# Effects of empagliflozin on conventional and exploratory acute and chronic kidney outcomes: an individual participant-level meta-analysis

William G Herrington, Zhaojing J Che, Rebecca Sardell, Alistair J Roddick, Parminder K Judge, Christoph Wanner, Sibylle J Hauske, Stefan Anker, Javed Butler, Gerasimos Filippatos, et al.

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## To cite this version:

William G Herrington, Zhaojing J Che, Rebecca Sardell, Alistair J Roddick, Parminder K Judge, et al. "Effects of empagliflozin on conventional and exploratory acute and chronic kidney outcomes: an individual participant-level meta-analysis." The Lancet. Diabetes & Endocrinology, 2025, 10.1016/S2213-8587(25)00222-0. hal-05316232

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# Effects of empagliflozin on conventional and exploratory acute and chronic kidney outcomes: an individual participant-level meta-analysis

William G Herrington*, Zhaojing J Che*, Rebecca Sardell, Alistair J Roddick, Parminder K Judge, Christoph Wanner, Sibylle J Hauske†, Stefan Anker, Javed Butler, Gerasimos Filippatos, Milton Packer, Faiez Zannad, Dominik Steubl, Martina Brueckmann, Jennifer B Green, Jonathan R Emberson, Martin J Landray, Colin Baigent, Richard Haynes*, Natalie Staplin*

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## Summary

**Background** Uncertainty remains about effects of sodium-glucose co-transporter-2 (SGLT2) inhibition on kidney outcomes in individuals with slowly progressive chronic kidney disease (eg, low albuminuria) and those at risk of large acute estimated glomerular filtration rate (eGFR) dips on initiation of such treatment. We aimed to explore the effects of empagliflozin on a range of kidney outcomes in these population subtypes.

**Methods** In this meta-analysis, we used individual-level data from 23340 participants in four large placebo-controlled trials (EMPA-REG OUTCOME, EMPEROR-Reduced, EMPEROR-Preserved, and EMPA-KIDNEY) to assess the effects of empagliflozin on conventional and exploratory acute and chronic kidney outcomes. We then assessed whether effects varied by predicted size of the acute eGFR dip on treatment initiation or among other key population subtypes using tests for heterogeneity and trend. The individual-level data were requested from Boehringer Ingelheim (Ingelheim, Germany).

**Findings** Compared with placebo, allocation to empagliflozin reduced the risk of a marker of acute kidney injury (a ≥50% increase in serum creatinine in consecutive follow-up samples) by 20% (hazard ratio 0.80 [95% CI 0.72–0.88]; 1573 outcomes), acute kidney injury adverse events by 27% (0.73 [0.63–0.85]; 694 outcomes), a categorical chronic kidney disease progression outcome by 30% (0.70 [0.63–0.78]; 1403 outcomes), and kidney failure by 34% (0.66 [0.55–0.79]; 490 outcomes). Empagliflozin slowed a chronic annual rate of eGFR decline by 64% (95% CI 59–69) and off-treatment dip-free slope—a post-hoc outcome using randomisation and off-treatment eGFR values available in a subset of 10630 participants—by 64% (54–73). These kidney benefits were similar in subgroups divided by predicted size of acute eGFR dip, and were present irrespective of diabetes or heart failure status, level of kidney function, or albuminuria.

**Interpretation** SGLT2 inhibition reduces risk of acute and chronic kidney outcomes irrespective of the size of the acute dip in eGFR. Kidney benefits are evident irrespective of diabetes status, heart failure status, primary cause of kidney disease, and markers of severity of these diseases.

**Funding** None.

**Copyright** © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

## Introduction

It is estimated that around 850 million adults globally have chronic kidney disease (CKD)¹ and that at least 64 million people are affected by heart failure.² Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce risk of kidney failure and cardiovascular disease in individuals with CKD or heart failure irrespective of diabetes status,³ and are recommended as a standard of care in these conditions.⁴,⁵ To ensure maximum benefits, SGLT2 inhibitor treatment should be continued long term.⁶ There remain uncertainties, however, on the effects of SGLT2 inhibitors on kidney outcomes in certain population subtypes with an indication for prescription. These subtypes include trial participants at lower risk of CKD progression (eg, when albuminuria is low and in

the absence of diabetes) and those at risk of large acute estimated glomerular filtration rate (eGFR) dips on initiation of treatment.

When SGLT2 inhibitors cause a large acute dip in eGFR on initiation,⁷ there are concerns about risk of acute kidney injury (AKI) and attenuated benefits on CKD progression. These concerns can be particularly relevant in individuals with chronic heart failure when intraglomerular pressure could be low.⁸ Reassuringly, SGLT2 inhibition has also been shown to reduce risk of adverse events of AKI,³ and data from four large placebo-controlled trials have shown how some of the acute dip in eGFR on initiation of SGLT2 inhibitors reverses on systematic eGFR re-measurement, even after several years of SGLT2 inhibitor use.⁷,⁹⁻¹¹ Nevertheless, it remains

### *Lancet Diabetes Endocrinol* 2025

Published Online

October 10, 2025

https://doi.org/10.1016/
S2213-8587(25)00222-0

See Online/Comment

https://doi.org/10.1016/
S2213-8587(25)00224-4

*Joint contributions

†Deceased

Renal Studies Group, Clinical
Trial Service Unit and
Epidemiological Studies Unit,
Nuffield Department of
Population Health, University
of Oxford, Oxford, UK

(Prof W G Herrington FRCP,

Z J Che PhD, R Sardell PhD,

A J Roddick MBBS, P K Judge PhD,

Prof C Wanner MD,

Prof J R Emberson PhD,

Prof M J Landray FMedSci,

Prof C Baigent FMedSci,

Prof R Haynes DM,

N Staplin PhD); Oxford Kidney

Unit, Oxford University

Hospitals NHS Foundation

Trust, Oxford, UK

(Prof W G Herrington,

A J Roddick, P K Judge,

Prof R Haynes); Würzburg

University Clinic, Würzburg,

Germany (Prof C Wanner);

Vth Department of Medicine,

University Medical Centre

Mannheim, University of

Heidelberg, Heidelberg,

Germany (S J Hauske MD);

Boehringer Ingelheim

International, Ingelheim,

Germany (S J Hauske,

D Steubl MD,

Prof M Brueckmann MD);

Department of Cardiology and

Berlin Institute of Health

Center for Regenerative

Therapies, German Center for

Cardiovascular Research

Partner Site Berlin, Charité

Universitätsmedizin, Berlin,

Germany (S Anker MD); Baylor

Scott and White Research

Institute, Dallas, TX, USA

(Prof J Butler MD); Department

of Medicine, University of

Mississippi, Jackson, MS, USA
(Prof J Butler); Department of
Cardiology, Attikon University
Hospital, School of Medicine,
National and Kapodistrian
University of Athens, Athens,
Greece (Prof G Filippatos MD);
Baylor Heart and Vascular
Institute, Baylor University
Medical Center, Dallas, TX, USA
(Prof M Packer MD); University
of Lorraine, Inserm, Centre
d'Investigations Cliniques
Plurithématique 14-33, and
Inserm U1116, Nancy, France
(Prof F Zannad PhD MD); Faculty
of Medicine Mannheim,
University of Heidelberg,
Mannheim, Germany
(Prof M Brueckmann); Division
of Endocrinology, Department
of Medicine, Duke Clinical
Research Institute, Duke
University Medical Center,
Durham, NC, USA
(Prof J B Green MD)
Correspondence to:
Prof William G Herrington, Renal
Studies Group, Clinical Trial
Service Unit and Epidemiological
Studies Unit, Nuffield
Department of Population
Health, Oxford OX3 7LF, UK
will.herrington@ndph.ox.ac.uk

Research in context

Evidence before this study

Our previous systematic review and collaborative tabular meta-
analysis of large randomised placebo-controlled trials showed
that sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce
the risk of progression of chronic kidney disease and acute kidney
injury in individuals with chronic kidney disease or heart failure,
irrespective of diabetes status. A limitation of this previous study
was the inability to assess subgroups of particular
interest (eg, by predicted size of acute estimated glomerular
filtration rate [eGFR] dip, and by albuminuria among patients
without diabetes). This was largely due to insufficient individual
participant data. As part of the systematic review, we identified
some SGLT2 inhibitor trials that had explored whether the effects
of SGLT2 inhibition are modified by the observed acute eGFR dip,
but found no published study that had used an unbiased
approach that preserves the balance of randomisation.

Added value of this study

This study addressed the limitations of our previous study by
using individual participant-level data from trials representing
the key populations for whom there are indications for use of
SGLT2 inhibition, and by exploring in detail the effects on a range
of kidney outcomes in these key subgroups. The study included
derivation of models to predict the size of the acute eGFR dip. We
found strong evidence for important clinical benefits in all

outcomes, including the definitive kidney-related clinical
outcome of kidney failure. Compared with placebo, empagliflozin
reduced the risk of increases of 50% or more in serum creatinine
between consecutive follow-up visits by 20%, and reports of
acute kidney injury adverse events by 27%. The risk of kidney
failure was reduced by 34%. Benefits were evident irrespective of
the predicted size of the acute dip in eGFR and irrespective of
diabetes status, heart failure status, primary cause of kidney
disease, level of albuminuria, and other markers of severity of
these diseases. After the acute dip, the rate of chronic eGFR slope
was reduced by 64%. These relative benefits were greatest in
participants with diabetes (slowing the rate of decline by 74%),
but were also evident in participants without diabetes with low
albuminuria (where the rate of decline was slowed by 47%).
Findings were consistent when conventional and exploratory
outcome derivation approaches were employed.

Implications of all the available evidence

The findings support arguments for widespread early use of
SGLT2 inhibitors to reduce the global burden of kidney disease,
and simplification of current guidance on whom to treat. The
findings also provide reassuring data for prescribers regarding
kidney safety and acute eGFR dips, and should discourage routine
remeasurement of eGFR after initiation in most individuals.

important to further explore the clinical relevance of the
acute eGFR dip to determine whether large dips attenuate
the benefits of SGLT2 inhibitors, or whether dips are an
epiphenomenon without clinical importance. Previous
exploration of these hypotheses has been limited to non-
randomised approaches.⁹,¹⁰,¹²,¹³ Individual participant-level
data meta-analysis provides an opportunity to model
every participant's predicted size of acute eGFR dip on
initiation of an SGLT2 inhibitor, allowing for unbiased
subgroup analyses of effects of allocation to empagliflozin
versus placebo on kidney outcomes by size of predicted
acute eGFR dip.

The acute eGFR dip also makes the assessment of annual rate of eGFR decline (ie, slope analyses) more complex, particularly when the rate of CKD progression is slow. There are uncertainties about whether conventional chronic eGFR slope analysis—an assessment that excludes eGFR data before the acute eGFR dip—represents a reliable method to assess treatment effects.¹⁴ These complexities have contributed to uncertainties about the benefits of SGLT2 inhibition on risk of CKD progression in trials where the numbers of kidney failure outcomes were too low to confirm benefits on hard clinical kidney outcomes.⁸

In this report, we aimed to explore the uncertainties
on the effects of SGLT2 inhibitors on kidney outcomes
in trial participants at lower risk of CKD progression and
at different risks of acute eGFR dips. This necessitated
availability of individual participant-level data to derive

subgroups of participants of particular interest, and to
explore with sufficient statistical power a range of
conventional and exploratory AKI and CKD outcomes.

Methods

Search strategy and selection criteria

We have previously reported results of our literature
searches for large, double-blind, placebo-controlled SGLT2
inhibitor trials, which were all deemed to have low risk of
bias in assessments using the Cochrane Risk of Bias tool
version 2 (PROSPERO CRD42022351618; appendix p 2).¹⁵
We developed an outline protocol for this meta-analysis
using individual participant data from the four large
empagliflozin trials. Individual participant-level data were
sought from Boehringer Ingelheim (Ingelheim, Germany).
Four trials were included in the outline protocol:
EMPA-REG OUTCOME² in type 2 diabetes and previous
atherosclerotic cardiovascular disease, EMPEROR-
Reduced¹⁶ and EMPEROR-Preserved¹⁷ in heart failure, and
EMPA-KIDNEY in CKD at risk of progression.⁶,¹⁸ The
EMPA-MI trial was not included because safety laboratory
parameters including eGFR during follow-up were not
collected.¹⁹ All trials were approved by the relevant ethics
committees, and all participants provided written informed
consent for research participation.

Definitions

Based on individual trial follow-up schedules, the acute dip in eGFR was measured as the percentage change

For the protocol of the four
large empagliflozin trial renal
meta-analysis see https://www.
empakidney.org/downloads
during early follow-up, defined as between baseline and approximately 4 weeks after randomisation in the EMPA-REG OUTCOME and EMPEROR trials, and as about 2 months in the EMPA-KIDNEY trial.

The two categorical AKI outcomes were: an exploratory outcome defined as a 50% or greater increase in follow-up serum creatinine (measured by either a local or central laboratory) compared with the most recent laboratory value taken less than 1 year previously, based on KDIGO AKI definitions and a prespecified approach used in another CKD trial;²⁰,²¹ and the MedDRA Preferred Term for AKI (appendix pp 3–5).

The categorical CKD outcomes were kidney failure and a composite of kidney disease progression, defined as a composite of kidney failure (ie, initiation of maintenance dialysis or receipt of a kidney transplant, a sustained decrease in the eGFR to <15 mL/min per 1.73 m² [or <10 mL/min per 1.73 m² in EMPA-KIDNEY or when baseline eGFR was <30 mL/min per 1.73 m²], or death from kidney failure) or a sustained decrease in eGFR from baseline of at least 40%.

The continuous CKD outcomes were based on annual rate of eGFR change (ie, eGFR slopes). This surrogate has particular advantages when trial follow-up is short and when assessing for differences between subgroups. Effects on eGFR slopes on a relative scale are emphasised because any subgroup analysis based on absolute effects combines any between-subgroup differences in baseline absolute risk plus any differences in the relative effect of empagliflozin.¹¹ Results on mean annual rates of change in eGFR after the acute dip during early follow-up (chronic slopes; 4 weeks or 2 months after randomisation to the final follow-up visit) were emphasised over effects on eGFR from randomisation to the final follow-up visit (total slopes) because the acute dip results in a systematic underestimate of effects and cannot be extrapolated long term. Post hoc, to validate the emphasis on chronic eGFR slope, we explored the effects on change in eGFR from randomisation to the off-treatment value of eGFR, when participants were not taking allocated active or placebo empagliflozin (off-treatment dip-free slopes; appendix pp 6–7).

The heart failure outcomes were a composite of cardiovascular death or hospitalisation for heart failure with or without kidney disease progression, and hospitalisation for heart failure alone.

## Data analysis

A multivariable model was used for predicting acute eGFR dip. The model used data from participants allocated to empagliflozin representing all four included trials. Relationships between continuous predictors and the dependent variable that exhibited non-linearity (ie, eGFR and urinary albumin-to-creatinine ratio [uACR]) were categorised into five equally sized subgroups. Initial univariable analyses were done to identify independent predictors of acute eGFR dips. Characteristics with

p values less than 0.2 were carried forward into multivariable modelling. For the final model, backward selection was used, retaining variables that were statistically significant at a 5% level. Model performance was validated internally using bootstrapping to estimate potential optimism and account for any overfitting (appendix pp 8–11).

For the meta-analysis of the effects of empagliflozin versus placebo on outcomes, all participants were analysed according to their original allocated groups, irrespective of whether or not they remained adherent with study treatment (ie, intention-to-treat principles were followed).²² The empagliflozin 25 mg and 10 mg groups from the EMPA-REG OUTCOME trial were combined and compared with matching placebo. Analyses of allocation to empagliflozin versus placebo were done for each trial separately and then combined using an inverse-variance weighted approach.

Categorical outcomes were assessed in separate Cox models adjusted, where possible, for age (continuous), sex (male or female, from medical records), diabetes status, eGFR (categorical: <30, ≥30 to <45, ≥45 to <60, or ≥60 mL/min per 1.73 m²), and uACR (categorical: <30, ≥30 to <300, ≥300 to <1000, or ≥1000 mg/g). Effects of allocation to empagliflozin versus placebo on chronic and total eGFR slope were estimated using shared parameter models¹ adjusted for the same variables as the Cox models (with the exception of eGFR). Models estimating chronic slope were additionally adjusted for baseline eGFR (as a continuous variable) and the interaction between baseline eGFR and follow-up time. This approach jointly models the annual rate of change in eGFR using a linear mixed model (with random effects for each participant's slope and intercept) and the time-to-event initiation of maintenance dialysis, receipt of a kidney transplant, or death (using a Weibull survival model in which the scale parameter is assumed to be linearly related to the random effects from the linear mixed model). The model output can be thought of as the average of the participant-specific slopes, conditional on the baseline covariates in the model, and in the hypothetical scenario where eGFR had continued to be measured beyond the time of maintenance kidney replacement therapy or death. As with other methods, the estimates these outputs provide merely reflect averages over the follow-up period of interest (hence, in the context of a drug that has an initial acute effect, the total slope requires careful interpretation). Off-treatment dip-free slopes were calculated by dividing the difference between off-treatment eGFR and randomisation eGFR by the time between the two measurements. Differences in mean slopes were then calculated by adjusted linear regression using the same covariates as the Cox models (appendix pp 12–14).

To assess for effect modification between important prespecified subgroups, the proportional effects of empagliflozin in the different subgroups were
estimated and compared with the overall effect using standard χ² tests for heterogeneity or, where appropriate, χ² tests for trend. Subgroups included groups by predicted size of acute eGFR dip, diabetes status, primary cause of kidney disease, heart failure status, and markers of disease severity including eGFR, uACR, and N-terminal pro-B-type natriuretic peptide

(NT-proBNP; see outline protocol for details of subgroup definitions). In addition, we explored how the previously identified effect modification by baseline level of uACR might vary by baseline diabetes status in analyses stratified by diabetes and then by uACR of 200 mg/g or higher versus uACR lower than 200 mg/g (these cutoffs were selected because KDIGO use them

<table><thead><tr><th></th><th>EMPA-REG OUTCOME (n=7020)</th><th>EMPEROR-Reduced (n=3726)</th><th>EMPEROR-Preserved (n=5985)</th><th>EMPA-KIDNEY (n=6609)</th><th>Overall (n=23 340)</th></tr></thead><tbody><tr><td>Age at randomisation, years</td><td>63.1 (8.6)</td><td>66.8 (11.0)</td><td>71.9 (9.4)</td><td>63.8 (13.9)</td><td>66.2 (11.5)</td></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>5016 (71.5%)</td><td>2836 (76.1%)</td><td>3311 (55.3%)</td><td>4417 (66.8%)</td><td>15 580 (66.8%)</td></tr><tr><td>Female</td><td>2004 (28.5%)</td><td>890 (23.9%)</td><td>2674 (44.7%)</td><td>2192 (33.2%)</td><td>7760 (33.2%)</td></tr><tr><td>Race</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>White</td><td>5081 (72.4%)</td><td>2626 (70.5%)</td><td>4539 (75.8%)</td><td>3859 (58.4%)</td><td>16 105 (69.0%)</td></tr><tr><td>Black or African American</td><td>357 (5.1%)</td><td>257 (6.9%)</td><td>258 (4.3%)</td><td>262 (4.0%)</td><td>1134 (4.9%)</td></tr><tr><td>Asian</td><td>1517 (21.6%)</td><td>671 (18.0%)</td><td>824 (13.8%)</td><td>2393 (36.2%)</td><td>5405 (23.2%)</td></tr><tr><td>Other (eg, mixed race)</td><td>64 (0.9%)</td><td>114 (3.1%)</td><td>362 (6.0%)</td><td>95 (1.4%)</td><td>635 (2.7%)</td></tr><tr><td>Previous disease</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Previous diabetes</td><td>7020 (100%)</td><td>1853 (49.7%)</td><td>2936 (49.1%)</td><td>3004 (45.5%)</td><td>14 813 (63.5%)</td></tr><tr><td>History of atherosclerotic disease</td><td>6999 (99.7%)</td><td>2403 (64.5%)</td><td>3319 (55.5%)</td><td>1765 (26.7%)</td><td>14 486 (62.1%)</td></tr><tr><td>History of heart failure</td><td>706 (10.1%)</td><td>3726 (100%)</td><td>5985 (100%)</td><td>658 (10.0%)</td><td>11 075 (47.5%)</td></tr><tr><td>Clinical measurements</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Systolic blood pressure, mm Hg</td><td>135.4 (17.0)</td><td>122.0 (15.7)</td><td>131.8 (15.6)</td><td>136.5 (18.3)</td><td>132.7 (17.6)</td></tr><tr><td>Diastolic blood pressure, mm Hg</td><td>76.7 (9.9)</td><td>73.9 (10.8)</td><td>75.7 (10.6)</td><td>78.1 (11.8)</td><td>76.4 (10.9)</td></tr><tr><td>BMI, kg/m²</td><td>30.6 (5.3)</td><td>27.9 (5.4)</td><td>29.8 (5.9)</td><td>29.7 (6.8)</td><td>29.7 (6.0)</td></tr><tr><td>eGFR, mL/min per 1.73 m²</td><td>75.6 (19.8)</td><td>62.0 (21.6)</td><td>60.6 (19.8)</td><td>37.3 (14.4)</td><td>58.8 (23.9)</td></tr><tr><td>&lt;30</td><td>31 (0.4%)</td><td>204 (5.5%)</td><td>309 (5.2%)</td><td>2282 (34.5%)</td><td>2826 (12.1%)</td></tr><tr><td>≥30 to &lt;45</td><td>513 (7.3%)</td><td>693 (18.6%)</td><td>1114 (18.6%)</td><td>2928 (44.3%)</td><td>5248 (22.5%)</td></tr><tr><td>≥45 to &lt;60</td><td>1066 (15.2%)</td><td>900 (24.2%)</td><td>1562 (26.1%)</td><td>888 (13.4%)</td><td>4416 (18.9%)</td></tr><tr><td>≥60</td><td>5408 (77.0%)</td><td>1927 (51.7%)</td><td>2998 (50.1%)</td><td>511 (7.7%)</td><td>10 844 (46.5%)</td></tr><tr><td>Data missing</td><td>2 (&lt;0.1%)</td><td>2 (0.1%)</td><td>2 (&lt;0.1%)</td><td>0</td><td>6 (&lt;0.1%)</td></tr><tr><td>Geometric mean uACR, mg/g</td><td>26 (25–27)</td><td>30 (28–31)</td><td>28 (27–29)</td><td>222 (212–233)</td><td>50 (48–51)</td></tr><tr><td>Median uACR, mg/g</td><td>18 (7–72)</td><td>22 (8–81)</td><td>21 (8–71)</td><td>329 (49–1069)</td><td>34 (9–236)</td></tr><tr><td>&lt;30</td><td>4171 (59.4%)</td><td>2076 (55.7%)</td><td>3472 (58.0%)</td><td>1328 (20.1%)</td><td>11 047 (47.3%)</td></tr><tr><td>≥30 to &lt;300</td><td>2013 (28.7%)</td><td>1235 (33.1%)</td><td>1858 (31.0%)</td><td>1864 (28.2%)</td><td>6970 (29.9%)</td></tr><tr><td>≥300 to &lt;1000</td><td>478 (6.8%)</td><td>243 (6.5%)</td><td>374 (6.2%)</td><td>1664 (25.2%)</td><td>2759 (11.8%)</td></tr><tr><td>≥1000</td><td>291 (4.1%)</td><td>153 (4.1%)</td><td>256 (4.3%)</td><td>1753 (26.5%)</td><td>2453 (10.5%)</td></tr><tr><td>Data missing</td><td>67 (1.0%)</td><td>19 (0.5%)</td><td>25 (0.4%)</td><td>0</td><td>111 (0.5%)</td></tr><tr><td>NT-proBNP, ng/L</td><td>NR</td><td>1910 (1115–3476)</td><td>974 (498–1734)</td><td>160 (69–419)</td><td>709 (207–1680)</td></tr><tr><td>HbA₁c, %</td><td>8.1% (0–8)</td><td>6.6% (1–4)</td><td>6.5% (1–3)</td><td>6.3% (1–2)</td><td>6.9% (1–4)</td></tr><tr><td>HbA₁c, mmol/mol</td><td>64.7 (9.3)</td><td>48.2 (15.5)</td><td>47.2 (14.5)</td><td>45.5 (13.6)</td><td>52.0 (15.5)</td></tr><tr><td>Concomitant medication use</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Renin-angiotensin system inhibitor</td><td>5677 (80.9%)</td><td>3289 (88.3%)</td><td>4833 (80.8%)</td><td>5628 (85.2%)</td><td>19 427 (83.2%)</td></tr><tr><td>Any diuretic</td><td>3035 (43.2%)</td><td>3541 (95.0%)</td><td>5160 (86.2%)</td><td>2815 (42.6%)</td><td>14 551 (62.3%)</td></tr><tr><td>Kidney failure risk, %</td><td>0.04% (0.01–0.26)</td><td>0.21% (0.03–1.25)</td><td>0.18% (0.03–1.01)</td><td>9.26% (2.70–29.20)</td><td>0.38% (0.03–4.06)</td></tr><tr><td>Cause of kidney disease</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Diabetic kidney disease</td><td>NR</td><td>185 (5.0%)</td><td>371 (6.2%)</td><td>2057 (31.1%)</td><td>2613 (11.2%)</td></tr><tr><td>Hypertensive or renovascular disease</td><td>NR</td><td>160 (4.3%)</td><td>428 (7.2%)</td><td>1445 (21.9%)</td><td>2033 (8.7%)</td></tr><tr><td>Glomerular disease</td><td>NR</td><td>5 (0.1%)</td><td>11 (0.2%)</td><td>1669 (25.3%)</td><td>1685 (7.2%)</td></tr><tr><td>Other or unknown</td><td>NR</td><td>195 (5.2%)</td><td>358 (6.0%)</td><td>1438 (21.8%)</td><td>1991 (8.5%)</td></tr><tr><td>No reported disease</td><td>NR</td><td>3181 (85.4%)</td><td>4817 (80.5%)</td><td>0</td><td>7998 (34.3%)</td></tr></tbody></table>

(Table 1 continues on next page)
<table><thead><tr><th></th><th>EMPA-REG OUTCOME<br>(n=7020)</th><th>EMPEROR-Reduced<br>(n=3726)</th><th>EMPEROR-Preserved<br>(n=5985)</th><th>EMPA-KIDNEY<br>(n=6609)</th><th>Overall<br>(n=23 340)</th></tr></thead><tbody><tr><td>(Continued from previous page)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Predicted acute dip, %*</td><td>-3.5% (2.1)</td><td>-4.9% (2.0)</td><td>-5.3% (2.0)</td><td>-6.1% (2.5)</td><td>-4.9% (2.4)</td></tr><tr><td>-5% or higher (smallest dip or<br>increase)</td><td>5437 (77.5%)</td><td>1904 (51.1%)</td><td>2604 (43.5%)</td><td>1984 (30.0%)</td><td>11 929 (51.1%)</td></tr><tr><td>-7% to lower than -5%</td><td>1140 (16.2%)</td><td>1223 (32.8%)</td><td>2225 (37.2%)</td><td>1834 (27.8%)</td><td>6422 (27.5%)</td></tr><tr><td>-8% to lower than -7%</td><td>231 (3.3%)</td><td>314 (8.4%)</td><td>649 (10.8%)</td><td>872 (13.2%)</td><td>2066 (8.9%)</td></tr><tr><td>Lower than -8% (largest dip)</td><td>124 (1.8%)</td><td>209 (5.6%)</td><td>477 (8.0%)</td><td>1256 (19.0%)</td><td>2066 (8.9%)</td></tr><tr><td>Data missing†</td><td>88 (1.3%)</td><td>76 (2.0%)</td><td>30 (0.5%)</td><td>663 (10.0%)</td><td>857 (3.7%)</td></tr><tr><td>Follow-up, years</td><td>3.1 (2.2-3.6)</td><td>1.3 (0.9-1.8)</td><td>2.2 (1.5-2.7)</td><td>2.0 (1.5-2.4)</td><td>2.2 (1.5-2.8)</td></tr></tbody><tfoot><tr><td colspan="6">Data are mean (SD), n (%), or median (IQR). eGFR=estimated glomerular filtration rate. uACR=urine albumin-to-creatinine ratio. NT-proBNP=N-terminal pro-B-type natriuretic peptide. NR=not recorded. *Predicted acute dip—defined as the percentage change in eGFR during early follow-up, ie, between 0 and 1 months (or 2 months in EMPA-KIDNEY)—is estimated from the multivariable risk model (appendix pp 20–21). †Participants with missing values for some characteristics that are included in the model were recorded as missing a predicted acute dip.</td></tr><tr><td colspan="6"><b>Table 1: Baseline characteristics of participants from the four included trials</b></td></tr></tfoot></table>

for different classes of recommendation for use of SGLT2 inhibitors in CKD¹).

The University of Oxford (Oxford, UK) carried out the analyses directly on trial datasets using SAS version 9.4.

## Role of the funding source

There was no funding source for this study.

## Results

The four trials with available individual participant data comprised 23340 participants, including 7020 people with type 2 diabetes and previous atherosclerotic cardiovascular disease from EMPA-REG OUTCOME, 9711 with heart failure from the EMPEROR Program, and 6609 with CKD at risk of progression from EMPA-KIDNEY. Overall, 11 075 (47.5%) participants reported a history of heart failure, mean eGFR was 58.8 mL/min per 1.73 m² (SD 23.9; 12 490 [53.5%] participants had an eGFR <60mL/min per 1.73 m²), and median uACR was 34 mg/g (IQR 9–236; table 1). The wide range of eGFR and uACR reflected in the four trials resulted in an overall median 5-year kidney failure risk of 0.38% (IQR 0.03–4.06). The 5-year kidney failure risk was highest in EMPA-KIDNEY (9.3%) and lowest in EMPA-REG OUTCOME (<0.1%).

10630 (45.5%) participants had an off-treatment eGFR after discontinuing study treatment, of whom 6250 were allocated to empagliflozin. Baseline characteristics for this subset of participants were balanced between treatment groups (appendix p 16). In this subset, the mean acute dip in eGFR in the empagliflozin group was 3.5 mL/min per 1.73 m² (SD 8.0) and the mean increase in eGFR after discontinuation of empagliflozin (the un-dip) was 2.8 mL/min per 1.73 m² (8.3; appendix p 19).

All 12228 participants allocated to empagliflozin in the four trials were considered for inclusion in acute eGFR prediction dip models. After model selection,

independent predictors of larger acute eGFR dips included older age (0.9% larger dip per 10 years), higher eGFR (4.6% larger dip for eGFR ≥85 vs <37 mL/min per 1.73 m²) and level of albuminuria (2.9% larger dip for uACR ≥324 vs <6 mg/g), renin–angiotensin system inhibitor and diuretic use (1.1% and 0.8% larger dips with use vs non-use, respectively), higher systolic blood pressure (0.9% larger dip per 17 mm Hg), lower haematocrit (0.9% larger dip per 4.8%), and lower BMI (0.5% larger dip per 5.8 kg/m²). The prediction model showed good discrimination and calibration with observed dip (appendix pp 20–21).

The model was then applied to 22 483 (96.3%) participants from both the empagliflozin and placebo groups. The model predicted a mean acute eGFR dip of 4.9% (SD 2.4), with 11 929 (51.1%) participants predicted to have an acute eGFR dip smaller than 5%. 2066 (8.9%) participants were predicted to dip by more than 8% (table 1).

An acute eGFR dip of more than 14% was observed in 29 482 (24.5%) of 12 031 participants in the empagliflozin groups and 12 191 (12.5%) of 9761 participants in the placebo groups (appendix p 17), with 465 (2.1%) of these 21 792 participants observed to dip by more than 30%. This result was also about twice as likely among those allocated to empagliflozin compared with placebo (311 [2.6%] vs 154 [1.6%] participants).

Overall, only 400 (1.8%) of 21 792 surviving participants stopped the study empagliflozin or placebo treatment by the time of their early follow-up visit. Participants with larger predicted acute eGFR dips were slightly more likely to discontinue empagliflozin or placebo early; 36 (1.9%) of 1884 participants with the largest predicted dip (>8%) discontinued, versus 175 (1.6%) of 11 286 participants with the smallest predicted dip (≤5%; appendix p 17). After adjustment for other predictors of stopping early, participants with higher eGFR and those in EMPA-KIDNEY (vs the other trials) had lower odds of early study treatment discontinuation (appendix p 22).
There were 1573 outcomes of a 50% or greater increase in serum creatinine in consecutive follow-up blood samples. Compared with placebo, allocation to empagliflozin reduced the risk of a 50% or greater increase in serum creatinine by 20% (hazard ratio [HR] 0.80 [95% CI 0.72-0.88]; table 2). These HRs were similar in analyses by predicted size of acute eGFR dip (trend p=0.52). There was also no evidence of effect modification by diabetes status (heterogeneity p=0.34), by primary kidney diagnosis (heterogeneity p=0.39), by baseline eGFR (trend p=0.84) or albuminuria (trend p=0.24), by previous heart failure (heterogeneity p=0.34), or by baseline NT-proBNP (trend p=0.78; figure 1).

There were 694 first reports of AKI adverse events. Overall, allocation to empagliflozin reduced risk of AKI by 27% (HR 0.73 [95% CI 0.63-0.85]) with similar HRs by predicted size of acute eGFR dip (trend p=0.65) and in other subgroups (all heterogeneity or trend p>0.05; figure 1). There was a small number of adverse events of AKI during the early follow-up period, with no significant excess risk in either group (17 [0.1%] in the empagliflozin group vs 14 [0.1%] in the placebo group; HR 0.88 [0.41-1.88]).

There were 1403 categorical kidney disease progression outcomes based on a composite comprising a 40% or greater decline in eGFR from baseline (which included 490 kidney failure outcomes). Compared

with placebo, allocation to empagliflozin reduced the risk of kidney disease progression by 30% (HR 0.70 [95% CI 0.63-0.78]). HRs by predicted size of acute eGFR dip were similar (trend p=0.65), as were HRs by diabetes status (heterogeneity p=0.09), by primary kidney diagnosis (heterogeneity p=0.47), across the uACR subgroups (trend p=0.64), and by heart failure status or NT-proBNP (all heterogeneity or trend p>0.05; figure 2). The overall effect on kidney failure was a 34% reduction in risk (HR 0.66 [0.55-0.79]). Effects on kidney failure were similar across all the subgroups, including those with and without heart failure (all heterogeneity or trend p>0.05; figure 2).

Analyses using the chronic eGFR slope showed that empagliflozin slowed the annual rate of decline by 64% (95% CI 59-69). These relative effects appeared to be largest in the EMPA-REG OUTCOME trial compared with the EMPEROR Program and EMPA-KIDNEY trials (figure 3). Repeating slope analyses by trial population using an exploratory post-hoc method applied to eGFR measured at baseline and then after study treatment discontinuation (off-treatment dip-free slopes) found similar effects on off-treatment dip-free slope to the chronic eGFR slope analyses (albeit with sparse data available from EMPA-KIDNEY, which had only 762 participants with systematic post-treatment eGFR measurements; table 2, figure 3, appendix p 18).

<table><thead><tr><th colspan="2">Additional definition details</th><th>Number of participants with outcome</th><th>Effect of empagliflozin, hazard ratio or relative difference in slope, % (95% CI)</th></tr></thead><tbody><tr><td colspan="4"><strong>Acute kidney injury outcomes: categorical</strong></td></tr><tr><td>≥50% increase in creatinine in consecutive follow-up samples within 1 year</td><td>Based on serial follow-up creatinine values (ie, excludes acute eGFR dip)</td><td>1573</td><td>0.80 (0.72-0.88)</td></tr><tr><td>Acute kidney injury</td><td>Adverse event reports using MedDRA Preferred Term of acute kidney injury</td><td>694</td><td>0.73 (0.63-0.85)</td></tr><tr><td colspan="4"><strong>Chronic kidney disease outcomes: categorical and continuous</strong></td></tr><tr><td>Kidney disease progression</td><td>Composite of kidney failure or a sustained decrease in eGFR of at least 40% from baseline</td><td>1403</td><td>0.70 (0.63-0.78)</td></tr><tr><td>Kidney failure</td><td>Initiation of maintenance dialysis or receipt of a kidney transplant, a sustained decrease in eGFR to less than 15 mL/min per 1.73 m² (or &lt;10 mL/min per 1.73 m² in EMPA-KIDNEY or when baseline eGFR was &lt;30 mL/min per 1.73 m²) or death from kidney failure</td><td>490</td><td>0.66 (0.55-0.79)</td></tr><tr><td>Off-treatment dip-free slope (exploratory post hoc)</td><td>Change in eGFR over time from baseline to systematically measured off-treatment eGFR collected after treatment discontinuation</td><td>10 630*</td><td>64% slower (54-73)</td></tr><tr><td>Total slope</td><td>Change in eGFR over time from baseline to final follow-up (ie, includes acute dip and chronic slope)</td><td>23 340</td><td>42% slower (37-47)</td></tr><tr><td>Chronic slope</td><td>Change in eGFR over time from first follow-up measurement to final follow-up (ie, excludes effect of acute dip)</td><td>23 165†</td><td>64% slower (59-69)</td></tr><tr><td colspan="4"><strong>Cardiovascular and cardiorenal outcomes: categorical</strong></td></tr><tr><td>Cardiovascular death or hospitalisation for heart failure</td><td>..</td><td>2520</td><td>0.75 (0.69-0.81)</td></tr><tr><td>Hospitalisation for heart failure</td><td>..</td><td>1614</td><td>0.70 (0.64-0.77)</td></tr><tr><td>Cardiovascular death, or hospitalisation for heart failure or kidney disease progression (post-hoc composite)</td><td>..</td><td>3646</td><td>0.73 (0.68-0.78)</td></tr></tbody></table>

Further details of outcome definitions and modelling approaches are provided in the online supplemental methods (appendix p 14). eGFR=estimated glomerular filtration rate. *EMPA-REG OUTCOME and the EMPEROR programme systematically measured eGFR 30 days after discontinuation of study treatment. EMPA-KIDNEY measured eGFR in a subset from the UK 4 weeks after final follow-up. 186 (0.7%) of 12 850 participants in the empagliflozin group and 89 (0.8%) of 10 490 participants in the placebo group were missing chronic slope data.

<caption>**Table 2: Kidney outcomes assessed and effect of empagliflozin in the four trials combined**</caption>
<figure><img src="image_5.png" /><figcaption>**Figure 1:** Effect of empagliflozin on acute kidney injury outcomes</figcaption></figure>

50% or greater increases in creatinine in consecutive follow-up samples and adverse event reports of acute kidney injury, overall and by subgroups. eGFR=estimated glomerular filtration rate. uACR=urine albumin-to-creatinine ratio. NT-proBNP=N-terminal pro-B-type natriuretic peptide. *Subgroup-specific analyses were excluded if the trial subgroup included fewer than ten outcomes (with these outcomes only presented in the overall row). †Predicted acute eGFR dips are the estimated percentage changes in eGFR between 0 and 4 weeks in the EMPA-REG OUTCOME, EMPEROR-Preserved, and EMPEROR-Reduced trials, and 2 months in the EMPA-KIDNEY trial. ‡Data missing rows, which count the participants with missing values for predicted acute eGFR dip and NT-proBNP, were excluded from the trend tests.

Effects on chronic slope were greater among participants with diabetes (74%) than those without diabetes (42%; heterogeneity p<0.0001). There was

apparent effect modification by albuminuria in univariate subgroup analyses (trend p<0.0001). In analyses stratified by diabetes status, evidence of effect
<figure><img src="image_6.png" /><figcaption>**Figure 2:** Effect of empagliflozin on chronic kidney outcomes</figcaption></figure>

Effects on categorical kidney disease progression outcome and kidney failure, overall and by subgroups. Kidney disease progression is defined as kidney failure (ie, initiation of maintenance dialysis or receipt of a kidney transplant, a sustained decrease in eGFR to <15 mL/min per 1.73 m² [or <10 mL/min per 1.73 m² in EMPA-KIDNEY or when baseline eGFR was <30 mL/min per 1.73 m²], or death from kidney failure) or a sustained decrease from baseline in eGFR of at least 40%. eGFR=estimated glomerular filtration rate. uACR=urine albumin-to-creatinine ratio. NT-proBNP=N-terminal pro-B-type natriuretic peptide. *Subgroup-specific analyses were excluded if the trial subgroup included fewer than ten outcomes (with these outcomes only presented in the overall row). †Predicted acute eGFR dips are the estimated percentage changes in eGFR between 0 and 4 weeks in the EMPA-REG OUTCOME, EMPEROR-Preserved, and EMPEROR-Reduced trials, and 2 months in the EMPA-KIDNEY trial. ‡Data missing rows, which count the participants with missing values for predicted acute eGFR dip and NT-proBNP, were excluded from the trend tests.
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<table><thead><tr><th></th><th>Empagliflozin</th><th>Placebo</th><th>Absolute difference (95% CI)</th><th>Relative difference (95% CI)</th></tr></thead><tbody><tr><td>EMPA-REG OUTCOME</td><td>-0.38 (0.06)</td><td>-2.07 (0.08)</td><td>1.68 (1.49 to 1.88)</td><td>-81% (-91 to -72)</td></tr><tr><td>EMPEROR programme</td><td>-1.11 (0.11)</td><td>-2.67 (0.11)</td><td>1.55 (1.27 to 1.84)</td><td>-58% (-69 to -48)</td></tr><tr><td>EMPA-KIDNEY</td><td>-1.37 (0.08)</td><td>-2.74 (0.08)</td><td>1.37 (1.15 to 1.59)</td><td>-50% (-58 to -42)</td></tr></tbody></table>

<table><caption>**Figure 3:** Effect of empagliflozin on chronic and off-treatment dip-free eGFR slopes</caption><thead><tr><th>Empagliflozin</th><th>Placebo</th><th>Absolute difference (95% CI)</th><th>Relative difference (95% CI)</th></tr></thead><tbody><tr><td>-0.36 (0.08)</td><td>-2.10 (0.12)</td><td>1.75 (1.46 to 2.03)</td><td>-83% (-97 to -69)</td></tr><tr><td>-1.42 (0.17)</td><td>-2.83 (0.17)</td><td>1.41 (0.94 to 1.89)</td><td>-50% (-67 to -33)</td></tr><tr><td>-1.71 (0.17)</td><td>-2.57 (0.17)</td><td>0.87 (0.40 to 1.34)</td><td>-34% (-52 to -15)</td></tr></tbody></table>

Error bars are 95% CIs. Mean post-treatment values are plotted at the median time since randomisation. 6437 EMPA-KIDNEY participants were included in the chronic slope analysis and 762 were included in the off-treatment dip-free slope analysis. eGFR=estimated glomerular filtration rate.

modification by uACR in participants with diabetes remained (heterogeneity p<0.0001 for uACR above vs below 200 mg/g), with no significant effect modification identified in participants without diabetes (heterogeneity p=0.31 for uACR above vs below 200 mg/g). Effects on chronic eGFR slope also appeared to be larger in participants with higher eGFR (trend p=0.0021; figure 4). Analyses using total eGFR slope (ie, acute and chronic slope combined) showed that empagliflozin slowed the annual rate of decline by 42% (95% CI 37–47; table 2, appendix p 23). Allocation to empagliflozin reduced risk of the composite of cardiovascular death or hospitalisation for heart failure by 25% (HR 0.75 [95% CI 0.69–0.81]; 2520 outcomes), with similar HRs by predicted size of acute eGFR dip (trend p=0.31) and other subgroups (all heterogeneity or trend p>0.05; appendix p 24). These findings were mirrored for the outcome of hospitalisation for heart failure alone (HR 0.70 [0.64–0.77]; 1614 outcomes; table 2, appendix p 25).

Discussion

The overarching objective of this study was to use randomised approaches and individual participant-level data to explore in more detail the effects of SGLT2 inhibition on a wider range of kidney outcomes and subgroups than has previously been possible. The included trials represent the different key populations for which SGLT2 inhibitors are indicated. Compared with placebo,

empagliflozin reduced the risk of 50% or greater increases in serum creatinine between consecutive follow-up visits by 20% and reports of AKI adverse events by 27%. The risk of kidney failure was reduced by 34%, with relative benefits similar across studied participant groups, including participants with and without heart failure. All these benefits were irrespective of the predicted size of the acute dip in eGFR. We emphasise eGFR slope analyses as a sensitive method to explore treatment effects in subgroups. After the acute dip, the overall rate of chronic eGFR slope was reduced by 64%. These relative benefits were greatest in participants with diabetes (slowing the rate of decline by 74%), but were also clearly evident in participants without diabetes with low albuminuria (where the rate of decline was slowed by 47%).

Previous analyses have suggested that benefits on chronic eGFR decline are greater in individuals with CKD with low versus high levels of baseline albuminuria.¹¹ Our new analyses include more data in individuals without diabetes, and found that benefits on eGFR slope—whether based on chronic, total, or off-treatment dip-free slope—are evident in participants with a uACR of less than 200 mg/g or of 200 mg/g and higher, irrespective of diabetes status (figure 4, appendix p 23). These findings substantially reinforce the conclusion that neither the presence nor the absence of diabetes, nor albuminuria, should be used to determine whether to treat with an SGLT2 inhibitor,¹¹ and they support strengthening
<figure><img src="image_9.png" /><figcaption>**Figure 4: Effect of empagliflozin on chronic eGFR slopes overall and by subgroups**</figcaption></figure>

eGFR=estimated glomerular filtration rate. uACR=urine albumin-to-creatinine ratio. NT-proBNP=N-terminal pro-B-type natriuretic peptide. *Predicted acute eGFR dips are the estimated percentage changes in eGFR between 0 and 4 weeks in the EMPA-REG OUTCOME, EMPEROR-Preserved, and EMPEROR-Reduced trials, and 2 months in the EMPA-KIDNEY trial. †Data missing rows, which count the participants with missing values for predicted acute eGFR dip and NT-proBNP, were excluded from the trend tests.

international guideline recommendations for use of SGLT2 inhibitors among individuals with CKD without albuminuria.⁴ Although greater relative benefits observed at lower levels of albuminuria did not translate

into greater absolute benefits due to lower baseline risk of progression in such participants (appendix p 18), the findings also support arguments to start SGLT2 inhibitors early in CKD to maximise long-term benefits rather than waiting for albuminuria to develop.

The finding that the predicted size of the acute eGFR dip did not modify the important benefits on either AKI or CKD outcomes is important and consistent with non-randomised observations that acute dips on initiation are not associated with future risk of AKI.⁹,¹⁰ We also found no clear extra risk of adverse events attributed to AKI around the time of the acute eGFR dip. Inclusion of these results and other similarly reassuring published trial findings³,²⁰,²³ in the safety sections of SGLT2 inhibitor labels would be useful information for prescribers, as current labelling only raises concerns. For example, labels list increases in creatinine as an adverse event (the presented data suggest this is a benign effect),²⁴ and AKI is listed as an adverse reaction identified from post-marketing experience (despite the limitations of voluntary reports in establishing causal relationships between drugs and common conditions).²⁵,²⁶

We emphasise results from chronic eGFR slope because, after the acute eGFR dip, this result is most meaningful to individuals wishing to understand the effect of SGLT2 inhibition on their individual eGFR decline over the long term. It was reassuring to observe that at a group level the size of the un-dip was approximately 80% of the size of the observed acute eGFR dip (mean acute dip 3.5 mL/min per 1.73 m² vs un-dip 2.8 mL/min per 1.73 m²). At an individual level, the relationship between the size of the acute dip and un-dip was weak (appendix p 19). The weak relationship could be largely explained by within-person variability in eGFR—resulting from measurement error and natural short-term biological variation—generally being larger than the size of the acute dip. However, this finding also raises a hypothesis that the effect of SGLT2 inhibitors on the acute dip—which is presumed to result from an intrarenal haemodynamic effect—varies over time within individuals. Such a phenomenon could complicate any eGFR slope analysis that assumes the effect of SGLT2 inhibition on the acute dip is constant. Nevertheless, it was reassuring that the analyses of chronic eGFR slope reported similar absolute rates of annual eGFR decline and almost identical effects of empagliflozin to the results from exploratory off-treatment dip-free slope analyses (figure 3). To reduce uncertainty about renal efficacy, we recommend that future trials aiming primarily to assess effects on kidney disease progression are designed to include large numbers of kidney failure outcomes, wherever possible.

The observations in the present work, all taken together, suggest that routine remeasurement of eGFR shortly after initiation of SGLT2 inhibitors is not justifiable in
many individuals, and that a population-based approach with simple implementation should be used in most clinical situations. This approach is analogous to the fire-and-forget approaches recommended for primary prevention using lipid lowering in CKD.²⁷ Current advice for additional monitoring of eGFR after initiation²⁵,²⁶ could be revised to highlight when eGFR monitoring might be important—for example, when multiple risk-modifying therapies that cause volume depletion are initiated simultaneously. Our acute eGFR dip model was not externally validated because its purpose was to order participants by predicted risk for the subgroup analyses. Nevertheless, we predict that larger acute eGFR dips will be observed in individuals who are older, have high albuminuria, have high eGFR, use a renin–angiotensin system inhibitor or diuretics, have high systolic blood pressure, or have low BMI.

The strengths of this meta-analysis include the use of individual participant data from the key populations for which SGLT2 inhibitor treatment is indicated, the wide range of kidney outcomes, and nearly 500 kidney failure outcomes. eGFR slope analyses also enabled statistically powerful approaches to assess in detail for effect modification. Other studies of acute eGFR dips have relied on non-randomised approaches.⁹,¹⁰,¹²,¹³ Although the presented methodology overcomes this limitation, individual-level data from trials assessing other SGLT2 inhibitors were unavailable. Reassuringly, the results from the subset of trials in this report are clear and consistent with the AKI, kidney failure, and cardiovascular outcomes reported in our previous comprehensive systematic review and meta-analysis (appendix p 15). As other work has suggested a class effect on kidney and heart failure outcomes,³,²⁰,²⁸ it appears reasonable to generalise the findings of this study to other SGLT2 inhibitors. Complete ascertainment of all AKI events is challenging to achieve, and only one trial has adjudicated AKI outcomes. This limitation precluded more detailed exploration of effects on different causes of AKI. Systematic and blinded follow-up in the four trials means that HRs are likely to be unbiased.

In summary, in individuals with heart failure, CKD, or diabetes and previous cardiovascular disease, SGLT2 inhibition reduces risk of different conventional and exploratory acute and chronic kidney outcomes irrespective of the size of the acute dip in eGFR, and with benefits that are present irrespective of diabetes status, heart failure status, primary cause of kidney disease, level of albuminuria, and other markers of severity of these diseases. The findings support arguments for widespread early use of SGLT2 inhibitors to reduce the global burden of kidney disease, to simplify current guidance on whom to treat, and to discourage routine remeasurement of eGFR after initiation.

### Contributors

WGH, NS, CB, and RH conceived the meta-analysis and developed its protocol. MP, JB, GF, SA, and FZ refined the analyses and introduced the

approach of off-treatment dip-free slopes. ZJC and NS analysed the raw data, with validation by RS. WGH and NS wrote the first draft of the manuscript with ZJC and RH. ZJC, RS, and NS accessed and verified the data. All authors contributed to data collection, interpretation, and manuscript review, and were responsible for the decision to submit for publication.

### Declaration of interests

This paper has not been published previously in whole or part. The Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford (Oxford, UK) has a staff policy of not accepting honoraria or other payments from the pharmaceutical industry, except for the reimbursement of costs to participate in scientific meetings (https:// www.ctsu.ox.ac.uk/about/ctsu_honoraria_25june14-1.pdf). WGH, ZJC, RS, AJR, PKJ, CW, JG, JRE, MJL, CB, RH, and NS report institutional grant funding from Boehringer Ingelheim and Eli Lilly for the EMPA-KIDNEY trial. NS reports institutional grant funding from Novo Nordisk. RH reports institutional grant funding from Novartis; and trial drug supply from Roche and Regeneron. CW reports consultancy fees as part of his affiliation with Würzburg from AstraZeneca, Bayer, Boehringer Ingelheim, MSD, and Novo Nordisk. JG reports grants to her institution from Merck, Roche, Eli Lilly, and Bluedrop; and consultancy fees from AstraZeneca, Bayer, Boehringer Ingelheim, Corcept, Vertex, Valo, Eli Lilly, Mineralys, and Novo Nordisk. SJH, DS, and MB report employment by Boehringer Ingelheim. JB reports consulting fees from Abbott, Adaptyx, American Regent, Amgen, AskBio, AstraZeneca, Bayer, Boehringer Ingelheim, 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, Novo Nordisk, Pfizer, Pharmacosmos, PharmaIN, Prolaio, Pulnovo, Regeneron, Renibus, Reprieve, Roche, RyCarma, Saillent, Salamandra, Salubris, SC Pharma, SQ Innovation, Secretome, Sequana, Transmural, TikkunLev, Tenex, Tricog, Ultromics, Vera, and Zoll; and personal honoraria for lectures from Boehringer Ingelheim, Lilly, Impulse Dynamics, and Merck. GF reports grants from Boehringer Ingelheim and the European Commission; honoraria from Bayer, Boehringer Ingelheim, Servier, and Novartis; and other fees from Medtronic, Bayer, Vifor, Amgen, Impulse Dynamics, Cardior, and Novo Nordisk. MP reports personal fees for consulting from AbbVie, Altimmune, Amgen, Ardelyx, AstraZeneca, Boehringer Ingelheim, Caladrius, Casana, CSL Behring, Cytokinetics, Imara, Lilly, Moderna, Novartis, Reata, Regeneron, Relypsa, and Salamandra. FZ reports consulting fees from Applied Therapeutics, Bayer, Bioeutics, Boehringer Ingelheim, Cereno Scientific, CellProthera, CVRx, Merck, Owkin, and RoboCure; and stock options in G3 Pharmaceutical and Cereno Pharmaceutical.

### Data sharing

Please see the primary publications in the New England Journal of Medicine from each of the included trials for information on applying to use individual participant-level data. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

### Acknowledgments

We thank the participants, the local site staff, regional coordinating centre staff, and all members of committees that made these trials possible. No funding from industry was provided for this meta-analysis, but each included individual trial was funded by Boehringer Ingelheim. Coauthor representatives of Boehringer Ingelheim reviewed the draft manuscript. This paper is dedicated to the memory of Sibylle Hauske (1976–2025).

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