Key Summary Points
Why carry out this study? |
Novel therapies are emerging for the prevention of chronic kidney disease (CKD) progression in patients with type 2 diabetes (T2D). Understanding the characteristics and patterns of use of those receiving glucagon-like peptide-1 receptor agonists (GLP-1 RAs) is a first step in evaluating the feasibility and clinical relevance of potential comparative analyses in the evolving field of CKD treatment. |
This multinational, multicohort study in Japan, Europe, and the United States described cohorts of patients with CKD and T2D who initiated a GLP-1 RA during the period of 2012–2021. |
What was learned from the study? |
We observed a steady increase of GLP-1 RA use across multinational data sources of patients with T2D and CKD during the study period. Findings suggest that GLP-1 RA therapies were used mainly for poor T2D metabolic control and in the presence of obesity and that their use was independent of CKD severity. |
Introduction
Type 2 diabetes (T2D) is a major cause of chronic kidney disease (CKD) worldwide [1, 2]. Diabetes with CKD is the most common underlying cause of kidney failure, accounting for almost 50% of cases in the developed world [3]. Additionally, people with T2D and CKD are at elevated risk of cardiovascular disease [4]. Available therapies for preventing and reducing the risk of CKD progression among patients with T2D include renin-angiotensin system inhibitor drugs (e.g., angiotensin-converting enzyme inhibitors [ACEi], angiotensin receptor blockers [ARBs]), sodium-glucose cotransporter 2 inhibitors (SGLT2i), and nonsteroidal mineralocorticoid receptor antagonists [5].
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may also have favorable renal effects given their direct and indirect mechanisms of action, including stimulating pathways responsible for reducing oxidative stress in the kidneys, reducing inflammation, and promoting weight loss and glucose control [6, 7]. GLP-1 RAs have been shown to have beneficial effects on kidney outcomes [8]. Specifically, evidence from cardiovascular outcomes trials, in which kidney outcomes have been captured as secondary endpoints, and from observational studies suggests that treatment with a GLP-1 RA is associated with cardiovascular benefits in patients with prediabetes and T2D, and with lower risks of composite kidney outcomes in patients with T2D [8, 9, 10, 11, 12, 13–14]. Moreover, FLOW, the first randomized trial designed to study the effects of GLP-1 RAs on kidney outcomes, showed a 24% reduction in the risk (hazard ratio = 0.76; 95% confidence interval, 0.66–0.88) of a primary composite kidney outcome (kidney failure onset, at least a 50% reduction in estimated glomerular filtration rate [eGFR] from baseline, or death from kidney-related or cardiovascular causes) when comparing patients initiating semaglutide with patients initiating placebo [15, 16]. In 2025, the United States (US) Food and Drug Administration approved the GLP-1 RA semaglutide to reduce the risk of worsening CKD [17], and the KDIGO (Kidney Disease: Improving Global Outcomes) guidelines recommend GLP-1 RAs as a second-line therapy for patients with T2D and CKD who have not met their glycemic targets despite use of metformin and an SGLT2i or for those who are unable to tolerate these medications [5].
With the rapidly changing treatment landscape for patients with T2D and CKD, the emergence of new therapies for the prevention of CKD progression (e.g., finerenone) and with a GLP-1 RA having received approval in a CKD indication, it is of interest to understand the use of GLP-1 RAs in routine clinical practice among patients who have T2D and CKD. The overall aim of this multidatabase, multinational observational cohort study was to describe the profiles and treatment patterns of adults with CKD and T2D who initiate GLP-1 RAs, using real-world data from population-based data sources in Europe, Japan, and the US. The study was performed as part of the FOUNTAIN (FinerenOne mUlti-database NeTwork for evidence generAtIoN) platform [18].
Methods
Study Design and Setting
This was a multinational cohort study using secondary data from five participating data sources in Europe (the Danish National Health Registers [DNHR], the PHARMO Data Network [PHARMO], and the Valencia Health System Integrated Database [VID]), Japan (the Japan Chronic Kidney Disease Database Extension [J-CKD-DB-Ex]), and the US (Optum’s de-identified Clinformatics® Data Mart Database [CDM]). Appendix A (Online Supplement) describes the data sources in detail. Similar methodologies have been presented in a previously published companion study to clinically profile and evaluate treatment patterns in SGLT2i initiators using the same data sources [19].
This study used de-identified data from electronic health records (EHRs). Access to the data sources was through a collaboration model for DNHR, VID, PHARMO, J-CKD-DB-Ex and through a license for CDM. Ethics committee review was waived for DNHR and PHARMO. The study protocol was reviewed and approved by the Comité Ético de Investigación con Medicamentos del Hospital Clínico Universitario de Valencia for VID (2022/164). This study protocol was reviewed and approved by the ethics committee of the Shiga University of Medical Science for J-CKD-DB-Ex (R2022-143). CDM 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.
Study Population
Figure B1 (Appendix B) depicts the cohort eligibility criteria, cohort entry, baseline assessment periods, and follow-up for the study population. For each data source, the source population included all adult patients (aged ≥ 18 years) with at least 12 months of continuous enrollment and recorded evidence of both CKD and T2D, in any order prior to or on cohort entry date, and who initiated a GLP-1 RA from January 1, 2012, through June 30, 2021 (December 2020 for PHARMO). In Japan, the study period began on January 1, 2014, the date on which the J-CKD-DB-Ex had the first recorded information. Exclusion criteria were evidence of type 1 diabetes (T1D), kidney cancer, or kidney failure prior to or on cohort entry date. New users were patients with an outpatient prescription or dispensing (hereafter, prescription) for a GLP-1 RA and no prescription for any other medication in that class during the previous 12 months. The index date was the date of the first eligible GLP-1 RA prescription that fulfilled the definition of new use during the study period. At the time of each potential index date, patients were assessed for the inclusion and exclusion criteria; the earliest prescription date that met these criteria was selected as the index date. All patients meeting the study eligibility criteria were included in the study cohort.
The definition of active registration and the algorithms for T1D and T2D were specific to each data source (see Table A1). CKD was assessed through diagnosis codes (a single diagnosis code for stage 2, 3, or 4 or stage unspecified), through eGFR test results (occurrence of two different eGFR test results ≥ 15 ml/min/1.73 m2 and < 60 ml/min/1.73 m2 separated by at least 90 days and ≤ 540 days), or through urine albumin-to-creatinine ratio (ACR) test results (two different results ≥ 30 mg/g separated by at least 90 days and ≤ 540 days). Chronic kidney failure was defined according to diagnosis codes for CKD stage 5, occurrence of two eGFR results < 15 ml/min/1.73 m2 separated by at least 90 days and ≤ 540 days, receipt of chronic dialysis, or kidney transplantation. Kidney cancer was assessed by diagnosis codes (see Table A1).
Variables
Exposures
Exposures to medications of interest were identified from prescriptions in the EHRs or administrative claims data for prescription of medications, depending on the data source (see Appendix A). The index GLP-1 RA was defined by Anatomical Therapeutic Chemical (ATC) codes (Table C1, Appendix C), and current use of the index GLP-1 RA is defined in Appendix B.
Table 1. Selected baseline characteristics of GLP-1 RA new users by data source
Characteristic | DNHR (N = 18,929) | PHARMO (N = 476) | VID (N = 11,798) | J-CKD-DB-Ex (N = 329) | CDM (N = 70,158) |
|---|---|---|---|---|---|
Demographic and lifestyle characteristics | |||||
Age at the index date, years | |||||
Mean (SD) | 66.2 (11.7) | 66.6 (8.8) | 67.3 (10.6) | 66.1 (13.6) | 67.9 (10.1) |
Sex, n (%) | |||||
Male | 11,250 (59.4) | 222 (46.6) | 6549 (55.5) | 196 (59.6) | 33,652 (48.0) |
Female | 7679 (40.6) | 254 (53.4) | 5249 (44.5) | 133 (40.4) | 36,502 (52.0) |
Calendar year of index date, n (%)a | |||||
2012 | 436 (2.3) | 23 (4.8) | 188 (1.6) | N/A | 1879 (2.7) |
2013 | 290 (1.5) | 26 (5.5) | 350 (3.0) | N/A | 1956 (2.8) |
2014 | 270 (1.4) | 19 (4.0) | 587 (5.0) | N/A | 2030 (2.9) |
2015 | 590 (3.1) | 15 (3.2) | 682 (5.8) | 24 (7.3) | 2885 (4.1) |
2016 | 1024 (5.4) | 14 (2.9) | 909 (7.7) | 41 (12.5) | 4308 (6.1) |
2017 | 1585 (8.4) | 39 (8.2) | 1098 (9.3) | 44 (13.4) | 7424 (10.6) |
2018 | 2526 (13.3) | 75 (15.8) | 1768 (15.0) | 45 (13.7) | 10,249 (14.6) |
2019 | 3976 (21.0) | 150 (31.5) | 2504 (21.2) | 65 (19.8) | 13,876 (19.8) |
2020 | 4981 (26.3) | 115 (24.2) | 2070 (17.6) | 75 (22.8) | 14,866 (21.2) |
2021 | 3252 (17.2) | N/A | 1642 (13.9) | 35 (10.6) | 10,685 (15.2) |
BMI (calculated as kg/m2), n (%)b | |||||
< 20 (underweight) | N/A | 0 (0) | 2 (0.2) | N/A | 136 (0.2) |
20–24.9 (normal) | N/A | 6 (1.3) | 114 (1.0) | N/A | 1043 (1.5) |
25–29.9 (overweight) | N/A | 68 (14.3) | 1263 (10.7) | N/A | 4589 (6.5) |
30–39.9 (obese) | N/A | 283 (59.5) | 7167 (60.8) | N/A | 15,039 (21.4) |
≥ 40 (severely obese) | N/A | 93 (19.5) | 2026 (17.2) | N/A | 9711 (13.8) |
Missing | N/A | 26 (5.5) | 1226 (10.4) | N/A | 39,640 (56.5) |
Obesity, n (%)c | |||||
Yes | 6063 (32.0) | 397 (83.4) | 10,635 (90.1) | 51 (15.5) | 37,234 (53.1) |
Comorbidities | |||||
Macrovascular complications of diabetes, n (%) | |||||
CHD | 5557 (29.4) | 157 (33.0) | 3171 (26.9) | 197 (59.9) | 23,175 (33.0) |
Cerebrovascular disease | 2397 (12.7) | 51 (10.7) | 1488 (12.6) | 171 (52.0) | 8670 (12.4) |
Peripheral vascular disease | 3088 (16.3) | 65 (13.7) | 3041 (25.8) | 72 (21.9) | 20,167 (28.7) |
CVD risk factors, n (%) | |||||
Hypertension | 15,204 (80.3) | 359 (75.4) | 10,974 (93.0) | 293 (89.1) | 65,828 (93.8) |
Hypercholesterolemia | 6337 (33.5) | 174 (36.6) | 9611 (81.5) | 278 (84.5) | 62,519 (89.1) |
Congestive heart failure | 2448 (12.9) | 73 (15.3) | 634 (5.4) | 194 (59.0) | 14,543 (20.7) |
Severe liver disease | 107 (0.6) | 23 (4.8) | 733 (6.2) | 16 (4.9) | 707 (1.0) |
HIV infection | 32 (0.2) | 1 (0.2) | 41 (0.4) | 16 (4.9) | 346 (0.5) |
Dementia | 260 (1.4) | 8 (1.7) | 279 (2.4) | 20 (6.1) | 2808 (4.0) |
COPD | 1929 (10.2) | 79 (16.6) | 2048 (17.4) | 105 (31.9) | 13,891 (19.8) |
Malignancy (other than kidney cancer and nonmelanoma skin cancers) | 2344 (12.4) | 99 (20.8) | 2814 (23.9) | 97 (29.5) | 8347 (11.9) |
Comedications | |||||
Cardiovascular medications in the 180 days before or on the index date, n (%) | |||||
Thiazide-like diuretics | 3018 (15.9) | 121 (25.4) | 678 (5.8) | 28 (8.5) | 23,031 (32.8) |
Loop diuretics | 5332 (28.2) | 107 (22.5) | 3771 (32.0) | 52 (15.8) | 17,894 (25.5) |
Potassium-sparing diuretics | 163 (0.9) | 12 (2.5) | 160 (1.4) | 0 (0) | 1703 (2.4) |
ACEi | 6755 (35.7) | 185 (38.9) | 2314 (19.6) | 21 (6.4) | 28,644 (40.8) |
ARB | 8227 (43.5) | 221 (46.4) | 7546 (64.0) | 181 (55.0) | 36,000 (51.3) |
Beta-blockers | 7598 (40.1) | 268 (56.3) | 4577 (38.8) | 83 (25.2) | 35,476 (50.6) |
Direct renin inhibitors | 20 (0.1) | 4 (0.8) | 33 (0.3) | 1 (0.3) | 59 (0.1) |
Angiotensin receptor-neprilysin inhibitors | 67 (0.4) | 1 (0.2) | 190 (1.6) | 0 (0) | 625 (0.9) |
Calcium channel blockers | 7537 (39.8) | 159 (33.4) | 3484 (29.5) | 176 (53.5) | 23,752 (33.9) |
Other antihypertensives | 0 (0) | 16 (3.4) | 1906 (16.2) | 33 (10.0) | 4939 (7.0) |
Statins | 14,501 (76.6) | 371 (77.9) | 9320 (79.0) | 160 (48.6) | 53,632 (76.4) |
Lipid-lowering drugs other than statins | 1094 (5.8) | 47 (9.9) | 2936 (24.9) | 67 (20.4) | 11,962 (17.1) |
Anticoagulants | 3134 (16.6) | 73 (15.3) | 2296 (19.5) | 34 (10.3) | 7786 (11.1) |
Digoxin | 851 (4.5) | 12 (2.5) | 274 (2.3) | 1 (0.3) | 1164 (1.7) |
Nitrates and other vasodilators | 1191 (6.3) | 40 (8.4) | 811 (6.9) | 28 (8.5) | 5920 (8.4) |
Aspirin and other antiplatelet agents | 7713 (40.7) | 191 (40.1) | 5135 (43.5) | 110 (33.4) | 9339 (13.3) |
ACEi angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blocker, BMI body mass index, CDM Optum’s de-identified Clinformatics® Data Mart Database, CHD coronary heart disease, COPD chronic obstructive pulmonary disease, CVD cardiovascular disease, DNHR Danish National Health Registers, GLP-1 RA glucagon-like peptide-1 receptor agonist, HIV human immunodeficiency virus, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, N/A not available, PHARMO PHARMO Data Network, SD standard deviation, VID Valencia Health System Integrated Database
aBy design, only 6 months of observation were included in 2021, with the exception of PHARMO, where the end of the study period was December 2020
bInformation regarding BMI was only available for VID, PHARMO, and CDM, but with CDM missing BMI data for 56.5% of patients. Smoking status was not available in all data sources and is not presented, nor is alcohol use presented
cDefined by a BMI measurement > 30 or a diagnosis code indicating obesity
Cohort Characteristics
The demographic and lifestyle variables that were available at the index date in each data source included age, sex, and obesity. Using all available history, clinical characteristics at baseline were evaluated, including markers of the severity of T2D (e.g., hemoglobin A1c [HbA1c], diabetes severity index), markers of impaired kidney function (e.g., diagnosis code, eGFR, ACR, medication use, clinical conditions associated with CKD, and hospitalization for acute kidney injury), medications other than glucose-lowering drugs (GLDs) used during the 180 days preceding or on the index date, and cardiovascular comorbidities at any time preceding or on the index date. In addition, demographic and clinical characteristics at baseline were also stratified by whether an ACR test had been performed in the 365 days preceding or on the index date (Table C4, Appendix C).
Treatment Utilization
Outcomes related to treatment utilization during follow-up included treatment discontinuation (i.e., the end of current use), which was defined as the date following the last day of current use; treatment switches; and add-on treatments. Duration of both the index medication episode and the total exposure to a GLP-1 RA over all follow-up time, regardless of discontinuations, were described.
Statistical Analyses
Each research partner performed statistical analyses independently according to a common protocol and a common statistical analysis plan with data source–specific adaptations. Analyses were programmed in SAS version 9.4 or higher (SAS Institute, Inc.), except at VID, where R version 4.1.0 was used. Aggregated results were provided to the coordinating center. All analyses were descriptive. Frequency distributions (counts, percentages) were reported for categorical variables and means, standard deviations, medians, and 1st and 99th percentiles were computed for continuous variables. In accordance with country-specific data privacy standards, categorical variables with low frequencies for a specific level were masked. The percentage of missing data for individual variables was described.
For reliability of long-term follow-up in all data sources and comparability of follow-up available for different data sources, the analyses of treatment changes over time were conducted up to 3 years after the index date. Each of the following treatment states was assessed at discrete “checkpoints” at 90 days, 180 days, 270 days, 1 year, 2 years, and 3 years following the index date: (1) treated with index medication (i.e., the patient was in any period of continuous current-use on the checkpoint date); (2) untreated with index medication; (3) death (which was not available in J-CKD-DB-Ex); and (4) lost to follow-up, end of study, or censored. Sankey diagrams were generated to illustrate the movement of patients between different treatment states across all checkpoints [20].
Results
After applying inclusion and exclusion criteria, the final analysis cohorts of GLP-1 RA new users with T2D and CKD comprised 18,929 patients in DNHR, 476 in PHARMO, 11,798 in VID, 329 in J-CKD-DB-Ex, and 70,158 in CDM (Table C2, Appendix C).
Table 2. Markers of severity of T2D at the index date for new users of GLP-1 RA by data source
DNHR (N = 18,929) | PHARMO (N = 476) | VID (N = 11,798) | J-CKD-DB-Ex (N = 329) | CDM (N = 70,158) | |
|---|---|---|---|---|---|
Duration of T2D at index date, years | |||||
Mean (SD) | 11.6 (6.7) | 13.6 (10.1) | 9.5 (4.1) | 8.6 (5.5) | 4.9 (3.4) |
Median | 11 | 13.2 | 10.1 | 7.9 | 4 |
1st, 99th percentiles | 0, 27 | 2, 35 | 0, 17 | 0, 22 | 0, 14 |
Medications for T2D (hypoglycemic agents) ever prescribed from 180 days before and including the index date, n (%) | |||||
SGLT2i and fixed-dose combinations | 5282 (27.9) | 31 (6.5) | 4890 (41.5) | 174 (52.9) | 9359 (13.3) |
Metformin and fixed-dose combinations | 14,454 (76.4) | 385 (80.9) | 7570 (64.2) | 199 (60.5) | 36,888 (52.6) |
Sulfonylureas and fixed-dose combinations | 2532 (13.4) | 269 (56.5) | 1040 (8.8) | 115 (35.0) | 26,089 (37.2) |
Alpha-glucosidase inhibitors | NR | 1 (0.2) | 24 (0.2) | 129 (39.2) | 369 (0.5) |
Thiazolidinediones | 10 (0.1) | 5 (1.1) | 609 (5.2) | 56 (17.0) | 6334 (9.0) |
Dipeptidyl peptidase-4 inhibitors and fixed-dose combinations | 6662 (35.2) | 64 (13.4) | 7418 (62.9) | 255 (77.5) | 17,042 (24.3) |
Meglitinides (including repaglinide, nateglinide, mitiglinide) | 56 (0.3) | 2 (0.4) | 2563 (21.7) | 118 (35.9) | 1000 (1.4) |
Number of T2D drug classes other than insulin ever used in the 180 days before and including the index date, n (%) | |||||
0 | 2390 (12.6) | 47 (9.9) | 723 (6.1) | 18 (5.5) | 16,185 (23.1) |
1 | 7266 (38.4) | 160 (33.6) | 2855 (24.2) | 32 (9.7) | 23,640 (33.7) |
2 | 6402 (33.8) | 213 (44.7) | 4345 (36.8) | 61 (18.5) | 19,721 (28.1) |
3 | 2554 (13.5) | 54 (11.3) | 3000 (25.4) | 83 (25.2) | 8635 (12.3) |
4 + | 317 (1.7) | 2 (0.4) | 875 (7.4) | 135 (41.0) | 1977 (2.8) |
Insulin use recorded in the 180 days before and including the index date, n (%) | 7322 (38.7) | 299 (13.2) | 6259 (53.1) | 281 (85.4) | 31,424 (44.8) |
HbA1c, n (%) | |||||
HbA1c ≤ 53 mmol/mol or ≤ 7% | 2620 (13.8) | 22 (4.6) | 1900 (16.1) | 67 (20.4) | 7235 (10.3) |
HbA1c > 53 mmol/mol and ≤ 63.9 mmol/mol or > 7% and ≤ 8% | 4897 (25.9) | 87 (18.3) | 2716 (23.0) | 82 (24.9) | 8846 (12.6) |
HbA1c > 63.9 mmol/mol and ≤ 74.9 mmol/mol or > 8% and ≤ 9% | 5107 (27.0) | 89 (18.7) | 2588 (21.9) | 76 (23.1) | 8228 (11.7) |
HbA1c > 74.9 mmol/mol or > 9% | 5923 (31.3) | 126 (26.5) | 2801 (23.7) | 98 (29.8) | 12,498 (17.8) |
HbA1c missing | 382 (2.0) | 152 (31.9) | 1793 (15.2) | 6 (1.8) | 33,351 (47.5) |
Other key medical conditions, n (%) | |||||
Hyperkaliemia | 205 (1.1) | 6 (1.3) | 1326 (11.2) | 44 (13.4) | 5368 (7.7) |
Amputation | 476 (2.5) | 4 (0.8) | 195 (1.7) | 0 (0) | 1676 (2.4) |
The Diabetes Severity Complications Index | |||||
Key diagnoses for scoring of the index score, n (%) | |||||
Retinopathy | 3976 (21.0) | 13 (2.7) | 3615 (30.6) | 82 (24.9) | 17,400 (24.8) |
Nephropathy | 16,345 (86.3) | 452 (95.0) | 11,798 (100.0) | 130 (39.5) | 45,553 (64.9) |
Neuropathy | 4380 (23.1) | 22 (4.6) | 2835 (24.0) | 113 (34.3) | 31,388 (44.7) |
Cerebrovascular | 2397 (12.7) | 28 (5.9) | 1532 (13.0) | 171 (52.0) | 8670 (12.4) |
Cardiovascular | 7917 (41.8) | 149 (31.3) | 5536 (46.9) | 276 (83.9) | 35,373 (50.4) |
Peripheral vascular disease | 3171 (16.8) | 27 (5.7) | 3628 (30.8) | 57 (17.3) | 20,546 (29.3) |
Metabolic complications | 992 (5.2) | 0 (0) | 1896 (16.1) | 7 (2.1) | 4994 (7.1) |
Index score | |||||
Mean (SD) | 2.6 (1.7) | 2.2 (1.6) | 4.44 (2.1) | 4.1 (2.2) | 3.1 (2.2) |
Median | 2 | 2 | 4 | 4 | 3 |
1st, 99th percentiles | 0, 7 | 0, 7 | 2, 10 | 0, 9 | 0, 9 |
CDM Optum’s de-identified Clinformatics® Data Mart Database, DNHR Danish National Health Registers, GLP-1 RA glucagon-like peptide-1 receptor agonist, HbA1c hemoglobin A1c (glycated hemoglobin), J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, N number, NR not reported, PHARMO PHARMO Data Network, SD standard deviation, SGLT2i sodium-glucose cotransporter 2 inhibitors, T2D type 2 diabetes, VID Valencia Health System Integrated Database
Baseline Demographic Characteristics
The mean age of GLP-1 RA initiators in the data sources was similar, ranging from 66.1 years in J-CKD-DB-Ex to 67.9 years in CDM (Table 1). Across data sources, there was a steady increase in GLP-1 RA new users from 2012 (when between 1.6% [VID] and 4.8% [PHARMO] of GLP-1 RA initiators started therapy) to 2019 (when between 19.8% [J-CKD-DB-Ex and CDM] and 31.5% [PHARMO] started therapy). Men represented a lower percentage of new users of GLP-1 RAs in CDM (48.0%) and PHARMO (46.6%) compared with J-CKD-DB-Ex (59.6%) and the other European data sources (DNHR, 59.4%; VID, 55.5%). The prevalence of obesity was quite variable, ranging from 15.5% (J-CKD-DB-Ex) to 90.1% (VID).
Markers of Severity of T2D and GLD Use at the Index Date
The estimated median duration of a T2D diagnosis at study inclusion was longest in PHARMO (13.2 years) and shortest in CDM (4 years), and the median score for the Diabetes Severity Complications Index was higher in J-CKD-DB-Ex and VID (both 4) than in CDM (3) and DNHR and PHARMO (both 2) (Table 2). The percentage of patients with HbA1c values > 53 mmol/mol (or > 7%) at baseline ranged from 42.1% in CDM to 84.2% in DNHR (the proportion of each cohort with missing HbA1c values ranged from 1.8% in J-CKD-DB-Ex to 47.5% in CDM). The percentage of patients with HbA1c levels > 74.9 mmol/mol or > 9% ranged from 17.8% in CDM to 31.3% in DNHR.
In the 180 days before and including the GLP-1 RA initiation date, insulin use was recorded for between 13.2% (PHARMO) and 85.4% (J-CKD-DB-Ex) of patients. The majority (> 60%) of patients in DNHR, PHARMO, VID, and CDM had used either one or two medications in a GLD class other than GLP-1 RAs. In the J-CKD-DB-Ex cohort, 41.0% of patients had been prescribed therapies in 4 or more drug classes during this time. The most common medications for T2D prescribed in the 180 days before and including the date of GLP-1 RA initiation varied by data source (Table 2). Metformin and fixed-dose combinations were the most common GLDs in DNHR (76.4%), PHARMO (80.9%), and CDM (52.6%), whereas dipeptidyl peptidase-4 inhibitor (DPP-4) was most common in J‑CKD-DB-Ex (77.5%). In VID, similar proportions of patients had recorded use of metformin (64.2%) or DPP-4 (62.9%).
Markers of Severity of Impaired Kidney Function at the Index Date
The estimated approximate median duration of CKD at the time of GLP-1 RA initiation based on all available data in each data source was 2 years in VID; 3 years in J-CKD-DB-Ex, CDM, and DNHR; and 7 years in PHARMO (Table C3, Appendix C). Based on CKD stage at baseline defined by eGFR or diagnosis code (Fig. 1), approximately half of the patients in J-CKD-DB-Ex (47.1%) and PHARMO (48.1%) had stage 3 CKD compared with 41.3% of patients in VID, 35.3% of patients in DNHR, and 27.6% of patients in CDM (Table C3, Appendix C). The percentage of patients diagnosed with stage 1 CKD ranged from approximately 10% to 32% in the European data sources, was 8.6% in CDM, and was lowest in J-CKD-DB-Ex at 6.4%, which has a database entry requirement for CKD that is defined as proteinuria and/or an eGFR value < 60 ml/min/1.73 m2. Approximately 26% to 36% of patients were diagnosed with stage 2 CKD at baseline. Severe CKD (stage 4) ranged from 3.9% in DNHR to 11.6% in J-CKD-DB-Ex. Similar proportions of patients in DNHR, PHARMO, VID, and J-CKD-DB-Ex (20.6% in VID to 25.6% in DNHR) and 10.2% of patients in CDM were in ACR category A1, although a large percentage of patients in these data sources did not have an ACR laboratory assessment recorded in the year before the index date, with the exception of DNHR (Fig. 2). Baseline characteristics stratified by ACR or no ACR assessment are listed in Table C4 (Appendix C). A high proportion of GLP-1 RA initiators had used medication classes of interest with potential CKD-protective effect before initiating a GLP-1 RA, with ACEi or ARB and SGLT2i used most frequently (Fig. 3).
Table 3. Characteristics of the Index GLP-1 RA at baseline and during follow-up, by data source
DNHR (N = 18,929) | PHARMO (N = 476) | VID (N = 11,798) | J-CKD-DB-Ex (N = 329) | CDM (N = 70,158) | |
|---|---|---|---|---|---|
Classification of the index GLP-1 RA at the index date, n (%) | |||||
Monotherapy | 4696 (24.8) | 90 (18.9) | 1342 (11.4) | 212 (64.4) | 14,535 (20.7) |
Combination therapy | 1061 (5.6) | 13 (2.7) | 254 (2.2) | 4 (1.2) | 1804 (2.6) |
Add-on | 8588 (45.4) | 298 (62.6) | 7335 (62.2) | 104 (31.6) | 40,500 (57.7) |
Switch | 2272 (12.0) | 19 (4.0) | 497 (4.2) | 4 (1.2) | 5244 (7.5) |
Add-on and switch | 1104 (5.8) | 21 (4.4) | 1404 (11.9) | 5 (1.5) | 2042 (2.9) |
Indeterminate | 1208 (6.4) | 35 (7.4) | 966 (8.2) | 0 (0) | 6033 (8.6) |
Duration of initial exposure episode after cohort entry, months | |||||
Mean (SD) | 17.9 (18.1) | 3.0 (3.1) | 17.6 (17.3) | 12.9 (14.4) | 9.4 (12.3) |
Median | 11.5 | 2.3 | 12.4 | 7.2 | 4 |
1st, 99th percentiles | 0, 87 | 0, 16 | 0, 79 | 0, 62 | 1, 57 |
Days’ supply of index GLP-1 RA, days | |||||
Mean (SD) | 102.1 (66.1) | 31.7 (30.4) | 26.8 (4.2) | 21.5 (27.9) | 40.4 (23.4) |
Median | 86 | 28 | 28 | 21 | 30 |
1st, 99th percentiles | 36, 320 | 7, 150 | 11, 30 | 1, 121 | 7, 90 |
Number of prescriptions or dispensings during follow-up for the GLP-1 RA drug class | |||||
Mean (SD) | 14.0 (15.3) | 6.8 (9.4) | 20.5 (21.8) | 19.6 (26.8) | 7.1 (10.0) |
Median | 9 | 4 | 13 | 12 | 3 |
1st, 99th percentiles | 1, 71 | 0, 48 | 1, 101 | 1, 130 | 1, 49 |
Number of distinct “current-use” periods (treatment episodes) during follow-up for the index GLP-1 RA drug class, n (%) | |||||
1 | 16,266 (85.9) | 259 (54.4) | 8193 (69.4) | 230 (69.9) | 28,047 (40.0) |
2 | 1983 (10.5) | 75 (15.8) | 2201 (18.7) | 55 (16.7) | 16,028 (22.8) |
3 | 444 (2.3) | 41 (8.6) | 822 (7.0) | 20 (6.1) | 9629 (13.7) |
4 | 140 (0.7) | 35 (7.4) | 322 (2.7) | 9 (2.7) | 5798 (8.3) |
5 + | 96 (0.5) | 66 (13.9) | 260 (2.2) | 15 (4.6) | 10,656 (15.2) |
Number of distinct prescriptions or dispensings during follow-up for the index GLP-1 RA drug class | |||||
Mean (SD) | 15.8 (16.2) | 13.4 (12.9) | 27.0 (26.4) | 26.0 (33.9) | 13.9 (14.7) |
Median | 11 | 10 | 20 | 17 | 9 |
1st, 99th percentiles | 1, 75 | 1, 67 | 1, 118 | 1, 159 | 1, 68 |
Number of discontinuations (interruptions) of current use during follow-up, n (%) | |||||
0 | 13,140 (69.4) | 259 (54.4) | 8193 (69.4) | 151 (45.9) | 28,047 (40.0) |
1 | 4595 (24.3) | 75 (15.8) | 2201 (18.7) | 114 (34.7) | 16,028 (22.8) |
2 | 849 (4.5) | 41 (8.6) | 822 (7.0) | 32 (9.7) | 9629 (13.7) |
3 | 206 (1.1) | 35 (7.4) | 322 (2.7) | 14 (4.3) | 5798 (8.3) |
4 | 79 (0.4) | 22 (4.6) | 131 (1.1) | 6 (1.8) | 3701 (5.3) |
5 + | 60 (0.3) | 44 (9.2) | 129 (1.1) | 12 (3.6) | 6955 (9.9) |
Number of patients with an interruption of current use lasting 90 days or more, n (%) | 1438 (7.6) | 74 (15.5) | 1416 (12.0) | 127 (38.6) | 28,959 (41.3) |
Duration of total exposure to index therapy, months | |||||
Mean (SD) | 20.7 (19.9) | 12.1 (14.9) | 23.2 (21.0) | 17.6 (17.8) | 21.9 (20.0) |
Median | 14.7 | 5.2 | 18.1 | 10.9 | 16.1 |
1st, 99th percentile | 0, 96 | 1, 66 | 0, 89 | 0, 70 | 2, 89 |
Other drug classes started during follow-up, n (%) | |||||
SGLT2i | 6770 (35.8) | 29 (6.1) | 6700 (56.8) | 131 (39.8) | 6815 (9.7) |
ACEi/ARB | 15,011 (79.3) | 368 (77.3) | 9650 (81.8) | 200 (60.8) | 2948 (4.2) |
Duration of total follow-up, months | |||||
Mean (SD) | 26.0 (23.1) | 24.8 (24.0) | 31.8 (25.4) | 25.4 (20.3) | 23.8 (21.7) |
Median | 19.9 | 17.2 | 25.5 | 19.5 | 17.8 |
1st, 99th percentiles | 0, 106 | 0, 103 | 0, 101 | 0, 75 | 0, 99 |
ACEi angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blocker, CDM Optum’s de-identified Clinformatics® Data Mart Database, DNHR Danish National Health Registers, GLP-1 RA glucagon-like peptide-1 receptor agonist, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, PHARMO PHARMO Data Network, SD standard deviation, SGLT2i sodium-glucose cotransporter 2 inhibitors, VID Valencia Health System Integrated Database
[See PDF for image]
Fig. 1
CKD stage at the index date defined according to eGFR value or diagnosis code among those with measured baseline values,a by data source. CDM Optum’s de-identified Clinformatics® Data Mart Database, CKD chronic kidney disease, DNHR Danish National Health Registers, eGFR estimated glomerular filtration rate, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, PHARMO PHARMO Data Network, VID Valencia Health System Integrated Database. aThe proportion of patients with unknown or unspecified CKD stage was 1.1% in DNHR, 0.6% in PHARMO, 4.7% in VID, 0% in J-CKD-DB-Ex, and 32.8% in CDM. All patients met the inclusion eligibility criteria for CKD, which was assessed through diagnosis codes, eGFR test results, or albumin-to-creatinine ratio test results
[See PDF for image]
Fig. 2
ACR categories at the index date among those with measured baseline values, a by data source. ACR urine albumin-to-creatinine ratio, CDM Optum’s de-identified Clinformatics® Data Mart Database, CKD chronic kidney disease, DNHR Danish National Health Registers, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, PHARMO PHARMO Data Network, VID Valencia Health System Integrated Database. aThe proportion of patients with no assessment of urine ACR recorded in the year before the index date was 19.0% in DNHR, 64.9% in PHARMO, 34.3% in VID, 38.0% in J-CKD-DB-Ex, and 73.2% in CDM
[See PDF for image]
Fig. 3
Historical, previous, and current use of CKD-protective medications of interest in relation to the index GLP-1 RA medication. ACEi angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blocker, CDM Optum’s de-identified Clinformatics® Data Mart Database, CKD chronic kidney disease, DNHR Danish National Health Registers, GLP-1 RA glucagon-like peptide-1 receptor agonist, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, PHARMO PHARMO Data Network, SGLT2i sodium-glucose cotransporter 2 inhibitors, VID Valencia Health System Integrated Database. By design, GLP-1 RA use could not occur from − 365 days to the day before the index date; thus, no percentages were reported for these measures across any data source
Baseline Comorbidities and Comedications
Across data sources, the most common comorbid condition was hypertension, ranging from 75.4% in PHARMO to 93.8% in CDM, followed by hypercholesterolemia, present in more than 80% of patients in J-CKD-DB-Ex, VID, and CDM (Table 1). Diagnoses of macrovascular complications, specifically coronary heart disease, ranged from 26.9% (VID) to 33.0% (PHARMO and CDM) and 59.9% (J-CKD-DB-Ex). Cerebrovascular disease ranged from 10.7% (PHARMO) to 52.0% (J-CKD-DB-Ex), and congestive heart failure ranged from 5.4% (VID) to 59.0% (J-CKD-DB-Ex).
Loop diuretics were commonly prescribed in some data sources, ranging from 15.8% in J-CKD-DB-Ex to 32.0% in VID, and thiazide-like diuretics were commonly prescribed in PHARMO (25.4%) and CDM (32.8%). Comparable proportions of patients were prescribed ACEi and ARBs in DNHR, PHARMO, and CDM: in these data sources, ACEi were prescribed to approximately 35% to 40% of patients and ARBs to approximately 44% to 51% of patients. In J-CKD-DB-Ex, only 6% of patients were prescribed an ACEi, and 55% an ARB; in VID, approximately 20% of patients were prescribed an ACEi and 64% an ARB. Calcium channel blockers were prescribed to 54% of patients in J-CKD-DB-Ex and 40% of patients in DNHR, and to approximately 30% of patients in the other data sources. Statins were prescribed to over 75% of patients in DNHR, PHARMO, VID, and CDM and to approximately 50% of patients in J-CKD-DB-Ex. Use of other lipid-lowering medications varied across data sources.
Characteristics of the Index Medication at Baseline and During Follow-up
The median duration of the initial exposure episode to a GLP-1 RA was greater than 2 months in all data sources, ranging from 2.3 months (PHARMO) to 12.4 months (VID) (Table 3). Between 7.6% (DNHR) and 41.3% (CDM) of GLP-1 RA initiators had an interruption of current use lasting 90 days or more during follow-up; interruptions were most common in CDM and J-CKD-DB-Ex. The total observed median duration of exposure to the index GLP-1 RA ranged from 5.2 months (PHARMO) to 18.1 months (VID), and across all data sources, the median duration of follow-up from the date of GLP-1 RA initiation was more than 17 months.
Use of individual GLP-1 RAs varied across data sources (Fig. 4). During the study period, semaglutide was the most prescribed GLP-1 RA in DNHR (60.1%), while dulaglutide was most prescribed in CDM (37.9%) and VID (41.7%). In J-CKD-DB-Ex, liraglutide was the most prescribed GLP-1 RA, with 60.2% of new users prescribed that drug. In PHARMO, liraglutide and semaglutide were comparably prescribed. Exenatide was prescribed to 6.3% of patients in VID and 11.6% of patients in CDM.
[See PDF for image]
Fig. 4
Type of GLP-1 RA at initiation. CDM Optum’s de-identified Clinformatics® Data Mart Database, DNHR Danish National Health Registers, GLP-1 RA glucagon-like peptide-1 receptor agonist, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, PHARMO PHARMO Data Network, VID Valencia Health System Integrated Database
Treatment Changes Over Time for GLP-1 RA During Follow-up
During the study period, the proportions of patients observed to be receiving GLP-1 RA treatment at each assessment time point (90 days, 180 days, 270 days, 1 year, 2 years, 3 years) were similar among the European data sources and somewhat lower in J-CKD-DB-Ex and CDM (Fig. 5). A common pattern across all data sources was that the largest proportional increase in the “no exposure” treatment state occurred within the first 6 months of the index date. Specifically, the greatest increase occurred either between the index and 90-day time points in VID (no exposure at 90 days: 7%), J-CKD-DB-Ex (no exposure at 90 days: 20%), and CDM (no exposure at 90 days: 21%) or between the 90- and 180-day time points in the DNHR (no exposure at 90 days: 3%; no exposure at 180 days: 12%). In PHARMO, the increase in the “no exposure” treatment state was similar at these time points. A small proportion of nonusers who remained under observation changed treatment state and became current users at each time.
[See PDF for image]
Fig. 5
Treatment states at specific time points for GLP-1 RA initiators for each data source. i. DNHR (median follow-up time, 19.9 months), ii. PHARMO (median follow-up time, 17.2 months), iii. VID (median follow-up time, 25.5 months), iv. J-CKD-DB-Ex (median follow-up time, 19.5 months), v. CDM (median follow-up time, 17.8 months). CDM Optum’s de-identified Clinformatics® Data Mart Database, DNHR Danish National Health Registers, GLP-1 RA glucagon-like peptide-1 receptor agonist, J-CKD-DB-Ex Japan Chronic Kidney Disease Database Extension, PHARMO PHARMO Data Network, VID Valencia Health System Integrated Database. Sankey diagrams display the proportion of the population at each time point in each of the treatment states for each data source. The connecting bars between time points show the proportion of the population that moved from 1 state to a different state at the next time point. These figures display proportions of the population over time and are not an analysis of individual patient trajectories: a patient may move between treatment states over time (e.g., begin as “treated,” move to “untreated” at the next time point, then move back to “treated” at the next). If a death occurred, the patient was placed in a separate category and remained in that state for each subsequent checkpoint. Note that death information was not available in J-CKD-DB-Ex. The height of the bar at each time point displays the relative size of the cohort remaining under observation at each time point. Patients who were lost to follow-up are not included in the percentage calculations at each time point; thus, the percentages sum to 100% for each time point; integers displayed may not sum to 100% due to rounding. The percentages describe the number of patients still under observation at that time point
Among patients under observation at yearly time points during the study period, the proportion of patients currently receiving GLP-1 RA treatment was lowest for CDM (1 year, 52%; 2 years, 42%; 3 years, 33%). For DNHR, PHARMO, VID, and J-CKD-DB-Ex, the proportions of patients who were observed to be receiving current treatment were similar at each yearly time point: 71% to 78% at year 1, 64% to 69% at year 2, and 54% to 60% at year 3. These percentages include both patients who had remained on treatment continuously up to the given time point and patients who had discontinued and restarted the medications.
Discussion
Describing the characteristics of patients with CKD and T2D and evaluating treatment patterns in different settings is an important first step to guide future research on new treatments to prevent CKD progression. In this multicountry, multicohort study to clinically profile the population with both CKD and T2D initiating a GLP-1 RA and examine patterns of use during the study period of 2012–2021, the median duration of the initial GLP-1 RA exposure episode ranged from 2.3 months (PHARMO) to 12.4 months (VID); variations in this observation were potentially influenced by the shortest duration of the study period in PHARMO. Between 7.6% (DNHR) and 41.3% (CDM) of GLP-1 RA initiators had an interruption of current use lasting 90 days or more during follow-up. At the 1-year time point, the percentage of patients who were currently receiving GLP-1 RA treatment was more than 70% across data sources, except in CDM (52%), potentially as a result of loss to follow-up or a change in healthcare coverage for the CDM cohort. Of note, rates of GLP-1 RA initiation increased throughout the study period, with 1.6% to 4.8% of patients in each data source initiating treatment in 2012 and 19.8% to 31.5% of patients in each data source initiating therapy in 2019, consistent with previous evidence showing increases in new users of GLP-1 RA over time [21, 22].
Durations of T2D and CKD are difficult to assess in secondary data, and the estimated median durations of T2D and CKD among GLP-1 RA initiators were variable across data sources. The median duration of T2D among GLP-1 RA initiators ranged from 4 to 12 years, with the lowest duration noted in CDM, which is composed of enrollees of a US commercial insurer, and the highest noted in the European databases, which are composed of EHRs from national or regional healthcare systems. The median duration of CKD ranged from 2 to 7 years across data sources. Importantly, the duration of chronic conditions such as T2D and CKD before index treatment is initiated is dependent on the length and completeness of pre-index data. Enrollee turnover is high in US commercial health plans [23], resulting in shorter pre-index time than in countries in which most residents have national insurance over their lifetime. Although the interpretation of database-specific median duration of T2D and CKD may be limited due to specific database characteristics and differences in health systems across countries, results show that median duration of T2D was consistently higher than median duration of CKD, indicating that, generally, T2D diagnoses seem to precede CKD diagnoses.
While markers of T2D varied across data sources, most patients across all data sources had HbA1c levels > 8%, and prior evidence suggests that those with higher HbA1c levels were more likely to be prescribed a GLP-1 RA [24]. Diabetes severity score was higher in J-CKD-DB-Ex and VID than in other data sources, and differences in health systems and access-to-care factors may contribute to the differences observed. Most patients across data sources were on another GLD in the 6 months before the index date, and metformin was the most commonly prescribed drug in all data sources, with the exception of J-CKD-DB-Ex, in which DPP-4 s were the most prescribed before index. Potential explanations for these differences might be regional differences in treatment guidelines and practices, including preferential use of DPP-4s in Japan [25, 26–27]. Variability was observed in patterns of insulin use across data sources, suggesting different practice patterns for GLP-1 RA use. In particular, the use of insulin at baseline was observed in most individuals (85.4%) in J-CKD-DB-Ex, suggesting the use of a GLP-1 RA after the introduction of insulin during the study period.
The prevalence of obesity was quite variable, ranging from 15.5% (J-CKD-DB-Ex) to 90.1% (VID). This variation may reflect fundamental differences in the clinical characteristics of the study cohorts (e.g., less obesity among Japanese people with T2D), differences in the data sources (e.g., varying means of capturing data on exact body mass index [BMI] versus diagnoses of obesity), and differences in treatment patterns (e.g., with preferential prescribing of GLP-1 RA therapies for individuals with obesity and required documentation of obesity diagnoses for prior authorization of GLP-1 RAs in some settings). For instance, in the Danish cohort, proportionally fewer individuals were classified as having obesity (32%, based on hospital diagnoses only) than in a clinical cohort of individuals with newly diagnosed T2D in the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) (52% with BMI ≥ 30, based on weight and height recordings) [28], highlighting that the prevalence of clinical characteristics can vary by how variables are captured in data sources. Moreover, in Spain, prescription of GLP-1 RA is only authorized in patients with a BMI greater than 30, which could account for the very high recorded prevalence of obesity in VID [29]. Broadly, our findings may point to the use of GLP-1 RA therapy for glycemic control and obesity, irrespective of CKD severity, during the study period of 2012–2021.
The greatest proportion of patients in each database cohort were classified as having stage 3 CKD based on eGFR values or a diagnostic code (range, 27.6% [CDM] to 48.1% [PHARMO]); however, between 35% and 60% of patients had CKD stage 1 or 2. CKD stage 3, based on either eGFR value or diagnosis code, was lowest in CDM, which contained limited information on laboratory measures such as eGFR and ACR, potentially resulting in an undercapture of cases of CKD. Moderate or severe CKD was comparably higher in the DNHR and J-CKD-DB-Ex cohorts than in the other data sources, with DNHR, J-CKD-DB-Ex, and VID also having the lowest percentage of patients missing ACR values.
The most common CKD-protective drug class used previously or recently in relation to the GLP-1 RA index date was either ACEi or ARB across all data sources, consistent with other studies that have noted antihypertensive agents to be most frequently prescribed for patients with CKD and T2D [30]; SGLT2i use was also common. ACEi or ARB and SGLT2i use were in line with CKD and T2D recommendations at the time of the study [5]. Although GLP-1 RAs were not approved therapies for CKD during the study period, they have been shown to have beneficial effects on kidney outcomes [8]. With regard to patterns of GLP-1 RA use, exenatide is not recommended for patients with low eGFR values (< 30 ml/min or stage 4 or 5 CKD) [9, 31, 32], but 6.3% of patients in VID and 11.6% of patients in CDM were prescribed this medication. We did not match the type of medication with patient eGFR values; thus, it is plausible that the patients in VID and CDM who were prescribed these medications had higher eGFR values. Broadly, our findings may point to the use of GLP-1 RA therapy for glycemic control in individuals with T2D and CKD, regardless of the severity of CKD, many of whom present with obesity.
The companion analysis to clinically profile and evaluate treatment patterns in SGLT2i initiators using the same data sources revealed some differences in cohorts treated with GLP-1 RAs versus those treated with SGLT2i [19]. While the GLP-1 RA cohorts and SGLT2i cohorts identified in these analyses are not mutually exclusive, some interesting patterns emerge. Obesity was more common in the GLP-1 RA cohorts than in the SGLT2i cohorts, reflective of the indication for GLP-1 RAs during the study periods, as was baseline use of insulin. The severity of T2D was also greater in the GLP-1 RA cohorts than in the SGLT2i cohorts, and the GLP-1 RA cohorts tended to have worse kidney function at baseline than the SGLT2i cohorts. These findings suggest later use of GLP-1 RAs relative to SGLT2i in the T2D treatment pathway. At the 1-year mark, a greater proportion of patients were currently using GLP-1 RAs versus users of SGLT2is at the same time point, which might suggest patients had better persistence to GLP-1 RA medications than SGLT2i medications. However, many other factors could explain the results, such as differences in patient profiles and characteristics, differences in side effects, and variations in patient preferences that were not measured in this study.
While the purpose of the current study was to describe the use of GLP-1 RAs in patients with CKD and T2D, future research should explore safety outcomes associated with the use of GLP-1 RA therapies in clinical practice. In addition, exploration of the clinical profiles of and treatment patterns among individuals initiating GLP-1 RA therapies for other disease states such as those diagnosed with obesity, atherosclerotic cardiovascular disease, and heart failure may enhance understanding of the populations that may benefit from these therapies.
Strengths of this analysis include the use of healthcare data from multiple countries and data sources, most population based, and the use of a common protocol and statistical analysis plan with data source-specific adaptations, as required. Nonetheless, limitations are noted. In DNHR, while capturing laboratory measurements and prescriptions from general practitioners (GPs), conditions usually managed by GPs (e.g., hypercholesterolemia) may be underestimated because GP data are not routinely captured. In PHARMO, GPs are not required to code all diagnoses using codes and can include this information in free-text notes instead, which were not used in the present study. Additionally, certain conditions (e.g., hypertension) may not have diagnostic codes assigned and might be recorded solely through laboratory values or clinical measurements, which may also lead to the underestimation of these conditions in this study where diagnostic codes alone were used to define a condition. BMI data were not available in J-CKD-DB-Ex; thus, there is the potential for underreporting of obesity in J-CKD-DB-Ex. Further, undercapture of medications in J-CKD-DB-Ex is plausible if medications were dispensed before entry into the J-CKD-DB-Ex cohort or at a hospital outside the catchment area of this registry. Undercapture of medications in CDM is plausible if patients paid out of pocket or sought care out of a network where a prescription was written. In the case of countries that do not have universal health coverage, including coverage for medications, there may also be the possibility of misclassifying new users of GLP-1 RAs. We were not able to assess across data sources for what condition(s) the GLP-1 RAs were prescribed nor the indications; the mode of administration also was not evaluated and may have influenced persistence patterns. As with all secondary database studies, the primary purpose of these data sources is for billing or routine health administration, not research; therefore, mechanisms such as healthcare use patterns can lead to missing data or misclassification. A prescription could refer to a written prescription for a medication or a dispensing of the drug. However, neither of these implies that an individual took the drug as intended or was exposed to the drug, including any refills. Finally, although formal comparisons across data sources were not conducted, differences in the observed results could reflect this inherent heterogeneity among the data sources in the type and nature of data captured as well as differences in healthcare systems, treatment guidelines, country-specific clinical practices, formulary policies, and application of diagnostic coding systems in the participating countries.
Conclusions
The present study characterized new users of GLP-1 RAs among patients with a diagnosis of CKD and T2D in five databases in Europe, Japan, and the US in a period largely before the approval of new CKD indications for existing treatments (e.g., SGLT2i and GLP-1 RA) and new treatments (2012–2021). We observed a steady increase of GLP-1 RA use across data sources during the study period, and persistence with treatment was high. Findings suggest that GLP-1 RA therapies were used for poor T2D metabolic control and in the presence of obesity and that their use was independent of CKD severity. The treatment landscape for the prevention of CKD progression in patients with T2D is evolving rapidly. Understanding the characteristics and patterns of use of those receiving GLP-1 RAs is a first step in evaluating the feasibility and clinical relevance of potential comparative analyses in the evolving field of CKD treatment.
Medical Writing/Editorial Assistance
Kate Lothman 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: Manel Pladevall-Vila, Ryan Ziemiecki, Catherine B. Johannes, Anam M. Khan, Daniel Mines, J. Bradley Layton, Alfredo E. Farjat, David Vizcaya, Nikolaus G. Oberprieler; Data curation: Ron M.C. Herings, Craig I. Coleman, Hiroshi Kanegae, Yuichiro Yano, Naoki Kashihara, Reimar W. Thomsen, Ina Trolle Andersen, Christian Fynbo Christiansen, Frederik Pagh Bredahl Kristensen, Isabel Hurtado, Celia Robles Cabaniñas, Clara Rodríguez Bernal, Aníbal García-Sempere; Formal analysis: Brenda N. Baak, Jetty A. Overbeek, Fernie J.A. Penning-van Beest, Craig I. Coleman, Hiroshi Kanegae Yuichiro Yano, Naoki Kashihara, Reimar W. Thomsen, Ina Trolle Andersen, Christian Fynbo Christiansen, Frederik Pagh Bredahl Kristensen, Isabel Hurtado, Celia Robles Cabaniñas, Clara Rodríguez Bernal, Aníbal García-Sempere; Funding acquisition: Nikolaus G. Oberprieler; Investigation: J. Bradley Layton, Catherine B. Johannes, Ryan Ziemiecki, Manel Pladevall-Vila, Anam M. Khan, Daniel Mines, David Vizcaya, Nikolaus G. Oberprieler, Reimar W. Thomsen, Ina Trolle Andersen, Christian Fynbo Christiansen, Frederik Pagh Bredahl Kristensen, Isabel Hurtado, Celia Robles Cabaniñas, Aníbal García-Sempere, Clara Rodríguez Bernal; Methodology: J. Bradley Layton, Catherine B. Johannes, Ryan Ziemiecki, Manel Pladevall-Vila, Anam M. Khan, Daniel Mines, Alfredo E. Farjat, David Vizcaya, Nikolaus G. Oberprieler, Hiroshi Kanegae, Yuichiro Yano, Naoki Kashihara, Suguru Okami, Satoshi Yamashita, Reimar W. Thomsen, Ina Trolle Andersen, Christian Fynbo Christiansen, Frederik Pagh Bredahl Kristensen, Isabel Hurtado, Celia Robles Cabaniñas, Aníbal García-Sempere, Clara Rodríguez Bernal, Brenda N. Baak, Jetty A. Overbeek, Fernie J.A. Penning-van Beest; Project administration: Catherine B. Johannes, Ryan Ziemiecki, Manel Pladevall-Vila, Anam M. Khan, J. Bradley Layton, David Vizcaya, Nikolaus G. Oberprieler, Fangfang Liu, Brenda N. Baak, Craig I. Coleman, Suguru Okami; Supervision: Craig I. Coleman, Catherine B. Johannes, Ryan Ziemiecki, Manel Pladevall-Vila, J. Bradley Layton, Alfredo E. Farjat, David Vizcaya, Fangfang Liu, Nikolaus G. Oberprieler; Validation: Ryan Ziemiecki; Writing—review & editing: Manel Pladevall-Vila, Ryan Ziemiecki, Catherine B. Johannes, Anam M. Khan, Daniel Mines, Natalie Ebert, Csaba P. Kovesdy, Reimar W. Thomsen, Brenda N. Baak, Aníbal García-Sempere, Hiroshi Kanegae, Craig I. Coleman, Michael Walsh, Ina Trolle Andersen, Clara Rodríguez Bernal, Celia Robles Cabaniñas, Christian Fynbo Christiansen, Alfredo E. Farjat, Alain Gay, Patrick Gee, Ron M.C. Herings, Isabel Hurtado, Naoki Kashihara, Frederik Pagh Bredahl Kristensen, Fangfang Liu, Suguru Okami, Jetty A. Overbeek, Fernie J.A. Penning-van Beest, Satoshi Yamashita, Yuichiro Yano, J. Bradley Layton, David Vizcaya, and Nikolaus G. Oberprieler.
Funding
Bayer AG funded this research, the development of this article, and the Rapid Service Fee for publication. Authors affiliated with Bayer were involved in the study design, analyses, and development of this publication.
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 was an employee of Bayer when this research was conducted and is now an employee of Clario, Philadelphia, PA, USA. David Vizcaya was an employee of Bayer when this research was conducted and is now an employee of Alexion Pharma S.L., Barcelona, Spain. Catherine B. Johannes, Ryan Ziemiecki, Anam M. Khan, and J. Bradley Layton are employees of RTI Health Solutions, which received research funding for this study from Bayer. Manel Pladevall-Vila and Daniel Mines were employees of RTI Health Solutions at the time of the study. Brenda N. Baak, Jetty A. Overbeek, Fernie J.A. Penning-van Beest, and Ron M.C. Herings are employees of the PHARMO Institute for Drug Outcomes Research. 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. 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 presentations and lectures on medical research (both with and without financial compensation) for pharmaceutical companies, including AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi. Reimar W. Thomsen, Ina Trolle Andersen, Christian Fynbo Christiansen, and Frederik Pagh Bredahl Kristensen 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. Access to the data sources was through a collaboration model for DNHR, VID, PHARMO, J-CKD-DB-Ex and through a license for CDM. Ethics committee review was waived for DNHR and PHARMO. The study protocol was reviewed and approved by the Comité Ético de Investigación con Medicamentos del Hospital Clínico Universitario de Valencia for VID (2022/164). This study protocol was reviewed and approved by the ethics committee of the Shiga University of Medical Science for J-CKD-DB-Ex (R2022-143). CDM 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. Tuttle, KR; Bakris, GL; Bilous, RW et al. Diabetic kidney disease: a report from an ADA Consensus Conference. Am J Kidney Dis; 2014; 64,
4. Afkarian, M; Sachs, MC; Kestenbaum, B et al. Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol; 2013; 24,
5. KDIGO. Kidney Disease: Improving Global Outcomes Work Group (KDIGO). Clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int; 2022; 2022,
6. Greco, EV; Russo, G; Giandalia, A et al. GLP-1 receptor agonists and kidney protection. Medicina (Kaunas); 2019; 55,
7. Michos, ED; Bakris, GL; Rodbard, HW; Tuttle, KR. Glucagon-like peptide-1 receptor agonists in diabetic kidney disease: a review of their kidney and heart protection. Am J Prev Cardiol.; 2023; 14, [DOI: https://dx.doi.org/10.1016/j.ajpc.2023.100502] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37313358][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258236]100502.
8. 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,
9. Sheahan, KH; Wahlberg, EA; Gilbert, MP. An overview of GLP-1 agonists and recent cardiovascular outcomes trials. Postgrad Med J; 2020; 96,
10. 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 [DOI: https://dx.doi.org/10.1136/bmj-2022-074068] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37024129][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077111]e074068.
11. Pasternak, B; Wintzell, V; Eliasson, B et al. Use of glucagon-like peptide 1 receptor agonists and risk of serious renal events: Scandinavian cohort study. Diabetes Care; 2020; 43,
12. Xie, Y; Bowe, B; Gibson, AK et al. Comparative effectiveness of SGLT2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, and sulfonylureas on risk of kidney outcomes: emulation of a target trial using health care databases. Diabetes Care; 2020; 43,
13. Xu, Y; Fu, EL; Clase, CM et al. GLP-1 receptor agonist versus DPP-4 inhibitor and kidney and cardiovascular outcomes in clinical practice in type-2 diabetes. Kidney Int; 2022; 101,
14. Salamah, HM; Marey, A; Abugdida, M et al. Efficacy and safety of glucagon-like peptide-1 receptor agonists on prediabetes: a systematic review and meta-analysis of randomized controlled trials. Diabetol Metab Syndr; 2024; 16,
15. ClinicalTrials.gov NCT03819153. A research study to see how semaglutide works compared to placebo in people with type 2 diabetes and chronic kidney disease (FLOW). 30 April 2024. https://clinicaltrials.gov/study/NCT03819153. Accessed 30 April 2024.
16. Perkovic, V; Tuttle, KR; Rossing, P et al. Effects of semaglutide on chronic kidney disease in patients with type 2 diabetes. N Engl J Med; 2024; 391,
17. Ozempic (semaglutide) prescribing information. 2025. https://www.novo-pi.com/ozempic.pdf. Accessed 10 February 2025.
18. 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,
19. Johannes, CB; Ziemiecki, R; Pladevall-Vila, M et al. Clinical profile and treatment adherence in patients with type 2 diabetes and chronic kidney disease who initiate an SGLT2 inhibitor: a multi-cohort study. Diabetes Ther.; 2024; 8, 9.
20. Gatto, NM; Campbell, UB; Rubinstein, E et al. The structured process to identify fit-for-purpose data: a data feasibility assessment framework. Clin Pharmacol Ther; 2022; 111,
21. Yeo, YH; Rezaie, A; Hsieh, TY et al. Shifting trends in the indication of glucagon-like peptide-1 receptor agonist prescriptions: a nationwide analysis. Ann Intern Med; 2024; 9, 63.
22. Pottegard, A; Andersen, JH; Sondergaard, J; Thomsen, RW; Vilsboll, T. Changes in the use of glucose-lowering drugs: a Danish nationwide study. Diabetes Obes Metab; 2023; 25,
23. Fang, H; Frean, M; Sylwestrzak, G; Ukert, B. Trends in disenrollment and reenrollment within US commercial health insurance plans, 2006–2018. JAMA Netw Open; 2022; 5,
24. Lamprea-Montealegre, JA; Madden, E; Tummalapalli, SL et al. Prescription patterns of cardiovascular- and kidney-protective therapies among patients with type 2 diabetes and chronic kidney disease. Diabetes Care; 2022; 45,
25. Bouchi, R; Sugiyama, T; Goto, A et al. Retrospective nationwide study on the trends in first-line antidiabetic medication for patients with type 2 diabetes in Japan. J Diabetes Investig; 2022; 13,
26. Bouchi, R; Kondo, T; Ohta, Y et al. A consensus statement from the Japan Diabetes Society: a proposed algorithm for pharmacotherapy in people with type 2 diabetes. J Diabetes Investig; 2023; 14,
27. Bouchi, R; Kondo, T; Ohta, Y et al. A consensus statement from the Japan Diabetes Society: a proposed algorithm for pharmacotherapy in people with type 2 diabetes—2nd edition (English version). J Diabetes Investig.; 2024; 15,
28. Christensen, DH; Nicolaisen, SK; Berencsi, K et al. Danish Centre for Strategic Research in Type 2 Diabetes (DD2) project cohort of newly diagnosed patients with type 2 diabetes: a cohort profile. BMJ Open; 2018; 8,
29. Bellido, V; Abreu Padin, C; Catarig, AM et al. Once-weekly semaglutide use in patients with type 2 diabetes: results from the SURE Spain multicentre, prospective, observational study. J Clin Med; 2022; 11, 17. [DOI: https://dx.doi.org/10.3390/jcm11174938]
30. Feng, XS; Farej, R; Dean, BB et al. CKD prevalence among patients with and without type 2 diabetes: regional differences in the United States. Kidney Med; 2022; 4,
31. Yu, JH; Park, SY; Lee, DY; Kim, NH; Seo, JA. GLP-1 receptor agonists in diabetic kidney disease: current evidence and future directions. Kidney Res Clin Pract; 2022; 41,
32. Marx, N; Husain, M; Lehrke, M; Verma, S; Sattar, N. GLP-1 receptor agonists for the reduction of atherosclerotic cardiovascular risk in patients with type 2 diabetes. Circulation; 2022; 146,
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
Novel therapies are emerging for the prevention of chronic kidney disease (CKD) progression in patients with type 2 diabetes (T2D). Within the FOUNTAIN platform (NCT05526157; EUPAS48148), this real-world study aimed to characterize cohorts of adults with CKD and T2D starting therapy with a glucagon-like peptide-1 receptor agonist (GLP-1 RA) in Europe, Japan, and the United States (US) during 2012–2021.
Methods
This multinational, multicohort study was conducted in five data sources: the Danish National Health Registers (DNHR) (Denmark), PHARMO Data Network (PHARMO) (The Netherlands), Valencia Health System Integrated Database (VID) (Spain), Japan Chronic Kidney Disease Database Extension (J-CKD-DB-Ex) (Japan), and Optum’s de-identified Clinformatics® Data Mart Database (CDM) (US). Eligible patients had T2D (defined by data source-specific algorithms) and CKD (based on diagnosis codes, estimated glomerular filtration rate values, and/or urine albumin-to-creatinine ratio) and initiated an GLP-1 RA during 2012–2021. Baseline demographic, lifestyle, and clinical characteristics were analyzed, and treatment patterns were described.
Results
Study cohorts included 18,929 GLP-1 RA initiators in DNHR; 476 in PHARMO; 11,798 in VID; 329 in J-CKD-DB-Ex; and 70,158 in CDM. Across cohorts, mean age ranged from 66.1 years in J-CKD-DB-Ex to 67.9 years in CDM, and between 46.6% (PHARMO) and 59.6% (J-CKD-DB-Ex) of patients were men. There was a steady increase in GLP-1 RA initiators from 2012 (when 1.6–4.8% of GLP-1 RA initiators started therapy) to 2019 (when 19.8–31.5% started therapy). The median duration of initial treatment with a GLP-1 RA ranged from 2.3 months (PHARMO) to 12.4 months (VID). At 1-year follow-up, between 52% (CDM) and 78% (DNHR) of patients were receiving treatment. Findings suggested that GLP-1 RA use was independent of CKD severity.
Conclusions
During 2012–2021, GLP-1 RA use steadily increased across multinational cohorts of patients with T2D and CKD, and persistence with treatment was high. GLP-1 use was independent of CKD severity.
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, Barcelona, Spain; Henry Ford Health System, The Center for Health Policy and Health Services Research, Detroit, USA (GRID:grid.239864.2) (ISNI:0000 0000 8523 7701)
2 RTI Health Solutions, Research Triangle Park, USA (GRID:grid.416262.5) (ISNI:0000 0004 0629 621X)
3 RTI Health Solutions, Waltham, USA (GRID:grid.416262.5) (ISNI:0000 0004 0629 621X)
4 Henry Ford Health System, The Center for Health Policy and Health Services Research, Detroit, USA (GRID:grid.239864.2) (ISNI:0000 0000 8523 7701)
5 Institute of Public Health, Charité – Universitätsmedizin Berlin, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662)
6 University of Tennessee Health Science Center, Division of Nephrology, Department of Medicine, Memphis, USA (GRID:grid.267301.1) (ISNI:0000 0004 0386 9246)
7 Aarhus University and Aarhus University Hospital, Department of Clinical Epidemiology, Aarhus, Denmark (GRID:grid.7048.b) (ISNI:0000 0001 1956 2722)
8 PHARMO Institute for Drug Outcomes Research, Utrecht, The Netherlands (GRID:grid.418604.f) (ISNI:0000 0004 1786 4649)
9 Health Services Research and Pharmacoepidemiology Unit, Fisabio, Spain (GRID:grid.428862.2) (ISNI:0000 0004 0506 9859)
10 Genki Plaza Medical Centre for Health Care, Tokyo, Japan (GRID:grid.512765.2)
11 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)
12 McMaster University, Division of Nephrology, Department of Medicine, Hamilton, Canada (GRID:grid.25073.33) (ISNI:0000 0004 1936 8227)
13 Bayer AG, Leverkusen, Germany (GRID:grid.420044.6) (ISNI:0000 0004 0374 4101)
14 National Kidney Foundation Advocacy, Richmond, USA (GRID:grid.420044.6)
15 Kawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan (GRID:grid.415086.e) (ISNI:0000 0001 1014 2000)
16 Juntendo University, Department of General Medicine, Faculty of Medicine, Tokyo, Japan (GRID:grid.258269.2) (ISNI:0000 0004 1762 2738)