SGLT2 Inhibitors and Kidney Outcomes by Glomerular Filtration Rate and Albuminuria: A Meta-Analysis

Brendon L Neuen; Robert A Fletcher; Stefan D Anker; Deepak L Bhatt; Javed Butler; David Z I Cherney; Kieran F Docherty; Silvio E Inzucchi; Meg J Jardine; Kenneth W Mahaffey; Finnian R McCausland; Darren K McGuire; John J V McMurray; Bruce Neal; Milton Packer; Siddharth M Patel; Vlado Perkovic; Marc S Sabatine; Rebecca J Sardell; Scott D Solomon; Muthiah Vaduganathan; Christoph Wanner; David C Wheeler; Faiez Zannad; Richard Haynes; Natalie Staplin; William G Herrington; Hiddo J L Heerspink

doi:10.1001/jama.2025.20834

. 2025 Nov 7;335(3):233–244. doi: 10.1001/jama.2025.20834

SGLT2 Inhibitors and Kidney Outcomes by Glomerular Filtration Rate and Albuminuria

A Meta-Analysis

Brendon L Neuen ^1,^2,^✉, Robert A Fletcher ^1,³, Stefan D Anker ⁴, Deepak L Bhatt ⁵, Javed Butler ⁶, David Z I Cherney ⁷, Kieran F Docherty ⁸, Silvio E Inzucchi ⁹, Meg J Jardine ¹⁰, Kenneth W Mahaffey ¹¹, Finnian R McCausland ¹², Darren K McGuire ¹³, John J V McMurray ⁸, Bruce Neal ^1,¹⁴, Milton Packer ^14,¹⁵, Siddharth M Patel ¹⁶, Vlado Perkovic ¹, Marc S Sabatine ¹⁶, Rebecca J Sardell ¹⁷, Scott D Solomon ¹², Muthiah Vaduganathan ¹², Christoph Wanner ^17,¹⁸, David C Wheeler ¹⁹, Faiez Zannad ²⁰, Richard Haynes ^17,²¹, Natalie Staplin ¹⁷, William G Herrington ^17,²¹, Hiddo J L Heerspink ^1,^22,^✉, for the SGLT2 Inhibitor Meta-Analysis Cardio-Renal Trialists’ Consortium (SMART-C)

¹The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

²Department of Renal Medicine, Royal North Shore Hospital, Sydney, New South Wales, Australia

³Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit and Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom

⁴Department of Cardiology (CVK) of German Heart Center Charité, Institute of Health Center for Regenerative Therapies (BCRT), German Centre for Cardiovascular Research (DZHK) partner site Berlin, Charité Universitätsmedizin, Berlin, Germany

⁵Mount Sinai Heart, New York, New York

⁶Baylor Scott and White Research Institute, Temple, Texas

⁷Toronto General Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada

⁸School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom

⁹Yale University School of Medicine, New Haven, Connecticut

¹⁰NHMRC Clinical Trials Centre, University of Sydney, Sydney, New South Wales, Australia

¹¹Stanford Center for Clinical Research, Department of Medicine, Stanford University, Stanford, California

¹²Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

¹³University of Texas Southwestern Medical Center, Dallas

¹⁴Imperial College London, London, United Kingdom

¹⁵Baylor University Medical Center, Dallas, Texas

¹⁶TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

¹⁷Renal Studies Group, Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, United Kingdom

¹⁸University Hospital of Würzburg, Würzburg, Germany

¹⁹Department of Renal Medicine, University College London, London, United Kingdom

²⁰Université de Lorraine, Inserm and CHRU de Nancy, Vandœuvre-lès-Nancy, France

²¹Oxford Kidney Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom

²²Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands

Group Information: The SGLT2 Inhibitor Meta-Analysis Cardio-Renal Trialists’ Consortium (SMART-C) members appear in Supplement 2.

Accepted for Publication: October 12, 2025.

Published Online: November 7, 2025. doi:10.1001/jama.2025.20834

^✉

Corresponding Author: Brendon L. Neuen, PhD (bneuen@georgeinstitute.org.au), and Hiddo J. L. Heerspink, PhD (h.j.lambers.heerspink@umcg.nl), SMART-C Secretariat, The George Institute for Global Health, University of New South Wales, Level 8, 55 Botany St, Randwick, Sydney, NSW 2031, Australia.

Author Contributions: Drs Neuen and Heerspink had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Neuen, Bhatt, Butler, Cherney, Jardine, Mahaffey, McGuire, Packer, Perkovic, Solomon, Wanner, Wheeler, Zannad, Haynes, Herrington, Heerspink.

Acquisition, analysis, or interpretation of data: Neuen, Fletcher, Anker, Bhatt, Butler, Cherney, Docherty, Inzucchi, Mahaffey, McCausland, McGuire, McMurray, Neal, Packer, Patel, Sabatine, Sardell, Vaduganathan, Wanner, Wheeler, Zannad, Haynes, Staplin, Herrington, Heerspink.

Drafting of the manuscript: Neuen, Fletcher, Butler, Jardine, Sardell, Wheeler, Heerspink.

Critical review of the manuscript for important intellectual content: Neuen, Fletcher, Anker, Bhatt, Butler, Cherney, Docherty, Inzucchi, Mahaffey, McCausland, McGuire, McMurray, Neal, Packer, Patel, Perkovic, Sabatine, Solomon, Vaduganathan, Wanner, Wheeler, Zannad, Haynes, Staplin, Herrington.

Statistical analysis: Fletcher, McCausland, Sardell, Staplin.

Obtained funding: Neuen, Mahaffey, Packer, Perkovic, Solomon, Haynes.

Administrative, technical, or material support: Neuen, Herrington, Heerspink.

Supervision: Neuen, Butler, Cherney, Jardine, Neal, Perkovic, Solomon, Wanner, Heerspink.

Conflict of Interest Disclosures: Dr Neuen reported receiving personal fees from AstraZeneca, Alexion, Bayer, Boehringer Ingelheim, Cornerstone Medical Education, Menarini, MJH Life Sciences, Janssen, Limbic, Medscape, Novo Nordisk, Otsuka, Travere, and Vera Therapeutics outside the submitted work. Dr Fletcher reported the receipt of studentship awards from the HDR-UK-Turing Wellcome Programme in Health Data Science. Dr Anker reported receiving grants from Gran CSL/Vifor & Abbott Laboratories and personal fees from Actimed, Alleviant, AstraZeneca, Bayer, Berlin Heals, BiMyo, Boehringer Ingelheim, Brahms, Cardiac Dimensions, Cardior, Cordio, Corvia, CVRx, Cytokinetics, Edwards, Impulse Dynamics, Lilly, Mankind Pharma, Novo Nordisk, Occlutech, Pfizer, Regeneron, Relaxera, Repairon, Scirent, Sensible Medical, Vectorious, Veru, Vivus, and V-Wave outside the submitted work and being named co-inventor of 2 patent applications regarding MR-proANP (DE 102007010834 & DE 102007022367) issued to Brahms GmbH. Dr Bhatt reported receiving grants from Lexicon, Boehringer Ingelheim, and AstraZeneca outside the submitted work and having a patent for sotagliflozin pending assigned to Brigham and Women’s Hospital who assigned to Lexicon; and serving on an advisory board for Angiowave, Antlia Bioscience, Bayer, Boehringer Ingelheim, CellProthera, Cereno Scientific, E-Star Biotech, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, NirvaMed, Novo Nordisk, Repair Biotechnologies, Stasys, Tourmaline Bio; the board of directors for American Heart Association New York City, Angiowave (stock options), Bristol Myers Squibb (stock), DRS.LINQ (stock options), and High Enroll (stock); consulting for Alnylam, Altimmune, Broadview Ventures, Corcept Therapeutics, Corsera, GlaxoSmithKline, Hims, SERB, SFJ, Summa Therapeutics, and Worldwide Clinical Trials; serving on data and safety monitoring committees for Acesion Pharma, Assistance Publique-Hôpitaux de Paris, Baim Institute for Clinical Research, Boston Scientific (Chair, PEITHO trial), Cleveland Clinic, Contego Medical (Chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ABILITY-DM trial, funded by Concept Medical; for ALLAY-HF, funded by Alleviant Medical), Novartis, and Population Health Research Institute; Rutgers University (for the NIH-funded MINT Trial); receiving honoraria from American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Chair, ACC Accreditation Oversight Committee), Arnold and Porter law firm (work related to Sanofi/Bristol Myers Squibb clopidogrel litigation), Baim Institute for Clinical Research (AEGIS-II executive committee funded by CSL Behring), Belvoir Publications (editor in chief, Harvard Heart Letter), Canadian Medical and Surgical Knowledge Translation Research Group (clinical trial steering committees), CSL Behring (AHA lecture), Duke Clinical Research Institute, Engage Health Media, HMP Global (editor in chief, Journal of Invasive Cardiology), Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Oakstone CME (Course Director, Comprehensive Review of Interventional Cardiology), Philips (Becker’s Webinar on AI), Population Health Research Institute, WebMD (CME steering committees), and Wiley (steering committee); serving as deputy editor of Clinical Cardiology and Progress in Cardiovascular Diseases; being named on a patent for sotagliflozin assigned to Brigham and Women’s Hospital who assigned to Lexicon; receiving research funding from Abbott, Acesion Pharma, Afimmune, Alnylam, Amarin, Amgen, AstraZeneca, Atricure, Bayer, Boehringer Ingelheim, Boston Scientific, CellProthera, Cereno Scientific, Chiesi, Cleerly, CSL Behring, Faraday Pharmaceuticals, Fractyl, Idorsia, Janssen, Javelin, Lexicon, Lilly, Medtronic, Merck, MiRUS, Moderna, Novartis, Novo Nordisk, Pfizer, PhaseBio, Regeneron, Reid Hoffman Foundation, Roche, Sanofi, Stasys, and 89Bio; and receiving royalties from Elsevier. Dr Butler reported receiving personal fees from consulting for Abbott, Adaptyx, American Regent, Amgen, AskBio, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardiac Dimension, Cardior, CSL Vifor, CVRx, Cytokinetics, Daxor, Diastol, Edwards, Element Sciences, Faraday, Idorsia, Impulse Dynamics, Imbria, Innolife, Intellia, Inventiva, Levator, Lexicon, Eli Lilly, Mankind, Medtronic, Merck, New Amsterdam, Novartis, NovoNordisk, Pfizer, Pharmacosmos, Pharmain, Prolaio, Pulnovo, Regeneron, Renibus, Reprieve, Roche, Rycarma, Saillent, Salamandra, Salubris, SC Pharma, SQ Innovation, Secretome, Sequanna, Transmural, TekkunLev, Tenex, Tricog, Ultromic, Vera, and Zoll outside the submitted work. Dr Cherney reported receiving honoraria from Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, AbbVie, Janssen, AMGEN, Bayer, Prometic, BMS, Maze, Gilead, CSL-Behring, Otsuka, Novartis, Youngene, Lexicon, Inversago, GSK, Biobridge, Vantage, Altimmune and Novo Nordisk and grants from Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, CSL-Behring, Lexicon, Novo Nordisk, and Bayer during the conduct of the study and honoraria from Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, AbbVie, Janssen, AMGEN, Bayer, Prometic, BMS, Maze, Gilead, CSL-Behring, Otsuka, Novartis, Youngene, Lexicon, Inversago, GSK, Biobridge, Vantage, Altimmune, and Novo Nordisk and grants from Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, CSL-Behring, Lexicon, Novo Nordisk, and Bayer outside the submitted work. Dr Docherty reported being remunerated by AstraZeneca for time spent working on clinical trials, personal fees from Bayer (advisory board fees and clinical end point adjudication committee), other from AstraZeneca (research grant paid to institution), other from Boehringer Ingelheim (research grant paid to institution), other from Roche Diagnostics (research grant paid to institution), personal fees from Abbott (advisory board fees), other from FIRE-1 (member of clinical trial committee), personal fees from AstraZeneca (speaker fees), personal fees from Radcliffe Cardiology (speaker fees), and personal fees from Translational Medicine Academy (speaker fees) outside the submitted work. Dr Inzucchi reported receiving personal fees from Boehringer Ingelheim, AstraZeneca, Novo Nordisk, and Bayer outside the submitted work. Dr Jardine reported being supported by an Australian government National Health and Medical Research Council investigator grant; being responsible for research projects that have received unrestricted funding from Amgen, Baxter, CSL, Eli Lilly, Gambro, and MSD; serving on advisory boards sponsored by Akebia, Astra Zeneca, Baxter, Bayer, Boehringer Ingelheim, Merck Sharp & Dohme, Occuryx, and Vifor; serving on steering committees for trials sponsored by Chinook, CSL, Janssen and Boehringer Ingelheim; receiving funding support for an investigator-initiated trial from Dimerix, Kensana; speaking at scientific meetings sponsored by Amgen, Boehringer Ingelheim, Janssen, Novo Nordisk, Roche, and Vifor; receiving fees for educational activities from Cesas Medical, Medcon International PACE CME, and Medscape; and providing consultancy or receiving honoraria or travel support directed to her institution from Amgen, Baxter, CSL, Eli Lilly, Gambro, and MSD outside the submitted work. Dr Mahaffey reported receiving personal fees from Eli Lilly and Johnson & Johnson and grants from Johnson & Johnson outside the submitted work. Dr McCausland reported receiving research fees from Novartis, Lexicon, AstraZeneca, and NIH paid directly to his institution; consulting fees from Aquapass, GSK, and Zydus Therapeutics; expert witness fees from Rubin-Anders Scientific; travel/speakers fees from Bayer and Global Learning Collaborative; serving on a data and safety monitoring board for AstraZeneca; and spousal consulting fees from Vera Therapeutics and Alexion outside the submitted work. Dr McGuire reported receiving personal fees from Novo Nordisk, Boehringer Ingelheim, AstraZeneca, Lilly, Lexicon, Amgen, Neurotronic, Kailera, Alveus, Regor, Roche Genentech, Amsterdam, Metsera, and Novartis outside the submitted work. Dr McMurray reported receiving payment to Glasgow University from British Heart Foundation, National Institutes of Health/National Heart, Lung, and Blood Institute, AstraZeneca, Bayer, Cardurion, Cytokinetics, Novartis, and Roche Diagnostics; personal fees for consulting from Alynylam Pharmaceuticals, AnaCardio, AstraZeneca, Bayer, Cardurion, Cytokinetics, Novartis, River BioMedics, Biohaven Pharmaceuticals, DalCor Pharmaceuticals; personal lecture fees from Alkem Metabolics, AstraZeneca, Canadian Medical and Surgical Knowledge, Centrix Healthcare, Emcure Pharmaceuticals, Eris Lifesciences, Hikma Pharmaceuticals, Imagica Health, Intas Pharmaceuticals, J.B. Chemicals & Pharmaceuticals, Lupin Pharmaceuticals, Medscape/Heart.Org, ProAdWise Communications, Radcliffe Cardiology, Sun Pharmaceuticals, Translational Medicine Academy, Regeneron, MCI India, Hilton Pharmaceuticals, IMEDIC Pharmaceuticals Micro Labs Ltd, ARMGO Pharmaceuticals; and serving as director of Global Clinical Trial Partners outside the submitted work. Dr Neal reported receiving grants for funding support for the CANVAS and CREDENCE trials from Janssen Pharmaceuticals paid to the institution during the conduct of the study. Dr Packer reported receiving personal fees from 89bio, AbbVie, Actavis, Alnylam, Ardelyx, ARMGO, Attralus, AstraZeneca, Altimmune, Amgen, Biopeutics, Boehringer Ingelheim, Caladrius, Casana, CSL Behring, Cytokinetics, Daiichi Sankyo, Imara, Lilly, Medtronic, Moderna, Novartis, Novo Nordisk, Pharmacosmos, Regeneron, Salamandra, and V-Wave outside the submitted work. Dr Patel reported receiving grants from AstraZeneca for statistical support during the conduct of the study and grants from Anthos Therapeutics/Novartis and grants from Harvard Catalyst/The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health) award (K12TR004381) outside the submitted work; and being a member of the TIMI Study Group, which has received institutional research grant support through Brigham and Women’s Hospital from Abbott, Abiomed, Inc, Amgen, Anthos Therapeutics, ARCA Biopharma Inc, AstraZeneca, Boehringer Ingelheim, Cleerly Inc, Daiichi Sankyo, Ionis Pharmaceuticals Inc, Janssen Research and Development LLC, MedImmune, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals Inc, Roche, Saghmos Therapeutics Inc, Softcell Medical Limited, The Medicines Company, Verve Therapeutics, Inc, and Zora Biosciences. Dr Perkovic reported receiving personal fees from AstraZeneca, Bayer, Biogen, Boehringer Ingelheim, Chinook, GlaxoSmithKline, Guard Therapeutics, Incyte, Janssen, Mineralys, Novo Nordisk, Novartis, Otsuka, Shanxi Micot, Travere, Tricida, and Vifor outside the submitted work. Dr Sabatine reported receiving grants from AstraZeneca to the TIMI Study Group at Brigham and Women’s Hospital and personal fees from AstraZeneca during the conduct of the study and grants to the TIMI Study Group at Brigham and Women’s Hospital from Abbott and Amgen; personal fees from Amgen, AMPEL BioSolutions, Anthos Therapeutics, Beren Therapeutics, CCRN (Canadian CV Research Network), Dr Reddy’s Laboratories, General Medicines, Merck, NATF (North American Thrombosis Forum), Novo Nordisk, and Precision BioSciences and institutional research grants to the TIMI Study Group at Brigham and Women’s Hospital from Anthos Therapeutics, Boehringer Ingelheim, Daiichi Sankyo, IONIS, Marea, Merck, Novartis, Pfizer, Saghmos Therapeutics, and Verve Therapeutics outside the submitted work; and being a member of the TIMI Study Group, which has also received institutional research grant support through Brigham and Women’s Hospital from Abiomed Inc, ARCA Biopharma Inc, Cleerly Inc, Janssen Research and Development LLC, MedImmune, Regeneron Pharmaceuticals Inc, Roche, Softcell Medical Limited, The Medicines Company, and Zora Biosciences. Dr Solomon reported receiving grants from Alexion, Alnylam, Applied Therapeutics, AstraZeneca, Bellerophon, Bayer, BMS, Boston Scientific, Cardior, Cytokinetics, Edgewise, BridgeBio, Gossamer, GSK, Ionis, Lilly, NIH/NHLBI, Novartis, Novo Nordisk, Respicardia, Sanofi Pasteur, Tenaya, Theracos, US2.AI and personal fees from Abbott, Action, Akros, Alexion, Alnylam, Amgen, Arena, Askbio, AstraZeneca, Bayer, BMS, Bridgebio, Cardior, Cardurion, Corvia, Cytokinetics, GSK, Intellia, Lilly, Novartis, Roche, Theracos, Quantum Genomics, Tenaya, Sanofi-Pasteur, Dinaqor, Tremeau, CellProThera, Moderna, American Regent, Sarepta, Lexicon, Anacardio, Akros, Valo, Synhale, and Recordati outside the submitted work. Dr Vaduganathan reported receiving personal fees from Alnylam Pharmaceuticals, American Regent, Amgen, AstraZeneca, Bayer AG, Baxter Healthcare, BMS, Boehringer Ingelheim, Chiesi, Cytokinetics, Esperion, Fresenius Medical Care, Idorsia Pharmaceuticals, Lexicon Pharmaceuticals, Merck, Milestone Pharmaceuticals, Novartis, Novo Nordisk, Pharmacosmos, Recordati, Relypsa, Roche Diagnostics, Sanofi, and Tricog Health for serving as a consultant or advisory board member and personal fees from Amgen, AstraZeneca, Bayer AG, Boehringer Ingelheim, Galmed, Impulse Dynamics, Novartis, Occlutech, and Pharmacosmos for serving on clinical trial committees outside the submitted work. Dr Wanner reported receiving personal fees from companies similar to the companion SMART-C paper during the conduct of the study. Dr Wheeler reported receiving nonfinancial support from AstraZeneca and Boehringer Ingelheim Travel during the conduct of the study and personal fees from Bayer, CSL, GSK, Menarini, Pathalys, ProKidney, Purespring, Merck, Vifor, Medice, and Vertex outside the submitted work. Dr Zannad reported receiving personal fees from Boehringer during the conduct of the study and personal fees from Acceleron, Alnylam, Bayer, Biopeutics, Boehringer, Cambrian, Cellprothera, Centrix, Cereno pharmaceutical, Corteria, CRISPR Therapeutics, CVRx, CVCT, Daiichi Sankyo, Eshmoun, eSpace, G3Pharmaceutical, Inventiva, HighTide, KBP, Lilly, Lupin, Merck, NovoNordisk, Opalia Recordati, Otsuka, Owkin, Polygon, Ribocure, Roche, Viatris, and Vivek-India outside the submitted work and Equities in G3Pharmaceutical, Cereno pharmaceutical, and Eshmoun. Dr Haynes reported receiving grants to the institution from Boehringer Ingelheim and Lilly To institution during the conduct of the study. Dr Staplin reported receiving grants paid to the institution from Boehringer Ingelheim Eli Lilly and Company, and Novo Nordisk outside the submitted work. Dr Herrington reported receiving grants from Boehringer Ingelheim and grants from Eli Lilly during the conduct of the study. Dr Heerspink reported receiving honoraria to the institution from AstraZeneca and Janssen during the conduct of the study and grants paid to the institution from Bayer and Boehringer Ingelheim and honoraria from Amgen, Alexion, Biocity, Eli Lilly, Dimerix, Novartis, Novo Nordisk, Roche, and Travere outside the submitted work. No other disclosures were reported.

Funding/Support: SMART-C is supported by a National Health and Medical Research Council of Australia Emerging Leader Investigator Grant (grant No. 2026621) and a Ramaciotti Foundation Health Investment Grant (grant No. 2023HIG69), both awarded to Dr Neuen. No funding from industry was provided for this meta-analysis, but each included individual trial was funded by industry funders. The EMPA-REG OUTCOME, EMPEROR-Reduced, EMPEROR-Preserved, and EMPA-KIDNEY trials were funded by Boehringer Ingelheim. The CANVAS Program and CREDENCE trials were funded by Janssen Research & Development. The DECLARE-TIMI 58, DAPA-HF, DAPA-CKD, and DELIVER trials were funded by AstraZeneca.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The SGLT2 Inhibitor Meta-Analysis Cardio-Renal Trialists’ Consortium (SMART-C) members appear in Supplement 2.

Meeting Presentation: This article was presented at the ASN Kidney Week; November 7, 2025; Houston, Texas.

Data Sharing Statement: See Supplement 3.

Additional Contributions: We thank the participants, the local site staff, regional coordinating center staff, and all members of committees that made these trials possible.

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Corresponding author.

PMCID: PMC12595549 PMID: 41203232

Key Points

Question

Do the kidney protective effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors vary by estimated glomerular filtration rate (eGFR) or albuminuria?

Findings

In this meta-analysis that included 70 361 participants in 10 trials of SGLT2 inhibitors vs placebo, SGLT2 inhibitors significantly reduced progression of chronic kidney disease (CKD), serious acute kidney injury, and kidney failure. The relative risk reduction for CKD progression was consistent regardless of eGFR and albuminuria. Significant reductions in the annual rate of eGFR decline were observed across all subgroups, including when participants with and without diabetes were analyzed separately.

Meaning

These findings support the routine use of SGLT2 inhibitors to improve kidney outcomes across the full spectrum of kidney function and albuminuria among patients with type 2 diabetes, CKD, or heart failure.

Abstract

Importance

Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce chronic kidney disease (CKD) progression in individuals with type 2 diabetes, CKD, or heart failure. However, their effects in those with stage 4 CKD or little to no albuminuria remain uncertain.

Objective

To assess whether estimated glomerular filtration rate (eGFR) or degree of albuminuria, measured by urinary albumin to creatinine ratio (UACR), modifies the effects of SGLT2 inhibitors on kidney outcomes.

Data Sources

SGLT2 inhibitor trials participating in the SGLT2 Inhibitor Meta-Analysis Cardio-Renal Trialists’ Consortium (SMART-C).

Study Selection

Randomized, double-blind, placebo-controlled trials within SMART-C evaluating an SGLT2 inhibitor with label indications for reducing CKD progression including at least 500 participants in each group with at least 6 months of follow-up.

Data Extraction and Synthesis

Treatment effects in individual trials were pooled using inverse variance–weighted meta-analysis.

Main Outcomes and Measures

CKD progression, defined as kidney failure, at least 50% reduction in eGFR, or death due to kidney failure. Other outcomes included annual rate of eGFR decline and kidney failure.

Results

Among 70 361 participants (mean [SD] age, 64.8 [8.7] years; 24 595 [35.0%] females) in 10 randomized trials, 2314 (3.3%) experienced CKD progression and 988 (1.4%) reached kidney failure. SGLT2 inhibitors reduced the risk of CKD progression (25.4 vs 40.3 events per 1000 patient-years; hazard ratio [HR], 0.62 [95% CI, 0.57-0.68]), irrespective of baseline eGFR (HR of 0.61 [95% CI, 0.52-0.71] for eGFR ≥60 mL/min/1.73 m²; 0.57 [95% CI, 0.47-0.70] for eGFR of 45 to <60 mL/min/1.73 m²; 0.64 [95% CI, 0.54-0.75] for eGFR of 30 to <45 mL/min/1.73 m²; and 0.71 [95% CI, 0.60-0.83] for eGFR <30 mL/min/1.73 m²; P for trend = .16) and baseline albuminuria (HR of 0.58 [95% CI, 0.44-0.76] for albuminuria ≤30 mg/g; 0.74 [95% CI, 0.57-0.96] for >30-300 mg/g; and 0.57 [95% CI, 0.52-0.64] for more than 300 mg/g; P for trend = .49). Although the magnitude of protection varied, SGLT2 inhibitors reduced the annual rate of eGFR decline across all eGFR and UACR subgroups, including when participants with and without diabetes were analyzed separately. SGLT2 inhibitors also reduced the risk of kidney failure alone (HR, 0.66 [95% CI, 0.58-0.75]).

Conclusions and Relevance

In this meta-analysis, SGLT2 inhibitors were found to lower the risk of CKD progression regardless of baseline eGFR or albuminuria, including in patients with stage 4 CKD or minimal albuminuria, supporting their routine use to improve kidney outcomes across the full spectrum of kidney function among patients with type 2 diabetes, CKD, or heart failure.

This meta-analysis examines whether estimated glomerular filtration rate or degree of albuminuria, as measured by urinary albumin to creatinine ratio, modifies the effects of sodium-glucose cotransporter 2 inhibitors on kidney outcomes.

Introduction

Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce the risk of kidney disease progression, cardiovascular events, and mortality in patients with type 2 diabetes, chronic kidney disease (CKD), and heart failure.¹ As a result, major clinical practice guidelines now recommend their use in these populations.^2,3,4

Because their glucose-lowering efficacy declines at lower estimated glomerular filtration rate (eGFR) values and due to concerns about the risk of precipitating acute kidney injury (AKI), the use of SGLT2 inhibitors—originally developed as glucose-lowering agents—has historically been restricted in patients with impaired kidney function.⁵ Although regulatory thresholds for initiation have been revised in recent years, routinely collected data from health systems worldwide indicate that patients with lower eGFR remain less likely to receive an SGLT2 inhibitor.⁶ Accordingly, patients with advanced CKD (eGFR <30 mL/min/1.73 m²) were underrepresented in most SGLT2 inhibitor trials, and real-world uptake in this group is particularly limited, despite their markedly elevated risk of kidney failure and cardiovascular disease.

Additionally, several guidelines offer varying recommendations on the use of SGLT2 inhibitors in CKD based on albuminuria. Based on completed outcomes trials and their inclusion criteria, the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines give a strong recommendation (1A) for SGLT2 inhibitor use in patients without type 2 diabetes and albumin to creatinine ratio (UACR) greater than or equal to 200 mg/g, compared with a weaker recommendation (2B) for those with UACR less than 200 mg/g.² Similarly, in the UK, the National Institute for Health and Care Excellence offers varying recommendations for individuals with type 2 diabetes based on UACR thresholds of 30 mg/g and 300 mg/g.⁷ These discrepancies are reflected across several implementation documents and guidelines,^8,9 highlighting ongoing uncertainty about whether the kidney-protective effects of SGLT2 inhibitors vary according to baseline albuminuria levels.

To address these uncertainties, a collaborative meta-analysis of trials participating in the SGLT2 Inhibitor Meta-Analysis Cardio-Renal Trialists’ Consortium (SMART-C) was conducted to evaluate the impact of eGFR and albuminuria on the kidney-protective effects of SGLT2 inhibitors.

Methods

SMART-C comprises completed randomized, double-blind, placebo-controlled outcome trials with at least 500 participants in each treatment group and follow-up of at least 6 months.¹ The consortium is led by an academic steering committee comprising representatives from each of the participating trials. A systematic review was not performed. This analysis was restricted to trials within SMART-C evaluating SGLT2 inhibitors with a label indication for CKD progression (canagliflozin, dapagliflozin, and empagliflozin). Trials in which longitudinal kidney outcomes were not systematically collected were excluded.

This meta-analysis is one of 2 SMART-C companion analyses. The current study focuses on the effects of SGLT2 inhibitors on kidney outcomes across the full spectrum of eGFR and albuminuria, while the companion meta-analysis summarizes the net absolute benefits of SGLT2 inhibitors on kidney, cause-specific hospitalization, and cause-specific mortality outcomes by diabetes status and albuminuria.¹⁰ Both were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline.¹¹

This meta-analysis includes data from 10 trials (eFigure 1 in Supplement 1): 3 trials enrolled participants with type 2 diabetes at high atherosclerotic cardiovascular risk (EMPA-REG OUTCOME, CANVAS Program, and DECLARE-TIMI 58),^12,13,14 4 trials studied participants with heart failure across the spectrum of ejection fraction (EMPEROR-Reduced, EMPEROR-Preserved, DAPA-HF, and DELIVER),^15,16,17,18 and 3 were primary kidney outcome trials (CREDENCE, DAPA-CKD, and EMPA-KIDNEY).^19,20,21 Protocols for each of the included trials were approved by relevant ethics committees, and all participants provided written informed consent. SGLT2 inhibitor trials participating in SMART-C evaluating effects in the post–myocardial infarction setting (DAPA-MI and EMPACT-MI) were not included because longitudinal kidney outcomes were not systematically recorded.^22,23 Trials of sotagliflozin, an SGLT1/2 inhibitor, and ertugliflozin were also excluded because these agents do not have a label indication for kidney outcomes. Risk of bias was assessed by the SMART-C secretariats (B.L.N. and H.J.L.H.) using the Cochrane Risk of Bias 2 Tool.

Outcomes

The primary outcome in this meta-analysis was CKD progression, defined as kidney failure, at least 50% reduction in eGFR, or death due to kidney failure. Kidney failure was defined as receiving dialysis, kidney transplant, or eGFR less than 15 mL/min/1.73 m². Secondary clinical outcomes included (1) kidney failure, ≥50% reduction in eGFR, or death due to cardiovascular disease or kidney failure; (2) kidney failure; (3) kidney failure or all-cause mortality; and (4) serious AKI. AKI was defined based on investigator-reported adverse events (using the Medical Dictionary for Regulatory Activities term acute kidney injury) and restricted to those identified as serious adverse events (ie, life-threatening, leading to or prolonging hospitalization, or causing persistent or significant disability).

Additionally, we evaluated effects on the annualized rate of decline in eGFR (eGFR slope) as a secondary outcome.²⁴ Analysis of eGFR slope provides additional statistical power compared with clinical outcomes to evaluate effects on kidney function across eGFR and UACR subgroups. Because SGLT2 inhibitors cause an acute, hemodynamic reduction in eGFR that differs from their long-term effect, we evaluated outcomes over distinct periods. The acute effect was defined as the change in eGFR from treatment initiation to the first postrandomization assessment (typically within 3 months). Chronic eGFR slope was defined as the difference in annualized rate of eGFR decline between treatment and placebo, calculated from the first postrandomization measurement until the end of follow-up. We also evaluated total eGFR slope, incorporating all eGFR measurements from randomization to the end of the study.

Subgroups

We evaluated treatment effects according to baseline eGFR (<30, 30 to <45, 45 to <60, and ≥60 mL/min/1.73 m²) and UACR (<30, 30-300, and >300 mg/g) categories. Additionally, we combined eGFR and UACR using the KDIGO classification of CKD into low-, moderate-, high-, and very high–risk categories. We further examined whether the effects of SGLT2 inhibitors on kidney outcomes are modified by eGFR, UACR, and KDIGO risk categories separately in patients with and without diabetes. Although there were very few participants with eGFR less than 20 mL/min/1.73 m² at randomization based on the lower eGFR thresholds for trial entry, we divided the subgroup with eGFR less than 30 mL/min/1.73 m² into less than 30 mL/min/1.73 m² and less than 20 mL/min/1.73 m² in exploratory analyses to examine treatment effects in these individuals in whom treatment initiation is not currently recommended by guidelines.

Statistical Analysis

We conducted a 2-stage meta-analysis with outcome definitions harmonized across trials, in line with previous SMART-C collaborative meta-analyses.^1,25,26 Treatment effects were obtained from individual trials and pooled using inverse variance weighted meta-analysis. Two trials (DAPA-HF and DELIVER) recorded eGFR at baseline but not UACR. These trials were included in overall treatment effect estimates and subgroup analyses by baseline eGFR because all participants had the opportunity to have eGFR evaluated at baseline, but were excluded from subgroup analyses by baseline UACR. Data were independently verified at the trial level by each collaborating group and subsequently subjected to central quality control by 2 authors at the SMART-C data coordinating center The George Institute for Global Health.

We compared characteristics of participants at baseline across the aforementioned eGFR and UACR categories. We used the inverse variance–weighted method to calculate the weighted pooled mean and SD for continuous variables and pooled number and percentage for categorical variables.

Treatment effects on clinical outcomes were estimated in individual trials using Cox regression models with covariate stratification as prespecified in each trial. We obtained summary effect estimates, overall and according to baseline eGFR and UACR, by pooling log-transformed hazards ratios (HRs) using inverse variance–weighted means. We assessed effect modification across ordered eGFR, UACR, and KDIGO risk categories using Cochran-Armitage tests for trend.

Consistent with the consortium’s previous work, we used a 2-slope mixed-effects linear spline model with an unstructured covariance matrix for eGFR slope analyses. Acute, chronic, and total slopes were defined based on this 2-slope model, with a knot placed at the first eGFR value after randomization. Models included 2-way and 3-way interaction terms between randomized treatment, eGFR, or UACR, and the 2-slope linear spline for follow-up time. A detailed description of these methods, including worked examples for calculating eGFR slope as used in SMART-C publications, is publicly available on GitHub. For all eGFR slope analyses, we report both relative effects (percentage change) and absolute effects (mL/min/1.73 m²/y). Because absolute effects on chronic and total eGFR slopes are influenced by the underlying rate of CKD progression, any observed heterogeneity in absolute effects may reflect differences in baseline risk and may not be generalizable. Because our primary aim was to assess effect modification by eGFR and UACR, we focused on relative effects for chronic eGFR slope, which are more likely to be generalizable to routine clinical populations. Relative acute effects were calculated by dividing the absolute treatment effect and its 95% CI by the mean baseline eGFR, while relative effects on chronic eGFR scope were calculated by dividing the absolute treatment effect and its 95% CI by the rate of eGFR decline in the placebo group. This approach is consistent with previous work²⁵ and the accompanying companion meta-analysis.¹⁰

Effect estimates on eGFR slope in individual trials were meta-analyzed using the approach described previously, with Cochran-Armitage tests for trend used to evaluate heterogeneity across ordered eGFR, UACR, and KDIGO subgroups.

All analyses including clinical outcomes and eGFR slope were conducted using an intention-to-treat approach. All P values were 2-sided, with P <.05 considered statistically significant. Trial-level meta-analysis was performed using R version 4.3.1 (R Foundation).

Results

The meta-analysis included 70 361 participants enrolled in 10 randomized, double-blind, placebo-controlled trials (eTable 1 in Supplement 1). Risk of bias was low in all trials (eTable 2 in Supplement 1). Characteristics of participants across eGFR and UACR categories are displayed in the Table. In lower eGFR categories, participants were more likely to have UACR greater than 300 mg/g and less likely to have atherosclerotic cardiovascular disease or diabetes, reflecting that those with reduced eGFR were predominantly enrolled from CKD trials. Similarly, in higher UACR categories, higher percentages of participants had eGFR less than 45 mL/min/1.73 m² and were male and lower percentages had atherosclerotic cardiovascular disease or diabetes. Overall, 2314 participants (3.3%) experienced the primary outcome of CKD progression and 5739 (8.2%) experienced CKD progression or death due to cardiovascular causes; 988 (1.4%) reached kidney failure and 6996 (9.9%) reached kidney failure or died due to any cause. A total of 1344 participants (1.9%) had serious AKI.

Table. Characteristics of Participants at Baseline According to eGFR and UACR at Randomization in the 10 Included Trials^a.

	eGFR, mL/min/1.73 m²				UACR, mg/g
	<30 (n = 3895)	30 to <45 (n = 10 891)	45 to <60 (n = 12 167)	≥60 (n = 43 147)	≤30 (n = 29 767)	>30 to 300 (n = 14 176)	>300 (n = 14 873)
Age, mean (SD), y	67.9 (11.5)	69.4 (9.8)	68.8 (8.6)	63.5 (8.0)	64.6 (7.8)	65.4 (8.6)	62.7 (10.2)
Male, No. (%)	2485 (63.8)	6836 (62.8)	7804 (64.1)	28 442 (65.9)	18 356 (61.7)	9667 (68.2)	10 250 (68.9)
Female, No. (%)	1410 (36.2)	4055 (37.2)	4363 (35.9)	14 705 (34.1)	11 411 (38.3)	4509 (31.8)	4623 (31.1)
Race, No. (%)^b
Asian	1053 (27.0)	2629 (24.1)	2511 (20.6)	7530 (17.5)	4018 (13.5)	2815 (19.9)	4472 (30.1)
Black	174 (4.5)	460 (4.2)	493 (4.1)	1742 (4.0)	1181 (4.0)	615 (4.3)	668 (4.5)
White	2504 (64.3)	7324 (67.2)	8585 (70.6)	32 042 (74.3)	23 437 (78.7)	10 224 (72.1)	8801 (59.2)
Other	164 (4.2)	463 (4.3)	561 (4.6)	1805 (4.2)	1096 (3.7)	505 (3.6)	926 (6.2)
Medical history
Atherosclerotic cardiovascular disease, No. (%)^c	1687 (43.3)	6023 (55.3)	7831 (64.4)	25 649 (59.4)	18 512 (62.2)	8920 (62.9)	6931 (46.6)
Diabetes, No. (%)^d	2262 (58.1)	6961 (63.9)	8369 (68.8)	36 558 (84.7)	25 829 (86.8)	11 748 (82.9)	11 369 (76.4)
Heart failure, No. (%)^e	959 (24.6)	4186 (38.4)	5579 (45.9)	12 982 (30.1)	6645 (22.3)	3614 (25.5)	2364 (15.9)
Systolic BP, mean (SD), mm Hg	135.9 (18.2)	133.0 (17.0)	132.2 (16.1)	133.3 (15.8)	132.2 (15.1)	135.2 (16.1)	140.1 (16.9)
Estimated GFR, mL/min/1.73 m², No. (%)
≥60				43 147	23 435 (78.7)	8612 (60.8)	4652 (31.3)
45 to <60			12 167		3353 (11.3)	2372 (16.7)	3518 (23.7)
30 to <45		10 891			2195 (7.4)	2231 (15.7)	4524 (30.4)
<30	3895				614 (2.1)	901 (6.4)	2161 (14.5)
UACR, mg/g, No. (%)
≤30	614 (15.8)	2195 (20.2)	3351 (27.5)	23 433 (54.3)	29 767
>30 to 300	901 (23.1)	2231 (20.5)	2374 (19.5)	8614 (20.0)		14 176
>300	2161 (55.5)	4524 (41.5)	3518 (28.9)	4652 (10.8)			14 873
RAS blockade, No. (%)	3218 (82.6)	9481 (87.1)	10 643 (87.5)	35 837 (83.1)	24 085 (80.9)	11 819 (83.4)	13 756 (92.5)

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Abbreviations: BP, blood pressure; eGFR, estimated glomerular filtration rate; RAS, renin-angiotensin system; UACR, urinary albumin-to-creatinine ratio.

^{^a}

See eTable 1 in Supplement 1 for additional details of included trials.

^{^b}

Race was self-reported in all trials based on fixed categories. Additional information on the other category cannot be provided because data collection varied across trials.

^{^c}

Atherosclerotic cardiovascular disease was defined as a history of coronary artery disease, peripheral artery disease, or cerebrovascular disease.

^{^d}

Diabetes status was based on investigator-reported medical history.

^{^e}

History of heart failure was based on investigator-reported medical history (outside of the dedicated heart failure trials).

The incidence of the primary outcome of CKD progression was lower with SGLT2 inhibitors compared with placebo (25.4 vs 40.3 events per 1000 patient-years; HR, 0.62 [95% CI, 0.57-0.68]). SGLT2 inhibitors also lowered the risks of all secondary kidney outcomes studied, including CKD progression or cardiovascular death (HR, 0.75 [95% CI, 0.72-0.80]), kidney failure alone (HR, 0.66 [95% CI, 0.58-0.75]), kidney failure or all-cause mortality (HR, 0.85 [95% CI, 0.81-0.89]), and serious AKI (HR, 0.74 [95% CI, 0.66-0.82]) (eFigure 2 in Supplement 1).

The effect of SGLT2 inhibitors on CKD progression was similar across eGFR categories: greater than or equal to 60 mL/min/1.73 m² (HR, 0.61 [95% CI, 0.52-0.71]), 45 to less than 60 mL/min/1.73 m² (HR, 0.57 [95% CI, 0.47-0.70]), 30 to less than 45 mL/min/1.73 m² (HR, 0.64 [95% CI, 0.54-0.75]), and less than 30 mL/min/1.73 m² (HR, 0.71 [95% CI, 0.60-0.83]; P for trend = .16; Figure 1). In an exploratory analysis, the effect was also consistent when participants with stage 4 CKD were divided into eGFR categories of 20 to 30 mL/min/1.73 m² and less than 20 mL/min/1.73 m², although there were only 254 participants (0.4%) in the subgroup of eGFR less than 20 mL/min/1.73 m² (P for trend = .12; eFigure 3 in Supplement 1). In analyses further stratified by diabetes status, there was no evidence that the effect of SGLT2 inhibitors on CKD progression was modified by eGFR in participants with diabetes (P for trend = .39) or those without diabetes (P for trend = .57) (Figure 1).

Figure 1. — Includes data from 10 trials. Box sizes are inversely proportional to the standard error of the treatment effect. Bars indicate 95% CIs. Chronic kidney disease (CKD) progression is defined as ≥50% reduction in eGFR, kidney failure, or death due to kidney failure. GFR indicates glomerular filtration rate; and SGLT2, sodium-glucose cotransporter 2.

SGLT2 inhibitors reduced risk of CKD progression regardless of baseline UACR, with benefit in participants with UACR less than or equal to 30 mg/g (HR, 0.58 [95% CI, 0.44-0.76]), greater than 30 to 300 mg/g (HR, 0.74 [95% CI, 0.57-0.96]), and greater than 300 mg/g (HR, 0.57 [95% CI, 0.52-0.64]; P for trend = .49; Figure 2). In analyses treating participants with and without diabetes separately, there was also no evidence of effect modification by baseline UACR in either population (P for trend = .96 and .07 for diabetes and no diabetes, respectively; Figure 2), although there were very few events in participants without diabetes and UACR less than or equal to 30 mg/g. SGLT2 inhibitors reduced the risk of CKD progression irrespective of KDIGO risk categories overall and in participants with and without diabetes (eFigure 4 in Supplement 1).

Figure 2. — Includes data from 8 trials. Box sizes are inversely proportional to the standard error of the treatment effect. Bars indicate 95% CIs. Chronic kidney disease (CKD) progression is defined as ≥50% reduction in estimated glomerular filtration rate, kidney failure, or death due to kidney failure. SGLT2 indicates sodium-glucose cotransporter 2; and UACR, urinary albumin to creatinine ratio.

SGLT2 inhibitors induced an acute decline in eGFR (relative change, −5% [95% CI, −4.7% to −5.2%]; absolute change, −2.19 mL/min/1.73 m² [95% CI, −2.31 to −2.08]; Figure 3). In relative terms, this acute reduction was more pronounced at lower baseline eGFR (P for trend <.001), whereas in absolute terms, the decline was attenuated at lower baseline eGFR (P for trend = .01; eFigure 5 in Supplement 1). The relative acute effect was also greater in participants with higher UACR (P for trend <.001), likely reflecting the lower mean baseline eGFR across higher UACR categories (Figure 3). Acute effects across KDIGO risk categories, and absolute effects across subgroups, are shown in eFigures 5-6 in Supplement 1.

SGLT2 inhibitors reduced the annual rate of eGFR decline (relative difference, −51% [95% CI, −54% to −49%]; absolute difference, 1.26 mL/min/1.73 m²/y [95% CI, 1.20-1.32]; Figure 4). Although there was some evidence that the magnitude of benefit varied across eGFR and UACR categories (P for trend = .02 and .002, respectively), there was clear and separate benefit for all eGFR and UACR subgroups, including eGFR less than 30 mL/min/1.73 m² (relative difference, −47% [95% CI, −59% to −34%]) and UACR less than or equal to 30 mg/g (relative difference, −54% [95% CI, −58% to −50%]) (Figure 4). Because the rate of eGFR decline increased with increasing UACR, absolute effects were generally largest in participants with UACR greater than or equal to 300 mg/g (eFigure 7 in Supplement 1). Patterns were similar across eGFR and UACR subgroups stratified by diabetes status (Figure 4) and KDIGO risk categories (eFigure 8 in Supplement 1) and when evaluating total eGFR slope (eFigure 9 in Supplement 1).

Figure 4. — Estimated glomerular filtration rate (eGFR) subgroups include data from 10 trials, while urinary albumin to creatinine ratio (UACR) subgroups include data from 8 trials. Box sizes are inversely proportional to the standard error (SE) of the treatment effect. Bars indicate 95% CIs. Relative differences are emphasized in this presentation. For absolute differences, which are primarily determined by the background rate of kidney disease progression, see eFigure 7 in Supplement 1. For details of the effect on total slope, see eFigure 9 in Supplement 1. SGLT2 indicates sodium-glucose cotransporter 2.

There was some evidence that the reduction in serious AKI with SGLT2 inhibitors was attenuated in participants with eGFR less than 30 mL/min/1.73 m² (P for trend = .02), but this effect was consistent regardless of UACR (P for trend = .49; eFigure 10 in Supplement 1).

Discussion

In this large, collaborative meta-analysis using individual participant data, SGLT2 inhibitors reduced the risk of all kidney outcomes studied, including kidney failure alone. Importantly, SGLT2 inhibitors lowered the risk of CKD progression across the full spectrum of eGFR and UACR, including among participants with eGFR less than 30 mL/min/1.73 m² and those with little to no albuminuria, for whom current guidelines offer weaker recommendations. Although few clinical outcomes were observed in participants without diabetes and with normal levels of albuminuria, analyses of eGFR slope, a more sensitive, continuous outcome, demonstrated clear benefits on kidney function decline across all eGFR and albuminuria subgroups. This was also true when participants with and without diabetes were assessed separately, and regardless of whether eGFR slope was expressed as relative or absolute differences. These findings, representing the totality of the large-scale randomized evidence for SGLT2 inhibitors approved for reducing CKD progression, provide the clearest support to date for routine use of SGLT2 inhibitors across the full spectrum of CKD, including in patients with stage 4 CKD, who are at the highest risk of kidney failure, and in those with little to no albuminuria.

These findings reinforce recommendations from major cardiovascular, endocrinology, and nephrology clinical practice guidelines to initiate SGLT2 inhibitors in patients with CKD and an eGFR greater than or equal to 20 mL/min/1.73 m², regardless of diabetes status.^2,27,28 Data from more than 27 million individuals in the CKD Prognosis Consortium indicate that patients with stage 4 CKD (eGFR <30 mL/min/1.73 m²) are at 110 to 580 times higher risk of kidney failure (depending on level of albuminuria) compared with those with eGFR greater than or equal to 90 mL/min/1.73 m² and with normal albuminuria.²⁹ Uptake of SGLT2 inhibitors among those with stage 4 CKD has been limited, partly because the level of evidence provided by this collaborative meta-analysis was not previously available, but also because of the continuing perception of SGLT2 inhibitors as glucose-lowering agents, whose glycosuric effect is almost completely abrogated at this level of kidney function.³⁰ Additionally, there were residual concerns regarding the safety in this population because of the relatively limited number of participants with stage 4 CKD enrolled in individual trials. This meta-analysis identified no increased risk of serious AKI, even among those with stage 4 CKD. The overall reduction in serious AKI indicates a strong case for updating labeling on AKI risk with SGLT2 inhibitors, which may help to assuage any remaining concerns about their safety in patients with stage 4 CKD. Better treatment for this very high–risk group offers a major opportunity to reduce the individual and societal burden of kidney failure.

Equally important, the clear kidney benefits observed in patients across the spectrum of albuminuria suggest that guidelines that offer weaker recommendations for patients with lower levels of albuminuria should be updated. These results extend a 2019 meta-analysis that reported no effect modification by baseline albuminuria,³¹ now in a much larger and broader population, including those without diabetes. Patients with severely increased albuminuria (UACR >300 mg/g) are at highest risk of CKD progression and the absolute benefits of SGLT2 inhibitors are largest in these individuals. However, the National Health and Nutrition Examination Survey estimates that 8.6% of adults in the US have a UACR of 30 to 300 mg/g, while only 1.6% have a UACR greater than 300 mg/g. Treating the 5-fold–larger population in the US with moderately increased albuminuria may deliver large additional health gains. The same is likely to be true in many other geographical regions where lower levels of albuminuria represent the majority of cases of CKD.³² When initiated in early-stage CKD, renin–angiotensin system blockade has been shown to provide more years free from kidney failure compared with intervention in advanced CKD.³³ Taken together, more widespread implementation of SGLT2 inhibitors in patients with lower levels of albuminuria and early-stage CKD, particularly those with concomitant diabetes, could have substantial benefits in preventing kidney function decline at a population level. Nevertheless, rates of CKD progression observed in trial populations, such as those included in this meta-analysis, are not necessarily generalizable to routine care. When considering the broader benefits of SGLT2 inhibitors on hospitalization and mortality outcomes, as summarized in the accompanying companion study,¹⁰ the totality of the available evidence indicates that the direct economic benefits to health systems are likely to be considerable because SGLT2 inhibitors are already available as generic medications in several countries, with patent expiry expected in most jurisdictions within the next few years. Economic evaluation in different settings may provide important information for policymakers and health systems.

The acute reductions in eGFR summarized in this meta-analysis are modest across all eGFR and UACR subgroups and considerably smaller than the 20% to 30% threshold at which temporary treatment discontinuation is recommended in clinical practice guidelines.² Although relative effects are most informative for evaluating long-term outcomes, acute declines in eGFR are typically viewed as a safety consideration and are therefore more commonly interpreted in absolute terms. The smaller absolute reductions observed at lower baseline eGFR resemble the pattern seen with renin–angiotensin system blockade.³⁴ Findings of this study should reassure clinicians that these effects are minimal at lower eGFR and should not preclude treatment initiation.

Limitations

The findings from this collaborative meta-analysis have some limitations. First, data on the effects of SGLT2 inhibitors in patients with eGFR less than 20 mL/min/1.73 m² was limited to 1 trial—EMPA-KIDNEY—precluding recommendations to initiate therapy below current thresholds. The RENAL LIFECYCLE trial will address this gap by evaluating dapagliflozin vs placebo in approximately 1500 patients with eGFR less than 25 mL/min/1.73 m², kidney failure requiring dialysis, or kidney transplant recipients.³⁵ Second, despite pooling of data across 10 trials, the number of primary outcome events was limited among participants without diabetes and with normal albuminuria (UACR <30 mg/g), reducing the statistical power to assess effect modification by albuminuria separately in patients without diabetes, though the use of the continuous outcome of eGFR slope provided better power to assess effects on kidney function in these individuals. Third, the distribution of albuminuria across eGFR subgroups in this meta-analysis reflects the entry criteria of the included trials and may not mirror routine clinical practice (eg, most participants with advanced CKD had severely increased albuminuria). However, the consistent effects across eGFR, UACR, and KDIGO risk categories support the generalizability of these relative effect estimates to broader CKD populations in clinical care. Fourth, although a 1-stage meta-analysis was not conducted, the individual participant-level approach to harmonizing outcomes is consistent with methods used by other large cardiometabolic consortia.²⁹

Conclusions

SGLT2 inhibitors lower the risk of CKD progression regardless of baseline eGFR or albuminuria and reduce serious acute kidney injury and kidney failure. These data provide strong support for their routine use to improve kidney outcomes across the spectrum of kidney function and albuminuria among patients with type 2 diabetes, CKD, or heart failure.

Supplement 1.

eTable 1. Summary of included trials.

eTable 2. Risk of bias assessment.

eFigure 1. PRISMA flow diagram.

eFigure 2. Summary of the effects of SGLT2i on clinical kidney outcomes.

eFigure 3. Effects of SGLT2i on CKD progression according to baseline eGFR, including in participants with eGFR <20 mL/min/1.73m2.

eFigure 4. Effects of SGLT2i on CKD progression according to baseline KDIGO risk categories, overall and by diabetes status.

eFigure 5. Absolute effects of SGLT2i on acute changes in eGFR according to eGFR and UACR, overall, and by diabetes status.

eFigure 6. Relative and absolute effects of SGLT2i on acute changes in eGFR according to baseline KDIGO risk, overall and by diabetes status.

eFigure 7. Absolute effects of SGLT2i on chronic eGFR slope according to baseline eGFR and UACR, overall, and by diabetes status.

eFigure 8. Relative and absolute effects of SGLT2i on chronic eGFR slope according to baseline KDIGO risk, overall, and by diabetes status.

eFigure 9. Relative and absolute effects of SGLT2i on total eGFR slope according to baseline eGFR and UACR, overall, and by diabetes status.

eFigure 10. Effects of SGLT2i on serious AKI by baseline eGFR and UACR.

jama-e2520834-s001.pdf^{(5.1MB, pdf)}

Supplement 2.

Nonauthor collaborators

jama-e2520834-s002.pdf^{(129.3KB, pdf)}

Supplement 3.

Data sharing statement

jama-e2520834-s003.pdf^{(14.1KB, pdf)}

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