Original ArticlesDiabetes and the Kidney

Network Meta-Analysis of Novel Glucose-Lowering Drugs on Risk of Acute Kidney Injury

Min Zhao, Shusen Sun, Zhenguang Huang, Tiansheng Wang and Huilin Tang
CJASN January 2021, 16 (1) 70-78; DOI: https://doi.org/10.2215/CJN.11220720

Abstract

Background and objectives Little is known about the comparative effects of dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), or sodium glucose cotransporter-2 (SGLT2) inhibitors on risk of AKI. This study aimed to compare the effects of these three novel classes of glucose-lowering drugs on AKI risk in patients with or without type 2 diabetes, by network meta-analysis of event-driven cardiovascular or kidney outcome trials.

Design, setting, participants, & measurements We systematically searched electronic databases up to September 2020, and included 20 event-driven cardiovascular or kidney outcome trials (18 trials included patients with type 2 diabetes only, and two trials included patients with or without type 2 diabetes). A network meta-analysis using a frequentist approach was performed to compare the effects of DPP-4 inhibitors, GLP-1RAs, or SGLT2 inhibitors on risk of AKI, and estimate the probability for each intervention as the safest one. The primary analysis included 18 trials with type 2 diabetes only, and a secondary analysis included 20 trials.

Results In the 18 trials with a total of 2051 AKI events (range: 1–300) among 156,690 patients with type 2 diabetes only, our network meta-analysis showed that SGLT2 inhibitors were associated with a lower risk of AKI compared with placebo (odds ratio, 0.76; 95% confidence interval, 0.66 to 0.88), whereas both DPP-4 inhibitors and GLP-1RAs had neutral effects on risk of AKI. Moreover, SGLT2 inhibitors were significantly associated with a lower risk in AKI than both GLP-1RAs (odds ratio, 0.79; 95% confidence interval, 0.65 to 0.97) and DPP-4 inhibitors (odds ratio, 0.68; 95% confidence interval, 0.54 to 0.86). SGLT2 inhibitors have the highest probability of being the safest intervention (84%). The results were similar in the secondary analysis.

Conclusions Current evidence indicates that SGLT2 inhibitors have a lower risk of AKI than both DPP-4 inhibitors and GLP-1RAs.

Introduction

Diabetes is an independent risk factor for AKI, and an episode of AKI is significantly associated with major adverse outcomes and death in diabetes (1). Over the past decades, three new classes of glucose-lowering drugs (glucagon-like peptide-1 receptor agonists [GLP-1RAs], dipeptidyl peptidase-4 [DPP-4] inhibitors, and sodium-glucose cotransporter-2 [SGLT2] inhibitors) have been introduced and have become common drugs for treating type 2 diabetes. In 2016, the US Food and Drug Administration (FDA) strengthened the warning on the increased risk of AKI associated with SGLT2 inhibitors (2). However, growing evidence showed that SGLT2 inhibitors would decrease the risk of AKI among patients with type 2 diabetes (3,4). There is some evidence indicating that GLP-1RAs might provide kidney protection (5), and a series of postmarketing case reports showed that GLP-1RAs were associated with the development of AKI (6). Similarly, the risk of AKI among patients taking DPP-4 inhibitors also remains controversial (7,8). Better understanding the risk of AKI among these three classes of glucose-lowering drugs is critically important and will help clinicians make decision in clinical practice. However, knowledge of the comparative effects of these three classes of glucose-lowering drugs on risk of AKI is limited because few head-to-head trials have been performed.

Unlike standard pairwise meta-analysis (comparing two treatments directly), a network meta-analysis allows us to compare multiple interventions simultaneously in a single analysis by combining both direct head-to-head trials and indirect comparison between the interventions across a network of studies (9). This enables us to estimate the comparative effects of DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors on risk of AKI, in absence of head-to-head trials, and rank the safest intervention. Moreover, to date, numerous event-driven cardiovascular or kidney outcome trials designed to meet regulatory requirements have been published. Therefore, a network meta-analysis of these trials can provide enough power and long-term follow-up to evaluate the risk of AKI associated with DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors in patients with or without type 2 diabetes, to assess their comparative effects of these three classes of drugs on AKI development, and to estimate the probability for each intervention as the safest one.

Materials and Methods

This meta-analysis was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for reporting of systematic reviews and meta-analyses of health care interventions (10).

Search Strategy and Study Selection

We systematically searched the PubMed, Embase, and the Cochrane Central Register of Controlled Trials up to September 2020 using relevant search terms (Supplemental Table 1). Two reviewers independently screened the article titles, abstracts, and full texts to identify potential trials according to the following inclusion criteria: (1) event-driven cardiovascular or kidney outcome trials; (2) novel glucose-lowering drugs, including DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors; (3) adults (aged ≥18 years) with or without type 2 diabetes; and (4) reporting the AKI events. AKI was defined by each trial or according to the Medical Dictionary for Regulatory Activities preferred terms for serious adverse events and/or adverse events (Supplemental Table 2). We also scanned the references of included publications to identify additional trials.

Data Extraction and Quality Assessment

Two reviewers independently extracted the data and assessed the risk of bias of included trials. We extracted the following information: first author (year), study characteristics, patients’ characteristics, type of treatments (DPP-4 inhibitors, GLP-1RAs, or SGLT2 inhibitors), and events of AKI. The events of AKI were retrieved from the publications and from ClinicalTrials.gov. If these data were unavailable, we extracted the data from ClinicalTrials.gov. The number of events from the publication was used in the meta-analysis in case these data were inconsistent between publication and ClinicalTrials.gov. We also assessed the risk of bias of included trials according to adjusted Cochrane risk of bias tool (11). Any disagreement was resolved through consensus.

Statistical Analyses

We calculated a pooled odds ratio (OR) and 95% confidence interval (95% CI) for the risk of AKI using the fixed-effects model. The magnitude of the heterogeneity between studies was assessed using I2 statistic, with a value <25%, ≥25% to <75%, and ≥75% being considered as low, moderate, and high levels of heterogeneity, respectively. We evaluated the effects of each class of glucose-lowering drug (DPP-4 inhibitors, GLP-1RAs, or SGLT2 inhibitors) on risk of AKI. A funnel plot and Egger regression were used to evaluate the publication bias (12).

To further explore the comparative effects of these three novel classes of glucose-lowering drugs on risk of AKI, we performed a frequentist network meta-analysis with a random-effects model, using the “mvmeta” command (13). We calculated ORs and 95% CIs between different interventions and estimated the probabilities for being the safest intervention by using surface under the cumulative ranking curve (13). We cannot assess the inconsistency of the network because the network in our analysis was star-shaped and did not have a closed loop. We performed a primary analysis by including the trials involving patients with type 2 diabetes only, and then a secondary analysis by including all trials involving patients with or without type 2 diabetes. The statistical analyses were performed using STATA (version 14; Stata Corp., College Station, TX), and risk of bias was assessed using Review Manager 5.3 (The Nordic Cochrane Centre, The Cochrane Collaboration, 2014).

Results

Of the 8206 citations identified through electronic databases in May 2020, 18 event-driven cardiovascular or kidney outcome trials were identified, and two additional trials were included in September 2020, resulting in a total of 20 trials involving 165,738 patients with or without type 2 diabetes included in this meta-analysis (Supplemental Figure 1) (14⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓–33). The basic characteristics of included trials are presented in Supplemental Table 3 and Table 1. Eighteen trials included patients with type 2 diabetes and two trials included those with or without type 2 diabetes (32,33). The assessment of risk of bias of included trials is shown in Supplemental Figure 2. All trials were well performed, but only 11 trials reported the outcome of AKI in their published article, and three trials confirmed the AKI event by event adjudication committee (21,26,33). The available interventions in this network analysis is presented in Figure 1. The network in our analysis was star-shaped and did not have a closed loop. Nineteen trials used placebo as a control group and one trial used glimepiride (19). The data source and definition of AKI for each trial are presented in Supplemental Table 4.

View this table:
Table 1.

Basic characteristics of included studies

Figure 1.
Figure 1.

Number of trials evaluating DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors for risk of AKI. The node (circle) reflects the number of participants randomly assigned to the treatment, and the line width reflects the number of direct head-to-head comparisons available in contributing trials. No connecting line between two treatments indicates that there was no head-to-head comparison. The numbers beside the lines indicate the number of trials comparing the two interventions. DPP-4 inhibitors, dipeptidyl peptidase-4 inhibitors; GLP-1RAs, glucagon-like peptide-1 receptor agonists; SGLT2 inhibitors, sodium-glucose cotransporter-2 inhibitors.

In the primary analysis, we included 18 trials involving 156,690 patients with type 2 diabetes only. Results of individual trials and pairwise meta-analysis are presented in Figure 2. Six trials evaluating DPP-4 inhibitors reported 534 cases of AKI among 53,747 patients with type 2 diabetes. DPP-4 inhibitors were not significantly associated with a higher risk of AKI than placebo or glimepiride (OR, 1.12; 95% CI, 0.95 to 1.33; I2=0%). Similarly, there was no significant difference between GLP-1RAs (359 cases/27,963 patients) and placebo (373 cases/28,010 patients) in the risk of AKI (OR, 0.96; 95% CI, 0.83 to 1.11; I2=0%). When pooling analysis of five trials (387 cases/26,765 patients versus 398 cases/20,205 patients), SGLT2 inhibitors were significantly associated with a lower risk of AKI than placebo (OR, 0.76; 95% CI, 0.66 to 0.88; I2=0%). No statistical heterogeneity was observed in the above analysis. There was no evidence of publication bias detected in the meta-analysis using Egger test (intercept =0.50; P=0.45) and funnel plot (Supplemental Figure 3).

Figure 2.
Figure 2.

Forest plot of pairwise meta-analysis showing a lower risk of AKI among type 2 diabetes patients taking SGLT2 inhibitors but not those taking DPP-4 inhibitors or GLP-1RAs. 95% CI, 95% confidence interval; OR, odds ratio.

In the network meta-analysis (Figure 3), SGLT2 inhibitors were significantly associated with a lower risk of AKI than placebo (OR, 0.76; 95% CI, 0.66 to 0.88). Moreover, SGLT2 inhibitors were significantly associated with a lower risk of AKI than both GLP-1RAs (OR, 0.79; 95% CI, 0.65 to 0.97) and DPP-4 inhibitors (OR, 0.68; 95% CI, 0.54 to 0.86). Among these three novel glucose-lowering drugs, the results from analysis with surface under the cumulative ranking curve indicated that there was an 83.5 probability of SGLT2 inhibitors being the safest intervention for risk of AKI, followed by GLP-1RAs (1%) and DPP-4 inhibitors (0%) (Supplemental Figure 4).

Figure 3.
Figure 3.

Forest plot of network meta-analysis showing a lower risk of AKI associated with SGLT2 inhibitors than placebo, DPP-4 inhibitors, or GLP-1RAs in patients with type 2 diabetes.

The results did not change in the secondary analysis including 20 trials involving patients with or without type 2 diabetes (see Supplemental Figure 5 for pairwise meta-analysis and Supplemental Figure 6 for network meta-analysis).

Discussion

In this study, we found a distinct difference in the influences of new classes of glucose-lowering drugs on the risk of AKI among patients with type 2 diabetes. SGLT2 inhibitors showed a protective effect on the risk of AKI, whereas both DPP-4 inhibitors and GLP-1RAs had neutral effects. Our network meta-analysis indicated that SGLT2 inhibitors were associated with a lower risk of AKI than both DPP-4 inhibitors and GLP-1RAs. Moreover, similar results were detected among patients with and without type 2 diabetes.

In view of a potential increase in risk of AKI raised by the FDA, it is interesting to find that SGLT2 inhibitors have a protection against AKI. These findings are consistent with previous results from meta-analysis and observational studies (3,34⇓–36). One meta-analysis of randomized trials showed that SGLT2 inhibitors were associated with a lower risk of both serious AKI events (OR, 0.64; 95% CI, 0.53 to 0.78; n=30 trials) and AKI events of any severity (OR, 0.75; 95% CI, 0.66 to 0.84; n=41 trials), and there was no difference between empagliflozin, dapagliflozin, and canagliflozin (3). Moreover, a lower risk of AKI associated with SGLT2 inhibitors was observed by meta-analysis of the observational studies (OR, 0.40; 95% CI, 0.33 to 0.48) (3). To date, the precise mechanism by which SGLT2 inhibitors provide AKI prevention remains unclear. A plausible mechanism includes reductions in intraglomerular pressure by enhancing afferent arteriolar vasoconstriction, which likely contribute to long-term renoprotection (37). Other factors may explain the beneficial effect on AKI. Principal among these is the effect of SGLT2 inhibition on kidney oxygen consumption (38). Oxygen requirements are known to increase in diabetic kidney disease, increasing susceptibility to hypoxic kidney injury (39). Sodium reabsorption on the basolateral aspect of the proximal tubule is an energy-dependent process via the N/K/ATPase transporter. Reductions in SGLT2-mediated sodium reabsorption may reduce energy expenditure in the proximal tubule, thus reducing susceptibility to ischemic-related insults (40). Additionally, direct effects, including reducing kidney inflammatory reactions and restoring the mode of cellular energy metabolism, were also suggested (40). However, these findings are limited to experimental models, and further studies are needed to explore their underlying mechanisms and how to apply these findings in clinical practice.

Although a series of case reports showed an association between GLP-1RA use and AKI risk (6), there is growing evidence indicating that GLP-1RAs could slow the progression of diabetic kidney disease (5,41). However, in this meta-analysis, we found no association between GLP-1RAs and risk of AKI. It should be noted that none of the included trials assessed AKI as a primary end point for GLP-1RAs; one ongoing kidney outcome, placebo-controlled trial of semaglutide (A Research Study to See How Semaglutide Works Compared to Placebo in People With Type 2 Diabetes and Chronic Kidney Disease; ClinicalTrials.gov identifier: NCT03819153) will provide meaningful information to address this issue (42).

The risk of AKI associated with DPP-4 inhibitors is inconsistent (7,8). One nested case-control study using Taiwan National Health Insurance Research Database showed that DPP-4 inhibitors were associated with an increased risk of AKI development as compared with nonusers (7), while another cohort study using the same database found a reduction in mild and severe forms of AKI among patients with diabetes who were taking DPP-4 inhibitors (8). The conflicting results might be caused by the confounders within these retrospective and observational studies. On the basis of the data from clinical trials, we did not find an association between DPP-4 inhibitors and risk of AKI. Further investigation is needed to confirm our findings regarding the risk of AKI associated with DPP-4 inhibitors.

To our knowledge, this is the first study evaluating the comparative effects of DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors on risk of AKI in patients with type 2 diabetes. Compared with previous meta-analyses that evaluated the AKI risk among patients taking SGLT2 inhibitors only (3,34,36), the advantages of this study included the use of a network meta-analysis to estimate their effects between SGLT2 inhibitors and DPP-4 inhibitors, and between SGLT2 inhibitors and GLP-1RAs, in the absence of direct head-to-head trials. Moreover, this network meta-analysis provides the most substantial evidence regarding the association between novel glucose-lowering drugs and the risk of AKI. However, we also acknowledge several limitations in this study. First, AKI was not reported as a primary outcome in these included trials, and only 11 trials reported these results in published articles. The AKI events were more likely to be under-reported. The range of AKI risk among included trials was from 1 in 14,752 to 184 in 4401, which might result in differences in the ORs of each trial (e.g., the OR within DPP-4 inhibitors class ranges from 0.94 to 2.00), despite the fact that no across-study statistical heterogeneity was observed in the meta-analysis. Moreover, the AKI event was confirmed by the event adjudication committee in only three trials. Therefore, the results should be interpreted with caution. Second, in many of the included trials, AKI events were investigator reported, and thus, there is likely some variability in the definitions, which may lead to clinical heterogeneity in this meta-analysis. Additionally, more granular data on AKI severity are probably not reported in most trials, and thus a secondary analysis is not permitted. Third, this study included only patients with established or at risk of cardiovascular disease or CKD, limiting its generalizability to other patient populations without these risks. Finally, there is no closed loop in the network, which precluded the evaluation of inconsistency. However, our results were reliable because the results from the network meta-analysis were consistent with those from the pairwise meta-analysis.

In summary, current evidence shows that SGLT2 inhibitors may decrease the risk of AKI, whereas no association is observed for either DPP-4 inhibitors or GLP-1RAs. Moreover, SGLT2 inhibitors are the best choice for reducing AKI risk. These findings enable health care clinicians to select the best glucose-lowering drug for patients with a high risk of AKI.

Disclosures

S. Sun reports receiving honoraria from Connecticut Pharmacists’ Association and serving as a scientific advisor or member of BioMed Research International and International Journal of Clinical Pharmacy. All remaining authors have nothing to disclose.

Funding

None.

Supplemental Material

This article contains the following supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.11220720/-/DCSupplemental.

Supplemental Table 1. Search strategy.

Supplemental Table 2. Terms used to identify the patients with AKI, on the basis of the Medical Dictionary for Regulatory Activities (MedDRA).

Supplemental Table 3. Baseline information and difference between groups in change from baseline to the last follow-up.

Supplemental Table 4. The data source and definition of the AKI.

Supplemental Figure 1. The flow chart of study selection.

Supplemental Figure 2. Risk of bias assessments for each study on the basis of adjusted Cochrane risk of bias tool.

Supplemental Figure 3. The publication bias assessment, using funnel plot, in patients with type 2 diabetes.

Supplemental Figure 4. Surface under the cumulative ranking curve analysis for AKI in patients with type 2 diabetes.

Supplemental Figure 5. Pairwise meta-analysis of the effect of novel glucose-lowering drugs on the risk of AKI in patients with or without type 2 diabetes.

Supplemental Figure 6. Network meta-analysis of the effects of DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors on risk of AKI in patients with or without type 2 diabetes.

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

  • See related editorial, “Forever Starts Now: Effects of Glucose-Lowering Therapies on Acute Kidney Injury,” on pages 6–8.

  • Received July 9, 2020.
  • Accepted November 17, 2020.

References

  1. ↵
    1. Monseu M,
    2. Gand E,
    3. Saulnier PJ,
    4. Ragot S,
    5. Piguel X,
    6. Zaoui P,
    7. Rigalleau V,
    8. Marechaud R,
    9. Roussel R,
    10. Hadjadj S,
    11. Halimi JM
    ; SURDIAGENE Study Group: Acute kidney injury predicts major adverse outcomes in diabetes: Synergic impact with low glomerular filtration rate and albuminuria. Diabetes Care 38: 2333–2340, 2015pmid:26512039
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. US Food and Drug Administration (FDA)
    : FDA Drug Safety Communication: FDA Strengthens Kidney Warnings for Diabetes Medicines Canagliflozin (Invokana, Invokamet) and Dapagliflozin, (farxiga, xigduo XR), 2016. Available at: https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-fda-strengthens-kidney-warnings-diabetes-medicines-canagliflozin. Accessed May 10, 2020
  3. ↵
    1. Menne J,
    2. Dumann E,
    3. Haller H,
    4. Schmidt BMW
    : Acute kidney injury and adverse renal events in patients receiving SGLT2-inhibitors: A systematic review and meta-analysis. PLoS Med 16: e1002983, 2019pmid:31815931
    OpenUrlPubMed
  4. ↵
    1. Zhang X,
    2. Zhong Z,
    3. Li Y,
    4. Li W
    : Long-term renal outcomes associated with sodium glucose cotransporter 2 inhibitors in patients with type 2 diabetes mellitus: A systematic review and meta-analysis. Diabetes Metab Res Rev 36: e3303, 2020pmid:32134558
    OpenUrlPubMed
  5. ↵
    1. Kristensen SL,
    2. Rørth R,
    3. Jhund PS,
    4. Docherty KF,
    5. Sattar N,
    6. Preiss D,
    7. Køber L,
    8. Petrie MC,
    9. McMurray JJV
    : Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: A systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol 7: 776–785, 2019pmid:31422062
    OpenUrlPubMed
  6. ↵
    1. Dubois-Laforgue D,
    2. Boutboul D,
    3. Lévy DJ,
    4. Joly D,
    5. Timsit J
    : Severe acute renal failure in patients treated with glucagon-like peptide-1 receptor agonists. Diabetes Res Clin Pract 103: e53–e55, 2014pmid:24447807
    OpenUrlPubMed
  7. ↵
    1. Shih CJ,
    2. Lee YJ,
    3. Lo YH,
    4. Kuo SC,
    5. Ou SM,
    6. Chen YT
    : Association between use of dipeptidyl peptidase-4 inhibitors and the risk of acute kidney injury: A nested case-control study. Mayo Clin Proc 91: 867–872, 2016pmid:27236426
    OpenUrlPubMed
  8. ↵
    1. Chao CT,
    2. Wang J,
    3. Wu HY,
    4. Chien KL,
    5. Hung KY
    : Dipeptidyl peptidase 4 inhibitor use is associated with a lower risk of incident acute kidney injury in patients with diabetes. Oncotarget 8: 53028–53040, 2017pmid:28881791
    OpenUrlCrossRefPubMed
  9. ↵
    1. Li T,
    2. Puhan MA,
    3. Vedula SS,
    4. Singh S,
    5. Dickersin K
    ; Ad Hoc Network Meta-analysis Methods Meeting Working Group: Network meta-analysis-highly attractive but more methodological research is needed. BMC Med 9: 79, 2011pmid:21707969
    OpenUrlCrossRefPubMed
  10. ↵
    1. Liberati A,
    2. Altman DG,
    3. Tetzlaff J,
    4. Mulrow C,
    5. Gøtzsche PC,
    6. Ioannidis JP,
    7. Clarke M,
    8. Devereaux PJ,
    9. Kleijnen J,
    10. Moher D
    : The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 339: b2700, 2009pmid:19622552
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Higgins JP,
    2. Altman DG,
    3. Gøtzsche PC,
    4. Jüni P,
    5. Moher D,
    6. Oxman AD,
    7. Savovic J,
    8. Schulz KF,
    9. Weeks L,
    10. Sterne JA
    ; Cochrane Bias Methods Group; Cochrane Statistical Methods Group: The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343: d5928, 2011pmid:22008217
    OpenUrlFREE Full Text
  12. ↵
    1. Lin L,
    2. Chu H
    : Quantifying publication bias in meta-analysis. Biometrics 74: 785–794, 2018pmid:29141096
    OpenUrlCrossRefPubMed
  13. ↵
    1. Chaimani A,
    2. Higgins JP,
    3. Mavridis D,
    4. Spyridonos P,
    5. Salanti G
    : Graphical tools for network meta-analysis in STATA. PLoS One 8: e76654, 2013pmid:24098547
    OpenUrlCrossRefPubMed
  14. ↵
    1. Scirica BM,
    2. Bhatt DL,
    3. Braunwald E,
    4. Steg PG,
    5. Davidson J,
    6. Hirshberg B,
    7. Ohman P,
    8. Frederich R,
    9. Wiviott SD,
    10. Hoffman EB,
    11. Cavender MA,
    12. Udell JA,
    13. Desai NR,
    14. Mosenzon O,
    15. McGuire DK,
    16. Ray KK,
    17. Leiter LA,
    18. Raz I
    ; SAVOR-TIMI 53 Steering Committee and Investigators: Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus. N Engl J Med 369: 1317–1326, 2013pmid:23992601
    OpenUrlCrossRefPubMed
  15. ↵
    1. White WB,
    2. Cannon CP,
    3. Heller SR,
    4. Nissen SE,
    5. Bergenstal RM,
    6. Bakris GL,
    7. Perez AT,
    8. Fleck PR,
    9. Mehta CR,
    10. Kupfer S,
    11. Wilson C,
    12. Cushman WC,
    13. Zannad F
    ; EXAMINE Investigators: Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N Engl J Med 369: 1327–1335, 2013pmid:23992602
    OpenUrlCrossRefPubMed
  16. ↵
    1. Green JB,
    2. Bethel MA,
    3. Armstrong PW,
    4. Buse JB,
    5. Engel SS,
    6. Garg J,
    7. Josse R,
    8. Kaufman KD,
    9. Koglin J,
    10. Korn S,
    11. Lachin JM,
    12. McGuire DK,
    13. Pencina MJ,
    14. Standl E,
    15. Stein PP,
    16. Suryawanshi S,
    17. Van de Werf F,
    18. Peterson ED,
    19. Holman RR
    ; TECOS Study Group: Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes [published correction appears in N Engl J Med 373: 586, 2015 10.1056/NEJMx150029]. N Engl J Med 373: 232–242, 2015pmid:26052984
    OpenUrlCrossRefPubMed
  17. ↵
    1. Gantz I,
    2. Chen M,
    3. Suryawanshi S,
    4. Ntabadde C,
    5. Shah S,
    6. O’Neill EA,
    7. Engel SS,
    8. Kaufman KD,
    9. Lai E
    : A randomized, placebo-controlled study of the cardiovascular safety of the once-weekly DPP-4 inhibitor omarigliptin in patients with type 2 diabetes mellitus. Cardiovasc Diabetol 16: 112, 2017pmid:28893244
    OpenUrlCrossRefPubMed
  18. ↵
    1. Rosenstock J,
    2. Perkovic V,
    3. Johansen OE,
    4. Cooper ME,
    5. Kahn SE,
    6. Marx N,
    7. Alexander JH,
    8. Pencina M,
    9. Toto RD,
    10. Wanner C,
    11. Zinman B,
    12. Woerle HJ,
    13. Baanstra D,
    14. Pfarr E,
    15. Schnaidt S,
    16. Meinicke T,
    17. George JT,
    18. von Eynatten M,
    19. McGuire DK
    ; CARMELINA Investigators: Effect of linagliptin vs placebo on major cardiovascular events in adults with type 2 diabetes and high cardiovascular and renal risk: The CARMELINA randomized clinical trial. JAMA 321: 69–79, 2019pmid:30418475
    OpenUrlCrossRefPubMed
  19. ↵
    1. Rosenstock J,
    2. Kahn SE,
    3. Johansen OE,
    4. Zinman B,
    5. Espeland MA,
    6. Woerle HJ,
    7. Pfarr E,
    8. Keller A,
    9. Mattheus M,
    10. Baanstra D,
    11. Meinicke T,
    12. George JT,
    13. von Eynatten M,
    14. McGuire DK,
    15. Marx N
    ; CAROLINA Investigators: Effect of linagliptin vs glimepiride on major adverse cardiovascular outcomes in patients with type 2 diabetes: The CAROLINA randomized clinical trial [published correction appears in JAMA 322: 2138, 2019 10.1001/jama.2019.19012]. JAMA 322: 1155–1166, 2019pmid:31536101
    OpenUrlPubMed
  20. ↵
    1. Pfeffer MA,
    2. Claggett B,
    3. Diaz R,
    4. Dickstein K,
    5. Gerstein HC,
    6. Køber LV,
    7. Lawson FC,
    8. Ping L,
    9. Wei X,
    10. Lewis EF,
    11. Maggioni AP,
    12. McMurray JJ,
    13. Probstfield JL,
    14. Riddle MC,
    15. Solomon SD,
    16. Tardif JC
    ; ELIXA Investigators: Lixisenatide in patients with type 2 diabetes and acute coronary syndrome. N Engl J Med 373: 2247–2257, 2015pmid:26630143
    OpenUrlCrossRefPubMed
  21. ↵
    1. Marso SP,
    2. Daniels GH,
    3. Brown-Frandsen K,
    4. Kristensen P,
    5. Mann JF,
    6. Nauck MA,
    7. Nissen SE,
    8. Pocock S,
    9. Poulter NR,
    10. Ravn LS,
    11. Steinberg WM,
    12. Stockner M,
    13. Zinman B,
    14. Bergenstal RM,
    15. Buse JB
    ; LEADER Steering Committee; LEADER Trial Investigators: Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med 375: 311–322, 2016pmid:27295427
    OpenUrlCrossRefPubMed
  22. ↵
    1. Marso SP,
    2. Bain SC,
    3. Consoli A,
    4. Eliaschewitz FG,
    5. Jódar E,
    6. Leiter LA,
    7. Lingvay I,
    8. Rosenstock J,
    9. Seufert J,
    10. Warren ML,
    11. Woo V,
    12. Hansen O,
    13. Holst AG,
    14. Pettersson J,
    15. Vilsbøll T
    ; SUSTAIN-6 Investigators: Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 375: 1834–1844, 2016pmid:27633186
    OpenUrlCrossRefPubMed
  23. ↵
    1. Holman RR,
    2. Bethel MA,
    3. Mentz RJ,
    4. Thompson VP,
    5. Lokhnygina Y,
    6. Buse JB,
    7. Chan JC,
    8. Choi J,
    9. Gustavson SM,
    10. Iqbal N,
    11. Maggioni AP,
    12. Marso SP,
    13. Öhman P,
    14. Pagidipati NJ,
    15. Poulter N,
    16. Ramachandran A,
    17. Zinman B,
    18. Hernandez AF
    ; EXSCEL Study Group: Effects of once-weekly exenatide on cardiovascular outcomes in type 2 diabetes. N Engl J Med 377: 1228–1239, 2017pmid:28910237
    OpenUrlCrossRefPubMed
  24. ↵
    1. Hernandez AF,
    2. Green JB,
    3. Janmohamed S,
    4. D’Agostino RB Sr.,
    5. Granger CB,
    6. Jones NP,
    7. Leiter LA,
    8. Rosenberg AE,
    9. Sigmon KN,
    10. Somerville MC,
    11. Thorpe KM,
    12. McMurray JJV,
    13. Del Prato S
    ; Harmony Outcomes Committees and Investigators: Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): A double-blind, randomised placebo-controlled trial. Lancet 392: 1519–1529, 2018pmid:30291013
    OpenUrlCrossRefPubMed
  25. ↵
    1. Gerstein HC,
    2. Colhoun HM,
    3. Dagenais GR,
    4. Diaz R,
    5. Lakshmanan M,
    6. Pais P,
    7. Probstfield J,
    8. Riesmeyer JS,
    9. Riddle MC,
    10. Rydén L,
    11. Xavier D,
    12. Atisso CM,
    13. Dyal L,
    14. Hall S,
    15. Rao-Melacini P,
    16. Wong G,
    17. Avezum A,
    18. Basile J,
    19. Chung N,
    20. Conget I,
    21. Cushman WC,
    22. Franek E,
    23. Hancu N,
    24. Hanefeld M,
    25. Holt S,
    26. Jansky P,
    27. Keltai M,
    28. Lanas F,
    29. Leiter LA,
    30. Lopez-Jaramillo P,
    31. Cardona Munoz EG,
    32. Pirags V,
    33. Pogosova N,
    34. Raubenheimer PJ,
    35. Shaw JE,
    36. Sheu WH,
    37. Temelkova-Kurktschiev T
    ; REWIND Investigators: Dulaglutide and cardiovascular outcomes in type 2 diabetes (REWIND): A double-blind, randomised placebo-controlled trial. Lancet 394: 121–130, 2019pmid:31189511
    OpenUrlCrossRefPubMed
  26. ↵
    1. Husain M,
    2. Birkenfeld AL,
    3. Donsmark M,
    4. Dungan K,
    5. Eliaschewitz FG,
    6. Franco DR,
    7. Jeppesen OK,
    8. Lingvay I,
    9. Mosenzon O,
    10. Pedersen SD,
    11. Tack CJ,
    12. Thomsen M,
    13. Vilsbøll T,
    14. Warren ML,
    15. Bain SC
    ; PIONEER 6 Investigators: Oral semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 381: 841–851, 2019pmid:31185157
    OpenUrlCrossRefPubMed
  27. ↵
    1. Zinman B,
    2. Wanner C,
    3. Lachin JM,
    4. Fitchett D,
    5. Bluhmki E,
    6. Hantel S,
    7. Mattheus M,
    8. Devins T,
    9. Johansen OE,
    10. Woerle HJ,
    11. Broedl UC,
    12. Inzucchi SE
    ; EMPA-REG OUTCOME Investigators: Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med 373: 2117–2128, 2015pmid:26378978
    OpenUrlCrossRefPubMed
  28. ↵
    1. Neal B,
    2. Perkovic V,
    3. Mahaffey KW,
    4. de Zeeuw D,
    5. Fulcher G,
    6. Erondu N,
    7. Shaw W,
    8. Law G,
    9. Desai M,
    10. Matthews DR
    ; CANVAS Program Collaborative Group: Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med 377: 644–657, 2017pmid:28605608
    OpenUrlCrossRefPubMed
  29. ↵
    1. Wiviott SD,
    2. Raz I,
    3. Bonaca MP,
    4. Mosenzon O,
    5. Kato ET,
    6. Cahn A,
    7. Silverman MG,
    8. Zelniker TA,
    9. Kuder JF,
    10. Murphy SA,
    11. Bhatt DL,
    12. Leiter LA,
    13. McGuire DK,
    14. Wilding JPH,
    15. Ruff CT,
    16. Gause-Nilsson IAM,
    17. Fredriksson M,
    18. Johansson PA,
    19. Langkilde AM,
    20. Sabatine MS
    ; DECLARE–TIMI 58 Investigators: Dapagliflozin and cardiovascular outcomes in type 2 diabetes. N Engl J Med 380: 347–357, 2019pmid:30415602
    OpenUrlCrossRefPubMed
  30. ↵
    1. Perkovic V,
    2. Jardine MJ,
    3. Neal B,
    4. Bompoint S,
    5. Heerspink HJL,
    6. Charytan DM,
    7. Edwards R,
    8. Agarwal R,
    9. Bakris G,
    10. Bull S,
    11. Cannon CP,
    12. Capuano G,
    13. Chu PL,
    14. de Zeeuw D,
    15. Greene T,
    16. Levin A,
    17. Pollock C,
    18. Wheeler DC,
    19. Yavin Y,
    20. Zhang H,
    21. Zinman B,
    22. Meininger G,
    23. Brenner BM,
    24. Mahaffey KW
    ; CREDENCE Trial Investigators: Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med 380: 2295–2306, 2019pmid:30990260
    OpenUrlCrossRefPubMed
  31. ↵
    1. Cannon CP,
    2. Pratley R,
    3. Dagogo-Jack S,
    4. Mancuso J,
    5. Huyck S,
    6. Masiukiewicz U,
    7. Charbonnel B,
    8. Frederich R,
    9. Gallo S,
    10. Cosentino F,
    11. Shih WJ,
    12. Gantz I,
    13. Terra SG,
    14. Cherney DZI,
    15. McGuire DK
    ; VERTIS CV Investigators: Cardiovascular outcomes with ertugliflozin in type 2 diabetes. N Engl J Med 383: 1425–1435, 2020pmid:32966714
    OpenUrlCrossRefPubMed
  32. ↵
    1. McMurray JJV,
    2. Solomon SD,
    3. Inzucchi SE,
    4. Køber L,
    5. Kosiborod MN,
    6. Martinez FA,
    7. Ponikowski P,
    8. Sabatine MS,
    9. Anand IS,
    10. Bělohlávek J,
    11. Böhm M,
    12. Chiang CE,
    13. Chopra VK,
    14. de Boer RA,
    15. Desai AS,
    16. Diez M,
    17. Drozdz J,
    18. Dukát A,
    19. Ge J,
    20. Howlett JG,
    21. Katova T,
    22. Kitakaze M,
    23. Ljungman CEA,
    24. Merkely B,
    25. Nicolau JC,
    26. O’Meara E,
    27. Petrie MC,
    28. Vinh PN,
    29. Schou M,
    30. Tereshchenko S,
    31. Verma S,
    32. Held C,
    33. DeMets DL,
    34. Docherty KF,
    35. Jhund PS,
    36. Bengtsson O,
    37. Sjöstrand M,
    38. Langkilde AM
    ; DAPA-HF Trial Committees and Investigators: Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med 381: 1995–2008, 2019pmid:31535829
    OpenUrlCrossRefPubMed
  33. ↵
    1. Heerspink HJL,
    2. Stefánsson BV,
    3. Correa-Rotter R,
    4. Chertow GM,
    5. Greene T,
    6. Hou FF,
    7. Mann JFE,
    8. McMurray JJV,
    9. Lindberg M,
    10. Rossing P,
    11. Sjöström CD,
    12. Toto RD,
    13. Langkilde AM,
    14. Wheeler DC
    ; DAPA-CKD Trial Committees and Investigators: Dapagliflozin in patients with chronic kidney disease. N Engl J Med 383: 1436–1446, 2020pmid:32970396
    OpenUrlCrossRefPubMed
  34. ↵
    1. Gilbert RE,
    2. Thorpe KE
    : Acute kidney injury with sodium-glucose co-transporter-2 inhibitors: A meta-analysis of cardiovascular outcome trials. Diabetes Obes Metab 21: 1996–2000, 2019pmid:31050116
    OpenUrlPubMed
  35. ↵
    1. Cahn A,
    2. Melzer-Cohen C,
    3. Pollack R,
    4. Chodick G,
    5. Shalev V
    : Acute renal outcomes with sodium-glucose co-transporter-2 inhibitors: Real-world data analysis. Diabetes Obes Metab 21: 340–348, 2019pmid:30207040
    OpenUrlCrossRefPubMed
  36. ↵
    1. Neuen BL,
    2. Young T,
    3. Heerspink HJL,
    4. Neal B,
    5. Perkovic V,
    6. Billot L,
    7. Mahaffey KW,
    8. Charytan DM,
    9. Wheeler DC,
    10. Arnott C,
    11. Bompoint S,
    12. Levin A,
    13. Jardine MJ
    : SGLT2 inhibitors for the prevention of kidney failure in patients with type 2 diabetes: A systematic review and meta-analysis. Lancet Diabetes Endocrinol 7: 845–854, 2019pmid:31495651
    OpenUrlCrossRefPubMed
  37. ↵
    1. Cherney DZ,
    2. Perkins BA,
    3. Soleymanlou N,
    4. Maione M,
    5. Lai V,
    6. Lee A,
    7. Fagan NM,
    8. Woerle HJ,
    9. Johansen OE,
    10. Broedl UC,
    11. von Eynatten M
    : Renal hemodynamic effect of sodium-glucose cotransporter 2 inhibition in patients with type 1 diabetes mellitus. Circulation 129: 587–597, 2014pmid:24334175
    OpenUrlAbstract/FREE Full Text
  38. ↵
    1. van Raalte DH,
    2. Cherney DZI
    : Sodium glucose cotransporter 2 inhibition and renal ischemia: Implications for future clinical trials. Kidney Int 94: 459–462, 2018pmid:30143067
    OpenUrlPubMed
  39. ↵
    1. Singh P,
    2. Ricksten SE,
    3. Bragadottir G,
    4. Redfors B,
    5. Nordquist L
    : Renal oxygenation and haemodynamics in acute kidney injury and chronic kidney disease. Clin Exp Pharmacol Physiol 40: 138–147, 2013pmid:23360244
    OpenUrlCrossRefPubMed
  40. ↵
    1. Ni L,
    2. Yuan C,
    3. Chen G,
    4. Zhang C,
    5. Wu X
    : SGLT2i: Beyond the glucose-lowering effect. Cardiovasc Diabetol 19: 98, 2020pmid:32590982
    OpenUrlPubMed
  41. ↵
    1. Pasternak B,
    2. Wintzell V,
    3. Eliasson B,
    4. Svensson AM,
    5. Franzén S,
    6. Gudbjörnsdottir S,
    7. Hveem K,
    8. Jonasson C,
    9. Melbye M,
    10. Svanström H,
    11. Ueda P
    : Use of glucagon-like peptide 1 receptor agonists and risk of serious renal events: Scandinavian cohort study. Diabetes Care 43: 1326–1335, 2020pmid:32295809
    OpenUrlAbstract/FREE Full Text
  42. ↵
    1. US National Library of Medicine
    : ClinicalTrials.gov: A Research Study to See How Semaglutide Works Compared to Placebo in People with Type 2 Diabetes and Chronic Kidney Disease (FLOW). 2019. Available at: https://clinicaltrials.gov/ct2/show/NCT03819153. Accessed May 28, 2020
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Network Meta-Analysis of Novel Glucose-Lowering Drugs on Risk of Acute Kidney Injury
Min Zhao, Shusen Sun, Zhenguang Huang, Tiansheng Wang, Huilin Tang
CJASN Jan 2021, 16 (1) 70-78; DOI: 10.2215/CJN.11220720
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