Outcomes With Finerenone in Patients With Chronic Kidney Disease and Type 2 Diabetes by Baseline Insulin Resistance

Metabolic and inflammatory abnormalities associated with diabetes and chronic kidney disease (CKD), such as insulin resistance, contribute to an increased risk of cardiovascular (CV) disease and progression to end-stage kidney disease (1,2).

Insulin resistance can exist in early-stage CKD, and prevalence increases as CKD progresses (1). Although the exact mechanisms of insulin resistance are not fully understood, resistance to the metabolic actions of insulin has been associated with heart, vasculature, and kidney dysfunction (2,3). In the kidneys, insulin acts on multiple sites along the nephron, including the glomerulus, and podocyte insulin signaling is known to play a critical role in the maintenance of glomerular function. For example, podocyte-specific insulin receptor–knockout mice develop a glomerular phenotype consistent with CKD in type 2 diabetes, including albuminuria (4).

The hyperinsulinemic-euglycemic clamp method is considered the gold standard technique to measure insulin action in vivo to precisely determine the rate of whole-body glucose disposal. However, this method is invasive and costly, meaning use is limited. Alternative, simple indices (e.g., homeostasis model assessment [HOMA], quantitative insulin sensitivity check index) have been developed and validated for clinical use (5). The estimated glucose disposal rate (eGDR) is another indicator originally developed as a validated score to measure insulin resistance in patients with type 1 diabetes based on waist circumference (WC), hypertension, and glycated hemoglobin (HbA_1c) (6–8). Recently, Penno et al. (9) validated eGDR as an insulin resistance index in patients with type 2 diabetes in a comparison with the hyperinsulinemic-euglycemic clamp technique. The study found that eGDR was independently associated with mortality in patients without CKD or with nonalbuminuric CKD but not in those with albuminuric CKD. Even though albuminuria is predictive of CV and kidney events, and eGDR was shown to be strongly associated with the development and progression of albuminuria in patients with type 2 diabetes, there is limited evidence on the predictive value of eGDR for kidney outcomes (9–11). A recent retrospective study reported that lower eGDR was a predictive biomarker for rapid eGFR decline in patients with type 2 diabetes. In patients with eGFR ≤60 mL/min/1.73 m², the predictive value of eGDR was superior to WC and HbA_1c, and similar to hypertension (11). In a different study, Zabala et al. (12) showed that changes in insulin resistance as measured by eGDR were associated with the risk of stroke and mortality in patients with type 2 diabetes.

Historically, patients with CKD have been excluded from many studies of insulin resistance (1). Given that insulin resistance contributes to CV and kidney morbidity and mortality, it is crucial to investigate the cardiorenal impact of existing therapies. Previous research has linked insulin resistance and enhanced mineralocorticoid receptor (MR) activation (13). In preclinical studies, aldosterone had deleterious effects on the CV system by promoting adipose tissue expansion via MR activation (14). In addition, a positive correlation between aldosterone and insulin resistance was reported in hypertensive patients (15). Steroidal MR antagonists have been shown to provide survival benefits in patients with heart failure (16,17). However, MR antagonist use in patients with CKD has been limited because of safety concerns (18).

Finerenone is a distinct, selective, nonsteroidal MR antagonist. Compared with steroidal MR antagonists, finerenone is thought to provide heart and kidney benefits with fewer side effects, particularly hyperkalemia (19,20). In preclinical models of obesity, data suggest finerenone may enhance insulin sensitivity by increasing interscapular brown adipose tissue recruitment (14). In clinical trials, finerenone reduced the risk of cardiorenal outcomes, compared with placebo, across a broad population of patients with CKD and type 2 diabetes (21).

This post hoc analysis aims to explore whether insulin resistance (estimated by eGDR) is associated with risk of cardiorenal outcomes and whether insulin resistance modifies the cardiorenal efficacy of finerenone in the Finerenone in Chronic Kidney Disease and Type 2 Diabetes: Combined FIDELIO-DKD and FIGARO-DKD Trial Programme Analysis (FIDELITY) pooled data set.

FIDELITY, a prespecified pooled analysis, combines individual patient-level data from Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease (FIDELIO-DKD; NCT02540993) and Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD; NCT02545049), two multicenter, phase 3, randomized, double-blind, placebo-controlled, parallel-group, event-driven trials (22,23). The study designs and efficacy and safety outcomes of these trials have been previously published (22–25). The trials were conducted in accordance with the principles of the Declaration of Helsinki, and the protocols were approved by relevant regulatory authorities and ethics committees for each trial site; written informed consent was obtained from all participants.

Briefly, eligible patients were adults (aged ≥18 years) with CKD (urine albumin-to-creatinine ratio [UACR] of ≥30 to <300 mg/g and estimated glomerular filtration rate [eGFR] of ≥25 to ≤90 mL/min/1.73 m², or UACR of ≥300 to ≤5,000 mg/g and eGFR of ≥25 mL/min/1.73 m²) and type 2 diabetes. Patients were required to be treated with maximum-tolerated dose of renin-angiotensin system therapy, with a serum potassium level of ≤4.8 mmol/L at run-in and screening visits, and HbA_1c of ≤12% (108 mmol/mol) (21). The use of insulin and other oral glucose-lowering agents, including dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, biguanides, sulfonylureas, α-glucosidase inhibitors, meglitinides, and thiazolidinediones, was not restricted during the trials (21).

Patients were randomly assigned (1:1) to receive once-daily oral treatment with finerenone (at titrated doses of 10 mg or 20 mg), or matching placebo (21). In this analysis, patients from the FIDELITY prespecified pooled analysis were stratified according to baseline insulin resistance, estimated by eGDR.

Efficacy outcomes included a CV composite outcome (defined as time to CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure) and a kidney composite outcome (defined as time to kidney failure, a sustained decrease of ≥57% in eGFR from baseline [equivalent to a doubling of the serum creatinine level] maintained for at least 4 weeks, or renal death). Kidney failure was defined as end-stage kidney disease (initiation of long-term dialysis for ≥90 days, kidney transplantation, or a sustained decrease in eGFR to <15 mL/min/1.73 m²). Safety outcomes and vital signs were also evaluated and included assessment of adverse events and central laboratory testing. Adverse events that occurred during the treatment period were defined as those that started or worsened during study drug intake or up to 3 days after any temporary or permanent interruption. All outcomes were adjudicated by independent clinical event committees blinded to treatment assignment. Data for the CV composite and kidney composite outcomes were prospectively collected for all patients in the combined FIDELITY data set.

Exploratory subgroup efficacy analyses were performed in the full analysis set, consisting of all randomized patients without any critical Good Clinical Practice violations. Post hoc laboratory parameter and safety analyses were performed in the safety analysis set, which included all randomized patients who had taken at least one dose of the study drug and were without any critical Good Clinical Practice violations. Analysis included descriptive statistics, time-to-event analyses, statistical test for interaction (subject to sufficient sample size within a given subgroup), and mixed models for repeated measures. Continuous population characteristics and demographics were summarized by mean and SD or median and interquartile range, depending on the distribution of the variable. Categorical characteristics and demographics were summarized by counts and percentages.

Insulin resistance was evaluated using eGDR (Supplementary Table 1) (7). Hypertension was considered present if a patient received more than one antihypertensive medication (renin-angiotensin system inhibitors, β-blockers, α-blockers, calcium antagonists, loop diuretics, or thiazide diuretics), or systolic blood pressure (SBP) of >140 mmHg or diastolic blood pressure of >90 mmHg. Composite outcomes were analyzed by defined categorical subgroups: <median eGDR and ≥median eGDR. The median eGDR value was selected for reference to use a simple, unbiased approach to categorizing the eGDR variable. Time-to-event treatment effects were analyzed using stratified Cox proportional hazards regression models and expressed as hazard ratios (HRs) with corresponding CIs. HRs (95% CI) were based on the stratified Cox proportional hazards model estimated within each level of the subgroup variable. The P interaction of the treatment assignment (finerenone or placebo) and each subgroup category was based on the Cox proportional hazards model, including the terms treatment group, the subgroup, and their interaction. Time-to-event analyses were stratified by region, UACR category at screening, eGFR category at screening, study, and CV disease history. The correlation between eGDR and log(UACR)/eGFR baseline values and their effect on outcomes were analyzed by Pearson correlation analysis.

The relationship of the CV and kidney composite outcomes with eGDR as a continuous variable was investigated using a similar stratified Cox proportional hazards model with cubic B-splines with three equally spaced knots across the range of eGDR. All Cox proportional hazards models were adjusted for blood pressure (systolic), sex, HbA_1c at baseline, and diabetes duration. Models were fitted separately in each treatment group (i.e., finerenone and placebo). Events were reported from randomization up to the end-of-study (EOS) visit. Patients without an event were censored at the date of their last contact, and complete information on all components of their respective outcomes was recorded. UACR over time was analyzed with a mixed model for repeated measures, adjusting for treatment group, stratification factors, baseline UACR value, and time of visit. Interaction terms for treatment group and visit, and baseline value and visit, were also included in the model.

As a sensitivity analysis, other known estimates of insulin resistance were calculated and analyzed. These included triglyceride (TG)/high-density lipoprotein (HDL)–cholesterol ratio, visceral adiposity index (VAI), and lipid accumulation product (LAP) index at baseline. Similar to eGDR, the medians of TG/HDL ratio, VAI, and LAP index were used to split the population (<median and ≥median), and the analyses described above were conducted. The correlations between eGDR and VAI, LAP and TG/HDL–cholesterol ratio baseline values, respectively, were analyzed by Pearson correlation analysis.

We conducted an additional sensitivity analysis with time-updated eGDR calculated based on the average of all of SBP measurements throughout the trial for each individual patient.

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The FIDELITY analysis included 13,026 patients in the full analysis set, over a median follow-up of 3.0 years (interquartile range 2.3–3.8 years). Full patient demographics and baseline characteristics for the FIDELITY study population have been previously published (21). In this post hoc analysis, median eGDR was 4.1 mg/kg/min; 6,484 (50%) patients had an eGDR of <median (considered as insulin resistant) and 6,480 (50%) had an eGDR of ≥median (considered as insulin sensitive) (Supplementary Fig. 1).

The baseline clinical and demographic characteristics of patients with an eGDR of ≥median and <median were generally comparable, with some key differences (Table 1). Compared with patients with an eGDR of ≥median, patients with an eGDR of <median had a longer mean duration of diabetes (16.1 vs. 14.7 years), and higher median UACR (535 vs. 493 mg/g), mean weight (99 vs. 77 kg), and mean WC (117 vs. 97 cm) at baseline. A greater proportion of patients in the eGDR <median subgroup identified as White compared with the eGDR ≥median subgroup (80% vs. 56%), while a substantially lower proportion identified as Asian (9% vs. 35%). At baseline, eGDR was significantly positively correlated with eGFR (r = 0.04 [95% CI 0.02–0.06; P < 0.0001]) and significantly negatively correlated with log(UACR) (r = –0.03 [95% CI –0.04 to –0.01; P = 0.004]). Significant negative correlations between eGDR and baseline VAI, LAP index, and TG/HDL ratio, respectively (P < 0.0001) were calculated (Supplementary Table 2).

As previously reported in the overall population of the FIDELITY analysis, the CV composite outcome of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure was observed in 825 of 6,519 (12.7%) patients receiving finerenone and in 939 of 6,507 (14.4%) patients receiving placebo (HR = 0.86; 95% CI 0.78–0.95) (21). In the overall population, irrespective of finerenone or placebo treatment, an increase in eGDR from baseline was significantly associated with a decreased risk of the CV composite outcome (HR = 0.88, 95% CI 0.86–0.91; P < 0.0001). Baseline eGDR <median was associated with a higher incidence rate (IR) of the CV composite outcome in both the finerenone and placebo groups versus eGDR ≥median (eGDR <median: IR per 100 patient-years [PY] 5.18, 95% CI 4.73–5.65 [for finerenone] and IR per 100 PY 6.34, 95% CI 5.83–6.86 [for placebo]; eGDR ≥median: IR per 100 PY 3.47, 95% CI 3.11–3.86 [for finerenone] and IR per 100 PY 3.76, 95% CI 3.38–4.15 [for placebo]) (Fig. 1). There was no significant heterogeneity for the effect of finerenone on the CV composite outcome by baseline eGDR (eGDR <median: HR = 0.81, 95% CI 0.72–0.92; eGDR ≥median: HR = 0.92, 95% CI 0.79–1.06; P interaction = 0.23). Calculations using time-updated eGDR yielded results similar to the analyses based on eGDR at baseline (Supplementary Fig. 2).

Similarly, there was not a statistically significant change in the effect of finerenone versus placebo on the CV composite outcome across the range of eGDR values at baseline (Wald test P = 0.063) (Fig. 2A).

Sensitivity analyses also showed there was no significant heterogeneity for the effect of finerenone on the CV composite outcome by baseline TG/HDL ratio (TG/HDL ratio <median: HR = 0.83, 95% CI 0.73–0.95; TG/HDL ratio ≥median: HR = 0.88, 95% CI 0.77–1.01; P interaction = 0.52), VAI (VAI <median: HR = 0.85, 95% CI 0.74–0.97; VAI ≥median: HR = 0.85, 95% CI 0.75–0.98; P interaction = 0.91), or LAP index (LAP index <median: HR = 0.86, 95% CI 0.75–0.99; LAP index ≥median: HR = 0.85, 95% CI 0.74–0.97; P interaction = 0.84).

As previously reported, the kidney composite outcome of kidney failure, a sustained ≥57% decrease in eGFR from baseline, or renal death was lower with finerenone versus placebo in the overall population of the FIDELITY analysis (HR = 0.77; 95% CI 0.67–0.88; P = 0.0002) (21). In the overall population, irrespective of finerenone or placebo, baseline eGDR was not associated with risk of the kidney composite outcome (HR = 1.00, 95% CI 0.96–1.04; P = 0.93). The IR of the kidney composite outcome was similar across eGDR subgroups in the finerenone and placebo groups (eGDR <median: IR per 100 PY 1.96, 95% CI 1.68–2.26 and IR per 100 PY 2.31, 95% CI 2.00–2.63, respectively; eGDR ≥median: IR per 100 PY 1.97, 95% CI 1.70–2.27 and IR per 100 PY 2.82, 95% CI 2.49–3.18, respectively) (Fig. 1). There was no significant heterogeneity for the effect of finerenone on the kidney composite outcome by baseline eGDR subgroups (eGDR <median: HR = 0.84, 95% CI 0.68–1.02; eGDR ≥median: HR = 0.70, 95% CI 0.58–0.85; P interaction = 0.28). Similarly, there was no statistically significant change in the effect of finerenone versus placebo on the kidney composite outcome across the range of eGDR values (Wald test P value = 0.51) (Fig. 2B).

Sensitivity analyses also showed that there was no significant heterogeneity for the effect of finerenone on the ≥57% kidney composite outcome by baseline TG/HDL (TG/HDL ratio <median: HR = 0.82, 95% CI 0.67–0.99; TG/HDL ratio ≥median: HR = 0.71, 95% CI 0.59–0.87; P interaction = 0.33), VAI (VAI <median: HR = 0.77, 95% CI 0.63–0.94; VAI ≥median: HR = 0.75, 95% CI 0.61–0.91; P interaction = 0.75), or LAP index (LAP index <median: HR = 0.74, 95% CI 0.61–0.90; LAP index ≥median: HR = 0.81, 95% CI 0.66–0.98; P interaction = 0.64).

Finerenone reduced UACR regardless of eGDR category (eGDR <median: ratio of least-squares [LS] mean 0.69 (95% CI 0.66–0.72; P < 0.0001; eGDR ≥median: ratio of LS mean 0.67 (95% CI 0.65–0.70; P < 0.0001) (Supplementary Fig. 3). Finerenone also slowed eGFR decline regardless of eGDR category (Supplementary Fig. 4). In patients with eGDR <median, LS mean change in chronic eGFR slope from month 4 to EOS visit was ‒2.7 mL/min/1.73 m² per year with finerenone and ‒3.7 mL/min/1.73 m² with placebo (between-group difference: 1.0 mL/min/1.73 m², 95% CI 0.76‒1.25; P < 0.0001). In patients with an eGDR ≥median, LS mean change in chronic eGFR slope from month 4 to EOS visit was –2.8 mL/min/1.73 m² per year with finerenone and –3.9 mL/min/1.73 m² with placebo (between-group difference: 1.1 mL/min/1.73 m², 95% CI 0.83–1.29; P < 0.0001).

Overall, the incidences of treatment-emergent adverse events and severe adverse events were balanced between the finerenone and placebo groups and between eGDR subgroups (Table 2). The incidence of investigator-reported, treatment-emergent hyperkalemia was higher in patients treated with finerenone versus placebo in both eGDR subgroups (eGDR <median: 14.2% vs. 6.0%, respectively; eGDR ≥median: 13.9% vs. 7.8%, respectively). However, hyperkalemia leading to discontinuation was low in the finerenone treatment group, with no notable differences between eGDR subgroups (eGDR <median: 1.9%; eGDR ≥median: 1.5%).

To date, insulin resistance has been poorly defined in patients with CKD and type 2 diabetes, and its independent role in CKD progression risk is unclear. Although initially developed for patients with type 1 diabetes, studies have shown that eGDR is well correlated with the gold standard hyperinsulinemic-euglycemic clamp method in patients with type 2 diabetes (12). Our analysis included patients with CKD, both those with insulin resistance and those with insulin sensitivity, as measured by eGDR at baseline. eGFR at baseline was similar between patients who were insulin resistant and patients who were insulin sensitive. This finding provides indirect support to the growing body of evidence that insulin resistance is not significantly associated with the development of CKD-related outcomes (1). When compared with the gold standard hyperinsulinemic-euglycemic clamp method, several other surrogate parameters of insulin resistance, such as HOMA, fasting insulin resistance index, Matsuda index, and Stumvoll index, did not appear to be dysregulated in patients with versus without CKD (26). In this analysis of the FIDELITY population, the median eGDR (4.1 mg/kg/min) was substantially lower than previous studies of the general population and of patients with type 1 diabetes. For context, the 2022 China Health and Retirement Longitudinal Study, (N = 8,267; general population), reported a median eGDR of 10.4 mg/kg/min (27), and two studies of patients with type 1 diabetes (N ≈ 200 per study) reported mean eGDR of 6.5–10.1 mg/kg/min (28,29). In these studies, low eGDR (high insulin resistance) was defined as those with eGDR <5.39 mg/kg/min in patients with type 1 diabetes (28) and <8.92 mg/kg/min in the general population (27). In patients with type 1 diabetes, mean eGDR in patients with CKD was significantly lower than in patients without (6.4 vs. 15.9 mg/kg/min; P < 0.001) (29), which aligns with our results, suggesting a widespread burden of insulin resistance in patients with CKD.

Marked differences in duration of diabetes, mean weight, WC, and ethnicity between patients with insulin resistance (eGDR <median) and patients who were insulin sensitive (eGDR ≥median) were observed. Simpler clinical indicators such as WC could be of substantial utility in clinical practice to offer guidance on the risk of CKD progression. An association between WC (or BMI) and CV and kidney outcomes in FIDELITY patients has been reported (30). Patients with low-risk WC (or BMI) experienced a higher kidney outcome event rate (and the opposite for CV outcome) compared with those with high and very high risk (30). Even though BMI is the most commonly used measure of obesity, growing evidence suggests WC is moderately superior to BMI for predicting cardiometabolic risk (30,31).

We note that our results suggest greater insulin resistance, as assessed by eGDR, in patients of White ethnicity. This appears to contradict the fact that diabetes disproportionately burdens certain racial and ethnic groups, such as Black and South East Asian (32,33). Previously, epidemiological studies have reported insulin resistance in these populations; however, more recent and direct methods of measurement have provided evidence that insulin sensitivity is not significantly different between ethnic groups.

In this post hoc analysis, finerenone provided consistent CV and kidney protection in patients with CKD and type 2 diabetes, irrespective of baseline insulin resistance. However, patients with insulin resistance (eGDR <median) were at greater risk of CV events compared with those without (eGDR ≥median). This aligns with previous studies, supporting the hypothesis that eGDR is an important predictor of CV disease. For example, in a cohort study of 104,697 patients with type 2 diabetes from the Swedish National Diabetes Register (2004–2016), low eGDR (<4 mg/kg/min), versus higher eGDR (4 to <8 mg/kg/min), was associated with an increased risk of stroke and CV mortality (12). Additionally, in the retrospective, population-based China Health and Retirement Longitudinal Study (N = 8,276), low eGDR was found to be positively correlated with risk of CV disease, stroke, and cardiac events (27). More recently, Peng et al. (11) reported that low eGDR (<6.34 mg/kg/min) at baseline was an independent risk factor for kidney outcomes in a retrospective cohort study of 956 Chinese patients.

In the present FIDELITY analysis, which used data from the largest clinical trial cohort of 13,026 patients with CKD and type 2 diabetes to date, the incidence of the kidney composite outcome was similar between eGDR subgroups (21). Even though data reported by Peng et al. (11) support an association between low eGDR and kidney function decline in patients with diabetes, our results suggest that changes in insulin kinetics in patients with diabetes with established CKD (assessed by UACR and eGFR) may no longer be predictive of CKD progression.

Furthermore, insulin signaling differs within the kidney in a cell type–specific manner (34). Because insulin resistance is often associated with podocyte dysfunction (35), it is possible to speculate that when CKD and glomerular filtration barrier dysfunction already exist, insulin resistance, assessed by eGDR, cannot further impair kidney outcomes. These findings support our preclinical hypothesis that finerenone may increase insulin sensitivity; even though it was not possible to evaluate the effect of finerenone on insulin resistance, by eGDR over time, because of a lack of follow-up data for WC, no changes in other algorithm components (i.e., weight and HbA_1c) from baseline to the EOS visit were observed. Moreover, results of the sensitivity analysis using baseline eGDR were similar to those based on the time-updated eGDR calculation. These findings suggest that changes in eGDR were not associated with temporal fluctuations in SBP and provide further validation for eGDR categories as a measure of changes in insulin resistance.

Multiple studies have demonstrated that insulin resistance is implicated in CV disease in patients with CKD, but its role in CKD progression is less clear (36). Few clinical studies have explored the role of insulin resistance in predicting the deterioration of kidney function in patients with CKD with or without type 2 diabetes. In a previous study in patients with CKD without diabetes, progression of kidney disease was slower in those patients with low insulin resistance, as measured by HOMA (37). Insulin resistance as measured by HOMA was shown to be a significant risk factor for CKD progression in hypertensive patients without diabetes (38).

Thus, further studies are required to examine whether the hemodynamic effects of insulin resistance in the kidneys differ between individuals with diabetes versus patients with advanced CKD with or without diabetes.

Furthermore, these exploratory analyses suggest that insulin resistance, as measured by eGDR, is associated with higher CV events regardless of treatment, but it is not associated with kidney outcomes. Compared with placebo, the effect of finerenone on the CV composite outcomes across the range of baseline eGDR values reached near statistical significance. Given that our analyses were hypothesis generating and not adequately powered to evaluate the statistical significance of any associations between eGDR with CV and kidney outcomes, this needs to be investigated in further studies, including randomized clinical trials.

In this post hoc analysis of the FIDELITY prespecified pooled analysis, the efficacy and safety of finerenone were not modified by baseline insulin resistance. A higher risk of CV—but not kidney—outcomes was observed in patients with CKD and type 2 diabetes with greater insulin resistance.

Acknowledgments. The authors are indebted to the participants, as well as the investigators and study sites. Medical writing assistance was provided by Emilie Topley of Chameleon Communications International Ltd and was funded by Bayer AG.

Funding. The FIDELIO-DKD and FIGARO-DKD trials were conducted and sponsored by Bayer AG. The sponsor participated in the analysis design, data collection, data analysis, data interpretation, and approval of the manuscript. Analyses were conducted by the sponsor, and all authors had access to and participated in the interpretation of the data.

Duality of Interest. T.E. has served as a paid consultant for Bayer, Lilly Deutschland, and Sanofi and reports personal fees from Bayer, Fresenius Medical Care Deutschland, Lilly Deutschland, Novo Nordisk, Sanofi, and Santis Pharmaceuticals. He has received research support from the European Foundation for the Study of Diabetes (EFSD Mentorship Programme supported by AstraZeneca) and Otsuka Pharma. S.D.A. reports grants and personal fees from Vifor and Abbott Vascular, and personal fees for consultancies, trial committee work, and/or lectures from Actimed, Amgen, AstraZeneca, Bayer, Bioventrix, Boehringer Ingelheim, B R A H M S, Cardiac Dimensions, Cardior, Cordio, CVRx, Edwards, Faraday, Impulse Dynamics, Janssen, Novartis, Occlutech, Pfizer, Respicardia, Servier, Vectorious, and V-Wave. He is named co-inventor of two patent applications regarding MR-proANP (DE 102007010834 and DE 102007022367), but he does not benefit personally from the related issued patents. L.M.R. reports receipt of consultancy fees from Bayer. P.F. reports consultation fees from AstraZeneca, Bayer, Boehringer Ingelheim, Lilly, Mundipharma, and Novo Nordisk. V.F. has served as a paid consultant for Abbott, Asahi, AstraZeneca, Bayer, Novo Nordisk, and Sanofi. He has patent and ownership interests in BRAVO4Health. G.E.U. has received research support (to Emory University) from Abbott, Bayer, and Dexcom Inc. A.L.B. reported research support from Boehringer Ingelheim and personal fees from AstraZeneca, Boehringer Ingelheim, and Novo Nordisk. All fees are given to Tübingen University. R.L. is a full-time employee of Bayer AG, Division Pharmaceuticals, Germany. C.S. is a full-time employee of Bayer PLC, Division Pharmaceuticals, U.K. K.R. is a full-time employee of Bayer AG. P.R. reported personal fees from Bayer during the conduct of the study. He has received research support and personal fees from AstraZeneca, Bayer, and Novo Nordisk and personal fees from Abbott, Astellas, Boehringer Ingelheim, Eli Lilly, Gilead, Mundipharma, and Sanofi, and all fees are given to Steno Diabetes Center Copenhagen. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. The Executive Committee designed the study in conjunction with the sponsor. T.E. wrote the first draft of the report. T.E., S.D.A., L.M.R., P.F., V.F., G.E.U., A.L.B., R.L., C.S., K.R., and P.R. had access to the study results, were involved in data analysis and interpretation, and in drafting and critically revising the report, and reviewed and approved the final submitted version of the report. T.E. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors made the decision to submit the manuscript for publication.

Prior Presentation. Parts of this study were presented at the 83rd Scientific Sessions of the American Diabetes Association, San Diego, CA, 23–26 June 2023.