. 2023 Nov 29;18(11):e0295059. doi: 10.1371/journal.pone.0295059

The effect of sodium-glucose cotransporter 2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes mellitus on cardiovascular and renal outcomes: A systematic review and meta-analysis

Carlos Ignacio Reyes-Farias ^1,^#, Marcelo Reategui-Diaz ^1,^*,^#, Franco Romani-Romani ¹, Larry Prokop ²

Editor: Edward Zimbudzi³

PMCID: PMC10686459 PMID: 38019892

Abstract

Background

Sodium-glucose cotransporter 2 (SGLT-2) inhibitors have shown a favorable effect on cardiovascular and renal outcomes in patients with type 2 diabetes mellitus (T2DM). However, their efficacy in patients with chronic kidney disease (CKD) with or without T2DM has not yet been analyzed.

Objective

To assess the cardiovascular and renal effects of SGLT-2 inhibitors in patients with CKD with and without T2DM, including all CKD patients in the current literature.

Methods

We searched MEDLINE, EMBASE, CENTRAL and Scopus for randomized controlled trials of SGLT-2 inhibitors that evaluated cardiovascular and kidney outcomes in patients with CKD, or trials in which these patients were a subgroup. We defined 2 primary outcomes: a composite of cardiovascular death or hospitalization for heart failure, and a composite renal outcome. For each outcome, we obtained overall hazard ratios with 95% confidence intervals by using a random effects model.

Results

We included 14 randomized controlled trials. SGLT-2 inhibitors decreased the hazard for the primary cardiovascular outcome (HR 0.76; [95% CI 0.72–0.79]) and the primary renal outcome (HR 0.69; [95% CI 0.61–0.79]) in patients with CKD with or without T2DM. We did not find significant differences in the subgroup analyses according to diabetes status, baseline eGFR values or the type of SGLT-2 inhibitor used.

Conclusion

In patients with CKD, treatment with SGLT-2 inhibitors in addition to standard therapy conferred protection against cardiovascular and renal outcomes. Further research on patients with non-diabetic CKD should be done to confirm the utility of these medications in this population. (PROSPERO ID: CRD42021275012).

Introduction

Chronic kidney disease (CKD) is an important cause of mortality worldwide [1]. Until recently, only angiotensin-converting enzyme (ACE) inhibitors and angiotensin-receptor blockers (ARBs) had been shown to decrease proteinuria and prevent progression to end-stage kidney disease [2,3].

Sodium-glucose cotransporter 2 (SGLT-2) inhibitors are used to treat people with type 2 diabetes mellitus (T2DM) [4,5]. They control glycemia and HbA1c by inhibiting the SGLT-2 cotransporter in proximal tubules, responsible for the reabsorption of the majority of the filtered glucose in the glomeruli. However, SGLT-2 inhibitors have other non-glycemic effects, such as lowering glomerular hyperfiltration and proteinuria, and anti-inflammatory properties in the glomeruli [6–9]. The relevance of these mechanisms has been reflected in the DAPA-CKD [10] and EMPA-KIDNEY [11] trials, which enrolled both diabetic and non-diabetic patients with CKD. These trials have shown a favorable effect of the use of SGLT-2 inhibitors on some cardiovascular and renal outcomes, whether the kidney injury was associated with T2DM or not. However, the evidence in patients with CKD without T2DM is still scarce.

Previous systematic reviews have demonstrated the efficacy of SGLT-2 inhibitors in reducing the risk of cardiovascular and renal events in patients with CKD and T2DM [12–15]. One systematic review included studies in both diabetic and non-diabetic CKD patients [16]. However, to date, there has been no comprehensive systematic review assessing the impact of SGLT-2 inhibitors on the entire CKD population, as provided in the available literature. Incorporating data from all CKD patients within clinical trials, not solely those designed exclusively for CKD patients but also those in which CKD patients were included as a subgroup, could enhance the precision of the effect estimates and improve the applicability of the findings.

The aim of this systematic review is to assess the cardiovascular and renal effects of SGLT-2 inhibitors in all available CKD patients in the current literature and to evaluate whether the presence of T2DM modifies their overall impact. This may help clarify a new therapeutic role for these drugs.

Methods

To conduct this study, we followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guideline [17]. The protocol of this systematic review and meta-analysis has been registered in PROSPERO (CRD42021275012).

Eligibility criteria

We included randomized controlled trials of SGLT-2 inhibitors that evaluated cardiovascular and kidney outcomes in patients with CKD, as well as trials in which CKD patients were included as a subgroup. For the latter, only the information from patients with CKD was extracted and used in this study. There were no restrictions regarding the type of SGLT-2 inhibitor, and the comparator group could receive either placebo and standard therapy or standard therapy alone. Patients needed to be adults with CKD, regardless of the definition of CKD, and with or without T2DM. Studies were included regardless of differences in time to follow-up.

We defined two primary outcomes. First, the cardiovascular composite outcome included hospitalization for heart failure (HF) or cardiovascular (CV) death. Second, a renal composite outcome was defined as doubling of serum creatinine, decline of eGFR > 50%, end-stage kidney disease, renal replacement therapy, transplantation, or renal death. The different definitions of primary outcomes are presented in the Supplement (S1 Table). The secondary outcomes were all-cause mortality; major adverse cardiovascular events (MACE, which consists of non-fatal stroke, non-fatal myocardial infarction and CV death); hospitalization for HF; and CV death.

Information sources and search strategy

A comprehensive search from each database’s inception to February 24th, 2023, was conducted with no language restriction. The databases included Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials (CENTRAL), and Scopus. The search strategy is detailed in the Supplement (S2 Table). We searched for additional records in the references section of the selected studies.

Screening of studies

Before the selection process, we performed a pilot test of the eligibility criteria to reach consistency between reviewers. The abstract screening and the full-text assessment for eligibility was done independently by two authors (C.R., M.R.) with DistillerSR Version 2.40.0 (Evidence Partners; 2022). The final decision was confirmed by a third person (O.J.P.) in case of disagreement.

Data extraction

Data collection and author contact

Data extraction was performed independently and individually by C.R. and M.R. using Microsoft® Excel® Version 2208. Then, both authors crosschecked each other’s extracted information to ensure consistency; if a disagreement was found, both reviewed the full-text article again. We requested unreported outcome data from the corresponding authors of each study and included the information they provided us in the analyses. We extracted the corresponding authors’ information and contacted them via e-mail once. Out of 15 emails sent, seven authors responded. Three authors shared published research papers which we had not retrieved with our search strategy.

Data items

Two authors independently extracted the following data of each study: publication year, trial registration number, inclusion and exclusion criteria, type of SGLT-2 inhibitor, comparator type (placebo or standard care) and follow-up time (years), patient baseline characteristics and clinical outcomes (primary and secondary outcomes as described above), and risk of bias indicators (shown in the section below). The baseline characteristics were sex, age (years), race, diabetes status (T2DM, no T2DM), diabetes medications (insulin, metformin, sulfonylurea, DPP-4 inhibitor, GLP-1 receptor agonist), HbA1C (percentage), CV history (any CV disease and HF) and CV medications (ACE inhibitors or ARB, β-Blocker, statin and diuretic), and renal baseline characteristics (estimated glomerular filtration rate [eGFR] < 60 ml/min/1.73m2 and urine albumin-to-creatinine ratio [UACR] ≥ 300 mg/g). We only considered the data from the patients with CKD according to the definition of each study and/or patients with an eGFR < 60 ml/min/1.73 m² (S1 Table).

Risk of bias assessment

Study quality was assessed independently and individually by C.R. and M.R. using the Cochrane Risk of Bias Tool 2. This tool consists of five domains (randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result) [18].

We used the two primary outcomes for our evaluation and rated the overall risk of bias of each study as ‘low’, ‘some concerns’, or ‘high’. Any disagreement was discussed between the authors to reach a consensus. We generated summary charts with the web-based version of the robvis package [19].

Quality of the evidence

Two authors (C.R., M.R.) independently assessed the quality of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system [20]. Disagreements were resolved by discussion.

The GRADE system consists of five domains (risk of bias, imprecision, inconsistency, indirectness, and publication bias) to assess the level of certainty of the evidence for each outcome as high, moderate, low, or very low. We considered a large magnitude of effect (Hazard ratio [HR] < 0.5), a dose-response gradient and a plausible confounding as criteria to increase the level of certainty [21].

We used the GRADEpro GDT software to elaborate ‘Summary of the evidence’ table [22]. All the reasons to rate up or down the quality of the evidence were documented as footnotes.

Statistical analysis

Patient baseline characteristics were summarized according to the type of variable. For qualitative variables, the frequency and percentage were reported; for quantitative variables, the mean and the standard deviation were used instead. If data was not directly available for the subgroup of all CKD patients in a study, we calculated it from the separate intervention and control groups. For the qualitative variables, we added up the frequencies and calculated the percentages based on the total number of participants. For the quantitative variables, we calculated the weighted means and weighted standard deviations, considering the intervention and control groups sizes.

Time-to-event CV and renal outcomes are presented as incidence rate (events per 100 person-years or events per 1000 person-years), and when appropriate, as cumulative incidence (events/total participants). All outcomes were summarized by using the reported HR and their corresponding 95% confidence intervals (CI) in each study.

We performed a meta-analysis including all studies with available data. We fit the random effects model with the Paule-Mandel estimation of the tau-squared [23], since it was more appropriate for binary outcomes than the restricted maximum likelihood method we originally considered. The heterogeneity was adjusted with the Hartung-Knapp method which accounts for the uncertainty in the estimate of tau-squared [24]. The I-squared statistic was calculated to assess the percentage of total variability attributed to heterogeneity among the true effects in our meta-analysis [25].

When a study reported effect estimates (HR) for complementary subgroups from within an outcome such as varying levels of eGFR (45 to < 60 ml/min/1.73 m² and eGFR < 45 ml/min/1.73 m²), we aggregated these subgroups with a fixed effects model as described by Borenstein et al. [26]. For instance, based on our example a new HR was generated for patients with eGFR < 60ml/min/1.73 m².

We performed subgroup analyses to detect differences in the effect of SGLT-2 inhibitors in patients according to the presence of T2DM, baseline eGFR values, and the type of SGLT-2 inhibitor. We assessed the interaction between the subgroups with a Wald-type test [27].

For each analysis we used the most comprehensive definition of CKD available in each study, which could include patients with either a reduced eGFR or significant albuminuria (S2 Table). If this definition was not provided, we established an eGFR < 60 ml/min/1.73 m² as diagnostic of CKD and included these patients in the meta-analysis.

Although this analysis was not described in our protocol, if the Hartung-Knapp adjustment was suspected to be interfering with the precision of a confidence interval, we performed a sensitivity analysis without the adjustment to confirm the result. Additionally, we performed a sensitivity analysis to re-assess our main meta-analysis only in patients with CKD defined as eGFR < 60 ml/min/1.73 m².

Publication bias was explored using contour enhanced funnel plots for the primary and secondary outcomes. Additionally, we performed the Egger’s regression test to statistically assess the risk of publication bias [28]. We originally intended to explore publication bias in outcomes with 10 or more studies; however, the large sample size of the included trials allowed us to evaluate all outcomes.

All the analyses were done with R version 4.2.1 [29] and RStudio version 2022.07.1+554 [30] using the meta package [31].

Results

Study selection

We retrieved 5009 records from databases. After duplicate removal (n = 1534) and abstract screening (n = 3475) we assessed 250 full-text articles for eligibility. Of these, 24 articles that corresponded to 13 clinical trials were included [10,11,32–53].

We searched for other documents by reviewing citations from included articles. With this strategy we identified four articles with additional outcome data from included trials [54–57], as well as two articles from a trial not previously found [58,59]. Furthermore, we obtained five articles from included trials through author contact [13,15,60–62]. In total, we included 35 articles from 14 trials (Fig 1).

Fig 1 — *Wrong study design includes articles of rationale and protocols; ^¶Wrong population includes articles of studies that analyzed subgroups of the trial sample that were not of interest for our review.

Baseline characteristics

We included 44 593 patients from 14 randomized placebo-controlled clinical trials [10,11,32–40,52,56,58] of five SGLT-2 inhibitors (empagliflozin [n = 4], canagliflozin [n = 3], dapagliflozin [n = 4], ertugliflozin [n = 1], and sotagliflozin [n = 2]) in patients with CKD. S3 Table shows the characteristics of the patients.

Out of the 14 studies included in the quantitative synthesis, five included only patients with CKD, and nine also included non-CKD patients as well. For the latter we only extracted the data from the CKD subgroups. Furthermore, six studies included patients with and without T2DM, while eight only considered patients with T2DM. Finally, only two studies made comparisons between patients with CKD and T2DM and patients with CKD without T2DM. For the subgroup analysis, except for the primary renal outcome, we only used data from the DAPA-CKD trial for the non-diabetic subgroups, since there was no other study that reported this data. Of note, every study except for two (the EMPEROR-Reduced and EMPEROR-Preserved trials) excluded patients with T1DM. The median follow-up ranged from 0.8 to 4.2 years across all the trials.

Risk of bias

The overall risk of bias was judged as ‘low’ in eight trials, ‘some concerns’ in six trials, and no trials were judged as ‘high’ risk of bias. Of the trials with ‘some concerns’, five of them had unclear information in the selection of the reported result and one had deviations from intended intervention. Additionally, the CANVAS Program did not make an adequate statement about missing outcome data neither in any of publications nor the protocol (S1 Fig).

Quality of the evidence

The ‘Summary of the evidence’ table showed moderate certainty of evidence for every outcome. Indirectness was deemed as ‘serious’ for every outcome, as most of the studies were designed to evaluate diseases other than CKD such as T2DM and HF (S4 Table).

Main analysis

Primary outcomes

SGLT-2 inhibitors decreased the hazard for the primary CV outcome in patients with CKD regardless of the presence of T2DM (HR 0.76; [95% CI 0.72–0.79]; τ² = 0; I² = 0%). Likewise, SGLT-2 inhibitors significantly reduced the occurrence of the primary renal outcome (HR 0.69; [95% CI 0.61–0.79]; τ² = 0.0093; I² = 23%) (Fig 2).

Fig 2 — (A) Primary cardiovascular outcome. (B) Primary renal outcome. ^¶Effect estimate calculated with a fixed effects model meta-analysis. CI, confidence interval; HR, hazard ratio; NA, not available.

Secondary outcomes

In patients with CKD with or without T2DM, SGLT-2 inhibitors decreased the risk for all-cause mortality (HR 0.87; [95% CI 0.79–0.94]; τ² = 0; I² = 0%), CV death (HR 0.86; [95% CI 0.81–0.91]; τ² = 0; I² = 0%), and hospitalization for HF (HR 0.67; [95% CI 0.62–0.73]; τ² = 0; I² = 0%) (Fig 3). However, SGLT-2 inhibitors did not reduce the risk for the MACE outcome (HR 0.86; [95% CI 0.74–1.01]; τ² = 0.0178; I² = 41%).

Fig 3 — (A) All-cause mortality. (B) MACE. (C) CV death. (D) Hospitalization for HF. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; HR, hazard ratio; NA, not available.

Subgroup analysis

The subgroup analyses according to the presence of T2DM are shown in Figs 4 and 5. We did not find significant differences between diabetic and nondiabetic patients in any outcome. We found a significant reduction of the hazard for the primary renal outcome in the subgroup of patients with T2DM (HR 0.65; [95% CI 0.59–0.72]; τ² = 0; I² = 0%). Although this protective effect was also found in the subgroup of patients without T2DM, this association was not significant, and pooled studies were heterogenous (HR 0.66; [95% CI 0.04–11.16]; τ² = 0.0764; I² = 75%).

Fig 4 — (A) Primary cardiovascular outcome. (B) Primary renal outcome. CI, confidence interval; HR, hazard ratio; NA, not available; T2DM, type 2 diabetes mellitus.

Fig 5 — (A) All-cause mortality. (B) MACE outcome. (C) CV death. (D) Hospitalization for HF. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; HR, hazard ratio; NA, not available; T2DM, type 2 diabetes mellitus.

We performed a subgroup analysis according to eGFR levels (S2–S4 Figs), which showed that SGLT-2 inhibitors had a protective effect for the primary and secondary outcomes regardless of baseline eGFR, as the test for subgroup differences was not significant. Likewise, we did not find differences in our subgroup analysis by the type of SGLT-2 inhibitor in any outcome (S5–S7 Figs).

Sensitivity analysis

We decided to perform the subgroup analysis for the primary renal outcome without the Hartung-Knapp adjustment, as it seemed to artificially increase the confidence interval. We found a narrower but still not statistically significant confidence interval for this subgroup (HR 0.66; [95% CI 0.43–1.02]; τ² = 0.0764; I² = 75%), and the test for subgroup differences remained not significant (p = 0.93).

The effect of SGLT-2 inhibitors when only considering patients with an eGFR < 60 ml/min/1.73 m² were consistent with the main analyses for the primary and secondary outcomes, except for the MACE outcome. The results of this sensitivity analysis are presented in the Supplement (S8 and S9 Figs).

Publication bias assessment

We only found a significant Egger’s test in the MACE and hospitalization for HF outcomes (p < 0.05) (S10 Fig). With regard to the availability of data, we were only able to extract data of patients with CKD without T2DM from 2 studies [10,11]. The other studies that included patients with CKD without T2DM did not report outcome data for this specific subgroup.

Discussion

In this systematic review and meta-analysis, moderate quality of evidence shows that SGLT-2 inhibitors administered to patients with CKD with or without T2DM reduce the risk of the primary cardiovascular and renal outcomes, as well as the hazard for all-cause mortality, CV death and hospitalization for HF outcomes.

Our results are consistent with systematic reviews that focused on patients with T2DM [12–15] and patients without T2DM [63]. Other reviews included both patient groups [16,60,64,65]; however, they had limitations, such as not including patients without T2DM in the subgroup analysis of CKD patients [64], not performing a statistical test for subgroup differences [60], focusing solely on a single clinically relevant outcome [65], and not including CKD patients from trials in which they were a subgroup [16]. In contrast, our approach included patients with CKD from all trials, even those trials in which CKD patients were a subgroup of the study.

By adopting this methodological approach, we increased the number of participants with CKD from 25 898 to 44 593, when compared to the study by Baigent et al. [16]. Additionally, we noted that in some subgroup analyses of their study, the “No diabetes” subgroup was composed predominantly of non-diabetic patients with HF rather than with CKD. Consequently, the statistical test for heterogeneity by diabetes status was more reliant on data from HF patients without T2DM than CKD patients without T2DM. In our subgroup analyses, we exclusively used data from CKD patients, allowing us to assess the heterogeneity in the effect of SGLT-2 inhibitors in non-diabetic CKD patients more directly.

The subgroup analysis that assesses the differences between patients with and without T2DM did not reveal any significant subgroup effect for any outcome. This suggests that the presence of T2DM does not modify the therapeutic effect of SGLT-2 inhibitors in patients with CKD. However, there were insufficient studies reporting information for patients without T2DM. Among the six trials that included patients with CKD and with or without T2DM [10,11,32,38,56,58], only the DAPA-CKD trial provided data for every outcome in CKD patients without T2DM [10,45,54], while the EMPA-KIDNEY trial only reported such data for our primary renal outcome. Furthermore, we found high heterogeneity between studies in the subgroup of patients without T2DM for the primary renal outcome. This may be explained by multiple factors, such as differences in inclusion criteria, sample size and outcome definition; the use of different SGLT-2 inhibitors (dapagliflozin and empagliflozin); varying eGFR levels; and baseline medication. Nevertheless, our subgroup analyses by baseline eGFR values and by the type of SGLT-2 inhibitor revealed no significant differences between the subgroups. This suggests that the heterogeneity introduced by these factors does not substantially impact the overall positive effect of SGLT-2 inhibitors in CKD patients.

Our main results on the primary CV outcome were similar to those reported in previous systematic reviews [14–16,60,63]. However, our results exhibit a narrower confidence interval than those previously described. Presumably, this difference is attributable to the inclusion of new SGLT-2 inhibitors trials in patients with CKD [10,11,33,40,52] that have been published since the publication of the other systematic reviews. Thus, we analyzed a greater number of patients with CKD, and this was reflected by a higher precision in our estimates.

We found a significant reduction of the hazard for the primary renal outcome in patients with CKD and the diabetic subgroup. Our result was expected as SGLT-2 inhibitors help control T2DM, which is a major risk for the development and progression of CKD [66]. This finding is also consistent with previous systematic reviews in the diabetic population [13,16,41,64].

On the other hand, we did not find a significant hazard reduction for the renal outcome in the subgroup of patients without T2DM. This can be attributed to the small number of studies (n = 2) included in this subgroup and the substantial heterogeneity between these two studies [10,11], which typically results in a low precision of the estimated between‐study variance when using the random-effects model [67]. In contrast, another systematic review [16] analyzed the data from patients with CKD without T2DM from the same clinical trials [10,11] and found a significant protective effect. We attribute this difference to two key factors. First, they redefined the renal composite outcome to include a sustained ≥50% decline in eGFR, which decreased the total number of events; and second, they used different statistical methods of analysis. Ultimately, the effect does not appear to be robust, as its statistical significance seems dependent on the method of analysis used.

The favorable effect of SGLT-2 inhibitors extends to certain secondary outcomes. However, our analysis revealed varied effects compared to existing literature. For the all-cause mortality outcome, we found a significant protective effect in CKD patients, similarly to Arnott et al. [15] but in contrast to other reviews [13,60]. Our results align with a review [14] that did not find a protective effect for the MACE outcome in CKD patients with T2DM, while other reviews [12,13,15] have shown otherwise. Regarding the cardiovascular death outcome, we found a significant protective effect in CKD patients with or without T2DM, and in those with CKD and T2DM. Two systematic reviews [15,60] support this result, but others do not [13,14,64]. Additionally, we found that SGLT-2 inhibitors have a strong protective effect for the hospitalization for HF outcome, which is consistent with prior systematic reviews [13,15,60,64]. This variability in results across systematic reviews is mostly explained by the gradual increase of published clinical trials in patients with CKD over time.

Notably, the focus of clinical trials of SGLT-2 inhibitors efficacy has shifted from patients with T2DM and CV disease to patients with HF and CKD regardless of the presence of T2DM. This can be attributed to the findings of preclinical studies that revealed that SGLT-2 inhibitors have other cardiorenal protective mechanisms independent of their glucose lowering effect, such as the reduction of glomerular hyperfiltration [6], tubular cell fibrosis and inflammation [7,8], and albuminuria [9]. The shift to study the clinical relevance of these mechanisms has had an impact on the absolute incidence of time-to-event outcomes in each clinical trial. For instance, a higher number of patients developed the primary CV outcome in the HF trials, which accounts for the higher weight these trials received in our meta-analyses. This may be explained by the fact that all the patients we selected from those trials had both HF and CKD, while only 10.9% of patients in the DAPA-CKD trial had HF and only 26.7% of patients in the EMPA-KIDNEY trial had established cardiovascular disease. Thus, CKD patients from HF trials were more susceptible to clinically unfavorable outcomes than patients from CKD trials.

This study represents a significant contribution to the field, as to the best our knowledge, this is the first systematic review that evaluated patients with CKD according to the definition of each trial, which often considered the presence of albuminuria. This approach allowed us to include patients with CKD with a preserved eGFR but a high albuminuria. Previous systematic reviews [12–15,60,64] established an eGFR < 60 ml/min/1.73 m² as the sole criterion for the diagnosis of CKD, potentially excluding data from patients with CKD that do not meet this threshold. A key strength of this systematic review is its comprehensive inclusion of CKD patients from every trial in the literature, even those from trials focused on other diseases such as T2DM or HF. Our approach greatly augmented our sample size and may increase the external validity of our estimates. To assess the robustness of our results, we conducted a sensitivity analysis only including patients with an eGFR < 60 ml/min/1.73 m². Additionally, we performed subgroup analyses according to baseline eGFR values and the type of SGLT-2 inhibitor. Finally, while there was some variability in the individual components of the composite kidney outcome in each trial, they mostly comprised clinically relevant markers such as doubling of serum creatinine, strong decline of eGFR, end-stage kidney disease, or renal death.

We recognize several limitations in our study. First, most of the clinical trials in our analysis considered the patients with CKD as a subgroup rather than as their main study population. Thus, these studies were not originally designed to detect the treatment effect in these patients. Second, some baseline and outcome data from the trials were missing. Consequently, we could not incorporate these elements into our results. For instance, we were unable to assess the effect of baseline hypoglycemic drugs in a meta-analysis, as comparisons according to this variable were scarcely reported in the records of the included trials. Third, we could only extract outcome data of patients with CKD without T2DM from two trials, which severely limited the external validity of our subgroup analyses. Finally, we did not evaluate the effect of SGLT-2 inhibitors according to specific causes of non-diabetic CKD, as we considered that the number of trials that reported this information was very low (n = 2).

In conclusion, our analysis demonstrates that SGLT-2 inhibitors, in addition to standard therapy, provide significant protection against cardiovascular and renal outcomes in CKD patients, regardless of their T2DM status. This finding aligns with the well-established roles of empagliflozin and dapagliflozin in patients with HF without diabetes [68], the indication of dapagliflozin for CKD patients without T2DM [69], and the recent findings from the EMPA-KIDNEY trial that show significant benefits of empagliflozin in CKD patients without T2DM [11]. The potential benefits of other SGLT-2 inhibitors in this context should be individually assessed in future clinical trials. Additionally, we suggest that future trials use a standardized renal composite outcome to facilitate comparisons between studies.

Supporting information

S1 Checklist. PRISMA 2020 checklist.

(DOCX)

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S1 Fig. Risk of bias assessment.

(A) Traffic light plot. (B) Bar plot.

(TIF)

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S2 Fig. Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD according to eGFR.

(A) Primary cardiovascular outcome. (B) Primary renal outcome. *Included patients with an eGFR of 25 to < 45 ml/min/1.73 m². ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; eGFR, estimated glomerular filtration rate; HR, hazard ratio; NA, not available.

(TIF)

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S3 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to eGFR.

(A) All-cause mortality. (B) MACE outcome. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; eGFR, estimated glomerular filtration rate; HR, hazard ratio; NA, not available.

(TIF)

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S4 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to eGFR.

(A) CV death. (B) Hospitalization for HF. *Included patients with an eGFR of 25 to < 45 ml/min/1.73 m². ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; eGFR, estimated glomerular filtration rate; HR, hazard ratio; NA, not available.

(TIF)

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S5 Fig. Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD according to the type of SGLT-2 inhibitor.

(A) Primary cardiovascular outcome. (B) Primary renal outcome. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; HR, hazard ratio; NA, not available.

(TIF)

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S6 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to the type of SGLT-2 inhibitor.

(A) All-cause mortality. (B) MACE outcome. CI, confidence interval; HR, hazard ratio; NA, not available.

(TIF)

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S7 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to the type of SGLT-2 inhibitor.

(A) CV death. (B) Hospitalization for HF. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; HR, hazard ratio; NA, not available.

(TIF)

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S8 Fig. Sensitivity analysis: Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD and an eGFR < 60 ml/min/1.73 m².

(A) Primary cardiovascular outcome. (B) Primary renal outcome. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; CKD, chronic kidney disease; HR, hazard ratio; NA, not available.

(TIF)

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S9 Fig. Sensitivity analysis: Effects of SGLT-2 inhibitors on the secondary outcomes in patients with CKD and an eGFR < 60 ml/min/1.73 m².

(A) All-cause mortality. (B) MACE outcome. (C) CV death. (D) Hospitalization for HF. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; CKD, chronic kidney disease; HR, hazard ratio; NA, not available.

(TIF)

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S10 Fig. Reporting bias assessment: Funnel plots and Egger’s tests.

(A) Primary cardiovascular outcome. (B) Primary renal outcome. (C) All-cause mortality. (D) MACE outcome. (E) CV death. (F) Hospitalization for HF.

(TIF)

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S1 Table. Definition of chronic kidney disease and the renal and cardiovascular composite outcomes in each study.

*We used an eGFR < 60 ml/min/1.73 m² as the definition of chronic kidney disease. CKD, chronic kidney disease; CV, cardiovascular; eGFR, estimated glomerular filtration rate; HF, heart failure; NA, not available; UACR, urine albumin-to-creatinine ratio.

(DOCX)

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S2 Table. Search Strategy.

(DOCX)

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S3 Table. Clinical trial baseline characteristics ordered according to publication date.

Data calculated from other publications of the included trial is shaded blue, since it was not directly available in the published articles. ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; CKD, chronic kidney disease; CV, cardiovascular; DPP-4, dipeptidyl peptidase-4; eGFR, estimated glomerular filtration rate; GLP-1, glucose-like peptide-1; HF, Heart failure; IQR, interquartile range; MRA, mineralocorticoid receptor antagonist; n, absolute frequency; NA, not available; SD, standard deviation; SGLT-2, sodium-glucose cotransporter 2; T2DM, type 2 diabetes mellitus; UACR, urine albumin-to-creatinine ratio.

(DOCX)

Click here for additional data file.^{(31.7KB, docx)}

S4 Table. Summary of the evidence.

(DOCX)

Click here for additional data file.^{(29.2KB, docx)}

S1 Database

(XLSX)

Click here for additional data file.^{(60.4KB, xlsx)}

Acknowledgments

We thank Oscar J Ponce M.D. (O.J.P.—Knowledge and Evaluation Research Unit, Mayo Clinic, USA) for helping in the development of the research protocol, as well as in the study selection process and data analysis. We thank Paolo Wong M.D. (Universidad de Piura, Peru) for comments on the manuscript.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

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7 Aug 2023

PONE-D-23-13221The effect of sodium-glucose cotransporter 2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes mellitus on cardiovascular and renal outcomes: a systematic review and meta-analysisPLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I appreciate the opportunity to review the manuscript titled "The effect of sodium-glucose cotransporter 2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes mellitus on cardiovascular and renal outcomes: a systematic review and meta-analysis." The authors conducted a systematic review and meta-analysis to investigate the cardiovascular and renal effects of SGLT-2 inhibitors in patients with chronic kidney disease (CKD) with and without type 2 diabetes mellitus (T2DM). The authors included not only clinical trials specifically focused on patients with CKD, but also trials dedicated to heart failure (HF) or T2DM that included CKD patients as a subgroup. I acknowledge the authors' time and effort in conducting this meta-analysis and believe that the manuscript has been improved through previous revision rounds. Below are my comments for the authors.

[1] In the Introduction section, the authors mention a recent meta-analysis study published in Lancet (Ref #16: Baigent C, Lancet 2022; 400: 1788–801). The authors state that this meta-analysis only analyzed data from trials that included CKD patients and did not consider data from trials where CKD patients were a subgroup. Therefore, the efficacy of SGLT-2 inhibitors in CKD subgroups with or without T2DM was not evaluated across all trials. It is true that the Lancet meta-analysis focused on combined outcomes from CKD trials (such as CREDENCE, SCORED, DAPA-CKD, and EMPA-KIDNEY trials), it did not include outcomes of CKD patients from trials dedicated to T2DM or HF (such as DECLARE-TIMI 58, CANVAS Program, VERTIS CV, EMPA-REG OUTCOME, DAPA-HF, EMPEROR-REDUCED, EMPEROR-PRESERVED, DELIVER, and SOLOIST-WHF). However, although the Lancet meta-analysis did not provide patient-level analysis, it is unclear if conducting an additional meta-analysis is necessary. Most trials dedicated to T2DM or HF have already shown consistent benefits of SGLT-2 inhibitors across various glomerular filtration rate (GFR) subgroups, and trials dedicated to CKD (DAPA-CKD and EMPA-KIDNEY) have demonstrated the benefits of SGLT-2 inhibitors regardless of the presence of T2DM. Therefore, it can be expected that including the CKD subgroup from trials dedicated to T2DM or HF would not significantly affect the outcomes. I recommend that the authors provide more reasonable background information in the Introduction section, focusing on why the current meta-analysis is required and clinically relevant. In my opinion, considering the consistent benefits shown in the aforementioned trials and the recently published meta-analysis in Lancet, it is uncertain whether the current meta-analysis adds significant additional knowledge or clinical relevance.

[2] In the Results section (Subgroup analysis), the authors state, "Although this protective effect was also found in the subgroup of patients without T2DM, this association was not significant, and the pooled studies were heterogeneous (HR 0.66; [95% CI 0.04-11.16]; τ2 = 0.0764; I2 = 75%)." This lack of significance and heterogeneity may be primarily attributed to the limited number of trials included. As explained by the authors in the Results section (Baseline characteristics), the DAPA-CKD trial and the EMPA-KIDNEY trial were the only sources of outcome data for the subgroup of patients with CKD but without T2DM. Moreover, there was significant heterogeneity between these two trials (DAPA-CKD vs. EMPA-KIDNEY trial). Unfortunately, this limitation is significant because the results from patients with CKD but without T2DM would be a crucial finding in terms of the novelty of the current meta-analysis.

[3] The authors rated the certainty of evidence for MACE and HF hospitalization outcomes as "low-quality evidence" and mentioned concerns of indirectness and publication bias. The suggestion of possible publication bias was based on the Egger's test, as shown in Supplementary Figure S4. However, it is important to note that funnel plot asymmetry can have causes other than publication bias. Additionally, the varying number of patients from each included trial should be considered, as certain trials were dedicated to CKD patients, resulting in larger numbers of patients with CKD compared to trials dedicated to DM or HF. I recommend that the authors provide additional reasons for rating the evidence as "low quality" for the MACE and HF hospitalization outcomes.

[4] Although the authors mention in the Limitation section, it would be interesting to explore and compare the outcomes between individual SGLT2 inhibitors. This could be considered as an additional analysis within the current study or as a separate study.

[5] Was there any difference in terms of GFR values? Since many trials conducted subgroup analyses based on GFR values (e.g., GFR 45-60 vs. GFR <45), it would enhance the novelty and relevance of the current study if the authors could provide subgroup analyses based on GFR values.

Reviewer #2: 1. “Finally, only two studies compared patients with CKD and T2DM with patients with CKD without T2DM.” The above sentence is misexpressed and prone to ambiguity.

2. When performing subgroup analysis, why chronic kidney disease was not grouped according to grade of kidney function, so as to determine the different levels of benefit of SGLT-2 inhibitors in chronic kidney disease.

3. The concept of chronic kidney disease may also include diabetic nephropathy, which in turn includes type 1 diabetic nephropathy and type 2 diabetic nephropathy. Are these concepts consistent with the concept of chronic kidney disease combined with diabetes in this study?

4. As the first systematic review that to evaluat patients with CKD according to the definition of each trial, which often considered the presence of albuminuria. It is indeed suggested that the clinical rational selection of hypoglycemic drugs, especially the hypoglycemic methods of patients with kidney disease are more optimized.

5. The inconsistence in co-use of hypoglycemic agents in patients enrolled in each RCT clinical study, and the uncertainty about whether patients were treated with nephrotic agents, should explain the bias caused by this factor.

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: None

**********

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PLoS One. 2023 Nov 29;18(11):e0295059. doi: 10.1371/journal.pone.0295059.r003

Author response to Decision Letter 0

16 Sep 2023

Reviewer #1:

Response: We appreciate your comments on our manuscript. We deeply respect the thoroughness of the research done by Baigent et al. However, by focusing on patients with CKD, we believe that our study possesses methodological advantages that contribute to the strengthening of the evidence for the use of SGLT-2 inhibitors specifically in patients with CKD, regardless of the presence of T2DM:

Our decision to include every published trial in our analysis, even those in which CKD patients were a subgroup of the study, allowed us to include a considerably larger sample of patients. We included 44 593 patients with CKD, almost double the number included by Baigent et al. (25 898 patients). Thus, the number of CKD patients who come from subgroups of T2DM and HF trials is substantial, and their clinical outcome data contributes to the meta-analysis, possibly giving it more robustness. Additionally, the inclusion of every trial allowed us to better identify and show the limitations of current literature, such as the heterogeneity in the definition of the renal composite outcome across trials or the lack of reporting of various cardiorenal outcomes in patients without T2DM from the EMPA-KIDNEY trial.

We assessed the heterogeneity between CKD patients with and without T2DM with a statistical test for every outcome, in order to confirm the absence of statistical differences between subgroups. Baigent et al. only performed this test in the overall sample of the trials, which included patients with and without CKD. This limitation is present in their Figure 3 (https://doi.org/10.1016/S0140-6736(22)02074-8). In their analysis, the subgroup “No diabetes” is further categorized in “Stable heart failure trials” and “Chronic kidney disease trials”.

By including “Stable heart failure trials” in the “No diabetes” subgroup, the positive effect of SGLT-2 inhibitors in patients from “Chronic kidney disease trials” may have been obscured by the already established benefits of these drugs in patients with HF but neither CKD nor T2DM. For instance, the outcome “Cardiovascular death or hospitalization for heart failure” shows a wide and non-significant confidence interval for “Chronic kidney disease trials” and gives more weight to the “Stable heart failure trials”. By doing so, the test for “Heterogeneity by diabetes status” is far more reliant on the data from patients with HF without T2DM than patients with CKD without T2DM. This limitation is present in the four outcomes shown in their Figure 3. Thus, it is uncertain whether there is heterogeneity between CKD patients with and without T2DM in these clinically relevant outcomes. In consequence, a more focused analysis was warranted.

The only analyses that provide a pure comparison between CKD patients with and without T2DM in the review by Baigent et al. are those reported in their Figure 2 and Supplementary Webfigure 2, and these only show the outcomes “Kidney disease progression” and “Kidney failure”, respectively. Additionally, those analyses did not include a vast number of participants with CKD from subgroups of HF and T2DM trials, as we mentioned before. On the other hand, our systematic review also evaluated kidney disease progression (“Primary renal outcome”) as well as other clinically relevant outcomes with a focus on patients with CKD, such as cardiovascular death, hospitalization for heart failure, all-cause mortality, and MACE (Major Adverse Cardiovascular Events). Of note, the work of Baigent et al. did not report the effect of SGLT-2 on MACE in any analysis.

Thanks to your and the other reviewer’s kind suggestions, we have included new subgroup analyses according to the eGFR values and individual SGLT-2 inhibitors. We believe that these additions have enriched our study with greater novelty and comprehensiveness. Further details regarding these new analyses are provided in their respective responses within this document, as well as in our manuscript.

As recommended, we have improved the Introduction and Discussion of our Manuscript to reflect the ideas discussed here.

Response: We appreciate the comment, and we agree with you. We believe that the high heterogeneity between the DAPA-CKD and the EMPA-KIDNEY trials, which leads to a wide confidence interval, is indeed a limitation. However, this is not an intrinsic limitation of our study, but of the available literature, which is still lacking in trials that assess the effect of SGLT-2 inhibitors in non-diabetic patients with CKD. This is reflected in the fact that the only outcome in which we could calculate an HR for CKD patients without T2DM was the Renal composite outcome, as only the DAPA-CKD trial reported data of this subgroup for the other clinical outcomes, unlike the EMPA-KIDNEY trial. We think our study helps by drawing attention to this limitation of the literature.

Regarding the lack of statistical significance and high heterogeneity between trials, we have commented in our Discussion section (Lines 327-344 of the Manuscript) how the selection of analytical methods may be the reason why our estimate for the kidney composite outcome differs from the one reported by Baigent et al (who obtained a statistically significant interval). This suggests that the overall effect of SGLT-2 inhibitors in the renal outcome in patients with CKD without T2DM may not be very robust, as its statistical significance seems to depend on the method of analysis used, and that more studies are needed.

Response: We agree with your comment. After reviewing the GRADE handbook for the assessment of the quality of the evidence as well as the Cochrane handbook for systematic reviews (Chapter 13), we have decided that there is no indication that the evidence for the MACE and hospitalization for HF is of low quality. Thus, we have corrected it to “moderate quality”.

We agree with you about the fact that funnel plot asymmetry and the Egger’s test are not absolute indicators of publication bias, as stated in the Cochrane Handbook (Chapter 13): “funnel plot asymmetry should not be considered to be diagnostic for the presence of non-reporting bias.” Other possible sources of this asymmetry can be poor methodological quality in smaller studies, true heterogeneity, artefactual or chance.

In our systematic review, we believe that the inclusion of the study by Wada et al. may be a source of funnel plot asymmetry, as it was considerably smaller than the other trials and restricted to Japanese population instead of being multinational. This conclusion relies on the following statement of the Cochrane handbook (Chapter 13): “Although funnel plot asymmetry has long been equated with non-reporting bias, the funnel plot should be seen as a generic means of displaying small-study effects: a tendency for the intervention effects estimated in smaller studies to differ from those estimated in larger studies.” Additionally, the GRADE tool handbook states that “in smaller studies, over-estimate of effect may yield an asymmetric funnel plot that could be explained by limitations other than publication bias such as a restrictive study population”, which may be the case of the trial by Wada et al.

Most of the trials included in our study report the MACE and hospitalization for HF outcomes appropriately. Consequently, there is no suggestion that these two outcomes have been intentionally underreported in published trials. Likewise, in our exhaustive literature search we did not find evidence of unpublished or unfinished studies with unfavorable results for these two outcomes that would lead to publication bias.

We believe this change does not affect our main results, and we have corrected the Manuscript and the S4 Table to reflect our reassessment of the quality of the evidence.

Schünemann H H, Brożek J, Guyatt G, Oxman A. GRADE handbook for grading quality of evidence and strength of recommendations. The GRADE Working Group; 2013. Available from: guidelinedevelopment.org/handbook

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook.

Response: We agree with your suggestion, and thus we have performed a new subgroup analysis according to the individual SGLT-2 inhibitors, which showed no differences across subgroups in the effect of SGLT-2 inhibitors on any outcome. The results of this analysis are shown on the new Supplementary figures S5-S7. We have commented on this finding in the Discussion section.

Response: We agree with your suggestion, and thus we have performed a new subgroup analysis according to eGFR values, which showed no differences across subgroups on any outcome. The results of this analysis are shown on the new Supplementary figures S2-S4. We have commented on this finding in the Discussion section.

Of note, for this analysis we had to retrieve an additional publication (Waijer et al., 2022) of an already included study (DAPA-CKD), as it contained the data for the outcomes stratified by eGFR. This article has been included in the Results section and Flowchart as “Other records from included trials”.

Waijer SW, Vart P, Cherney DZI, Chertow GM, Jongs N, Langkilde AM, et al. Effect of dapagliflozin on kidney and cardiovascular outcomes by baseline KDIGO risk categories: a post hoc analysis of the DAPA-CKD trial. Diabetologia. 2022;65: 1085–1097. doi:10.1007/s00125-022-05694-6

Reviewer #2:

1. “Finally, only two studies compared patients with CKD and T2DM with patients with CKD without T2DM.” The above sentence is misexpressed and prone to ambiguity.

Response: We agree with your suggestion, and thus we have enhanced the clarity of the sentence.

New version: “Finally, only two studies made comparisons between patients with CKD and T2DM and patients with CKD without T2DM”.

Response: In our study, the vast majority of patients with both CKD and diabetes had specifically Type 2 diabetes mellitus.

While in clinical practice it is true that type 1 diabetes mellitus (T1DM) may be a cause of nephropathy, patients with T1DM were excluded from almost every trial by design. The only exceptions are the EMPEROR-Reduced and EMPEROR-Preserved trials, which do not exclude patients with T1DM. Unfortunately, these trials only report the number of participants with “Diabetes” and do not specify the type of diabetes, so that the exact number of participants with T1DM in these trials is uncertain.

However, the EMPEROR trials explicitly excluded patients with a history of diabetic ketoacidosis. Diabetic ketoacidosis is a very frequent form of initial presentation of undiagnosed T1DM in children, reported in around 30% of cases, ranging from 12 to 80% depending on the study (Usher-Smith et al., Cherubini et al.). Moreover, the incidence of diabetic ketoacidosis in patients already diagnosed with T1DM is high, at around 5 to 7% (Kao et al.). Therefore, by excluding patients with a history of diabetic ketoacidosis, the EMPEROR trials excluded a large proportion of T1DM patients. It is safe to assume that the number of patients with T1DM and nephropathy in the EMPEROR trials was rather low, and thus the impact they could have on the overall outcome estimates in our meta-analyses was probably minimal.

We have added a comment about this in the Results – Baseline characteristics section (Lines 197-198 of the Manuscript).

Usher-Smith JA, Thompson M, Ercole A, Walter FM. Variation between countries in the frequency of diabetic ketoacidosis at first presentation of type 1 diabetes in children: a systematic review. Diabetologia. 2012 Nov;55(11):2878-94. doi: 10.1007/s00125-012-2690-2

Cherubini V, Grimsmann JM, Åkesson K, Birkebæk NH, Cinek O, Dovč K, et al. Temporal trends in diabetic ketoacidosis at diagnosis of paediatric type 1 diabetes between 2006 and 2016: results from 13 countries in three continents. Diabetologia. 2020;63(8):1530-1541. doi: 10.1007/s00125-020-05152-1

Kao KT, Islam N, Fox DA, Amed S. Incidence Trends of Diabetic Ketoacidosis in Children and Adolescents with Type 1 Diabetes in British Columbia, Canada. J Pediatr. 2020;221:165-173.e2. doi: 10.1016/j.jpeds.2020.02.069

4. As the first systematic review that to evaluate patients with CKD according to the definition of each trial, which often considered the presence of albuminuria. It is indeed suggested that the clinical rational selection of hypoglycemic drugs, especially the hypoglycemic methods of patients with kidney disease are more optimized.

Response: We believe that your points 4 and 5 were intended to be a single point, so we have addressed both here:

The inclusion of albuminuria as a diagnostic criterion of CKD could indeed be a factor of bias in our meta-analysis, considering that some drugs such as ACE inhibitors can influence it. Being aware of this, we decided to perform a sensitivity analysis with CKD exclusively defined by an eGFR < 60 ml/min/1.73 m2 regardless of albuminuria. The results were largely consistent with those of our main analysis, which included albuminuria in the definition of CKD.

We agree that the inconsistency of baseline hypoglycemic drugs could be a factor of bias as well. Unfortunately, comparisons according to baseline hypoglycemic drugs are scarcely reported in the records of the included trials, making it difficult to assess the effect of this variable in a meta-analysis. This is probably due to the design of all the included trials, which allowed most patients to continue taking their usual medication. In many cases patients used more than one drug, which made subgroup comparisons unfeasible. In our search we found a single analysis focusing on this issue from the VERTIS-CV trial by Dagogo-Jack et al., which showed no treatment effect modification of ertugliflozin on cardiorenal outcomes when stratified by baseline use of metformin, insulin, sulphonylureas and dipeptidyl peptidase‐4 inhibitors. We did not find information on the other SGLT-2 inhibitors.

We added a comment regarding this observation in the Discussion section (lines 394-397 of the Manuscript), as a limitation.

Dagogo‐Jack S, Cannon CP, Cherney DZI, Cosentino F, Liu J, Pong A, et al. Cardiorenal outcomes with ertugliflozin assessed according to baseline glucose‐lowering agent: An analysis from VERTIS CV. Diabetes Obes Metab. 2022;24: 1245–1254. doi:10.1111/dom.14691

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PLoS One. doi: 10.1371/journal.pone.0295059.r004

Decision Letter 1

Edward Zimbudzi

11 Oct 2023

PONE-D-23-13221R1The effect of sodium-glucose cotransporter 2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes mellitus on cardiovascular and renal outcomes: a systematic review and meta-analysisPLOS ONE

Dear Dr. Reategui-Diaz,

In particular, the discussion section in the current version of your manuscript is quite lengthy. This could be made more concise.

Please submit your revised manuscript by Nov 25 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Edward Zimbudzi

Academic Editor

PLOS ONE

Journal Requirements:

1. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I would like to commend the authors for their diligent efforts in addressing the previous review comments, which have led to a significant improvement in the manuscript. Additionally, I have some further comments to offer.

[1] The Discussion section appears quite lengthy. I recommend that the authors work on making it more concise.

[2] When explaining the subgroup analysis based on the type of SGLT-2 inhibitors, please consistently use the term "the type of SGLT-2 inhibitors" instead of simply referring to "the SGLT-2 inhibitor" throughout the manuscript.

[3] In order to emphasize the significance and relevance of the current study, please indicate that this study aimed to include all the available patients with CKD from the current literature, not only those from clinical trials dedicated to CKD, but also those where CKD patients were included as a subgroup.

[4] In the third paragraph of the Introduction section (lines 57 – 61), I would like to recommend the authors to rephrase the sentences. Because the aim of the present study appears in the next paragraph, the sentence above should highlight the needs for an additional meta-analysis, while avoiding the term “our results”. For example, “However, to date, there has been no comprehensive systematic review assessing the impact of SGLT-2 inhibitors on the entire CKD population, as provided in the available literature. Incorporating data from all CKD patients within clinical trials, not solely those designed exclusively for CKD patients but also those where CKD patients are included as a subgroup, could enhance the accuracy of effect estimates and enhance the applicability of the findings.” Please note that this is just a suggestion, and I hope the authors can refine the sentences further.

[5] Likewise, in the last paragraph of the Introduction section (lines 62 – 64), consider rephrasing it as follows: "This systematic review aims to assess the cardiovascular and renal effects of SGLT-2 inhibitors in all available CKD patients in the current literature and to evaluate whether the presence of T2DM modifies their overall impact." Please note that this is just a suggestion.

[6] In the Discussion section (line 299), please specify in the sentence that the total number of participants with CKD was compared to the study by Baigent et al.

Reviewer #2: The authors responded to all of the review questions one on one and included detailed evidence. In particular, authors assessed heterogeneity between CKD patients with and without T2DM and performed statistical tests for each outcome to confirm that there were no statistical differences between subgroups.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Hengfen Dai

**********

PLoS One. 2023 Nov 29;18(11):e0295059. doi: 10.1371/journal.pone.0295059.r005

Author response to Decision Letter 1

23 Oct 2023

Dear reviewers,

Thank you for your further suggestions about our manuscript. We have implemented the changes and improved our manuscript accordingly.

Sincerely,

Marcelo Reategui-Diaz

Reviewer #1:

[1] The Discussion section appears quite lengthy. I recommend that the authors work on making it more concise.

Response: We agree with your suggestion, and thus we have reduced the number of words from most of the Discussion section paragraphs. With these changes, the word count was reduced from 2236 to 1623 words.

Response: We agree with your suggestion. In the new version, we have used the term "the type of SGLT-2 inhibitors" more consistently across the manuscript.

Response: We agree with your suggestion. We modified the Abstract, Introduction, Methods and Discussion sections to emphasize that our study aimed to assess the impact of SGLT-2 inhibitors on the entire CKD population in the current literature, including both CKD trials and trials in which CKD patients were a subgroup.

Response: We modified the Introduction section to incorporate your suggestions in this paragraph (lines 57-62).

Response: We agree with your suggestion. We modified the Introduction section to incorporate your suggestion in this paragraph (lines 63-65).

[6] In the Discussion section (line 299), please specify in the sentence that the total number of participants with CKD was compared to the study by Baigent et al.

Response: We agree with your suggestion. We modified the Discussion section to incorporate your suggestion in this paragraph (line 286).

Reviewer #2:

The authors responded to all of the review questions one on one and included detailed evidence. In particular, authors assessed heterogeneity between CKD patients with and without T2DM and performed statistical tests for each outcome to confirm that there were no statistical differences between subgroups

Response: Thank you for your thorough evaluation of our manuscript.

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PLoS One. doi: 10.1371/journal.pone.0295059.r006

Decision Letter 2

Edward Zimbudzi

30 Oct 2023

PONE-D-23-13221R2The effect of sodium-glucose cotransporter 2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes mellitus on cardiovascular and renal outcomes: a systematic review and meta-analysisPLOS ONE

Dear Dr. Reategui-Diaz,

Please submit your revised manuscript by Dec 14 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Edward Zimbudzi

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Note that our reviewers suggest reorganising paragraphs in the discussion to improve the flow of this section. I recommend that you pay particular attention to the examples provided.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: I want to express my gratitude for the authors' dedication and time spent on the revision. The authors have made substantial efforts in addressing the comments from the previous revision. While I acknowledge the significant improvements in the manuscript, I still believe that the Discussion section could benefit from further refinement. Some paragraphs appear to disrupt the overall flow and readability. Specifically, the paragraph beginning with "Independently of their glucose-lowering effect..." seems inconsistent with the surrounding paragraphs. Additionally, the paragraph that starts with "To date, the role of empagliflozin and dapagliflozin..." doesn't seem to flow seamlessly with the adjacent paragraphs.

Would it be possible to consider reorganizing the paragraphs (not only the above two paragraphs I have mentioned, but also other paragraphs) within the Discussion section?

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********

PLoS One. 2023 Nov 29;18(11):e0295059. doi: 10.1371/journal.pone.0295059.r007

Author response to Decision Letter 2

8 Nov 2023

Dear reviewer,

Thank you for your comments about the Discussion section of our manuscript. We have implemented the changes and improved our manuscript accordingly.

We agree with your comments. In consequence, we have sorted the paragraphs to follow a more logical order. We also paid particular attention to the two paragraphs that were highlighted in your comment. We have merged those paragraphs with adjacent paragraphs seamlessly, so that the readability and connection of ideas are improved.

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PLoS One. doi: 10.1371/journal.pone.0295059.r008

Decision Letter 3

Edward Zimbudzi

15 Nov 2023

The effect of sodium-glucose cotransporter 2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes mellitus on cardiovascular and renal outcomes: a systematic review and meta-analysis

PONE-D-23-13221R3

Dear Dr. Reategui-Diaz,

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PLOS ONE

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PLoS One. doi: 10.1371/journal.pone.0295059.r009

Acceptance letter

Edward Zimbudzi

17 Nov 2023

PONE-D-23-13221R3

Dear Dr. Reategui-Diaz:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. PRISMA 2020 checklist.

(DOCX)

Click here for additional data file.^{(39KB, docx)}

S1 Fig. Risk of bias assessment.

(A) Traffic light plot. (B) Bar plot.

(TIF)

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S2 Fig. Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD according to eGFR.

(TIF)

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S3 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to eGFR.

(TIF)

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S4 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to eGFR.

(TIF)

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S5 Fig. Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD according to the type of SGLT-2 inhibitor.

(A) Primary cardiovascular outcome. (B) Primary renal outcome. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; HR, hazard ratio; NA, not available.

(TIF)

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S6 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to the type of SGLT-2 inhibitor.

(A) All-cause mortality. (B) MACE outcome. CI, confidence interval; HR, hazard ratio; NA, not available.

(TIF)

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S7 Fig. Effects of SGLT-2 inhibitors on two secondary outcomes in patients with CKD according to the type of SGLT-2 inhibitor.

(A) CV death. (B) Hospitalization for HF. ^¶Synthetic estimate created with a fixed effects model meta-analysis. CI, confidence interval; HR, hazard ratio; NA, not available.

(TIF)

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S8 Fig. Sensitivity analysis: Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD and an eGFR < 60 ml/min/1.73 m².

(TIF)

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S9 Fig. Sensitivity analysis: Effects of SGLT-2 inhibitors on the secondary outcomes in patients with CKD and an eGFR < 60 ml/min/1.73 m².

(TIF)

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S10 Fig. Reporting bias assessment: Funnel plots and Egger’s tests.

(A) Primary cardiovascular outcome. (B) Primary renal outcome. (C) All-cause mortality. (D) MACE outcome. (E) CV death. (F) Hospitalization for HF.

(TIF)

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S1 Table. Definition of chronic kidney disease and the renal and cardiovascular composite outcomes in each study.

(DOCX)

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S2 Table. Search Strategy.

(DOCX)

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S3 Table. Clinical trial baseline characteristics ordered according to publication date.

(DOCX)

Click here for additional data file.^{(31.7KB, docx)}

S4 Table. Summary of the evidence.

(DOCX)

Click here for additional data file.^{(29.2KB, docx)}

S1 Database

(XLSX)

Click here for additional data file.^{(60.4KB, xlsx)}

Attachment

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Attachment

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

PERMALINK

The effect of sodium-glucose cotransporter 2 inhibitors in patients with chronic kidney disease with or without type 2 diabetes mellitus on cardiovascular and renal outcomes: A systematic review and meta-analysis

Carlos Ignacio Reyes-Farias

Marcelo Reategui-Diaz

Franco Romani-Romani

Larry Prokop

Roles

Abstract

Background

Objective

Methods

Results

Conclusion

Introduction

Methods

Eligibility criteria

Information sources and search strategy

Screening of studies

Data extraction

Data collection and author contact

Data items

Risk of bias assessment

Quality of the evidence

Statistical analysis

Results

Study selection

Fig 1. PRISMA Flow chart.

Baseline characteristics

Risk of bias

Quality of the evidence

Main analysis

Primary outcomes

Fig 2. Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD with or without T2DM.

Secondary outcomes

Fig 3. Effects of SGLT-2 inhibitors on the secondary outcomes in patients with CKD with or without T2DM.

Subgroup analysis

Fig 4. Effects of SGLT-2 inhibitors on the primary outcomes in patients with CKD according to T2DM status.

Fig 5. Effects of SGLT-2 inhibitors on the secondary outcomes in patients with CKD according to T2DM status.

Sensitivity analysis

Publication bias assessment

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Author response to previous submission

Decision Letter 0

Edward Zimbudzi

Roles

Author response to Decision Letter 0

Decision Letter 1

Edward Zimbudzi

Roles

Author response to Decision Letter 1

Decision Letter 2

Edward Zimbudzi

Roles

Author response to Decision Letter 2

Decision Letter 3

Edward Zimbudzi

Roles

Acceptance letter

Edward Zimbudzi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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