Key Summary Points
Why carry out this study? |
People with chronic kidney disease (CKD) and type 2 diabetes (T2D) have an increased risk of kidney failure and cardiovascular disease |
The therapeutic landscape for prevention of CKD progression is changing rapidly. Before assessing new medications, it is important to understand the background incidence of cardiovascular events and progression of kidney disease in populations of patients with T2D and CKD using existing medications with expected cardiorenal protective effects like sodium-glucose cotransporter-2 inhibitors (SGLT2i) and the glucagon-like peptide-1 receptor agonists (GLP-1 RA) |
The objective of this multinational, multidatabase study was to describe the incidence of kidney and cardiovascular outcomes in separate cohorts of patients with CKD and T2D who initiated either an SGLT2i or a GLP-1 RA |
What was learned from the study? |
Differences in baseline clinical profile were observed for new users of GLP-1 RA and new users of SGLT2i, and crude incidence rates of kidney and heart failure tended to be higher in the GLP-1 RA cohorts than in the SGLT2i cohorts across data sources |
These findings allow a greater understanding of the incidence of kidney failure and cardiovascular outcomes in people receiving antidiabetic medications with cardiorenal protective effects, which is important for future studies comparing the incidence of kidney and cardiovascular outcomes related to new and existing CKD treatments |
Introduction
Type 2 diabetes (T2D) is a leading cause of chronic kidney disease (CKD) globally [1, 2]. People with T2D have a high prevalence and incidence of CKD, which presents an increased risk of kidney failure, cardiovascular disease, and death [3]. Cardiovascular complications are an especially relevant concern for individuals with both diabetes and CKD [4, 5]. The treatment approach for people with CKD is multidisciplinary, with the aim being primarily to slow the progression of disease to avoid dialysis or kidney transplantation and secondarily to reduce the cardiovascular risk that accompanies CKD. Currently available therapies with demonstrated benefits on cardiovascular events and kidney outcomes among patients with T2D include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1 RA), and non-steroidal mineralocorticoid receptor antagonists (ns-MRAs) [6]. Among the latter class, finerenone is the drug with more robust and well-documented evidence supporting cardiovascular and kidney benefits among patients with CKD and T2D; finerenone was approved by the US Food and Drug Administration (FDA) in 2021 and the European Medicines Agency (EMA) in 2022 specifically to prevent CKD disease progression and cardiovascular events in patients with CKD associated with T2D [7, 8–9].
The 2022 Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guidelines recommend SGLT2i as first-line therapy as part of a comprehensive management approach for people with T2D and CKD with an estimated glomerular filtration rate (eGFR) ≥ 20 ml/min per 1.73 m2 [6]. A growing body of clinical trial evidence suggests a protective effect of GLP-1 RA on cardiorenal outcomes compared with placebo or with standard of care [10, 11, 12–13]; indeed, the first GLP-1 RA was recently approved by the US FDA for reducing the risk of worsening kidney disease and cardiovascular death in patients with T2D and CKD [14]. While KDIGO guidelines do not explicitly recommend the use of GLP-1 RA as first-line therapy for CKD in T2D, KDIGO’s comprehensive risk reduction strategy includes GLP-1 RA as an additional therapy option for patients at risk of insufficient glycemic control [6, 13]. Systematic reviews and network meta-analyses of GLP-1 RA and SGLT2i in patients with T2D concluded that both medication classes have cardiovascular and kidney benefits but with notable differences in benefits and harms [12, 15].
KDIGO guidelines also suggest the addition of an ns-MRA such as finerenone to first-line therapy for patients who have T2D and who are at high risk of kidney disease progression and cardiovascular events [6]. Before conducting comparative analyses of new medications like finerenone in this therapeutic area, it is important to understand the background incidence rates (IRs) of cardiovascular events and progression of kidney disease in populations of patients with T2D and CKD using existing medications with expected cardiorenal protective effects in routine clinical care, like SGLT2i or GLP-1 RA. Performed as part of the FOUNTAIN (FinerenOne mUlti-database NeTwork for evidence generAtIoN) platform [16], this multidatabase, multinational, observational cohort study aimed to describe the incidence of kidney failure and cardiovascular events (i.e., acute coronary syndrome [ACS], stroke, heart failure [HF], atrial fibrillation [AF]) in separate, non-mutually exclusive cohorts of patients with both CKD and T2D initiating therapy with either an SGLT2i or GLP-1 RA. To evaluate the background incidence of cardiorenal outcomes, the separate cohorts were described from 2012 to 2019 before SGLT2i were approved specifically for CKD or cardiovascular disease and before finerenone became available.
Methods
Study Design and Setting
This was a multidatabase, multinational cohort study aiming to describe the incidence of cardiorenal events in separate, non-mutually exclusive cohorts of patients with both CKD and T2D initiating therapy with either an SGLT2i or GLP-1 RA using secondary data from four participating data sources: two population-based data sources in Europe (Danish National Health Registers [DNHR] and Valencia Health System Integrated Database [VID] in Spain); one hospital-based CKD registry in Japan (Japan Chronic Kidney Disease Database Extension [J-CKD-DB-Ex]); and one electronic health record (EHR) database in the US (Optum® de-identified Electronic Health Record data set [Optum® EHR]). Additional details are presented in Appendix A (Supplementary Material). The study was reviewed and approved by the relevant ethics committee for each data source in accordance with local regulations (Comité Ético de Investigación con Medicamentos del Hospital Clínico Universitario de Valencia for VID [2022/163]; the ethics committee of the Shiga University of Medical Science for J-CKD-DB-Ex [R2022-156]), determined to not constitute research involving human subjects according to 45 Code of Federal Regulations 46.102(f) and deemed exempt from board oversight (Optum EHR), or deemed exempt from review (DNHR). The institutional review board of RTI International deemed the study exempt from full review. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. Patient consent for participation and patient consent for publication are not applicable, except for J-CKD-DB-Ex, where informed consent was obtained through an opt-out method on the website of participating university hospitals.
Study Population
The source population included all adults (aged ≥ 18 years) with recorded evidence of CKD and T2D who initiated an SGLT2i or GLP-1 RA from 1 January 2012 through 31 December 2019 (all countries except Japan, where the study period began on 1 January 2014) with at least 12 months of continuous enrollment or registration in the data source (i.e., minimum 12-month lookback period) before medication initiation. Distinct, non-mutually exclusive cohorts of new users of SGLT2i and GLP-1 RA were evaluated separately; the same patient could be included in both cohorts if both an SGLT2i and GLP-1 RA were newly initiated during the study period. New users of SGLT2i or GLP-1 RA, separately, were defined as patients with an outpatient prescription or dispensing (hereafter referred to as “prescription”) for any medication in the respective SGLT2i or GLP-1 RA class and no prescription for any other medication in that class during the previous 12 months. The index prescription for each medication class was the first eligible prescription that fulfilled the definition of new use during the study period; the date of this prescription was the index date. Figure B1 (Appendix B, Supplementary Material) depicts the study design features regarding cohort eligibility, cohort entry, baseline assessment periods, and follow-up.
Variables
Eligibility Criteria
Eligibility criteria were common across data sources; T2D was defined by algorithms specific to each data source (Appendix A, Table A1), which incorporated diagnosis codes for T2D and/or prescription(s) for a glucose-lowering medication. CKD was defined as having one diagnosis code for CKD stage 2–4 or unspecified stage; two eGFR measurements between 15 and 60 ml/min/1.73 m2 separated by 90–540 days (i.e., CKD eGFR stage G3 or G4); or two urine albumin-to-creatinine ratio (UACR) measurements > 30 mg/g separated by 90–540 days (i.e., CKD albuminuria stage A2 or A3). Patients were excluded if they had type 1 diabetes, kidney cancer, or kidney failure on or ever prior to the index date. Kidney failure was defined as substantially impaired kidney function based on diagnosis codes for stage 5 CKD, occurrence of 2 eGFR results < 15 ml/min/1.73 m2 separated by 90–540 days, receipt of maintenance dialysis (≥ 3 sessions over ≥ 90 days during the baseline period), or kidney transplant.
Exposures
Exposures to medications of interest were identified from written prescription records in the EHR or administrative data for prescription or dispensing of medications, depending on the data source (Appendix A). Medication classes were defined by Anatomical Therapeutic Chemical (ATC) codes; for the Optum® EHR database, National Drug Codes (NDC) corresponding to the relevant ATC codes were identified (Appendix B, Table B1). SGLT2i- and GLP-1 RA-specific cohorts were defined and analyzed separately. Current-use periods of the study medications were defined as starting on the day after the index date to the end of presumed supply for consecutive prescriptions based on dispensed days’ supply data where available—DNHR current-use periods were estimated using the upper quartile of the times between prescriptions—plus a grace period of 30 days. Treatment discontinuation was defined by the date corresponding to the end of current use.
Demographic, Lifestyle, and Clinical Characteristics
Demographic and lifestyle variables and baseline clinical characteristics available in each data source at the index date included age, sex, smoking status, obesity (by diagnosis code or body mass index [BMI]), indicators of T2D severity, indicators of CKD severity, medications other than glucose-lowering drugs used in the 180 days before or on the index date, comorbidities before or on the index date, and healthcare resource utilization in the 180 days before the index date.
Outcomes
Primary outcomes were new-onset kidney failure, ACS, stroke, new-onset congestive HF, and new-onset AF (Appendix B, Table B2). To ensure identification of new-onset outcomes, all outcomes were assessed in outcome-specific analysis sets created by excluding those with a history of the outcome from the overall cohorts (except for kidney failure, as baseline kidney failure was an exclusion criterion). Secondary outcomes included estimates of kidney function based on laboratory measurements of eGFR (using the creatinine-based Chronic Kidney Disease Epidemiology Collaboration equation [17]) and serum potassium. Time to changes in eGFR level (≥ 30% or ≥ 57% decline from baseline) and time to serum potassium level > 5.5 mmol/l or > 6.0 mmol/l were evaluated in separate analysis-specific cohorts among those with available baseline laboratory measurements. Patients in each cohort were followed for primary and secondary outcomes from the day after the index date until the earliest of the following: end of the study period (31 December 2019), disenrollment from the data source or emigration from the data source catchment area, discontinuation of the index medication class, development of kidney failure or kidney cancer, or death.
Statistical Analyses
Statistical analyses were performed by each research partner in each data source according to a common statistical analysis plan with data source-specific adaptations. All analyses were descriptive, and no comparative analyses of outcomes between the medication-specific cohorts or data sources were performed. Baseline demographic and lifestyle characteristics, clinical characteristics of T2D and CKD (including treatments for the conditions and indicators of severity), comedications, and comorbidities were analyzed descriptively. Continuous variables were described as applicable with means, standard deviations, medians, first and third quartiles, and first and 99th percentiles, whereas categorical variables were reported as frequency counts and percentages. For each of the primary outcomes, crude IRs and 95% confidence intervals (CIs) were calculated as the number of events during follow-up divided by the total person-time at risk and expressed as a rate per 1000 person-years (PY). Within each medication-specific cohort analysis set or outcome-specific analysis set, the cumulative incidence of the outcome by follow-up time (i.e., risk) was estimated with the cumulative incidence function and plotted to visualize the occurrence of outcome events over time, accounting for censoring criteria and the competing risk of death [18]. Cumulative incidence curves for clinical outcomes were reported for each outcome at each 6-month interval throughout follow-up in each analysis set. Cumulative incidence for decreases in eGFR to set points or increases in serum potassium level was reported at 4, 12, 24, and 36 months.
Results
Attrition
After applying all inclusion and exclusion criteria, the final SGLT2i cohorts comprised 12,501 patients in DNHR, 22,404 in VID, 811 in J-CKD-DB-Ex, and 54,308 in Optum® EHR (Appendix C [Supplementary Material], Table C1). The final GLP-1 RA cohorts comprised 10,696 in DNHR, 8317 in VID, 219 in J-CKD-DB-Ex, and 78,934 in Optum® EHR (Appendix C, Table C2).
Baseline Demographic Characteristics
Within each data source, the mean ages of patients were generally similar for each medication cohort but varied across data sources, ranging from 62 to 70 years. Across data sources, the proportion of female patients ranged from 35.9 to 44.4% (SGLT2i cohorts) and from 40.0 to 51.3% (GLP-1 RA cohorts) (Table 1). Tables C3–C6 (Appendix C) summarize other baseline characteristics by data source. The proportion of patients with stage 3 CKD at baseline based on a diagnosis code or eGFR value ranged from 23.1 to 46.5% in SGLT2i cohorts and from 33.6 to 45.2% in GLP-1 RA cohorts. Laboratory data were quite complete in all data sources, except for UACR, for which 17.1 to 48.6% of participants were missing data. For eGFR at baseline, the percentage of patients with missing results was 10% or less in all cohorts (Appendix C, Tables C7–C8). Across all data sources, obesity and indicators of T2D severity (e.g., insulin use at the index date, higher HbA1c level) were more common at baseline in the GLP-1 RA cohorts than in the SGLT2i cohorts. The GLP-1 RA cohorts also tended to have worse renal function at baseline than did the SGLT2i cohorts.
Table 1. Selected baseline characteristics of SGLT2i and GLP-1 RA new users by data source
Characteristic | DNHR | J-CKD-DB-Ex | VID | Optum® EHR | ||||
|---|---|---|---|---|---|---|---|---|
SGLT2i (n = 12,501) | GLP-1 RA (n = 10,696) | SGLT2i (n = 811) | GLP-1 RA (n = 219) | SGLT2i (n = 22,404) | GLP-1 RA (n = 8317) | SGLT2i (n = 54,308) | GLP-1 RA (n = 78,934) | |
Age at the index date, years | ||||||||
Mean (SD) | 65.0 (11.3) | 65.3 (11.5) | 66.5 (11.7) | 66.8 (13.3) | 69.8 (11.1) | 66.9 (10.6) | 62.2 (11.0) | 62.2 (10.9) |
Median (1st, 99th percentiles) | 66 (35, 87) | 67 (34, 87) | 67.9 (36, 88) | 68.6 (31, 91) | 70.8 (42, 91) | 67.9 (40,88) | 63 (34, 84) | 63 (34, 84) |
Female sex, n (%) | 4485 (35.9) | 4281 (40.0) | 299 (36.9) | 97 (44.3) | 9106 (40.6) | 3768 (45.3) | 24,121 (44.4) | 40,515 (51.3) |
Obesity, yes (by diagnosis), n (%) | 3558 (28.5) | 3645 (34.1) | 79 (9.7) | 31 (14.2) | 15,221 (67.9) | 7531 (90.6) | 24,113 (44.4) | 38,526 (48.8) |
HbA1c, n (%) | ||||||||
HbA1c ≤ 53 mmol/mol or ≤ 7% | 1388 (11.1) | 1290 (12.1) | 258 (31.8) | 44 (20.1) | 4849 (21.6) | 1228 (14.8) | 11,598 (21.4) | 17,365 (22.0) |
HbA1c > 53 to ≤ 63.9 mmol/mol or > 7% to ≤ 8% | 3493 (27.9) | 2562 (24.0) | 275 (33.9) | 63 (28.8) | 5916 (26.4) | 1940 (23.3) | 14,331 (26.4) | 18,938 (24.0) |
HbA1c > 63.9 to ≤ 74.9 mmol/mol or > 8% to ≤ 9% | 3441 (27.5) | 2956 (27.6) | 161 (19.9) | 50 (22.8) | 4784 (21.4) | 1941 (23.3) | 9916 (18.3) | 13,931 (17.6) |
HbA1c > 74.9 mmol/mol or > 9% | 3971 (31.8) | 3613 (33.8) | 105 (12.9) | 58 (26.5) | 4216 (18.8) | 2029 (24.4) | 14,098 (26.0) | 20,937 (26.5) |
HbA1c missing | 208 (1.7) | 275 (2.6) | 12 (1.5) | 4 (1.8) | 2639 (11.8) | 1179 (14.2) | 4365 (8.0) | 7763 (9.8) |
CKD stage based on eGFR a or diagnosis code, bn (%) | ||||||||
Stage 1: eGFR ≥ 90, normal or high | 5056 (40.4) | 3507 (32.8) | 39 (4.8) | 13 (5.9) | 5103 (22.8) | 1834 (22.1) | 14,425 (26.6) | 16,653 (21.1) |
Stage 2: eGFR 60–89, mildly decreased | 4257 (34.1) | 2958 (27.7) | 346 (42.7) | 76 (34.7) | 7722 (34.5) | 2140 (25.7) | 20,226 (37.2) | 24,339 (30.8) |
Stage 3: eGFR 30–59, mildly to severely decreased | 2887 (23.1) | 3733 (34.9) | 377 (46.5) | 99 (45.2) | 8008 (35.7) | 3384 (40.7) | 14,022 (25.8) | 26,561 (33.6) |
Stage 3a: eGFR 45–59, mildly to moderately decreased | 2232 (17.9) | 2354 (22.0) | 259 (31.9) | 48 (21.9) | 5444 (24.3) | 1866 (22.4) | 10,871 (20.0) | 17,135 (21.7) |
Stage 3b: eGFR 30–44, moderately to severely decreased | 640 (5.1) | 1359 (12.7) | 118 (14.5) | 51 (23.3) | 2269 (10.1) | 1381 (16.6) | 2956 (5.4) | 8948 (11.3) |
Stage 3 without specification of substage | 15 (0.1) | 20 (0.2) | 0 (0.0) | 0 (0.0) | 295 (1.3) | 137 (1.7) | 195 (0.4) | 478 (0.6) |
Stage 4: eGFR 15–29, severely decreased | 102 (0.8) | 353 (3.3) | 43 (5.3) | 29 (13.2) | 434 (1.9) | 480 (5.8) | 466 (0.9) | 2334 (3.0) |
Stage 5: eGFR < 15 or treated by dialysis, kidney failure | NR | NR | 3 (0.4) | 2 (0.9) | 14 (0.1) | 17 (0.2) | 9 (< 0.1) | 56 (0.1) |
Unspecified stage | NR | NR | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 1189 (2.2) | 2573 (3.3) |
Missing stage | NR | NR | 3 (0.4) | 0 (0.0) | 818 (3.7) | 266 (3.2) | 3971 (7.3) | 6418 (8.1) |
CKD chronic kidney disease, DNHR Danish National Health Registers, eGFR estimated glomerular filtration rate, GLP-1 RA glucagon-like peptide-1 receptor agonists, HbA1c hemoglobin A1c, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, NR not reported, Optum® EHR Optum® de-identified Electronic Health Record data set, SD standard deviation, SGLT2i sodium-glucose cotransporter 2 inhibitors, T2D type 2 diabetes, VID Valencia Health System Integrated Database
aThe lookback period is study days (− 365, 0)
bThe lookback period uses all available data: study days (− ∞, 0)
Outcomes
In consideration of differences across data sources in healthcare setting, data type (e.g., administrative claims, EHR), and length of follow-up, primary outcomes (i.e., kidney failure, cardiovascular outcomes) and secondary outcomes (i.e., change in eGFR and serum potassium measurements) for SGLT2i and GLP-1 RA cohorts are presented separately by data source.
DNHR
SGLT2i Cohort
In the SGLT2i cohort, the IR of kidney failure was 0.27 events per 1000 PY during follow-up of approximately 80 months. Among cardiovascular outcomes, new-onset AF was most common (IR, 13.74/1000 PY), followed by stroke (IR, 9.75/1000 PY), ACS (IR, 7.51/1000 PY), and new-onset HF (IR, 5.44/1000 PY) (Fig. 1a). In alignment, the risk of AF was higher than all other outcomes at all timepoints, while risk of stroke and ACS was similar until approximately 2 years after the index date and again by the end of follow-up (i.e., maximum of approximately 80 months). The curves for HF and ACS were nearly superimposed until approximately 60 months of follow-up (Appendix C, Figure C1a). As illustrated by the cumulative incidence curve, there were very few kidney failure events in this cohort throughout the follow-up period.
[See PDF for image]
Fig. 1
Outcomes: DNHR. a Incidence rates of primary outcomes, SGLT2i cohort. b Cumulative incidence of secondary outcomes, SGLT2i cohort. c Incidence rates of primary outcomes, GLP-1 RA cohort. d Cumulative incidence of secondary outcomes, GLP-1 RA cohort. CI confidence internal, DNHR Danish National Health Registers, eGFR estimated glomerular filtration rate, GLP-1 RA glucagon-like peptide-1 receptor agonists, IR incidence rate, NR not reported, SGLT2i sodium-glucose cotransporter 2 inhibitors
The risk of eGFR declines ≥ 30% from baseline was 17% at 36 months, compared with 2% for eGFR declines ≥ 57% (Fig. 1b). Over the entire follow-up period, the risks of serum potassium values > 5.5 mmol/l and > 6.0 mmol/l were similar (36 months: 4% and 2%, respectively) (Fig. 1b).
GLP-1 RA Cohort
In the GLP-1 RA cohort, the IR of kidney failure was 1.20 events per 1000 PY during follow-up of approximately 95 months. Atrial fibrillation was the most common cardiovascular outcome (IR, 15.19/1000 PY), followed by stroke (IR, 8.90/1000 PY), HF (IR, 7.59/1000 PY), and ACS (IR, 7.44/1000 PY) (Fig. 1c). The risk of AF was higher than that of all other outcomes at all timepoints, while the risk of stroke, ACS, and HF was similar. Kidney failure incidence increased until approximately 50 months of follow-up and remained lower than the cardiovascular outcomes at all timepoints (Appendix C, Fig. C1b).
At 36 months, declines in eGFR ≥ 30% from baseline had a risk of 20% compared with 3% for eGFR declines ≥ 57%, and the risks of serum potassium values > 5.5 mmol/l and > 6.0 mmol/l were 5% and 2%, respectively (Fig. 1d).
J-CKD-DB-Ex
SGLT2i Cohort
In the SGLT2i cohort, the IR of kidney failure was 8.88 events per 1000 PY during follow-up of approximately 55 months. Among cardiovascular outcomes, new-onset HF was most common (IR, 115.50/1000 PY), followed by stroke (IR, 45.54/1000 PY), ACS (IR, 40.53/1000 PY), and AF (IR, 16.07/1000 PY) (Fig. 2a). The risk of HF, which tended to occur relatively early during follow-up, was higher than the risk of all other outcomes at each timepoint (Appendix C, Fig. C2a). Risk of stroke and ACS was similar throughout follow-up. Risk of kidney failure and AF was similar throughout follow-up without notable time trends.
[See PDF for image]
Fig. 2
Outcomes: J-CKD-DB-Ex. a Incidence rates of primary outcomes, SGLT2i cohort. b Cumulative incidence of secondary outcomes, SGLT2i cohort. c Incidence rates of primary outcomes, GLP-1 RA cohort. d Cumulative incidence of secondary outcomes, GLP-1 RA cohort. CI confidence internal, eGFR estimated glomerular filtration rate, GLP-1 RA glucagon-like peptide-1 receptor agonists, IR incidence rate, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, SGLT2i sodium-glucose cotransporter 2 inhibitors
At 36 months, the risk of eGFR declines ≥ 30% was 27% at 36 months compared with 4% for declines ≥ 57%, and risks of serum potassium values > 5.5 mmol/l and > 6.0 mmol/l were 16% and 4%, respectively (Fig. 2b).
GLP-1 RA Cohort
In the GLP-1 RA cohort, the IR of kidney failure was 21.65 events per 1000 PY during follow-up of approximately 55 months. New-onset HF was the most common cardiovascular outcome (IR, 177.21/1000 PY), followed by ACS (IR, 35.47/1000 PY), stroke (IR, 33.96/1000 PY), and AF (IR, 11.65/1000 PY) (Fig. 2c). The risk of HF deviated from that of the other outcomes in the first 3 months and remained the most common event at each timepoint, while risk of stroke, ACS, kidney failure, and AF was similar throughout the follow-up period, with overlapping curves (Appendix C, Fig. C2b).
At 36 months, the risk of eGFR declines ≥ 30% was 44%, while risk of declines ≥ 57% remained lower at 6%, and risks of serum potassium values > 5.5 mmol/l and > 6.0 mmol/l were 34% and 15%, respectively (Fig. 2d).
VID
SGLT2i Cohort
In the SGLT2i cohort, the IR of kidney failure was 0.58 events per 1000 PY during follow-up of approximately 75 months. Among cardiovascular outcomes, AF was most common (IR, 18.99/1000 PY), followed by HF (IR, 10.52/1000 PY), stroke (IR, 8.23/1000 PY), and ACS (5.66/1000 PY) (Fig. 3a). The risk of AF was higher than that of all other outcomes at all timepoints and began to deviate from the other curves at approximately 20 months, while curves for stroke, ACS, and HF were similar throughout the end of follow-up (Appendix C, Fig. C3a). The cumulative incidence curve for kidney failure remained relatively flat and near 0 throughout follow-up.
[See PDF for image]
Fig. 3
Outcomes: VID. a Incidence rates of primary outcomes, SGLT2i cohort. b Cumulative incidence of secondary outcomes, SGLT2i cohort. c Incidence rates of primary outcomes, GLP-1 RA cohort. d Cumulative incidence of secondary outcomes, GLP-1 RA cohort. CI confidence internal, eGFR estimated glomerular filtration rate, GLP-1 RA glucagon-like peptide-1 receptor agonists, IR incidence rate, SGLT2i sodium-glucose cotransporter 2 inhibitors, VID Valencia Health System Integrated Database
At 36 months, the risk of eGFR declines ≥ 30% from baseline was 26% while risk for declines ≥ 57% was 3% (Fig. 3b). Over the entire follow-up period, the risk of serum potassium values > 5.5 mmol/l was much higher than risk of values > 6.0 mmol/l, which remained relatively low (36 months: 17% and 5%, respectively) (Fig. 3b).
GLP-1 RA Cohort
In the GLP-1 RA cohort, the IR of kidney failure was 1.68 events per 1000 PY during follow-up of approximately 95 months. Among cardiovascular outcomes, AF was most common (IR, 21.93/1000 PY), followed by HF (IR, 13.76/1000 PY), stroke (IR, 9.07/1000 PY), and ACS (IR, 7.31/1000 PY) (Fig. 3c). The risk of AF was higher than all other outcomes at all timepoints; the curve for HF showed a similar pattern to that for AF, while the curves for stroke and ACS were almost identical throughout follow-up (Appendix C, Fig. C3b). The curve for kidney failure remained relatively flat and near 0.
At 36 months, risk for declines in eGFR ≥ 30% from baseline was 24% compared with 3% for declines ≥ 57%, and the risk of serum potassium values > 5.5 mmol/l was 20% compared with 7% for values > 6.0 mmol/l (Fig. 3d).
Optum® EHR
SGLT2i Cohort
In the SGLT2i cohort, the IR of kidney failure was 6.33 events per 1000 PY during follow-up of approximately 80 months. Among cardiovascular outcomes, AF was most common (IR, 26.28/1000 PY), followed by HF (IR, 13.01/1000 PY), ACS (IR, 9.54/1000 PY), and stroke (IR, 5.20/1000 PY) (Fig. 4a). The cumulative incidence curves for AF and HF were of similar magnitude and were higher than those for all other outcomes throughout follow-up (Appendix C, Fig. C4a). The curves for stroke and kidney failure were superimposed until 60 months of follow-up, while the curve for ACS was slightly greater than that for kidney failure at all timepoints.
[See PDF for image]
Fig. 4
Outcomes: Optum® EHR. a Incidence rates of primary outcomes, SGLT2i cohort. b Cumulative incidence of secondary outcomes, SGLT2i cohort. c Incidence rates of primary outcomes, GLP-1 RA cohort. d Cumulative incidence of secondary outcomes, GLP-1 RA cohort. CI confidence internal, eGFR estimated glomerular filtration rate, GLP-1 RA glucagon-like peptide-1 receptor agonists, IR incidence rate, Optum® EHR Optum® de-identified Electronic Health Record data set, SGLT2i sodium-glucose cotransporter 2 inhibitors
At 36 months, the risks of eGFR declines ≥ 30% and ≥ 57% from baseline were 31% and 7%, respectively, and the risks of serum potassium values > 5.5 mmol/l and > 6.0 mmol/l were 10% and 3%, respectively, at 36 months (Fig. 4b).
GLP-1 RA Cohort
In the GLP-1 RA cohort, the IR of kidney failure was 12.95 events per 1000 PY during follow-up of approximately 95 months. Among cardiovascular outcomes, AF (IR, 29.37/1000 PY) and HF (IR, 16.66/1000 PY) were most common, and the IR for ACS (IR, 10.21/1000 PY) was higher than that for stroke (IR, 5.41/1,000 PY) (Fig. 4c). During the follow-up period, the cumulative incidence curves for AF and HF were very similar and were higher than those for all other outcomes throughout follow-up (Appendix C, Fig. C4b). The curves for ACS and kidney failure were also similar and were higher than that for stroke throughout follow-up.
At 36 months, the risk of eGFR declines ≥ 30% from baseline was 35% at 36 months, while risk for declines ≥ 57% was 9%, and the risks of serum potassium values > 5.5 mmol/l and > 6.0 mmol/l were 12% and 4%, respectively (Fig. 4d).
Discussion
This multidatabase, multinational, observational cohort study describes the incidence of kidney failure and cardiovascular events in separate cohorts of patients with diagnoses of both CKD and T2D newly initiating therapy with either an SGLT2i or GLP-1 RA using 4 healthcare data sources: country-wide, population-based registry data for Denmark; regional, population-based databases for the Valencia region of Spain; a nationwide, clinical hospital-based registry of patients with CKD from 5 university hospitals in Japan; and an aggregated database of EHR data from hospitals and clinics throughout the US. The treatment landscape for the prevention of CKD progression in T2D is evolving rapidly, with new treatments in development [13, 19, 20]. To understand background incidence of cardiovascular events and progression of kidney disease in different populations of patients with T2D and CKD using existing medications with expected cardiorenal protective effects in real-world settings, the period of this study (2012–2019) occurred largely before the approval of new CKD and cardiovascular disease indications for existing treatments (SGLT2i and GLP-1 RA) and new treatments, such as finerenone [6, 9, 13, 21, 22].
The objective of this work was not to compare the effects of different medication classes, and no direct statistical comparisons were made. From the present descriptions of different medication user populations, we found IRs of kidney failure and HF across all data sources were overall numerically higher in the GLP-1 RA cohorts (1.20–21.65 and 7.59–177.21 per 1000 PY, respectively) than in the SGLT2i cohorts (0.27–8.88 and 5.44–115.50 per 1000 PY, respectively). These results align with differences in baseline patient characteristics (e.g., obesity, use of insulin, longer duration of T2D and CKD, greater severity of T2D, and worse renal function were more common in the GLP-1 RA cohorts than in the SGLT2i cohorts), though whether confounding factors fully account for the observed differences is uncertain and beyond the scope of this analysis. These cohort differences are also in line with those seen in previous clinical trials and comparative observational studies [20, 23, 24]. Across data sources, 36-month cumulative incidences of eGFR declines of ≥ 30% were higher in the GLP-1 RA cohorts (20–44%) than in SGLT2i cohorts (17–31%); similarly, 36-month cumulative incidences of elevated serum potassium values > 5.5 mmol/l were higher in the GLP-1 RA cohorts (5–34%) than in the SGLT2i cohorts (4–17%), likely reflecting the worse renal function at baseline in the GLP-1 RA cohort. Despite the relatively mild CKD observed in both the SGLT2i and GLP-1 RA study cohorts at baseline, these data suggest that during the period when these study data were generated, there was room for improvement in the treatment of patients with T2D and CKD to improve outcomes via improvements in adherence, combining SGLT2i and GLP-1 RA [25, 26], and/or adding new treatments such as finerenone [20].
Heterogeneity in the type and nature of data captured as well as differences in healthcare systems, treatment guidelines, country-specific clinical and prescribing practices, formulary policies, and application of diagnostic coding systems in the participating countries may have resulted in key differences between data sources that could have impacted the incidence of the primary and secondary outcomes of interest [27]. Notably, compared with the other data sources, the J-CKD-DB-Ex had higher IRs of all primary outcomes except new-onset AF. The population in the J-CKD-DB-Ex consisted of patients treated at university hospitals with higher levels of comorbidity—including cardiovascular complications such as congestive HF (56.6–60.4% versus 4.1–13.3% across DNHR, VID, and Optum® EHR databases)—and relatively more advanced CKD at baseline than those in the other data sources, which could partially explain the higher incidence of some of the outcomes in that data source. In DNHR, diagnoses made by general practitioners are not captured in the National Patient Registry unless a patient has a hospital encounter with that condition, while laboratory data (including UACR measurements) and filled prescription data are included across all sectors. Indication information was available in only 1 data source (VID); thus, we could not capture temporal trends within a given country regarding the timing of information on label expansions or the benefits of SGLT2i or GLP-1 RA medications for slowing CKD progression or preventing cardiovascular disease and HF outcomes.
Healthcare data from multiple countries and data sources allowed evaluation of study parameters in diverse settings, populations, and healthcare systems. The study was conducted using a common protocol and statistical analysis plan with efforts to harmonize approaches across all data sources. However, data source-specific adaptations were necessary, and the inherent heterogeneity among the data sources must be considered when interpreting results. For instance, the censoring methodology may impact treatment pattern results during follow-up; moreover, few patients were followed for > 60 months, and results observed after this time should be interpreted carefully. Missing data or misclassification of study variables is possible, though laboratory data were quite complete in all data sources, with the exception of UACR. While the percentage of patients with missing baseline eGFR results was ≤ 10% for both medication cohorts across data sources, the percentage of patients without a UACR assessment at baseline was 32–49% across all data sources except DNHR (< 20% of patients did not have a UACR result). As UACR is a critical parameter in assessing and defining risk, as well as for study cohort identification, misclassification of CKD severity in these data sources is possible [6, 28]. Notably, except for the GLP-1 RA cohort in the Optum® EHR database, all cohorts across data sources included a greater proportion of male than female patients; sex-related differences in kidney and cardiovascular disease risk factors and prevalence, as well as markers and staging of CKD, must be considered when interpreting findings [29]. Finally, no formal, direct comparisons of the medication-specific cohorts or data sources were planned or conducted. The results are purely descriptive within each cohort and data source, and no causal conclusions about medication effects can be drawn from the differences observed between cohorts and data sources. While descriptive, the results of this study provide valuable context, as both SGLT2i and GLP1-RA are used in populations with T2D and CKD in real-world settings, and an increased understanding of these patient populations and their respective incidence of cardiorenal outcomes is useful for formal comparisons of these and newer medications like finerenone in future studies.
Conclusions
In this multinational, multidatabase study of patients with CKD and T2D, IRs of clinical outcomes were described in users of SGLT2i or GLP-1 RA—antidiabetic medications with cardiorenal protective effects—using data from 2012 to 2019 before these effects were widely recognized or the medications were approved for these indications. The study was not designed to compare medication effects across the two cohorts, and it was noted that across all data sources and at baseline, new users of GLP-1 RA had a different baseline clinical profile from that of new users of SGLT2i. Understanding the incidence of cardiovascular outcomes and kidney failure in patients receiving antidiabetic medications with cardiorenal protective effects is a first step in designing future studies to compare the incidence of these outcomes related to new and existing treatments for CKD.
Medical Writing/Editorial Assistance
Kate Lothman and Gabrielle Dardis, PhD, of RTI Health Solutions provided medical writing support and John Forbes of RTI Health Solutions provided editorial support, with funding from Bayer AG, during manuscript development.
Author Contributions
Conceptualization: J. Bradley Layton, Ryan Ziemiecki, Catherine B. Johannes, Manel Pladevall-Vila, Anam M. Khan, Alfredo E. Farjat, David Vizcaya, Nikolaus G. Oberprieler; Data curation: Christian Fynbo Christiansen, Aníbal García-Sempere, Hiroshi Kanegae, Craig I. Coleman, Ina Trolle Andersen, Clara Rodríguez-Bernal, Celia Robles Cabaniñas, Reimar W. Thomsen, Isabel Hurtado, Naoki Kashihara, Philip Vestergaard Munch, Yuichiro Yano; Formal analysis: Christian Fynbo Christiansen, Aníbal García-Sempere, Hiroshi Kanegae, Craig I. Coleman, Ina Trolle Andersen, Clara Rodríguez-Bernal, Celia Robles Cabaniñas, Reimar W. Thomsen, Isabel Hurtado, Naoki Kashihara, Philip Vestergaard Munch, Yuichiro Yano; Funding acquisition: Craig I. Coleman, Nikolaus G. Oberprieler; Investigation: J. Bradley Layton, Ryan Ziemiecki, Catherine B. Johannes, Manel Pladevall-Vila, Anam M. Khan, Christian Fynbo Christiansen, Aníbal García-Sempere, Ina Trolle Andersen, Clara Rodríguez-Bernal, Celia Robles Cabaniñas, Reimar W. Thomsen, Isabel Hurtado, Philip Vestergaard Munch, David Vizcaya, Nikolaus G. Oberprieler; Methodology: J. Bradley Layton, Ryan Ziemiecki, Catherine B. Johannes, Manel Pladevall-Vila, Anam M. Khan, Christian Fynbo Christiansen, Aníbal García-Sempere, Hiroshi Kanegae, Ina Trolle Andersen, Clara Rodríguez-Bernal, Celia Robles Cabaniñas, Reimar W. Thomsen, Alfredo E. Farjat, Isabel Hurtado, Philip Vestergaard Munch, Suguru Okami, Satoshi Yamashita, Yuichiro Yano, David Vizcaya, Nikolaus G. Oberprieler; Project administration: J. Bradley Layton, Ryan Ziemiecki, Catherine B. Johannes, Manel Pladevall-Vila, Anam M. Khan, Craig I. Coleman, Fangfang Liu, Suguru Okami, David Vizcaya, Nikolaus G. Oberprieler; Supervision: J. Bradley Layton, Ryan Ziemiecki, Catherine B. Johannes, Manel Pladevall-Vila, Anam M. Khan, Craig I. Coleman, Alfredo E. Farjat, Fangfang Liu, David Vizcaya, Nikolaus G. Oberprieler; Validation: Ryan Ziemiecki; Writing—review & editing: J. Bradley Layton, Ryan Ziemiecki, Catherine B. Johannes, Manel Pladevall-Vila, Anam M. Khan, Natalie Ebert, Csaba P. Kovesdy, Christian Fynbo Christiansen, Aníbal García-Sempere, Hiroshi Kanegae, Craig I. Coleman, Michael Walsh, Ina Trolle Andersen, Clara Rodríguez-Bernal, Celia Robles Cabaniñas, Reimar W. Thomsen, Alfredo E. Farjat, Alain Gay, Patrick Gee, Isabel Hurtado, Naoki Kashihara, Philip Vestergaard Munch, Fangfang Liu, Suguru Okami, Satoshi Yamashita, Yuichiro Yano, David Vizcaya, and Nikolaus G. Oberprieler.
Funding
This study, development of this article, and the rapid service fee for publication was funded by Bayer AG. Authors affiliated with Bayer were involved in the study design, analyses, and development of this publication.
Data Availability
The datasets generated during and/or analyzed during the current study are not publicly available due to legal and policy restrictions regarding their use.
Declarations
Conflict of Interest
Alfredo E. Farjat, Fangfang Liu, Suguru Okami, Satoshi Yamashita, and Nikolaus G. Oberprieler are employees of Bayer, which funded this study. Alain Gay and David Vizcaya were employees of Bayer when this research was conducted. J. Bradley Layton, Ryan Ziemiecki, Catherine B. Johannes, and Anam M. Khan are employees of RTI Health Solutions, which received research funding for this study from Bayer; Manel Pladevall-Vila was an employee of RTI Health Solutions when this research was conducted. This independent research institute performs financially supported studies for government and related healthcare authorities and several pharmaceutical companies. Craig I. Coleman has received grant funding and consulting fees from Bayer AG and AstraZeneca Pharmaceuticals. Michael Walsh is employed by the Ontario Renal Network of Ontario Health; has received grant funding from the Canadian Institutes of Health Research, British Heart Foundation, Medical Research Future Fund, National Health and Medical Research Council (Australia), Health Research Council (New Zealand), Hamilton Academic Health Sciences Organization, Vifor; has received consulting fees for Otsuka, Glaxo-Smith Kline, Bayer, Visterra, Alexion; is a member of steering committees for the Canadian Institutes of Health Research, Medical Research Future Fund, National Health and Medical Research Council (Australia), Bayer, Otsuka; is on data safety monitoring boards for Hansa Pharmaceuticals, National Institute of Health Research (UK), Medical Research Council (UK), Roche; and is a member of event adjudication committees for Novo Nordisk and the Dutch Kidney Foundation. Csaba P. Kovesdy received consulting fees from Abbott, Akebia, Astra Zeneca, Bayer, Boehringer Ingelheim, Cara Therapeutics, CSL Behring, CSL Vifor, GSK, Pharmacosmos, ProKidney, Renibus and Takeda. Yuichiro Yano reports consultancy for Bayer. Naoki Kashihara reports research grants from Daiichi Sankyo, AstraZeneca, and Bayer. Natalie Ebert receives honoraria from Bayer AG. Reimar W. Thomsen has given single lectures on medical research (with and without compensation) for AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi. Christian Fynbo Christiansen, Reimar W. Thomsen, Ina Trolle Andersen, and Philip Vestergaard Munch are employees of Aarhus University, which receives institutional research funding from public and private entities, including regulators, pharmaceutical companies, and contract research organizations. This includes the present study. Aníbal García-Sempere, Clara Rodríguez-Bernal, Celia Robles Cabaniñas, and Isabel Hurtado are employed by FISABIO, a research body in Spain affiliated with the Health Department of the Valencia Government, which receives public and private funding to conduct biomedical research, including the present study.
Ethical Approval
This study used de-identified data from electronic health records. The study was reviewed and approved by the relevant ethics committee for each data source, in accordance with local regulations. Ethics committee review was waived for DNHR. The study protocol was reviewed and approved by the Comité Ético de Investigación con Medicamentos del Hospital Clínico Universitario de Valencia for VID (2022/163). This study protocol was reviewed and approved by the ethics committee of the Shiga University of Medical Science for J-CKD-DB-Ex (R2022-156). Optum EHR data are de‐identified and are compliant with the Health Insurance Portability and Accountability Act of 1996. This study was deemed to not constitute research involving human subjects according to 45 Code of Federal Regulations 46.102(f) and was deemed exempt from board oversight. The institutional review board of RTI International deemed the study exempt from full review. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. Patient consent for participation and patient consent for publication are not applicable, except for J-CKD-DB-Ex, where informed consent was obtained through an opt-out method on the website of participating university hospitals.
References
1. Thomas, MC; Cooper, ME; Zimmet, P. Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease. Nat Rev Nephrol; 2016; 12,
2. Li, H; Lu, W; Wang, A; Jiang, H; Lyu, J. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: estimates from Global Burden of Disease 2017. J Diabetes Investig; 2021; 12,
3. Webster, AC; Nagler, EV; Morton, RL; Masson, P. Chronic kidney disease. Lancet; 2017; 389,
4. Levey, AS; Atkins, R; Coresh, J et al. Chronic kidney disease as a global public health problem: approaches and initiatives - a position statement from Kidney Disease Improving Global Outcomes. Kidney Int; 2007; 72,
5. Levey, AS; Schoolwerth, AC; Burrows, NR et al. Comprehensive public health strategies for preventing the development, progression, and complications of CKD: report of an expert panel convened by the Centers for Disease Control and Prevention. Am J Kidney Dis; 2009; 53,
6. KDIGO. Kidney Disease: Improving Global Outcomes Work Group (KDIGO) 2022 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int; 2022; 102, pp. S1-127.
7. Kintscher, U; Edelmann, F. The non-steroidal mineralocorticoid receptor antagonist finerenone and heart failure with preserved ejection fraction. Cardiovasc Diabetol; 2023; 22,
8. Kerendia PI. Kerendia (finerenone) tablets. Bayer Healthcare. 2022. https://labeling.bayerhealthcare.com/html/products/pi/Kerendia_PI.pdf. Accessed 6 Nov 2023.
9. EMA. Bayer AG. Kerendia (finerenone). 2024. https://www.ema.europa.eu/en/medicines/human/EPAR/kerendia.
10. von Scholten, BJ; Kreiner, FF; Rasmussen, S; Rossing, P; Idorn, T. The potential of GLP-1 receptor agonists in type 2 diabetes and chronic kidney disease: from randomised trials to clinical practice. Ther Adv Endocrinol Metab.; 2022; 13, 20420188221112490.
11. Giugliano, D; Longo, M; Signoriello, S et al. The effect of DPP-4 inhibitors, GLP-1 receptor agonists and SGLT-2 inhibitors on cardiorenal outcomes: a network meta-analysis of 23 CVOTs. Cardiovasc Diabetol; 2022; 21,
12. Sattar, N; Lee, MMY; Kristensen, SL et al. Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of randomised trials. Lancet Diabetes Endocrinol; 2021; 9,
13. Shi, Q; Nong, K; Vandvik, PO et al. Benefits and harms of drug treatment for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials. BMJ; 2023; 381, 1:CAS:528:DC%2BB2MXlt12kt7Y%3D e074068.
14. Novo Nordisk. Press release: FDA approves Ozempic® (semaglutide) as the only GLP-1 RA to reduce the risk of worsening kidney disease and cardiovascular death in adults with type 2 diabetes and chronic kidney disease; 2025.
15. Palmer, SC; Tendal, B; Mustafa, RA et al. Sodium-glucose cotransporter protein-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials. BMJ; 2021; 372, m4573.
16. Oberprieler, NG; Pladevall-Vila, M; Johannes, C et al. FOUNTAIN: a modular research platform for integrated real-world evidence generation. BMC Med Res Methodol; 2024; 24,
17. Levey, AS; Stevens, LA; Schmid, CH et al. A new equation to estimate glomerular filtration rate. Ann Intern Med; 2009; 150,
18. Austin, PC; Lee, DS; Fine, JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation; 2016; 133,
19. Yau, K; Dharia, A; Alrowiyti, I; Cherney, DZI. Prescribing SGLT2 inhibitors in patients with CKD: expanding indications and practical considerations. Kidney Int Rep; 2022; 7,
20. Neuen, BL; Heerspink, HJL; Vart, P et al. Estimated lifetime cardiovascular, kidney, and mortality benefits of combination treatment with SGLT2 inhibitors, GLP-1 receptor agonists, and nonsteroidal MRA compared with conventional care in patients with type 2 diabetes and albuminuria. Circulation; 2024; 149,
21. EMA. Forxiga (dapagliflozin): overview. European Medicines Agency. 2021. https://www.ema.europa.eu/en/medicines/human/EPAR/forxiga. Accessed 17 Feb 2025.
22. Farxiga PI. Farxiga (dapagliflozin) tablets. AstraZeneca Pharmaceuticals LP. 2021. https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/202293s024lbl.pdf. Accessed 17 Feb 2025.
23. Patorno, E; Htoo, PT; Glynn, RJ et al. Sodium-glucose cotransporter-2 inhibitors versus glucagon-like peptide-1 receptor agonists and the risk for cardiovascular outcomes in routine care patients with diabetes across categories of cardiovascular disease. Ann Intern Med; 2021; 174,
24. Suzuki, Y; Kaneko, H; Nagasawa, H et al. Comparison of estimated glomerular filtration rate change with sodium-glucose cotransporter-2 inhibitors versus glucagon-like peptide-1 receptor agonists among people with diabetes: a propensity-score matching study. Diabetes Obes Metab; 2024; 26,
25. Riley, DR; Essa, H; Austin, P et al. All-cause mortality and cardiovascular outcomes with sodium-glucose co-transporter 2 inhibitors, glucagon-like peptide-1 receptor agonists and with combination therapy in people with type 2 diabetes. Diabetes Obes Metab; 2023; 25,
26. Muta, Y; Kobayashi, K; Toyoda, M et al. Influence of the combination of SGLT2 inhibitors and GLP-1 receptor agonists on eGFR decline in type 2 diabetes: post-hoc analysis of RECAP study. Front Pharmacol; 2024; 15, 1358573.1:CAS:528:DC%2BB2cXhtVegsbzF
27. Gini, R; Pajouheshnia, R; Gardarsdottir, H et al. Describing diversity of real world data sources in pharmacoepidemiologic studies: the DIVERSE scoping review. Pharmacoepidemiol Drug Saf; 2024; 33,
28. KDIGO. Kidney Disease: Improving Global Outcomes Work Group (KDIGO) 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int; 2024; 105,
29. Piani, F; Melena, I; Tommerdahl, KL et al. Sex-related differences in diabetic kidney disease: a review on the mechanisms and potential therapeutic implications. J Diabetes Complicat; 2021; 35,
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Introduction
People with chronic kidney disease (CKD) and type 2 diabetes (T2D) have an increased risk of kidney failure and cardiovascular disease. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) have shown cardiorenal protective effects. The objective of this multinational, multidatabase study was to describe the incidence of kidney and cardiovascular outcomes in separate, non–mutually exclusive cohorts of patients with CKD and T2D who initiated either an SGLT2i or a GLP-1 RA.
Methods
Data describing adults (≥ 18 years) with T2D and CKD who were new users of either SGLT2i or GLP-1 RA from 2012 to 2019 were assessed from population-based Danish National Health Registers (DNHR) and Valencia Health System Integrated Database (VID), hospital-based Japan Chronic Kidney Disease Database Extension (J-CKD-DB-Ex), and US Optum® de-identified Electronic Health Record dataset (Optum® EHR). Crude incidence rates (IRs) and 95% confidence intervals (CIs) for primary outcomes (kidney failure, acute coronary syndrome, stroke, new-onset congestive heart failure, new-onset atrial fibrillation) and cumulative incidence by follow-up time for primary and secondary outcomes (laboratory measurements of kidney function) were estimated.
Results
SGLT2i cohorts comprised 12,501 patients in DNHR, 22,404 in VID, 811 in J-CKD-DB-Ex, and 54,308 in Optum® EHR. GLP-1 RA cohorts comprised 10,696 in DNHR, 8317 in VID, 219 in J-CKD-DB-Ex, and 78,934 in Optum® EHR. Baseline clinical profile differences were observed for GLP-1 RA and SGLT2i new users, and crude IRs of kidney and heart failure tended to be higher in the GLP-1 RA cohorts than in the SGLT2i cohorts across data sources.
Conclusion
Understanding the incidence of kidney failure and cardiovascular outcomes in people receiving antidiabetic medications with cardiorenal protective effects is important for future studies aiming to compare the incidence of kidney and cardiovascular outcomes related to new and existing CKD treatments.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 RTI Health Solutions, Research Triangle Park, USA (GRID:grid.416262.5) (ISNI:0000 0004 0629 621X)
2 RTI Health Solutions, Waltham, USA (GRID:grid.416262.5) (ISNI:0000 0004 0629 621X)
3 RTI Health Solutions, Barcelona, Spain (GRID:grid.416262.5); The Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, USA (GRID:grid.239864.2) (ISNI:0000 0000 8523 7701)
4 Charité-Universitätsmedizin, Institute of Public Health, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662)
5 University of Tennessee Health Science Center, Division of Nephrology, Department of Medicine, Memphis, USA (GRID:grid.267301.1) (ISNI:0000 0004 0386 9246)
6 Aarhus University and Aarhus University Hospital, Department of Clinical Epidemiology, Aarhus, Denmark (GRID:grid.7048.b) (ISNI:0000 0001 1956 2722)
7 Health Services Research and Pharmacoepidemiology Unit, Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain (GRID:grid.428862.2) (ISNI:0000 0004 0506 9859)
8 Genki Plaza Medical Centre for Health Care, Tokyo, Japan (GRID:grid.512765.2)
9 University of Connecticut School of Pharmacy, Storrs, USA (GRID:grid.63054.34) (ISNI:0000 0001 0860 4915); Hartford Hospital, Evidence-Based Practice Center, Hartford, USA (GRID:grid.277313.3) (ISNI:0000 0001 0626 2712)
10 McMaster University, Division of Nephrology, Department of Medicine, Hamilton, Canada (GRID:grid.25073.33) (ISNI:0000 0004 1936 8227)
11 Bayer AG, Berlin, Germany (GRID:grid.420044.6) (ISNI:0000 0004 0374 4101)
12 National Kidney Foundation Advocacy, Richmond, USA (GRID:grid.420044.6)
13 Kawasaki Geriatric Medical Center, Okayama, Japan (GRID:grid.428862.2)
14 Juntendo University, Department of General Medicine, Faculty of Medicine, Tokyo, Japan (GRID:grid.258269.2) (ISNI:0000 0004 1762 2738)