Indirect comparison of finerenone and SGLT 2 inhibitors in established chronic kidney disease: evidence based on Bayesian methods

Indirect comparison of finerenone and SGLT 2 inhibitors in established chronic kidney disease: evidence based on Bayesian methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Indirect comparison of finerenone and SGLT 2 inhibitors in established chronic kidney disease: evidence based on Bayesian methods Hai-Bin Chen, Dong-Yi Li, Rong-Sen Meng, Yao-Lin Yang, Tian-Hao Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4131335/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Head-to-head comparison of finerenone and SGLT2 inhibitors in patients with established chronic kidney disease (CKD) remains absent. Methods and results All dedicated CKD trials about finerenone versus placebo or SGLT2 inhibitors versus placebo were searched. A Bayesian approach to network meta-analysis was applied. In patients with CKD, no significant difference in the composite of renal outcomes (OR 1.14, 95% CI 0.92–1.88), the composite of cardiovascular death or hospitalization for heart failure (OR 0.94, 95% CI 0.58–1.56), all-cause mortality (OR 1.04, 95% CI 0.78–1.43), and cardiovascular death (OR 0.99, 95% CI 0.73–1.35) was observed between finerenone and SGLT2 inhibitors. In patients with type 2 diabetes and CKD, no significant difference in the composite of renal outcomes (OR 0.97, 95% CI 0.50–1.69), the composite of cardiovascular death or hospitalization for heart failure (OR 0.86, 95% CI 0.48–1.62), all-cause mortality (OR 0.97, 95% CI 0.74–1.28), and cardiovascular death (OR 0.95, 95% CI 0.65–1.38) was observed between finerenone and SGLT2 inhibitors. We ranked the risk of the major outcomes in patients with CKD. As a result, dapagliflozin was identified as having the lowest risk of renal outcomes and all-cause mortality, while canagliflozin was identified as having the lowest risk of cardiovascular outcomes. Conclusions In patients with CKD, there was no significant difference in the major outcomes between finerenone and SGLT2 inhibitors; however, dapagliflozin and canagliflozin may be associated with the lowest risk of the major outcomes. SGLT2 inhibitors finerenone chronic kidney disease cardiovascular death renal events indirect comparison Figures Figure 1 Figure 2 Figure 3 Introduction Evidence supports the association of mineralocorticoid receptor overactivation with cardiovascular and renal disease 1 . Finerenone as a selective nonsteroidal mineralocorticoid receptor antagonist has proven cardioprotective and nephroprotective effects in patients with type 2 diabetes and chronic kidney disease (CKD) by anti-inflammatory and anti-fibrotic effects 2 – 4 . The FIDELIO-DKD trial 3 , which investigated finerenone in 5734 patients with type 2 diabetes and CKD, reported that finerenone did result in a lower risk of cardiovascular events and kidney function progression. The FIGARO-DKD trial 2 also reported that treatment with finerenone can improve cardiovascular outcomes compared with placebo in patients with type 2 diabetes and CKD. Sodium-glucose cotransporter-2 (SGLT2) inhibitors as hypoglycemic drugs have also showed remarkable benefits on cardiovascular deaths and kidney function progression in previous large cardiovascular outcomes trials 5 – 9 . The DAPA-CKD trial 10 as a large dedicated CKD showed that dapagliflozin was associated with lower risks of the composite of renal outcomes in patients with established CKD, regardless of patients with or without diabetes. A previous meta-analysis of three dedicated CKD trials also reported that SGLT2 inhibitors can reduce a composite of cardiorenal outcome by 27.5% 11 . As far as we know, there are no head-to-head trials comparing finerenone with SGLT2 inhibitors in patients with CKD. Although a previous study 12 tried to evaluate the effect of SGLT2 inhibitors versus finerenone by network meta-analysis based on the restricted maximum likelihood method, it was under-powered for the assessment of efficacy due to pooling several post-hoc analyses of CKD strata within trials that were not focusing on CKD. To assess indirectly the efficacy of finerenone versus SGLT2 inhibitors based on existing evidence in dedicated CKD trials, we performed a network meta-analysis of dedicated CKD trials by Bayesian methods. Methods Search strategy and selection criteria We followed the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) extension statement 13 for this study. Pubmed and EMBASE were searched from inception to November 1, 2023. All relevant phase III randomized controlled trials about finerenone versus placebo or SGLT2 inhibitors versus placebo in patients with CKD were searched for inclusion. Non-randomized, post-hoc analyses of CKD strata within trials that were not focusing on CKD, and observational trials were excluded. Two researchers (HB.C and RS.M) independently selected the study and assessed the eligibility of studies, with conflicts resolved by consensus. Data analysis Two researchers (HB.C and DY.L) independently performed data extraction for aggregated study-level data, with conflicts resolved by consensus. The major efficacy outcomes of interest were the composite of renal outcomes (including worsening estimated glomerular filtration rate, end-stage kidney disease, or renal death), the composite of cardiovascular death or hospitalization for heart failure, all-cause mortality, and cardiovascular death. We accepted the original trial definitions for all outcomes. The risk of bias in the included trials was assessed by the Cochrane Collaboration’s recommended tool 14 . Publication bias was not estimated due to the small number of studies included in this analysis. Statistical analysis Pooled effects were presented as odds ratio (OR) with corresponding 95% confidence intervals (CI). The heterogeneity test between studies was assessed by the Q test and I 2 statistic. We performed an indirect comparison of finerenone and SGLT2 inhibitors by Bayesian network meta-analysis. We performed statistical analysis by GeMTC package (version 1.0–1) in R4.1.3 (R Language and Environment for Statistical Computing) based on Markov chain Monte Carlo methods and sampling for four chains. We used a random effects model, with an initial burn-in period of 20,000 iterations, and then a posterior summary was generated based on a further 50,000 iterations. We could not evaluate the consistency by inconsistency factor due to the lack of non-direct comparisons between finerenone and SGLT2 inhibitors. If the results of comparisons suggested no significance, the ranking probability would be used to represent the likelihood. Results Six articles 2 , 3 , 10 , 15 – 17 were finally included in our study, involving two trials for finerenone and four trials for SGLT2 inhibitors. Only the DAPA-CKA and EMPA-KIDNEY trials focused on patients with CKD with or without type 2 diabetes, and the other four trials focused on patients with CKD and type 2 diabetes. The baseline characteristics of the patients enrolled in the six trials included in this study are shown in Table 1. All the trials were judged to be at low risk of bias via Cochrane’s Collaboration tool for risk assessment (Table S1 ). Indirect comparison of finerenone and SGLT2 inhibitors: the composite of renal outcomes The composite of renal outcomes showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 1.14, 95% CI 0.92–1.88, Fig. 1 , Table 2 ). In patients with type 2 diabetes and CKD, the composite of renal outcomes showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.97, 95% CI 0.50–1.69, Fig. 1 ). The composite of renal outcomes also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.51, 95% CI 0.71–3.17, Fig. 2 ), finerenone and canagliflozin (OR 1.27, 95% CI 0.62–2.67, Fig. 2 ), finerenone and empagliflozin (OR 1.14, 95% CI 0.55–2.40, Fig. 2 ), finerenone and sotagliflozin (OR 1.17, 95% CI 0.54–2.69, Fig. 2 ). We ranked the risk of the composite of renal outcomes in patients with CKD. As a result, dapagliflozin was identified as having the lowest risk of the composite of renal outcomes (Fig. 3 ). Table 1. Baseline characteristics of patients enrolled in the six trials FIDELIO-DKD FIGARA-DKD CREDENCE DAPA-CKD SCORED EMPA-KIDNEY Finerenone Placebo Finerenone Placebo Canagliflozin Placebo Dapagliflozin Placebo Sotagliflozin Placebo Canagliflozin Placebo Numbers, no. 2833 2841 3686 3666 2202 2199 2152 2152 5292 5292 3304 3305 Follow-up 2.6 years 3.4 years 2.62 years 2.4 years 16 months 2.0 years Mean age (years) 65.4 65.7 64.1 64.1 62.9 63.2 61.8 61.9 69.0 69.0 63.9 63.8 Female, no. (%) 880 (31.1) 811 (28.5) 1158 (31.4) 1089 (29.7) 762 (34.6) 732 (33.3) 709 (32.9) 716 (33.3) 2347 (44.3) 2407 (45.5) 1097 (33.2) 1095 (33.1) Race, no. (%) White 1777 (62.7) 1815 (63.9) 2672 (72.5) 2605 (71.1) 1487 (67.5) 144 (65.7) 1124 (52.2) 1166 (54.2) 4402 (83.2) 4347 (82.2) 1939 (58.7) 1920 (58.1) Black 140 (4.9) 124 (4.4) 113 (3.1) 145 (4.0) 112 (5.1) 112 (5.2) 104 (4.8) 87 (4.0) 176 (3.3) 188 (3.6) 128 (3.9) 134 (4.1) Asian 717 (25.3) 723 (25.4) 715 (19.4) 739 (20.2) 425 (19.3) 452 (20.6) 749 (34.8) 718 (33.4) 317 (6.0) 365 (6.9) 1194 (36.1) 1199 (36.3) Other 199 (7.0) 179 (6.3) 177 (4.8) 170 (4.6) 178 (8.1) 191 (8.7) 175 (8.1) 181 (8.4) 397 (7.5) 392 (7.3) 43 (1.3) 52 (1.6) T2DM, no. (%) 2833 (100) 2841 (100) 3686 (100) 3666 (100) 2202 (100) 2199 (100) 1455 (67.6) 1451 (67.4) 5292 (100) 5292 (100) 1525 (46.2) 1515 (45.8) Estimated GFR, mL/min/1.73m 2 44.4 44.3 67.6 68.0 56.3 56.0 43.2 43.0 44.4 44.7 37.4 37.3 Median UACR(mg/g) 833 867 302 315 923 931 965 934 74 75 331 327 Notes: GFR= glomerular filtration rate; T2DM= type 2 diabetes mellitus; UACR= urinary albumin-to-creatinine ratio; NA=not available; Table 2. OR and 95% CI for network meta-analysis of different outcomes for patients with CKD Outcomes Strategy Finerenone SGLT2 inhibitors Placebo A Finerenone 1.00 (1.00, 1.00) 1.14 (0.92, 1.88) 0.84 (0.53, 1.37) SGLT2 inhibitors 0.88 (0.50, 1.65) 1.00 (1.00, 1.00) 0.73 (0.53, 1.09) Placebo 1.19 (0.73, 1.90) 1.36 (0.92, 1.88) 1.00 (1.00, 1.00) B Finerenone 1.00 (1.00, 1.00) 0.94 (0.58, 1.56) 0.83 (0.55, 1.24) SGLT2 inhibitors 1.06 (0.64, 1.73) 1.00 (1.00, 1.00) 0.88 (0.65, 1.17) Placebo 1.21 (0.81, 1.81) 1.14 (0.85, 1.53) 1.00 (1.00, 1.00) C Finerenone 1.00 (1.00, 1.00) 1.04 (0.78, 1.43) 0.88 (0.70, 1.13) SGLT2 inhibitors 0.96 (0.70, 1.28) 1.00 (1.00, 1.00) 0.85 (0.70, 1.01) Placebo 1.13 (0.88, 1.43) 1.17 (0.99, 1.43) 1.00 (1.00, 1.00) D Finerenone 1.00 (1.00, 1.00) 0.99 (0.73, 1.35) 0.88 (0.69, 1.11) SGLT2 inhibitors 1.01 (0.74, 1.36) 1.00 (1.00, 1.00) 0.89 (0.73, 1.07) Placebo 1.14 (0.90, 1.46) 1.12 (0.93, 1.38) 1.00 (1.00, 1.00) Notes: Results are OR in the row-defining treatment compared with OR in the column-defining treatment. For efficacy, OR＜1 favor row-defining treatment. NA=not available; CKD=chronic kidney disease A: The composite of renal outcomes; B: The composite of cardiovascular dearth or hospitalization for heart failure C: All-cause mortality; D: Cardiovascular dearth; Indirect comparison of finerenone and SGLT2 inhibitors: the composite of cardiovascular death or hospitalization for heart failure The composite of cardiovascular death or hospitalization for heart failure showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.94, 95% CI 0.58–1.56, Fig. 1 , Table 2 ). In patients with type 2 diabetes and CKD, the composite of cardiovascular death or hospitalization for heart failure showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.86, 95% CI 0.48–1.62, Fig. 1 ). The composite of cardiovascular death or hospitalization for heart failure also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.15, 95% CI 0.68–2.01, Fig. 2 ), finerenone and canagliflozin (OR 1.22, 95% CI 0.72–2.03, Fig. 2 ), finerenone and empagliflozin (OR 0.97, 95% CI 0.56–1.64, Fig. 2 ), finerenone and sotagliflozin (OR 1.13, 95% CI 0.66–1.84, Fig. 2 ). We ranked the risk of the composite of cardiovascular death or hospitalization for heart failure in patients with CKD. As a result, canagliflozin was identified as having the lowest risk of the composite of cardiovascular death or hospitalization for heart failure (Fig. 3 ). Indirect comparison of finerenone and SGLT2 inhibitors: all-cause mortality All-cause mortality showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 1.04, 95% CI 0.78–1.43, Fig. 1 , Table 2 ). In patients with type 2 diabetes and CKD, all-cause mortality showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.97, 95% CI 0.74–1.28, Fig. 1 ). All-cause mortality also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.30, 95% CI 0.77–2.26, Fig. 2 ), finerenone and canagliflozin (OR 1.08, 95% CI 0.64–1.85, Fig. 2 ), finerenone and empagliflozin (OR 1.02, 95% CI 0.60–1.74, Fig. 2 ), finerenone and sotagliflozin (OR 0.89, 95% CI 0.53–1.48, Fig. 2 ). We ranked the risk of all-cause mortality in patients with CKD. As a result, dapagliflozin was identified as having the lowest risk of all-cause mortality (Fig. 3 ). Indirect comparison of finerenone and SGLT2 inhibitors: cardiovascular death Cardiovascular death showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.99, 95% CI 0.73–1.35, Fig. 1 , Table 2 ). In patients with type 2 diabetes and CKD, cardiovascular death showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.95, 95% CI 0.65–1.38, Fig. 1 ). Cardiovascular death also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.07, 95% CI 0.66–1.76, Fig. 2 ), finerenone and canagliflozin (OR 1.13, 95% CI 0.73–1.77, Fig. 2 ), finerenone and empagliflozin (OR 1.04, 95% CI 0.62–1.75, Fig. 2 ), finerenone and sotagliflozin (OR 0.96, 95% CI 0.63–1.48, Fig. 2 ). We ranked the risk of cardiovascular death in patients with CKD. As a result, canagliflozin was identified as having the lowest risk of all-cause mortality (Fig. 3 ). Qualitative assessment We assessed the risk of bias in the included trials using the Cochrane Collaboration’s recommended tool 14 . A summary of the quality assessment of each trial is shown in the table S1 . Heterogeneity test shows no evidence of heterogeneity for the composite of cardiovascular death or hospitalization for heart failure (I 2 = 0%, P = 0.50) all-cause mortality (I 2 = 19%, P = 0.29), and cardiovascular death (I 2 = 0%, P = 0.95). Heterogeneity test shows evidence of heterogeneity for the composite of renal outcomes (I 2 = 67%, P = 0.009). Discussion In this study, we sought to evaluate indirectly the efficacy of finerenone versus SGLT2 inhibitors in patients with established CKD by Bayesian network meta-analysis, which reported several findings that can be summarized as follows: in patients with CKD, there was no significant difference in the comparison between finerenone and SGLT2 inhibitors for the major efficacy outcomes of interest; in patients with type 2 diabetes and CKD, there was no significant difference in the comparison between finerenone and SGLT2 inhibitors for the major efficacy outcomes of interest; dapagliflozin may be associated with the lowest risk of the composite of renal outcomes and all-cause mortality, while canagliflozin may be associated with the lowest risk of cardiovascular outcomes by ranking probability. Finerenone and SGLT2 inhibitors are both proven to reduce cardiovascular events and kidney function progression in patients with CKD. Finerenone was a selective nonsteroidal mineralocorticoid receptor antagonist, which has anti-inflammatory and anti-fibrotic effects 4 . SGLT2 inhibitors reduce blood glucose by increasing renal glucose excretion 18 , which was developed as hypoglycemic drugs, with the exact mechanism to protect the heart and kidney may be related to sodium balance, energy homeostasis, and relief of cellular stress 19 . A previous meta-analysis 20 reported that finerenone was associated with a significant reduction in changing urinary albumin-to-creatinine ratio from baseline. The FIDELIO-DKD trial 3 as a large phase III randomized controlled trial reported that finerenone did result in a lower risk of cardiovascular events and kidney function progression in patients with type 2 diabetes and CKD. The FIGARO-DKD trial 2 also reported that finerenone can improve cardiovascular outcomes compared with a placebo in patients with type 2 diabetes and CKD. The FIDELITY trial 21 , which was an individual patient-level prespecified pooled analysis of the FIDELIO-DKD and FIGARO-DKD trials, reported that finerenone was associated with the reduced risk of cardiovascular and kidney outcomes across the spectrum of chronic kidney disease in patients with type 2 diabetes. Therefore, finerenone was approved by FDA to reduce the risk of sustained estimated glomerular filtration rate decline, end-stage renal disease, and cardiovascular outcomes in patients with CKD associated with type 2 diabetes 22 . A previous network meta-analysis 12 reported that SGLT2 inhibitors significantly reduced the risks of kidney function progression and hospitalization for heart failure compared to finerenone, which was inconsistent with our study. Perhaps it included several cardiovascular outcome trials of SGLT2 inhibitors, which were only post-hoc analyses of CKD strata within trials that were not focusing on CKD. Our study included dedicated CKD trials, demonstrating that finerenone and SGLT2 inhibitors had the similar risks of the composite of renal outcomes and the composite of cardiovascular death or hospitalization for heart failure by Bayesian network meta-analysis. Although the DAPA-CKD and EMPA-KIDNEY trials were excluded, which was focused on patients with CKD, regardless of diabetes or not, it failed to convert the results. For cardiovascular death and all-cause mortality, our results were consistent with the previous study 12 , which also reported that no significant difference between finerenone and SGLT2 inhibitors was observed. In clinical practice, both finerenone and SLT2 inhibitors can be prescribed for patients with type 2 diabetes and CKD; however, SGLT2 inhibitors seem to tend to be superior to finerenone for cardioprotective and nephroprotective effects for these populations according to our results by ranking probability. Further studies are needed to confirm due to the lack of head-to-head trials of comparisons between finerenone and SGLT2 inhibitors. Strengths of our study include the inclusion of only dedicated CKD trials and indirect comparison by Bayesian network meta-analysis based on existing evidence in dedicated CKD trials. Our study also has some limitations. Firstly, most trials were focused on patients with type 2 diabetes and CKD, only the DAPA-CKD and EMPA-KIDNEY trials focused on patients with CKD, regardless of diabetes or not. We accounted for this by sensitivity analysis by excluding these two trials. Furthermore, we evaluated outcomes using trial-level data rather than individual participant data. Finally, head-to-head comparisons of finerenone and SGLT2 inhibitors in patients with CKD remain absent and head-to-head comparisons are needed in the future. Conclusions In patients with CKD, there was no significant difference in the major outcomes between finerenone and SGLT2 inhibitors; however, dapagliflozin and canagliflozin may be associated with the lowest risk of the major outcomes. Declarations Conflict of interest statement The authors have no conflicts of interest to disclose. Ethics statement Not applicable, because this study was based on the published literature. Funding Declaration None. References Buonafine M, Bonnard B, Jaisser F. Mineralocorticoid Receptor and Cardiovascular Disease. Am J Hypertens. 2018;31(11):1165-1174. Pitt B, Filippatos G, Agarwal R, et al. Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. N Engl J Med. 2021;385(24):2252-2263. Bakris GL, Agarwal R, Anker SD, et al. Effect of Finerenone on Chronic Kidney Disease Outcomes in Type 2 Diabetes. N Engl J Med. 2020;383(23):2219-2229. Chaudhuri A, Ghanim H, Arora P. Improving the residual risk of renal and cardiovascular outcomes in diabetic kidney disease: A review of pathophysiology, mechanisms, and evidence from recent trials. Diabetes Obes Metab. 2022;24(3):365-376. Cannon CP, Pratley R, Dagogo-Jack S, et al. Cardiovascular Outcomes with Ertugliflozin in Type 2 Diabetes. N Engl J Med. 2020;383(15):1425-1435. Wiviott SD, Raz I, Bonaca MP, et al. Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2019;380(4):347-357. Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N Engl J Med. 2017;377(7):644-657. Wanner C, Inzucchi SE, Lachin JM, et al. Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes. N Engl J Med. 2016;375(4):323-334. Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. N Engl J Med. 2015;373(22):2117-2128. Heerspink HJL, Stefánsson BV, Correa-Rotter R, et al. 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Bhatt DL, Szarek M, Pitt B, et al. Sotagliflozin in Patients with Diabetes and Chronic Kidney Disease. N Engl J Med. 2021;384(2):129-139. Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. N Engl J Med e. 2019;380(24):2295-2306. Herrington WG, Staplin N, Wanner C, et al. Empagliflozin in Patients with Chronic Kidney Disease. N Engl J Med. 2023;388(2):117-127. Gallo LA, Wright EM, Vallon V. Probing SGLT2 as a therapeutic target for diabetes: basic physiology and consequences. Diab Vasc Dis Res. 2015;12(2):78-89. Griffin M, Rao VS, Ivey-Miranda J, et al. Empagliflozin in Heart Failure: Diuretic and Cardiorenal Effects. Circulation. 2020;142(11):1028-1039. Zheng Y, Ma S, Huang Q, et al. Meta-Analysis of the Efficacy and Safety of Finerenone in Diabetic Kidney Disease. Kidney Blood Press Res. 2022;47(4):219-228. Agarwal R, Filippatos G, Pitt B, et al. Cardiovascular and kidney outcomes with finerenone in patients with type 2 diabetes and chronic kidney disease: the FIDELITY pooled analysis. Eur Heart J. 2022;43(6):474-484. Administration UFD. FDA approves drug to reduce risk of serious kidney and heart complications in adults with chronic kidney disease associated with type 2 diabetes. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.Originaldata.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4131335","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287541875,"identity":"49147c23-1b05-4478-a083-7f164ccd9b25","order_by":0,"name":"Hai-Bin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACCQYGZiCZwMbAkPggoUJCjp8ULY8NPpyxMJZsIE4LQwIDA+MzyZltFYkbCGnhn9187HFBjUUen3RzgjTvPAnGDQzMDx/dwGfJnWPpxjOOSRSzyRxLMObdJsFszsBmbJyDR4uBRI6ZNA+bRGKbRE5CMlALm2UDD5s0fi3536R5/oG05H84zDtHgsfgAEEtOWzSvG0gLQmJjTMbJCQIapG4kWYmzdsH9ItEQjLDh2MSBpLNBPzCPyP5mTTPt7o8+RkJ6T8Saurq+9mbHz7GpwULYCZN+SgYBaNgFIwCLAAAFWFEhZ6VoDQAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Second Provincial General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hai-Bin","middleName":"","lastName":"Chen","suffix":""},{"id":287541876,"identity":"21060b18-1b24-45cf-8e84-eacf696c106c","order_by":1,"name":"Dong-Yi Li","email":"","orcid":"","institution":"Guangdong Second Provincial General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dong-Yi","middleName":"","lastName":"Li","suffix":""},{"id":287541877,"identity":"2622c79a-2f6a-45f1-b373-7863854d6811","order_by":2,"name":"Rong-Sen Meng","email":"","orcid":"","institution":"Guangdong Second Provincial General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rong-Sen","middleName":"","lastName":"Meng","suffix":""},{"id":287541878,"identity":"c0f7e2a0-e832-4cb6-9497-b1bc0f25dd31","order_by":3,"name":"Yao-Lin Yang","email":"","orcid":"","institution":"Guangdong Second Provincial General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yao-Lin","middleName":"","lastName":"Yang","suffix":""},{"id":287541879,"identity":"7aee70d8-1c3a-497f-8de5-7ef36212c3fc","order_by":4,"name":"Tian-Hao Yu","email":"","orcid":"","institution":"Guangdong Second Provincial General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tian-Hao","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-03-19 14:57:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4131335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4131335/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54366680,"identity":"5e8f24fa-8815-4cb0-a729-1196a05efae5","added_by":"auto","created_at":"2024-04-09 12:38:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99905,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of indirect comparison of finerenone and SGLT2 inhibitors;\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4131335/v1/3cfe72afe2e36b31919be450.png"},{"id":54366681,"identity":"cf343a19-835c-42df-b642-5a69e5590500","added_by":"auto","created_at":"2024-04-09 12:38:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154021,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of indirect comparison between finerenone and different SGLT2 inhibitors;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4131335/v1/2e67d46b9e6c2057f732190f.png"},{"id":54366679,"identity":"98e49f71-de36-4914-bb7b-3a750d75224a","added_by":"auto","created_at":"2024-04-09 12:38:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":704137,"visible":true,"origin":"","legend":"\u003cp\u003eThe plot of rank probability; A: The composite of renal outcomes; B: The composite of cardiovascular death or hospitalization for heart failure; C: All-cause mortality; D: Cardiovascular death\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4131335/v1/88ff7c69de2e339819c43129.png"},{"id":54862767,"identity":"412766c0-0c2d-4eb3-a136-754c3756cc14","added_by":"auto","created_at":"2024-04-17 20:17:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":894962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4131335/v1/7a5f8d0f-2e76-4944-9000-752c0fdef133.pdf"},{"id":54366682,"identity":"36e79326-a6f1-4212-8842-6301e83a17be","added_by":"auto","created_at":"2024-04-09 12:38:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23688,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4131335/v1/812b40b284e1701a396c7897.docx"},{"id":54366677,"identity":"fe24f46d-3b20-4037-8422-2fae83a922d2","added_by":"auto","created_at":"2024-04-09 12:38:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22646,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.Originaldata.docx","url":"https://assets-eu.researchsquare.com/files/rs-4131335/v1/148191334733d5eb499c57ec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Indirect comparison of finerenone and SGLT 2 inhibitors in established chronic kidney disease: evidence based on Bayesian methods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEvidence supports the association of mineralocorticoid receptor overactivation with cardiovascular and renal disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Finerenone as a selective nonsteroidal mineralocorticoid receptor antagonist has proven cardioprotective and nephroprotective effects in patients with type 2 diabetes and chronic kidney disease (CKD) by anti-inflammatory and anti-fibrotic effects\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The FIDELIO-DKD trial\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, which investigated finerenone in 5734 patients with type 2 diabetes and CKD, reported that finerenone did result in a lower risk of cardiovascular events and kidney function progression. The FIGARO-DKD trial\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e also reported that treatment with finerenone can improve cardiovascular outcomes compared with placebo in patients with type 2 diabetes and CKD.\u003c/p\u003e \u003cp\u003eSodium-glucose cotransporter-2 (SGLT2) inhibitors as hypoglycemic drugs have also showed remarkable benefits on cardiovascular deaths and kidney function progression in previous large cardiovascular outcomes trials\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The DAPA-CKD trial\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e as a large dedicated CKD showed that dapagliflozin was associated with lower risks of the composite of renal outcomes in patients with established CKD, regardless of patients with or without diabetes. A previous meta-analysis of three dedicated CKD trials also reported that SGLT2 inhibitors can reduce a composite of cardiorenal outcome by 27.5%\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs far as we know, there are no head-to-head trials comparing finerenone with SGLT2 inhibitors in patients with CKD. Although a previous study\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e tried to evaluate the effect of SGLT2 inhibitors versus finerenone by network meta-analysis based on the restricted maximum likelihood method, it was under-powered for the assessment of efficacy due to pooling several post-hoc analyses of CKD strata within trials that were not focusing on CKD. To assess indirectly the efficacy of finerenone versus SGLT2 inhibitors based on existing evidence in dedicated CKD trials, we performed a network meta-analysis of dedicated CKD trials by Bayesian methods.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eSearch strategy and selection criteria\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe followed the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) extension statement\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e for this study. Pubmed and EMBASE were searched from inception to November 1, 2023. All relevant phase III randomized controlled trials about finerenone versus placebo or SGLT2 inhibitors versus placebo in patients with CKD were searched for inclusion. Non-randomized, post-hoc analyses of CKD strata within trials that were not focusing on CKD, and observational trials were excluded. Two researchers (HB.C and RS.M) independently selected the study and assessed the eligibility of studies, with conflicts resolved by consensus.\u003c/p\u003e\u003cp\u003e \u003cb\u003eData analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTwo researchers (HB.C and DY.L) independently performed data extraction for aggregated study-level data, with conflicts resolved by consensus. The major efficacy outcomes of interest were the composite of renal outcomes (including worsening estimated glomerular filtration rate, end-stage kidney disease, or renal death), the composite of cardiovascular death or hospitalization for heart failure, all-cause mortality, and cardiovascular death. We accepted the original trial definitions for all outcomes. The risk of bias in the included trials was assessed by the Cochrane Collaboration’s recommended tool\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Publication bias was not estimated due to the small number of studies included in this analysis.\u003c/p\u003e\u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003ePooled effects were presented as odds ratio (OR) with corresponding 95% confidence intervals (CI). The heterogeneity test between studies was assessed by the Q test and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e statistic. We performed an indirect comparison of finerenone and SGLT2 inhibitors by Bayesian network meta-analysis. We performed statistical analysis by GeMTC package (version 1.0–1) in R4.1.3 (R Language and Environment for Statistical Computing) based on Markov chain Monte Carlo methods and sampling for four chains. We used a random effects model, with an initial burn-in period of 20,000 iterations, and then a posterior summary was generated based on a further 50,000 iterations. We could not evaluate the consistency by inconsistency factor due to the lack of non-direct comparisons between finerenone and SGLT2 inhibitors. If the results of comparisons suggested no significance, the ranking probability would be used to represent the likelihood.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSix articles\u003csup\u003e\u003cspan\u003e2\u003c/span\u003e,\u003cspan\u003e3\u003c/span\u003e,\u003cspan\u003e10\u003c/span\u003e,\u003cspan\u003e15\u003c/span\u003e\u0026ndash;\u003cspan\u003e17\u003c/span\u003e\u003c/sup\u003e were finally included in our study, involving two trials for finerenone and four trials for SGLT2 inhibitors. Only the DAPA-CKA and EMPA-KIDNEY trials focused on patients with CKD with or without type 2 diabetes, and the other four trials focused on patients with CKD and type 2 diabetes. The baseline characteristics of the patients enrolled in the six trials included in this study are shown in Table\u0026nbsp;1. All the trials were judged to be at low risk of bias via Cochrane\u0026rsquo;s Collaboration tool for risk assessment (Table \u003cspan\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect comparison of finerenone and SGLT2 inhibitors: the composite of renal outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe composite of renal outcomes showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 1.14, 95% CI 0.92\u0026ndash;1.88, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e, Table \u003cspan\u003e2\u003c/span\u003e). In patients with type 2 diabetes and CKD, the composite of renal outcomes showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.97, 95% CI 0.50\u0026ndash;1.69, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The composite of renal outcomes also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.51, 95% CI 0.71\u0026ndash;3.17, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and canagliflozin (OR 1.27, 95% CI 0.62\u0026ndash;2.67, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and empagliflozin (OR 1.14, 95% CI 0.55\u0026ndash;2.40, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and sotagliflozin (OR 1.17, 95% CI 0.54\u0026ndash;2.69, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). We ranked the risk of the composite of renal outcomes in patients with CKD. As a result, dapagliflozin was identified as having the lowest risk of the composite of renal outcomes (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1281\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.03125%\" colspan=\"13\"\u003e\n \u003cp\u003eTable 1. Baseline characteristics of patients enrolled in the six trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.90625%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.53125%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"3\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.515625%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIDELIO-DKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.140625%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIGARA-DKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.390625%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCREDENCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.3125%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAPA-CKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.390625%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCORED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0625%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEMPA-KIDNEY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.197028928850665%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333072713057076%\"\u003e\n \u003cp\u003eFinerenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.193119624706802%\" colspan=\"2\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.114933541829554%\"\u003e\n \u003cp\u003eFinerenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0367474589523065%\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.427677873338546%\"\u003e\n \u003cp\u003eCanagliflozin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8967943706020325%\" colspan=\"2\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.880375293197811%\"\u003e\n \u003cp\u003eDapagliflozin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.444096950742768%\" colspan=\"2\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.489444878811572%\"\u003e\n \u003cp\u003eSotagliflozin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.913213448006255%\" colspan=\"2\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.114933541829554%\" colspan=\"3\"\u003e\n \u003cp\u003eCanagliflozin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.958561376075059%\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.197028928850665%\"\u003e\n \u003cp\u003eNumbers, no.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333072713057076%\"\u003e\n \u003cp\u003e2833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.193119624706802%\" colspan=\"2\"\u003e\n \u003cp\u003e2841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.114933541829554%\"\u003e\n \u003cp\u003e3686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0367474589523065%\"\u003e\n \u003cp\u003e3666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.427677873338546%\"\u003e\n \u003cp\u003e2202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8967943706020325%\" colspan=\"2\"\u003e\n \u003cp\u003e2199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.880375293197811%\"\u003e\n \u003cp\u003e2152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.444096950742768%\" colspan=\"2\"\u003e\n \u003cp\u003e2152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.489444878811572%\"\u003e\n \u003cp\u003e5292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.913213448006255%\" colspan=\"2\"\u003e\n \u003cp\u003e5292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.114933541829554%\" colspan=\"3\"\u003e\n \u003cp\u003e3304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.958561376075059%\"\u003e\n \u003cp\u003e3305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\"\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.515625%\" colspan=\"3\"\u003e\n \u003cp\u003e2.6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.140625%\" colspan=\"2\"\u003e\n \u003cp\u003e3.4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.390625%\" colspan=\"3\"\u003e\n \u003cp\u003e2.62 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.3125%\" colspan=\"3\"\u003e\n \u003cp\u003e2.4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.390625%\" colspan=\"3\"\u003e\n \u003cp\u003e16 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0625%\" colspan=\"4\"\u003e\n \u003cp\u003e2.0 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e65.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e62.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e69.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\"\u003e\n \u003cp\u003e63.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\"\u003e\n \u003cp\u003e63.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e880 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e811 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e1158 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e1089 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e762 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e732 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e709 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e716 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2347 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e2407 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\"\u003e\n \u003cp\u003e1097 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\"\u003e\n \u003cp\u003e1095 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003eRace, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.09375%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e1777 (62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e1815 (63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e2672 (72.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e2605 (71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1487 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e144 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1124 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e1166 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e4402 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e4347 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1939 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1920 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e140 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e124 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e113 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e145 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e112 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e112 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e104 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e87 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e176 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e188 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e128 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e134 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e717 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e723 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e715 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e739 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e425 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e452 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e749 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e718 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e317 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e365 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1194 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1199 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e199 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e179 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e177 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e170 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e178 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e191 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e175 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e181 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e397 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e392 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e43 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e52 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003eT2DM, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e2833 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e2841 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e3686 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e3666 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2202 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e2199 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1455 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e1451 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e5292 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e5292 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1525 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1515 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003eEstimated GFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e67.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e68.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e56.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.1875%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedian UACR(mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.421875%\" colspan=\"2\"\u003e\n \u003cp\u003e833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.09375%\"\u003e\n \u003cp\u003e867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.03125%\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.53125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.859375%\" valign=\"bottom\"\u003e\n \u003cp\u003e931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.203125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109375%\" valign=\"bottom\"\u003e\n \u003cp\u003e934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.828125%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5625%\" valign=\"bottom\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"85.9375%\" colspan=\"15\"\u003e\n \u003cp\u003eNotes: \u0026nbsp;GFR= glomerular filtration rate; T2DM= type 2 diabetes mellitus; UACR= urinary albumin-to-creatinine ratio; NA=not available;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.796875%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.265625%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cbr\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eTable 2. OR and 95% CI for network meta-analysis of different outcomes for patients with CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eStrategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003eFinerenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003eSGLT2 inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eFinerenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.14 (0.92, 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.84 (0.53, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eSGLT2 inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e0.88 (0.50, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.73 (0.53, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.19 (0.73, 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.36 (0.92, 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eFinerenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e0.94 (0.58, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.83 (0.55, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eSGLT2 inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.06 (0.64, 1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.88 (0.65, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.21 (0.81, 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.14 (0.85, 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eFinerenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.04 (0.78, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.88 (0.70, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eSGLT2 inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e0.96 (0.70, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.85 (0.70, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.13 (0.88, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.17 (0.99, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eFinerenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e0.99 (0.73, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.88 (0.69, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003eSGLT2 inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.01 (0.74, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e0.89 (0.73, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.409395973154362%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.29530201342282%\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.13422818791946%\"\u003e\n \u003cp\u003e1.14 (0.90, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.63758389261745%\" colspan=\"2\"\u003e\n \u003cp\u003e1.12 (0.93, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.523489932885905%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eNotes: Results are OR in the row-defining treatment compared with OR in the column-defining treatment.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eFor efficacy, OR＜1 favor row-defining treatment. NA=not available; CKD=chronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.34673366834171%\" colspan=\"4\"\u003e\n \u003cp\u003eA: The composite of renal outcomes;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.65326633165829%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eB: The composite of cardiovascular dearth or hospitalization for heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.34673366834171%\" colspan=\"4\"\u003e\n \u003cp\u003eC: All-cause mortality; D: Cardiovascular dearth; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.65326633165829%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect comparison of finerenone and SGLT2 inhibitors: the composite of cardiovascular death or hospitalization for heart failure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe composite of cardiovascular death or hospitalization for heart failure showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.94, 95% CI 0.58\u0026ndash;1.56, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e, Table \u003cspan\u003e2\u003c/span\u003e). In patients with type 2 diabetes and CKD, the composite of cardiovascular death or hospitalization for heart failure showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.86, 95% CI 0.48\u0026ndash;1.62, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The composite of cardiovascular death or hospitalization for heart failure also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.15, 95% CI 0.68\u0026ndash;2.01, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and canagliflozin (OR 1.22, 95% CI 0.72\u0026ndash;2.03, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and empagliflozin (OR 0.97, 95% CI 0.56\u0026ndash;1.64, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and sotagliflozin (OR 1.13, 95% CI 0.66\u0026ndash;1.84, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). We ranked the risk of the composite of cardiovascular death or hospitalization for heart failure in patients with CKD. As a result, canagliflozin was identified as having the lowest risk of the composite of cardiovascular death or hospitalization for heart failure (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect comparison of finerenone and SGLT2 inhibitors: all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll-cause mortality showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 1.04, 95% CI 0.78\u0026ndash;1.43, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e, Table \u003cspan\u003e2\u003c/span\u003e). In patients with type 2 diabetes and CKD, all-cause mortality showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.97, 95% CI 0.74\u0026ndash;1.28, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). All-cause mortality also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.30, 95% CI 0.77\u0026ndash;2.26, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and canagliflozin (OR 1.08, 95% CI 0.64\u0026ndash;1.85, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and empagliflozin (OR 1.02, 95% CI 0.60\u0026ndash;1.74, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and sotagliflozin (OR 0.89, 95% CI 0.53\u0026ndash;1.48, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). We ranked the risk of all-cause mortality in patients with CKD. As a result, dapagliflozin was identified as having the lowest risk of all-cause mortality (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect comparison of finerenone and SGLT2 inhibitors: cardiovascular death\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiovascular death showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.99, 95% CI 0.73\u0026ndash;1.35, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e, Table \u003cspan\u003e2\u003c/span\u003e). In patients with type 2 diabetes and CKD, cardiovascular death showed no significant difference in the comparison between finerenone and SGLT2 inhibitors (OR 0.95, 95% CI 0.65\u0026ndash;1.38, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Cardiovascular death also showed no significant difference in the comparison between finerenone and dapagliflozin (OR 1.07, 95% CI 0.66\u0026ndash;1.76, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and canagliflozin (OR 1.13, 95% CI 0.73\u0026ndash;1.77, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and empagliflozin (OR 1.04, 95% CI 0.62\u0026ndash;1.75, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e), finerenone and sotagliflozin (OR 0.96, 95% CI 0.63\u0026ndash;1.48, Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). We ranked the risk of cardiovascular death in patients with CKD. As a result, canagliflozin was identified as having the lowest risk of all-cause mortality (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the risk of bias in the included trials using the Cochrane Collaboration\u0026rsquo;s recommended tool\u003csup\u003e\u003cspan\u003e14\u003c/span\u003e\u003c/sup\u003e. A summary of the quality assessment of each trial is shown in the table \u003cspan\u003eS1\u003c/span\u003e. Heterogeneity test shows no evidence of heterogeneity for the composite of cardiovascular death or hospitalization for heart failure (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, P\u0026thinsp;=\u0026thinsp;0.50) all-cause mortality (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;19%, P\u0026thinsp;=\u0026thinsp;0.29), and cardiovascular death (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, P\u0026thinsp;=\u0026thinsp;0.95). Heterogeneity test shows evidence of heterogeneity for the composite of renal outcomes (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;67%, P\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we sought to evaluate indirectly the efficacy of finerenone versus SGLT2 inhibitors in patients with established CKD by Bayesian network meta-analysis, which reported several findings that can be summarized as follows: in patients with CKD, there was no significant difference in the comparison between finerenone and SGLT2 inhibitors for the major efficacy outcomes of interest; in patients with type 2 diabetes and CKD, there was no significant difference in the comparison between finerenone and SGLT2 inhibitors for the major efficacy outcomes of interest; dapagliflozin may be associated with the lowest risk of the composite of renal outcomes and all-cause mortality, while canagliflozin may be associated with the lowest risk of cardiovascular outcomes by ranking probability.\u003c/p\u003e\u003cp\u003eFinerenone and SGLT2 inhibitors are both proven to reduce cardiovascular events and kidney function progression in patients with CKD. Finerenone was a selective nonsteroidal mineralocorticoid receptor antagonist, which has anti-inflammatory and anti-fibrotic effects\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. SGLT2 inhibitors reduce blood glucose by increasing renal glucose excretion\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which was developed as hypoglycemic drugs, with the exact mechanism to protect the heart and kidney may be related to sodium balance, energy homeostasis, and relief of cellular stress\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A previous meta-analysis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e reported that finerenone was associated with a significant reduction in changing urinary albumin-to-creatinine ratio from baseline. The FIDELIO-DKD trial\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e as a large phase III randomized controlled trial reported that finerenone did result in a lower risk of cardiovascular events and kidney function progression in patients with type 2 diabetes and CKD. The FIGARO-DKD trial\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e also reported that finerenone can improve cardiovascular outcomes compared with a placebo in patients with type 2 diabetes and CKD. The FIDELITY trial\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, which was an individual patient-level prespecified pooled analysis of the FIDELIO-DKD and FIGARO-DKD trials, reported that finerenone was associated with the reduced risk of cardiovascular and kidney outcomes across the spectrum of chronic kidney disease in patients with type 2 diabetes. Therefore, finerenone was approved by FDA to reduce the risk of sustained estimated glomerular filtration rate decline, end-stage renal disease, and cardiovascular outcomes in patients with CKD associated with type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA previous network meta-analysis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e reported that SGLT2 inhibitors significantly reduced the risks of kidney function progression and hospitalization for heart failure compared to finerenone, which was inconsistent with our study. Perhaps it included several cardiovascular outcome trials of SGLT2 inhibitors, which were only post-hoc analyses of CKD strata within trials that were not focusing on CKD. Our study included dedicated CKD trials, demonstrating that finerenone and SGLT2 inhibitors had the similar risks of the composite of renal outcomes and the composite of cardiovascular death or hospitalization for heart failure by Bayesian network meta-analysis. Although the DAPA-CKD and EMPA-KIDNEY trials were excluded, which was focused on patients with CKD, regardless of diabetes or not, it failed to convert the results. For cardiovascular death and all-cause mortality, our results were consistent with the previous study\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, which also reported that no significant difference between finerenone and SGLT2 inhibitors was observed. In clinical practice, both finerenone and SLT2 inhibitors can be prescribed for patients with type 2 diabetes and CKD; however, SGLT2 inhibitors seem to tend to be superior to finerenone for cardioprotective and nephroprotective effects for these populations according to our results by ranking probability. Further studies are needed to confirm due to the lack of head-to-head trials of comparisons between finerenone and SGLT2 inhibitors.\u003c/p\u003e\u003cp\u003eStrengths of our study include the inclusion of only dedicated CKD trials and indirect comparison by Bayesian network meta-analysis based on existing evidence in dedicated CKD trials. Our study also has some limitations. Firstly, most trials were focused on patients with type 2 diabetes and CKD, only the DAPA-CKD and EMPA-KIDNEY trials focused on patients with CKD, regardless of diabetes or not. We accounted for this by sensitivity analysis by excluding these two trials. Furthermore, we evaluated outcomes using trial-level data rather than individual participant data. Finally, head-to-head comparisons of finerenone and SGLT2 inhibitors in patients with CKD remain absent and head-to-head comparisons are needed in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn patients with CKD, there was no significant difference in the major outcomes between finerenone and SGLT2 inhibitors; however, dapagliflozin and canagliflozin may be associated with the lowest risk of the major outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, because this study was based on the published literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBuonafine M, Bonnard B, Jaisser F. Mineralocorticoid Receptor and Cardiovascular Disease. \u003cem\u003eAm J Hypertens. \u003c/em\u003e2018;31(11):1165-1174.\u003c/li\u003e\n\u003cli\u003ePitt B, Filippatos G, Agarwal R, et al. Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. \u003cem\u003eN Engl J Med. \u003c/em\u003e2021;385(24):2252-2263.\u003c/li\u003e\n\u003cli\u003eBakris GL, Agarwal R, Anker SD, et al. Effect of Finerenone on Chronic Kidney Disease Outcomes in Type 2 Diabetes. \u003cem\u003eN Engl J Med. \u003c/em\u003e2020;383(23):2219-2229.\u003c/li\u003e\n\u003cli\u003eChaudhuri A, Ghanim H, Arora P. Improving the residual risk of renal and cardiovascular outcomes in diabetic kidney disease: A review of pathophysiology, mechanisms, and evidence from recent trials. \u003cem\u003eDiabetes Obes Metab. \u003c/em\u003e2022;24(3):365-376.\u003c/li\u003e\n\u003cli\u003eCannon CP, Pratley R, Dagogo-Jack S, et al. 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Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. \u003cem\u003eN Engl J Med. \u003c/em\u003e2015;373(22):2117-2128.\u003c/li\u003e\n\u003cli\u003eHeerspink HJL, Stef\u0026aacute;nsson BV, Correa-Rotter R, et al. Dapagliflozin in Patients with Chronic Kidney Disease. \u003cem\u003eN Engl J Med. \u003c/em\u003e2020;383(15):1436-1446.\u003c/li\u003e\n\u003cli\u003eChen HB, Yang YL, Yu TH, Li YH. SGLT2 inhibitors for the composite of cardiorenal outcome in patients with chronic kidney disease: A systematic review and meta-analysis of randomized controlled trials. \u003cem\u003eEur J Pharmacol. \u003c/em\u003e2022;936:175354.\u003c/li\u003e\n\u003cli\u003eZhang Y, Jiang L, Wang J, et al. Network meta-analysis on the effects of finerenone versus SGLT2 inhibitors and GLP-1 receptor agonists on cardiovascular and renal outcomes in patients with type 2 diabetes mellitus and chronic kidney disease. \u003cem\u003eCardiovasc Diabetol. \u003c/em\u003e2022;21(1):232.\u003c/li\u003e\n\u003cli\u003eHutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. \u003cem\u003eAnn Intern Med. \u003c/em\u003e2015;162(11):777-784.\u003c/li\u003e\n\u003cli\u003eHiggins JP, Altman DG, G\u0026oslash;tzsche PC, et al. The Cochrane Collaboration\u0026apos;s tool for assessing risk of bias in randomised trials. \u003cem\u003eBMJ. \u003c/em\u003e2011;343:d5928.\u003c/li\u003e\n\u003cli\u003eBhatt DL, Szarek M, Pitt B, et al. Sotagliflozin in Patients with Diabetes and Chronic Kidney Disease. \u003cem\u003eN Engl J Med. \u003c/em\u003e2021;384(2):129-139.\u003c/li\u003e\n\u003cli\u003ePerkovic V, Jardine MJ, Neal B, et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. \u003cem\u003eN Engl J Med e. \u003c/em\u003e2019;380(24):2295-2306.\u003c/li\u003e\n\u003cli\u003eHerrington WG, Staplin N, Wanner C, et al. Empagliflozin in Patients with Chronic Kidney Disease. \u003cem\u003eN Engl J Med. \u003c/em\u003e2023;388(2):117-127.\u003c/li\u003e\n\u003cli\u003eGallo LA, Wright EM, Vallon V. Probing SGLT2 as a therapeutic target for diabetes: basic physiology and consequences. \u003cem\u003eDiab Vasc Dis Res. \u003c/em\u003e2015;12(2):78-89.\u003c/li\u003e\n\u003cli\u003eGriffin M, Rao VS, Ivey-Miranda J, et al. Empagliflozin in Heart Failure: Diuretic and Cardiorenal Effects. \u003cem\u003eCirculation. \u003c/em\u003e2020;142(11):1028-1039.\u003c/li\u003e\n\u003cli\u003eZheng Y, Ma S, Huang Q, et al. Meta-Analysis of the Efficacy and Safety of Finerenone in Diabetic Kidney Disease. \u003cem\u003eKidney Blood Press Res. \u003c/em\u003e2022;47(4):219-228.\u003c/li\u003e\n\u003cli\u003eAgarwal R, Filippatos G, Pitt B, et al. Cardiovascular and kidney outcomes with finerenone in patients with type 2 diabetes and chronic kidney disease: the FIDELITY pooled analysis. \u003cem\u003eEur Heart J. \u003c/em\u003e2022;43(6):474-484.\u003c/li\u003e\n\u003cli\u003eAdministration UFD. FDA approves drug to reduce risk of serious kidney and heart complications in adults with chronic kidney disease associated with type 2 diabetes.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SGLT2 inhibitors, finerenone, chronic kidney disease, cardiovascular death, renal events, indirect comparison","lastPublishedDoi":"10.21203/rs.3.rs-4131335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4131335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHead-to-head comparison of finerenone and SGLT2 inhibitors in patients with established chronic kidney disease (CKD) remains absent.\u003c/p\u003e\u003ch2\u003eMethods and results\u003c/h2\u003e \u003cp\u003eAll dedicated CKD trials about finerenone versus placebo or SGLT2 inhibitors versus placebo were searched. A Bayesian approach to network meta-analysis was applied. In patients with CKD, no significant difference in the composite of renal outcomes (OR 1.14, 95% CI 0.92\u0026ndash;1.88), the composite of cardiovascular death or hospitalization for heart failure (OR 0.94, 95% CI 0.58\u0026ndash;1.56), all-cause mortality (OR 1.04, 95% CI 0.78\u0026ndash;1.43), and cardiovascular death (OR 0.99, 95% CI 0.73\u0026ndash;1.35) was observed between finerenone and SGLT2 inhibitors. In patients with type 2 diabetes and CKD, no significant difference in the composite of renal outcomes (OR 0.97, 95% CI 0.50\u0026ndash;1.69), the composite of cardiovascular death or hospitalization for heart failure (OR 0.86, 95% CI 0.48\u0026ndash;1.62), all-cause mortality (OR 0.97, 95% CI 0.74\u0026ndash;1.28), and cardiovascular death (OR 0.95, 95% CI 0.65\u0026ndash;1.38) was observed between finerenone and SGLT2 inhibitors. We ranked the risk of the major outcomes in patients with CKD. As a result, dapagliflozin was identified as having the lowest risk of renal outcomes and all-cause mortality, while canagliflozin was identified as having the lowest risk of cardiovascular outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn patients with CKD, there was no significant difference in the major outcomes between finerenone and SGLT2 inhibitors; however, dapagliflozin and canagliflozin may be associated with the lowest risk of the major outcomes.\u003c/p\u003e","manuscriptTitle":"Indirect comparison of finerenone and SGLT 2 inhibitors in established chronic kidney disease: evidence based on Bayesian methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 12:38:21","doi":"10.21203/rs.3.rs-4131335/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59d46254-5cd4-492d-946c-74b8a0cf7ec4","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-17T20:09:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 12:38:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4131335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4131335","identity":"rs-4131335","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}