, monetary or otherwise, for their participation in the PRE-D Trial, although documented travel costs from the participants’ homes to the clinic were reimbursed.

Interventions

All participants received general advice on diet and physical activity based on national recommendations [24]. After the baseline test period, participants were randomly assigned to receive 13 weeks of intervention as follows: (1) dapagliflozin, 10 mg once daily; (2) metformin, 850 mg daily for 1 week and then 850 mg twice daily for the rest of the intervention; (3) exercise; or (4) control (habitual living). The exercise intervention consisted of unsupervised interval training, 5 days/week, 30 min/session, with alternating 3 min intervals aiming for intensities of ≥75% and ≤60% of peak heart rate by the end of each interval. To support adherence to the exercise protocol, the participants in the exercise group recorded exercise sessions using a heart rate monitor (Polar V800; Polar Electro, Kempele, Finland). Data from exercise sessions (heart rate, duration, frequency) were uploaded by the participants to an online platform (https://flow.polar.com) and brief feedback was provided weekly by research staff to the participants based on an evaluation of the individual exercise sessions.

For those allocated to active treatments, the treatment was initiated the day after the end of the baseline CGM measurement period. All active treatments were terminated just before the 13 week visit; participants in the exercise group returned the heart rate monitor at 13 weeks.

Procedures and outcomes

Outcomes

The primary efficacy outcome was the change in the mean amplitude of glycaemic excursions (MAGE) [25], a measure of glycaemic variability, from baseline to end of treatment (13 weeks). Secondary outcomes included changes from baseline to mid-point of treatment (6 weeks), end of treatment (13 weeks) and follow-up (26 weeks) for the following variables: MAGE (6 and 26 weeks); mean CGM glucose; SD of sensor glucose (SDCGM); CV of sensor glucose (CVCGM); daily time spent at glucose concentrations >6.1 mmol/l, >7.0 mmol/l, >7.8 mmol/l and >11.1 mmol/l measured by CGM; HbA1c; fasting plasma glucose concentration; fasting serum insulin concentration; body weight; WHR; BP; resting heart rate; and blood lipids. Additional secondary outcomes measured only at baseline, at 13 weeks and at 26 weeks included the following variables: plasma glucose and serum insulin levels during an OGTT; measures of body composition; peak heart rate and cardiorespiratory fitness (CRF). At baseline and after 13 and 26 weeks, a resting ECG was performed prior to exercise testing for safety assessment.

Clinic visits and free-living measurement periods

To assess eligibility for inclusion, potential participants attended a screening visit consisting of an interview and a medical examination [22]. On subsequent test days (6, 13 and 26 weeks), the participants arrived at the clinic at 08:00–09:00 hours after an overnight fast of at least 8 h. Participants were asked to abstain from strenuous physical activity for 48 h prior to the examinations at 0, 13 and 26 weeks, and those who were allocated to treatment with either dapagliflozin or metformin were asked not to take the study medication on the morning of the test day at week 13 [22]. The test day visits lasted approximately 5 h and were identical except for the test day at 6 weeks, which was of a shorter duration and did not include the ECG, OGTT and the measurement of CRF and body composition. When participants arrived at the test facility, the length of fasting and the time of their last exercise bout were registered, followed by measurements of body weight, plus waist and hip circumference, as described in detail previously [22]. Then the participants were fitted with a blinded CGM system (iPro2 CGM; Medtronic Denmark, Denmark) for the assessment of MAGE and other measures of glycaemic control in a free-living situation; glucose levels were monitored for six consecutive days with the iPro2 placed on the lower abdomen. To calibrate the CGM systems, the participants measured blood glucose levels using a glucose meter (Contour XT; Ascensia Diabetes Care Denmark, Denmark) at 1 h and 2 h after CGM insertion as well as at home before breakfast, before lunch, before dinner and before bedtime during the CGM measurement periods. MAGE was calculated by taking the arithmetic mean of the blood glucose increases or decreases when both ascending and descending segments exceeded the value of 1 SD of the blood glucose during a 24 h measurement period [25] (see electronic supplementary material [ESM] Methods and ESM Fig. 1 for details).

Next, BP and resting heart rate were measured and a resting ECG was recorded; the latter was used to screen for contraindications to perform the fitness test [22]. Fasting blood samples and urine samples were collected. This was followed by a total body dual-energy x-ray absorptiometry scan (Discovery DXA System; Hologic, MA, USA) to assess body composition. Participants wore light clothing and lay still in a supine position during the scan. A standard 75 g OGTT was then performed with blood sampled from an antecubital vein just before the ingestion of glucose and at 30, 60 and 120 min after ingestion. Participants were instructed to either sit or lie down during the OGTT but could read and listen to music/podcast/audiobooks. In addition, they answered electronic questionnaires administered by the staff. The incremental AUCs for glucose (iAUCglucose) and insulin (iAUCinsulin) were calculated based on the trapezoidal rule as measures of the overall glucose tolerance and insulin response during the OGTT. Finally, CRF (\( \dot{V}{\mathrm{O}}_{2\mathrm{peak}} \)) and peak heart rate were evaluated using an incremental test on a cycle ergometer (Monark LC4, Monark, Sweden) with indirect calorimetry. After a 6 min warm up (3 min at 30 W followed by 3 min at 60 W for women; 3 min at 40 W followed by 3 min at 80 W for men) the workload was increased by 20 W/min for women and 25 W/min for men until exhaustion. From trial initiation until 11 January 2018, a JAEGER Oxycon Pro analyser (Erich JAEGER, Germany) was used to measure oxygen and carbon dioxide exchange (210 tests). Due to an equipment malfunction, a Vyntus CPX (Vyaire, Germany) was used for the remainder of the trial (129 tests). Before leaving the facility, the participants were served a well-deserved lunch and were instructed to measure home blood glucose and fill in the dietary diary for the following days.

Sample analysis

Samples for the analysis of plasma glucose concentrations were placed on ice immediately following sampling. All samples were centrifuged shortly after collection at 1610 g for 15 min (Sigma 4 K15; Sigma, Germany), except for samples used for analysis of HbA1c and serum insulin concentrations. Samples for serum insulin analysis were centrifuged 30 min after collection. The samples were stored in a refrigerator for the remainder of the test day. Urine samples were cooled after collection. At the end of each test day, all samples from that day were analysed in-house at Steno Diabetes Center Copenhagen. Serum insulin was analysed using electro-chemiluminescence immunoassay (Cobas e411; Roche Diagnostics, Switzerland). HbA1c was measured by HPLC (Tosoh G8; Tosoh Corporation, Japan). Urinary glucose, plasma glucose, total cholesterol, HDL-cholesterol and triacylglycerols were analysed by colorimetric analysis (Vitros 5600; Ortho Clinical Diagnostics, USA). Plasma VLDL-cholesterol was calculated as plasma triacylglycerols (mmol/l)/2.2, and plasma LDL-cholesterol was calculated based on the Friedewald equation [26]. eGFR was calculated using the CKD-EPI formula [27].

Adverse events and treatment adherence monitoring

Adverse events were systematically registered and rated for severity at the 6, 13 and 26 week visits. For adverse events occurring between visits, the participants were instructed to contact the investigators who registered and rated the severity of the episode.

Adherence to treatment in the metformin and dapagliflozin groups was calculated from the amount of medication returned by the participants at the 6 and 13 week visits. Participants taking ≥80% of their medication were regarded as compliant. Adherence to the exercise intervention was calculated from the number and duration of the exercise sessions performed. Participants fulfilling the compound goal of completing ≥80% of the prescribed exercise sessions and ≥80% of the prescribed exercise volume were designated as adherent.

Sample size

The clinical target for glycaemic variability in prediabetes is unknown [28]. We designed the trial to have 80% power (α 0.05) to detect a minimal clinically important difference of 0.5 mmol/l change in glycaemic variability (MAGE) between two groups from baseline to 13 weeks with an SD of 0.6 mmol/l [22]. In our study population, this corresponds to ∼30% difference. Based on this assumption, at least 23 participants in each group were needed. To account for drop-outs, we included 30 participants in each group.

Randomisation

Randomisation was performed in blocks of 16 (4:4:4:4) without any stratification for baseline variables using a web-based system (EasyTrial). When using EasyTrial, the allocation of each participant is not determined until the individual participant is ready for randomisation; accordingly, no randomisation list was generated before the trial started. Participants were randomised at the end of the baseline visit but were blinded to group allocation until the end of the 6 days pre-intervention CGM measurement period. Documents and equipment/medication to be used during the intervention were supplied to the participants in non-see-through bags locked with a coded padlock. The participants received the code when finishing the baseline CGM measurement period and later returned the bags and locks in order to confirm that these had not been forced open.

Statistical methods

We performed pre-specified intention-to-treat analyses including all available data. It was a priori planned to compare the intervention effects between all four groups (six tests) for the primary outcome, MAGE. All pre-specified secondary outcomes were only compared with the control group. A per protocol analysis was conducted for MAGE, including participants attending the 13 week visit who were designated adherent. Outcomes were modelled by linear mixed-effects models with a participant-specific random intercept. The baseline level of the outcome was retained as an outcome in the model. Treatment groups and visits and their interaction were included as fixed effects. Additionally, for the primary outcome a post hoc sensitivity analysis including baseline as a covariate rather than as an outcome was performed (ESM Methods). Assumptions of normality and homogeneity of variances for residuals were assessed with graphical methods. Results for MAGE are presented as estimated mean differences in change with 95% CI and two-sided p values. Secondary outcomes are presented as estimated mean differences in change with 95% CIs; the widths of the CIs have not been adjusted for multiple testing. Outcomes for which the distribution of residuals did not conform to the parametric assumptions of the models were transformed using the natural logarithm (loge) prior to analyses and results are presented as the ratio between the relative change from baseline to follow-up within the two groups being compared (i.e. if group A changed 20% between visits 1 and 2 [A2/A1 = 1.2] and group B changed 10% [B2/B1 = 1.1], then the relative change between group A and B is 1.2/1.1 = 9.1%). Analyses of body composition, CRF and HDL-cholesterol were adjusted for sex. The analysis of CRF was further adjusted for the type of calorimeter used (ESM Methods, ESM Results and ESM Tables 1–3). All statistical analyses were conducted blinded to group allocation and interpretation of results were performed before unblinding. Statistical analyses were conducted in R version 3.6.0 (The R Foundation for Statistical Computing, www.R-project.org) and SAS version 9.4 (SAS Institute, Cary, NC, USA).

Results

Out of 404 individuals screened, 120 participants were randomly assigned to dapagliflozin (n = 30), metformin (n = 30), exercise (n = 30) or control (n = 30) (Fig. 1). Across groups, 44% were men, the median (Q1, Q3) MAGE was 1.6 (1.4, 2.2) mmol/l, the mean±SD HbA1c was 40.9 ± 2.3 mmol/mol (5.9 ± 0.2%), mean±SD BMI was 31.5 ± 5.1 kg/m2 and mean±SD age was 60 ± 9 years. Baseline characteristics of the participants by randomisation group are shown in Table 1.

Fig. 1
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Trial profile

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Table 1 Demographic and baseline characteristics
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The overall retention rate was 93%. A total of 26 participants in the dapagliflozin group and 25 in the metformin group took ≥80% of the administered medication (for further details on adherence to the interventions see ESM Table 4). In the exercise group, 24 individuals performed ≥80% of the required exercise training (ESM Table 5). Median (Q1, Q3) urinary glucose concentration was 122.1 (77.6, 163.7) mmol/l after 6 weeks and 76.4 (20.8, 123.3) mmol/l after 13 weeks of treatment with dapagliflozin, whereas it was <1.1 (<1.1, <1.1) mmol/l at baseline and at 26 weeks in the dapagliflozin group and in the other groups at all time points. Only a few changes in non-trial medicines were reported (ESM Results).

Primary outcome

Compared with the control group, MAGE decreased by 17.1% in the dapagliflozin group from baseline to 13 weeks; the effect in the exercise group was smaller and more uncertain (Fig. 2, Table 2). MAGE did not change in the metformin group compared with control. Results from the per protocol analysis and the post hoc baseline-adjusted sensitivity analysis showed smaller and more uncertain effects (ESM Tables 6, 7). Glycaemic variability calculated as CVCGM likewise showed smaller effect sizes for the dapagliflozin and exercise groups and no effects in the metformin group compared with control (Table 2). Compared with metformin, MAGE was reduced by 17.2% in the dapagliflozin group, but with no differences between the effects in the exercise and dapagliflozin groups (Fig. 2, Table 2). Both the dapagliflozin and exercise groups experienced a reduction in MAGE from baseline to 6 weeks and from baseline to 26 weeks compared with the control group (Fig. 2, Table 2). For details on the distribution of CGM-based measures see ESM Figs 2–7.

Fig. 2
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Intervention effects for the primary outcome, MAGE. Results were back-transformed for presentation and are presented as estimated conditional geometric means (95% CI) (a) and estimated relative changes from baseline (95% CI) (b)

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Table 2 Pairwise comparisons of the change in the primary outcome (MAGE) and other measures of glycaemic variability from baseline to 6, 13 and 26 weeks
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Secondary outcomes

Other glycaemic measures

In all three active groups, a mean decline in HbA1c of ~1 mmol/mol (~0.1%) was observed at 13 weeks compared with control (Table 3). At 26 weeks, HbA1c had returned to the baseline level in all groups. Mean fasting plasma glucose concentration decreased by 0.3 mmol/l in the metformin group at 13 weeks (Table 3). In the same period, the OGTT iAUCglucose increased by ∼40% in the metformin group compared with the control group; this increase was still present at 26 weeks (Table 3). At 13 weeks, a 20–24% decline was observed in the fasting serum insulin concentration in the metformin and dapagliflozin groups; these changes were maintained at the 26 week follow-up. The OGTT iAUCinsulin decreased by 20% in the dapagliflozin group and by 18% in the exercise group but was unchanged in the metformin group at 13 weeks. Participants in the exercise group reduced their time spent at >7.0 mmol/l when compared with those in the control group at 13 weeks (Table 3). At baseline, few participants spent a significant amount of time with a sensor glucose level of >7.8 or >11.1 mmol/l (Table 1) and these outcomes were consequently not analysed further.

Table 3 Change in secondary glycaemic outcomes between the three active groups and the control group from baseline to 6, 13 and 26 weeks
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Anthropometric measures

Reductions in body weight of ∼1 kg were observed in the dapagliflozin and metformin groups at 6 and 13 weeks; however, greater variation was observed at 13 weeks (Table 4). At 13 weeks, participants in the exercise group had on average reduced their body weight by 1.4 kg (∼1.2%) compared with control. This change was accompanied by mean reductions of 2.5 cm in waist circumference and 1.1 kg in fat mass. Individuals randomised to dapagliflozin had on average reduced their waist circumference by 2.4 cm. Fat-free mass was unaltered in all three treatment groups compared with control (Table 4).

Table 4 Change in anthropometric measures between the three active groups and the control group from baseline to 6, 13 and 26 weeks
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Markers of cardiometabolic function

None of the three treatments were associated with changes in BP compared with control (Table 5). A mean reduction in LDL-cholesterol (−0.3 mmol/l) was observed in the metformin and exercise groups after 6 weeks, but at 13 and 26 weeks these changes were no longer apparent (Table 5). Exercise was associated with reductions in VLDL-cholesterol and triacylglycerol concentrations by 17–19% after 13 weeks (Table 5). From baseline to 13 weeks, participants in the exercise group increased CRF (\( \dot{V}{\mathrm{O}}_{2\mathrm{peak}} \)) by 2.8 ml O2 min−1; kg−1, corresponding to an 11% increase, and decreased their resting heart rate by 4 beats/min compared with those in the control group; these changes were still present at 26 weeks (Table 5).

Table 5 Change in markers of cardiometabolic function between the three active groups and the control group from baseline to 6, 13 and 26 weeks
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Adverse events

One serious adverse event occurred in the control group (lung cancer). During the active intervention period (baseline to 13 weeks), 44% of the participants experienced at least one adverse event (dapagliflozin 57%, metformin 80%, exercise 23%, control 17%). Most adverse events were known side-effects of the two medications; in the exercise group most events were related to the musculoskeletal system (Fig. 3 and ESM Results, ESM Tables 8, 9).

Fig. 3
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The number of adverse events reported within each group from baseline to end of treatment (13 weeks) presented by organ system

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Discussion

We found that 13 weeks of dapagliflozin treatment reduced glycaemic variability (MAGE) compared with control or treatment with metformin in individuals with overweight and prediabetes diagnosed by HbA1c. A similar effect was observed for interval-based exercise, though with a slightly smaller effect size and greater uncertainty of the estimate. However, the effect sizes did not reach the predefined minimal clinically important difference, and were even smaller when reported as CVCGM, which is now recommended as the primary measure of glycaemic variability [19]. The lack of clinically relevant effects was supported by the analysis based on per protocol completers and by the post hoc analysis with adjustment for the baseline level of MAGE. Dapagliflozin, metformin and exercise were associated with reductions in HbA1c and improved markers of glucometabolic function; however, we observed an increase in post-load glucose levels in the metformin group. No major adverse events were recorded in the active treatment groups, and the exercise group experienced fewest events.

Raised fasting and OGTT-derived glucose concentrations as well as mean glucose levels are well-established risk factors for the development of diabetes, CVD and premature death [2, 29]. At the same time, glycaemic variability is emerging as an independent risk factor [18, 19, 30]. This is the first trial to assess the efficacy of SGLT2 inhibition on glycaemic variability in people with prediabetes diagnosed by HbA1c. We compared SGLT2 inhibition not only with a control group but also head-to-head with treatments that have proven efficacious in preventing diabetes in people with impaired glucose tolerance [3,4,5]. As we hypothesised, treatment with dapagliflozin or interval-based exercise was superior to metformin in improving glycaemic variability when measured in the week after the end of treatment. However, the effect sizes observed for MAGE were around half the size of what we expected and the variation in the effect estimates was large. In contrast to what we expected, exercise was not superior to treatment with dapagliflozin in reducing glycaemic variability, potentially because the exercise intensity in our study was too low to result in improvements in glycaemic variability of the magnitude observed in other studies [20, 21].

The PRE-D Trial was designed to investigate on-treatment effects on glycaemic variability (6 weeks) in addition to assessing both short-term (13 weeks) and longer-term (26 weeks) physiological post-treatment adaptations. Interestingly, small improvements in MAGE in the dapagliflozin group were observed during treatment, immediately after treatment and at the 26 week follow-up. Whether this longer-term effect represents an actual physiological legacy effect or whether it is a random finding is too early to conclude. An effect on glycaemic variability in the exercise group was also observed at the follow-up visit at 26 weeks. Support for the exercise programme was withdrawn at 13 weeks but the improvements in CRF and resting heart rate at 26 weeks indicate that the participants continued their exercise in the period between the last two visits, which could explain the beneficial effects on the glucometabolic outcomes observed at the end of the follow-up period in the exercise group.

We consider the use of a primary outcome obtained under ‘free-living’ conditions to be a major strength. Still, while this enables the evaluation of intervention effects in everyday life, it also poses a challenge as the clinical implications of CGM-derived measures, such as MAGE and CVCGM, are still unclear [18] and relevant effect sizes are unknown, particularly in a population with prediabetes. Furthermore, the use of CGM as a primary outcome is challenging because of lack of consensus in the data management process, making comparisons across trials difficult. During the period in which the trial was running, CVCGM was suggested as the preferred measure of glycaemic variability in diabetes [19]; this will hopefully improve and optimise comparison between studies using CGM data in the future. Our results indicate, however, that if CVCGM had been the primary outcome of the trial, the effect sizes would have been even smaller. The small effect sizes for MAGE are potentially explained by the poor overlap between HbA1c and glucose levels in the prediabetic range [9, 10], resulting in relatively low baseline levels of  MAGE compared with observations made in individuals with impaired fasting glucose or impaired glucose tolerance [30, 31]. Accordingly, the potential for improvement in MAGE in our study population was modest. Furthermore, the baseline MAGE levels in the control and metformin groups were relatively low, which could theoretically lead to a floor effect in these groups.

We included overweight or obese participants with prediabetes defined solely by HbA1c. This population is easily identified in the clinical setting compared with prediabetes defined by impaired fasting glucose or impaired glucose tolerance, but evidence for the optimal glucose-lowering strategy in this high-risk group has been lacking. Despite between-group variations in glycaemic variability, high inter-individual variation in post-load glucose and insulin levels, and relatively normal glycaemic control at baseline, all active treatments were associated with improvements in some of the secondary glucometabolic outcomes. Clinically relevant effect sizes for the glycaemic outcomes are unknown for this population and although we only observed small improvements in blood glucose levels, the size of the improvements in HbA1c were comparable to those obtained in the Diabetes Prevention Program [3] and could thus hypothetically have substantial long-term effects, especially when considering the marked improvements in both fasting and post-load insulin levels. This underlines the need for long-term diabetes prevention trials in individuals with prediabetes defined by HbA1c. However, the normal fasting and/or 2 h glucose levels exhibited by many of our participants at baseline indicate that individuals with prediabetic HbA1c levels in the low end of the range suggested by the ADA (39–47 mmol/mol [5.7–6.4%]) [17] may not be the optimal target group for interventions aimed at preventing diabetes. Potentially, the cut-points suggested by the International Expert Committee (42–47 mmol/mol [6.0–6.4%]) [32] may be more relevant for diabetes prevention efforts. Nevertheless, because HbA1c is closely associated with cardiovascular risk in both the non-diabetic and diabetic range [33], assessing the long-term effects of interventions targeting cardiometabolic risk (e.g. diet, exercise and/or SGLT2 inhibition [34, 35]) are still relevant for all individuals with prediabetes defined by the ADA’s criteria.

Moving beyond glucose metabolism, the interventions were associated with improvement in other markers of cardiometabolic function. Treatment with either dapagliflozin or exercise was associated with small improvements in body weight and waist circumference. In the dapagliflozin group, part of the initial weight loss was likely secondary to some degree of dehydration [13], as the weight loss was not accompanied by a loss of fat mass. Furthermore, the weight loss had diminished at 13 weeks. In the exercise group, the weight loss seemed primarily to be driven by a loss of fat mass. Exercise was also associated with improvement in CRF and decreases in plasma triacylglycerol levels; these are well-known beneficial effects of physical activity [36], representing improvements beyond those attained by the two pharmacological treatments. An increase in CRF similar to that observed in the exercise group has been proposed to be relevant for reducing the risk of CVD in a sedentary population [37].

In conclusion, treatment with dapagliflozin or interval-based exercise improved glycaemic variability compared head-to-head with habitual lifestyle or metformin therapy in individuals with prediabetes diagnosed by HbA1c but the effect sizes were small and likely of limited clinical relevance. All treatments were associated with small improvements in glucometabolic outcomes, and exercise was associated with additional cardiometabolic benefits. Given the risk of adverse events and limited effects on blood glucose levels, our results do not lend support to the benefits of the pharmacological interventions in the short-term outweighing the harms in this population with prediabetes, especially not metformin treatment. Additionally, our findings raise the question of whether individuals with HbA1c levels in the lower prediabetic range, as specified by the ADA, are ideal targets for pharmaceutical glucose-lowering interventions with metformin or SGLT2 inhibitors.